diff --git a/.ci/update_windows/update.py b/.ci/update_windows/update.py
index ef9374c4..127247b2 100755
--- a/.ci/update_windows/update.py
+++ b/.ci/update_windows/update.py
@@ -1,6 +1,9 @@
 import pygit2
 from datetime import datetime
 import sys
+import os
+import shutil
+import filecmp
 
 def pull(repo, remote_name='origin', branch='master'):
     for remote in repo.remotes:
@@ -42,7 +45,8 @@ def pull(repo, remote_name='origin', branch='master'):
                 raise AssertionError('Unknown merge analysis result')
 
 pygit2.option(pygit2.GIT_OPT_SET_OWNER_VALIDATION, 0)
-repo = pygit2.Repository(str(sys.argv[1]))
+repo_path = str(sys.argv[1])
+repo = pygit2.Repository(repo_path)
 ident = pygit2.Signature('comfyui', 'comfy@ui')
 try:
     print("stashing current changes")
@@ -51,7 +55,10 @@ except KeyError:
     print("nothing to stash")
 backup_branch_name = 'backup_branch_{}'.format(datetime.today().strftime('%Y-%m-%d_%H_%M_%S'))
 print("creating backup branch: {}".format(backup_branch_name))
-repo.branches.local.create(backup_branch_name, repo.head.peel())
+try:
+    repo.branches.local.create(backup_branch_name, repo.head.peel())
+except:
+    pass
 
 print("checking out master branch")
 branch = repo.lookup_branch('master')
@@ -63,3 +70,41 @@ pull(repo)
 
 print("Done!")
 
+self_update = True
+if len(sys.argv) > 2:
+    self_update = '--skip_self_update' not in sys.argv
+
+update_py_path = os.path.realpath(__file__)
+repo_update_py_path = os.path.join(repo_path, ".ci/update_windows/update.py")
+
+cur_path = os.path.dirname(update_py_path)
+
+
+req_path = os.path.join(cur_path, "current_requirements.txt")
+repo_req_path = os.path.join(repo_path, "requirements.txt")
+
+
+def files_equal(file1, file2):
+    try:
+        return filecmp.cmp(file1, file2, shallow=False)
+    except:
+        return False
+
+def file_size(f):
+    try:
+        return os.path.getsize(f)
+    except:
+        return 0
+
+
+if self_update and not files_equal(update_py_path, repo_update_py_path) and file_size(repo_update_py_path) > 10:
+    shutil.copy(repo_update_py_path, os.path.join(cur_path, "update_new.py"))
+    exit()
+
+if not os.path.exists(req_path) or not files_equal(repo_req_path, req_path):
+    import subprocess
+    try:
+        subprocess.check_call([sys.executable, '-s', '-m', 'pip', 'install', '-r', repo_req_path])
+        shutil.copy(repo_req_path, req_path)
+    except:
+        pass
diff --git a/.ci/update_windows/update_comfyui.bat b/.ci/update_windows/update_comfyui.bat
index 60d1e694..bb08c0de 100755
--- a/.ci/update_windows/update_comfyui.bat
+++ b/.ci/update_windows/update_comfyui.bat
@@ -1,2 +1,8 @@
+@echo off
 ..\python_embeded\python.exe .\update.py ..\ComfyUI\
-pause
+if exist update_new.py (
+  move /y update_new.py update.py
+  echo Running updater again since it got updated.
+  ..\python_embeded\python.exe .\update.py ..\ComfyUI\ --skip_self_update
+)
+if "%~1"=="" pause
diff --git a/.ci/update_windows/update_comfyui_and_python_dependencies.bat b/.ci/update_windows/update_comfyui_and_python_dependencies.bat
deleted file mode 100755
index b7308550..00000000
--- a/.ci/update_windows/update_comfyui_and_python_dependencies.bat
+++ /dev/null
@@ -1,3 +0,0 @@
-..\python_embeded\python.exe .\update.py ..\ComfyUI\
-..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu117 xformers -r ../ComfyUI/requirements.txt pygit2
-pause
diff --git a/.ci/update_windows_cu118/update_comfyui_and_python_dependencies.bat b/.ci/update_windows_cu118/update_comfyui_and_python_dependencies.bat
deleted file mode 100755
index c33adc0a..00000000
--- a/.ci/update_windows_cu118/update_comfyui_and_python_dependencies.bat
+++ /dev/null
@@ -1,11 +0,0 @@
-@echo off
-..\python_embeded\python.exe .\update.py ..\ComfyUI\
-echo
-echo This will try to update pytorch and all python dependencies, if you get an error wait for pytorch/xformers to fix their stuff
-echo You should not be running this anyways unless you really have to
-echo
-echo If you just want to update normally, close this and run update_comfyui.bat instead.
-echo
-pause
-..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu118 xformers -r ../ComfyUI/requirements.txt pygit2
-pause
diff --git a/.github/ISSUE_TEMPLATE/bug-report.yml b/.github/ISSUE_TEMPLATE/bug-report.yml
new file mode 100644
index 00000000..87bef368
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug-report.yml
@@ -0,0 +1,45 @@
+name: Bug Report
+description: "Something is broken inside of ComfyUI. (Do not use this if you're just having issues and need help, or if the issue relates to a custom node)"
+labels: [ "Potential Bug" ]
+body:
+    - type: markdown
+      attributes:
+        value: |
+                Before submitting a **Bug Report**, please ensure the following:
+
+                **1:** You are running the latest version of ComfyUI.
+                **2:** You have looked at the existing bug reports and made sure this isn't already reported.
+                **3:** This is an actual bug in ComfyUI, not just a support question and not caused by an custom node. A bug is when you can specify exact steps to replicate what went wrong and others will be able to repeat your steps and see the same issue happen.
+
+                If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.
+    - type: textarea
+      attributes:
+            label: Expected Behavior
+            description: "What you expected to happen."
+      validations:
+            required: true
+    - type: textarea
+      attributes:
+                label: Actual Behavior
+                description: "What actually happened. Please include a screenshot of the issue if possible."
+      validations:
+                required: true
+    - type: textarea
+      attributes:
+                label: Steps to Reproduce
+                description: "Describe how to reproduce the issue. Please be sure to attach a workflow JSON or PNG, ideally one that doesn't require custom nodes to test. If the bug open happens when certain custom nodes are used, most likely that custom node is what has the bug rather than ComfyUI, in which case it should be reported to the node's author."
+      validations:
+                required: true
+    - type: textarea
+      attributes:
+                label: Debug Logs
+                description: "Please copy the output from your terminal logs here."
+                render: powershell
+      validations:
+                required: true
+    - type: textarea
+      attributes:
+                label: Other
+                description: "Any other additional information you think might be helpful."
+      validations:
+                required: false
diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml
new file mode 100644
index 00000000..2c519ede
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -0,0 +1,8 @@
+blank_issues_enabled: true
+contact_links:
+  - name: ComfyUI Matrix Space
+    url: https://app.element.io/#/room/%23comfyui_space%3Amatrix.org
+    about: The ComfyUI Matrix Space is available for support and general discussion related to ComfyUI (Matrix is like Discord but open source).
+  - name: Comfy Org Discord
+    url: https://discord.gg/comfyorg
+    about: The Comfy Org Discord is available for support and general discussion related to ComfyUI.
diff --git a/.github/ISSUE_TEMPLATE/feature-request.yml b/.github/ISSUE_TEMPLATE/feature-request.yml
new file mode 100644
index 00000000..419721b6
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature-request.yml
@@ -0,0 +1,32 @@
+name: Feature Request
+description: "You have an idea for something new you would like to see added to ComfyUI's core."
+labels: [ "Feature" ]
+body:
+    - type: markdown
+      attributes:
+        value: |
+                Before submitting a **Feature Request**, please ensure the following:
+
+                **1:** You are running the latest version of ComfyUI.
+                **2:** You have looked to make sure there is not already a feature that does what you need, and there is not already a Feature Request listed for the same idea.
+                **3:** This is something that makes sense to add to ComfyUI Core, and wouldn't make more sense as a custom node.
+
+                If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.
+    - type: textarea
+      attributes:
+            label: Feature Idea
+            description: "Describe the feature you want to see."
+      validations:
+            required: true
+    - type: textarea
+      attributes:
+                label: Existing Solutions
+                description: "Please search through available custom nodes / extensions to see if there are existing custom solutions for this. If so, please link the options you found here as a reference."
+      validations:
+                required: false
+    - type: textarea
+      attributes:
+                label: Other
+                description: "Any other additional information you think might be helpful."
+      validations:
+                required: false
diff --git a/.github/ISSUE_TEMPLATE/user-support.yml b/.github/ISSUE_TEMPLATE/user-support.yml
new file mode 100644
index 00000000..df28804c
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/user-support.yml
@@ -0,0 +1,32 @@
+name: User Support
+description: "Use this if you need help with something, or you're experiencing an issue."
+labels: [ "User Support" ]
+body:
+    - type: markdown
+      attributes:
+        value: |
+            Before submitting a **User Report** issue, please ensure the following:
+
+            **1:** You are running the latest version of ComfyUI.
+            **2:** You have made an effort to find public answers to your question before asking here. In other words, you googled it first, and scrolled through recent help topics.
+
+                If unsure, ask on the [ComfyUI Matrix Space](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) or the [Comfy Org Discord](https://discord.gg/comfyorg) first.
+    - type: textarea
+      attributes:
+            label: Your question
+            description: "Post your question here. Please be as detailed as possible."
+      validations:
+            required: true
+    - type: textarea
+      attributes:
+                label: Logs
+                description: "If your question relates to an issue you're experiencing, please go to `Server` -> `Logs` -> potentially set `View Type` to `Debug` as well, then copypaste all the text into here."
+                render: powershell
+      validations:
+                required: false
+    - type: textarea
+      attributes:
+                label: Other
+                description: "Any other additional information you think might be helpful."
+      validations:
+                required: false
diff --git a/.github/workflows/test-browser.yml b/.github/workflows/test-browser.yml
new file mode 100644
index 00000000..65483b00
--- /dev/null
+++ b/.github/workflows/test-browser.yml
@@ -0,0 +1,63 @@
+# This is a temporary action during frontend TS migration.
+# This file should be removed after TS migration is completed.
+# The browser test is here to ensure TS repo is working the same way as the
+# current JS code.
+# If you are adding UI feature, please sync your changes to the TS repo:
+# huchenlei/ComfyUI_frontend and update test expectation files accordingly.
+name: Playwright Browser Tests CI
+
+on:
+  push:
+    branches: [ main, master ]
+  pull_request:
+    branches: [ main, master ]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+    - name: Checkout ComfyUI
+      uses: actions/checkout@v4
+      with:
+        repository: "comfyanonymous/ComfyUI"
+        path: "ComfyUI"
+    - name: Checkout ComfyUI_frontend
+      uses: actions/checkout@v4
+      with:
+        repository: "huchenlei/ComfyUI_frontend"
+        path: "ComfyUI_frontend"
+        ref: "fcc54d803e5b6a9b08a462a1d94899318c96dcbb"
+    - uses: actions/setup-node@v3
+      with:
+        node-version: lts/*
+    - uses: actions/setup-python@v4
+      with:
+        python-version: '3.10'
+    - name: Install requirements
+      run: |
+        python -m pip install --upgrade pip
+        pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+        pip install -r requirements.txt
+        pip install wait-for-it
+      working-directory: ComfyUI
+    - name: Start ComfyUI server
+      run: |
+        python main.py --cpu &
+        wait-for-it --service 127.0.0.1:8188 -t 600
+      working-directory: ComfyUI
+    - name: Install ComfyUI_frontend dependencies
+      run: |
+        npm ci
+      working-directory: ComfyUI_frontend
+    - name: Install Playwright Browsers
+      run: npx playwright install --with-deps
+      working-directory: ComfyUI_frontend
+    - name: Run Playwright tests
+      run: npx playwright test
+      working-directory: ComfyUI_frontend
+    - uses: actions/upload-artifact@v4
+      if: always()
+      with:
+        name: playwright-report
+        path: ComfyUI_frontend/playwright-report/
+        retention-days: 30
diff --git a/.github/workflows/windows_release_cu118_dependencies.yml b/.github/workflows/windows_release_cu118_dependencies.yml
deleted file mode 100644
index 75c42b62..00000000
--- a/.github/workflows/windows_release_cu118_dependencies.yml
+++ /dev/null
@@ -1,71 +0,0 @@
-name: "Windows Release cu118 dependencies"
-
-on:
-  workflow_dispatch:
-#  push:
-#    branches:
-#      - master
-
-jobs:
-  build_dependencies:
-    env:
-        # you need at least cuda 5.0 for some of the stuff compiled here.
-        TORCH_CUDA_ARCH_LIST: "5.0+PTX 6.0 6.1 7.0 7.5 8.0 8.6 8.9"
-        FORCE_CUDA: 1
-        MAX_JOBS: 1 # will crash otherwise
-        DISTUTILS_USE_SDK: 1 # otherwise distutils will complain on windows about multiple versions of msvc
-        XFORMERS_BUILD_TYPE: "Release"
-    runs-on: windows-latest
-    steps:
-        - name: Cache Built Dependencies
-          uses: actions/cache@v3
-          id: cache-cu118_python_stuff
-          with:
-            path: cu118_python_deps.tar
-            key: ${{ runner.os }}-build-cu118
-
-        - if: steps.cache-cu118_python_stuff.outputs.cache-hit != 'true'
-          uses: actions/checkout@v3
-
-        - if: steps.cache-cu118_python_stuff.outputs.cache-hit != 'true'
-          uses: actions/setup-python@v4
-          with:
-            python-version: '3.10.9'
-
-        - if: steps.cache-cu118_python_stuff.outputs.cache-hit != 'true'
-          uses: comfyanonymous/cuda-toolkit@test
-          id: cuda-toolkit
-          with:
-            cuda: '11.8.0'
-        # copied from xformers github
-        - name: Setup MSVC
-          uses: ilammy/msvc-dev-cmd@v1
-        - name: Configure Pagefile
-          # windows runners will OOM with many CUDA architectures
-          # we cheat here with a page file
-          uses: al-cheb/configure-pagefile-action@v1.3
-          with:
-            minimum-size: 2GB
-        # really unfortunate: https://github.com/ilammy/msvc-dev-cmd#name-conflicts-with-shell-bash
-        - name: Remove link.exe
-          shell: bash
-          run: rm /usr/bin/link
-
-        - if: steps.cache-cu118_python_stuff.outputs.cache-hit != 'true'
-          shell: bash
-          run: |
-            python -m pip wheel --no-cache-dir torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu118 -r requirements.txt pygit2 -w ./temp_wheel_dir
-            python -m pip install --no-cache-dir ./temp_wheel_dir/*
-            echo installed basic
-            git clone --recurse-submodules https://github.com/facebookresearch/xformers.git
-            cd xformers
-            python -m pip install --no-cache-dir wheel setuptools twine
-            echo building xformers
-            python setup.py bdist_wheel -d ../temp_wheel_dir/
-            cd ..
-            rm -rf xformers
-            ls -lah temp_wheel_dir
-            mv temp_wheel_dir cu118_python_deps
-            tar cf cu118_python_deps.tar cu118_python_deps
-
-
diff --git a/.github/workflows/windows_release_cu118_dependencies_2.yml b/.github/workflows/windows_release_cu118_dependencies_2.yml
deleted file mode 100644
index a7760b21..00000000
--- a/.github/workflows/windows_release_cu118_dependencies_2.yml
+++ /dev/null
@@ -1,37 +0,0 @@
-name: "Windows Release cu118 dependencies 2"
-
-on:
-  workflow_dispatch:
-    inputs:
-      xformers:
-        description: 'xformers version'
-        required: true
-        type: string
-        default: "xformers"
-
-#  push:
-#    branches:
-#      - master
-
-jobs:
-  build_dependencies:
-    runs-on: windows-latest
-    steps:
-        - uses: actions/checkout@v3
-        - uses: actions/setup-python@v4
-          with:
-            python-version: '3.10.9'
-
-        - shell: bash
-          run: |
-            python -m pip wheel --no-cache-dir torch torchvision torchaudio ${{ inputs.xformers }} --extra-index-url https://download.pytorch.org/whl/cu118 -r requirements.txt pygit2 -w ./temp_wheel_dir
-            python -m pip install --no-cache-dir ./temp_wheel_dir/*
-            echo installed basic
-            ls -lah temp_wheel_dir
-            mv temp_wheel_dir cu118_python_deps
-            tar cf cu118_python_deps.tar cu118_python_deps
-
-        - uses: actions/cache/save@v3
-          with:
-            path: cu118_python_deps.tar
-            key: ${{ runner.os }}-build-cu118
diff --git a/.github/workflows/windows_release_cu118_package.yml b/.github/workflows/windows_release_cu118_package.yml
deleted file mode 100644
index 0f0fbf28..00000000
--- a/.github/workflows/windows_release_cu118_package.yml
+++ /dev/null
@@ -1,79 +0,0 @@
-name: "Windows Release cu118 packaging"
-
-on:
-  workflow_dispatch:
-#  push:
-#    branches:
-#      - master
-
-jobs:
-  package_comfyui:
-    permissions:
-        contents: "write"
-        packages: "write"
-        pull-requests: "read"
-    runs-on: windows-latest
-    steps:
-        - uses: actions/cache/restore@v3
-          id: cache
-          with:
-            path: cu118_python_deps.tar
-            key: ${{ runner.os }}-build-cu118
-        - shell: bash
-          run: |
-            mv cu118_python_deps.tar ../
-            cd ..
-            tar xf cu118_python_deps.tar
-            pwd
-            ls
-
-        - uses: actions/checkout@v3
-          with:
-            fetch-depth: 0
-            persist-credentials: false
-        - shell: bash
-          run: |
-            cd ..
-            cp -r ComfyUI ComfyUI_copy
-            curl https://www.python.org/ftp/python/3.10.9/python-3.10.9-embed-amd64.zip -o python_embeded.zip
-            unzip python_embeded.zip -d python_embeded
-            cd python_embeded
-            echo 'import site' >> ./python310._pth
-            curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
-            ./python.exe get-pip.py
-            ./python.exe -s -m pip install ../cu118_python_deps/*
-            sed -i '1i../ComfyUI' ./python310._pth
-            cd ..
-
-            git clone https://github.com/comfyanonymous/taesd
-            cp taesd/*.pth ./ComfyUI_copy/models/vae_approx/
-
-            mkdir ComfyUI_windows_portable
-            mv python_embeded ComfyUI_windows_portable
-            mv ComfyUI_copy ComfyUI_windows_portable/ComfyUI
-
-            cd ComfyUI_windows_portable
-
-            mkdir update
-            cp -r ComfyUI/.ci/update_windows/* ./update/
-            cp -r ComfyUI/.ci/update_windows_cu118/* ./update/
-            cp -r ComfyUI/.ci/windows_base_files/* ./
-
-            cd ..
-
-            "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
-            mv ComfyUI_windows_portable.7z ComfyUI/new_ComfyUI_windows_portable_nvidia_cu118_or_cpu.7z
-
-            cd ComfyUI_windows_portable
-            python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu
-
-            ls
-
-        - name: Upload binaries to release
-          uses: svenstaro/upload-release-action@v2
-          with:
-                repo_token: ${{ secrets.GITHUB_TOKEN }}
-                file: new_ComfyUI_windows_portable_nvidia_cu118_or_cpu.7z
-                tag: "latest"
-                overwrite: true
-
diff --git a/.github/workflows/windows_release_dependencies.yml b/.github/workflows/windows_release_dependencies.yml
index aafe8a21..5aa57e7d 100644
--- a/.github/workflows/windows_release_dependencies.yml
+++ b/.github/workflows/windows_release_dependencies.yml
@@ -24,7 +24,7 @@ on:
         description: 'python patch version'
         required: true
         type: string
-        default: "6"
+        default: "8"
 #  push:
 #    branches:
 #      - master
@@ -33,18 +33,17 @@ jobs:
   build_dependencies:
     runs-on: windows-latest
     steps:
-        - uses: actions/checkout@v3
-        - uses: actions/setup-python@v4
+        - uses: actions/checkout@v4
+        - uses: actions/setup-python@v5
           with:
             python-version: 3.${{ inputs.python_minor }}.${{ inputs.python_patch }}
 
         - shell: bash
           run: |
             echo "@echo off
-            ..\python_embeded\python.exe .\update.py ..\ComfyUI\\
+            call update_comfyui.bat nopause
             echo -
-            echo This will try to update pytorch and all python dependencies, if you get an error wait for pytorch/xformers to fix their stuff
-            echo You should not be running this anyways unless you really have to
+            echo This will try to update pytorch and all python dependencies.
             echo -
             echo If you just want to update normally, close this and run update_comfyui.bat instead.
             echo -
@@ -59,7 +58,7 @@ jobs:
             mv temp_wheel_dir cu${{ inputs.cu }}_python_deps
             tar cf cu${{ inputs.cu }}_python_deps.tar cu${{ inputs.cu }}_python_deps
 
-        - uses: actions/cache/save@v3
+        - uses: actions/cache/save@v4
           with:
             path: |
               cu${{ inputs.cu }}_python_deps.tar
diff --git a/.github/workflows/windows_release_nightly_pytorch.yml b/.github/workflows/windows_release_nightly_pytorch.yml
index 90e09d27..e68011b6 100644
--- a/.github/workflows/windows_release_nightly_pytorch.yml
+++ b/.github/workflows/windows_release_nightly_pytorch.yml
@@ -7,7 +7,7 @@ on:
         description: 'cuda version'
         required: true
         type: string
-        default: "121"
+        default: "124"
 
       python_minor:
         description: 'python minor version'
@@ -19,7 +19,7 @@ on:
         description: 'python patch version'
         required: true
         type: string
-        default: "1"
+        default: "3"
 #  push:
 #    branches:
 #      - master
@@ -32,11 +32,11 @@ jobs:
         pull-requests: "read"
     runs-on: windows-latest
     steps:
-        - uses: actions/checkout@v3
+        - uses: actions/checkout@v4
           with:
             fetch-depth: 0
             persist-credentials: false
-        - uses: actions/setup-python@v4
+        - uses: actions/setup-python@v5
           with:
             python-version: 3.${{ inputs.python_minor }}.${{ inputs.python_patch }}
         - shell: bash
@@ -49,7 +49,7 @@ jobs:
             echo 'import site' >> ./python3${{ inputs.python_minor }}._pth
             curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
             ./python.exe get-pip.py
-            python -m pip wheel torch torchvision torchaudio --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir
+            python -m pip wheel torch torchvision torchaudio mpmath==1.3.0 numpy==1.26.4 --pre --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2 -w ../temp_wheel_dir
             ls ../temp_wheel_dir
             ./python.exe -s -m pip install --pre ../temp_wheel_dir/*
             sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth
@@ -68,12 +68,12 @@ jobs:
             cp -r ComfyUI/.ci/update_windows/* ./update/
             cp -r ComfyUI/.ci/windows_base_files/* ./
 
-            echo "..\python_embeded\python.exe .\update.py ..\ComfyUI\\
+            echo "call update_comfyui.bat nopause
             ..\python_embeded\python.exe -s -m pip install --upgrade --pre torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/nightly/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2
             pause" > ./update/update_comfyui_and_python_dependencies.bat
             cd ..
 
-            "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI_windows_portable_nightly_pytorch
+            "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI_windows_portable_nightly_pytorch
             mv ComfyUI_windows_portable_nightly_pytorch.7z ComfyUI/ComfyUI_windows_portable_nvidia_or_cpu_nightly_pytorch.7z
 
             cd ComfyUI_windows_portable_nightly_pytorch
diff --git a/.github/workflows/windows_release_package.yml b/.github/workflows/windows_release_package.yml
index 87d37c24..020741c4 100644
--- a/.github/workflows/windows_release_package.yml
+++ b/.github/workflows/windows_release_package.yml
@@ -19,7 +19,7 @@ on:
         description: 'python patch version'
         required: true
         type: string
-        default: "6"
+        default: "8"
 #  push:
 #    branches:
 #      - master
@@ -32,7 +32,7 @@ jobs:
         pull-requests: "read"
     runs-on: windows-latest
     steps:
-        - uses: actions/cache/restore@v3
+        - uses: actions/cache/restore@v4
           id: cache
           with:
             path: |
@@ -48,7 +48,7 @@ jobs:
             pwd
             ls
 
-        - uses: actions/checkout@v3
+        - uses: actions/checkout@v4
           with:
             fetch-depth: 0
             persist-credentials: false
@@ -82,7 +82,7 @@ jobs:
 
             cd ..
 
-            "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
+            "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=8 -mfb=64 -md=32m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
             mv ComfyUI_windows_portable.7z ComfyUI/new_ComfyUI_windows_portable_nvidia_cu${{ inputs.cu }}_or_cpu.7z
 
             cd ComfyUI_windows_portable
diff --git a/.gitignore b/.gitignore
index 9f038924..a9beebe7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -9,10 +9,12 @@ __pycache__/
 !custom_nodes/example_node.py.example
 extra_model_paths.yaml
 /.vs
+.vscode/
 .idea/
 venv/
 /web/extensions/*
 !/web/extensions/logging.js.example
 !/web/extensions/core/
 /tests-ui/data/object_info.json
-/user/
\ No newline at end of file
+/user/
+*.log
\ No newline at end of file
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 00000000..048f127e
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,41 @@
+# Contributing to ComfyUI
+
+Welcome, and thank you for your interest in contributing to ComfyUI!
+
+There are several ways in which you can contribute, beyond writing code. The goal of this document is to provide a high-level overview of how you can get involved.
+
+## Asking Questions
+
+Have a question? Instead of opening an issue, please ask on [Discord](https://comfy.org/discord) or [Matrix](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) channels. Our team and the community will help you.
+
+## Providing Feedback
+
+Your comments and feedback are welcome, and the development team is available via a handful of different channels.
+
+See the `#bug-report`, `#feature-request` and `#feedback` channels on Discord.
+
+## Reporting Issues
+
+Have you identified a reproducible problem in ComfyUI? Do you have a feature request? We want to hear about it! Here's how you can report your issue as effectively as possible.
+
+
+### Look For an Existing Issue
+
+Before you create a new issue, please do a search in [open issues](https://github.com/comfyanonymous/ComfyUI/issues) to see if the issue or feature request has already been filed.
+
+If you find your issue already exists, make relevant comments and add your [reaction](https://github.com/blog/2119-add-reactions-to-pull-requests-issues-and-comments). Use a reaction in place of a "+1" comment:
+
+* 👍 - upvote
+* 👎 - downvote
+
+If you cannot find an existing issue that describes your bug or feature, create a new issue. We have an issue template in place to organize new issues.
+
+
+### Creating Pull Requests
+
+* Please refer to the article on [creating pull requests](https://github.com/comfyanonymous/ComfyUI/wiki/How-to-Contribute-Code) and contributing to this project.
+
+
+## Thank You
+
+Your contributions to open source, large or small, make great projects like this possible. Thank you for taking the time to contribute.
diff --git a/README.md b/README.md
index ff3ab642..35e3238a 100644
--- a/README.md
+++ b/README.md
@@ -11,16 +11,16 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
 
 ## Features
 - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
-- Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/) and [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/)
+- Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/), [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/), [SD3](https://comfyanonymous.github.io/ComfyUI_examples/sd3/) and [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
 - Asynchronous Queue system
 - Many optimizations: Only re-executes the parts of the workflow that changes between executions.
-- Command line option: ```--lowvram``` to make it work on GPUs with less than 3GB vram (enabled automatically on GPUs with low vram)
+- Smart memory management: can automatically run models on GPUs with as low as 1GB vram.
 - Works even if you don't have a GPU with: ```--cpu``` (slow)
 - Can load ckpt, safetensors and diffusers models/checkpoints. Standalone VAEs and CLIP models.
 - Embeddings/Textual inversion
 - [Loras (regular, locon and loha)](https://comfyanonymous.github.io/ComfyUI_examples/lora/)
 - [Hypernetworks](https://comfyanonymous.github.io/ComfyUI_examples/hypernetworks/)
-- Loading full workflows (with seeds) from generated PNG files.
+- Loading full workflows (with seeds) from generated PNG, WebP and FLAC files.
 - Saving/Loading workflows as Json files.
 - Nodes interface can be used to create complex workflows like one for [Hires fix](https://comfyanonymous.github.io/ComfyUI_examples/2_pass_txt2img/) or much more advanced ones.
 - [Area Composition](https://comfyanonymous.github.io/ComfyUI_examples/area_composition/)
@@ -41,29 +41,32 @@ Workflow examples can be found on the [Examples page](https://comfyanonymous.git
 
 ## Shortcuts
 
-| Keybind                   | Explanation                                                                                                        |
-|---------------------------|--------------------------------------------------------------------------------------------------------------------|
-| Ctrl + Enter              | Queue up current graph for generation                                                                              |
-| Ctrl + Shift + Enter      | Queue up current graph as first for generation                                                                     |
-| Ctrl + Z/Ctrl + Y         | Undo/Redo                                                                                                          |
-| Ctrl + S                  | Save workflow                                                                                                      |
-| Ctrl + O                  | Load workflow                                                                                                      |
-| Ctrl + A                  | Select all nodes                                                                                                   |
-| Alt + C                   | Collapse/uncollapse selected nodes                                                                                 |
-| Ctrl + M                  | Mute/unmute selected nodes                                                                                         |
-| Ctrl + B                  | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through)            |
-| Delete/Backspace          | Delete selected nodes                                                                                              |
-| Ctrl + Delete/Backspace   | Delete the current graph                                                                                           |
-| Space                     | Move the canvas around when held and moving the cursor                                                             |
-| Ctrl/Shift + Click        | Add clicked node to selection                                                                                      |
-| Ctrl + C/Ctrl + V         | Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes)                     |
-| Ctrl + C/Ctrl + Shift + V | Copy and paste selected nodes (maintaining connections from outputs of unselected nodes to inputs of pasted nodes) |
-| Shift + Drag              | Move multiple selected nodes at the same time                                                                      |
-| Ctrl + D                  | Load default graph                                                                                                 |
-| Q                         | Toggle visibility of the queue                                                                                     |
-| H                         | Toggle visibility of history                                                                                       |
-| R                         | Refresh graph                                                                                                      |
-| Double-Click LMB          | Open node quick search palette                                                                                     |
+| Keybind                            | Explanation                                                                                                        |
+|------------------------------------|--------------------------------------------------------------------------------------------------------------------|
+| Ctrl + Enter                       | Queue up current graph for generation                                                                              |
+| Ctrl + Shift + Enter               | Queue up current graph as first for generation                                                                     |
+| Ctrl + Z/Ctrl + Y                  | Undo/Redo                                                                                                          |
+| Ctrl + S                           | Save workflow                                                                                                      |
+| Ctrl + O                           | Load workflow                                                                                                      |
+| Ctrl + A                           | Select all nodes                                                                                                   |
+| Alt + C                            | Collapse/uncollapse selected nodes                                                                                 |
+| Ctrl + M                           | Mute/unmute selected nodes                                                                                         |
+| Ctrl + B                           | Bypass selected nodes (acts like the node was removed from the graph and the wires reconnected through)            |
+| Delete/Backspace                   | Delete selected nodes                                                                                              |
+| Ctrl + Backspace                   | Delete the current graph                                                                                           |
+| Space                              | Move the canvas around when held and moving the cursor                                                             |
+| Ctrl/Shift + Click                 | Add clicked node to selection                                                                                      |
+| Ctrl + C/Ctrl + V                  | Copy and paste selected nodes (without maintaining connections to outputs of unselected nodes)                     |
+| Ctrl + C/Ctrl + Shift + V          | Copy and paste selected nodes (maintaining connections from outputs of unselected nodes to inputs of pasted nodes) |
+| Shift + Drag                       | Move multiple selected nodes at the same time                                                                      |
+| Ctrl + D                           | Load default graph                                                                                                 |
+| Alt + `+`                          | Canvas Zoom in                                                                                                     |
+| Alt + `-`                          | Canvas Zoom out                                                                                                    |
+| Ctrl + Shift + LMB + Vertical drag | Canvas Zoom in/out                                                                                                 |
+| Q                                  | Toggle visibility of the queue                                                                                     |
+| H                                  | Toggle visibility of history                                                                                       |
+| R                                  | Refresh graph                                                                                                      |
+| Double-Click LMB                   | Open node quick search palette                                                                                     |
 
 Ctrl can also be replaced with Cmd instead for macOS users
 
@@ -99,11 +102,11 @@ Put your VAE in: models/vae
 ### AMD GPUs (Linux only)
 AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
 
-```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.7```
+```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.0```
 
 This is the command to install the nightly with ROCm 6.0 which might have some performance improvements:
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.0```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.1```
 
 ### NVIDIA
 
@@ -113,7 +116,7 @@ Nvidia users should install stable pytorch using this command:
 
 This is the command to install pytorch nightly instead which might have performance improvements:
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu121```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu124```
 
 #### Troubleshooting
 
@@ -133,7 +136,16 @@ After this you should have everything installed and can proceed to running Comfy
 
 ### Others:
 
-#### [Intel Arc](https://github.com/comfyanonymous/ComfyUI/discussions/476)
+#### Intel GPUs
+
+Intel GPU support is available for all Intel GPUs supported by Intel's Extension for Pytorch (IPEX) with the support requirements listed in the [Installation](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu) page. Choose your platform and method of install and follow the instructions. The steps are as follows:
+
+1. Start by installing the drivers or kernel listed or newer in the Installation page of IPEX linked above for Windows and Linux if needed.
+1. Follow the instructions to install [Intel's oneAPI Basekit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html) for your platform.
+1. Install the packages for IPEX using the instructions provided in the Installation page for your platform.
+1. Follow the [ComfyUI manual installation](#manual-install-windows-linux) instructions for Windows and Linux and run ComfyUI normally as described above after everything is installed.
+
+Additional discussion and help can be found [here](https://github.com/comfyanonymous/ComfyUI/discussions/476).
 
 #### Apple Mac silicon
 
@@ -142,7 +154,7 @@ You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS ve
 1. Install pytorch nightly. For instructions, read the [Accelerated PyTorch training on Mac](https://developer.apple.com/metal/pytorch/) Apple Developer guide (make sure to install the latest pytorch nightly).
 1. Follow the [ComfyUI manual installation](#manual-install-windows-linux) instructions for Windows and Linux.
 1. Install the ComfyUI [dependencies](#dependencies). If you have another Stable Diffusion UI [you might be able to reuse the dependencies](#i-already-have-another-ui-for-stable-diffusion-installed-do-i-really-have-to-install-all-of-these-dependencies).
-1. Launch ComfyUI by running `python main.py --force-fp16`. Note that --force-fp16 will only work if you installed the latest pytorch nightly.
+1. Launch ComfyUI by running `python main.py`
 
 > **Note**: Remember to add your models, VAE, LoRAs etc. to the corresponding Comfy folders, as discussed in [ComfyUI manual installation](#manual-install-windows-linux).
 
@@ -195,30 +207,29 @@ To use a textual inversion concepts/embeddings in a text prompt put them in the
 ```embedding:embedding_filename.pt```
 
 
-## How to increase generation speed?
-
-Make sure you use the regular loaders/Load Checkpoint node to load checkpoints. It will auto pick the right settings depending on your GPU.
-
-You can set this command line setting to disable the upcasting to fp32 in some cross attention operations which will increase your speed. Note that this will very likely give you black images on SD2.x models. If you use xformers or pytorch attention this option does not do anything.
-
-```--dont-upcast-attention```
-
 ## How to show high-quality previews?
 
 Use ```--preview-method auto``` to enable previews.
 
 The default installation includes a fast latent preview method that's low-resolution. To enable higher-quality previews with [TAESD](https://github.com/madebyollin/taesd), download the [taesd_decoder.pth](https://github.com/madebyollin/taesd/raw/main/taesd_decoder.pth) (for SD1.x and SD2.x) and [taesdxl_decoder.pth](https://github.com/madebyollin/taesd/raw/main/taesdxl_decoder.pth) (for SDXL) models and place them in the `models/vae_approx` folder. Once they're installed, restart ComfyUI to enable high-quality previews.
 
+## How to use TLS/SSL?
+Generate a self-signed certificate (not appropriate for shared/production use) and key by running the command: `openssl req -x509 -newkey rsa:4096 -keyout key.pem -out cert.pem -sha256 -days 3650 -nodes -subj "/C=XX/ST=StateName/L=CityName/O=CompanyName/OU=CompanySectionName/CN=CommonNameOrHostname"`
+
+Use `--tls-keyfile key.pem --tls-certfile cert.pem` to enable TLS/SSL, the app will now be accessible with `https://...` instead of `http://...`.
+
+> Note: Windows users can use [alexisrolland/docker-openssl](https://github.com/alexisrolland/docker-openssl) or one of the [3rd party binary distributions](https://wiki.openssl.org/index.php/Binaries) to run the command example above. 
+<br/><br/>If you use a container, note that the volume mount `-v` can be a relative path so `... -v ".\:/openssl-certs" ...` would create the key & cert files in the current directory of your command prompt or powershell terminal.
+
 ## Support and dev channel
 
 [Matrix space: #comfyui_space:matrix.org](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) (it's like discord but open source).
 
+See also: [https://www.comfy.org/](https://www.comfy.org/)
+
 # QA
 
-### Why did you make this?
+### Which GPU should I buy for this?
 
-I wanted to learn how Stable Diffusion worked in detail. I also wanted something clean and powerful that would let me experiment with SD without restrictions.
+[See this page for some recommendations](https://github.com/comfyanonymous/ComfyUI/wiki/Which-GPU-should-I-buy-for-ComfyUI)
 
-### Who is this for?
-
-This is for anyone that wants to make complex workflows with SD or that wants to learn more how SD works. The interface follows closely how SD works and the code should be much more simple to understand than other SD UIs.
diff --git a/app/user_manager.py b/app/user_manager.py
index 209094af..53dff18b 100644
--- a/app/user_manager.py
+++ b/app/user_manager.py
@@ -2,6 +2,8 @@ import json
 import os
 import re
 import uuid
+import glob
+import shutil
 from aiohttp import web
 from comfy.cli_args import args
 from folder_paths import user_directory
@@ -56,16 +58,16 @@ class UserManager():
         if os.path.commonpath((root_dir, user_root)) != root_dir:
             return None
 
-        parent = user_root
-
         if file is not None:
             # prevent leaving /{type}/{user}
             path = os.path.abspath(os.path.join(user_root, file))
             if os.path.commonpath((user_root, path)) != user_root:
                 return None
 
+        parent = os.path.split(path)[0]
+
         if create_dir and not os.path.exists(parent):
-            os.mkdir(parent)
+            os.makedirs(parent, exist_ok=True)
 
         return path
 
@@ -108,33 +110,96 @@ class UserManager():
             user_id = self.add_user(username)
             return web.json_response(user_id)
 
-        @routes.get("/userdata/{file}")
-        async def getuserdata(request):
-            file = request.match_info.get("file", None)
-            if not file:
+        @routes.get("/userdata")
+        async def listuserdata(request):
+            directory = request.rel_url.query.get('dir', '')
+            if not directory:
                 return web.Response(status=400)
                 
-            path = self.get_request_user_filepath(request, file)
+            path = self.get_request_user_filepath(request, directory)
             if not path:
                 return web.Response(status=403)
             
             if not os.path.exists(path):
                 return web.Response(status=404)
             
-            return web.FileResponse(path)
+            recurse = request.rel_url.query.get('recurse', '').lower() == "true"
+            results = glob.glob(os.path.join(
+                glob.escape(path), '**/*'), recursive=recurse)
+            results = [os.path.relpath(x, path) for x in results if os.path.isfile(x)]
+            
+            split_path = request.rel_url.query.get('split', '').lower() == "true"
+            if split_path:
+                results = [[x] + x.split(os.sep) for x in results]
 
-        @routes.post("/userdata/{file}")
-        async def post_userdata(request):
-            file = request.match_info.get("file", None)
+            return web.json_response(results)
+
+        def get_user_data_path(request, check_exists = False, param = "file"):
+            file = request.match_info.get(param, None)
             if not file:
                 return web.Response(status=400)
                 
             path = self.get_request_user_filepath(request, file)
             if not path:
                 return web.Response(status=403)
+            
+            if check_exists and not os.path.exists(path):
+                return web.Response(status=404)
+            
+            return path
+
+        @routes.get("/userdata/{file}")
+        async def getuserdata(request):
+            path = get_user_data_path(request, check_exists=True)
+            if not isinstance(path, str):
+                return path
+            
+            return web.FileResponse(path)
+
+        @routes.post("/userdata/{file}")
+        async def post_userdata(request):
+            path = get_user_data_path(request)
+            if not isinstance(path, str):
+                return path
+            
+            overwrite = request.query["overwrite"] != "false"
+            if not overwrite and os.path.exists(path):
+                return web.Response(status=409)
 
             body = await request.read()
+
             with open(path, "wb") as f:
                 f.write(body)
                 
-            return web.Response(status=200)
+            resp = os.path.relpath(path, self.get_request_user_filepath(request, None))
+            return web.json_response(resp)
+
+        @routes.delete("/userdata/{file}")
+        async def delete_userdata(request):
+            path = get_user_data_path(request, check_exists=True)
+            if not isinstance(path, str):
+                return path
+
+            os.remove(path)
+                
+            return web.Response(status=204)
+
+        @routes.post("/userdata/{file}/move/{dest}")
+        async def move_userdata(request):
+            source = get_user_data_path(request, check_exists=True)
+            if not isinstance(source, str):
+                return source
+            
+            dest = get_user_data_path(request, check_exists=False, param="dest")
+            if not isinstance(source, str):
+                return dest
+            
+            overwrite = request.query["overwrite"] != "false"
+            if not overwrite and os.path.exists(dest):
+                return web.Response(status=409)
+
+            print(f"moving '{source}' -> '{dest}'")
+            shutil.move(source, dest)
+                
+            resp = os.path.relpath(dest, self.get_request_user_filepath(request, None))
+            return web.json_response(resp)
diff --git a/comfy/cldm/cldm.py b/comfy/cldm/cldm.py
index 5eee5a51..d4d32b87 100644
--- a/comfy/cldm/cldm.py
+++ b/comfy/cldm/cldm.py
@@ -13,7 +13,46 @@ from ..ldm.modules.diffusionmodules.util import (
 from ..ldm.modules.attention import SpatialTransformer
 from ..ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample
 from ..ldm.util import exists
+from collections import OrderedDict
 import comfy.ops
+from comfy.ldm.modules.attention import optimized_attention
+
+class OptimizedAttention(nn.Module):
+    def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.heads = nhead
+        self.c = c
+
+        self.in_proj = operations.Linear(c, c * 3, bias=True, dtype=dtype, device=device)
+        self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device)
+
+    def forward(self, x):
+        x = self.in_proj(x)
+        q, k, v = x.split(self.c, dim=2)
+        out = optimized_attention(q, k, v, self.heads)
+        return self.out_proj(out)
+
+class QuickGELU(nn.Module):
+    def forward(self, x: torch.Tensor):
+        return x * torch.sigmoid(1.702 * x)
+
+class ResBlockUnionControlnet(nn.Module):
+    def __init__(self, dim, nhead, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.attn = OptimizedAttention(dim, nhead, dtype=dtype, device=device, operations=operations)
+        self.ln_1 = operations.LayerNorm(dim, dtype=dtype, device=device)
+        self.mlp = nn.Sequential(
+            OrderedDict([("c_fc", operations.Linear(dim, dim * 4, dtype=dtype, device=device)), ("gelu", QuickGELU()),
+                         ("c_proj", operations.Linear(dim * 4, dim, dtype=dtype, device=device))]))
+        self.ln_2 = operations.LayerNorm(dim, dtype=dtype, device=device)
+
+    def attention(self, x: torch.Tensor):
+        return self.attn(x)
+
+    def forward(self, x: torch.Tensor):
+        x = x + self.attention(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
 
 class ControlledUnetModel(UNetModel):
     #implemented in the ldm unet
@@ -52,6 +91,8 @@ class ControlNet(nn.Module):
         adm_in_channels=None,
         transformer_depth_middle=None,
         transformer_depth_output=None,
+        attn_precision=None,
+        union_controlnet=False,
         device=None,
         operations=comfy.ops.disable_weight_init,
         **kwargs,
@@ -202,7 +243,7 @@ class ControlNet(nn.Module):
                             SpatialTransformer(
                                 ch, num_heads, dim_head, depth=num_transformers, context_dim=context_dim,
                                 disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
-                                use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
+                                use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations
                             )
                         )
                 self.input_blocks.append(TimestepEmbedSequential(*layers))
@@ -262,7 +303,7 @@ class ControlNet(nn.Module):
             mid_block += [SpatialTransformer(  # always uses a self-attn
                             ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim,
                             disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
-                            use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
+                            use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations
                         ),
             ResBlock(
                 ch,
@@ -279,6 +320,65 @@ class ControlNet(nn.Module):
         self.middle_block_out = self.make_zero_conv(ch, operations=operations, dtype=self.dtype, device=device)
         self._feature_size += ch
 
+        if union_controlnet:
+            self.num_control_type = 6
+            num_trans_channel = 320
+            num_trans_head = 8
+            num_trans_layer = 1
+            num_proj_channel = 320
+            # task_scale_factor = num_trans_channel ** 0.5
+            self.task_embedding = nn.Parameter(torch.empty(self.num_control_type, num_trans_channel, dtype=self.dtype, device=device))
+
+            self.transformer_layes = nn.Sequential(*[ResBlockUnionControlnet(num_trans_channel, num_trans_head, dtype=self.dtype, device=device, operations=operations) for _ in range(num_trans_layer)])
+            self.spatial_ch_projs = operations.Linear(num_trans_channel, num_proj_channel, dtype=self.dtype, device=device)
+            #-----------------------------------------------------------------------------------------------------
+
+            control_add_embed_dim = 256
+            class ControlAddEmbedding(nn.Module):
+                def __init__(self, in_dim, out_dim, num_control_type, dtype=None, device=None, operations=None):
+                    super().__init__()
+                    self.num_control_type = num_control_type
+                    self.in_dim = in_dim
+                    self.linear_1 = operations.Linear(in_dim * num_control_type, out_dim, dtype=dtype, device=device)
+                    self.linear_2 = operations.Linear(out_dim, out_dim, dtype=dtype, device=device)
+                def forward(self, control_type, dtype, device):
+                    c_type = torch.zeros((self.num_control_type,), device=device)
+                    c_type[control_type] = 1.0
+                    c_type = timestep_embedding(c_type.flatten(), self.in_dim, repeat_only=False).to(dtype).reshape((-1, self.num_control_type * self.in_dim))
+                    return self.linear_2(torch.nn.functional.silu(self.linear_1(c_type)))
+
+            self.control_add_embedding = ControlAddEmbedding(control_add_embed_dim, time_embed_dim, self.num_control_type, dtype=self.dtype, device=device, operations=operations)
+        else:
+            self.task_embedding = None
+            self.control_add_embedding = None
+
+    def union_controlnet_merge(self, hint, control_type, emb, context):
+        # Equivalent to: https://github.com/xinsir6/ControlNetPlus/tree/main
+        inputs = []
+        condition_list = []
+
+        for idx in range(min(1, len(control_type))):
+            controlnet_cond = self.input_hint_block(hint[idx], emb, context)
+            feat_seq = torch.mean(controlnet_cond, dim=(2, 3))
+            if idx < len(control_type):
+                feat_seq += self.task_embedding[control_type[idx]]
+
+            inputs.append(feat_seq.unsqueeze(1))
+            condition_list.append(controlnet_cond)
+
+        x = torch.cat(inputs, dim=1)
+        x = self.transformer_layes(x)
+        controlnet_cond_fuser = None
+        for idx in range(len(control_type)):
+            alpha = self.spatial_ch_projs(x[:, idx])
+            alpha = alpha.unsqueeze(-1).unsqueeze(-1)
+            o = condition_list[idx] + alpha
+            if controlnet_cond_fuser is None:
+                controlnet_cond_fuser = o
+            else:
+                controlnet_cond_fuser += o
+        return controlnet_cond_fuser
+
     def make_zero_conv(self, channels, operations=None, dtype=None, device=None):
         return TimestepEmbedSequential(operations.conv_nd(self.dims, channels, channels, 1, padding=0, dtype=dtype, device=device))
 
@@ -286,9 +386,21 @@ class ControlNet(nn.Module):
         t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False).to(x.dtype)
         emb = self.time_embed(t_emb)
 
-        guided_hint = self.input_hint_block(hint, emb, context)
+        guided_hint = None
+        if self.control_add_embedding is not None: #Union Controlnet
+            control_type = kwargs.get("control_type", [])
 
-        outs = []
+            emb += self.control_add_embedding(control_type, emb.dtype, emb.device)
+            if len(control_type) > 0:
+                if len(hint.shape) < 5:
+                    hint = hint.unsqueeze(dim=0)
+                guided_hint = self.union_controlnet_merge(hint, control_type, emb, context)
+
+        if guided_hint is None:
+            guided_hint = self.input_hint_block(hint, emb, context)
+
+        out_output = []
+        out_middle = []
 
         hs = []
         if self.num_classes is not None:
@@ -303,10 +415,10 @@ class ControlNet(nn.Module):
                 guided_hint = None
             else:
                 h = module(h, emb, context)
-            outs.append(zero_conv(h, emb, context))
+            out_output.append(zero_conv(h, emb, context))
 
         h = self.middle_block(h, emb, context)
-        outs.append(self.middle_block_out(h, emb, context))
+        out_middle.append(self.middle_block_out(h, emb, context))
 
-        return outs
+        return {"middle": out_middle, "output": out_output}
 
diff --git a/comfy/cldm/mmdit.py b/comfy/cldm/mmdit.py
new file mode 100644
index 00000000..025c2fb5
--- /dev/null
+++ b/comfy/cldm/mmdit.py
@@ -0,0 +1,77 @@
+import torch
+from typing import Dict, Optional
+import comfy.ldm.modules.diffusionmodules.mmdit
+
+class ControlNet(comfy.ldm.modules.diffusionmodules.mmdit.MMDiT):
+    def __init__(
+        self,
+        num_blocks = None,
+        dtype = None,
+        device = None,
+        operations = None,
+        **kwargs,
+    ):
+        super().__init__(dtype=dtype, device=device, operations=operations, final_layer=False, num_blocks=num_blocks, **kwargs)
+        # controlnet_blocks
+        self.controlnet_blocks = torch.nn.ModuleList([])
+        for _ in range(len(self.joint_blocks)):
+            self.controlnet_blocks.append(operations.Linear(self.hidden_size, self.hidden_size, device=device, dtype=dtype))
+
+        self.pos_embed_input = comfy.ldm.modules.diffusionmodules.mmdit.PatchEmbed(
+            None,
+            self.patch_size,
+            self.in_channels,
+            self.hidden_size,
+            bias=True,
+            strict_img_size=False,
+            dtype=dtype,
+            device=device,
+            operations=operations
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        timesteps: torch.Tensor,
+        y: Optional[torch.Tensor] = None,
+        context: Optional[torch.Tensor] = None,
+        hint = None,
+    ) -> torch.Tensor:
+
+        #weird sd3 controlnet specific stuff
+        y = torch.zeros_like(y)
+
+        if self.context_processor is not None:
+            context = self.context_processor(context)
+
+        hw = x.shape[-2:]
+        x = self.x_embedder(x) + self.cropped_pos_embed(hw, device=x.device).to(dtype=x.dtype, device=x.device)
+        x += self.pos_embed_input(hint)
+
+        c = self.t_embedder(timesteps, dtype=x.dtype)
+        if y is not None and self.y_embedder is not None:
+            y = self.y_embedder(y)
+            c = c + y
+
+        if context is not None:
+            context = self.context_embedder(context)
+
+        output = []
+
+        blocks = len(self.joint_blocks)
+        for i in range(blocks):
+            context, x = self.joint_blocks[i](
+                context,
+                x,
+                c=c,
+                use_checkpoint=self.use_checkpoint,
+            )
+
+            out = self.controlnet_blocks[i](x)
+            count = self.depth // blocks
+            if i == blocks - 1:
+                count -= 1
+            for j in range(count):
+                output.append(out)
+
+        return {"output": output}
diff --git a/comfy/cli_args.py b/comfy/cli_args.py
index b4bbfbfa..b72bf399 100644
--- a/comfy/cli_args.py
+++ b/comfy/cli_args.py
@@ -35,6 +35,8 @@ parser = argparse.ArgumentParser()
 
 parser.add_argument("--listen", type=str, default="127.0.0.1", metavar="IP", nargs="?", const="0.0.0.0", help="Specify the IP address to listen on (default: 127.0.0.1). If --listen is provided without an argument, it defaults to 0.0.0.0. (listens on all)")
 parser.add_argument("--port", type=int, default=8188, help="Set the listen port.")
+parser.add_argument("--tls-keyfile", type=str, help="Path to TLS (SSL) key file. Enables TLS, makes app accessible at https://... requires --tls-certfile to function")
+parser.add_argument("--tls-certfile", type=str, help="Path to TLS (SSL) certificate file. Enables TLS, makes app accessible at https://... requires --tls-keyfile to function")
 parser.add_argument("--enable-cors-header", type=str, default=None, metavar="ORIGIN", nargs="?", const="*", help="Enable CORS (Cross-Origin Resource Sharing) with optional origin or allow all with default '*'.")
 parser.add_argument("--max-upload-size", type=float, default=100, help="Set the maximum upload size in MB.")
 
@@ -49,7 +51,6 @@ cm_group = parser.add_mutually_exclusive_group()
 cm_group.add_argument("--cuda-malloc", action="store_true", help="Enable cudaMallocAsync (enabled by default for torch 2.0 and up).")
 cm_group.add_argument("--disable-cuda-malloc", action="store_true", help="Disable cudaMallocAsync.")
 
-parser.add_argument("--dont-upcast-attention", action="store_true", help="Disable upcasting of attention. Can boost speed but increase the chances of black images.")
 
 fp_group = parser.add_mutually_exclusive_group()
 fp_group.add_argument("--force-fp32", action="store_true", help="Force fp32 (If this makes your GPU work better please report it).")
@@ -74,6 +75,7 @@ fpte_group.add_argument("--fp8_e5m2-text-enc", action="store_true", help="Store
 fpte_group.add_argument("--fp16-text-enc", action="store_true", help="Store text encoder weights in fp16.")
 fpte_group.add_argument("--fp32-text-enc", action="store_true", help="Store text encoder weights in fp32.")
 
+parser.add_argument("--force-channels-last", action="store_true", help="Force channels last format when inferencing the models.")
 
 parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")
 
@@ -94,6 +96,11 @@ attn_group.add_argument("--use-pytorch-cross-attention", action="store_true", he
 
 parser.add_argument("--disable-xformers", action="store_true", help="Disable xformers.")
 
+upcast = parser.add_mutually_exclusive_group()
+upcast.add_argument("--force-upcast-attention", action="store_true", help="Force enable attention upcasting, please report if it fixes black images.")
+upcast.add_argument("--dont-upcast-attention", action="store_true", help="Disable all upcasting of attention. Should be unnecessary except for debugging.")
+
+
 vram_group = parser.add_mutually_exclusive_group()
 vram_group.add_argument("--gpu-only", action="store_true", help="Store and run everything (text encoders/CLIP models, etc... on the GPU).")
 vram_group.add_argument("--highvram", action="store_true", help="By default models will be unloaded to CPU memory after being used. This option keeps them in GPU memory.")
@@ -111,9 +118,13 @@ parser.add_argument("--quick-test-for-ci", action="store_true", help="Quick test
 parser.add_argument("--windows-standalone-build", action="store_true", help="Windows standalone build: Enable convenient things that most people using the standalone windows build will probably enjoy (like auto opening the page on startup).")
 
 parser.add_argument("--disable-metadata", action="store_true", help="Disable saving prompt metadata in files.")
+parser.add_argument("--disable-all-custom-nodes", action="store_true", help="Disable loading all custom nodes.")
 
 parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.")
 
+parser.add_argument("--verbose", action="store_true", help="Enables more debug prints.")
+
+
 if comfy.options.args_parsing:
     args = parser.parse_args()
 else:
@@ -124,3 +135,10 @@ if args.windows_standalone_build:
 
 if args.disable_auto_launch:
     args.auto_launch = False
+
+import logging
+logging_level = logging.INFO
+if args.verbose:
+    logging_level = logging.DEBUG
+
+logging.basicConfig(format="%(message)s", level=logging_level)
diff --git a/comfy/clip_model.py b/comfy/clip_model.py
index 09e7bbca..14f43c56 100644
--- a/comfy/clip_model.py
+++ b/comfy/clip_model.py
@@ -97,7 +97,7 @@ class CLIPTextModel_(torch.nn.Module):
         x = self.embeddings(input_tokens)
         mask = None
         if attention_mask is not None:
-            mask = 1.0 - attention_mask.to(x.dtype).unsqueeze(1).unsqueeze(1).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
+            mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
             mask = mask.masked_fill(mask.to(torch.bool), float("-inf"))
 
         causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1)
@@ -119,6 +119,9 @@ class CLIPTextModel(torch.nn.Module):
         super().__init__()
         self.num_layers = config_dict["num_hidden_layers"]
         self.text_model = CLIPTextModel_(config_dict, dtype, device, operations)
+        embed_dim = config_dict["hidden_size"]
+        self.text_projection = operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device)
+        self.text_projection.weight.copy_(torch.eye(embed_dim))
         self.dtype = dtype
 
     def get_input_embeddings(self):
@@ -128,7 +131,10 @@ class CLIPTextModel(torch.nn.Module):
         self.text_model.embeddings.token_embedding = embeddings
 
     def forward(self, *args, **kwargs):
-        return self.text_model(*args, **kwargs)
+        x = self.text_model(*args, **kwargs)
+        out = self.text_projection(x[2])
+        return (x[0], x[1], out, x[2])
+
 
 class CLIPVisionEmbeddings(torch.nn.Module):
     def __init__(self, embed_dim, num_channels=3, patch_size=14, image_size=224, dtype=None, device=None, operations=None):
diff --git a/comfy/clip_vision.py b/comfy/clip_vision.py
index 8c77ee7a..acc86be8 100644
--- a/comfy/clip_vision.py
+++ b/comfy/clip_vision.py
@@ -2,6 +2,7 @@ from .utils import load_torch_file, transformers_convert, state_dict_prefix_repl
 import os
 import torch
 import json
+import logging
 
 import comfy.ops
 import comfy.model_patcher
@@ -99,7 +100,7 @@ def load_clipvision_from_sd(sd, prefix="", convert_keys=False):
     clip = ClipVisionModel(json_config)
     m, u = clip.load_sd(sd)
     if len(m) > 0:
-        print("missing clip vision:", m)
+        logging.warning("missing clip vision: {}".format(m))
     u = set(u)
     keys = list(sd.keys())
     for k in keys:
diff --git a/comfy/conds.py b/comfy/conds.py
index 23fa4887..660690af 100644
--- a/comfy/conds.py
+++ b/comfy/conds.py
@@ -29,7 +29,12 @@ class CONDRegular:
 
 class CONDNoiseShape(CONDRegular):
     def process_cond(self, batch_size, device, area, **kwargs):
-        data = self.cond[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]]
+        data = self.cond
+        if area is not None:
+            dims = len(area) // 2
+            for i in range(dims):
+                data = data.narrow(i + 2, area[i + dims], area[i])
+
         return self._copy_with(comfy.utils.repeat_to_batch_size(data, batch_size).to(device))
 
 
diff --git a/comfy/controlnet.py b/comfy/controlnet.py
index d9d990a7..84286f1f 100644
--- a/comfy/controlnet.py
+++ b/comfy/controlnet.py
@@ -1,14 +1,18 @@
 import torch
 import math
 import os
+import logging
 import comfy.utils
 import comfy.model_management
 import comfy.model_detection
 import comfy.model_patcher
 import comfy.ops
+import comfy.latent_formats
 
 import comfy.cldm.cldm
 import comfy.t2i_adapter.adapter
+import comfy.ldm.cascade.controlnet
+import comfy.cldm.mmdit
 
 
 def broadcast_image_to(tensor, target_batch_size, batched_number):
@@ -35,18 +39,24 @@ class ControlBase:
         self.cond_hint = None
         self.strength = 1.0
         self.timestep_percent_range = (0.0, 1.0)
+        self.latent_format = None
+        self.vae = None
         self.global_average_pooling = False
         self.timestep_range = None
+        self.compression_ratio = 8
+        self.upscale_algorithm = 'nearest-exact'
 
         if device is None:
             device = comfy.model_management.get_torch_device()
         self.device = device
         self.previous_controlnet = None
 
-    def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0)):
+    def set_cond_hint(self, cond_hint, strength=1.0, timestep_percent_range=(0.0, 1.0), vae=None):
         self.cond_hint_original = cond_hint
         self.strength = strength
         self.timestep_percent_range = timestep_percent_range
+        if self.latent_format is not None:
+            self.vae = vae
         return self
 
     def pre_run(self, model, percent_to_timestep_function):
@@ -77,43 +87,37 @@ class ControlBase:
         c.strength = self.strength
         c.timestep_percent_range = self.timestep_percent_range
         c.global_average_pooling = self.global_average_pooling
+        c.compression_ratio = self.compression_ratio
+        c.upscale_algorithm = self.upscale_algorithm
+        c.latent_format = self.latent_format
+        c.vae = self.vae
 
     def inference_memory_requirements(self, dtype):
         if self.previous_controlnet is not None:
             return self.previous_controlnet.inference_memory_requirements(dtype)
         return 0
 
-    def control_merge(self, control_input, control_output, control_prev, output_dtype):
+    def control_merge(self, control, control_prev, output_dtype):
         out = {'input':[], 'middle':[], 'output': []}
 
-        if control_input is not None:
-            for i in range(len(control_input)):
-                key = 'input'
-                x = control_input[i]
-                if x is not None:
-                    x *= self.strength
-                    if x.dtype != output_dtype:
-                        x = x.to(output_dtype)
-                out[key].insert(0, x)
-
-        if control_output is not None:
+        for key in control:
+            control_output = control[key]
+            applied_to = set()
             for i in range(len(control_output)):
-                if i == (len(control_output) - 1):
-                    key = 'middle'
-                    index = 0
-                else:
-                    key = 'output'
-                    index = i
                 x = control_output[i]
                 if x is not None:
                     if self.global_average_pooling:
                         x = torch.mean(x, dim=(2, 3), keepdim=True).repeat(1, 1, x.shape[2], x.shape[3])
 
-                    x *= self.strength
+                    if x not in applied_to: #memory saving strategy, allow shared tensors and only apply strength to shared tensors once
+                        applied_to.add(x)
+                        x *= self.strength
+
                     if x.dtype != output_dtype:
                         x = x.to(output_dtype)
 
                 out[key].append(x)
+
         if control_prev is not None:
             for x in ['input', 'middle', 'output']:
                 o = out[x]
@@ -128,18 +132,22 @@ class ControlBase:
                             if o[i].shape[0] < prev_val.shape[0]:
                                 o[i] = prev_val + o[i]
                             else:
-                                o[i] += prev_val
+                                o[i] = prev_val + o[i] #TODO: change back to inplace add if shared tensors stop being an issue
         return out
 
 class ControlNet(ControlBase):
-    def __init__(self, control_model, global_average_pooling=False, device=None, load_device=None, manual_cast_dtype=None):
+    def __init__(self, control_model=None, global_average_pooling=False, compression_ratio=8, latent_format=None, device=None, load_device=None, manual_cast_dtype=None):
         super().__init__(device)
         self.control_model = control_model
         self.load_device = load_device
-        self.control_model_wrapped = comfy.model_patcher.ModelPatcher(self.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device())
+        if control_model is not None:
+            self.control_model_wrapped = comfy.model_patcher.ModelPatcher(self.control_model, load_device=load_device, offload_device=comfy.model_management.unet_offload_device())
+
+        self.compression_ratio = compression_ratio
         self.global_average_pooling = global_average_pooling
         self.model_sampling_current = None
         self.manual_cast_dtype = manual_cast_dtype
+        self.latent_format = latent_format
 
     def get_control(self, x_noisy, t, cond, batched_number):
         control_prev = None
@@ -158,11 +166,21 @@ class ControlNet(ControlBase):
             dtype = self.manual_cast_dtype
 
         output_dtype = x_noisy.dtype
-        if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
+        if self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]:
             if self.cond_hint is not None:
                 del self.cond_hint
             self.cond_hint = None
-            self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * 8, x_noisy.shape[2] * 8, 'nearest-exact', "center").to(dtype).to(self.device)
+            compression_ratio = self.compression_ratio
+            if self.vae is not None:
+                compression_ratio *= self.vae.downscale_ratio
+            self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, x_noisy.shape[3] * compression_ratio, x_noisy.shape[2] * compression_ratio, self.upscale_algorithm, "center")
+            if self.vae is not None:
+                loaded_models = comfy.model_management.loaded_models(only_currently_used=True)
+                self.cond_hint = self.vae.encode(self.cond_hint.movedim(1, -1))
+                comfy.model_management.load_models_gpu(loaded_models)
+            if self.latent_format is not None:
+                self.cond_hint = self.latent_format.process_in(self.cond_hint)
+            self.cond_hint = self.cond_hint.to(device=self.device, dtype=dtype)
         if x_noisy.shape[0] != self.cond_hint.shape[0]:
             self.cond_hint = broadcast_image_to(self.cond_hint, x_noisy.shape[0], batched_number)
 
@@ -174,10 +192,12 @@ class ControlNet(ControlBase):
         x_noisy = self.model_sampling_current.calculate_input(t, x_noisy)
 
         control = self.control_model(x=x_noisy.to(dtype), hint=self.cond_hint, timesteps=timestep.float(), context=context.to(dtype), y=y)
-        return self.control_merge(None, control, control_prev, output_dtype)
+        return self.control_merge(control, control_prev, output_dtype)
 
     def copy(self):
-        c = ControlNet(self.control_model, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)
+        c = ControlNet(None, global_average_pooling=self.global_average_pooling, load_device=self.load_device, manual_cast_dtype=self.manual_cast_dtype)
+        c.control_model = self.control_model
+        c.control_model_wrapped = self.control_model_wrapped
         self.copy_to(c)
         return c
 
@@ -195,7 +215,7 @@ class ControlNet(ControlBase):
         super().cleanup()
 
 class ControlLoraOps:
-    class Linear(torch.nn.Module):
+    class Linear(torch.nn.Module, comfy.ops.CastWeightBiasOp):
         def __init__(self, in_features: int, out_features: int, bias: bool = True,
                     device=None, dtype=None) -> None:
             factory_kwargs = {'device': device, 'dtype': dtype}
@@ -214,7 +234,7 @@ class ControlLoraOps:
             else:
                 return torch.nn.functional.linear(input, weight, bias)
 
-    class Conv2d(torch.nn.Module):
+    class Conv2d(torch.nn.Module, comfy.ops.CastWeightBiasOp):
         def __init__(
             self,
             in_channels,
@@ -287,13 +307,13 @@ class ControlLora(ControlNet):
         for k in sd:
             weight = sd[k]
             try:
-                comfy.utils.set_attr(self.control_model, k, weight)
+                comfy.utils.set_attr_param(self.control_model, k, weight)
             except:
                 pass
 
         for k in self.control_weights:
             if k not in {"lora_controlnet"}:
-                comfy.utils.set_attr(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device()))
+                comfy.utils.set_attr_param(self.control_model, k, self.control_weights[k].to(dtype).to(comfy.model_management.get_torch_device()))
 
     def copy(self):
         c = ControlLora(self.control_weights, global_average_pooling=self.global_average_pooling)
@@ -312,15 +332,49 @@ class ControlLora(ControlNet):
     def inference_memory_requirements(self, dtype):
         return comfy.utils.calculate_parameters(self.control_weights) * comfy.model_management.dtype_size(dtype) + ControlBase.inference_memory_requirements(self, dtype)
 
+def load_controlnet_mmdit(sd):
+    new_sd = comfy.model_detection.convert_diffusers_mmdit(sd, "")
+    model_config = comfy.model_detection.model_config_from_unet(new_sd, "", True)
+    num_blocks = comfy.model_detection.count_blocks(new_sd, 'joint_blocks.{}.')
+    for k in sd:
+        new_sd[k] = sd[k]
+
+    supported_inference_dtypes = model_config.supported_inference_dtypes
+
+    controlnet_config = model_config.unet_config
+    unet_dtype = comfy.model_management.unet_dtype(supported_dtypes=supported_inference_dtypes)
+    load_device = comfy.model_management.get_torch_device()
+    manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
+    if manual_cast_dtype is not None:
+        operations = comfy.ops.manual_cast
+    else:
+        operations = comfy.ops.disable_weight_init
+
+    control_model = comfy.cldm.mmdit.ControlNet(num_blocks=num_blocks, operations=operations, device=load_device, dtype=unet_dtype, **controlnet_config)
+    missing, unexpected = control_model.load_state_dict(new_sd, strict=False)
+
+    if len(missing) > 0:
+        logging.warning("missing controlnet keys: {}".format(missing))
+
+    if len(unexpected) > 0:
+        logging.debug("unexpected controlnet keys: {}".format(unexpected))
+
+    latent_format = comfy.latent_formats.SD3()
+    latent_format.shift_factor = 0 #SD3 controlnet weirdness
+    control = ControlNet(control_model, compression_ratio=1, latent_format=latent_format, load_device=load_device, manual_cast_dtype=manual_cast_dtype)
+    return control
+
+
 def load_controlnet(ckpt_path, model=None):
     controlnet_data = comfy.utils.load_torch_file(ckpt_path, safe_load=True)
     if "lora_controlnet" in controlnet_data:
         return ControlLora(controlnet_data)
 
     controlnet_config = None
+    supported_inference_dtypes = None
+
     if "controlnet_cond_embedding.conv_in.weight" in controlnet_data: #diffusers format
-        unet_dtype = comfy.model_management.unet_dtype()
-        controlnet_config = comfy.model_detection.unet_config_from_diffusers_unet(controlnet_data, unet_dtype)
+        controlnet_config = comfy.model_detection.unet_config_from_diffusers_unet(controlnet_data)
         diffusers_keys = comfy.utils.unet_to_diffusers(controlnet_config)
         diffusers_keys["controlnet_mid_block.weight"] = "middle_block_out.0.weight"
         diffusers_keys["controlnet_mid_block.bias"] = "middle_block_out.0.bias"
@@ -359,10 +413,18 @@ def load_controlnet(ckpt_path, model=None):
             if k in controlnet_data:
                 new_sd[diffusers_keys[k]] = controlnet_data.pop(k)
 
+        if "control_add_embedding.linear_1.bias" in controlnet_data: #Union Controlnet
+            controlnet_config["union_controlnet"] = True
+            for k in list(controlnet_data.keys()):
+                new_k = k.replace('.attn.in_proj_', '.attn.in_proj.')
+                new_sd[new_k] = controlnet_data.pop(k)
+
         leftover_keys = controlnet_data.keys()
         if len(leftover_keys) > 0:
-            print("leftover keys:", leftover_keys)
+            logging.warning("leftover keys: {}".format(leftover_keys))
         controlnet_data = new_sd
+    elif "controlnet_blocks.0.weight" in controlnet_data: #SD3 diffusers format
+        return load_controlnet_mmdit(controlnet_data)
 
     pth_key = 'control_model.zero_convs.0.0.weight'
     pth = False
@@ -376,16 +438,24 @@ def load_controlnet(ckpt_path, model=None):
     else:
         net = load_t2i_adapter(controlnet_data)
         if net is None:
-            print("error checkpoint does not contain controlnet or t2i adapter data", ckpt_path)
+            logging.error("error checkpoint does not contain controlnet or t2i adapter data {}".format(ckpt_path))
         return net
 
     if controlnet_config is None:
-        unet_dtype = comfy.model_management.unet_dtype()
-        controlnet_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, unet_dtype, True).unet_config
+        model_config = comfy.model_detection.model_config_from_unet(controlnet_data, prefix, True)
+        supported_inference_dtypes = model_config.supported_inference_dtypes
+        controlnet_config = model_config.unet_config
+
     load_device = comfy.model_management.get_torch_device()
+    if supported_inference_dtypes is None:
+        unet_dtype = comfy.model_management.unet_dtype()
+    else:
+        unet_dtype = comfy.model_management.unet_dtype(supported_dtypes=supported_inference_dtypes)
+
     manual_cast_dtype = comfy.model_management.unet_manual_cast(unet_dtype, load_device)
     if manual_cast_dtype is not None:
         controlnet_config["operations"] = comfy.ops.manual_cast
+    controlnet_config["dtype"] = unet_dtype
     controlnet_config.pop("out_channels")
     controlnet_config["hint_channels"] = controlnet_data["{}input_hint_block.0.weight".format(prefix)].shape[1]
     control_model = comfy.cldm.cldm.ControlNet(**controlnet_config)
@@ -403,7 +473,7 @@ def load_controlnet(ckpt_path, model=None):
                             cd = controlnet_data[x]
                             cd += model_sd[sd_key].type(cd.dtype).to(cd.device)
             else:
-                print("WARNING: Loaded a diff controlnet without a model. It will very likely not work.")
+                logging.warning("WARNING: Loaded a diff controlnet without a model. It will very likely not work.")
 
         class WeightsLoader(torch.nn.Module):
             pass
@@ -412,7 +482,12 @@ def load_controlnet(ckpt_path, model=None):
         missing, unexpected = w.load_state_dict(controlnet_data, strict=False)
     else:
         missing, unexpected = control_model.load_state_dict(controlnet_data, strict=False)
-    print(missing, unexpected)
+
+    if len(missing) > 0:
+        logging.warning("missing controlnet keys: {}".format(missing))
+
+    if len(unexpected) > 0:
+        logging.debug("unexpected controlnet keys: {}".format(unexpected))
 
     global_average_pooling = False
     filename = os.path.splitext(ckpt_path)[0]
@@ -423,11 +498,13 @@ def load_controlnet(ckpt_path, model=None):
     return control
 
 class T2IAdapter(ControlBase):
-    def __init__(self, t2i_model, channels_in, device=None):
+    def __init__(self, t2i_model, channels_in, compression_ratio, upscale_algorithm, device=None):
         super().__init__(device)
         self.t2i_model = t2i_model
         self.channels_in = channels_in
         self.control_input = None
+        self.compression_ratio = compression_ratio
+        self.upscale_algorithm = upscale_algorithm
 
     def scale_image_to(self, width, height):
         unshuffle_amount = self.t2i_model.unshuffle_amount
@@ -447,13 +524,13 @@ class T2IAdapter(ControlBase):
                 else:
                     return None
 
-        if self.cond_hint is None or x_noisy.shape[2] * 8 != self.cond_hint.shape[2] or x_noisy.shape[3] * 8 != self.cond_hint.shape[3]:
+        if self.cond_hint is None or x_noisy.shape[2] * self.compression_ratio != self.cond_hint.shape[2] or x_noisy.shape[3] * self.compression_ratio != self.cond_hint.shape[3]:
             if self.cond_hint is not None:
                 del self.cond_hint
             self.control_input = None
             self.cond_hint = None
-            width, height = self.scale_image_to(x_noisy.shape[3] * 8, x_noisy.shape[2] * 8)
-            self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, width, height, 'nearest-exact', "center").float().to(self.device)
+            width, height = self.scale_image_to(x_noisy.shape[3] * self.compression_ratio, x_noisy.shape[2] * self.compression_ratio)
+            self.cond_hint = comfy.utils.common_upscale(self.cond_hint_original, width, height, self.upscale_algorithm, "center").float().to(self.device)
             if self.channels_in == 1 and self.cond_hint.shape[1] > 1:
                 self.cond_hint = torch.mean(self.cond_hint, 1, keepdim=True)
         if x_noisy.shape[0] != self.cond_hint.shape[0]:
@@ -464,19 +541,21 @@ class T2IAdapter(ControlBase):
             self.control_input = self.t2i_model(self.cond_hint.to(x_noisy.dtype))
             self.t2i_model.cpu()
 
-        control_input = list(map(lambda a: None if a is None else a.clone(), self.control_input))
-        mid = None
-        if self.t2i_model.xl == True:
-            mid = control_input[-1:]
-            control_input = control_input[:-1]
-        return self.control_merge(control_input, mid, control_prev, x_noisy.dtype)
+        control_input = {}
+        for k in self.control_input:
+            control_input[k] = list(map(lambda a: None if a is None else a.clone(), self.control_input[k]))
+
+        return self.control_merge(control_input, control_prev, x_noisy.dtype)
 
     def copy(self):
-        c = T2IAdapter(self.t2i_model, self.channels_in)
+        c = T2IAdapter(self.t2i_model, self.channels_in, self.compression_ratio, self.upscale_algorithm)
         self.copy_to(c)
         return c
 
 def load_t2i_adapter(t2i_data):
+    compression_ratio = 8
+    upscale_algorithm = 'nearest-exact'
+
     if 'adapter' in t2i_data:
         t2i_data = t2i_data['adapter']
     if 'adapter.body.0.resnets.0.block1.weight' in t2i_data: #diffusers format
@@ -504,13 +583,22 @@ def load_t2i_adapter(t2i_data):
         if cin == 256 or cin == 768:
             xl = True
         model_ad = comfy.t2i_adapter.adapter.Adapter(cin=cin, channels=[channel, channel*2, channel*4, channel*4][:4], nums_rb=2, ksize=ksize, sk=True, use_conv=use_conv, xl=xl)
+    elif "backbone.0.0.weight" in keys:
+        model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.0.weight'].shape[1], proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63])
+        compression_ratio = 32
+        upscale_algorithm = 'bilinear'
+    elif "backbone.10.blocks.0.weight" in keys:
+        model_ad = comfy.ldm.cascade.controlnet.ControlNet(c_in=t2i_data['backbone.0.weight'].shape[1], bottleneck_mode="large", proj_blocks=[0, 4, 8, 12, 51, 55, 59, 63])
+        compression_ratio = 1
+        upscale_algorithm = 'nearest-exact'
     else:
         return None
+
     missing, unexpected = model_ad.load_state_dict(t2i_data)
     if len(missing) > 0:
-        print("t2i missing", missing)
+        logging.warning("t2i missing {}".format(missing))
 
     if len(unexpected) > 0:
-        print("t2i unexpected", unexpected)
+        logging.debug("t2i unexpected {}".format(unexpected))
 
-    return T2IAdapter(model_ad, model_ad.input_channels)
+    return T2IAdapter(model_ad, model_ad.input_channels, compression_ratio, upscale_algorithm)
diff --git a/comfy/diffusers_convert.py b/comfy/diffusers_convert.py
index a9eb9302..ed2a45fe 100644
--- a/comfy/diffusers_convert.py
+++ b/comfy/diffusers_convert.py
@@ -1,5 +1,6 @@
 import re
 import torch
+import logging
 
 # conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py
 
@@ -177,7 +178,7 @@ def convert_vae_state_dict(vae_state_dict):
     for k, v in new_state_dict.items():
         for weight_name in weights_to_convert:
             if f"mid.attn_1.{weight_name}.weight" in k:
-                print(f"Reshaping {k} for SD format")
+                logging.debug(f"Reshaping {k} for SD format")
                 new_state_dict[k] = reshape_weight_for_sd(v)
     return new_state_dict
 
@@ -205,6 +206,21 @@ textenc_pattern = re.compile("|".join(protected.keys()))
 # Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
 code2idx = {"q": 0, "k": 1, "v": 2}
 
+# This function exists because at the time of writing torch.cat can't do fp8 with cuda
+def cat_tensors(tensors):
+    x = 0
+    for t in tensors:
+        x += t.shape[0]
+
+    shape = [x] + list(tensors[0].shape)[1:]
+    out = torch.empty(shape, device=tensors[0].device, dtype=tensors[0].dtype)
+
+    x = 0
+    for t in tensors:
+        out[x:x + t.shape[0]] = t
+        x += t.shape[0]
+
+    return out
 
 def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""):
     new_state_dict = {}
@@ -237,20 +253,24 @@ def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""):
             capture_qkv_bias[k_pre][code2idx[k_code]] = v
             continue
 
-        relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k)
-        new_state_dict[relabelled_key] = v
+        text_proj = "transformer.text_projection.weight"
+        if k.endswith(text_proj):
+            new_state_dict[k.replace(text_proj, "text_projection")] = v.transpose(0, 1).contiguous()
+        else:
+            relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k)
+            new_state_dict[relabelled_key] = v
 
     for k_pre, tensors in capture_qkv_weight.items():
         if None in tensors:
             raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")
         relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)
-        new_state_dict[relabelled_key + ".in_proj_weight"] = torch.cat(tensors)
+        new_state_dict[relabelled_key + ".in_proj_weight"] = cat_tensors(tensors)
 
     for k_pre, tensors in capture_qkv_bias.items():
         if None in tensors:
             raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing")
         relabelled_key = textenc_pattern.sub(lambda m: protected[re.escape(m.group(0))], k_pre)
-        new_state_dict[relabelled_key + ".in_proj_bias"] = torch.cat(tensors)
+        new_state_dict[relabelled_key + ".in_proj_bias"] = cat_tensors(tensors)
 
     return new_state_dict
 
diff --git a/comfy/extra_samplers/uni_pc.py b/comfy/extra_samplers/uni_pc.py
index 08bf0fc9..a30d1d03 100644
--- a/comfy/extra_samplers/uni_pc.py
+++ b/comfy/extra_samplers/uni_pc.py
@@ -358,9 +358,6 @@ class UniPC:
         thresholding=False,
         max_val=1.,
         variant='bh1',
-        noise_mask=None,
-        masked_image=None,
-        noise=None,
     ):
         """Construct a UniPC. 
 
@@ -372,9 +369,6 @@ class UniPC:
         self.predict_x0 = predict_x0
         self.thresholding = thresholding
         self.max_val = max_val
-        self.noise_mask = noise_mask
-        self.masked_image = masked_image
-        self.noise = noise
 
     def dynamic_thresholding_fn(self, x0, t=None):
         """
@@ -391,10 +385,7 @@ class UniPC:
         """
         Return the noise prediction model.
         """
-        if self.noise_mask is not None:
-            return self.model(x, t) * self.noise_mask
-        else:
-            return self.model(x, t)
+        return self.model(x, t)
 
     def data_prediction_fn(self, x, t):
         """
@@ -409,8 +400,6 @@ class UniPC:
             s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
             s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
             x0 = torch.clamp(x0, -s, s) / s
-        if self.noise_mask is not None:
-            x0 = x0 * self.noise_mask + (1. - self.noise_mask) * self.masked_image
         return x0
 
     def model_fn(self, x, t):
@@ -723,8 +712,6 @@ class UniPC:
             assert timesteps.shape[0] - 1 == steps
             # with torch.no_grad():
             for step_index in trange(steps, disable=disable_pbar):
-                if self.noise_mask is not None:
-                    x = x * self.noise_mask + (1. - self.noise_mask) * (self.masked_image * self.noise_schedule.marginal_alpha(timesteps[step_index]) + self.noise * self.noise_schedule.marginal_std(timesteps[step_index]))
                 if step_index == 0:
                     vec_t = timesteps[0].expand((x.shape[0]))
                     model_prev_list = [self.model_fn(x, vec_t)]
@@ -766,7 +753,7 @@ class UniPC:
                                 model_x = self.model_fn(x, vec_t)
                             model_prev_list[-1] = model_x
                 if callback is not None:
-                    callback(step_index, model_prev_list[-1], x, steps)
+                    callback({'x': x, 'i': step_index, 'denoised': model_prev_list[-1]})
         else:
             raise NotImplementedError()
         # if denoise_to_zero:
@@ -858,7 +845,7 @@ def predict_eps_sigma(model, input, sigma_in, **kwargs):
     return  (input - model(input, sigma_in, **kwargs)) / sigma
 
 
-def sample_unipc(model, noise, image, sigmas, max_denoise, extra_args=None, callback=None, disable=False, noise_mask=None, variant='bh1'):
+def sample_unipc(model, noise, sigmas, extra_args=None, callback=None, disable=False, variant='bh1'):
         timesteps = sigmas.clone()
         if sigmas[-1] == 0:
             timesteps = sigmas[:]
@@ -867,16 +854,7 @@ def sample_unipc(model, noise, image, sigmas, max_denoise, extra_args=None, call
             timesteps = sigmas.clone()
         ns = SigmaConvert()
 
-        if image is not None:
-            img = image * ns.marginal_alpha(timesteps[0])
-            if max_denoise:
-                noise_mult = 1.0
-            else:
-                noise_mult = ns.marginal_std(timesteps[0])
-            img += noise * noise_mult
-        else:
-            img = noise
-
+        noise = noise / torch.sqrt(1.0 + timesteps[0] ** 2.0)
         model_type = "noise"
 
         model_fn = model_wrapper(
@@ -888,7 +866,10 @@ def sample_unipc(model, noise, image, sigmas, max_denoise, extra_args=None, call
         )
 
         order = min(3, len(timesteps) - 2)
-        uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, noise_mask=noise_mask, masked_image=image, noise=noise, variant=variant)
-        x = uni_pc.sample(img, timesteps=timesteps, skip_type="time_uniform", method="multistep", order=order, lower_order_final=True, callback=callback, disable_pbar=disable)
+        uni_pc = UniPC(model_fn, ns, predict_x0=True, thresholding=False, variant=variant)
+        x = uni_pc.sample(noise, timesteps=timesteps, skip_type="time_uniform", method="multistep", order=order, lower_order_final=True, callback=callback, disable_pbar=disable)
         x /= ns.marginal_alpha(timesteps[-1])
         return x
+
+def sample_unipc_bh2(model, noise, sigmas, extra_args=None, callback=None, disable=False):
+    return sample_unipc(model, noise, sigmas, extra_args, callback, disable, variant='bh2')
\ No newline at end of file
diff --git a/comfy/gligen.py b/comfy/gligen.py
index 71892dfb..59252276 100644
--- a/comfy/gligen.py
+++ b/comfy/gligen.py
@@ -2,7 +2,8 @@ import torch
 from torch import nn
 from .ldm.modules.attention import CrossAttention
 from inspect import isfunction
-
+import comfy.ops
+ops = comfy.ops.manual_cast
 
 def exists(val):
     return val is not None
@@ -22,7 +23,7 @@ def default(val, d):
 class GEGLU(nn.Module):
     def __init__(self, dim_in, dim_out):
         super().__init__()
-        self.proj = nn.Linear(dim_in, dim_out * 2)
+        self.proj = ops.Linear(dim_in, dim_out * 2)
 
     def forward(self, x):
         x, gate = self.proj(x).chunk(2, dim=-1)
@@ -35,14 +36,14 @@ class FeedForward(nn.Module):
         inner_dim = int(dim * mult)
         dim_out = default(dim_out, dim)
         project_in = nn.Sequential(
-            nn.Linear(dim, inner_dim),
+            ops.Linear(dim, inner_dim),
             nn.GELU()
         ) if not glu else GEGLU(dim, inner_dim)
 
         self.net = nn.Sequential(
             project_in,
             nn.Dropout(dropout),
-            nn.Linear(inner_dim, dim_out)
+            ops.Linear(inner_dim, dim_out)
         )
 
     def forward(self, x):
@@ -57,11 +58,12 @@ class GatedCrossAttentionDense(nn.Module):
             query_dim=query_dim,
             context_dim=context_dim,
             heads=n_heads,
-            dim_head=d_head)
+            dim_head=d_head,
+            operations=ops)
         self.ff = FeedForward(query_dim, glu=True)
 
-        self.norm1 = nn.LayerNorm(query_dim)
-        self.norm2 = nn.LayerNorm(query_dim)
+        self.norm1 = ops.LayerNorm(query_dim)
+        self.norm2 = ops.LayerNorm(query_dim)
 
         self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)))
         self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)))
@@ -87,17 +89,18 @@ class GatedSelfAttentionDense(nn.Module):
 
         # we need a linear projection since we need cat visual feature and obj
         # feature
-        self.linear = nn.Linear(context_dim, query_dim)
+        self.linear = ops.Linear(context_dim, query_dim)
 
         self.attn = CrossAttention(
             query_dim=query_dim,
             context_dim=query_dim,
             heads=n_heads,
-            dim_head=d_head)
+            dim_head=d_head,
+            operations=ops)
         self.ff = FeedForward(query_dim, glu=True)
 
-        self.norm1 = nn.LayerNorm(query_dim)
-        self.norm2 = nn.LayerNorm(query_dim)
+        self.norm1 = ops.LayerNorm(query_dim)
+        self.norm2 = ops.LayerNorm(query_dim)
 
         self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)))
         self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)))
@@ -126,14 +129,14 @@ class GatedSelfAttentionDense2(nn.Module):
 
         # we need a linear projection since we need cat visual feature and obj
         # feature
-        self.linear = nn.Linear(context_dim, query_dim)
+        self.linear = ops.Linear(context_dim, query_dim)
 
         self.attn = CrossAttention(
-            query_dim=query_dim, context_dim=query_dim, dim_head=d_head)
+            query_dim=query_dim, context_dim=query_dim, dim_head=d_head, operations=ops)
         self.ff = FeedForward(query_dim, glu=True)
 
-        self.norm1 = nn.LayerNorm(query_dim)
-        self.norm2 = nn.LayerNorm(query_dim)
+        self.norm1 = ops.LayerNorm(query_dim)
+        self.norm2 = ops.LayerNorm(query_dim)
 
         self.register_parameter('alpha_attn', nn.Parameter(torch.tensor(0.)))
         self.register_parameter('alpha_dense', nn.Parameter(torch.tensor(0.)))
@@ -201,11 +204,11 @@ class PositionNet(nn.Module):
         self.position_dim = fourier_freqs * 2 * 4  # 2 is sin&cos, 4 is xyxy
 
         self.linears = nn.Sequential(
-            nn.Linear(self.in_dim + self.position_dim, 512),
+            ops.Linear(self.in_dim + self.position_dim, 512),
             nn.SiLU(),
-            nn.Linear(512, 512),
+            ops.Linear(512, 512),
             nn.SiLU(),
-            nn.Linear(512, out_dim),
+            ops.Linear(512, out_dim),
         )
 
         self.null_positive_feature = torch.nn.Parameter(
@@ -215,16 +218,15 @@ class PositionNet(nn.Module):
 
     def forward(self, boxes, masks, positive_embeddings):
         B, N, _ = boxes.shape
-        dtype = self.linears[0].weight.dtype
-        masks = masks.unsqueeze(-1).to(dtype)
-        positive_embeddings = positive_embeddings.to(dtype)
+        masks = masks.unsqueeze(-1)
+        positive_embeddings = positive_embeddings
 
         # embedding position (it may includes padding as placeholder)
-        xyxy_embedding = self.fourier_embedder(boxes.to(dtype))  # B*N*4 --> B*N*C
+        xyxy_embedding = self.fourier_embedder(boxes)  # B*N*4 --> B*N*C
 
         # learnable null embedding
-        positive_null = self.null_positive_feature.view(1, 1, -1)
-        xyxy_null = self.null_position_feature.view(1, 1, -1)
+        positive_null = self.null_positive_feature.to(device=boxes.device, dtype=boxes.dtype).view(1, 1, -1)
+        xyxy_null = self.null_position_feature.to(device=boxes.device, dtype=boxes.dtype).view(1, 1, -1)
 
         # replace padding with learnable null embedding
         positive_embeddings = positive_embeddings * \
@@ -251,7 +253,7 @@ class Gligen(nn.Module):
         def func(x, extra_options):
             key = extra_options["transformer_index"]
             module = self.module_list[key]
-            return module(x, objs)
+            return module(x, objs.to(device=x.device, dtype=x.dtype))
         return func
 
     def set_position(self, latent_image_shape, position_params, device):
diff --git a/comfy/k_diffusion/deis.py b/comfy/k_diffusion/deis.py
new file mode 100644
index 00000000..60741065
--- /dev/null
+++ b/comfy/k_diffusion/deis.py
@@ -0,0 +1,121 @@
+#Taken from: https://github.com/zju-pi/diff-sampler/blob/main/gits-main/solver_utils.py
+#under Apache 2 license
+import torch
+import numpy as np
+
+# A pytorch reimplementation of DEIS (https://github.com/qsh-zh/deis).
+#############################
+### Utils for DEIS solver ###
+#############################
+#----------------------------------------------------------------------------
+# Transfer from the input time (sigma) used in EDM to that (t) used in DEIS.
+
+def edm2t(edm_steps, epsilon_s=1e-3, sigma_min=0.002, sigma_max=80):
+    vp_sigma = lambda beta_d, beta_min: lambda t: (np.e ** (0.5 * beta_d * (t ** 2) + beta_min * t) - 1) ** 0.5
+    vp_sigma_inv = lambda beta_d, beta_min: lambda sigma: ((beta_min ** 2 + 2 * beta_d * (sigma ** 2 + 1).log()).sqrt() - beta_min) / beta_d
+    vp_beta_d = 2 * (np.log(torch.tensor(sigma_min).cpu() ** 2 + 1) / epsilon_s - np.log(torch.tensor(sigma_max).cpu() ** 2 + 1)) / (epsilon_s - 1)
+    vp_beta_min = np.log(torch.tensor(sigma_max).cpu() ** 2 + 1) - 0.5 * vp_beta_d
+    t_steps = vp_sigma_inv(vp_beta_d.clone().detach().cpu(), vp_beta_min.clone().detach().cpu())(edm_steps.clone().detach().cpu())
+    return t_steps, vp_beta_min, vp_beta_d + vp_beta_min
+
+#----------------------------------------------------------------------------
+
+def cal_poly(prev_t, j, taus):
+    poly = 1
+    for k in range(prev_t.shape[0]):
+        if k == j:
+            continue
+        poly *= (taus - prev_t[k]) / (prev_t[j] - prev_t[k])
+    return poly
+
+#----------------------------------------------------------------------------
+# Transfer from t to alpha_t.
+
+def t2alpha_fn(beta_0, beta_1, t):
+    return torch.exp(-0.5 * t ** 2 * (beta_1 - beta_0) - t * beta_0)
+
+#----------------------------------------------------------------------------
+
+def cal_intergrand(beta_0, beta_1, taus):
+    with torch.inference_mode(mode=False):
+        taus = taus.clone()
+        beta_0 = beta_0.clone()
+        beta_1 = beta_1.clone()
+        with torch.enable_grad():
+            taus.requires_grad_(True)
+            alpha = t2alpha_fn(beta_0, beta_1, taus)
+            log_alpha = alpha.log()
+            log_alpha.sum().backward()
+            d_log_alpha_dtau = taus.grad
+    integrand = -0.5 * d_log_alpha_dtau / torch.sqrt(alpha * (1 - alpha))
+    return integrand
+
+#----------------------------------------------------------------------------
+
+def get_deis_coeff_list(t_steps, max_order, N=10000, deis_mode='tab'):
+    """
+    Get the coefficient list for DEIS sampling.
+
+    Args:
+        t_steps: A pytorch tensor. The time steps for sampling.
+        max_order: A `int`. Maximum order of the solver. 1 <= max_order <= 4
+        N: A `int`. Use how many points to perform the numerical integration when deis_mode=='tab'.
+        deis_mode: A `str`. Select between 'tab' and 'rhoab'. Type of DEIS.
+    Returns:
+        A pytorch tensor. A batch of generated samples or sampling trajectories if return_inters=True.
+    """
+    if deis_mode == 'tab':
+        t_steps, beta_0, beta_1 = edm2t(t_steps)
+        C = []
+        for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])):
+            order = min(i+1, max_order)
+            if order == 1:
+                C.append([])
+            else:
+                taus = torch.linspace(t_cur, t_next, N)   # split the interval for integral appximation
+                dtau = (t_next - t_cur) / N
+                prev_t = t_steps[[i - k for k in range(order)]]
+                coeff_temp = []
+                integrand = cal_intergrand(beta_0, beta_1, taus)
+                for j in range(order):
+                    poly = cal_poly(prev_t, j, taus)
+                    coeff_temp.append(torch.sum(integrand * poly) * dtau)
+                C.append(coeff_temp)
+
+    elif deis_mode == 'rhoab':
+        # Analytical solution, second order
+        def get_def_intergral_2(a, b, start, end, c):
+            coeff = (end**3 - start**3) / 3 - (end**2 - start**2) * (a + b) / 2 + (end - start) * a * b
+            return coeff / ((c - a) * (c - b))
+
+        # Analytical solution, third order
+        def get_def_intergral_3(a, b, c, start, end, d):
+            coeff = (end**4 - start**4) / 4 - (end**3 - start**3) * (a + b + c) / 3 \
+                    + (end**2 - start**2) * (a*b + a*c + b*c) / 2 - (end - start) * a * b * c
+            return coeff / ((d - a) * (d - b) * (d - c))
+
+        C = []
+        for i, (t_cur, t_next) in enumerate(zip(t_steps[:-1], t_steps[1:])):
+            order = min(i, max_order)
+            if order == 0:
+                C.append([])
+            else:
+                prev_t = t_steps[[i - k for k in range(order+1)]]
+                if order == 1:
+                    coeff_cur = ((t_next - prev_t[1])**2 - (t_cur - prev_t[1])**2) / (2 * (t_cur - prev_t[1]))
+                    coeff_prev1 = (t_next - t_cur)**2 / (2 * (prev_t[1] - t_cur))
+                    coeff_temp = [coeff_cur, coeff_prev1]
+                elif order == 2:
+                    coeff_cur = get_def_intergral_2(prev_t[1], prev_t[2], t_cur, t_next, t_cur)
+                    coeff_prev1 = get_def_intergral_2(t_cur, prev_t[2], t_cur, t_next, prev_t[1])
+                    coeff_prev2 = get_def_intergral_2(t_cur, prev_t[1], t_cur, t_next, prev_t[2])
+                    coeff_temp = [coeff_cur, coeff_prev1, coeff_prev2]
+                elif order == 3:
+                    coeff_cur = get_def_intergral_3(prev_t[1], prev_t[2], prev_t[3], t_cur, t_next, t_cur)
+                    coeff_prev1 = get_def_intergral_3(t_cur, prev_t[2], prev_t[3], t_cur, t_next, prev_t[1])
+                    coeff_prev2 = get_def_intergral_3(t_cur, prev_t[1], prev_t[3], t_cur, t_next, prev_t[2])
+                    coeff_prev3 = get_def_intergral_3(t_cur, prev_t[1], prev_t[2], t_cur, t_next, prev_t[3])
+                    coeff_temp = [coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3]
+                C.append(coeff_temp)
+    return C
+
diff --git a/comfy/k_diffusion/sampling.py b/comfy/k_diffusion/sampling.py
index 761c2e0e..763d8cc7 100644
--- a/comfy/k_diffusion/sampling.py
+++ b/comfy/k_diffusion/sampling.py
@@ -7,7 +7,8 @@ import torchsde
 from tqdm.auto import trange, tqdm
 
 from . import utils
-
+from . import deis
+import comfy.model_patcher
 
 def append_zero(x):
     return torch.cat([x, x.new_zeros([1])])
@@ -129,8 +130,13 @@ def sample_euler(model, x, sigmas, extra_args=None, callback=None, disable=None,
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
     for i in trange(len(sigmas) - 1, disable=disable):
-        gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
-        sigma_hat = sigmas[i] * (gamma + 1)
+        if s_churn > 0:
+            gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
+            sigma_hat = sigmas[i] * (gamma + 1)
+        else:
+            gamma = 0
+            sigma_hat = sigmas[i]
+
         if gamma > 0:
             eps = torch.randn_like(x) * s_noise
             x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
@@ -170,7 +176,13 @@ def sample_heun(model, x, sigmas, extra_args=None, callback=None, disable=None,
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
     for i in trange(len(sigmas) - 1, disable=disable):
-        gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
+        if s_churn > 0:
+            gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
+            sigma_hat = sigmas[i] * (gamma + 1)
+        else:
+            gamma = 0
+            sigma_hat = sigmas[i]
+
         sigma_hat = sigmas[i] * (gamma + 1)
         if gamma > 0:
             eps = torch.randn_like(x) * s_noise
@@ -199,8 +211,13 @@ def sample_dpm_2(model, x, sigmas, extra_args=None, callback=None, disable=None,
     extra_args = {} if extra_args is None else extra_args
     s_in = x.new_ones([x.shape[0]])
     for i in trange(len(sigmas) - 1, disable=disable):
-        gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
-        sigma_hat = sigmas[i] * (gamma + 1)
+        if s_churn > 0:
+            gamma = min(s_churn / (len(sigmas) - 1), 2 ** 0.5 - 1) if s_tmin <= sigmas[i] <= s_tmax else 0.
+            sigma_hat = sigmas[i] * (gamma + 1)
+        else:
+            gamma = 0
+            sigma_hat = sigmas[i]
+
         if gamma > 0:
             eps = torch.randn_like(x) * s_noise
             x = x + eps * (sigma_hat ** 2 - sigmas[i] ** 2) ** 0.5
@@ -527,6 +544,9 @@ def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None,
 @torch.no_grad()
 def sample_dpmpp_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
     """DPM-Solver++ (stochastic)."""
+    if len(sigmas) <= 1:
+        return x
+
     sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
     seed = extra_args.get("seed", None)
     noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
@@ -595,6 +615,8 @@ def sample_dpmpp_2m(model, x, sigmas, extra_args=None, callback=None, disable=No
 @torch.no_grad()
 def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
     """DPM-Solver++(2M) SDE."""
+    if len(sigmas) <= 1:
+        return x
 
     if solver_type not in {'heun', 'midpoint'}:
         raise ValueError('solver_type must be \'heun\' or \'midpoint\'')
@@ -642,6 +664,9 @@ def sample_dpmpp_2m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
 def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
     """DPM-Solver++(3M) SDE."""
 
+    if len(sigmas) <= 1:
+        return x
+
     seed = extra_args.get("seed", None)
     sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
     noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=seed, cpu=True) if noise_sampler is None else noise_sampler
@@ -690,18 +715,27 @@ def sample_dpmpp_3m_sde(model, x, sigmas, extra_args=None, callback=None, disabl
 
 @torch.no_grad()
 def sample_dpmpp_3m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+    if len(sigmas) <= 1:
+        return x
+
     sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
     noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
     return sample_dpmpp_3m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler)
 
 @torch.no_grad()
 def sample_dpmpp_2m_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, solver_type='midpoint'):
+    if len(sigmas) <= 1:
+        return x
+
     sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
     noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
     return sample_dpmpp_2m_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, solver_type=solver_type)
 
 @torch.no_grad()
 def sample_dpmpp_sde_gpu(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None, r=1 / 2):
+    if len(sigmas) <= 1:
+        return x
+
     sigma_min, sigma_max = sigmas[sigmas > 0].min(), sigmas.max()
     noise_sampler = BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=extra_args.get("seed", None), cpu=False) if noise_sampler is None else noise_sampler
     return sample_dpmpp_sde(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, eta=eta, s_noise=s_noise, noise_sampler=noise_sampler, r=r)
@@ -748,7 +782,7 @@ def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, n
 
         x = denoised
         if sigmas[i + 1] > 0:
-            x += sigmas[i + 1] * noise_sampler(sigmas[i], sigmas[i + 1])
+            x = model.inner_model.inner_model.model_sampling.noise_scaling(sigmas[i + 1], noise_sampler(sigmas[i], sigmas[i + 1]), x)
     return x
 
 
@@ -808,3 +842,209 @@ def sample_heunpp2(model, x, sigmas, extra_args=None, callback=None, disable=Non
             d_prime = w1 * d + w2 * d_2 + w3 * d_3
             x = x + d_prime * dt
     return x
+
+
+#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
+#under Apache 2 license
+def sample_ipndm(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4):
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+
+    x_next = x
+
+    buffer_model = []
+    for i in trange(len(sigmas) - 1, disable=disable):
+        t_cur = sigmas[i]
+        t_next = sigmas[i + 1]
+
+        x_cur = x_next
+
+        denoised = model(x_cur, t_cur * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+        d_cur = (x_cur - denoised) / t_cur
+
+        order = min(max_order, i+1)
+        if order == 1:      # First Euler step.
+            x_next = x_cur + (t_next - t_cur) * d_cur
+        elif order == 2:    # Use one history point.
+            x_next = x_cur + (t_next - t_cur) * (3 * d_cur - buffer_model[-1]) / 2
+        elif order == 3:    # Use two history points.
+            x_next = x_cur + (t_next - t_cur) * (23 * d_cur - 16 * buffer_model[-1] + 5 * buffer_model[-2]) / 12
+        elif order == 4:    # Use three history points.
+            x_next = x_cur + (t_next - t_cur) * (55 * d_cur - 59 * buffer_model[-1] + 37 * buffer_model[-2] - 9 * buffer_model[-3]) / 24
+
+        if len(buffer_model) == max_order - 1:
+            for k in range(max_order - 2):
+                buffer_model[k] = buffer_model[k+1]
+            buffer_model[-1] = d_cur
+        else:
+            buffer_model.append(d_cur)
+
+    return x_next
+
+#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
+#under Apache 2 license
+def sample_ipndm_v(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=4):
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+
+    x_next = x
+    t_steps = sigmas
+
+    buffer_model = []
+    for i in trange(len(sigmas) - 1, disable=disable):
+        t_cur = sigmas[i]
+        t_next = sigmas[i + 1]
+
+        x_cur = x_next
+
+        denoised = model(x_cur, t_cur * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+        d_cur = (x_cur - denoised) / t_cur
+
+        order = min(max_order, i+1)
+        if order == 1:      # First Euler step.
+            x_next = x_cur + (t_next - t_cur) * d_cur
+        elif order == 2:    # Use one history point.
+            h_n = (t_next - t_cur)
+            h_n_1 = (t_cur - t_steps[i-1])
+            coeff1 = (2 + (h_n / h_n_1)) / 2
+            coeff2 = -(h_n / h_n_1) / 2
+            x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1])
+        elif order == 3:    # Use two history points.
+            h_n = (t_next - t_cur)
+            h_n_1 = (t_cur - t_steps[i-1])
+            h_n_2 = (t_steps[i-1] - t_steps[i-2])
+            temp = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2
+            coeff1 = (2 + (h_n / h_n_1)) / 2 + temp
+            coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp
+            coeff3 = temp * h_n_1 / h_n_2
+            x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2])
+        elif order == 4:    # Use three history points.
+            h_n = (t_next - t_cur)
+            h_n_1 = (t_cur - t_steps[i-1])
+            h_n_2 = (t_steps[i-1] - t_steps[i-2])
+            h_n_3 = (t_steps[i-2] - t_steps[i-3])
+            temp1 = (1 - h_n / (3 * (h_n + h_n_1)) * (h_n * (h_n + h_n_1)) / (h_n_1 * (h_n_1 + h_n_2))) / 2
+            temp2 = ((1 - h_n / (3 * (h_n + h_n_1))) / 2 + (1 - h_n / (2 * (h_n + h_n_1))) * h_n / (6 * (h_n + h_n_1 + h_n_2))) \
+                   * (h_n * (h_n + h_n_1) * (h_n + h_n_1 + h_n_2)) / (h_n_1 * (h_n_1 + h_n_2) * (h_n_1 + h_n_2 + h_n_3))
+            coeff1 = (2 + (h_n / h_n_1)) / 2 + temp1 + temp2
+            coeff2 = -(h_n / h_n_1) / 2 - (1 + h_n_1 / h_n_2) * temp1 - (1 + (h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3)))) * temp2
+            coeff3 = temp1 * h_n_1 / h_n_2 + ((h_n_1 / h_n_2) + (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * (1 + h_n_2 / h_n_3)) * temp2
+            coeff4 = -temp2 * (h_n_1 * (h_n_1 + h_n_2) / (h_n_2 * (h_n_2 + h_n_3))) * h_n_1 / h_n_2
+            x_next = x_cur + (t_next - t_cur) * (coeff1 * d_cur + coeff2 * buffer_model[-1] + coeff3 * buffer_model[-2] + coeff4 * buffer_model[-3])
+
+        if len(buffer_model) == max_order - 1:
+            for k in range(max_order - 2):
+                buffer_model[k] = buffer_model[k+1]
+            buffer_model[-1] = d_cur.detach()
+        else:
+            buffer_model.append(d_cur.detach())
+
+    return x_next
+
+#From https://github.com/zju-pi/diff-sampler/blob/main/diff-solvers-main/solvers.py
+#under Apache 2 license
+@torch.no_grad()
+def sample_deis(model, x, sigmas, extra_args=None, callback=None, disable=None, max_order=3, deis_mode='tab'):
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+
+    x_next = x
+    t_steps = sigmas
+
+    coeff_list = deis.get_deis_coeff_list(t_steps, max_order, deis_mode=deis_mode)
+
+    buffer_model = []
+    for i in trange(len(sigmas) - 1, disable=disable):
+        t_cur = sigmas[i]
+        t_next = sigmas[i + 1]
+
+        x_cur = x_next
+
+        denoised = model(x_cur, t_cur * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+        d_cur = (x_cur - denoised) / t_cur
+
+        order = min(max_order, i+1)
+        if t_next <= 0:
+            order = 1
+
+        if order == 1:          # First Euler step.
+            x_next = x_cur + (t_next - t_cur) * d_cur
+        elif order == 2:        # Use one history point.
+            coeff_cur, coeff_prev1 = coeff_list[i]
+            x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1]
+        elif order == 3:        # Use two history points.
+            coeff_cur, coeff_prev1, coeff_prev2 = coeff_list[i]
+            x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2]
+        elif order == 4:        # Use three history points.
+            coeff_cur, coeff_prev1, coeff_prev2, coeff_prev3 = coeff_list[i]
+            x_next = x_cur + coeff_cur * d_cur + coeff_prev1 * buffer_model[-1] + coeff_prev2 * buffer_model[-2] + coeff_prev3 * buffer_model[-3]
+
+        if len(buffer_model) == max_order - 1:
+            for k in range(max_order - 2):
+                buffer_model[k] = buffer_model[k+1]
+            buffer_model[-1] = d_cur.detach()
+        else:
+            buffer_model.append(d_cur.detach())
+
+    return x_next
+
+@torch.no_grad()
+def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    extra_args = {} if extra_args is None else extra_args
+
+    temp = [0]
+    def post_cfg_function(args):
+        temp[0] = args["uncond_denoised"]
+        return args["denoised"]
+
+    model_options = extra_args.get("model_options", {}).copy()
+    extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+    s_in = x.new_ones([x.shape[0]])
+    for i in trange(len(sigmas) - 1, disable=disable):
+        sigma_hat = sigmas[i]
+        denoised = model(x, sigma_hat * s_in, **extra_args)
+        d = to_d(x, sigma_hat, temp[0])
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
+        dt = sigmas[i + 1] - sigma_hat
+        # Euler method
+        x = denoised + d * sigmas[i + 1]
+    return x
+
+@torch.no_grad()
+def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+    """Ancestral sampling with Euler method steps."""
+    extra_args = {} if extra_args is None else extra_args
+    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+
+    temp = [0]
+    def post_cfg_function(args):
+        temp[0] = args["uncond_denoised"]
+        return args["denoised"]
+
+    model_options = extra_args.get("model_options", {}).copy()
+    extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+    s_in = x.new_ones([x.shape[0]])
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        d = to_d(x, sigmas[i], temp[0])
+        # Euler method
+        dt = sigma_down - sigmas[i]
+        x = denoised + d * sigma_down
+        if sigmas[i + 1] > 0:
+            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+    return x
diff --git a/comfy/latent_formats.py b/comfy/latent_formats.py
index 2252a075..4b4a9eda 100644
--- a/comfy/latent_formats.py
+++ b/comfy/latent_formats.py
@@ -1,6 +1,8 @@
+import torch
 
 class LatentFormat:
     scale_factor = 1.0
+    latent_channels = 4
     latent_rgb_factors = None
     taesd_decoder_name = None
 
@@ -23,8 +25,9 @@ class SD15(LatentFormat):
         self.taesd_decoder_name = "taesd_decoder"
 
 class SDXL(LatentFormat):
+    scale_factor = 0.13025
+
     def __init__(self):
-        self.scale_factor = 0.13025
         self.latent_rgb_factors = [
                     #   R        G        B
                     [ 0.3920,  0.4054,  0.4549],
@@ -34,6 +37,105 @@ class SDXL(LatentFormat):
                 ]
         self.taesd_decoder_name = "taesdxl_decoder"
 
+class SDXL_Playground_2_5(LatentFormat):
+    def __init__(self):
+        self.scale_factor = 0.5
+        self.latents_mean = torch.tensor([-1.6574, 1.886, -1.383, 2.5155]).view(1, 4, 1, 1)
+        self.latents_std = torch.tensor([8.4927, 5.9022, 6.5498, 5.2299]).view(1, 4, 1, 1)
+
+        self.latent_rgb_factors = [
+                    #   R        G        B
+                    [ 0.3920,  0.4054,  0.4549],
+                    [-0.2634, -0.0196,  0.0653],
+                    [ 0.0568,  0.1687, -0.0755],
+                    [-0.3112, -0.2359, -0.2076]
+                ]
+        self.taesd_decoder_name = "taesdxl_decoder"
+
+    def process_in(self, latent):
+        latents_mean = self.latents_mean.to(latent.device, latent.dtype)
+        latents_std = self.latents_std.to(latent.device, latent.dtype)
+        return (latent - latents_mean) * self.scale_factor / latents_std
+
+    def process_out(self, latent):
+        latents_mean = self.latents_mean.to(latent.device, latent.dtype)
+        latents_std = self.latents_std.to(latent.device, latent.dtype)
+        return latent * latents_std / self.scale_factor + latents_mean
+
+
 class SD_X4(LatentFormat):
     def __init__(self):
         self.scale_factor = 0.08333
+        self.latent_rgb_factors = [
+            [-0.2340, -0.3863, -0.3257],
+            [ 0.0994,  0.0885, -0.0908],
+            [-0.2833, -0.2349, -0.3741],
+            [ 0.2523, -0.0055, -0.1651]
+        ]
+
+class SC_Prior(LatentFormat):
+    latent_channels = 16
+    def __init__(self):
+        self.scale_factor = 1.0
+        self.latent_rgb_factors = [
+            [-0.0326, -0.0204, -0.0127],
+            [-0.1592, -0.0427,  0.0216],
+            [ 0.0873,  0.0638, -0.0020],
+            [-0.0602,  0.0442,  0.1304],
+            [ 0.0800, -0.0313, -0.1796],
+            [-0.0810, -0.0638, -0.1581],
+            [ 0.1791,  0.1180,  0.0967],
+            [ 0.0740,  0.1416,  0.0432],
+            [-0.1745, -0.1888, -0.1373],
+            [ 0.2412,  0.1577,  0.0928],
+            [ 0.1908,  0.0998,  0.0682],
+            [ 0.0209,  0.0365, -0.0092],
+            [ 0.0448, -0.0650, -0.1728],
+            [-0.1658, -0.1045, -0.1308],
+            [ 0.0542,  0.1545,  0.1325],
+            [-0.0352, -0.1672, -0.2541]
+        ]
+
+class SC_B(LatentFormat):
+    def __init__(self):
+        self.scale_factor = 1.0 / 0.43
+        self.latent_rgb_factors = [
+            [ 0.1121,  0.2006,  0.1023],
+            [-0.2093, -0.0222, -0.0195],
+            [-0.3087, -0.1535,  0.0366],
+            [ 0.0290, -0.1574, -0.4078]
+        ]
+
+class SD3(LatentFormat):
+    latent_channels = 16
+    def __init__(self):
+        self.scale_factor = 1.5305
+        self.shift_factor = 0.0609
+        self.latent_rgb_factors = [
+            [-0.0645,  0.0177,  0.1052],
+            [ 0.0028,  0.0312,  0.0650],
+            [ 0.1848,  0.0762,  0.0360],
+            [ 0.0944,  0.0360,  0.0889],
+            [ 0.0897,  0.0506, -0.0364],
+            [-0.0020,  0.1203,  0.0284],
+            [ 0.0855,  0.0118,  0.0283],
+            [-0.0539,  0.0658,  0.1047],
+            [-0.0057,  0.0116,  0.0700],
+            [-0.0412,  0.0281, -0.0039],
+            [ 0.1106,  0.1171,  0.1220],
+            [-0.0248,  0.0682, -0.0481],
+            [ 0.0815,  0.0846,  0.1207],
+            [-0.0120, -0.0055, -0.0867],
+            [-0.0749, -0.0634, -0.0456],
+            [-0.1418, -0.1457, -0.1259]
+        ]
+        self.taesd_decoder_name = "taesd3_decoder"
+
+    def process_in(self, latent):
+        return (latent - self.shift_factor) * self.scale_factor
+
+    def process_out(self, latent):
+        return (latent / self.scale_factor) + self.shift_factor
+
+class StableAudio1(LatentFormat):
+    latent_channels = 64
diff --git a/comfy/ldm/audio/autoencoder.py b/comfy/ldm/audio/autoencoder.py
new file mode 100644
index 00000000..8123e66a
--- /dev/null
+++ b/comfy/ldm/audio/autoencoder.py
@@ -0,0 +1,282 @@
+# code adapted from: https://github.com/Stability-AI/stable-audio-tools
+
+import torch
+from torch import nn
+from typing import Literal, Dict, Any
+import math
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+def vae_sample(mean, scale):
+        stdev = nn.functional.softplus(scale) + 1e-4
+        var = stdev * stdev
+        logvar = torch.log(var)
+        latents = torch.randn_like(mean) * stdev + mean
+
+        kl = (mean * mean + var - logvar - 1).sum(1).mean()
+
+        return latents, kl
+
+class VAEBottleneck(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.is_discrete = False
+
+    def encode(self, x, return_info=False, **kwargs):
+        info = {}
+
+        mean, scale = x.chunk(2, dim=1)
+
+        x, kl = vae_sample(mean, scale)
+
+        info["kl"] = kl
+
+        if return_info:
+            return x, info
+        else:
+            return x
+
+    def decode(self, x):
+        return x
+
+
+def snake_beta(x, alpha, beta):
+    return x + (1.0 / (beta + 0.000000001)) * pow(torch.sin(x * alpha), 2)
+
+# Adapted from https://github.com/NVIDIA/BigVGAN/blob/main/activations.py under MIT license
+class SnakeBeta(nn.Module):
+
+    def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=True):
+        super(SnakeBeta, self).__init__()
+        self.in_features = in_features
+
+        # initialize alpha
+        self.alpha_logscale = alpha_logscale
+        if self.alpha_logscale: # log scale alphas initialized to zeros
+            self.alpha = nn.Parameter(torch.zeros(in_features) * alpha)
+            self.beta = nn.Parameter(torch.zeros(in_features) * alpha)
+        else: # linear scale alphas initialized to ones
+            self.alpha = nn.Parameter(torch.ones(in_features) * alpha)
+            self.beta = nn.Parameter(torch.ones(in_features) * alpha)
+
+        # self.alpha.requires_grad = alpha_trainable
+        # self.beta.requires_grad = alpha_trainable
+
+        self.no_div_by_zero = 0.000000001
+
+    def forward(self, x):
+        alpha = self.alpha.unsqueeze(0).unsqueeze(-1).to(x.device) # line up with x to [B, C, T]
+        beta = self.beta.unsqueeze(0).unsqueeze(-1).to(x.device)
+        if self.alpha_logscale:
+            alpha = torch.exp(alpha)
+            beta = torch.exp(beta)
+        x = snake_beta(x, alpha, beta)
+
+        return x
+
+def WNConv1d(*args, **kwargs):
+    try:
+        return torch.nn.utils.parametrizations.weight_norm(ops.Conv1d(*args, **kwargs))
+    except:
+        return torch.nn.utils.weight_norm(ops.Conv1d(*args, **kwargs)) #support pytorch 2.1 and older
+
+def WNConvTranspose1d(*args, **kwargs):
+    try:
+        return torch.nn.utils.parametrizations.weight_norm(ops.ConvTranspose1d(*args, **kwargs))
+    except:
+        return torch.nn.utils.weight_norm(ops.ConvTranspose1d(*args, **kwargs)) #support pytorch 2.1 and older
+
+def get_activation(activation: Literal["elu", "snake", "none"], antialias=False, channels=None) -> nn.Module:
+    if activation == "elu":
+        act = torch.nn.ELU()
+    elif activation == "snake":
+        act = SnakeBeta(channels)
+    elif activation == "none":
+        act = torch.nn.Identity()
+    else:
+        raise ValueError(f"Unknown activation {activation}")
+
+    if antialias:
+        act = Activation1d(act)
+
+    return act
+
+
+class ResidualUnit(nn.Module):
+    def __init__(self, in_channels, out_channels, dilation, use_snake=False, antialias_activation=False):
+        super().__init__()
+
+        self.dilation = dilation
+
+        padding = (dilation * (7-1)) // 2
+
+        self.layers = nn.Sequential(
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels),
+            WNConv1d(in_channels=in_channels, out_channels=out_channels,
+                      kernel_size=7, dilation=dilation, padding=padding),
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=out_channels),
+            WNConv1d(in_channels=out_channels, out_channels=out_channels,
+                      kernel_size=1)
+        )
+
+    def forward(self, x):
+        res = x
+
+        #x = checkpoint(self.layers, x)
+        x = self.layers(x)
+
+        return x + res
+
+class EncoderBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False):
+        super().__init__()
+
+        self.layers = nn.Sequential(
+            ResidualUnit(in_channels=in_channels,
+                         out_channels=in_channels, dilation=1, use_snake=use_snake),
+            ResidualUnit(in_channels=in_channels,
+                         out_channels=in_channels, dilation=3, use_snake=use_snake),
+            ResidualUnit(in_channels=in_channels,
+                         out_channels=in_channels, dilation=9, use_snake=use_snake),
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels),
+            WNConv1d(in_channels=in_channels, out_channels=out_channels,
+                      kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2)),
+        )
+
+    def forward(self, x):
+        return self.layers(x)
+
+class DecoderBlock(nn.Module):
+    def __init__(self, in_channels, out_channels, stride, use_snake=False, antialias_activation=False, use_nearest_upsample=False):
+        super().__init__()
+
+        if use_nearest_upsample:
+            upsample_layer = nn.Sequential(
+                nn.Upsample(scale_factor=stride, mode="nearest"),
+                WNConv1d(in_channels=in_channels,
+                        out_channels=out_channels,
+                        kernel_size=2*stride,
+                        stride=1,
+                        bias=False,
+                        padding='same')
+            )
+        else:
+            upsample_layer = WNConvTranspose1d(in_channels=in_channels,
+                               out_channels=out_channels,
+                               kernel_size=2*stride, stride=stride, padding=math.ceil(stride/2))
+
+        self.layers = nn.Sequential(
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=in_channels),
+            upsample_layer,
+            ResidualUnit(in_channels=out_channels, out_channels=out_channels,
+                         dilation=1, use_snake=use_snake),
+            ResidualUnit(in_channels=out_channels, out_channels=out_channels,
+                         dilation=3, use_snake=use_snake),
+            ResidualUnit(in_channels=out_channels, out_channels=out_channels,
+                         dilation=9, use_snake=use_snake),
+        )
+
+    def forward(self, x):
+        return self.layers(x)
+
+class OobleckEncoder(nn.Module):
+    def __init__(self,
+                 in_channels=2,
+                 channels=128,
+                 latent_dim=32,
+                 c_mults = [1, 2, 4, 8],
+                 strides = [2, 4, 8, 8],
+                 use_snake=False,
+                 antialias_activation=False
+        ):
+        super().__init__()
+
+        c_mults = [1] + c_mults
+
+        self.depth = len(c_mults)
+
+        layers = [
+            WNConv1d(in_channels=in_channels, out_channels=c_mults[0] * channels, kernel_size=7, padding=3)
+        ]
+
+        for i in range(self.depth-1):
+            layers += [EncoderBlock(in_channels=c_mults[i]*channels, out_channels=c_mults[i+1]*channels, stride=strides[i], use_snake=use_snake)]
+
+        layers += [
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[-1] * channels),
+            WNConv1d(in_channels=c_mults[-1]*channels, out_channels=latent_dim, kernel_size=3, padding=1)
+        ]
+
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class OobleckDecoder(nn.Module):
+    def __init__(self,
+                 out_channels=2,
+                 channels=128,
+                 latent_dim=32,
+                 c_mults = [1, 2, 4, 8],
+                 strides = [2, 4, 8, 8],
+                 use_snake=False,
+                 antialias_activation=False,
+                 use_nearest_upsample=False,
+                 final_tanh=True):
+        super().__init__()
+
+        c_mults = [1] + c_mults
+
+        self.depth = len(c_mults)
+
+        layers = [
+            WNConv1d(in_channels=latent_dim, out_channels=c_mults[-1]*channels, kernel_size=7, padding=3),
+        ]
+
+        for i in range(self.depth-1, 0, -1):
+            layers += [DecoderBlock(
+                in_channels=c_mults[i]*channels,
+                out_channels=c_mults[i-1]*channels,
+                stride=strides[i-1],
+                use_snake=use_snake,
+                antialias_activation=antialias_activation,
+                use_nearest_upsample=use_nearest_upsample
+                )
+            ]
+
+        layers += [
+            get_activation("snake" if use_snake else "elu", antialias=antialias_activation, channels=c_mults[0] * channels),
+            WNConv1d(in_channels=c_mults[0] * channels, out_channels=out_channels, kernel_size=7, padding=3, bias=False),
+            nn.Tanh() if final_tanh else nn.Identity()
+        ]
+
+        self.layers = nn.Sequential(*layers)
+
+    def forward(self, x):
+        return self.layers(x)
+
+
+class AudioOobleckVAE(nn.Module):
+    def __init__(self,
+                 in_channels=2,
+                 channels=128,
+                 latent_dim=64,
+                 c_mults = [1, 2, 4, 8, 16],
+                 strides = [2, 4, 4, 8, 8],
+                 use_snake=True,
+                 antialias_activation=False,
+                 use_nearest_upsample=False,
+                 final_tanh=False):
+        super().__init__()
+        self.encoder = OobleckEncoder(in_channels, channels, latent_dim * 2, c_mults, strides, use_snake, antialias_activation)
+        self.decoder = OobleckDecoder(in_channels, channels, latent_dim, c_mults, strides, use_snake, antialias_activation,
+                                      use_nearest_upsample=use_nearest_upsample, final_tanh=final_tanh)
+        self.bottleneck = VAEBottleneck()
+
+    def encode(self, x):
+        return self.bottleneck.encode(self.encoder(x))
+
+    def decode(self, x):
+        return self.decoder(self.bottleneck.decode(x))
+
diff --git a/comfy/ldm/audio/dit.py b/comfy/ldm/audio/dit.py
new file mode 100644
index 00000000..1c1112c5
--- /dev/null
+++ b/comfy/ldm/audio/dit.py
@@ -0,0 +1,888 @@
+# code adapted from: https://github.com/Stability-AI/stable-audio-tools
+
+from comfy.ldm.modules.attention import optimized_attention
+import typing as tp
+
+import torch
+
+from einops import rearrange
+from torch import nn
+from torch.nn import functional as F
+import math
+
+class FourierFeatures(nn.Module):
+    def __init__(self, in_features, out_features, std=1., dtype=None, device=None):
+        super().__init__()
+        assert out_features % 2 == 0
+        self.weight = nn.Parameter(torch.empty(
+            [out_features // 2, in_features], dtype=dtype, device=device))
+
+    def forward(self, input):
+        f = 2 * math.pi * input @ self.weight.T.to(dtype=input.dtype, device=input.device)
+        return torch.cat([f.cos(), f.sin()], dim=-1)
+
+# norms
+class LayerNorm(nn.Module):
+    def __init__(self, dim, bias=False, fix_scale=False, dtype=None, device=None):
+        """
+        bias-less layernorm has been shown to be more stable. most newer models have moved towards rmsnorm, also bias-less
+        """
+        super().__init__()
+
+        self.gamma = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
+
+        if bias:
+            self.beta = nn.Parameter(torch.empty(dim, dtype=dtype, device=device))
+        else:
+            self.beta = None
+
+    def forward(self, x):
+        beta = self.beta
+        if self.beta is not None:
+            beta = beta.to(dtype=x.dtype, device=x.device)
+        return F.layer_norm(x, x.shape[-1:], weight=self.gamma.to(dtype=x.dtype, device=x.device), bias=beta)
+
+class GLU(nn.Module):
+    def __init__(
+        self,
+        dim_in,
+        dim_out,
+        activation,
+        use_conv = False,
+        conv_kernel_size = 3,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.act = activation
+        self.proj = operations.Linear(dim_in, dim_out * 2, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim_in, dim_out * 2, conv_kernel_size, padding = (conv_kernel_size // 2), dtype=dtype, device=device)
+        self.use_conv = use_conv
+
+    def forward(self, x):
+        if self.use_conv:
+            x = rearrange(x, 'b n d -> b d n')
+            x = self.proj(x)
+            x = rearrange(x, 'b d n -> b n d')
+        else:
+            x = self.proj(x)
+
+        x, gate = x.chunk(2, dim = -1)
+        return x * self.act(gate)
+
+class AbsolutePositionalEmbedding(nn.Module):
+    def __init__(self, dim, max_seq_len):
+        super().__init__()
+        self.scale = dim ** -0.5
+        self.max_seq_len = max_seq_len
+        self.emb = nn.Embedding(max_seq_len, dim)
+
+    def forward(self, x, pos = None, seq_start_pos = None):
+        seq_len, device = x.shape[1], x.device
+        assert seq_len <= self.max_seq_len, f'you are passing in a sequence length of {seq_len} but your absolute positional embedding has a max sequence length of {self.max_seq_len}'
+
+        if pos is None:
+            pos = torch.arange(seq_len, device = device)
+
+        if seq_start_pos is not None:
+            pos = (pos - seq_start_pos[..., None]).clamp(min = 0)
+
+        pos_emb = self.emb(pos)
+        pos_emb = pos_emb * self.scale
+        return pos_emb
+
+class ScaledSinusoidalEmbedding(nn.Module):
+    def __init__(self, dim, theta = 10000):
+        super().__init__()
+        assert (dim % 2) == 0, 'dimension must be divisible by 2'
+        self.scale = nn.Parameter(torch.ones(1) * dim ** -0.5)
+
+        half_dim = dim // 2
+        freq_seq = torch.arange(half_dim).float() / half_dim
+        inv_freq = theta ** -freq_seq
+        self.register_buffer('inv_freq', inv_freq, persistent = False)
+
+    def forward(self, x, pos = None, seq_start_pos = None):
+        seq_len, device = x.shape[1], x.device
+
+        if pos is None:
+            pos = torch.arange(seq_len, device = device)
+
+        if seq_start_pos is not None:
+            pos = pos - seq_start_pos[..., None]
+
+        emb = torch.einsum('i, j -> i j', pos, self.inv_freq)
+        emb = torch.cat((emb.sin(), emb.cos()), dim = -1)
+        return emb * self.scale
+
+class RotaryEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim,
+        use_xpos = False,
+        scale_base = 512,
+        interpolation_factor = 1.,
+        base = 10000,
+        base_rescale_factor = 1.
+    ):
+        super().__init__()
+        # proposed by reddit user bloc97, to rescale rotary embeddings to longer sequence length without fine-tuning
+        # has some connection to NTK literature
+        # https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/
+        base *= base_rescale_factor ** (dim / (dim - 2))
+
+        inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer('inv_freq', inv_freq)
+
+        assert interpolation_factor >= 1.
+        self.interpolation_factor = interpolation_factor
+
+        if not use_xpos:
+            self.register_buffer('scale', None)
+            return
+
+        scale = (torch.arange(0, dim, 2) + 0.4 * dim) / (1.4 * dim)
+
+        self.scale_base = scale_base
+        self.register_buffer('scale', scale)
+
+    def forward_from_seq_len(self, seq_len, device, dtype):
+        # device = self.inv_freq.device
+
+        t = torch.arange(seq_len, device=device, dtype=dtype)
+        return self.forward(t)
+
+    def forward(self, t):
+        # device = self.inv_freq.device
+        device = t.device
+        dtype = t.dtype
+
+        # t = t.to(torch.float32)
+
+        t = t / self.interpolation_factor
+
+        freqs = torch.einsum('i , j -> i j', t, self.inv_freq.to(dtype=dtype, device=device))
+        freqs = torch.cat((freqs, freqs), dim = -1)
+
+        if self.scale is None:
+            return freqs, 1.
+
+        power = (torch.arange(seq_len, device = device) - (seq_len // 2)) / self.scale_base
+        scale = self.scale.to(dtype=dtype, device=device) ** rearrange(power, 'n -> n 1')
+        scale = torch.cat((scale, scale), dim = -1)
+
+        return freqs, scale
+
+def rotate_half(x):
+    x = rearrange(x, '... (j d) -> ... j d', j = 2)
+    x1, x2 = x.unbind(dim = -2)
+    return torch.cat((-x2, x1), dim = -1)
+
+def apply_rotary_pos_emb(t, freqs, scale = 1):
+    out_dtype = t.dtype
+
+    # cast to float32 if necessary for numerical stability
+    dtype = t.dtype #reduce(torch.promote_types, (t.dtype, freqs.dtype, torch.float32))
+    rot_dim, seq_len = freqs.shape[-1], t.shape[-2]
+    freqs, t = freqs.to(dtype), t.to(dtype)
+    freqs = freqs[-seq_len:, :]
+
+    if t.ndim == 4 and freqs.ndim == 3:
+        freqs = rearrange(freqs, 'b n d -> b 1 n d')
+
+    # partial rotary embeddings, Wang et al. GPT-J
+    t, t_unrotated = t[..., :rot_dim], t[..., rot_dim:]
+    t = (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale)
+
+    t, t_unrotated = t.to(out_dtype), t_unrotated.to(out_dtype)
+
+    return torch.cat((t, t_unrotated), dim = -1)
+
+class FeedForward(nn.Module):
+    def __init__(
+        self,
+        dim,
+        dim_out = None,
+        mult = 4,
+        no_bias = False,
+        glu = True,
+        use_conv = False,
+        conv_kernel_size = 3,
+        zero_init_output = True,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+        inner_dim = int(dim * mult)
+
+        # Default to SwiGLU
+
+        activation = nn.SiLU()
+
+        dim_out = dim if dim_out is None else dim_out
+
+        if glu:
+            linear_in = GLU(dim, inner_dim, activation, dtype=dtype, device=device, operations=operations)
+        else:
+            linear_in = nn.Sequential(
+                Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
+                operations.Linear(dim, inner_dim, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(dim, inner_dim, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device),
+                Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
+                activation
+            )
+
+        linear_out = operations.Linear(inner_dim, dim_out, bias = not no_bias, dtype=dtype, device=device) if not use_conv else operations.Conv1d(inner_dim, dim_out, conv_kernel_size, padding = (conv_kernel_size // 2), bias = not no_bias, dtype=dtype, device=device)
+
+        # # init last linear layer to 0
+        # if zero_init_output:
+        #     nn.init.zeros_(linear_out.weight)
+        #     if not no_bias:
+        #         nn.init.zeros_(linear_out.bias)
+
+
+        self.ff = nn.Sequential(
+            linear_in,
+            Rearrange('b d n -> b n d') if use_conv else nn.Identity(),
+            linear_out,
+            Rearrange('b n d -> b d n') if use_conv else nn.Identity(),
+        )
+
+    def forward(self, x):
+        return self.ff(x)
+
+class Attention(nn.Module):
+    def __init__(
+        self,
+        dim,
+        dim_heads = 64,
+        dim_context = None,
+        causal = False,
+        zero_init_output=True,
+        qk_norm = False,
+        natten_kernel_size = None,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.dim_heads = dim_heads
+        self.causal = causal
+
+        dim_kv = dim_context if dim_context is not None else dim
+
+        self.num_heads = dim // dim_heads
+        self.kv_heads = dim_kv // dim_heads
+
+        if dim_context is not None:
+            self.to_q = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+            self.to_kv = operations.Linear(dim_kv, dim_kv * 2, bias=False, dtype=dtype, device=device)
+        else:
+            self.to_qkv = operations.Linear(dim, dim * 3, bias=False, dtype=dtype, device=device)
+
+        self.to_out = operations.Linear(dim, dim, bias=False, dtype=dtype, device=device)
+
+        # if zero_init_output:
+        #     nn.init.zeros_(self.to_out.weight)
+
+        self.qk_norm = qk_norm
+
+
+    def forward(
+        self,
+        x,
+        context = None,
+        mask = None,
+        context_mask = None,
+        rotary_pos_emb = None,
+        causal = None
+    ):
+        h, kv_h, has_context = self.num_heads, self.kv_heads, context is not None
+
+        kv_input = context if has_context else x
+
+        if hasattr(self, 'to_q'):
+            # Use separate linear projections for q and k/v
+            q = self.to_q(x)
+            q = rearrange(q, 'b n (h d) -> b h n d', h = h)
+
+            k, v = self.to_kv(kv_input).chunk(2, dim=-1)
+
+            k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = kv_h), (k, v))
+        else:
+            # Use fused linear projection
+            q, k, v = self.to_qkv(x).chunk(3, dim=-1)
+            q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v))
+
+        # Normalize q and k for cosine sim attention
+        if self.qk_norm:
+            q = F.normalize(q, dim=-1)
+            k = F.normalize(k, dim=-1)
+
+        if rotary_pos_emb is not None and not has_context:
+            freqs, _ = rotary_pos_emb
+
+            q_dtype = q.dtype
+            k_dtype = k.dtype
+
+            q = q.to(torch.float32)
+            k = k.to(torch.float32)
+            freqs = freqs.to(torch.float32)
+
+            q = apply_rotary_pos_emb(q, freqs)
+            k = apply_rotary_pos_emb(k, freqs)
+
+            q = q.to(q_dtype)
+            k = k.to(k_dtype)
+
+        input_mask = context_mask
+
+        if input_mask is None and not has_context:
+            input_mask = mask
+
+        # determine masking
+        masks = []
+        final_attn_mask = None # The mask that will be applied to the attention matrix, taking all masks into account
+
+        if input_mask is not None:
+            input_mask = rearrange(input_mask, 'b j -> b 1 1 j')
+            masks.append(~input_mask)
+
+        # Other masks will be added here later
+
+        if len(masks) > 0:
+            final_attn_mask = ~or_reduce(masks)
+
+        n, device = q.shape[-2], q.device
+
+        causal = self.causal if causal is None else causal
+
+        if n == 1 and causal:
+            causal = False
+
+        if h != kv_h:
+            # Repeat interleave kv_heads to match q_heads
+            heads_per_kv_head = h // kv_h
+            k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v))
+
+        out = optimized_attention(q, k, v, h, skip_reshape=True)
+        out = self.to_out(out)
+
+        if mask is not None:
+            mask = rearrange(mask, 'b n -> b n 1')
+            out = out.masked_fill(~mask, 0.)
+
+        return out
+
+class ConformerModule(nn.Module):
+    def __init__(
+        self,
+        dim,
+        norm_kwargs = {},
+    ):
+
+        super().__init__()
+
+        self.dim = dim
+
+        self.in_norm = LayerNorm(dim, **norm_kwargs)
+        self.pointwise_conv = nn.Conv1d(dim, dim, kernel_size=1, bias=False)
+        self.glu = GLU(dim, dim, nn.SiLU())
+        self.depthwise_conv = nn.Conv1d(dim, dim, kernel_size=17, groups=dim, padding=8, bias=False)
+        self.mid_norm = LayerNorm(dim, **norm_kwargs) # This is a batch norm in the original but I don't like batch norm
+        self.swish = nn.SiLU()
+        self.pointwise_conv_2 = nn.Conv1d(dim, dim, kernel_size=1, bias=False)
+
+    def forward(self, x):
+        x = self.in_norm(x)
+        x = rearrange(x, 'b n d -> b d n')
+        x = self.pointwise_conv(x)
+        x = rearrange(x, 'b d n -> b n d')
+        x = self.glu(x)
+        x = rearrange(x, 'b n d -> b d n')
+        x = self.depthwise_conv(x)
+        x = rearrange(x, 'b d n -> b n d')
+        x = self.mid_norm(x)
+        x = self.swish(x)
+        x = rearrange(x, 'b n d -> b d n')
+        x = self.pointwise_conv_2(x)
+        x = rearrange(x, 'b d n -> b n d')
+
+        return x
+
+class TransformerBlock(nn.Module):
+    def __init__(
+            self,
+            dim,
+            dim_heads = 64,
+            cross_attend = False,
+            dim_context = None,
+            global_cond_dim = None,
+            causal = False,
+            zero_init_branch_outputs = True,
+            conformer = False,
+            layer_ix = -1,
+            remove_norms = False,
+            attn_kwargs = {},
+            ff_kwargs = {},
+            norm_kwargs = {},
+            dtype=None,
+            device=None,
+            operations=None,
+    ):
+
+        super().__init__()
+        self.dim = dim
+        self.dim_heads = dim_heads
+        self.cross_attend = cross_attend
+        self.dim_context = dim_context
+        self.causal = causal
+
+        self.pre_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
+
+        self.self_attn = Attention(
+            dim,
+            dim_heads = dim_heads,
+            causal = causal,
+            zero_init_output=zero_init_branch_outputs,
+            dtype=dtype,
+            device=device,
+            operations=operations,
+            **attn_kwargs
+        )
+
+        if cross_attend:
+            self.cross_attend_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
+            self.cross_attn = Attention(
+                dim,
+                dim_heads = dim_heads,
+                dim_context=dim_context,
+                causal = causal,
+                zero_init_output=zero_init_branch_outputs,
+                dtype=dtype,
+                device=device,
+                operations=operations,
+                **attn_kwargs
+            )
+
+        self.ff_norm = LayerNorm(dim, dtype=dtype, device=device, **norm_kwargs) if not remove_norms else nn.Identity()
+        self.ff = FeedForward(dim, zero_init_output=zero_init_branch_outputs, dtype=dtype, device=device, operations=operations,**ff_kwargs)
+
+        self.layer_ix = layer_ix
+
+        self.conformer = ConformerModule(dim, norm_kwargs=norm_kwargs) if conformer else None
+
+        self.global_cond_dim = global_cond_dim
+
+        if global_cond_dim is not None:
+            self.to_scale_shift_gate = nn.Sequential(
+                nn.SiLU(),
+                nn.Linear(global_cond_dim, dim * 6, bias=False)
+            )
+
+            nn.init.zeros_(self.to_scale_shift_gate[1].weight)
+            #nn.init.zeros_(self.to_scale_shift_gate_self[1].bias)
+
+    def forward(
+        self,
+        x,
+        context = None,
+        global_cond=None,
+        mask = None,
+        context_mask = None,
+        rotary_pos_emb = None
+    ):
+        if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None:
+
+            scale_self, shift_self, gate_self, scale_ff, shift_ff, gate_ff = self.to_scale_shift_gate(global_cond).unsqueeze(1).chunk(6, dim = -1)
+
+            # self-attention with adaLN
+            residual = x
+            x = self.pre_norm(x)
+            x = x * (1 + scale_self) + shift_self
+            x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb)
+            x = x * torch.sigmoid(1 - gate_self)
+            x = x + residual
+
+            if context is not None:
+                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask)
+
+            if self.conformer is not None:
+                x = x + self.conformer(x)
+
+            # feedforward with adaLN
+            residual = x
+            x = self.ff_norm(x)
+            x = x * (1 + scale_ff) + shift_ff
+            x = self.ff(x)
+            x = x * torch.sigmoid(1 - gate_ff)
+            x = x + residual
+
+        else:
+            x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb)
+
+            if context is not None:
+                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask)
+
+            if self.conformer is not None:
+                x = x + self.conformer(x)
+
+            x = x + self.ff(self.ff_norm(x))
+
+        return x
+
+class ContinuousTransformer(nn.Module):
+    def __init__(
+        self,
+        dim,
+        depth,
+        *,
+        dim_in = None,
+        dim_out = None,
+        dim_heads = 64,
+        cross_attend=False,
+        cond_token_dim=None,
+        global_cond_dim=None,
+        causal=False,
+        rotary_pos_emb=True,
+        zero_init_branch_outputs=True,
+        conformer=False,
+        use_sinusoidal_emb=False,
+        use_abs_pos_emb=False,
+        abs_pos_emb_max_length=10000,
+        dtype=None,
+        device=None,
+        operations=None,
+        **kwargs
+        ):
+
+        super().__init__()
+
+        self.dim = dim
+        self.depth = depth
+        self.causal = causal
+        self.layers = nn.ModuleList([])
+
+        self.project_in = operations.Linear(dim_in, dim, bias=False, dtype=dtype, device=device) if dim_in is not None else nn.Identity()
+        self.project_out = operations.Linear(dim, dim_out, bias=False, dtype=dtype, device=device) if dim_out is not None else nn.Identity()
+
+        if rotary_pos_emb:
+            self.rotary_pos_emb = RotaryEmbedding(max(dim_heads // 2, 32))
+        else:
+            self.rotary_pos_emb = None
+
+        self.use_sinusoidal_emb = use_sinusoidal_emb
+        if use_sinusoidal_emb:
+            self.pos_emb = ScaledSinusoidalEmbedding(dim)
+
+        self.use_abs_pos_emb = use_abs_pos_emb
+        if use_abs_pos_emb:
+            self.pos_emb = AbsolutePositionalEmbedding(dim, abs_pos_emb_max_length)
+
+        for i in range(depth):
+            self.layers.append(
+                TransformerBlock(
+                    dim,
+                    dim_heads = dim_heads,
+                    cross_attend = cross_attend,
+                    dim_context = cond_token_dim,
+                    global_cond_dim = global_cond_dim,
+                    causal = causal,
+                    zero_init_branch_outputs = zero_init_branch_outputs,
+                    conformer=conformer,
+                    layer_ix=i,
+                    dtype=dtype,
+                    device=device,
+                    operations=operations,
+                    **kwargs
+                )
+            )
+
+    def forward(
+        self,
+        x,
+        mask = None,
+        prepend_embeds = None,
+        prepend_mask = None,
+        global_cond = None,
+        return_info = False,
+        **kwargs
+    ):
+        batch, seq, device = *x.shape[:2], x.device
+
+        info = {
+            "hidden_states": [],
+        }
+
+        x = self.project_in(x)
+
+        if prepend_embeds is not None:
+            prepend_length, prepend_dim = prepend_embeds.shape[1:]
+
+            assert prepend_dim == x.shape[-1], 'prepend dimension must match sequence dimension'
+
+            x = torch.cat((prepend_embeds, x), dim = -2)
+
+            if prepend_mask is not None or mask is not None:
+                mask = mask if mask is not None else torch.ones((batch, seq), device = device, dtype = torch.bool)
+                prepend_mask = prepend_mask if prepend_mask is not None else torch.ones((batch, prepend_length), device = device, dtype = torch.bool)
+
+                mask = torch.cat((prepend_mask, mask), dim = -1)
+
+        # Attention layers
+
+        if self.rotary_pos_emb is not None:
+            rotary_pos_emb = self.rotary_pos_emb.forward_from_seq_len(x.shape[1], dtype=x.dtype, device=x.device)
+        else:
+            rotary_pos_emb = None
+
+        if self.use_sinusoidal_emb or self.use_abs_pos_emb:
+            x = x + self.pos_emb(x)
+
+        # Iterate over the transformer layers
+        for layer in self.layers:
+            x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs)
+            # x = checkpoint(layer, x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs)
+
+            if return_info:
+                info["hidden_states"].append(x)
+
+        x = self.project_out(x)
+
+        if return_info:
+            return x, info
+
+        return x
+
+class AudioDiffusionTransformer(nn.Module):
+    def __init__(self,
+        io_channels=64,
+        patch_size=1,
+        embed_dim=1536,
+        cond_token_dim=768,
+        project_cond_tokens=False,
+        global_cond_dim=1536,
+        project_global_cond=True,
+        input_concat_dim=0,
+        prepend_cond_dim=0,
+        depth=24,
+        num_heads=24,
+        transformer_type: tp.Literal["continuous_transformer"] = "continuous_transformer",
+        global_cond_type: tp.Literal["prepend", "adaLN"] = "prepend",
+        audio_model="",
+        dtype=None,
+        device=None,
+        operations=None,
+        **kwargs):
+
+        super().__init__()
+
+        self.dtype = dtype
+        self.cond_token_dim = cond_token_dim
+
+        # Timestep embeddings
+        timestep_features_dim = 256
+
+        self.timestep_features = FourierFeatures(1, timestep_features_dim, dtype=dtype, device=device)
+
+        self.to_timestep_embed = nn.Sequential(
+            operations.Linear(timestep_features_dim, embed_dim, bias=True, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(embed_dim, embed_dim, bias=True, dtype=dtype, device=device),
+        )
+
+        if cond_token_dim > 0:
+            # Conditioning tokens
+
+            cond_embed_dim = cond_token_dim if not project_cond_tokens else embed_dim
+            self.to_cond_embed = nn.Sequential(
+                operations.Linear(cond_token_dim, cond_embed_dim, bias=False, dtype=dtype, device=device),
+                nn.SiLU(),
+                operations.Linear(cond_embed_dim, cond_embed_dim, bias=False, dtype=dtype, device=device)
+            )
+        else:
+            cond_embed_dim = 0
+
+        if global_cond_dim > 0:
+            # Global conditioning
+            global_embed_dim = global_cond_dim if not project_global_cond else embed_dim
+            self.to_global_embed = nn.Sequential(
+                operations.Linear(global_cond_dim, global_embed_dim, bias=False, dtype=dtype, device=device),
+                nn.SiLU(),
+                operations.Linear(global_embed_dim, global_embed_dim, bias=False, dtype=dtype, device=device)
+            )
+
+        if prepend_cond_dim > 0:
+            # Prepend conditioning
+            self.to_prepend_embed = nn.Sequential(
+                operations.Linear(prepend_cond_dim, embed_dim, bias=False, dtype=dtype, device=device),
+                nn.SiLU(),
+                operations.Linear(embed_dim, embed_dim, bias=False, dtype=dtype, device=device)
+            )
+
+        self.input_concat_dim = input_concat_dim
+
+        dim_in = io_channels + self.input_concat_dim
+
+        self.patch_size = patch_size
+
+        # Transformer
+
+        self.transformer_type = transformer_type
+
+        self.global_cond_type = global_cond_type
+
+        if self.transformer_type == "continuous_transformer":
+
+            global_dim = None
+
+            if self.global_cond_type == "adaLN":
+                # The global conditioning is projected to the embed_dim already at this point
+                global_dim = embed_dim
+
+            self.transformer = ContinuousTransformer(
+                dim=embed_dim,
+                depth=depth,
+                dim_heads=embed_dim // num_heads,
+                dim_in=dim_in * patch_size,
+                dim_out=io_channels * patch_size,
+                cross_attend = cond_token_dim > 0,
+                cond_token_dim = cond_embed_dim,
+                global_cond_dim=global_dim,
+                dtype=dtype,
+                device=device,
+                operations=operations,
+                **kwargs
+            )
+        else:
+            raise ValueError(f"Unknown transformer type: {self.transformer_type}")
+
+        self.preprocess_conv = operations.Conv1d(dim_in, dim_in, 1, bias=False, dtype=dtype, device=device)
+        self.postprocess_conv = operations.Conv1d(io_channels, io_channels, 1, bias=False, dtype=dtype, device=device)
+
+    def _forward(
+        self,
+        x,
+        t,
+        mask=None,
+        cross_attn_cond=None,
+        cross_attn_cond_mask=None,
+        input_concat_cond=None,
+        global_embed=None,
+        prepend_cond=None,
+        prepend_cond_mask=None,
+        return_info=False,
+        **kwargs):
+
+        if cross_attn_cond is not None:
+            cross_attn_cond = self.to_cond_embed(cross_attn_cond)
+
+        if global_embed is not None:
+            # Project the global conditioning to the embedding dimension
+            global_embed = self.to_global_embed(global_embed)
+
+        prepend_inputs = None
+        prepend_mask = None
+        prepend_length = 0
+        if prepend_cond is not None:
+            # Project the prepend conditioning to the embedding dimension
+            prepend_cond = self.to_prepend_embed(prepend_cond)
+
+            prepend_inputs = prepend_cond
+            if prepend_cond_mask is not None:
+                prepend_mask = prepend_cond_mask
+
+        if input_concat_cond is not None:
+
+            # Interpolate input_concat_cond to the same length as x
+            if input_concat_cond.shape[2] != x.shape[2]:
+                input_concat_cond = F.interpolate(input_concat_cond, (x.shape[2], ), mode='nearest')
+
+            x = torch.cat([x, input_concat_cond], dim=1)
+
+        # Get the batch of timestep embeddings
+        timestep_embed = self.to_timestep_embed(self.timestep_features(t[:, None]).to(x.dtype)) # (b, embed_dim)
+
+        # Timestep embedding is considered a global embedding. Add to the global conditioning if it exists
+        if global_embed is not None:
+            global_embed = global_embed + timestep_embed
+        else:
+            global_embed = timestep_embed
+
+        # Add the global_embed to the prepend inputs if there is no global conditioning support in the transformer
+        if self.global_cond_type == "prepend":
+            if prepend_inputs is None:
+                # Prepend inputs are just the global embed, and the mask is all ones
+                prepend_inputs = global_embed.unsqueeze(1)
+                prepend_mask = torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)
+            else:
+                # Prepend inputs are the prepend conditioning + the global embed
+                prepend_inputs = torch.cat([prepend_inputs, global_embed.unsqueeze(1)], dim=1)
+                prepend_mask = torch.cat([prepend_mask, torch.ones((x.shape[0], 1), device=x.device, dtype=torch.bool)], dim=1)
+
+            prepend_length = prepend_inputs.shape[1]
+
+        x = self.preprocess_conv(x) + x
+
+        x = rearrange(x, "b c t -> b t c")
+
+        extra_args = {}
+
+        if self.global_cond_type == "adaLN":
+            extra_args["global_cond"] = global_embed
+
+        if self.patch_size > 1:
+            x = rearrange(x, "b (t p) c -> b t (c p)", p=self.patch_size)
+
+        if self.transformer_type == "x-transformers":
+            output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, **extra_args, **kwargs)
+        elif self.transformer_type == "continuous_transformer":
+            output = self.transformer(x, prepend_embeds=prepend_inputs, context=cross_attn_cond, context_mask=cross_attn_cond_mask, mask=mask, prepend_mask=prepend_mask, return_info=return_info, **extra_args, **kwargs)
+
+            if return_info:
+                output, info = output
+        elif self.transformer_type == "mm_transformer":
+            output = self.transformer(x, context=cross_attn_cond, mask=mask, context_mask=cross_attn_cond_mask, **extra_args, **kwargs)
+
+        output = rearrange(output, "b t c -> b c t")[:,:,prepend_length:]
+
+        if self.patch_size > 1:
+            output = rearrange(output, "b (c p) t -> b c (t p)", p=self.patch_size)
+
+        output = self.postprocess_conv(output) + output
+
+        if return_info:
+            return output, info
+
+        return output
+
+    def forward(
+        self,
+        x,
+        timestep,
+        context=None,
+        context_mask=None,
+        input_concat_cond=None,
+        global_embed=None,
+        negative_global_embed=None,
+        prepend_cond=None,
+        prepend_cond_mask=None,
+        mask=None,
+        return_info=False,
+        control=None,
+        transformer_options={},
+        **kwargs):
+            return self._forward(
+                x,
+                timestep,
+                cross_attn_cond=context,
+                cross_attn_cond_mask=context_mask,
+                input_concat_cond=input_concat_cond,
+                global_embed=global_embed,
+                prepend_cond=prepend_cond,
+                prepend_cond_mask=prepend_cond_mask,
+                mask=mask,
+                return_info=return_info,
+                **kwargs
+            )
diff --git a/comfy/ldm/audio/embedders.py b/comfy/ldm/audio/embedders.py
new file mode 100644
index 00000000..82a3210c
--- /dev/null
+++ b/comfy/ldm/audio/embedders.py
@@ -0,0 +1,108 @@
+# code adapted from: https://github.com/Stability-AI/stable-audio-tools
+
+import torch
+import torch.nn as nn
+from torch import Tensor, einsum
+from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, TypeVar, Union
+from einops import rearrange
+import math
+import comfy.ops
+
+class LearnedPositionalEmbedding(nn.Module):
+    """Used for continuous time"""
+
+    def __init__(self, dim: int):
+        super().__init__()
+        assert (dim % 2) == 0
+        half_dim = dim // 2
+        self.weights = nn.Parameter(torch.empty(half_dim))
+
+    def forward(self, x: Tensor) -> Tensor:
+        x = rearrange(x, "b -> b 1")
+        freqs = x * rearrange(self.weights, "d -> 1 d") * 2 * math.pi
+        fouriered = torch.cat((freqs.sin(), freqs.cos()), dim=-1)
+        fouriered = torch.cat((x, fouriered), dim=-1)
+        return fouriered
+
+def TimePositionalEmbedding(dim: int, out_features: int) -> nn.Module:
+    return nn.Sequential(
+        LearnedPositionalEmbedding(dim),
+        comfy.ops.manual_cast.Linear(in_features=dim + 1, out_features=out_features),
+    )
+
+
+class NumberEmbedder(nn.Module):
+    def __init__(
+        self,
+        features: int,
+        dim: int = 256,
+    ):
+        super().__init__()
+        self.features = features
+        self.embedding = TimePositionalEmbedding(dim=dim, out_features=features)
+
+    def forward(self, x: Union[List[float], Tensor]) -> Tensor:
+        if not torch.is_tensor(x):
+            device = next(self.embedding.parameters()).device
+            x = torch.tensor(x, device=device)
+        assert isinstance(x, Tensor)
+        shape = x.shape
+        x = rearrange(x, "... -> (...)")
+        embedding = self.embedding(x)
+        x = embedding.view(*shape, self.features)
+        return x  # type: ignore
+
+
+class Conditioner(nn.Module):
+    def __init__(
+            self,
+            dim: int,
+            output_dim: int,
+            project_out: bool = False
+            ):
+
+        super().__init__()
+
+        self.dim = dim
+        self.output_dim = output_dim
+        self.proj_out = nn.Linear(dim, output_dim) if (dim != output_dim or project_out) else nn.Identity()
+
+    def forward(self, x):
+        raise NotImplementedError()
+
+class NumberConditioner(Conditioner):
+    '''
+        Conditioner that takes a list of floats, normalizes them for a given range, and returns a list of embeddings
+    '''
+    def __init__(self,
+                output_dim: int,
+                min_val: float=0,
+                max_val: float=1
+                ):
+        super().__init__(output_dim, output_dim)
+
+        self.min_val = min_val
+        self.max_val = max_val
+
+        self.embedder = NumberEmbedder(features=output_dim)
+
+    def forward(self, floats, device=None):
+            # Cast the inputs to floats
+            floats = [float(x) for x in floats]
+
+            if device is None:
+                device = next(self.embedder.parameters()).device
+
+            floats = torch.tensor(floats).to(device)
+
+            floats = floats.clamp(self.min_val, self.max_val)
+
+            normalized_floats = (floats - self.min_val) / (self.max_val - self.min_val)
+
+            # Cast floats to same type as embedder
+            embedder_dtype = next(self.embedder.parameters()).dtype
+            normalized_floats = normalized_floats.to(embedder_dtype)
+
+            float_embeds = self.embedder(normalized_floats).unsqueeze(1)
+
+            return [float_embeds, torch.ones(float_embeds.shape[0], 1).to(device)]
diff --git a/comfy/ldm/cascade/common.py b/comfy/ldm/cascade/common.py
new file mode 100644
index 00000000..124902c0
--- /dev/null
+++ b/comfy/ldm/cascade/common.py
@@ -0,0 +1,161 @@
+"""
+    This file is part of ComfyUI.
+    Copyright (C) 2024 Stability AI
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+"""
+
+import torch
+import torch.nn as nn
+from comfy.ldm.modules.attention import optimized_attention
+
+class Linear(torch.nn.Linear):
+    def reset_parameters(self):
+        return None
+
+class Conv2d(torch.nn.Conv2d):
+    def reset_parameters(self):
+        return None
+
+class OptimizedAttention(nn.Module):
+    def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.heads = nhead
+
+        self.to_q = operations.Linear(c, c, bias=True, dtype=dtype, device=device)
+        self.to_k = operations.Linear(c, c, bias=True, dtype=dtype, device=device)
+        self.to_v = operations.Linear(c, c, bias=True, dtype=dtype, device=device)
+
+        self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device)
+
+    def forward(self, q, k, v):
+        q = self.to_q(q)
+        k = self.to_k(k)
+        v = self.to_v(v)
+
+        out = optimized_attention(q, k, v, self.heads)
+
+        return self.out_proj(out)
+
+class Attention2D(nn.Module):
+    def __init__(self, c, nhead, dropout=0.0, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.attn = OptimizedAttention(c, nhead, dtype=dtype, device=device, operations=operations)
+        # self.attn = nn.MultiheadAttention(c, nhead, dropout=dropout, bias=True, batch_first=True, dtype=dtype, device=device)
+
+    def forward(self, x, kv, self_attn=False):
+        orig_shape = x.shape
+        x = x.view(x.size(0), x.size(1), -1).permute(0, 2, 1)  # Bx4xHxW -> Bx(HxW)x4
+        if self_attn:
+            kv = torch.cat([x, kv], dim=1)
+        # x = self.attn(x, kv, kv, need_weights=False)[0]
+        x = self.attn(x, kv, kv)
+        x = x.permute(0, 2, 1).view(*orig_shape)
+        return x
+
+
+def LayerNorm2d_op(operations):
+    class LayerNorm2d(operations.LayerNorm):
+        def __init__(self, *args, **kwargs):
+            super().__init__(*args, **kwargs)
+
+        def forward(self, x):
+            return super().forward(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
+    return LayerNorm2d
+
+class GlobalResponseNorm(nn.Module):
+    "from https://github.com/facebookresearch/ConvNeXt-V2/blob/3608f67cc1dae164790c5d0aead7bf2d73d9719b/models/utils.py#L105"
+    def __init__(self, dim, dtype=None, device=None):
+        super().__init__()
+        self.gamma = nn.Parameter(torch.zeros(1, 1, 1, dim, dtype=dtype, device=device))
+        self.beta = nn.Parameter(torch.zeros(1, 1, 1, dim, dtype=dtype, device=device))
+
+    def forward(self, x):
+        Gx = torch.norm(x, p=2, dim=(1, 2), keepdim=True)
+        Nx = Gx / (Gx.mean(dim=-1, keepdim=True) + 1e-6)
+        return self.gamma.to(device=x.device, dtype=x.dtype) * (x * Nx) + self.beta.to(device=x.device, dtype=x.dtype) + x
+
+
+class ResBlock(nn.Module):
+    def __init__(self, c, c_skip=0, kernel_size=3, dropout=0.0, dtype=None, device=None, operations=None):  # , num_heads=4, expansion=2):
+        super().__init__()
+        self.depthwise = operations.Conv2d(c, c, kernel_size=kernel_size, padding=kernel_size // 2, groups=c, dtype=dtype, device=device)
+        #         self.depthwise = SAMBlock(c, num_heads, expansion)
+        self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.channelwise = nn.Sequential(
+            operations.Linear(c + c_skip, c * 4, dtype=dtype, device=device),
+            nn.GELU(),
+            GlobalResponseNorm(c * 4, dtype=dtype, device=device),
+            nn.Dropout(dropout),
+            operations.Linear(c * 4, c, dtype=dtype, device=device)
+        )
+
+    def forward(self, x, x_skip=None):
+        x_res = x
+        x = self.norm(self.depthwise(x))
+        if x_skip is not None:
+            x = torch.cat([x, x_skip], dim=1)
+        x = self.channelwise(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
+        return x + x_res
+
+
+class AttnBlock(nn.Module):
+    def __init__(self, c, c_cond, nhead, self_attn=True, dropout=0.0, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.self_attn = self_attn
+        self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.attention = Attention2D(c, nhead, dropout, dtype=dtype, device=device, operations=operations)
+        self.kv_mapper = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(c_cond, c, dtype=dtype, device=device)
+        )
+
+    def forward(self, x, kv):
+        kv = self.kv_mapper(kv)
+        x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn)
+        return x
+
+
+class FeedForwardBlock(nn.Module):
+    def __init__(self, c, dropout=0.0, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.norm = LayerNorm2d_op(operations)(c, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.channelwise = nn.Sequential(
+            operations.Linear(c, c * 4, dtype=dtype, device=device),
+            nn.GELU(),
+            GlobalResponseNorm(c * 4, dtype=dtype, device=device),
+            nn.Dropout(dropout),
+            operations.Linear(c * 4, c, dtype=dtype, device=device)
+        )
+
+    def forward(self, x):
+        x = x + self.channelwise(self.norm(x).permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
+        return x
+
+
+class TimestepBlock(nn.Module):
+    def __init__(self, c, c_timestep, conds=['sca'], dtype=None, device=None, operations=None):
+        super().__init__()
+        self.mapper = operations.Linear(c_timestep, c * 2, dtype=dtype, device=device)
+        self.conds = conds
+        for cname in conds:
+            setattr(self, f"mapper_{cname}", operations.Linear(c_timestep, c * 2, dtype=dtype, device=device))
+
+    def forward(self, x, t):
+        t = t.chunk(len(self.conds) + 1, dim=1)
+        a, b = self.mapper(t[0])[:, :, None, None].chunk(2, dim=1)
+        for i, c in enumerate(self.conds):
+            ac, bc = getattr(self, f"mapper_{c}")(t[i + 1])[:, :, None, None].chunk(2, dim=1)
+            a, b = a + ac, b + bc
+        return x * (1 + a) + b
diff --git a/comfy/ldm/cascade/controlnet.py b/comfy/ldm/cascade/controlnet.py
new file mode 100644
index 00000000..7a52c3c2
--- /dev/null
+++ b/comfy/ldm/cascade/controlnet.py
@@ -0,0 +1,93 @@
+"""
+    This file is part of ComfyUI.
+    Copyright (C) 2024 Stability AI
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+"""
+
+import torch
+import torchvision
+from torch import nn
+from .common import LayerNorm2d_op
+
+
+class CNetResBlock(nn.Module):
+    def __init__(self, c, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.blocks = nn.Sequential(
+            LayerNorm2d_op(operations)(c, dtype=dtype, device=device),
+            nn.GELU(),
+            operations.Conv2d(c, c, kernel_size=3, padding=1),
+            LayerNorm2d_op(operations)(c, dtype=dtype, device=device),
+            nn.GELU(),
+            operations.Conv2d(c, c, kernel_size=3, padding=1),
+        )
+
+    def forward(self, x):
+        return x + self.blocks(x)
+
+
+class ControlNet(nn.Module):
+    def __init__(self, c_in=3, c_proj=2048, proj_blocks=None, bottleneck_mode=None, dtype=None, device=None, operations=nn):
+        super().__init__()
+        if bottleneck_mode is None:
+            bottleneck_mode = 'effnet'
+        self.proj_blocks = proj_blocks
+        if bottleneck_mode == 'effnet':
+            embd_channels = 1280
+            self.backbone = torchvision.models.efficientnet_v2_s().features.eval()
+            if c_in != 3:
+                in_weights = self.backbone[0][0].weight.data
+                self.backbone[0][0] = operations.Conv2d(c_in, 24, kernel_size=3, stride=2, bias=False, dtype=dtype, device=device)
+                if c_in > 3:
+                    # nn.init.constant_(self.backbone[0][0].weight, 0)
+                    self.backbone[0][0].weight.data[:, :3] = in_weights[:, :3].clone()
+                else:
+                    self.backbone[0][0].weight.data = in_weights[:, :c_in].clone()
+        elif bottleneck_mode == 'simple':
+            embd_channels = c_in
+            self.backbone = nn.Sequential(
+                operations.Conv2d(embd_channels, embd_channels * 4, kernel_size=3, padding=1, dtype=dtype, device=device),
+                nn.LeakyReLU(0.2, inplace=True),
+                operations.Conv2d(embd_channels * 4, embd_channels, kernel_size=3, padding=1, dtype=dtype, device=device),
+            )
+        elif bottleneck_mode == 'large':
+            self.backbone = nn.Sequential(
+                operations.Conv2d(c_in, 4096 * 4, kernel_size=1, dtype=dtype, device=device),
+                nn.LeakyReLU(0.2, inplace=True),
+                operations.Conv2d(4096 * 4, 1024, kernel_size=1, dtype=dtype, device=device),
+                *[CNetResBlock(1024, dtype=dtype, device=device, operations=operations) for _ in range(8)],
+                operations.Conv2d(1024, 1280, kernel_size=1, dtype=dtype, device=device),
+            )
+            embd_channels = 1280
+        else:
+            raise ValueError(f'Unknown bottleneck mode: {bottleneck_mode}')
+        self.projections = nn.ModuleList()
+        for _ in range(len(proj_blocks)):
+            self.projections.append(nn.Sequential(
+                operations.Conv2d(embd_channels, embd_channels, kernel_size=1, bias=False, dtype=dtype, device=device),
+                nn.LeakyReLU(0.2, inplace=True),
+                operations.Conv2d(embd_channels, c_proj, kernel_size=1, bias=False, dtype=dtype, device=device),
+            ))
+            # nn.init.constant_(self.projections[-1][-1].weight, 0)  # zero output projection
+        self.xl = False
+        self.input_channels = c_in
+        self.unshuffle_amount = 8
+
+    def forward(self, x):
+        x = self.backbone(x)
+        proj_outputs = [None for _ in range(max(self.proj_blocks) + 1)]
+        for i, idx in enumerate(self.proj_blocks):
+            proj_outputs[idx] = self.projections[i](x)
+        return {"input": proj_outputs[::-1]}
diff --git a/comfy/ldm/cascade/stage_a.py b/comfy/ldm/cascade/stage_a.py
new file mode 100644
index 00000000..ca8867ea
--- /dev/null
+++ b/comfy/ldm/cascade/stage_a.py
@@ -0,0 +1,255 @@
+"""
+    This file is part of ComfyUI.
+    Copyright (C) 2024 Stability AI
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+"""
+
+import torch
+from torch import nn
+from torch.autograd import Function
+
+class vector_quantize(Function):
+    @staticmethod
+    def forward(ctx, x, codebook):
+        with torch.no_grad():
+            codebook_sqr = torch.sum(codebook ** 2, dim=1)
+            x_sqr = torch.sum(x ** 2, dim=1, keepdim=True)
+
+            dist = torch.addmm(codebook_sqr + x_sqr, x, codebook.t(), alpha=-2.0, beta=1.0)
+            _, indices = dist.min(dim=1)
+
+            ctx.save_for_backward(indices, codebook)
+            ctx.mark_non_differentiable(indices)
+
+            nn = torch.index_select(codebook, 0, indices)
+            return nn, indices
+
+    @staticmethod
+    def backward(ctx, grad_output, grad_indices):
+        grad_inputs, grad_codebook = None, None
+
+        if ctx.needs_input_grad[0]:
+            grad_inputs = grad_output.clone()
+        if ctx.needs_input_grad[1]:
+            # Gradient wrt. the codebook
+            indices, codebook = ctx.saved_tensors
+
+            grad_codebook = torch.zeros_like(codebook)
+            grad_codebook.index_add_(0, indices, grad_output)
+
+        return (grad_inputs, grad_codebook)
+
+
+class VectorQuantize(nn.Module):
+    def __init__(self, embedding_size, k, ema_decay=0.99, ema_loss=False):
+        """
+        Takes an input of variable size (as long as the last dimension matches the embedding size).
+        Returns one tensor containing the nearest neigbour embeddings to each of the inputs,
+        with the same size as the input, vq and commitment components for the loss as a touple
+        in the second output and the indices of the quantized vectors in the third:
+        quantized, (vq_loss, commit_loss), indices
+        """
+        super(VectorQuantize, self).__init__()
+
+        self.codebook = nn.Embedding(k, embedding_size)
+        self.codebook.weight.data.uniform_(-1./k, 1./k)
+        self.vq = vector_quantize.apply
+
+        self.ema_decay = ema_decay
+        self.ema_loss = ema_loss
+        if ema_loss:
+            self.register_buffer('ema_element_count', torch.ones(k))
+            self.register_buffer('ema_weight_sum', torch.zeros_like(self.codebook.weight))
+
+    def _laplace_smoothing(self, x, epsilon):
+        n = torch.sum(x)
+        return ((x + epsilon) / (n + x.size(0) * epsilon) * n)
+
+    def _updateEMA(self, z_e_x, indices):
+        mask = nn.functional.one_hot(indices, self.ema_element_count.size(0)).float()
+        elem_count = mask.sum(dim=0)
+        weight_sum = torch.mm(mask.t(), z_e_x)
+
+        self.ema_element_count = (self.ema_decay * self.ema_element_count) + ((1-self.ema_decay) * elem_count)
+        self.ema_element_count = self._laplace_smoothing(self.ema_element_count, 1e-5)
+        self.ema_weight_sum = (self.ema_decay * self.ema_weight_sum) + ((1-self.ema_decay) * weight_sum)
+
+        self.codebook.weight.data = self.ema_weight_sum / self.ema_element_count.unsqueeze(-1)
+
+    def idx2vq(self, idx, dim=-1):
+        q_idx = self.codebook(idx)
+        if dim != -1:
+            q_idx = q_idx.movedim(-1, dim)
+        return q_idx
+
+    def forward(self, x, get_losses=True, dim=-1):
+        if dim != -1:
+            x = x.movedim(dim, -1)
+        z_e_x = x.contiguous().view(-1, x.size(-1)) if len(x.shape) > 2 else x
+        z_q_x, indices = self.vq(z_e_x, self.codebook.weight.detach())
+        vq_loss, commit_loss = None, None
+        if self.ema_loss and self.training:
+            self._updateEMA(z_e_x.detach(), indices.detach())
+        # pick the graded embeddings after updating the codebook in order to have a more accurate commitment loss
+        z_q_x_grd = torch.index_select(self.codebook.weight, dim=0, index=indices)
+        if get_losses:
+            vq_loss = (z_q_x_grd - z_e_x.detach()).pow(2).mean()
+            commit_loss = (z_e_x - z_q_x_grd.detach()).pow(2).mean()
+
+        z_q_x = z_q_x.view(x.shape)
+        if dim != -1:
+            z_q_x = z_q_x.movedim(-1, dim)
+        return z_q_x, (vq_loss, commit_loss), indices.view(x.shape[:-1])
+
+
+class ResBlock(nn.Module):
+    def __init__(self, c, c_hidden):
+        super().__init__()
+        # depthwise/attention
+        self.norm1 = nn.LayerNorm(c, elementwise_affine=False, eps=1e-6)
+        self.depthwise = nn.Sequential(
+            nn.ReplicationPad2d(1),
+            nn.Conv2d(c, c, kernel_size=3, groups=c)
+        )
+
+        # channelwise
+        self.norm2 = nn.LayerNorm(c, elementwise_affine=False, eps=1e-6)
+        self.channelwise = nn.Sequential(
+            nn.Linear(c, c_hidden),
+            nn.GELU(),
+            nn.Linear(c_hidden, c),
+        )
+
+        self.gammas = nn.Parameter(torch.zeros(6), requires_grad=True)
+
+        # Init weights
+        def _basic_init(module):
+            if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
+                torch.nn.init.xavier_uniform_(module.weight)
+                if module.bias is not None:
+                    nn.init.constant_(module.bias, 0)
+
+        self.apply(_basic_init)
+
+    def _norm(self, x, norm):
+        return norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2)
+
+    def forward(self, x):
+        mods = self.gammas
+
+        x_temp = self._norm(x, self.norm1) * (1 + mods[0]) + mods[1]
+        try:
+            x = x + self.depthwise(x_temp) * mods[2]
+        except: #operation not implemented for bf16
+            x_temp = self.depthwise[0](x_temp.float()).to(x.dtype)
+            x = x + self.depthwise[1](x_temp) * mods[2]
+
+        x_temp = self._norm(x, self.norm2) * (1 + mods[3]) + mods[4]
+        x = x + self.channelwise(x_temp.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) * mods[5]
+
+        return x
+
+
+class StageA(nn.Module):
+    def __init__(self, levels=2, bottleneck_blocks=12, c_hidden=384, c_latent=4, codebook_size=8192):
+        super().__init__()
+        self.c_latent = c_latent
+        c_levels = [c_hidden // (2 ** i) for i in reversed(range(levels))]
+
+        # Encoder blocks
+        self.in_block = nn.Sequential(
+            nn.PixelUnshuffle(2),
+            nn.Conv2d(3 * 4, c_levels[0], kernel_size=1)
+        )
+        down_blocks = []
+        for i in range(levels):
+            if i > 0:
+                down_blocks.append(nn.Conv2d(c_levels[i - 1], c_levels[i], kernel_size=4, stride=2, padding=1))
+            block = ResBlock(c_levels[i], c_levels[i] * 4)
+            down_blocks.append(block)
+        down_blocks.append(nn.Sequential(
+            nn.Conv2d(c_levels[-1], c_latent, kernel_size=1, bias=False),
+            nn.BatchNorm2d(c_latent),  # then normalize them to have mean 0 and std 1
+        ))
+        self.down_blocks = nn.Sequential(*down_blocks)
+        self.down_blocks[0]
+
+        self.codebook_size = codebook_size
+        self.vquantizer = VectorQuantize(c_latent, k=codebook_size)
+
+        # Decoder blocks
+        up_blocks = [nn.Sequential(
+            nn.Conv2d(c_latent, c_levels[-1], kernel_size=1)
+        )]
+        for i in range(levels):
+            for j in range(bottleneck_blocks if i == 0 else 1):
+                block = ResBlock(c_levels[levels - 1 - i], c_levels[levels - 1 - i] * 4)
+                up_blocks.append(block)
+            if i < levels - 1:
+                up_blocks.append(
+                    nn.ConvTranspose2d(c_levels[levels - 1 - i], c_levels[levels - 2 - i], kernel_size=4, stride=2,
+                                       padding=1))
+        self.up_blocks = nn.Sequential(*up_blocks)
+        self.out_block = nn.Sequential(
+            nn.Conv2d(c_levels[0], 3 * 4, kernel_size=1),
+            nn.PixelShuffle(2),
+        )
+
+    def encode(self, x, quantize=False):
+        x = self.in_block(x)
+        x = self.down_blocks(x)
+        if quantize:
+            qe, (vq_loss, commit_loss), indices = self.vquantizer.forward(x, dim=1)
+            return qe, x, indices, vq_loss + commit_loss * 0.25
+        else:
+            return x
+
+    def decode(self, x):
+        x = self.up_blocks(x)
+        x = self.out_block(x)
+        return x
+
+    def forward(self, x, quantize=False):
+        qe, x, _, vq_loss = self.encode(x, quantize)
+        x = self.decode(qe)
+        return x, vq_loss
+
+
+class Discriminator(nn.Module):
+    def __init__(self, c_in=3, c_cond=0, c_hidden=512, depth=6):
+        super().__init__()
+        d = max(depth - 3, 3)
+        layers = [
+            nn.utils.spectral_norm(nn.Conv2d(c_in, c_hidden // (2 ** d), kernel_size=3, stride=2, padding=1)),
+            nn.LeakyReLU(0.2),
+        ]
+        for i in range(depth - 1):
+            c_in = c_hidden // (2 ** max((d - i), 0))
+            c_out = c_hidden // (2 ** max((d - 1 - i), 0))
+            layers.append(nn.utils.spectral_norm(nn.Conv2d(c_in, c_out, kernel_size=3, stride=2, padding=1)))
+            layers.append(nn.InstanceNorm2d(c_out))
+            layers.append(nn.LeakyReLU(0.2))
+        self.encoder = nn.Sequential(*layers)
+        self.shuffle = nn.Conv2d((c_hidden + c_cond) if c_cond > 0 else c_hidden, 1, kernel_size=1)
+        self.logits = nn.Sigmoid()
+
+    def forward(self, x, cond=None):
+        x = self.encoder(x)
+        if cond is not None:
+            cond = cond.view(cond.size(0), cond.size(1), 1, 1, ).expand(-1, -1, x.size(-2), x.size(-1))
+            x = torch.cat([x, cond], dim=1)
+        x = self.shuffle(x)
+        x = self.logits(x)
+        return x
diff --git a/comfy/ldm/cascade/stage_b.py b/comfy/ldm/cascade/stage_b.py
new file mode 100644
index 00000000..7c3d8fea
--- /dev/null
+++ b/comfy/ldm/cascade/stage_b.py
@@ -0,0 +1,256 @@
+"""
+    This file is part of ComfyUI.
+    Copyright (C) 2024 Stability AI
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+"""
+
+import math
+import torch
+from torch import nn
+from .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock
+
+class StageB(nn.Module):
+    def __init__(self, c_in=4, c_out=4, c_r=64, patch_size=2, c_cond=1280, c_hidden=[320, 640, 1280, 1280],
+                 nhead=[-1, -1, 20, 20], blocks=[[2, 6, 28, 6], [6, 28, 6, 2]],
+                 block_repeat=[[1, 1, 1, 1], [3, 3, 2, 2]], level_config=['CT', 'CT', 'CTA', 'CTA'], c_clip=1280,
+                 c_clip_seq=4, c_effnet=16, c_pixels=3, kernel_size=3, dropout=[0, 0, 0.0, 0.0], self_attn=True,
+                 t_conds=['sca'], stable_cascade_stage=None, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.dtype = dtype
+        self.c_r = c_r
+        self.t_conds = t_conds
+        self.c_clip_seq = c_clip_seq
+        if not isinstance(dropout, list):
+            dropout = [dropout] * len(c_hidden)
+        if not isinstance(self_attn, list):
+            self_attn = [self_attn] * len(c_hidden)
+
+        # CONDITIONING
+        self.effnet_mapper = nn.Sequential(
+            operations.Conv2d(c_effnet, c_hidden[0] * 4, kernel_size=1, dtype=dtype, device=device),
+            nn.GELU(),
+            operations.Conv2d(c_hidden[0] * 4, c_hidden[0], kernel_size=1, dtype=dtype, device=device),
+            LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        )
+        self.pixels_mapper = nn.Sequential(
+            operations.Conv2d(c_pixels, c_hidden[0] * 4, kernel_size=1, dtype=dtype, device=device),
+            nn.GELU(),
+            operations.Conv2d(c_hidden[0] * 4, c_hidden[0], kernel_size=1, dtype=dtype, device=device),
+            LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        )
+        self.clip_mapper = operations.Linear(c_clip, c_cond * c_clip_seq, dtype=dtype, device=device)
+        self.clip_norm = operations.LayerNorm(c_cond, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+
+        self.embedding = nn.Sequential(
+            nn.PixelUnshuffle(patch_size),
+            operations.Conv2d(c_in * (patch_size ** 2), c_hidden[0], kernel_size=1, dtype=dtype, device=device),
+            LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        )
+
+        def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True):
+            if block_type == 'C':
+                return ResBlock(c_hidden, c_skip, kernel_size=kernel_size, dropout=dropout, dtype=dtype, device=device, operations=operations)
+            elif block_type == 'A':
+                return AttnBlock(c_hidden, c_cond, nhead, self_attn=self_attn, dropout=dropout, dtype=dtype, device=device, operations=operations)
+            elif block_type == 'F':
+                return FeedForwardBlock(c_hidden, dropout=dropout, dtype=dtype, device=device, operations=operations)
+            elif block_type == 'T':
+                return TimestepBlock(c_hidden, c_r, conds=t_conds, dtype=dtype, device=device, operations=operations)
+            else:
+                raise Exception(f'Block type {block_type} not supported')
+
+        # BLOCKS
+        # -- down blocks
+        self.down_blocks = nn.ModuleList()
+        self.down_downscalers = nn.ModuleList()
+        self.down_repeat_mappers = nn.ModuleList()
+        for i in range(len(c_hidden)):
+            if i > 0:
+                self.down_downscalers.append(nn.Sequential(
+                    LayerNorm2d_op(operations)(c_hidden[i - 1], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device),
+                    operations.Conv2d(c_hidden[i - 1], c_hidden[i], kernel_size=2, stride=2, dtype=dtype, device=device),
+                ))
+            else:
+                self.down_downscalers.append(nn.Identity())
+            down_block = nn.ModuleList()
+            for _ in range(blocks[0][i]):
+                for block_type in level_config[i]:
+                    block = get_block(block_type, c_hidden[i], nhead[i], dropout=dropout[i], self_attn=self_attn[i])
+                    down_block.append(block)
+            self.down_blocks.append(down_block)
+            if block_repeat is not None:
+                block_repeat_mappers = nn.ModuleList()
+                for _ in range(block_repeat[0][i] - 1):
+                    block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device))
+                self.down_repeat_mappers.append(block_repeat_mappers)
+
+        # -- up blocks
+        self.up_blocks = nn.ModuleList()
+        self.up_upscalers = nn.ModuleList()
+        self.up_repeat_mappers = nn.ModuleList()
+        for i in reversed(range(len(c_hidden))):
+            if i > 0:
+                self.up_upscalers.append(nn.Sequential(
+                    LayerNorm2d_op(operations)(c_hidden[i], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device),
+                    operations.ConvTranspose2d(c_hidden[i], c_hidden[i - 1], kernel_size=2, stride=2, dtype=dtype, device=device),
+                ))
+            else:
+                self.up_upscalers.append(nn.Identity())
+            up_block = nn.ModuleList()
+            for j in range(blocks[1][::-1][i]):
+                for k, block_type in enumerate(level_config[i]):
+                    c_skip = c_hidden[i] if i < len(c_hidden) - 1 and j == k == 0 else 0
+                    block = get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i],
+                                      self_attn=self_attn[i])
+                    up_block.append(block)
+            self.up_blocks.append(up_block)
+            if block_repeat is not None:
+                block_repeat_mappers = nn.ModuleList()
+                for _ in range(block_repeat[1][::-1][i] - 1):
+                    block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device))
+                self.up_repeat_mappers.append(block_repeat_mappers)
+
+        # OUTPUT
+        self.clf = nn.Sequential(
+            LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device),
+            operations.Conv2d(c_hidden[0], c_out * (patch_size ** 2), kernel_size=1, dtype=dtype, device=device),
+            nn.PixelShuffle(patch_size),
+        )
+
+        # --- WEIGHT INIT ---
+    #     self.apply(self._init_weights)  # General init
+    #     nn.init.normal_(self.clip_mapper.weight, std=0.02)  # conditionings
+    #     nn.init.normal_(self.effnet_mapper[0].weight, std=0.02)  # conditionings
+    #     nn.init.normal_(self.effnet_mapper[2].weight, std=0.02)  # conditionings
+    #     nn.init.normal_(self.pixels_mapper[0].weight, std=0.02)  # conditionings
+    #     nn.init.normal_(self.pixels_mapper[2].weight, std=0.02)  # conditionings
+    #     torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02)  # inputs
+    #     nn.init.constant_(self.clf[1].weight, 0)  # outputs
+    # 
+    #     # blocks
+    #     for level_block in self.down_blocks + self.up_blocks:
+    #         for block in level_block:
+    #             if isinstance(block, ResBlock) or isinstance(block, FeedForwardBlock):
+    #                 block.channelwise[-1].weight.data *= np.sqrt(1 / sum(blocks[0]))
+    #             elif isinstance(block, TimestepBlock):
+    #                 for layer in block.modules():
+    #                     if isinstance(layer, nn.Linear):
+    #                         nn.init.constant_(layer.weight, 0)
+    # 
+    # def _init_weights(self, m):
+    #     if isinstance(m, (nn.Conv2d, nn.Linear)):
+    #         torch.nn.init.xavier_uniform_(m.weight)
+    #         if m.bias is not None:
+    #             nn.init.constant_(m.bias, 0)
+
+    def gen_r_embedding(self, r, max_positions=10000):
+        r = r * max_positions
+        half_dim = self.c_r // 2
+        emb = math.log(max_positions) / (half_dim - 1)
+        emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp()
+        emb = r[:, None] * emb[None, :]
+        emb = torch.cat([emb.sin(), emb.cos()], dim=1)
+        if self.c_r % 2 == 1:  # zero pad
+            emb = nn.functional.pad(emb, (0, 1), mode='constant')
+        return emb
+
+    def gen_c_embeddings(self, clip):
+        if len(clip.shape) == 2:
+            clip = clip.unsqueeze(1)
+        clip = self.clip_mapper(clip).view(clip.size(0), clip.size(1) * self.c_clip_seq, -1)
+        clip = self.clip_norm(clip)
+        return clip
+
+    def _down_encode(self, x, r_embed, clip):
+        level_outputs = []
+        block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers)
+        for down_block, downscaler, repmap in block_group:
+            x = downscaler(x)
+            for i in range(len(repmap) + 1):
+                for block in down_block:
+                    if isinstance(block, ResBlock) or (
+                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+                                                                                  ResBlock)):
+                        x = block(x)
+                    elif isinstance(block, AttnBlock) or (
+                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+                                                                                  AttnBlock)):
+                        x = block(x, clip)
+                    elif isinstance(block, TimestepBlock) or (
+                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+                                                                                  TimestepBlock)):
+                        x = block(x, r_embed)
+                    else:
+                        x = block(x)
+                if i < len(repmap):
+                    x = repmap[i](x)
+            level_outputs.insert(0, x)
+        return level_outputs
+
+    def _up_decode(self, level_outputs, r_embed, clip):
+        x = level_outputs[0]
+        block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers)
+        for i, (up_block, upscaler, repmap) in enumerate(block_group):
+            for j in range(len(repmap) + 1):
+                for k, block in enumerate(up_block):
+                    if isinstance(block, ResBlock) or (
+                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+                                                                                  ResBlock)):
+                        skip = level_outputs[i] if k == 0 and i > 0 else None
+                        if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)):
+                            x = torch.nn.functional.interpolate(x, skip.shape[-2:], mode='bilinear',
+                                                                align_corners=True)
+                        x = block(x, skip)
+                    elif isinstance(block, AttnBlock) or (
+                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+                                                                                  AttnBlock)):
+                        x = block(x, clip)
+                    elif isinstance(block, TimestepBlock) or (
+                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+                                                                                  TimestepBlock)):
+                        x = block(x, r_embed)
+                    else:
+                        x = block(x)
+                if j < len(repmap):
+                    x = repmap[j](x)
+            x = upscaler(x)
+        return x
+
+    def forward(self, x, r, effnet, clip, pixels=None, **kwargs):
+        if pixels is None:
+            pixels = x.new_zeros(x.size(0), 3, 8, 8)
+
+        # Process the conditioning embeddings
+        r_embed = self.gen_r_embedding(r).to(dtype=x.dtype)
+        for c in self.t_conds:
+            t_cond = kwargs.get(c, torch.zeros_like(r))
+            r_embed = torch.cat([r_embed, self.gen_r_embedding(t_cond).to(dtype=x.dtype)], dim=1)
+        clip = self.gen_c_embeddings(clip)
+
+        # Model Blocks
+        x = self.embedding(x)
+        x = x + self.effnet_mapper(
+            nn.functional.interpolate(effnet, size=x.shape[-2:], mode='bilinear', align_corners=True))
+        x = x + nn.functional.interpolate(self.pixels_mapper(pixels), size=x.shape[-2:], mode='bilinear',
+                                          align_corners=True)
+        level_outputs = self._down_encode(x, r_embed, clip)
+        x = self._up_decode(level_outputs, r_embed, clip)
+        return self.clf(x)
+
+    def update_weights_ema(self, src_model, beta=0.999):
+        for self_params, src_params in zip(self.parameters(), src_model.parameters()):
+            self_params.data = self_params.data * beta + src_params.data.clone().to(self_params.device) * (1 - beta)
+        for self_buffers, src_buffers in zip(self.buffers(), src_model.buffers()):
+            self_buffers.data = self_buffers.data * beta + src_buffers.data.clone().to(self_buffers.device) * (1 - beta)
diff --git a/comfy/ldm/cascade/stage_c.py b/comfy/ldm/cascade/stage_c.py
new file mode 100644
index 00000000..c85da1f0
--- /dev/null
+++ b/comfy/ldm/cascade/stage_c.py
@@ -0,0 +1,273 @@
+"""
+    This file is part of ComfyUI.
+    Copyright (C) 2024 Stability AI
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+"""
+
+import torch
+from torch import nn
+import math
+from .common import AttnBlock, LayerNorm2d_op, ResBlock, FeedForwardBlock, TimestepBlock
+# from .controlnet import ControlNetDeliverer
+
+class UpDownBlock2d(nn.Module):
+    def __init__(self, c_in, c_out, mode, enabled=True, dtype=None, device=None, operations=None):
+        super().__init__()
+        assert mode in ['up', 'down']
+        interpolation = nn.Upsample(scale_factor=2 if mode == 'up' else 0.5, mode='bilinear',
+                                    align_corners=True) if enabled else nn.Identity()
+        mapping = operations.Conv2d(c_in, c_out, kernel_size=1, dtype=dtype, device=device)
+        self.blocks = nn.ModuleList([interpolation, mapping] if mode == 'up' else [mapping, interpolation])
+
+    def forward(self, x):
+        for block in self.blocks:
+            x = block(x)
+        return x
+
+
+class StageC(nn.Module):
+    def __init__(self, c_in=16, c_out=16, c_r=64, patch_size=1, c_cond=2048, c_hidden=[2048, 2048], nhead=[32, 32],
+                 blocks=[[8, 24], [24, 8]], block_repeat=[[1, 1], [1, 1]], level_config=['CTA', 'CTA'],
+                 c_clip_text=1280, c_clip_text_pooled=1280, c_clip_img=768, c_clip_seq=4, kernel_size=3,
+                 dropout=[0.0, 0.0], self_attn=True, t_conds=['sca', 'crp'], switch_level=[False], stable_cascade_stage=None,
+                 dtype=None, device=None, operations=None):
+        super().__init__()
+        self.dtype = dtype
+        self.c_r = c_r
+        self.t_conds = t_conds
+        self.c_clip_seq = c_clip_seq
+        if not isinstance(dropout, list):
+            dropout = [dropout] * len(c_hidden)
+        if not isinstance(self_attn, list):
+            self_attn = [self_attn] * len(c_hidden)
+
+        # CONDITIONING
+        self.clip_txt_mapper = operations.Linear(c_clip_text, c_cond, dtype=dtype, device=device)
+        self.clip_txt_pooled_mapper = operations.Linear(c_clip_text_pooled, c_cond * c_clip_seq, dtype=dtype, device=device)
+        self.clip_img_mapper = operations.Linear(c_clip_img, c_cond * c_clip_seq, dtype=dtype, device=device)
+        self.clip_norm = operations.LayerNorm(c_cond, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+
+        self.embedding = nn.Sequential(
+            nn.PixelUnshuffle(patch_size),
+            operations.Conv2d(c_in * (patch_size ** 2), c_hidden[0], kernel_size=1, dtype=dtype, device=device),
+            LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6)
+        )
+
+        def get_block(block_type, c_hidden, nhead, c_skip=0, dropout=0, self_attn=True):
+            if block_type == 'C':
+                return ResBlock(c_hidden, c_skip, kernel_size=kernel_size, dropout=dropout, dtype=dtype, device=device, operations=operations)
+            elif block_type == 'A':
+                return AttnBlock(c_hidden, c_cond, nhead, self_attn=self_attn, dropout=dropout, dtype=dtype, device=device, operations=operations)
+            elif block_type == 'F':
+                return FeedForwardBlock(c_hidden, dropout=dropout, dtype=dtype, device=device, operations=operations)
+            elif block_type == 'T':
+                return TimestepBlock(c_hidden, c_r, conds=t_conds, dtype=dtype, device=device, operations=operations)
+            else:
+                raise Exception(f'Block type {block_type} not supported')
+
+        # BLOCKS
+        # -- down blocks
+        self.down_blocks = nn.ModuleList()
+        self.down_downscalers = nn.ModuleList()
+        self.down_repeat_mappers = nn.ModuleList()
+        for i in range(len(c_hidden)):
+            if i > 0:
+                self.down_downscalers.append(nn.Sequential(
+                    LayerNorm2d_op(operations)(c_hidden[i - 1], elementwise_affine=False, eps=1e-6),
+                    UpDownBlock2d(c_hidden[i - 1], c_hidden[i], mode='down', enabled=switch_level[i - 1], dtype=dtype, device=device, operations=operations)
+                ))
+            else:
+                self.down_downscalers.append(nn.Identity())
+            down_block = nn.ModuleList()
+            for _ in range(blocks[0][i]):
+                for block_type in level_config[i]:
+                    block = get_block(block_type, c_hidden[i], nhead[i], dropout=dropout[i], self_attn=self_attn[i])
+                    down_block.append(block)
+            self.down_blocks.append(down_block)
+            if block_repeat is not None:
+                block_repeat_mappers = nn.ModuleList()
+                for _ in range(block_repeat[0][i] - 1):
+                    block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device))
+                self.down_repeat_mappers.append(block_repeat_mappers)
+
+        # -- up blocks
+        self.up_blocks = nn.ModuleList()
+        self.up_upscalers = nn.ModuleList()
+        self.up_repeat_mappers = nn.ModuleList()
+        for i in reversed(range(len(c_hidden))):
+            if i > 0:
+                self.up_upscalers.append(nn.Sequential(
+                    LayerNorm2d_op(operations)(c_hidden[i], elementwise_affine=False, eps=1e-6),
+                    UpDownBlock2d(c_hidden[i], c_hidden[i - 1], mode='up', enabled=switch_level[i - 1], dtype=dtype, device=device, operations=operations)
+                ))
+            else:
+                self.up_upscalers.append(nn.Identity())
+            up_block = nn.ModuleList()
+            for j in range(blocks[1][::-1][i]):
+                for k, block_type in enumerate(level_config[i]):
+                    c_skip = c_hidden[i] if i < len(c_hidden) - 1 and j == k == 0 else 0
+                    block = get_block(block_type, c_hidden[i], nhead[i], c_skip=c_skip, dropout=dropout[i],
+                                      self_attn=self_attn[i])
+                    up_block.append(block)
+            self.up_blocks.append(up_block)
+            if block_repeat is not None:
+                block_repeat_mappers = nn.ModuleList()
+                for _ in range(block_repeat[1][::-1][i] - 1):
+                    block_repeat_mappers.append(operations.Conv2d(c_hidden[i], c_hidden[i], kernel_size=1, dtype=dtype, device=device))
+                self.up_repeat_mappers.append(block_repeat_mappers)
+
+        # OUTPUT
+        self.clf = nn.Sequential(
+            LayerNorm2d_op(operations)(c_hidden[0], elementwise_affine=False, eps=1e-6, dtype=dtype, device=device),
+            operations.Conv2d(c_hidden[0], c_out * (patch_size ** 2), kernel_size=1, dtype=dtype, device=device),
+            nn.PixelShuffle(patch_size),
+        )
+
+        # --- WEIGHT INIT ---
+    #     self.apply(self._init_weights)  # General init
+    #     nn.init.normal_(self.clip_txt_mapper.weight, std=0.02)  # conditionings
+    #     nn.init.normal_(self.clip_txt_pooled_mapper.weight, std=0.02)  # conditionings
+    #     nn.init.normal_(self.clip_img_mapper.weight, std=0.02)  # conditionings
+    #     torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02)  # inputs
+    #     nn.init.constant_(self.clf[1].weight, 0)  # outputs
+    # 
+    #     # blocks
+    #     for level_block in self.down_blocks + self.up_blocks:
+    #         for block in level_block:
+    #             if isinstance(block, ResBlock) or isinstance(block, FeedForwardBlock):
+    #                 block.channelwise[-1].weight.data *= np.sqrt(1 / sum(blocks[0]))
+    #             elif isinstance(block, TimestepBlock):
+    #                 for layer in block.modules():
+    #                     if isinstance(layer, nn.Linear):
+    #                         nn.init.constant_(layer.weight, 0)
+    # 
+    # def _init_weights(self, m):
+    #     if isinstance(m, (nn.Conv2d, nn.Linear)):
+    #         torch.nn.init.xavier_uniform_(m.weight)
+    #         if m.bias is not None:
+    #             nn.init.constant_(m.bias, 0)
+
+    def gen_r_embedding(self, r, max_positions=10000):
+        r = r * max_positions
+        half_dim = self.c_r // 2
+        emb = math.log(max_positions) / (half_dim - 1)
+        emb = torch.arange(half_dim, device=r.device).float().mul(-emb).exp()
+        emb = r[:, None] * emb[None, :]
+        emb = torch.cat([emb.sin(), emb.cos()], dim=1)
+        if self.c_r % 2 == 1:  # zero pad
+            emb = nn.functional.pad(emb, (0, 1), mode='constant')
+        return emb
+
+    def gen_c_embeddings(self, clip_txt, clip_txt_pooled, clip_img):
+        clip_txt = self.clip_txt_mapper(clip_txt)
+        if len(clip_txt_pooled.shape) == 2:
+            clip_txt_pooled = clip_txt_pooled.unsqueeze(1)
+        if len(clip_img.shape) == 2:
+            clip_img = clip_img.unsqueeze(1)
+        clip_txt_pool = self.clip_txt_pooled_mapper(clip_txt_pooled).view(clip_txt_pooled.size(0), clip_txt_pooled.size(1) * self.c_clip_seq, -1)
+        clip_img = self.clip_img_mapper(clip_img).view(clip_img.size(0), clip_img.size(1) * self.c_clip_seq, -1)
+        clip = torch.cat([clip_txt, clip_txt_pool, clip_img], dim=1)
+        clip = self.clip_norm(clip)
+        return clip
+
+    def _down_encode(self, x, r_embed, clip, cnet=None):
+        level_outputs = []
+        block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers)
+        for down_block, downscaler, repmap in block_group:
+            x = downscaler(x)
+            for i in range(len(repmap) + 1):
+                for block in down_block:
+                    if isinstance(block, ResBlock) or (
+                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+                                                                                  ResBlock)):
+                        if cnet is not None:
+                            next_cnet = cnet.pop()
+                            if next_cnet is not None:
+                                x = x + nn.functional.interpolate(next_cnet, size=x.shape[-2:], mode='bilinear',
+                                                                  align_corners=True).to(x.dtype)
+                        x = block(x)
+                    elif isinstance(block, AttnBlock) or (
+                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+                                                                                  AttnBlock)):
+                        x = block(x, clip)
+                    elif isinstance(block, TimestepBlock) or (
+                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+                                                                                  TimestepBlock)):
+                        x = block(x, r_embed)
+                    else:
+                        x = block(x)
+                if i < len(repmap):
+                    x = repmap[i](x)
+            level_outputs.insert(0, x)
+        return level_outputs
+
+    def _up_decode(self, level_outputs, r_embed, clip, cnet=None):
+        x = level_outputs[0]
+        block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers)
+        for i, (up_block, upscaler, repmap) in enumerate(block_group):
+            for j in range(len(repmap) + 1):
+                for k, block in enumerate(up_block):
+                    if isinstance(block, ResBlock) or (
+                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+                                                                                  ResBlock)):
+                        skip = level_outputs[i] if k == 0 and i > 0 else None
+                        if skip is not None and (x.size(-1) != skip.size(-1) or x.size(-2) != skip.size(-2)):
+                            x = torch.nn.functional.interpolate(x, skip.shape[-2:], mode='bilinear',
+                                                                align_corners=True)
+                        if cnet is not None:
+                            next_cnet = cnet.pop()
+                            if next_cnet is not None:
+                                x = x + nn.functional.interpolate(next_cnet, size=x.shape[-2:], mode='bilinear',
+                                                                  align_corners=True).to(x.dtype)
+                        x = block(x, skip)
+                    elif isinstance(block, AttnBlock) or (
+                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+                                                                                  AttnBlock)):
+                        x = block(x, clip)
+                    elif isinstance(block, TimestepBlock) or (
+                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
+                                                                                  TimestepBlock)):
+                        x = block(x, r_embed)
+                    else:
+                        x = block(x)
+                if j < len(repmap):
+                    x = repmap[j](x)
+            x = upscaler(x)
+        return x
+
+    def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control=None, **kwargs):
+        # Process the conditioning embeddings
+        r_embed = self.gen_r_embedding(r).to(dtype=x.dtype)
+        for c in self.t_conds:
+            t_cond = kwargs.get(c, torch.zeros_like(r))
+            r_embed = torch.cat([r_embed, self.gen_r_embedding(t_cond).to(dtype=x.dtype)], dim=1)
+        clip = self.gen_c_embeddings(clip_text, clip_text_pooled, clip_img)
+
+        if control is not None:
+            cnet = control.get("input")
+        else:
+            cnet = None
+
+        # Model Blocks
+        x = self.embedding(x)
+        level_outputs = self._down_encode(x, r_embed, clip, cnet)
+        x = self._up_decode(level_outputs, r_embed, clip, cnet)
+        return self.clf(x)
+
+    def update_weights_ema(self, src_model, beta=0.999):
+        for self_params, src_params in zip(self.parameters(), src_model.parameters()):
+            self_params.data = self_params.data * beta + src_params.data.clone().to(self_params.device) * (1 - beta)
+        for self_buffers, src_buffers in zip(self.buffers(), src_model.buffers()):
+            self_buffers.data = self_buffers.data * beta + src_buffers.data.clone().to(self_buffers.device) * (1 - beta)
diff --git a/comfy/ldm/cascade/stage_c_coder.py b/comfy/ldm/cascade/stage_c_coder.py
new file mode 100644
index 00000000..0cb7c49f
--- /dev/null
+++ b/comfy/ldm/cascade/stage_c_coder.py
@@ -0,0 +1,95 @@
+"""
+    This file is part of ComfyUI.
+    Copyright (C) 2024 Stability AI
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+"""
+import torch
+import torchvision
+from torch import nn
+
+
+# EfficientNet
+class EfficientNetEncoder(nn.Module):
+    def __init__(self, c_latent=16):
+        super().__init__()
+        self.backbone = torchvision.models.efficientnet_v2_s().features.eval()
+        self.mapper = nn.Sequential(
+            nn.Conv2d(1280, c_latent, kernel_size=1, bias=False),
+            nn.BatchNorm2d(c_latent, affine=False),  # then normalize them to have mean 0 and std 1
+        )
+        self.mean = nn.Parameter(torch.tensor([0.485, 0.456, 0.406]))
+        self.std = nn.Parameter(torch.tensor([0.229, 0.224, 0.225]))
+
+    def forward(self, x):
+        x = x * 0.5 + 0.5
+        x = (x - self.mean.view([3,1,1])) / self.std.view([3,1,1])
+        o = self.mapper(self.backbone(x))
+        return o
+
+
+# Fast Decoder for Stage C latents. E.g. 16 x 24 x 24 -> 3 x 192 x 192
+class Previewer(nn.Module):
+    def __init__(self, c_in=16, c_hidden=512, c_out=3):
+        super().__init__()
+        self.blocks = nn.Sequential(
+            nn.Conv2d(c_in, c_hidden, kernel_size=1),  # 16 channels to 512 channels
+            nn.GELU(),
+            nn.BatchNorm2d(c_hidden),
+
+            nn.Conv2d(c_hidden, c_hidden, kernel_size=3, padding=1),
+            nn.GELU(),
+            nn.BatchNorm2d(c_hidden),
+
+            nn.ConvTranspose2d(c_hidden, c_hidden // 2, kernel_size=2, stride=2),  # 16 -> 32
+            nn.GELU(),
+            nn.BatchNorm2d(c_hidden // 2),
+
+            nn.Conv2d(c_hidden // 2, c_hidden // 2, kernel_size=3, padding=1),
+            nn.GELU(),
+            nn.BatchNorm2d(c_hidden // 2),
+
+            nn.ConvTranspose2d(c_hidden // 2, c_hidden // 4, kernel_size=2, stride=2),  # 32 -> 64
+            nn.GELU(),
+            nn.BatchNorm2d(c_hidden // 4),
+
+            nn.Conv2d(c_hidden // 4, c_hidden // 4, kernel_size=3, padding=1),
+            nn.GELU(),
+            nn.BatchNorm2d(c_hidden // 4),
+
+            nn.ConvTranspose2d(c_hidden // 4, c_hidden // 4, kernel_size=2, stride=2),  # 64 -> 128
+            nn.GELU(),
+            nn.BatchNorm2d(c_hidden // 4),
+
+            nn.Conv2d(c_hidden // 4, c_hidden // 4, kernel_size=3, padding=1),
+            nn.GELU(),
+            nn.BatchNorm2d(c_hidden // 4),
+
+            nn.Conv2d(c_hidden // 4, c_out, kernel_size=1),
+        )
+
+    def forward(self, x):
+        return (self.blocks(x) - 0.5) * 2.0
+
+class StageC_coder(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.previewer = Previewer()
+        self.encoder = EfficientNetEncoder()
+
+    def encode(self, x):
+        return self.encoder(x)
+
+    def decode(self, x):
+        return self.previewer(x)
diff --git a/comfy/ldm/models/autoencoder.py b/comfy/ldm/models/autoencoder.py
index b91ec324..f5f4de28 100644
--- a/comfy/ldm/models/autoencoder.py
+++ b/comfy/ldm/models/autoencoder.py
@@ -1,6 +1,4 @@
 import torch
-# import pytorch_lightning as pl
-import torch.nn.functional as F
 from contextlib import contextmanager
 from typing import Any, Dict, List, Optional, Tuple, Union
 
diff --git a/comfy/ldm/modules/attention.py b/comfy/ldm/modules/attention.py
index 9c9cb761..65a8bcf4 100644
--- a/comfy/ldm/modules/attention.py
+++ b/comfy/ldm/modules/attention.py
@@ -3,9 +3,10 @@ import torch
 import torch.nn.functional as F
 from torch import nn, einsum
 from einops import rearrange, repeat
-from typing import Optional, Any
+from typing import Optional
+import logging
 
-from .diffusionmodules.util import checkpoint, AlphaBlender, timestep_embedding
+from .diffusionmodules.util import AlphaBlender, timestep_embedding
 from .sub_quadratic_attention import efficient_dot_product_attention
 
 from comfy import model_management
@@ -18,13 +19,14 @@ from comfy.cli_args import args
 import comfy.ops
 ops = comfy.ops.disable_weight_init
 
-# CrossAttn precision handling
-if args.dont_upcast_attention:
-    print("disabling upcasting of attention")
-    _ATTN_PRECISION = "fp16"
-else:
-    _ATTN_PRECISION = "fp32"
+FORCE_UPCAST_ATTENTION_DTYPE = model_management.force_upcast_attention_dtype()
 
+def get_attn_precision(attn_precision):
+    if args.dont_upcast_attention:
+        return None
+    if FORCE_UPCAST_ATTENTION_DTYPE is not None:
+        return FORCE_UPCAST_ATTENTION_DTYPE
+    return attn_precision
 
 def exists(val):
     return val is not None
@@ -84,23 +86,35 @@ class FeedForward(nn.Module):
 def Normalize(in_channels, dtype=None, device=None):
     return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True, dtype=dtype, device=device)
 
-def attention_basic(q, k, v, heads, mask=None):
-    b, _, dim_head = q.shape
-    dim_head //= heads
+def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
+    attn_precision = get_attn_precision(attn_precision)
+
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+
     scale = dim_head ** -0.5
 
     h = heads
-    q, k, v = map(
-        lambda t: t.unsqueeze(3)
-        .reshape(b, -1, heads, dim_head)
-        .permute(0, 2, 1, 3)
-        .reshape(b * heads, -1, dim_head)
-        .contiguous(),
-        (q, k, v),
-    )
+    if skip_reshape:
+         q, k, v = map(
+            lambda t: t.reshape(b * heads, -1, dim_head),
+            (q, k, v),
+        )
+    else:
+        q, k, v = map(
+            lambda t: t.unsqueeze(3)
+            .reshape(b, -1, heads, dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b * heads, -1, dim_head)
+            .contiguous(),
+            (q, k, v),
+        )
 
     # force cast to fp32 to avoid overflowing
-    if _ATTN_PRECISION =="fp32":
+    if attn_precision == torch.float32:
         sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale
     else:
         sim = einsum('b i d, b j d -> b i j', q, k) * scale
@@ -114,7 +128,12 @@ def attention_basic(q, k, v, heads, mask=None):
             mask = repeat(mask, 'b j -> (b h) () j', h=h)
             sim.masked_fill_(~mask, max_neg_value)
         else:
-            sim += mask
+            if len(mask.shape) == 2:
+                bs = 1
+            else:
+                bs = mask.shape[0]
+            mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])
+            sim.add_(mask)
 
     # attention, what we cannot get enough of
     sim = sim.softmax(dim=-1)
@@ -129,18 +148,29 @@ def attention_basic(q, k, v, heads, mask=None):
     return out
 
 
-def attention_sub_quad(query, key, value, heads, mask=None):
-    b, _, dim_head = query.shape
-    dim_head //= heads
+def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False):
+    attn_precision = get_attn_precision(attn_precision)
+
+    if skip_reshape:
+        b, _, _, dim_head = query.shape
+    else:
+        b, _, dim_head = query.shape
+        dim_head //= heads
 
     scale = dim_head ** -0.5
-    query = query.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
-    value = value.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
 
-    key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1)
+    if skip_reshape:
+        query = query.reshape(b * heads, -1, dim_head)
+        value = value.reshape(b * heads, -1, dim_head)
+        key = key.reshape(b * heads, -1, dim_head).movedim(1, 2)
+    else:
+        query = query.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
+        value = value.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 1, 3).reshape(b * heads, -1, dim_head)
+        key = key.unsqueeze(3).reshape(b, -1, heads, dim_head).permute(0, 2, 3, 1).reshape(b * heads, dim_head, -1)
+
 
     dtype = query.dtype
-    upcast_attention = _ATTN_PRECISION =="fp32" and query.dtype != torch.float32
+    upcast_attention = attn_precision == torch.float32 and query.dtype != torch.float32
     if upcast_attention:
         bytes_per_token = torch.finfo(torch.float32).bits//8
     else:
@@ -165,6 +195,13 @@ def attention_sub_quad(query, key, value, heads, mask=None):
     if query_chunk_size is None:
         query_chunk_size = 512
 
+    if mask is not None:
+        if len(mask.shape) == 2:
+            bs = 1
+        else:
+            bs = mask.shape[0]
+        mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])
+
     hidden_states = efficient_dot_product_attention(
         query,
         key,
@@ -182,29 +219,43 @@ def attention_sub_quad(query, key, value, heads, mask=None):
     hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2)
     return hidden_states
 
-def attention_split(q, k, v, heads, mask=None):
-    b, _, dim_head = q.shape
-    dim_head //= heads
+def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
+    attn_precision = get_attn_precision(attn_precision)
+
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+
     scale = dim_head ** -0.5
 
     h = heads
-    q, k, v = map(
-        lambda t: t.unsqueeze(3)
-        .reshape(b, -1, heads, dim_head)
-        .permute(0, 2, 1, 3)
-        .reshape(b * heads, -1, dim_head)
-        .contiguous(),
-        (q, k, v),
-    )
+    if skip_reshape:
+         q, k, v = map(
+            lambda t: t.reshape(b * heads, -1, dim_head),
+            (q, k, v),
+        )
+    else:
+        q, k, v = map(
+            lambda t: t.unsqueeze(3)
+            .reshape(b, -1, heads, dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b * heads, -1, dim_head)
+            .contiguous(),
+            (q, k, v),
+        )
 
     r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
 
     mem_free_total = model_management.get_free_memory(q.device)
 
-    if _ATTN_PRECISION =="fp32":
+    if attn_precision == torch.float32:
         element_size = 4
+        upcast = True
     else:
         element_size = q.element_size()
+        upcast = False
 
     gb = 1024 ** 3
     tensor_size = q.shape[0] * q.shape[1] * k.shape[1] * element_size
@@ -223,6 +274,13 @@ def attention_split(q, k, v, heads, mask=None):
         raise RuntimeError(f'Not enough memory, use lower resolution (max approx. {max_res}x{max_res}). '
                             f'Need: {mem_required/64/gb:0.1f}GB free, Have:{mem_free_total/gb:0.1f}GB free')
 
+    if mask is not None:
+        if len(mask.shape) == 2:
+            bs = 1
+        else:
+            bs = mask.shape[0]
+        mask = mask.reshape(bs, -1, mask.shape[-2], mask.shape[-1]).expand(b, heads, -1, -1).reshape(-1, mask.shape[-2], mask.shape[-1])
+
     # print("steps", steps, mem_required, mem_free_total, modifier, q.element_size(), tensor_size)
     first_op_done = False
     cleared_cache = False
@@ -231,7 +289,7 @@ def attention_split(q, k, v, heads, mask=None):
             slice_size = q.shape[1] // steps if (q.shape[1] % steps) == 0 else q.shape[1]
             for i in range(0, q.shape[1], slice_size):
                 end = i + slice_size
-                if _ATTN_PRECISION =="fp32":
+                if upcast:
                     with torch.autocast(enabled=False, device_type = 'cuda'):
                         s1 = einsum('b i d, b j d -> b i j', q[:, i:end].float(), k.float()) * scale
                 else:
@@ -255,12 +313,12 @@ def attention_split(q, k, v, heads, mask=None):
                 model_management.soft_empty_cache(True)
                 if cleared_cache == False:
                     cleared_cache = True
-                    print("out of memory error, emptying cache and trying again")
+                    logging.warning("out of memory error, emptying cache and trying again")
                     continue
                 steps *= 2
                 if steps > 64:
                     raise e
-                print("out of memory error, increasing steps and trying again", steps)
+                logging.warning("out of memory error, increasing steps and trying again {}".format(steps))
             else:
                 raise e
 
@@ -277,26 +335,41 @@ def attention_split(q, k, v, heads, mask=None):
 BROKEN_XFORMERS = False
 try:
     x_vers = xformers.__version__
-    #I think 0.0.23 is also broken (q with bs bigger than 65535 gives CUDA error)
-    BROKEN_XFORMERS = x_vers.startswith("0.0.21") or x_vers.startswith("0.0.22") or x_vers.startswith("0.0.23")
+    # XFormers bug confirmed on all versions from 0.0.21 to 0.0.26 (q with bs bigger than 65535 gives CUDA error)
+    BROKEN_XFORMERS = x_vers.startswith("0.0.2") and not x_vers.startswith("0.0.20")
 except:
     pass
 
-def attention_xformers(q, k, v, heads, mask=None):
-    b, _, dim_head = q.shape
-    dim_head //= heads
+def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+
+    disabled_xformers = False
+
     if BROKEN_XFORMERS:
         if b * heads > 65535:
-            return attention_pytorch(q, k, v, heads, mask)
+            disabled_xformers = True
 
-    q, k, v = map(
-        lambda t: t.unsqueeze(3)
-        .reshape(b, -1, heads, dim_head)
-        .permute(0, 2, 1, 3)
-        .reshape(b * heads, -1, dim_head)
-        .contiguous(),
-        (q, k, v),
-    )
+    if not disabled_xformers:
+        if torch.jit.is_tracing() or torch.jit.is_scripting():
+            disabled_xformers = True
+
+    if disabled_xformers:
+        return attention_pytorch(q, k, v, heads, mask)
+
+    if skip_reshape:
+         q, k, v = map(
+            lambda t: t.reshape(b * heads, -1, dim_head),
+            (q, k, v),
+        )
+    else:
+        q, k, v = map(
+            lambda t: t.reshape(b, -1, heads, dim_head),
+            (q, k, v),
+        )
 
     if mask is not None:
         pad = 8 - q.shape[1] % 8
@@ -306,21 +379,30 @@ def attention_xformers(q, k, v, heads, mask=None):
 
     out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask)
 
-    out = (
-        out.unsqueeze(0)
-        .reshape(b, heads, -1, dim_head)
-        .permute(0, 2, 1, 3)
-        .reshape(b, -1, heads * dim_head)
-    )
+    if skip_reshape:
+        out = (
+            out.unsqueeze(0)
+            .reshape(b, heads, -1, dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b, -1, heads * dim_head)
+        )
+    else:
+        out = (
+            out.reshape(b, -1, heads * dim_head)
+        )
+
     return out
 
-def attention_pytorch(q, k, v, heads, mask=None):
-    b, _, dim_head = q.shape
-    dim_head //= heads
-    q, k, v = map(
-        lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
-        (q, k, v),
-    )
+def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
+    if skip_reshape:
+        b, _, _, dim_head = q.shape
+    else:
+        b, _, dim_head = q.shape
+        dim_head //= heads
+        q, k, v = map(
+            lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
+            (q, k, v),
+        )
 
     out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
     out = (
@@ -332,17 +414,17 @@ def attention_pytorch(q, k, v, heads, mask=None):
 optimized_attention = attention_basic
 
 if model_management.xformers_enabled():
-    print("Using xformers cross attention")
+    logging.info("Using xformers cross attention")
     optimized_attention = attention_xformers
 elif model_management.pytorch_attention_enabled():
-    print("Using pytorch cross attention")
+    logging.info("Using pytorch cross attention")
     optimized_attention = attention_pytorch
 else:
     if args.use_split_cross_attention:
-        print("Using split optimization for cross attention")
+        logging.info("Using split optimization for cross attention")
         optimized_attention = attention_split
     else:
-        print("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
+        logging.info("Using sub quadratic optimization for cross attention, if you have memory or speed issues try using: --use-split-cross-attention")
         optimized_attention = attention_sub_quad
 
 optimized_attention_masked = optimized_attention
@@ -364,10 +446,11 @@ def optimized_attention_for_device(device, mask=False, small_input=False):
 
 
 class CrossAttention(nn.Module):
-    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., dtype=None, device=None, operations=ops):
+    def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0., attn_precision=None, dtype=None, device=None, operations=ops):
         super().__init__()
         inner_dim = dim_head * heads
         context_dim = default(context_dim, query_dim)
+        self.attn_precision = attn_precision
 
         self.heads = heads
         self.dim_head = dim_head
@@ -389,15 +472,15 @@ class CrossAttention(nn.Module):
             v = self.to_v(context)
 
         if mask is None:
-            out = optimized_attention(q, k, v, self.heads)
+            out = optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision)
         else:
-            out = optimized_attention_masked(q, k, v, self.heads, mask)
+            out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision)
         return self.to_out(out)
 
 
 class BasicTransformerBlock(nn.Module):
     def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True, ff_in=False, inner_dim=None,
-                 disable_self_attn=False, disable_temporal_crossattention=False, switch_temporal_ca_to_sa=False, dtype=None, device=None, operations=ops):
+                 disable_self_attn=False, disable_temporal_crossattention=False, switch_temporal_ca_to_sa=False, attn_precision=None, dtype=None, device=None, operations=ops):
         super().__init__()
 
         self.ff_in = ff_in or inner_dim is not None
@@ -405,6 +488,7 @@ class BasicTransformerBlock(nn.Module):
             inner_dim = dim
 
         self.is_res = inner_dim == dim
+        self.attn_precision = attn_precision
 
         if self.ff_in:
             self.norm_in = operations.LayerNorm(dim, dtype=dtype, device=device)
@@ -412,7 +496,7 @@ class BasicTransformerBlock(nn.Module):
 
         self.disable_self_attn = disable_self_attn
         self.attn1 = CrossAttention(query_dim=inner_dim, heads=n_heads, dim_head=d_head, dropout=dropout,
-                              context_dim=context_dim if self.disable_self_attn else None, dtype=dtype, device=device, operations=operations)  # is a self-attention if not self.disable_self_attn
+                              context_dim=context_dim if self.disable_self_attn else None, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations)  # is a self-attention if not self.disable_self_attn
         self.ff = FeedForward(inner_dim, dim_out=dim, dropout=dropout, glu=gated_ff, dtype=dtype, device=device, operations=operations)
 
         if disable_temporal_crossattention:
@@ -426,20 +510,16 @@ class BasicTransformerBlock(nn.Module):
                 context_dim_attn2 = context_dim
 
             self.attn2 = CrossAttention(query_dim=inner_dim, context_dim=context_dim_attn2,
-                                heads=n_heads, dim_head=d_head, dropout=dropout, dtype=dtype, device=device, operations=operations)  # is self-attn if context is none
+                                heads=n_heads, dim_head=d_head, dropout=dropout, attn_precision=self.attn_precision, dtype=dtype, device=device, operations=operations)  # is self-attn if context is none
             self.norm2 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
 
         self.norm1 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
         self.norm3 = operations.LayerNorm(inner_dim, dtype=dtype, device=device)
-        self.checkpoint = checkpoint
         self.n_heads = n_heads
         self.d_head = d_head
         self.switch_temporal_ca_to_sa = switch_temporal_ca_to_sa
 
     def forward(self, x, context=None, transformer_options={}):
-        return checkpoint(self._forward, (x, context, transformer_options), self.parameters(), self.checkpoint)
-
-    def _forward(self, x, context=None, transformer_options={}):
         extra_options = {}
         block = transformer_options.get("block", None)
         block_index = transformer_options.get("block_index", 0)
@@ -456,6 +536,7 @@ class BasicTransformerBlock(nn.Module):
 
         extra_options["n_heads"] = self.n_heads
         extra_options["dim_head"] = self.d_head
+        extra_options["attn_precision"] = self.attn_precision
 
         if self.ff_in:
             x_skip = x
@@ -566,7 +647,7 @@ class SpatialTransformer(nn.Module):
     def __init__(self, in_channels, n_heads, d_head,
                  depth=1, dropout=0., context_dim=None,
                  disable_self_attn=False, use_linear=False,
-                 use_checkpoint=True, dtype=None, device=None, operations=ops):
+                 use_checkpoint=True, attn_precision=None, dtype=None, device=None, operations=ops):
         super().__init__()
         if exists(context_dim) and not isinstance(context_dim, list):
             context_dim = [context_dim] * depth
@@ -584,7 +665,7 @@ class SpatialTransformer(nn.Module):
 
         self.transformer_blocks = nn.ModuleList(
             [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
-                                   disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, dtype=dtype, device=device, operations=operations)
+                                   disable_self_attn=disable_self_attn, checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=dtype, device=device, operations=operations)
                 for d in range(depth)]
         )
         if not use_linear:
@@ -605,7 +686,7 @@ class SpatialTransformer(nn.Module):
         x = self.norm(x)
         if not self.use_linear:
             x = self.proj_in(x)
-        x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
+        x = x.movedim(1, 3).flatten(1, 2).contiguous()
         if self.use_linear:
             x = self.proj_in(x)
         for i, block in enumerate(self.transformer_blocks):
@@ -613,7 +694,7 @@ class SpatialTransformer(nn.Module):
             x = block(x, context=context[i], transformer_options=transformer_options)
         if self.use_linear:
             x = self.proj_out(x)
-        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
+        x = x.reshape(x.shape[0], h, w, x.shape[-1]).movedim(3, 1).contiguous()
         if not self.use_linear:
             x = self.proj_out(x)
         return x + x_in
@@ -640,6 +721,7 @@ class SpatialVideoTransformer(SpatialTransformer):
         disable_self_attn=False,
         disable_temporal_crossattention=False,
         max_time_embed_period: int = 10000,
+        attn_precision=None,
         dtype=None, device=None, operations=ops
     ):
         super().__init__(
@@ -652,6 +734,7 @@ class SpatialVideoTransformer(SpatialTransformer):
             context_dim=context_dim,
             use_linear=use_linear,
             disable_self_attn=disable_self_attn,
+            attn_precision=attn_precision,
             dtype=dtype, device=device, operations=operations
         )
         self.time_depth = time_depth
@@ -681,6 +764,7 @@ class SpatialVideoTransformer(SpatialTransformer):
                     inner_dim=time_mix_inner_dim,
                     disable_self_attn=disable_self_attn,
                     disable_temporal_crossattention=disable_temporal_crossattention,
+                    attn_precision=attn_precision,
                     dtype=dtype, device=device, operations=operations
                 )
                 for _ in range(self.depth)
diff --git a/comfy/ldm/modules/diffusionmodules/mmdit.py b/comfy/ldm/modules/diffusionmodules/mmdit.py
new file mode 100644
index 00000000..92745153
--- /dev/null
+++ b/comfy/ldm/modules/diffusionmodules/mmdit.py
@@ -0,0 +1,978 @@
+import logging
+import math
+from typing import Dict, Optional
+
+import numpy as np
+import torch
+import torch.nn as nn
+from .. import attention
+from einops import rearrange, repeat
+
+def default(x, y):
+    if x is not None:
+        return x
+    return y
+
+class Mlp(nn.Module):
+    """ MLP as used in Vision Transformer, MLP-Mixer and related networks
+    """
+    def __init__(
+            self,
+            in_features,
+            hidden_features=None,
+            out_features=None,
+            act_layer=nn.GELU,
+            norm_layer=None,
+            bias=True,
+            drop=0.,
+            use_conv=False,
+            dtype=None,
+            device=None,
+            operations=None,
+    ):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        drop_probs = drop
+        linear_layer = partial(operations.Conv2d, kernel_size=1) if use_conv else operations.Linear
+
+        self.fc1 = linear_layer(in_features, hidden_features, bias=bias, dtype=dtype, device=device)
+        self.act = act_layer()
+        self.drop1 = nn.Dropout(drop_probs)
+        self.norm = norm_layer(hidden_features) if norm_layer is not None else nn.Identity()
+        self.fc2 = linear_layer(hidden_features, out_features, bias=bias, dtype=dtype, device=device)
+        self.drop2 = nn.Dropout(drop_probs)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop1(x)
+        x = self.norm(x)
+        x = self.fc2(x)
+        x = self.drop2(x)
+        return x
+
+class PatchEmbed(nn.Module):
+    """ 2D Image to Patch Embedding
+    """
+    dynamic_img_pad: torch.jit.Final[bool]
+
+    def __init__(
+            self,
+            img_size: Optional[int] = 224,
+            patch_size: int = 16,
+            in_chans: int = 3,
+            embed_dim: int = 768,
+            norm_layer = None,
+            flatten: bool = True,
+            bias: bool = True,
+            strict_img_size: bool = True,
+            dynamic_img_pad: bool = True,
+            dtype=None,
+            device=None,
+            operations=None,
+    ):
+        super().__init__()
+        self.patch_size = (patch_size, patch_size)
+        if img_size is not None:
+            self.img_size = (img_size, img_size)
+            self.grid_size = tuple([s // p for s, p in zip(self.img_size, self.patch_size)])
+            self.num_patches = self.grid_size[0] * self.grid_size[1]
+        else:
+            self.img_size = None
+            self.grid_size = None
+            self.num_patches = None
+
+        # flatten spatial dim and transpose to channels last, kept for bwd compat
+        self.flatten = flatten
+        self.strict_img_size = strict_img_size
+        self.dynamic_img_pad = dynamic_img_pad
+
+        self.proj = operations.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size, bias=bias, dtype=dtype, device=device)
+        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
+
+    def forward(self, x):
+        B, C, H, W = x.shape
+        # if self.img_size is not None:
+        #     if self.strict_img_size:
+        #         _assert(H == self.img_size[0], f"Input height ({H}) doesn't match model ({self.img_size[0]}).")
+        #         _assert(W == self.img_size[1], f"Input width ({W}) doesn't match model ({self.img_size[1]}).")
+        #     elif not self.dynamic_img_pad:
+        #         _assert(
+        #             H % self.patch_size[0] == 0,
+        #             f"Input height ({H}) should be divisible by patch size ({self.patch_size[0]})."
+        #         )
+        #         _assert(
+        #             W % self.patch_size[1] == 0,
+        #             f"Input width ({W}) should be divisible by patch size ({self.patch_size[1]})."
+        #         )
+        if self.dynamic_img_pad:
+            pad_h = (self.patch_size[0] - H % self.patch_size[0]) % self.patch_size[0]
+            pad_w = (self.patch_size[1] - W % self.patch_size[1]) % self.patch_size[1]
+            x = torch.nn.functional.pad(x, (0, pad_w, 0, pad_h), mode='reflect')
+        x = self.proj(x)
+        if self.flatten:
+            x = x.flatten(2).transpose(1, 2)  # NCHW -> NLC
+        x = self.norm(x)
+        return x
+
+def modulate(x, shift, scale):
+    if shift is None:
+        shift = torch.zeros_like(scale)
+    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+
+#################################################################################
+#                   Sine/Cosine Positional Embedding Functions                  #
+#################################################################################
+
+
+def get_2d_sincos_pos_embed(
+    embed_dim,
+    grid_size,
+    cls_token=False,
+    extra_tokens=0,
+    scaling_factor=None,
+    offset=None,
+):
+    """
+    grid_size: int of the grid height and width
+    return:
+    pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
+    """
+    grid_h = np.arange(grid_size, dtype=np.float32)
+    grid_w = np.arange(grid_size, dtype=np.float32)
+    grid = np.meshgrid(grid_w, grid_h)  # here w goes first
+    grid = np.stack(grid, axis=0)
+    if scaling_factor is not None:
+        grid = grid / scaling_factor
+    if offset is not None:
+        grid = grid - offset
+
+    grid = grid.reshape([2, 1, grid_size, grid_size])
+    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
+    if cls_token and extra_tokens > 0:
+        pos_embed = np.concatenate(
+            [np.zeros([extra_tokens, embed_dim]), pos_embed], axis=0
+        )
+    return pos_embed
+
+
+def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
+    assert embed_dim % 2 == 0
+
+    # use half of dimensions to encode grid_h
+    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
+    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)
+
+    emb = np.concatenate([emb_h, emb_w], axis=1)  # (H*W, D)
+    return emb
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+    """
+    embed_dim: output dimension for each position
+    pos: a list of positions to be encoded: size (M,)
+    out: (M, D)
+    """
+    assert embed_dim % 2 == 0
+    omega = np.arange(embed_dim // 2, dtype=np.float64)
+    omega /= embed_dim / 2.0
+    omega = 1.0 / 10000**omega  # (D/2,)
+
+    pos = pos.reshape(-1)  # (M,)
+    out = np.einsum("m,d->md", pos, omega)  # (M, D/2), outer product
+
+    emb_sin = np.sin(out)  # (M, D/2)
+    emb_cos = np.cos(out)  # (M, D/2)
+
+    emb = np.concatenate([emb_sin, emb_cos], axis=1)  # (M, D)
+    return emb
+
+def get_1d_sincos_pos_embed_from_grid_torch(embed_dim, pos, device=None, dtype=torch.float32):
+    omega = torch.arange(embed_dim // 2, device=device, dtype=dtype)
+    omega /= embed_dim / 2.0
+    omega = 1.0 / 10000**omega  # (D/2,)
+    pos = pos.reshape(-1)  # (M,)
+    out = torch.einsum("m,d->md", pos, omega)  # (M, D/2), outer product
+    emb_sin = torch.sin(out)  # (M, D/2)
+    emb_cos = torch.cos(out)  # (M, D/2)
+    emb = torch.cat([emb_sin, emb_cos], dim=1)  # (M, D)
+    return emb
+
+def get_2d_sincos_pos_embed_torch(embed_dim, w, h, val_center=7.5, val_magnitude=7.5, device=None, dtype=torch.float32):
+    small = min(h, w)
+    val_h = (h / small) * val_magnitude
+    val_w = (w / small) * val_magnitude
+    grid_h, grid_w = torch.meshgrid(torch.linspace(-val_h + val_center, val_h + val_center, h, device=device, dtype=dtype), torch.linspace(-val_w + val_center, val_w + val_center, w, device=device, dtype=dtype), indexing='ij')
+    emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype)
+    emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype)
+    emb = torch.cat([emb_w, emb_h], dim=1)  # (H*W, D)
+    return emb
+
+
+#################################################################################
+#               Embedding Layers for Timesteps and Class Labels                 #
+#################################################################################
+
+
+class TimestepEmbedder(nn.Module):
+    """
+    Embeds scalar timesteps into vector representations.
+    """
+
+    def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+
+    @staticmethod
+    def timestep_embedding(t, dim, max_period=10000):
+        """
+        Create sinusoidal timestep embeddings.
+        :param t: a 1-D Tensor of N indices, one per batch element.
+                          These may be fractional.
+        :param dim: the dimension of the output.
+        :param max_period: controls the minimum frequency of the embeddings.
+        :return: an (N, D) Tensor of positional embeddings.
+        """
+        half = dim // 2
+        freqs = torch.exp(
+            -math.log(max_period)
+            * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device)
+            / half
+        )
+        args = t[:, None].float() * freqs[None]
+        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+        if dim % 2:
+            embedding = torch.cat(
+                [embedding, torch.zeros_like(embedding[:, :1])], dim=-1
+            )
+        if torch.is_floating_point(t):
+            embedding = embedding.to(dtype=t.dtype)
+        return embedding
+
+    def forward(self, t, dtype, **kwargs):
+        t_freq = self.timestep_embedding(t, self.frequency_embedding_size).to(dtype)
+        t_emb = self.mlp(t_freq)
+        return t_emb
+
+
+class VectorEmbedder(nn.Module):
+    """
+    Embeds a flat vector of dimension input_dim
+    """
+
+    def __init__(self, input_dim: int, hidden_size: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.mlp = nn.Sequential(
+            operations.Linear(input_dim, hidden_size, bias=True, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        emb = self.mlp(x)
+        return emb
+
+
+#################################################################################
+#                                 Core DiT Model                                #
+#################################################################################
+
+
+def split_qkv(qkv, head_dim):
+    qkv = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, -1, head_dim).movedim(2, 0)
+    return qkv[0], qkv[1], qkv[2]
+
+def optimized_attention(qkv, num_heads):
+    return attention.optimized_attention(qkv[0], qkv[1], qkv[2], num_heads)
+
+class SelfAttention(nn.Module):
+    ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug")
+
+    def __init__(
+        self,
+        dim: int,
+        num_heads: int = 8,
+        qkv_bias: bool = False,
+        qk_scale: Optional[float] = None,
+        proj_drop: float = 0.0,
+        attn_mode: str = "xformers",
+        pre_only: bool = False,
+        qk_norm: Optional[str] = None,
+        rmsnorm: bool = False,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+
+        self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
+        if not pre_only:
+            self.proj = operations.Linear(dim, dim, dtype=dtype, device=device)
+            self.proj_drop = nn.Dropout(proj_drop)
+        assert attn_mode in self.ATTENTION_MODES
+        self.attn_mode = attn_mode
+        self.pre_only = pre_only
+
+        if qk_norm == "rms":
+            self.ln_q = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
+            self.ln_k = RMSNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
+        elif qk_norm == "ln":
+            self.ln_q = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
+            self.ln_k = operations.LayerNorm(self.head_dim, elementwise_affine=True, eps=1.0e-6, dtype=dtype, device=device)
+        elif qk_norm is None:
+            self.ln_q = nn.Identity()
+            self.ln_k = nn.Identity()
+        else:
+            raise ValueError(qk_norm)
+
+    def pre_attention(self, x: torch.Tensor) -> torch.Tensor:
+        B, L, C = x.shape
+        qkv = self.qkv(x)
+        q, k, v = split_qkv(qkv, self.head_dim)
+        q = self.ln_q(q).reshape(q.shape[0], q.shape[1], -1)
+        k = self.ln_k(k).reshape(q.shape[0], q.shape[1], -1)
+        return (q, k, v)
+
+    def post_attention(self, x: torch.Tensor) -> torch.Tensor:
+        assert not self.pre_only
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        qkv = self.pre_attention(x)
+        x = optimized_attention(
+            qkv, num_heads=self.num_heads
+        )
+        x = self.post_attention(x)
+        return x
+
+
+class RMSNorm(torch.nn.Module):
+    def __init__(
+        self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None
+    ):
+        """
+        Initialize the RMSNorm normalization layer.
+        Args:
+            dim (int): The dimension of the input tensor.
+            eps (float, optional): A small value added to the denominator for numerical stability. Default is 1e-6.
+        Attributes:
+            eps (float): A small value added to the denominator for numerical stability.
+            weight (nn.Parameter): Learnable scaling parameter.
+        """
+        super().__init__()
+        self.eps = eps
+        self.learnable_scale = elementwise_affine
+        if self.learnable_scale:
+            self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype))
+        else:
+            self.register_parameter("weight", None)
+
+    def _norm(self, x):
+        """
+        Apply the RMSNorm normalization to the input tensor.
+        Args:
+            x (torch.Tensor): The input tensor.
+        Returns:
+            torch.Tensor: The normalized tensor.
+        """
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
+
+    def forward(self, x):
+        """
+        Forward pass through the RMSNorm layer.
+        Args:
+            x (torch.Tensor): The input tensor.
+        Returns:
+            torch.Tensor: The output tensor after applying RMSNorm.
+        """
+        x = self._norm(x)
+        if self.learnable_scale:
+            return x * self.weight.to(device=x.device, dtype=x.dtype)
+        else:
+            return x
+
+
+class SwiGLUFeedForward(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        hidden_dim: int,
+        multiple_of: int,
+        ffn_dim_multiplier: Optional[float] = None,
+    ):
+        """
+        Initialize the FeedForward module.
+
+        Args:
+            dim (int): Input dimension.
+            hidden_dim (int): Hidden dimension of the feedforward layer.
+            multiple_of (int): Value to ensure hidden dimension is a multiple of this value.
+            ffn_dim_multiplier (float, optional): Custom multiplier for hidden dimension. Defaults to None.
+
+        Attributes:
+            w1 (ColumnParallelLinear): Linear transformation for the first layer.
+            w2 (RowParallelLinear): Linear transformation for the second layer.
+            w3 (ColumnParallelLinear): Linear transformation for the third layer.
+
+        """
+        super().__init__()
+        hidden_dim = int(2 * hidden_dim / 3)
+        # custom dim factor multiplier
+        if ffn_dim_multiplier is not None:
+            hidden_dim = int(ffn_dim_multiplier * hidden_dim)
+        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+
+        self.w1 = nn.Linear(dim, hidden_dim, bias=False)
+        self.w2 = nn.Linear(hidden_dim, dim, bias=False)
+        self.w3 = nn.Linear(dim, hidden_dim, bias=False)
+
+    def forward(self, x):
+        return self.w2(nn.functional.silu(self.w1(x)) * self.w3(x))
+
+
+class DismantledBlock(nn.Module):
+    """
+    A DiT block with gated adaptive layer norm (adaLN) conditioning.
+    """
+
+    ATTENTION_MODES = ("xformers", "torch", "torch-hb", "math", "debug")
+
+    def __init__(
+        self,
+        hidden_size: int,
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        attn_mode: str = "xformers",
+        qkv_bias: bool = False,
+        pre_only: bool = False,
+        rmsnorm: bool = False,
+        scale_mod_only: bool = False,
+        swiglu: bool = False,
+        qk_norm: Optional[str] = None,
+        dtype=None,
+        device=None,
+        operations=None,
+        **block_kwargs,
+    ):
+        super().__init__()
+        assert attn_mode in self.ATTENTION_MODES
+        if not rmsnorm:
+            self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        else:
+            self.norm1 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        self.attn = SelfAttention(
+            dim=hidden_size,
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            attn_mode=attn_mode,
+            pre_only=pre_only,
+            qk_norm=qk_norm,
+            rmsnorm=rmsnorm,
+            dtype=dtype,
+            device=device,
+            operations=operations
+        )
+        if not pre_only:
+            if not rmsnorm:
+                self.norm2 = operations.LayerNorm(
+                    hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device
+                )
+            else:
+                self.norm2 = RMSNorm(hidden_size, elementwise_affine=False, eps=1e-6)
+        mlp_hidden_dim = int(hidden_size * mlp_ratio)
+        if not pre_only:
+            if not swiglu:
+                self.mlp = Mlp(
+                    in_features=hidden_size,
+                    hidden_features=mlp_hidden_dim,
+                    act_layer=lambda: nn.GELU(approximate="tanh"),
+                    drop=0,
+                    dtype=dtype,
+                    device=device,
+                    operations=operations
+                )
+            else:
+                self.mlp = SwiGLUFeedForward(
+                    dim=hidden_size,
+                    hidden_dim=mlp_hidden_dim,
+                    multiple_of=256,
+                )
+        self.scale_mod_only = scale_mod_only
+        if not scale_mod_only:
+            n_mods = 6 if not pre_only else 2
+        else:
+            n_mods = 4 if not pre_only else 1
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(), operations.Linear(hidden_size, n_mods * hidden_size, bias=True, dtype=dtype, device=device)
+        )
+        self.pre_only = pre_only
+
+    def pre_attention(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
+        if not self.pre_only:
+            if not self.scale_mod_only:
+                (
+                    shift_msa,
+                    scale_msa,
+                    gate_msa,
+                    shift_mlp,
+                    scale_mlp,
+                    gate_mlp,
+                ) = self.adaLN_modulation(c).chunk(6, dim=1)
+            else:
+                shift_msa = None
+                shift_mlp = None
+                (
+                    scale_msa,
+                    gate_msa,
+                    scale_mlp,
+                    gate_mlp,
+                ) = self.adaLN_modulation(
+                    c
+                ).chunk(4, dim=1)
+            qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa))
+            return qkv, (
+                x,
+                gate_msa,
+                shift_mlp,
+                scale_mlp,
+                gate_mlp,
+            )
+        else:
+            if not self.scale_mod_only:
+                (
+                    shift_msa,
+                    scale_msa,
+                ) = self.adaLN_modulation(
+                    c
+                ).chunk(2, dim=1)
+            else:
+                shift_msa = None
+                scale_msa = self.adaLN_modulation(c)
+            qkv = self.attn.pre_attention(modulate(self.norm1(x), shift_msa, scale_msa))
+            return qkv, None
+
+    def post_attention(self, attn, x, gate_msa, shift_mlp, scale_mlp, gate_mlp):
+        assert not self.pre_only
+        x = x + gate_msa.unsqueeze(1) * self.attn.post_attention(attn)
+        x = x + gate_mlp.unsqueeze(1) * self.mlp(
+            modulate(self.norm2(x), shift_mlp, scale_mlp)
+        )
+        return x
+
+    def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
+        assert not self.pre_only
+        qkv, intermediates = self.pre_attention(x, c)
+        attn = optimized_attention(
+            qkv,
+            num_heads=self.attn.num_heads,
+        )
+        return self.post_attention(attn, *intermediates)
+
+
+def block_mixing(*args, use_checkpoint=True, **kwargs):
+    if use_checkpoint:
+        return torch.utils.checkpoint.checkpoint(
+            _block_mixing, *args, use_reentrant=False, **kwargs
+        )
+    else:
+        return _block_mixing(*args, **kwargs)
+
+
+def _block_mixing(context, x, context_block, x_block, c):
+    context_qkv, context_intermediates = context_block.pre_attention(context, c)
+
+    x_qkv, x_intermediates = x_block.pre_attention(x, c)
+
+    o = []
+    for t in range(3):
+        o.append(torch.cat((context_qkv[t], x_qkv[t]), dim=1))
+    qkv = tuple(o)
+
+    attn = optimized_attention(
+        qkv,
+        num_heads=x_block.attn.num_heads,
+    )
+    context_attn, x_attn = (
+        attn[:, : context_qkv[0].shape[1]],
+        attn[:, context_qkv[0].shape[1] :],
+    )
+
+    if not context_block.pre_only:
+        context = context_block.post_attention(context_attn, *context_intermediates)
+
+    else:
+        context = None
+    x = x_block.post_attention(x_attn, *x_intermediates)
+    return context, x
+
+
+class JointBlock(nn.Module):
+    """just a small wrapper to serve as a fsdp unit"""
+
+    def __init__(
+        self,
+        *args,
+        **kwargs,
+    ):
+        super().__init__()
+        pre_only = kwargs.pop("pre_only")
+        qk_norm = kwargs.pop("qk_norm", None)
+        self.context_block = DismantledBlock(*args, pre_only=pre_only, qk_norm=qk_norm, **kwargs)
+        self.x_block = DismantledBlock(*args, pre_only=False, qk_norm=qk_norm, **kwargs)
+
+    def forward(self, *args, **kwargs):
+        return block_mixing(
+            *args, context_block=self.context_block, x_block=self.x_block, **kwargs
+        )
+
+
+class FinalLayer(nn.Module):
+    """
+    The final layer of DiT.
+    """
+
+    def __init__(
+        self,
+        hidden_size: int,
+        patch_size: int,
+        out_channels: int,
+        total_out_channels: Optional[int] = None,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        super().__init__()
+        self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.linear = (
+            operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
+            if (total_out_channels is None)
+            else operations.Linear(hidden_size, total_out_channels, bias=True, dtype=dtype, device=device)
+        )
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(), operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device)
+        )
+
+    def forward(self, x: torch.Tensor, c: torch.Tensor) -> torch.Tensor:
+        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
+        x = modulate(self.norm_final(x), shift, scale)
+        x = self.linear(x)
+        return x
+
+class SelfAttentionContext(nn.Module):
+    def __init__(self, dim, heads=8, dim_head=64, dtype=None, device=None, operations=None):
+        super().__init__()
+        dim_head = dim // heads
+        inner_dim = dim
+
+        self.heads = heads
+        self.dim_head = dim_head
+
+        self.qkv = operations.Linear(dim, dim * 3, bias=True, dtype=dtype, device=device)
+
+        self.proj = operations.Linear(inner_dim, dim, dtype=dtype, device=device)
+
+    def forward(self, x):
+        qkv = self.qkv(x)
+        q, k, v = split_qkv(qkv, self.dim_head)
+        x = optimized_attention((q.reshape(q.shape[0], q.shape[1], -1), k, v), self.heads)
+        return self.proj(x)
+
+class ContextProcessorBlock(nn.Module):
+    def __init__(self, context_size, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.norm1 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.attn = SelfAttentionContext(context_size, dtype=dtype, device=device, operations=operations)
+        self.norm2 = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.mlp = Mlp(in_features=context_size, hidden_features=(context_size * 4), act_layer=lambda: nn.GELU(approximate="tanh"), drop=0, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, x):
+        x += self.attn(self.norm1(x))
+        x += self.mlp(self.norm2(x))
+        return x
+
+class ContextProcessor(nn.Module):
+    def __init__(self, context_size, num_layers, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.layers = torch.nn.ModuleList([ContextProcessorBlock(context_size, dtype=dtype, device=device, operations=operations) for i in range(num_layers)])
+        self.norm = operations.LayerNorm(context_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+
+    def forward(self, x):
+        for i, l in enumerate(self.layers):
+            x = l(x)
+        return self.norm(x)
+
+class MMDiT(nn.Module):
+    """
+    Diffusion model with a Transformer backbone.
+    """
+
+    def __init__(
+        self,
+        input_size: int = 32,
+        patch_size: int = 2,
+        in_channels: int = 4,
+        depth: int = 28,
+        # hidden_size: Optional[int] = None,
+        # num_heads: Optional[int] = None,
+        mlp_ratio: float = 4.0,
+        learn_sigma: bool = False,
+        adm_in_channels: Optional[int] = None,
+        context_embedder_config: Optional[Dict] = None,
+        compile_core: bool = False,
+        use_checkpoint: bool = False,
+        register_length: int = 0,
+        attn_mode: str = "torch",
+        rmsnorm: bool = False,
+        scale_mod_only: bool = False,
+        swiglu: bool = False,
+        out_channels: Optional[int] = None,
+        pos_embed_scaling_factor: Optional[float] = None,
+        pos_embed_offset: Optional[float] = None,
+        pos_embed_max_size: Optional[int] = None,
+        num_patches = None,
+        qk_norm: Optional[str] = None,
+        qkv_bias: bool = True,
+        context_processor_layers = None,
+        context_size = 4096,
+        num_blocks = None,
+        final_layer = True,
+        dtype = None, #TODO
+        device = None,
+        operations = None,
+    ):
+        super().__init__()
+        self.dtype = dtype
+        self.learn_sigma = learn_sigma
+        self.in_channels = in_channels
+        default_out_channels = in_channels * 2 if learn_sigma else in_channels
+        self.out_channels = default(out_channels, default_out_channels)
+        self.patch_size = patch_size
+        self.pos_embed_scaling_factor = pos_embed_scaling_factor
+        self.pos_embed_offset = pos_embed_offset
+        self.pos_embed_max_size = pos_embed_max_size
+
+        # hidden_size = default(hidden_size, 64 * depth)
+        # num_heads = default(num_heads, hidden_size // 64)
+
+        # apply magic --> this defines a head_size of 64
+        self.hidden_size = 64 * depth
+        num_heads = depth
+        if num_blocks is None:
+            num_blocks = depth
+
+        self.depth = depth
+        self.num_heads = num_heads
+
+        self.x_embedder = PatchEmbed(
+            input_size,
+            patch_size,
+            in_channels,
+            self.hidden_size,
+            bias=True,
+            strict_img_size=self.pos_embed_max_size is None,
+            dtype=dtype,
+            device=device,
+            operations=operations
+        )
+        self.t_embedder = TimestepEmbedder(self.hidden_size, dtype=dtype, device=device, operations=operations)
+
+        self.y_embedder = None
+        if adm_in_channels is not None:
+            assert isinstance(adm_in_channels, int)
+            self.y_embedder = VectorEmbedder(adm_in_channels, self.hidden_size, dtype=dtype, device=device, operations=operations)
+
+        if context_processor_layers is not None:
+            self.context_processor = ContextProcessor(context_size, context_processor_layers, dtype=dtype, device=device, operations=operations)
+        else:
+            self.context_processor = None
+
+        self.context_embedder = nn.Identity()
+        if context_embedder_config is not None:
+            if context_embedder_config["target"] == "torch.nn.Linear":
+                self.context_embedder = operations.Linear(**context_embedder_config["params"], dtype=dtype, device=device)
+
+        self.register_length = register_length
+        if self.register_length > 0:
+            self.register = nn.Parameter(torch.randn(1, register_length, self.hidden_size, dtype=dtype, device=device))
+
+        # num_patches = self.x_embedder.num_patches
+        # Will use fixed sin-cos embedding:
+        # just use a buffer already
+        if num_patches is not None:
+            self.register_buffer(
+                "pos_embed",
+                torch.empty(1, num_patches, self.hidden_size, dtype=dtype, device=device),
+            )
+        else:
+            self.pos_embed = None
+
+        self.use_checkpoint = use_checkpoint
+        self.joint_blocks = nn.ModuleList(
+            [
+                JointBlock(
+                    self.hidden_size,
+                    num_heads,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=qkv_bias,
+                    attn_mode=attn_mode,
+                    pre_only=(i == num_blocks - 1) and final_layer,
+                    rmsnorm=rmsnorm,
+                    scale_mod_only=scale_mod_only,
+                    swiglu=swiglu,
+                    qk_norm=qk_norm,
+                    dtype=dtype,
+                    device=device,
+                    operations=operations
+                )
+                for i in range(num_blocks)
+            ]
+        )
+
+        if final_layer:
+            self.final_layer = FinalLayer(self.hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations)
+
+        if compile_core:
+            assert False
+            self.forward_core_with_concat = torch.compile(self.forward_core_with_concat)
+
+    def cropped_pos_embed(self, hw, device=None):
+        p = self.x_embedder.patch_size[0]
+        h, w = hw
+        # patched size
+        h = (h + 1) // p
+        w = (w + 1) // p
+        if self.pos_embed is None:
+            return get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, device=device)
+        assert self.pos_embed_max_size is not None
+        assert h <= self.pos_embed_max_size, (h, self.pos_embed_max_size)
+        assert w <= self.pos_embed_max_size, (w, self.pos_embed_max_size)
+        top = (self.pos_embed_max_size - h) // 2
+        left = (self.pos_embed_max_size - w) // 2
+        spatial_pos_embed = rearrange(
+            self.pos_embed,
+            "1 (h w) c -> 1 h w c",
+            h=self.pos_embed_max_size,
+            w=self.pos_embed_max_size,
+        )
+        spatial_pos_embed = spatial_pos_embed[:, top : top + h, left : left + w, :]
+        spatial_pos_embed = rearrange(spatial_pos_embed, "1 h w c -> 1 (h w) c")
+        # print(spatial_pos_embed, top, left, h, w)
+        # # t = get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, 7.875, 7.875, device=device) #matches exactly for 1024 res
+        # t = get_2d_sincos_pos_embed_torch(self.hidden_size, w, h, 7.5, 7.5, device=device) #scales better
+        # # print(t)
+        # return t
+        return spatial_pos_embed
+
+    def unpatchify(self, x, hw=None):
+        """
+        x: (N, T, patch_size**2 * C)
+        imgs: (N, H, W, C)
+        """
+        c = self.out_channels
+        p = self.x_embedder.patch_size[0]
+        if hw is None:
+            h = w = int(x.shape[1] ** 0.5)
+        else:
+            h, w = hw
+            h = (h + 1) // p
+            w = (w + 1) // p
+        assert h * w == x.shape[1]
+
+        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
+        x = torch.einsum("nhwpqc->nchpwq", x)
+        imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
+        return imgs
+
+    def forward_core_with_concat(
+        self,
+        x: torch.Tensor,
+        c_mod: torch.Tensor,
+        context: Optional[torch.Tensor] = None,
+        control = None,
+    ) -> torch.Tensor:
+        if self.register_length > 0:
+            context = torch.cat(
+                (
+                    repeat(self.register, "1 ... -> b ...", b=x.shape[0]),
+                    default(context, torch.Tensor([]).type_as(x)),
+                ),
+                1,
+            )
+
+        # context is B, L', D
+        # x is B, L, D
+        blocks = len(self.joint_blocks)
+        for i in range(blocks):
+            context, x = self.joint_blocks[i](
+                context,
+                x,
+                c=c_mod,
+                use_checkpoint=self.use_checkpoint,
+            )
+            if control is not None:
+                control_o = control.get("output")
+                if i < len(control_o):
+                    add = control_o[i]
+                    if add is not None:
+                        x += add
+
+        x = self.final_layer(x, c_mod)  # (N, T, patch_size ** 2 * out_channels)
+        return x
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        t: torch.Tensor,
+        y: Optional[torch.Tensor] = None,
+        context: Optional[torch.Tensor] = None,
+        control = None,
+    ) -> torch.Tensor:
+        """
+        Forward pass of DiT.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of class labels
+        """
+
+        if self.context_processor is not None:
+            context = self.context_processor(context)
+
+        hw = x.shape[-2:]
+        x = self.x_embedder(x) + self.cropped_pos_embed(hw, device=x.device).to(dtype=x.dtype, device=x.device)
+        c = self.t_embedder(t, dtype=x.dtype)  # (N, D)
+        if y is not None and self.y_embedder is not None:
+            y = self.y_embedder(y)  # (N, D)
+            c = c + y  # (N, D)
+
+        if context is not None:
+            context = self.context_embedder(context)
+
+        x = self.forward_core_with_concat(x, c, context, control)
+
+        x = self.unpatchify(x, hw=hw)  # (N, out_channels, H, W)
+        return x[:,:,:hw[-2],:hw[-1]]
+
+
+class OpenAISignatureMMDITWrapper(MMDiT):
+    def forward(
+        self,
+        x: torch.Tensor,
+        timesteps: torch.Tensor,
+        context: Optional[torch.Tensor] = None,
+        y: Optional[torch.Tensor] = None,
+        control = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        return super().forward(x, timesteps, context=context, y=y, control=control)
+
diff --git a/comfy/ldm/modules/diffusionmodules/model.py b/comfy/ldm/modules/diffusionmodules/model.py
index cc81c1f2..04eb83b2 100644
--- a/comfy/ldm/modules/diffusionmodules/model.py
+++ b/comfy/ldm/modules/diffusionmodules/model.py
@@ -3,8 +3,8 @@ import math
 import torch
 import torch.nn as nn
 import numpy as np
-from einops import rearrange
 from typing import Optional, Any
+import logging
 
 from comfy import model_management
 import comfy.ops
@@ -190,7 +190,7 @@ def slice_attention(q, k, v):
             steps *= 2
             if steps > 128:
                 raise e
-            print("out of memory error, increasing steps and trying again", steps)
+            logging.warning("out of memory error, increasing steps and trying again {}".format(steps))
 
     return r1
 
@@ -235,7 +235,7 @@ def pytorch_attention(q, k, v):
         out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=0.0, is_causal=False)
         out = out.transpose(2, 3).reshape(B, C, H, W)
     except model_management.OOM_EXCEPTION as e:
-        print("scaled_dot_product_attention OOMed: switched to slice attention")
+        logging.warning("scaled_dot_product_attention OOMed: switched to slice attention")
         out = slice_attention(q.view(B, -1, C), k.view(B, -1, C).transpose(1, 2), v.view(B, -1, C).transpose(1, 2)).reshape(B, C, H, W)
     return out
 
@@ -268,13 +268,13 @@ class AttnBlock(nn.Module):
                                         padding=0)
 
         if model_management.xformers_enabled_vae():
-            print("Using xformers attention in VAE")
+            logging.info("Using xformers attention in VAE")
             self.optimized_attention = xformers_attention
         elif model_management.pytorch_attention_enabled():
-            print("Using pytorch attention in VAE")
+            logging.info("Using pytorch attention in VAE")
             self.optimized_attention = pytorch_attention
         else:
-            print("Using split attention in VAE")
+            logging.info("Using split attention in VAE")
             self.optimized_attention = normal_attention
 
     def forward(self, x):
@@ -562,7 +562,7 @@ class Decoder(nn.Module):
         block_in = ch*ch_mult[self.num_resolutions-1]
         curr_res = resolution // 2**(self.num_resolutions-1)
         self.z_shape = (1,z_channels,curr_res,curr_res)
-        print("Working with z of shape {} = {} dimensions.".format(
+        logging.debug("Working with z of shape {} = {} dimensions.".format(
             self.z_shape, np.prod(self.z_shape)))
 
         # z to block_in
diff --git a/comfy/ldm/modules/diffusionmodules/openaimodel.py b/comfy/ldm/modules/diffusionmodules/openaimodel.py
index 998afd97..ba8fc2c4 100644
--- a/comfy/ldm/modules/diffusionmodules/openaimodel.py
+++ b/comfy/ldm/modules/diffusionmodules/openaimodel.py
@@ -4,6 +4,7 @@ import torch as th
 import torch.nn as nn
 import torch.nn.functional as F
 from einops import rearrange
+import logging
 
 from .util import (
     checkpoint,
@@ -257,7 +258,7 @@ class ResBlock(TimestepBlock):
         else:
             if emb_out is not None:
                 if self.exchange_temb_dims:
-                    emb_out = rearrange(emb_out, "b t c ... -> b c t ...")
+                    emb_out = emb_out.movedim(1, 2)
                 h = h + emb_out
             h = self.out_layers(h)
         return self.skip_connection(x) + h
@@ -359,7 +360,7 @@ def apply_control(h, control, name):
             try:
                 h += ctrl
             except:
-                print("warning control could not be applied", h.shape, ctrl.shape)
+                logging.warning("warning control could not be applied {} {}".format(h.shape, ctrl.shape))
     return h
 
 class UNetModel(nn.Module):
@@ -430,6 +431,7 @@ class UNetModel(nn.Module):
         video_kernel_size=None,
         disable_temporal_crossattention=False,
         max_ddpm_temb_period=10000,
+        attn_precision=None,
         device=None,
         operations=ops,
     ):
@@ -484,7 +486,6 @@ class UNetModel(nn.Module):
         self.predict_codebook_ids = n_embed is not None
 
         self.default_num_video_frames = None
-        self.default_image_only_indicator = None
 
         time_embed_dim = model_channels * 4
         self.time_embed = nn.Sequential(
@@ -497,7 +498,7 @@ class UNetModel(nn.Module):
             if isinstance(self.num_classes, int):
                 self.label_emb = nn.Embedding(num_classes, time_embed_dim, dtype=self.dtype, device=device)
             elif self.num_classes == "continuous":
-                print("setting up linear c_adm embedding layer")
+                logging.debug("setting up linear c_adm embedding layer")
                 self.label_emb = nn.Linear(1, time_embed_dim)
             elif self.num_classes == "sequential":
                 assert adm_in_channels is not None
@@ -550,13 +551,14 @@ class UNetModel(nn.Module):
                     disable_self_attn=disable_self_attn,
                     disable_temporal_crossattention=disable_temporal_crossattention,
                     max_time_embed_period=max_ddpm_temb_period,
+                    attn_precision=attn_precision,
                     dtype=self.dtype, device=device, operations=operations
                 )
             else:
                 return SpatialTransformer(
                                 ch, num_heads, dim_head, depth=depth, context_dim=context_dim,
                                 disable_self_attn=disable_self_attn, use_linear=use_linear_in_transformer,
-                                use_checkpoint=use_checkpoint, dtype=self.dtype, device=device, operations=operations
+                                use_checkpoint=use_checkpoint, attn_precision=attn_precision, dtype=self.dtype, device=device, operations=operations
                             )
 
         def get_resblock(
@@ -708,27 +710,30 @@ class UNetModel(nn.Module):
                 device=device,
                 operations=operations
             )]
-        if transformer_depth_middle >= 0:
-            mid_block += [get_attention_layer(  # always uses a self-attn
-                            ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim,
-                            disable_self_attn=disable_middle_self_attn, use_checkpoint=use_checkpoint
-                        ),
-            get_resblock(
-                merge_factor=merge_factor,
-                merge_strategy=merge_strategy,
-                video_kernel_size=video_kernel_size,
-                ch=ch,
-                time_embed_dim=time_embed_dim,
-                dropout=dropout,
-                out_channels=None,
-                dims=dims,
-                use_checkpoint=use_checkpoint,
-                use_scale_shift_norm=use_scale_shift_norm,
-                dtype=self.dtype,
-                device=device,
-                operations=operations
-            )]
-        self.middle_block = TimestepEmbedSequential(*mid_block)
+
+        self.middle_block = None
+        if transformer_depth_middle >= -1:
+            if transformer_depth_middle >= 0:
+                mid_block += [get_attention_layer(  # always uses a self-attn
+                                ch, num_heads, dim_head, depth=transformer_depth_middle, context_dim=context_dim,
+                                disable_self_attn=disable_middle_self_attn, use_checkpoint=use_checkpoint
+                            ),
+                get_resblock(
+                    merge_factor=merge_factor,
+                    merge_strategy=merge_strategy,
+                    video_kernel_size=video_kernel_size,
+                    ch=ch,
+                    time_embed_dim=time_embed_dim,
+                    dropout=dropout,
+                    out_channels=None,
+                    dims=dims,
+                    use_checkpoint=use_checkpoint,
+                    use_scale_shift_norm=use_scale_shift_norm,
+                    dtype=self.dtype,
+                    device=device,
+                    operations=operations
+                )]
+            self.middle_block = TimestepEmbedSequential(*mid_block)
         self._feature_size += ch
 
         self.output_blocks = nn.ModuleList([])
@@ -827,7 +832,7 @@ class UNetModel(nn.Module):
         transformer_patches = transformer_options.get("patches", {})
 
         num_video_frames = kwargs.get("num_video_frames", self.default_num_video_frames)
-        image_only_indicator = kwargs.get("image_only_indicator", self.default_image_only_indicator)
+        image_only_indicator = kwargs.get("image_only_indicator", None)
         time_context = kwargs.get("time_context", None)
 
         assert (y is not None) == (
@@ -858,7 +863,8 @@ class UNetModel(nn.Module):
                     h = p(h, transformer_options)
 
         transformer_options["block"] = ("middle", 0)
-        h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)
+        if self.middle_block is not None:
+            h = forward_timestep_embed(self.middle_block, h, emb, context, transformer_options, time_context=time_context, num_video_frames=num_video_frames, image_only_indicator=image_only_indicator)
         h = apply_control(h, control, 'middle')
 
 
diff --git a/comfy/ldm/modules/diffusionmodules/util.py b/comfy/ldm/modules/diffusionmodules/util.py
index 5a6aa7d7..ce14ad5e 100644
--- a/comfy/ldm/modules/diffusionmodules/util.py
+++ b/comfy/ldm/modules/diffusionmodules/util.py
@@ -46,23 +46,25 @@ class AlphaBlender(nn.Module):
         else:
             raise ValueError(f"unknown merge strategy {self.merge_strategy}")
 
-    def get_alpha(self, image_only_indicator: torch.Tensor) -> torch.Tensor:
+    def get_alpha(self, image_only_indicator: torch.Tensor, device) -> torch.Tensor:
         # skip_time_mix = rearrange(repeat(skip_time_mix, 'b -> (b t) () () ()', t=t), '(b t) 1 ... -> b 1 t ...', t=t)
         if self.merge_strategy == "fixed":
             # make shape compatible
             # alpha = repeat(self.mix_factor, '1 -> b () t  () ()', t=t, b=bs)
-            alpha = self.mix_factor.to(image_only_indicator.device)
+            alpha = self.mix_factor.to(device)
         elif self.merge_strategy == "learned":
-            alpha = torch.sigmoid(self.mix_factor.to(image_only_indicator.device))
+            alpha = torch.sigmoid(self.mix_factor.to(device))
             # make shape compatible
             # alpha = repeat(alpha, '1 -> s () ()', s = t * bs)
         elif self.merge_strategy == "learned_with_images":
-            assert image_only_indicator is not None, "need image_only_indicator ..."
-            alpha = torch.where(
-                image_only_indicator.bool(),
-                torch.ones(1, 1, device=image_only_indicator.device),
-                rearrange(torch.sigmoid(self.mix_factor.to(image_only_indicator.device)), "... -> ... 1"),
-            )
+            if image_only_indicator is None:
+                alpha = rearrange(torch.sigmoid(self.mix_factor.to(device)), "... -> ... 1")
+            else:
+                alpha = torch.where(
+                    image_only_indicator.bool(),
+                    torch.ones(1, 1, device=image_only_indicator.device),
+                    rearrange(torch.sigmoid(self.mix_factor.to(image_only_indicator.device)), "... -> ... 1"),
+                )
             alpha = rearrange(alpha, self.rearrange_pattern)
             # make shape compatible
             # alpha = repeat(alpha, '1 -> s () ()', s = t * bs)
@@ -76,7 +78,7 @@ class AlphaBlender(nn.Module):
         x_temporal,
         image_only_indicator=None,
     ) -> torch.Tensor:
-        alpha = self.get_alpha(image_only_indicator)
+        alpha = self.get_alpha(image_only_indicator, x_spatial.device)
         x = (
             alpha.to(x_spatial.dtype) * x_spatial
             + (1.0 - alpha).to(x_spatial.dtype) * x_temporal
diff --git a/comfy/ldm/modules/sub_quadratic_attention.py b/comfy/ldm/modules/sub_quadratic_attention.py
index cb0896b0..1bc4138c 100644
--- a/comfy/ldm/modules/sub_quadratic_attention.py
+++ b/comfy/ldm/modules/sub_quadratic_attention.py
@@ -14,6 +14,7 @@ import torch
 from torch import Tensor
 from torch.utils.checkpoint import checkpoint
 import math
+import logging
 
 try:
 	from typing import Optional, NamedTuple, List, Protocol
@@ -170,7 +171,7 @@ def _get_attention_scores_no_kv_chunking(
         attn_probs = attn_scores.softmax(dim=-1)
         del attn_scores
     except model_management.OOM_EXCEPTION:
-        print("ran out of memory while running softmax in  _get_attention_scores_no_kv_chunking, trying slower in place softmax instead")
+        logging.warning("ran out of memory while running softmax in  _get_attention_scores_no_kv_chunking, trying slower in place softmax instead")
         attn_scores -= attn_scores.max(dim=-1, keepdim=True).values
         torch.exp(attn_scores, out=attn_scores)
         summed = torch.sum(attn_scores, dim=-1, keepdim=True)
diff --git a/comfy/lora.py b/comfy/lora.py
index 5e4009b4..03743177 100644
--- a/comfy/lora.py
+++ b/comfy/lora.py
@@ -1,4 +1,5 @@
 import comfy.utils
+import logging
 
 LORA_CLIP_MAP = {
     "mlp.fc1": "mlp_fc1",
@@ -20,8 +21,16 @@ def load_lora(lora, to_load):
             alpha = lora[alpha_name].item()
             loaded_keys.add(alpha_name)
 
+        dora_scale_name = "{}.dora_scale".format(x)
+        dora_scale = None
+        if dora_scale_name in lora.keys():
+            dora_scale = lora[dora_scale_name]
+            loaded_keys.add(dora_scale_name)
+
         regular_lora = "{}.lora_up.weight".format(x)
         diffusers_lora = "{}_lora.up.weight".format(x)
+        diffusers2_lora = "{}.lora_B.weight".format(x)
+        diffusers3_lora = "{}.lora.up.weight".format(x)
         transformers_lora = "{}.lora_linear_layer.up.weight".format(x)
         A_name = None
 
@@ -33,6 +42,14 @@ def load_lora(lora, to_load):
             A_name = diffusers_lora
             B_name = "{}_lora.down.weight".format(x)
             mid_name = None
+        elif diffusers2_lora in lora.keys():
+            A_name = diffusers2_lora
+            B_name = "{}.lora_A.weight".format(x)
+            mid_name = None
+        elif diffusers3_lora in lora.keys():
+            A_name = diffusers3_lora
+            B_name = "{}.lora.down.weight".format(x)
+            mid_name = None
         elif transformers_lora in lora.keys():
             A_name = transformers_lora
             B_name ="{}.lora_linear_layer.down.weight".format(x)
@@ -43,7 +60,7 @@ def load_lora(lora, to_load):
             if mid_name is not None and mid_name in lora.keys():
                 mid = lora[mid_name]
                 loaded_keys.add(mid_name)
-            patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid))
+            patch_dict[to_load[x]] = ("lora", (lora[A_name], lora[B_name], alpha, mid, dora_scale))
             loaded_keys.add(A_name)
             loaded_keys.add(B_name)
 
@@ -64,7 +81,7 @@ def load_lora(lora, to_load):
                 loaded_keys.add(hada_t1_name)
                 loaded_keys.add(hada_t2_name)
 
-            patch_dict[to_load[x]] = ("loha", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2))
+            patch_dict[to_load[x]] = ("loha", (lora[hada_w1_a_name], lora[hada_w1_b_name], alpha, lora[hada_w2_a_name], lora[hada_w2_b_name], hada_t1, hada_t2, dora_scale))
             loaded_keys.add(hada_w1_a_name)
             loaded_keys.add(hada_w1_b_name)
             loaded_keys.add(hada_w2_a_name)
@@ -116,7 +133,7 @@ def load_lora(lora, to_load):
             loaded_keys.add(lokr_t2_name)
 
         if (lokr_w1 is not None) or (lokr_w2 is not None) or (lokr_w1_a is not None) or (lokr_w2_a is not None):
-            patch_dict[to_load[x]] = ("lokr", (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2))
+            patch_dict[to_load[x]] = ("lokr", (lokr_w1, lokr_w2, alpha, lokr_w1_a, lokr_w1_b, lokr_w2_a, lokr_w2_b, lokr_t2, dora_scale))
 
         #glora
         a1_name = "{}.a1.weight".format(x)
@@ -124,7 +141,7 @@ def load_lora(lora, to_load):
         b1_name = "{}.b1.weight".format(x)
         b2_name = "{}.b2.weight".format(x)
         if a1_name in lora:
-            patch_dict[to_load[x]] = ("glora", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha))
+            patch_dict[to_load[x]] = ("glora", (lora[a1_name], lora[a2_name], lora[b1_name], lora[b2_name], alpha, dora_scale))
             loaded_keys.add(a1_name)
             loaded_keys.add(a2_name)
             loaded_keys.add(b1_name)
@@ -156,7 +173,8 @@ def load_lora(lora, to_load):
 
     for x in lora.keys():
         if x not in loaded_keys:
-            print("lora key not loaded", x)
+            logging.warning("lora key not loaded: {}".format(x))
+
     return patch_dict
 
 def model_lora_keys_clip(model, key_map={}):
@@ -197,16 +215,36 @@ def model_lora_keys_clip(model, key_map={}):
                     key_map[lora_key] = k
                     lora_key = "text_encoder.text_model.encoder.layers.{}.{}".format(b, c) #diffusers lora
                     key_map[lora_key] = k
+                    lora_key = "lora_prior_te_text_model_encoder_layers_{}_{}".format(b, LORA_CLIP_MAP[c]) #cascade lora: TODO put lora key prefix in the model config
+                    key_map[lora_key] = k
+
+    for k in sdk: #OneTrainer SD3 lora
+        if k.startswith("t5xxl.transformer.") and k.endswith(".weight"):
+            l_key = k[len("t5xxl.transformer."):-len(".weight")]
+            lora_key = "lora_te3_{}".format(l_key.replace(".", "_"))
+            key_map[lora_key] = k
+
+    k = "clip_g.transformer.text_projection.weight"
+    if k in sdk:
+        key_map["lora_prior_te_text_projection"] = k #cascade lora?
+        # key_map["text_encoder.text_projection"] = k #TODO: check if other lora have the text_projection too
+        key_map["lora_te2_text_projection"] = k #OneTrainer SD3 lora
+
+    k = "clip_l.transformer.text_projection.weight"
+    if k in sdk:
+        key_map["lora_te1_text_projection"] = k #OneTrainer SD3 lora, not necessary but omits warning
 
     return key_map
 
 def model_lora_keys_unet(model, key_map={}):
-    sdk = model.state_dict().keys()
+    sd = model.state_dict()
+    sdk = sd.keys()
 
     for k in sdk:
         if k.startswith("diffusion_model.") and k.endswith(".weight"):
             key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_")
             key_map["lora_unet_{}".format(key_lora)] = k
+            key_map["lora_prior_unet_{}".format(key_lora)] = k #cascade lora: TODO put lora key prefix in the model config
 
     diffusers_keys = comfy.utils.unet_to_diffusers(model.model_config.unet_config)
     for k in diffusers_keys:
@@ -221,4 +259,19 @@ def model_lora_keys_unet(model, key_map={}):
                 if diffusers_lora_key.endswith(".to_out.0"):
                     diffusers_lora_key = diffusers_lora_key[:-2]
                 key_map[diffusers_lora_key] = unet_key
+
+    if isinstance(model, comfy.model_base.SD3): #Diffusers lora SD3
+        diffusers_keys = comfy.utils.mmdit_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
+        for k in diffusers_keys:
+            if k.endswith(".weight"):
+                to = diffusers_keys[k]
+                key_lora = "transformer.{}".format(k[:-len(".weight")]) #regular diffusers sd3 lora format
+                key_map[key_lora] = to
+
+                key_lora = "base_model.model.{}".format(k[:-len(".weight")]) #format for flash-sd3 lora and others?
+                key_map[key_lora] = to
+
+                key_lora = "lora_transformer_{}".format(k[:-len(".weight")].replace(".", "_")) #OneTrainer lora
+                key_map[key_lora] = to
+
     return key_map
diff --git a/comfy/model_base.py b/comfy/model_base.py
index aafb88e0..80f6667e 100644
--- a/comfy/model_base.py
+++ b/comfy/model_base.py
@@ -1,20 +1,32 @@
 import torch
+import logging
 from comfy.ldm.modules.diffusionmodules.openaimodel import UNetModel, Timestep
+from comfy.ldm.cascade.stage_c import StageC
+from comfy.ldm.cascade.stage_b import StageB
 from comfy.ldm.modules.encoders.noise_aug_modules import CLIPEmbeddingNoiseAugmentation
 from comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAugmentation
+from comfy.ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper
+import comfy.ldm.audio.dit
+import comfy.ldm.audio.embedders
 import comfy.model_management
 import comfy.conds
 import comfy.ops
 from enum import Enum
 from . import utils
+import comfy.latent_formats
+import math
 
 class ModelType(Enum):
     EPS = 1
     V_PREDICTION = 2
     V_PREDICTION_EDM = 3
+    STABLE_CASCADE = 4
+    EDM = 5
+    FLOW = 6
+    V_PREDICTION_CONTINUOUS = 7
 
 
-from comfy.model_sampling import EPS, V_PREDICTION, ModelSamplingDiscrete, ModelSamplingContinuousEDM
+from comfy.model_sampling import EPS, V_PREDICTION, EDM, ModelSamplingDiscrete, ModelSamplingContinuousEDM, StableCascadeSampling, ModelSamplingContinuousV
 
 
 def model_sampling(model_config, model_type):
@@ -27,6 +39,18 @@ def model_sampling(model_config, model_type):
     elif model_type == ModelType.V_PREDICTION_EDM:
         c = V_PREDICTION
         s = ModelSamplingContinuousEDM
+    elif model_type == ModelType.FLOW:
+        c = comfy.model_sampling.CONST
+        s = comfy.model_sampling.ModelSamplingDiscreteFlow
+    elif model_type == ModelType.STABLE_CASCADE:
+        c = EPS
+        s = StableCascadeSampling
+    elif model_type == ModelType.EDM:
+        c = EDM
+        s = ModelSamplingContinuousEDM
+    elif model_type == ModelType.V_PREDICTION_CONTINUOUS:
+        c = V_PREDICTION
+        s = ModelSamplingContinuousV
 
     class ModelSampling(s, c):
         pass
@@ -35,7 +59,7 @@ def model_sampling(model_config, model_type):
 
 
 class BaseModel(torch.nn.Module):
-    def __init__(self, model_config, model_type=ModelType.EPS, device=None):
+    def __init__(self, model_config, model_type=ModelType.EPS, device=None, unet_model=UNetModel):
         super().__init__()
 
         unet_config = model_config.unet_config
@@ -48,16 +72,20 @@ class BaseModel(torch.nn.Module):
                 operations = comfy.ops.manual_cast
             else:
                 operations = comfy.ops.disable_weight_init
-            self.diffusion_model = UNetModel(**unet_config, device=device, operations=operations)
+            self.diffusion_model = unet_model(**unet_config, device=device, operations=operations)
+            if comfy.model_management.force_channels_last():
+                self.diffusion_model.to(memory_format=torch.channels_last)
+                logging.debug("using channels last mode for diffusion model")
         self.model_type = model_type
         self.model_sampling = model_sampling(model_config, model_type)
 
         self.adm_channels = unet_config.get("adm_in_channels", None)
         if self.adm_channels is None:
             self.adm_channels = 0
-        self.inpaint_model = False
-        print("model_type", model_type.name)
-        print("adm", self.adm_channels)
+
+        self.concat_keys = ()
+        logging.info("model_type {}".format(model_type.name))
+        logging.debug("adm {}".format(self.adm_channels))
 
     def apply_model(self, x, t, c_concat=None, c_crossattn=None, control=None, transformer_options={}, **kwargs):
         sigma = t
@@ -96,8 +124,7 @@ class BaseModel(torch.nn.Module):
 
     def extra_conds(self, **kwargs):
         out = {}
-        if self.inpaint_model:
-            concat_keys = ("mask", "masked_image")
+        if len(self.concat_keys) > 0:
             cond_concat = []
             denoise_mask = kwargs.get("concat_mask", kwargs.get("denoise_mask", None))
             concat_latent_image = kwargs.get("concat_latent_image", None)
@@ -114,24 +141,16 @@ class BaseModel(torch.nn.Module):
 
             concat_latent_image = utils.resize_to_batch_size(concat_latent_image, noise.shape[0])
 
-            if len(denoise_mask.shape) == len(noise.shape):
-                denoise_mask = denoise_mask[:,:1]
+            if denoise_mask is not None:
+                if len(denoise_mask.shape) == len(noise.shape):
+                    denoise_mask = denoise_mask[:,:1]
 
-            denoise_mask = denoise_mask.reshape((-1, 1, denoise_mask.shape[-2], denoise_mask.shape[-1]))
-            if denoise_mask.shape[-2:] != noise.shape[-2:]:
-                denoise_mask = utils.common_upscale(denoise_mask, noise.shape[-1], noise.shape[-2], "bilinear", "center")
-            denoise_mask = utils.resize_to_batch_size(denoise_mask.round(), noise.shape[0])
+                denoise_mask = denoise_mask.reshape((-1, 1, denoise_mask.shape[-2], denoise_mask.shape[-1]))
+                if denoise_mask.shape[-2:] != noise.shape[-2:]:
+                    denoise_mask = utils.common_upscale(denoise_mask, noise.shape[-1], noise.shape[-2], "bilinear", "center")
+                denoise_mask = utils.resize_to_batch_size(denoise_mask.round(), noise.shape[0])
 
-            def blank_inpaint_image_like(latent_image):
-                blank_image = torch.ones_like(latent_image)
-                # these are the values for "zero" in pixel space translated to latent space
-                blank_image[:,0] *= 0.8223
-                blank_image[:,1] *= -0.6876
-                blank_image[:,2] *= 0.6364
-                blank_image[:,3] *= 0.1380
-                return blank_image
-
-            for ck in concat_keys:
+            for ck in self.concat_keys:
                 if denoise_mask is not None:
                     if ck == "mask":
                         cond_concat.append(denoise_mask.to(device))
@@ -141,7 +160,7 @@ class BaseModel(torch.nn.Module):
                     if ck == "mask":
                         cond_concat.append(torch.ones_like(noise)[:,:1])
                     elif ck == "masked_image":
-                        cond_concat.append(blank_inpaint_image_like(noise))
+                        cond_concat.append(self.blank_inpaint_image_like(noise))
             data = torch.cat(cond_concat, dim=1)
             out['c_concat'] = comfy.conds.CONDNoiseShape(data)
 
@@ -157,6 +176,10 @@ class BaseModel(torch.nn.Module):
         if cross_attn_cnet is not None:
             out['crossattn_controlnet'] = comfy.conds.CONDCrossAttn(cross_attn_cnet)
 
+        c_concat = kwargs.get("noise_concat", None)
+        if c_concat is not None:
+            out['c_concat'] = comfy.conds.CONDNoiseShape(c_concat)
+
         return out
 
     def load_model_weights(self, sd, unet_prefix=""):
@@ -169,10 +192,10 @@ class BaseModel(torch.nn.Module):
         to_load = self.model_config.process_unet_state_dict(to_load)
         m, u = self.diffusion_model.load_state_dict(to_load, strict=False)
         if len(m) > 0:
-            print("unet missing:", m)
+            logging.warning("unet missing: {}".format(m))
 
         if len(u) > 0:
-            print("unet unexpected:", u)
+            logging.warning("unet unexpected: {}".format(u))
         del to_load
         return self
 
@@ -194,9 +217,6 @@ class BaseModel(torch.nn.Module):
         unet_state_dict = self.diffusion_model.state_dict()
         unet_state_dict = self.model_config.process_unet_state_dict_for_saving(unet_state_dict)
 
-        if self.get_dtype() == torch.float16:
-            extra_sds = map(lambda sd: utils.convert_sd_to(sd, torch.float16), extra_sds)
-
         if self.model_type == ModelType.V_PREDICTION:
             unet_state_dict["v_pred"] = torch.tensor([])
 
@@ -206,7 +226,16 @@ class BaseModel(torch.nn.Module):
         return unet_state_dict
 
     def set_inpaint(self):
-        self.inpaint_model = True
+        self.concat_keys = ("mask", "masked_image")
+        def blank_inpaint_image_like(latent_image):
+            blank_image = torch.ones_like(latent_image)
+            # these are the values for "zero" in pixel space translated to latent space
+            blank_image[:,0] *= 0.8223
+            blank_image[:,1] *= -0.6876
+            blank_image[:,2] *= 0.6364
+            blank_image[:,3] *= 0.1380
+            return blank_image
+        self.blank_inpaint_image_like = blank_inpaint_image_like
 
     def memory_required(self, input_shape):
         if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention():
@@ -214,11 +243,11 @@ class BaseModel(torch.nn.Module):
             if self.manual_cast_dtype is not None:
                 dtype = self.manual_cast_dtype
             #TODO: this needs to be tweaked
-            area = input_shape[0] * input_shape[2] * input_shape[3]
+            area = input_shape[0] * math.prod(input_shape[2:])
             return (area * comfy.model_management.dtype_size(dtype) / 50) * (1024 * 1024)
         else:
             #TODO: this formula might be too aggressive since I tweaked the sub-quad and split algorithms to use less memory.
-            area = input_shape[0] * input_shape[2] * input_shape[3]
+            area = input_shape[0] * math.prod(input_shape[2:])
             return (((area * 0.6) / 0.9) + 1024) * (1024 * 1024)
 
 
@@ -362,10 +391,39 @@ class SVD_img2vid(BaseModel):
         if "time_conditioning" in kwargs:
             out["time_context"] = comfy.conds.CONDCrossAttn(kwargs["time_conditioning"])
 
-        out['image_only_indicator'] = comfy.conds.CONDConstant(torch.zeros((1,), device=device))
         out['num_video_frames'] = comfy.conds.CONDConstant(noise.shape[0])
         return out
 
+class SV3D_u(SVD_img2vid):
+    def encode_adm(self, **kwargs):
+        augmentation = kwargs.get("augmentation_level", 0)
+
+        out = []
+        out.append(self.embedder(torch.flatten(torch.Tensor([augmentation]))))
+
+        flat = torch.flatten(torch.cat(out)).unsqueeze(dim=0)
+        return flat
+
+class SV3D_p(SVD_img2vid):
+    def __init__(self, model_config, model_type=ModelType.V_PREDICTION_EDM, device=None):
+        super().__init__(model_config, model_type, device=device)
+        self.embedder_512 = Timestep(512)
+
+    def encode_adm(self, **kwargs):
+        augmentation = kwargs.get("augmentation_level", 0)
+        elevation = kwargs.get("elevation", 0) #elevation and azimuth are in degrees here
+        azimuth = kwargs.get("azimuth", 0)
+        noise = kwargs.get("noise", None)
+
+        out = []
+        out.append(self.embedder(torch.flatten(torch.Tensor([augmentation]))))
+        out.append(self.embedder_512(torch.deg2rad(torch.fmod(torch.flatten(90 - torch.Tensor([elevation])), 360.0))))
+        out.append(self.embedder_512(torch.deg2rad(torch.fmod(torch.flatten(torch.Tensor([azimuth])), 360.0))))
+
+        out = list(map(lambda a: utils.resize_to_batch_size(a, noise.shape[0]), out))
+        return torch.cat(out, dim=1)
+
+
 class Stable_Zero123(BaseModel):
     def __init__(self, model_config, model_type=ModelType.EPS, device=None, cc_projection_weight=None, cc_projection_bias=None):
         super().__init__(model_config, model_type, device=device)
@@ -427,3 +485,154 @@ class SD_X4Upscaler(BaseModel):
         out['c_concat'] = comfy.conds.CONDNoiseShape(image)
         out['y'] = comfy.conds.CONDRegular(noise_level)
         return out
+
+class IP2P:
+    def extra_conds(self, **kwargs):
+        out = {}
+
+        image = kwargs.get("concat_latent_image", None)
+        noise = kwargs.get("noise", None)
+        device = kwargs["device"]
+
+        if image is None:
+            image = torch.zeros_like(noise)
+
+        if image.shape[1:] != noise.shape[1:]:
+            image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
+
+        image = utils.resize_to_batch_size(image, noise.shape[0])
+
+        out['c_concat'] = comfy.conds.CONDNoiseShape(self.process_ip2p_image_in(image))
+        adm = self.encode_adm(**kwargs)
+        if adm is not None:
+            out['y'] = comfy.conds.CONDRegular(adm)
+        return out
+
+class SD15_instructpix2pix(IP2P, BaseModel):
+    def __init__(self, model_config, model_type=ModelType.EPS, device=None):
+        super().__init__(model_config, model_type, device=device)
+        self.process_ip2p_image_in = lambda image: image
+
+class SDXL_instructpix2pix(IP2P, SDXL):
+    def __init__(self, model_config, model_type=ModelType.EPS, device=None):
+        super().__init__(model_config, model_type, device=device)
+        if model_type == ModelType.V_PREDICTION_EDM:
+            self.process_ip2p_image_in = lambda image: comfy.latent_formats.SDXL().process_in(image) #cosxl ip2p
+        else:
+            self.process_ip2p_image_in = lambda image: image #diffusers ip2p
+
+
+class StableCascade_C(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=StageC)
+        self.diffusion_model.eval().requires_grad_(False)
+
+    def extra_conds(self, **kwargs):
+        out = {}
+        clip_text_pooled = kwargs["pooled_output"]
+        if clip_text_pooled is not None:
+            out['clip_text_pooled'] = comfy.conds.CONDRegular(clip_text_pooled)
+
+        if "unclip_conditioning" in kwargs:
+            embeds = []
+            for unclip_cond in kwargs["unclip_conditioning"]:
+                weight = unclip_cond["strength"]
+                embeds.append(unclip_cond["clip_vision_output"].image_embeds.unsqueeze(0) * weight)
+            clip_img = torch.cat(embeds, dim=1)
+        else:
+            clip_img = torch.zeros((1, 1, 768))
+        out["clip_img"] = comfy.conds.CONDRegular(clip_img)
+        out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,)))
+        out["crp"] = comfy.conds.CONDRegular(torch.zeros((1,)))
+
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['clip_text'] = comfy.conds.CONDCrossAttn(cross_attn)
+        return out
+
+
+class StableCascade_B(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=StageB)
+        self.diffusion_model.eval().requires_grad_(False)
+
+    def extra_conds(self, **kwargs):
+        out = {}
+        noise = kwargs.get("noise", None)
+
+        clip_text_pooled = kwargs["pooled_output"]
+        if clip_text_pooled is not None:
+            out['clip'] = comfy.conds.CONDRegular(clip_text_pooled)
+
+        #size of prior doesn't really matter if zeros because it gets resized but I still want it to get batched
+        prior = kwargs.get("stable_cascade_prior", torch.zeros((1, 16, (noise.shape[2] * 4) // 42, (noise.shape[3] * 4) // 42), dtype=noise.dtype, layout=noise.layout, device=noise.device))
+
+        out["effnet"] = comfy.conds.CONDRegular(prior)
+        out["sca"] = comfy.conds.CONDRegular(torch.zeros((1,)))
+        return out
+
+
+class SD3(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=OpenAISignatureMMDITWrapper)
+
+    def encode_adm(self, **kwargs):
+        return kwargs["pooled_output"]
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+        return out
+
+    def memory_required(self, input_shape):
+        if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention():
+            dtype = self.get_dtype()
+            if self.manual_cast_dtype is not None:
+                dtype = self.manual_cast_dtype
+            #TODO: this probably needs to be tweaked
+            area = input_shape[0] * input_shape[2] * input_shape[3]
+            return (area * comfy.model_management.dtype_size(dtype) * 0.012) * (1024 * 1024)
+        else:
+            area = input_shape[0] * input_shape[2] * input_shape[3]
+            return (area * 0.3) * (1024 * 1024)
+
+
+class StableAudio1(BaseModel):
+    def __init__(self, model_config, seconds_start_embedder_weights, seconds_total_embedder_weights, model_type=ModelType.V_PREDICTION_CONTINUOUS, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.audio.dit.AudioDiffusionTransformer)
+        self.seconds_start_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512)
+        self.seconds_total_embedder = comfy.ldm.audio.embedders.NumberConditioner(768, min_val=0, max_val=512)
+        self.seconds_start_embedder.load_state_dict(seconds_start_embedder_weights)
+        self.seconds_total_embedder.load_state_dict(seconds_total_embedder_weights)
+
+    def extra_conds(self, **kwargs):
+        out = {}
+
+        noise = kwargs.get("noise", None)
+        device = kwargs["device"]
+
+        seconds_start = kwargs.get("seconds_start", 0)
+        seconds_total = kwargs.get("seconds_total", int(noise.shape[-1] / 21.53))
+
+        seconds_start_embed = self.seconds_start_embedder([seconds_start])[0].to(device)
+        seconds_total_embed = self.seconds_total_embedder([seconds_total])[0].to(device)
+
+        global_embed = torch.cat([seconds_start_embed, seconds_total_embed], dim=-1).reshape((1, -1))
+        out['global_embed'] = comfy.conds.CONDRegular(global_embed)
+
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            cross_attn = torch.cat([cross_attn.to(device), seconds_start_embed.repeat((cross_attn.shape[0], 1, 1)), seconds_total_embed.repeat((cross_attn.shape[0], 1, 1))], dim=1)
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+        return out
+
+    def state_dict_for_saving(self, clip_state_dict=None, vae_state_dict=None, clip_vision_state_dict=None):
+        sd = super().state_dict_for_saving(clip_state_dict=clip_state_dict, vae_state_dict=vae_state_dict, clip_vision_state_dict=clip_vision_state_dict)
+        d = {"conditioner.conditioners.seconds_start.": self.seconds_start_embedder.state_dict(), "conditioner.conditioners.seconds_total.": self.seconds_total_embedder.state_dict()}
+        for k in d:
+            s = d[k]
+            for l in s:
+                sd["{}{}".format(k, l)] = s[l]
+        return sd
diff --git a/comfy/model_detection.py b/comfy/model_detection.py
index ea824c44..0b678480 100644
--- a/comfy/model_detection.py
+++ b/comfy/model_detection.py
@@ -1,5 +1,9 @@
 import comfy.supported_models
 import comfy.supported_models_base
+import comfy.utils
+import math
+import logging
+import torch
 
 def count_blocks(state_dict_keys, prefix_string):
     count = 0
@@ -25,12 +29,82 @@ def calculate_transformer_depth(prefix, state_dict_keys, state_dict):
         context_dim = state_dict['{}0.attn2.to_k.weight'.format(transformer_prefix)].shape[1]
         use_linear_in_transformer = len(state_dict['{}1.proj_in.weight'.format(prefix)].shape) == 2
         time_stack = '{}1.time_stack.0.attn1.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn1.to_q.weight'.format(prefix) in state_dict
-        return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack
+        time_stack_cross = '{}1.time_stack.0.attn2.to_q.weight'.format(prefix) in state_dict or '{}1.time_mix_blocks.0.attn2.to_q.weight'.format(prefix) in state_dict
+        return last_transformer_depth, context_dim, use_linear_in_transformer, time_stack, time_stack_cross
     return None
 
-def detect_unet_config(state_dict, key_prefix, dtype):
+def detect_unet_config(state_dict, key_prefix):
     state_dict_keys = list(state_dict.keys())
 
+    if '{}joint_blocks.0.context_block.attn.qkv.weight'.format(key_prefix) in state_dict_keys: #mmdit model
+        unet_config = {}
+        unet_config["in_channels"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[1]
+        patch_size = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[2]
+        unet_config["patch_size"] = patch_size
+        final_layer = '{}final_layer.linear.weight'.format(key_prefix)
+        if final_layer in state_dict:
+            unet_config["out_channels"] = state_dict[final_layer].shape[0] // (patch_size * patch_size)
+
+        unet_config["depth"] = state_dict['{}x_embedder.proj.weight'.format(key_prefix)].shape[0] // 64
+        unet_config["input_size"] = None
+        y_key = '{}y_embedder.mlp.0.weight'.format(key_prefix)
+        if y_key in state_dict_keys:
+            unet_config["adm_in_channels"] = state_dict[y_key].shape[1]
+
+        context_key = '{}context_embedder.weight'.format(key_prefix)
+        if context_key in state_dict_keys:
+            in_features = state_dict[context_key].shape[1]
+            out_features = state_dict[context_key].shape[0]
+            unet_config["context_embedder_config"] = {"target": "torch.nn.Linear", "params": {"in_features": in_features, "out_features": out_features}}
+        num_patches_key = '{}pos_embed'.format(key_prefix)
+        if num_patches_key in state_dict_keys:
+            num_patches = state_dict[num_patches_key].shape[1]
+            unet_config["num_patches"] = num_patches
+            unet_config["pos_embed_max_size"] = round(math.sqrt(num_patches))
+
+        rms_qk = '{}joint_blocks.0.context_block.attn.ln_q.weight'.format(key_prefix)
+        if rms_qk in state_dict_keys:
+            unet_config["qk_norm"] = "rms"
+
+        unet_config["pos_embed_scaling_factor"] = None #unused for inference
+        context_processor = '{}context_processor.layers.0.attn.qkv.weight'.format(key_prefix)
+        if context_processor in state_dict_keys:
+            unet_config["context_processor_layers"] = count_blocks(state_dict_keys, '{}context_processor.layers.'.format(key_prefix) + '{}.')
+        return unet_config
+
+    if '{}clf.1.weight'.format(key_prefix) in state_dict_keys: #stable cascade
+        unet_config = {}
+        text_mapper_name = '{}clip_txt_mapper.weight'.format(key_prefix)
+        if text_mapper_name in state_dict_keys:
+            unet_config['stable_cascade_stage'] = 'c'
+            w = state_dict[text_mapper_name]
+            if w.shape[0] == 1536: #stage c lite
+                unet_config['c_cond'] = 1536
+                unet_config['c_hidden'] = [1536, 1536]
+                unet_config['nhead'] = [24, 24]
+                unet_config['blocks'] = [[4, 12], [12, 4]]
+            elif w.shape[0] == 2048: #stage c full
+                unet_config['c_cond'] = 2048
+        elif '{}clip_mapper.weight'.format(key_prefix) in state_dict_keys:
+            unet_config['stable_cascade_stage'] = 'b'
+            w = state_dict['{}down_blocks.1.0.channelwise.0.weight'.format(key_prefix)]
+            if w.shape[-1] == 640:
+                unet_config['c_hidden'] = [320, 640, 1280, 1280]
+                unet_config['nhead'] = [-1, -1, 20, 20]
+                unet_config['blocks'] = [[2, 6, 28, 6], [6, 28, 6, 2]]
+                unet_config['block_repeat'] = [[1, 1, 1, 1], [3, 3, 2, 2]]
+            elif w.shape[-1] == 576: #stage b lite
+                unet_config['c_hidden'] = [320, 576, 1152, 1152]
+                unet_config['nhead'] = [-1, 9, 18, 18]
+                unet_config['blocks'] = [[2, 4, 14, 4], [4, 14, 4, 2]]
+                unet_config['block_repeat'] = [[1, 1, 1, 1], [2, 2, 2, 2]]
+        return unet_config
+
+    if '{}transformer.rotary_pos_emb.inv_freq'.format(key_prefix) in state_dict_keys: #stable audio dit
+        unet_config = {}
+        unet_config["audio_model"] = "dit1.0"
+        return unet_config
+
     unet_config = {
         "use_checkpoint": False,
         "image_size": 32,
@@ -45,7 +119,6 @@ def detect_unet_config(state_dict, key_prefix, dtype):
     else:
         unet_config["adm_in_channels"] = None
 
-    unet_config["dtype"] = dtype
     model_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[0]
     in_channels = state_dict['{}input_blocks.0.0.weight'.format(key_prefix)].shape[1]
 
@@ -64,6 +137,7 @@ def detect_unet_config(state_dict, key_prefix, dtype):
     use_linear_in_transformer = False
 
     video_model = False
+    video_model_cross = False
 
     current_res = 1
     count = 0
@@ -107,6 +181,7 @@ def detect_unet_config(state_dict, key_prefix, dtype):
                         context_dim = out[1]
                         use_linear_in_transformer = out[2]
                         video_model = out[3]
+                        video_model_cross = out[4]
                 else:
                     transformer_depth.append(0)
 
@@ -123,8 +198,10 @@ def detect_unet_config(state_dict, key_prefix, dtype):
     channel_mult.append(last_channel_mult)
     if "{}middle_block.1.proj_in.weight".format(key_prefix) in state_dict_keys:
         transformer_depth_middle = count_blocks(state_dict_keys, '{}middle_block.1.transformer_blocks.'.format(key_prefix) + '{}')
-    else:
+    elif "{}middle_block.0.in_layers.0.weight".format(key_prefix) in state_dict_keys:
         transformer_depth_middle = -1
+    else:
+        transformer_depth_middle = -2
 
     unet_config["in_channels"] = in_channels
     unet_config["out_channels"] = out_channels
@@ -145,28 +222,36 @@ def detect_unet_config(state_dict, key_prefix, dtype):
         unet_config["video_kernel_size"] = [3, 1, 1]
         unet_config["use_temporal_resblock"] = True
         unet_config["use_temporal_attention"] = True
+        unet_config["disable_temporal_crossattention"] = not video_model_cross
     else:
         unet_config["use_temporal_resblock"] = False
         unet_config["use_temporal_attention"] = False
 
     return unet_config
 
-def model_config_from_unet_config(unet_config):
+def model_config_from_unet_config(unet_config, state_dict=None):
     for model_config in comfy.supported_models.models:
-        if model_config.matches(unet_config):
+        if model_config.matches(unet_config, state_dict):
             return model_config(unet_config)
 
-    print("no match", unet_config)
+    logging.error("no match {}".format(unet_config))
     return None
 
-def model_config_from_unet(state_dict, unet_key_prefix, dtype, use_base_if_no_match=False):
-    unet_config = detect_unet_config(state_dict, unet_key_prefix, dtype)
-    model_config = model_config_from_unet_config(unet_config)
+def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=False):
+    unet_config = detect_unet_config(state_dict, unet_key_prefix)
+    model_config = model_config_from_unet_config(unet_config, state_dict)
     if model_config is None and use_base_if_no_match:
         return comfy.supported_models_base.BASE(unet_config)
     else:
         return model_config
 
+def unet_prefix_from_state_dict(state_dict):
+    if "model.model.postprocess_conv.weight" in state_dict: #audio models
+        unet_key_prefix = "model.model."
+    else:
+        unet_key_prefix = "model.diffusion_model."
+    return unet_key_prefix
+
 def convert_config(unet_config):
     new_config = unet_config.copy()
     num_res_blocks = new_config.get("num_res_blocks", None)
@@ -206,7 +291,7 @@ def convert_config(unet_config):
     return new_config
 
 
-def unet_config_from_diffusers_unet(state_dict, dtype):
+def unet_config_from_diffusers_unet(state_dict, dtype=None):
     match = {}
     transformer_depth = []
 
@@ -214,6 +299,7 @@ def unet_config_from_diffusers_unet(state_dict, dtype):
     down_blocks = count_blocks(state_dict, "down_blocks.{}")
     for i in range(down_blocks):
         attn_blocks = count_blocks(state_dict, "down_blocks.{}.attentions.".format(i) + '{}')
+        res_blocks = count_blocks(state_dict, "down_blocks.{}.resnets.".format(i) + '{}')
         for ab in range(attn_blocks):
             transformer_count = count_blocks(state_dict, "down_blocks.{}.attentions.{}.transformer_blocks.".format(i, ab) + '{}')
             transformer_depth.append(transformer_count)
@@ -222,8 +308,8 @@ def unet_config_from_diffusers_unet(state_dict, dtype):
 
         attn_res *= 2
         if attn_blocks == 0:
-            transformer_depth.append(0)
-            transformer_depth.append(0)
+            for i in range(res_blocks):
+                transformer_depth.append(0)
 
     match["transformer_depth"] = transformer_depth
 
@@ -289,6 +375,12 @@ def unet_config_from_diffusers_unet(state_dict, dtype):
                               'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
                               'use_temporal_attention': False, 'use_temporal_resblock': False}
 
+    SDXL_diffusers_ip2p = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+                              'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 8, 'model_channels': 320,
+                              'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 10, 10], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': 10,
+                              'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 0, 2, 2, 2, 10, 10, 10],
+                              'use_temporal_attention': False, 'use_temporal_resblock': False}
+
     SSD_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
               'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
               'num_res_blocks': [2, 2, 2], 'transformer_depth': [0, 0, 2, 2, 4, 4], 'transformer_depth_output': [0, 0, 0, 1, 1, 2, 10, 4, 4],
@@ -301,7 +393,32 @@ def unet_config_from_diffusers_unet(state_dict, dtype):
               'channel_mult': [1, 2, 4], 'transformer_depth_middle': -1, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
               'use_temporal_attention': False, 'use_temporal_resblock': False}
 
-    supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega]
+    KOALA_700M = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+              'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
+              'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 2, 5], 'transformer_depth_output': [0, 0, 2, 2, 5, 5],
+              'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
+              'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+    KOALA_1B = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+              'num_classes': 'sequential', 'adm_in_channels': 2816, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320,
+              'num_res_blocks': [1, 1, 1], 'transformer_depth': [0, 2, 6], 'transformer_depth_output': [0, 0, 2, 2, 6, 6],
+              'channel_mult': [1, 2, 4], 'transformer_depth_middle': 6, 'use_linear_in_transformer': True, 'context_dim': 2048, 'num_head_channels': 64,
+              'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+    SD09_XS = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+            'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1],
+            'transformer_depth': [1, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': True,
+            'context_dim': 1024, 'num_head_channels': 64, 'transformer_depth_output': [1, 1, 1, 1, 1, 1],
+            'use_temporal_attention': False, 'use_temporal_resblock': False, 'disable_self_attentions': [True, False, False]}
+
+    SD_XS = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False,
+            'adm_in_channels': None, 'dtype': dtype, 'in_channels': 4, 'model_channels': 320, 'num_res_blocks': [1, 1, 1],
+            'transformer_depth': [0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': False,
+            'context_dim': 768, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 1, 1, 1, 1],
+            'use_temporal_attention': False, 'use_temporal_resblock': False}
+
+
+    supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B, SD09_XS, SD_XS, SDXL_diffusers_ip2p]
 
     for unet_config in supported_models:
         matches = True
@@ -313,8 +430,44 @@ def unet_config_from_diffusers_unet(state_dict, dtype):
             return convert_config(unet_config)
     return None
 
-def model_config_from_diffusers_unet(state_dict, dtype):
-    unet_config = unet_config_from_diffusers_unet(state_dict, dtype)
+def model_config_from_diffusers_unet(state_dict):
+    unet_config = unet_config_from_diffusers_unet(state_dict)
     if unet_config is not None:
         return model_config_from_unet_config(unet_config)
     return None
+
+def convert_diffusers_mmdit(state_dict, output_prefix=""):
+    num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.')
+    if num_blocks > 0:
+        depth = state_dict["pos_embed.proj.weight"].shape[0] // 64
+        out_sd = {}
+        sd_map = comfy.utils.mmdit_to_diffusers({"depth": depth, "num_blocks": num_blocks}, output_prefix=output_prefix)
+        for k in sd_map:
+            weight = state_dict.get(k, None)
+            if weight is not None:
+                t = sd_map[k]
+
+                if not isinstance(t, str):
+                    if len(t) > 2:
+                        fun = t[2]
+                    else:
+                        fun = lambda a: a
+                    offset = t[1]
+                    if offset is not None:
+                        old_weight = out_sd.get(t[0], None)
+                        if old_weight is None:
+                            old_weight = torch.empty_like(weight)
+                            old_weight = old_weight.repeat([3] + [1] * (len(old_weight.shape) - 1))
+
+                        w = old_weight.narrow(offset[0], offset[1], offset[2])
+                    else:
+                        old_weight = weight
+                        w = weight
+                    w[:] = fun(weight)
+                    t = t[0]
+                    out_sd[t] = old_weight
+                else:
+                    out_sd[t] = weight
+                state_dict.pop(k)
+
+    return out_sd
diff --git a/comfy/model_management.py b/comfy/model_management.py
index 949f3f83..ab21bf66 100644
--- a/comfy/model_management.py
+++ b/comfy/model_management.py
@@ -1,10 +1,12 @@
 import psutil
+import logging
 from enum import Enum
 from comfy.cli_args import args
-import comfy.utils
 import torch
 import sys
 import os.path
+import platform
+
 
 class VRAMState(Enum):
     DISABLED = 0    #No vram present: no need to move models to vram
@@ -30,7 +32,7 @@ lowvram_available = True
 xpu_available = False
 
 if args.deterministic:
-    print("Using deterministic algorithms for pytorch")
+    logging.info("Using deterministic algorithms for pytorch")
     torch.use_deterministic_algorithms(True, warn_only=True)
 
 directml_enabled = False
@@ -42,7 +44,7 @@ if args.directml is not None:
         directml_device = torch_directml.device()
     else:
         directml_device = torch_directml.device(device_index)
-    print("Using directml with device:", torch_directml.device_name(device_index))
+    logging.info("Using directml with device: {}".format(torch_directml.device_name(device_index)))
     # torch_directml.disable_tiled_resources(True)
     lowvram_available = False #TODO: need to find a way to get free memory in directml before this can be enabled by default.
 
@@ -83,7 +85,7 @@ def get_torch_device():
         return torch.device("cpu")
     else:
         if is_intel_xpu():
-            return torch.device("xpu")
+            return torch.device("xpu", torch.xpu.current_device())
         else:
             return torch.device(torch.cuda.current_device())
 
@@ -120,8 +122,8 @@ def get_total_memory(dev=None, torch_total_too=False):
         elif is_intel_xpu():
             stats = torch.xpu.memory_stats(dev)
             mem_reserved = stats['reserved_bytes.all.current']
-            mem_total = torch.xpu.get_device_properties(dev).total_memory
             mem_total_torch = mem_reserved
+            mem_total = torch.xpu.get_device_properties(dev).total_memory
         else:
             stats = torch.cuda.memory_stats(dev)
             mem_reserved = stats['reserved_bytes.all.current']
@@ -135,12 +137,13 @@ def get_total_memory(dev=None, torch_total_too=False):
         return mem_total
 
 total_vram = get_total_memory(get_torch_device()) / (1024 * 1024)
-total_ram = get_total_memory(torch.device("cpu")) / (1024 * 1024)
-print("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram))
-if not args.normalvram and not args.cpu:
-    if lowvram_available and total_vram <= 4096:
-        print("Trying to enable lowvram mode because your GPU seems to have 4GB or less. If you don't want this use: --normalvram")
-        set_vram_to = VRAMState.LOW_VRAM
+total_ram = psutil.virtual_memory().total / (1024 * 1024)
+logging.info("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram))
+
+try:
+    logging.info("pytorch version: {}".format(torch.version.__version__))
+except:
+    pass
 
 try:
     OOM_EXCEPTION = torch.cuda.OutOfMemoryError
@@ -162,12 +165,10 @@ else:
             pass
         try:
             XFORMERS_VERSION = xformers.version.__version__
-            print("xformers version:", XFORMERS_VERSION)
+            logging.info("xformers version: {}".format(XFORMERS_VERSION))
             if XFORMERS_VERSION.startswith("0.0.18"):
-                print()
-                print("WARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.")
-                print("Please downgrade or upgrade xformers to a different version.")
-                print()
+                logging.warning("\nWARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.")
+                logging.warning("Please downgrade or upgrade xformers to a different version.\n")
                 XFORMERS_ENABLED_VAE = False
         except:
             pass
@@ -186,7 +187,7 @@ if args.use_pytorch_cross_attention:
     ENABLE_PYTORCH_ATTENTION = True
     XFORMERS_IS_AVAILABLE = False
 
-VAE_DTYPE = torch.float32
+VAE_DTYPES = [torch.float32]
 
 try:
     if is_nvidia():
@@ -195,7 +196,7 @@ try:
             if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
                 ENABLE_PYTORCH_ATTENTION = True
             if torch.cuda.is_bf16_supported() and torch.cuda.get_device_properties(torch.cuda.current_device()).major >= 8:
-                VAE_DTYPE = torch.bfloat16
+                VAE_DTYPES = [torch.bfloat16] + VAE_DTYPES
     if is_intel_xpu():
         if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
             ENABLE_PYTORCH_ATTENTION = True
@@ -203,17 +204,10 @@ except:
     pass
 
 if is_intel_xpu():
-    VAE_DTYPE = torch.bfloat16
+    VAE_DTYPES = [torch.bfloat16] + VAE_DTYPES
 
 if args.cpu_vae:
-    VAE_DTYPE = torch.float32
-
-if args.fp16_vae:
-    VAE_DTYPE = torch.float16
-elif args.bf16_vae:
-    VAE_DTYPE = torch.bfloat16
-elif args.fp32_vae:
-    VAE_DTYPE = torch.float32
+    VAE_DTYPES = [torch.float32]
 
 
 if ENABLE_PYTORCH_ATTENTION:
@@ -232,11 +226,11 @@ elif args.highvram or args.gpu_only:
 FORCE_FP32 = False
 FORCE_FP16 = False
 if args.force_fp32:
-    print("Forcing FP32, if this improves things please report it.")
+    logging.info("Forcing FP32, if this improves things please report it.")
     FORCE_FP32 = True
 
 if args.force_fp16:
-    print("Forcing FP16.")
+    logging.info("Forcing FP16.")
     FORCE_FP16 = True
 
 if lowvram_available:
@@ -250,12 +244,12 @@ if cpu_state != CPUState.GPU:
 if cpu_state == CPUState.MPS:
     vram_state = VRAMState.SHARED
 
-print(f"Set vram state to: {vram_state.name}")
+logging.info(f"Set vram state to: {vram_state.name}")
 
 DISABLE_SMART_MEMORY = args.disable_smart_memory
 
 if DISABLE_SMART_MEMORY:
-    print("Disabling smart memory management")
+    logging.info("Disabling smart memory management")
 
 def get_torch_device_name(device):
     if hasattr(device, 'type'):
@@ -273,11 +267,10 @@ def get_torch_device_name(device):
         return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device))
 
 try:
-    print("Device:", get_torch_device_name(get_torch_device()))
+    logging.info("Device: {}".format(get_torch_device_name(get_torch_device())))
 except:
-    print("Could not pick default device.")
+    logging.warning("Could not pick default device.")
 
-print("VAE dtype:", VAE_DTYPE)
 
 current_loaded_models = []
 
@@ -292,8 +285,10 @@ def module_size(module):
 class LoadedModel:
     def __init__(self, model):
         self.model = model
-        self.model_accelerated = False
         self.device = model.load_device
+        self.weights_loaded = False
+        self.real_model = None
+        self.currently_used = True
 
     def model_memory(self):
         return self.model.model_size()
@@ -304,55 +299,40 @@ class LoadedModel:
         else:
             return self.model_memory()
 
-    def model_load(self, lowvram_model_memory=0):
-        patch_model_to = None
-        if lowvram_model_memory == 0:
-            patch_model_to = self.device
+    def model_load(self, lowvram_model_memory=0, force_patch_weights=False):
+        patch_model_to = self.device
 
         self.model.model_patches_to(self.device)
         self.model.model_patches_to(self.model.model_dtype())
 
+        load_weights = not self.weights_loaded
+
         try:
-            self.real_model = self.model.patch_model(device_to=patch_model_to) #TODO: do something with loras and offloading to CPU
+            if lowvram_model_memory > 0 and load_weights:
+                self.real_model = self.model.patch_model_lowvram(device_to=patch_model_to, lowvram_model_memory=lowvram_model_memory, force_patch_weights=force_patch_weights)
+            else:
+                self.real_model = self.model.patch_model(device_to=patch_model_to, patch_weights=load_weights)
         except Exception as e:
             self.model.unpatch_model(self.model.offload_device)
             self.model_unload()
             raise e
 
-        if lowvram_model_memory > 0:
-            print("loading in lowvram mode", lowvram_model_memory/(1024 * 1024))
-            mem_counter = 0
-            for m in self.real_model.modules():
-                if hasattr(m, "comfy_cast_weights"):
-                    m.prev_comfy_cast_weights = m.comfy_cast_weights
-                    m.comfy_cast_weights = True
-                    module_mem = module_size(m)
-                    if mem_counter + module_mem < lowvram_model_memory:
-                        m.to(self.device)
-                        mem_counter += module_mem
-                elif hasattr(m, "weight"): #only modules with comfy_cast_weights can be set to lowvram mode
-                    m.to(self.device)
-                    mem_counter += module_size(m)
-                    print("lowvram: loaded module regularly", m)
-
-            self.model_accelerated = True
-
         if is_intel_xpu() and not args.disable_ipex_optimize:
-            self.real_model = torch.xpu.optimize(self.real_model.eval(), inplace=True, auto_kernel_selection=True, graph_mode=True)
+            self.real_model = ipex.optimize(self.real_model.eval(), graph_mode=True, concat_linear=True)
 
+        self.weights_loaded = True
         return self.real_model
 
-    def model_unload(self):
-        if self.model_accelerated:
-            for m in self.real_model.modules():
-                if hasattr(m, "prev_comfy_cast_weights"):
-                    m.comfy_cast_weights = m.prev_comfy_cast_weights
-                    del m.prev_comfy_cast_weights
+    def should_reload_model(self, force_patch_weights=False):
+        if force_patch_weights and self.model.lowvram_patch_counter > 0:
+            return True
+        return False
 
-            self.model_accelerated = False
-
-        self.model.unpatch_model(self.model.offload_device)
+    def model_unload(self, unpatch_weights=True):
+        self.model.unpatch_model(self.model.offload_device, unpatch_weights=unpatch_weights)
         self.model.model_patches_to(self.model.offload_device)
+        self.weights_loaded = self.weights_loaded and not unpatch_weights
+        self.real_model = None
 
     def __eq__(self, other):
         return self.model is other.model
@@ -360,31 +340,58 @@ class LoadedModel:
 def minimum_inference_memory():
     return (1024 * 1024 * 1024)
 
-def unload_model_clones(model):
+def unload_model_clones(model, unload_weights_only=True, force_unload=True):
     to_unload = []
     for i in range(len(current_loaded_models)):
         if model.is_clone(current_loaded_models[i].model):
             to_unload = [i] + to_unload
 
+    if len(to_unload) == 0:
+        return True
+
+    same_weights = 0
     for i in to_unload:
-        print("unload clone", i)
-        current_loaded_models.pop(i).model_unload()
+        if model.clone_has_same_weights(current_loaded_models[i].model):
+            same_weights += 1
+
+    if same_weights == len(to_unload):
+        unload_weight = False
+    else:
+        unload_weight = True
+
+    if not force_unload:
+        if unload_weights_only and unload_weight == False:
+            return None
+
+    for i in to_unload:
+        logging.debug("unload clone {} {}".format(i, unload_weight))
+        current_loaded_models.pop(i).model_unload(unpatch_weights=unload_weight)
+
+    return unload_weight
 
 def free_memory(memory_required, device, keep_loaded=[]):
-    unloaded_model = False
+    unloaded_model = []
+    can_unload = []
+
     for i in range(len(current_loaded_models) -1, -1, -1):
-        if not DISABLE_SMART_MEMORY:
-            if get_free_memory(device) > memory_required:
-                break
         shift_model = current_loaded_models[i]
         if shift_model.device == device:
             if shift_model not in keep_loaded:
-                m = current_loaded_models.pop(i)
-                m.model_unload()
-                del m
-                unloaded_model = True
+                can_unload.append((sys.getrefcount(shift_model.model), shift_model.model_memory(), i))
+                shift_model.currently_used = False
 
-    if unloaded_model:
+    for x in sorted(can_unload):
+        i = x[-1]
+        if not DISABLE_SMART_MEMORY:
+            if get_free_memory(device) > memory_required:
+                break
+        current_loaded_models[i].model_unload()
+        unloaded_model.append(i)
+
+    for i in sorted(unloaded_model, reverse=True):
+        current_loaded_models.pop(i)
+
+    if len(unloaded_model) > 0:
         soft_empty_cache()
     else:
         if vram_state != VRAMState.HIGH_VRAM:
@@ -392,24 +399,37 @@ def free_memory(memory_required, device, keep_loaded=[]):
             if mem_free_torch > mem_free_total * 0.25:
                 soft_empty_cache()
 
-def load_models_gpu(models, memory_required=0):
+def load_models_gpu(models, memory_required=0, force_patch_weights=False):
     global vram_state
 
     inference_memory = minimum_inference_memory()
     extra_mem = max(inference_memory, memory_required)
 
+    models = set(models)
+
     models_to_load = []
     models_already_loaded = []
     for x in models:
         loaded_model = LoadedModel(x)
+        loaded = None
 
-        if loaded_model in current_loaded_models:
-            index = current_loaded_models.index(loaded_model)
-            current_loaded_models.insert(0, current_loaded_models.pop(index))
-            models_already_loaded.append(loaded_model)
-        else:
+        try:
+            loaded_model_index = current_loaded_models.index(loaded_model)
+        except:
+            loaded_model_index = None
+
+        if loaded_model_index is not None:
+            loaded = current_loaded_models[loaded_model_index]
+            if loaded.should_reload_model(force_patch_weights=force_patch_weights): #TODO: cleanup this model reload logic
+                current_loaded_models.pop(loaded_model_index).model_unload(unpatch_weights=True)
+                loaded = None
+            else:
+                loaded.currently_used = True
+                models_already_loaded.append(loaded)
+
+        if loaded is None:
             if hasattr(x, "model"):
-                print(f"Requested to load {x.model.__class__.__name__}")
+                logging.info(f"Requested to load {x.model.__class__.__name__}")
             models_to_load.append(loaded_model)
 
     if len(models_to_load) == 0:
@@ -419,17 +439,22 @@ def load_models_gpu(models, memory_required=0):
                 free_memory(extra_mem, d, models_already_loaded)
         return
 
-    print(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}")
+    logging.info(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}")
 
     total_memory_required = {}
     for loaded_model in models_to_load:
-        unload_model_clones(loaded_model.model)
-        total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device)
+        if unload_model_clones(loaded_model.model, unload_weights_only=True, force_unload=False) == True:#unload clones where the weights are different
+            total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device)
 
     for device in total_memory_required:
         if device != torch.device("cpu"):
             free_memory(total_memory_required[device] * 1.3 + extra_mem, device, models_already_loaded)
 
+    for loaded_model in models_to_load:
+        weights_unloaded = unload_model_clones(loaded_model.model, unload_weights_only=False, force_unload=False) #unload the rest of the clones where the weights can stay loaded
+        if weights_unloaded is not None:
+            loaded_model.weights_loaded = not weights_unloaded
+
     for loaded_model in models_to_load:
         model = loaded_model.model
         torch_dev = model.load_device
@@ -442,15 +467,13 @@ def load_models_gpu(models, memory_required=0):
             model_size = loaded_model.model_memory_required(torch_dev)
             current_free_mem = get_free_memory(torch_dev)
             lowvram_model_memory = int(max(64 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 ))
-            if model_size > (current_free_mem - inference_memory): #only switch to lowvram if really necessary
-                vram_set_state = VRAMState.LOW_VRAM
-            else:
+            if model_size <= (current_free_mem - inference_memory): #only switch to lowvram if really necessary
                 lowvram_model_memory = 0
 
         if vram_set_state == VRAMState.NO_VRAM:
             lowvram_model_memory = 64 * 1024 * 1024
 
-        cur_loaded_model = loaded_model.model_load(lowvram_model_memory)
+        cur_loaded_model = loaded_model.model_load(lowvram_model_memory, force_patch_weights=force_patch_weights)
         current_loaded_models.insert(0, loaded_model)
     return
 
@@ -458,11 +481,25 @@ def load_models_gpu(models, memory_required=0):
 def load_model_gpu(model):
     return load_models_gpu([model])
 
-def cleanup_models():
+def loaded_models(only_currently_used=False):
+    output = []
+    for m in current_loaded_models:
+        if only_currently_used:
+            if not m.currently_used:
+                continue
+
+        output.append(m.model)
+    return output
+
+def cleanup_models(keep_clone_weights_loaded=False):
     to_delete = []
     for i in range(len(current_loaded_models)):
         if sys.getrefcount(current_loaded_models[i].model) <= 2:
-            to_delete = [i] + to_delete
+            if not keep_clone_weights_loaded:
+                to_delete = [i] + to_delete
+            #TODO: find a less fragile way to do this.
+            elif sys.getrefcount(current_loaded_models[i].real_model) <= 3: #references from .real_model + the .model
+                to_delete = [i] + to_delete
 
     for i in to_delete:
         x = current_loaded_models.pop(i)
@@ -506,7 +543,7 @@ def unet_inital_load_device(parameters, dtype):
     else:
         return cpu_dev
 
-def unet_dtype(device=None, model_params=0):
+def unet_dtype(device=None, model_params=0, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]):
     if args.bf16_unet:
         return torch.bfloat16
     if args.fp16_unet:
@@ -516,20 +553,31 @@ def unet_dtype(device=None, model_params=0):
     if args.fp8_e5m2_unet:
         return torch.float8_e5m2
     if should_use_fp16(device=device, model_params=model_params, manual_cast=True):
-        return torch.float16
+        if torch.float16 in supported_dtypes:
+            return torch.float16
+    if should_use_bf16(device, model_params=model_params, manual_cast=True):
+        if torch.bfloat16 in supported_dtypes:
+            return torch.bfloat16
     return torch.float32
 
 # None means no manual cast
-def unet_manual_cast(weight_dtype, inference_device):
+def unet_manual_cast(weight_dtype, inference_device, supported_dtypes=[torch.float16, torch.bfloat16, torch.float32]):
     if weight_dtype == torch.float32:
         return None
 
-    fp16_supported = comfy.model_management.should_use_fp16(inference_device, prioritize_performance=False)
+    fp16_supported = should_use_fp16(inference_device, prioritize_performance=False)
     if fp16_supported and weight_dtype == torch.float16:
         return None
 
-    if fp16_supported:
+    bf16_supported = should_use_bf16(inference_device)
+    if bf16_supported and weight_dtype == torch.bfloat16:
+        return None
+
+    if fp16_supported and torch.float16 in supported_dtypes:
         return torch.float16
+
+    elif bf16_supported and torch.bfloat16 in supported_dtypes:
+        return torch.bfloat16
     else:
         return torch.float32
 
@@ -543,8 +591,6 @@ def text_encoder_device():
     if args.gpu_only:
         return get_torch_device()
     elif vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.NORMAL_VRAM:
-        if is_intel_xpu():
-            return torch.device("cpu")
         if should_use_fp16(prioritize_performance=False):
             return get_torch_device()
         else:
@@ -585,9 +631,22 @@ def vae_offload_device():
     else:
         return torch.device("cpu")
 
-def vae_dtype():
-    global VAE_DTYPE
-    return VAE_DTYPE
+def vae_dtype(device=None, allowed_dtypes=[]):
+    global VAE_DTYPES
+    if args.fp16_vae:
+        return torch.float16
+    elif args.bf16_vae:
+        return torch.bfloat16
+    elif args.fp32_vae:
+        return torch.float32
+
+    for d in allowed_dtypes:
+        if d == torch.float16 and should_use_fp16(device, prioritize_performance=False):
+            return d
+        if d in VAE_DTYPES:
+            return d
+
+    return VAE_DTYPES[0]
 
 def get_autocast_device(dev):
     if hasattr(dev, 'type'):
@@ -605,11 +664,47 @@ def supports_dtype(device, dtype): #TODO
         return True
     return False
 
+def supports_cast(device, dtype): #TODO
+    if dtype == torch.float32:
+        return True
+    if dtype == torch.float16:
+        return True
+    if is_device_mps(device):
+        return False
+    if directml_enabled: #TODO: test this
+        return False
+    if dtype == torch.bfloat16:
+        return True
+    if dtype == torch.float8_e4m3fn:
+        return True
+    if dtype == torch.float8_e5m2:
+        return True
+    return False
+
 def device_supports_non_blocking(device):
     if is_device_mps(device):
         return False #pytorch bug? mps doesn't support non blocking
+    if is_intel_xpu():
+        return False
+    if args.deterministic: #TODO: figure out why deterministic breaks non blocking from gpu to cpu (previews)
+        return False
+    if directml_enabled:
+        return False
     return True
 
+def device_should_use_non_blocking(device):
+    if not device_supports_non_blocking(device):
+        return False
+    return False
+    # return True #TODO: figure out why this causes memory issues on Nvidia and possibly others
+
+def force_channels_last():
+    if args.force_channels_last:
+        return True
+
+    #TODO
+    return False
+
 def cast_to_device(tensor, device, dtype, copy=False):
     device_supports_cast = False
     if tensor.dtype == torch.float32 or tensor.dtype == torch.float16:
@@ -620,7 +715,7 @@ def cast_to_device(tensor, device, dtype, copy=False):
         elif is_intel_xpu():
             device_supports_cast = True
 
-    non_blocking = device_supports_non_blocking(device)
+    non_blocking = device_should_use_non_blocking(device)
 
     if device_supports_cast:
         if copy:
@@ -661,8 +756,22 @@ def pytorch_attention_flash_attention():
         #TODO: more reliable way of checking for flash attention?
         if is_nvidia(): #pytorch flash attention only works on Nvidia
             return True
+        if is_intel_xpu():
+            return True
     return False
 
+def force_upcast_attention_dtype():
+    upcast = args.force_upcast_attention
+    try:
+        if platform.mac_ver()[0] in ['14.5']: #black image bug on OSX Sonoma 14.5
+            upcast = True
+    except:
+        pass
+    if upcast:
+        return torch.float32
+    else:
+        return None
+
 def get_free_memory(dev=None, torch_free_too=False):
     global directml_enabled
     if dev is None:
@@ -684,10 +793,10 @@ def get_free_memory(dev=None, torch_free_too=False):
         elif is_intel_xpu():
             stats = torch.xpu.memory_stats(dev)
             mem_active = stats['active_bytes.all.current']
-            mem_allocated = stats['allocated_bytes.all.current']
             mem_reserved = stats['reserved_bytes.all.current']
             mem_free_torch = mem_reserved - mem_active
-            mem_free_total = torch.xpu.get_device_properties(dev).total_memory - mem_allocated
+            mem_free_xpu = torch.xpu.get_device_properties(dev).total_memory - mem_reserved
+            mem_free_total = mem_free_xpu + mem_free_torch
         else:
             stats = torch.cuda.memory_stats(dev)
             mem_active = stats['active_bytes.all.current']
@@ -709,17 +818,20 @@ def mps_mode():
     global cpu_state
     return cpu_state == CPUState.MPS
 
-def is_device_cpu(device):
+def is_device_type(device, type):
     if hasattr(device, 'type'):
-        if (device.type == 'cpu'):
+        if (device.type == type):
             return True
     return False
 
+def is_device_cpu(device):
+    return is_device_type(device, 'cpu')
+
 def is_device_mps(device):
-    if hasattr(device, 'type'):
-        if (device.type == 'mps'):
-            return True
-    return False
+    return is_device_type(device, 'mps')
+
+def is_device_cuda(device):
+    return is_device_type(device, 'cuda')
 
 
 def should_use_fp16(device=None, model_params=0, prioritize_performance=True, manual_cast=False):
@@ -732,9 +844,9 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma
     if FORCE_FP16:
         return True
 
-    if device is not None: #TODO
+    if device is not None:
         if is_device_mps(device):
-            return False
+            return True
 
     if FORCE_FP32:
         return False
@@ -742,8 +854,11 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma
     if directml_enabled:
         return False
 
-    if cpu_mode() or mps_mode():
-        return False #TODO ?
+    if mps_mode():
+        return True
+
+    if cpu_mode():
+        return False
 
     if is_intel_xpu():
         return True
@@ -762,7 +877,7 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma
     #FP16 is confirmed working on a 1080 (GP104) but it's a bit slower than FP32 so it should only be enabled
     #when the model doesn't actually fit on the card
     #TODO: actually test if GP106 and others have the same type of behavior
-    nvidia_10_series = ["1080", "1070", "titan x", "p3000", "p3200", "p4000", "p4200", "p5000", "p5200", "p6000", "1060", "1050"]
+    nvidia_10_series = ["1080", "1070", "titan x", "p3000", "p3200", "p4000", "p4200", "p5000", "p5200", "p6000", "1060", "1050", "p40", "p100", "p6", "p4"]
     for x in nvidia_10_series:
         if x in props.name.lower():
             fp16_works = True
@@ -783,6 +898,43 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma
 
     return True
 
+def should_use_bf16(device=None, model_params=0, prioritize_performance=True, manual_cast=False):
+    if device is not None:
+        if is_device_cpu(device): #TODO ? bf16 works on CPU but is extremely slow
+            return False
+
+    if device is not None: #TODO not sure about mps bf16 support
+        if is_device_mps(device):
+            return False
+
+    if FORCE_FP32:
+        return False
+
+    if directml_enabled:
+        return False
+
+    if cpu_mode() or mps_mode():
+        return False
+
+    if is_intel_xpu():
+        return True
+
+    if device is None:
+        device = torch.device("cuda")
+
+    props = torch.cuda.get_device_properties(device)
+    if props.major >= 8:
+        return True
+
+    bf16_works = torch.cuda.is_bf16_supported()
+
+    if bf16_works or manual_cast:
+        free_model_memory = (get_free_memory() * 0.9 - minimum_inference_memory())
+        if (not prioritize_performance) or model_params * 4 > free_model_memory:
+            return True
+
+    return False
+
 def soft_empty_cache(force=False):
     global cpu_state
     if cpu_state == CPUState.MPS:
@@ -799,6 +951,7 @@ def unload_all_models():
 
 
 def resolve_lowvram_weight(weight, model, key): #TODO: remove
+    print("WARNING: The comfy.model_management.resolve_lowvram_weight function will be removed soon, please stop using it.")
     return weight
 
 #TODO: might be cleaner to put this somewhere else
diff --git a/comfy/model_patcher.py b/comfy/model_patcher.py
index a88b737c..b949031e 100644
--- a/comfy/model_patcher.py
+++ b/comfy/model_patcher.py
@@ -1,9 +1,61 @@
 import torch
 import copy
 import inspect
+import logging
+import uuid
 
 import comfy.utils
 import comfy.model_management
+from comfy.types import UnetWrapperFunction
+
+
+def weight_decompose(dora_scale, weight, lora_diff, alpha, strength):
+    dora_scale = comfy.model_management.cast_to_device(dora_scale, weight.device, torch.float32)
+    lora_diff *= alpha
+    weight_calc = weight + lora_diff.type(weight.dtype)
+    weight_norm = (
+        weight_calc.transpose(0, 1)
+        .reshape(weight_calc.shape[1], -1)
+        .norm(dim=1, keepdim=True)
+        .reshape(weight_calc.shape[1], *[1] * (weight_calc.dim() - 1))
+        .transpose(0, 1)
+    )
+
+    weight_calc *= (dora_scale / weight_norm).type(weight.dtype)
+    if strength != 1.0:
+        weight_calc -= weight
+        weight += strength * (weight_calc)
+    else:
+        weight[:] = weight_calc
+    return weight
+
+
+def set_model_options_patch_replace(model_options, patch, name, block_name, number, transformer_index=None):
+    to = model_options["transformer_options"].copy()
+
+    if "patches_replace" not in to:
+        to["patches_replace"] = {}
+    else:
+        to["patches_replace"] = to["patches_replace"].copy()
+
+    if name not in to["patches_replace"]:
+        to["patches_replace"][name] = {}
+    else:
+        to["patches_replace"][name] = to["patches_replace"][name].copy()
+
+    if transformer_index is not None:
+        block = (block_name, number, transformer_index)
+    else:
+        block = (block_name, number)
+    to["patches_replace"][name][block] = patch
+    model_options["transformer_options"] = to
+    return model_options
+
+def set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=False):
+    model_options["sampler_post_cfg_function"] = model_options.get("sampler_post_cfg_function", []) + [post_cfg_function]
+    if disable_cfg1_optimization:
+        model_options["disable_cfg1_optimization"] = True
+    return model_options
 
 class ModelPatcher:
     def __init__(self, model, load_device, offload_device, size=0, current_device=None, weight_inplace_update=False):
@@ -23,13 +75,14 @@ class ModelPatcher:
             self.current_device = current_device
 
         self.weight_inplace_update = weight_inplace_update
+        self.model_lowvram = False
+        self.lowvram_patch_counter = 0
+        self.patches_uuid = uuid.uuid4()
 
     def model_size(self):
         if self.size > 0:
             return self.size
-        model_sd = self.model.state_dict()
         self.size = comfy.model_management.module_size(self.model)
-        self.model_keys = set(model_sd.keys())
         return self.size
 
     def clone(self):
@@ -37,10 +90,12 @@ class ModelPatcher:
         n.patches = {}
         for k in self.patches:
             n.patches[k] = self.patches[k][:]
+        n.patches_uuid = self.patches_uuid
 
         n.object_patches = self.object_patches.copy()
         n.model_options = copy.deepcopy(self.model_options)
-        n.model_keys = self.model_keys
+        n.backup = self.backup
+        n.object_patches_backup = self.object_patches_backup
         return n
 
     def is_clone(self, other):
@@ -48,6 +103,19 @@ class ModelPatcher:
             return True
         return False
 
+    def clone_has_same_weights(self, clone):
+        if not self.is_clone(clone):
+            return False
+
+        if len(self.patches) == 0 and len(clone.patches) == 0:
+            return True
+
+        if self.patches_uuid == clone.patches_uuid:
+            if len(self.patches) != len(clone.patches):
+                logging.warning("WARNING: something went wrong, same patch uuid but different length of patches.")
+            else:
+                return True
+
     def memory_required(self, input_shape):
         return self.model.memory_required(input_shape=input_shape)
 
@@ -60,13 +128,14 @@ class ModelPatcher:
             self.model_options["disable_cfg1_optimization"] = True
 
     def set_model_sampler_post_cfg_function(self, post_cfg_function, disable_cfg1_optimization=False):
-        self.model_options["sampler_post_cfg_function"] = self.model_options.get("sampler_post_cfg_function", []) + [post_cfg_function]
-        if disable_cfg1_optimization:
-            self.model_options["disable_cfg1_optimization"] = True
+        self.model_options = set_model_options_post_cfg_function(self.model_options, post_cfg_function, disable_cfg1_optimization)
 
-    def set_model_unet_function_wrapper(self, unet_wrapper_function):
+    def set_model_unet_function_wrapper(self, unet_wrapper_function: UnetWrapperFunction):
         self.model_options["model_function_wrapper"] = unet_wrapper_function
 
+    def set_model_denoise_mask_function(self, denoise_mask_function):
+        self.model_options["denoise_mask_function"] = denoise_mask_function
+
     def set_model_patch(self, patch, name):
         to = self.model_options["transformer_options"]
         if "patches" not in to:
@@ -74,16 +143,7 @@ class ModelPatcher:
         to["patches"][name] = to["patches"].get(name, []) + [patch]
 
     def set_model_patch_replace(self, patch, name, block_name, number, transformer_index=None):
-        to = self.model_options["transformer_options"]
-        if "patches_replace" not in to:
-            to["patches_replace"] = {}
-        if name not in to["patches_replace"]:
-            to["patches_replace"][name] = {}
-        if transformer_index is not None:
-            block = (block_name, number, transformer_index)
-        else:
-            block = (block_name, number)
-        to["patches_replace"][name][block] = patch
+        self.model_options = set_model_options_patch_replace(self.model_options, patch, name, block_name, number, transformer_index=transformer_index)
 
     def set_model_attn1_patch(self, patch):
         self.set_model_patch(patch, "attn1_patch")
@@ -115,6 +175,15 @@ class ModelPatcher:
     def add_object_patch(self, name, obj):
         self.object_patches[name] = obj
 
+    def get_model_object(self, name):
+        if name in self.object_patches:
+            return self.object_patches[name]
+        else:
+            if name in self.object_patches_backup:
+                return self.object_patches_backup[name]
+            else:
+                return comfy.utils.get_attr(self.model, name)
+
     def model_patches_to(self, device):
         to = self.model_options["transformer_options"]
         if "patches" in to:
@@ -142,13 +211,25 @@ class ModelPatcher:
 
     def add_patches(self, patches, strength_patch=1.0, strength_model=1.0):
         p = set()
+        model_sd = self.model.state_dict()
         for k in patches:
-            if k in self.model_keys:
-                p.add(k)
-                current_patches = self.patches.get(k, [])
-                current_patches.append((strength_patch, patches[k], strength_model))
-                self.patches[k] = current_patches
+            offset = None
+            function = None
+            if isinstance(k, str):
+                key = k
+            else:
+                offset = k[1]
+                key = k[0]
+                if len(k) > 2:
+                    function = k[2]
 
+            if key in model_sd:
+                p.add(k)
+                current_patches = self.patches.get(key, [])
+                current_patches.append((strength_patch, patches[k], strength_model, offset, function))
+                self.patches[key] = current_patches
+
+        self.patches_uuid = uuid.uuid4()
         return list(p)
 
     def get_key_patches(self, filter_prefix=None):
@@ -174,37 +255,41 @@ class ModelPatcher:
                     sd.pop(k)
         return sd
 
+    def patch_weight_to_device(self, key, device_to=None):
+        if key not in self.patches:
+            return
+
+        weight = comfy.utils.get_attr(self.model, key)
+
+        inplace_update = self.weight_inplace_update
+
+        if key not in self.backup:
+            self.backup[key] = weight.to(device=self.offload_device, copy=inplace_update)
+
+        if device_to is not None:
+            temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
+        else:
+            temp_weight = weight.to(torch.float32, copy=True)
+        out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype)
+        if inplace_update:
+            comfy.utils.copy_to_param(self.model, key, out_weight)
+        else:
+            comfy.utils.set_attr_param(self.model, key, out_weight)
+
     def patch_model(self, device_to=None, patch_weights=True):
         for k in self.object_patches:
-            old = getattr(self.model, k)
+            old = comfy.utils.set_attr(self.model, k, self.object_patches[k])
             if k not in self.object_patches_backup:
                 self.object_patches_backup[k] = old
-            setattr(self.model, k, self.object_patches[k])
 
         if patch_weights:
             model_sd = self.model_state_dict()
             for key in self.patches:
                 if key not in model_sd:
-                    print("could not patch. key doesn't exist in model:", key)
+                    logging.warning("could not patch. key doesn't exist in model: {}".format(key))
                     continue
 
-                weight = model_sd[key]
-
-                inplace_update = self.weight_inplace_update
-
-                if key not in self.backup:
-                    self.backup[key] = weight.to(device=self.offload_device, copy=inplace_update)
-
-                if device_to is not None:
-                    temp_weight = comfy.model_management.cast_to_device(weight, device_to, torch.float32, copy=True)
-                else:
-                    temp_weight = weight.to(torch.float32, copy=True)
-                out_weight = self.calculate_weight(self.patches[key], temp_weight, key).to(weight.dtype)
-                if inplace_update:
-                    comfy.utils.copy_to_param(self.model, key, out_weight)
-                else:
-                    comfy.utils.set_attr(self.model, key, out_weight)
-                del temp_weight
+                self.patch_weight_to_device(key, device_to)
 
             if device_to is not None:
                 self.model.to(device_to)
@@ -212,11 +297,71 @@ class ModelPatcher:
 
         return self.model
 
+    def patch_model_lowvram(self, device_to=None, lowvram_model_memory=0, force_patch_weights=False):
+        self.patch_model(device_to, patch_weights=False)
+
+        logging.info("loading in lowvram mode {}".format(lowvram_model_memory/(1024 * 1024)))
+        class LowVramPatch:
+            def __init__(self, key, model_patcher):
+                self.key = key
+                self.model_patcher = model_patcher
+            def __call__(self, weight):
+                return self.model_patcher.calculate_weight(self.model_patcher.patches[self.key], weight, self.key)
+
+        mem_counter = 0
+        patch_counter = 0
+        for n, m in self.model.named_modules():
+            lowvram_weight = False
+            if hasattr(m, "comfy_cast_weights"):
+                module_mem = comfy.model_management.module_size(m)
+                if mem_counter + module_mem >= lowvram_model_memory:
+                    lowvram_weight = True
+
+            weight_key = "{}.weight".format(n)
+            bias_key = "{}.bias".format(n)
+
+            if lowvram_weight:
+                if weight_key in self.patches:
+                    if force_patch_weights:
+                        self.patch_weight_to_device(weight_key)
+                    else:
+                        m.weight_function = LowVramPatch(weight_key, self)
+                        patch_counter += 1
+                if bias_key in self.patches:
+                    if force_patch_weights:
+                        self.patch_weight_to_device(bias_key)
+                    else:
+                        m.bias_function = LowVramPatch(bias_key, self)
+                        patch_counter += 1
+
+                m.prev_comfy_cast_weights = m.comfy_cast_weights
+                m.comfy_cast_weights = True
+            else:
+                if hasattr(m, "weight"):
+                    self.patch_weight_to_device(weight_key, device_to)
+                    self.patch_weight_to_device(bias_key, device_to)
+                    m.to(device_to)
+                    mem_counter += comfy.model_management.module_size(m)
+                    logging.debug("lowvram: loaded module regularly {} {}".format(n, m))
+
+        self.model_lowvram = True
+        self.lowvram_patch_counter = patch_counter
+        return self.model
+
     def calculate_weight(self, patches, weight, key):
         for p in patches:
-            alpha = p[0]
+            strength = p[0]
             v = p[1]
             strength_model = p[2]
+            offset = p[3]
+            function = p[4]
+            if function is None:
+                function = lambda a: a
+
+            old_weight = None
+            if offset is not None:
+                old_weight = weight
+                weight = weight.narrow(offset[0], offset[1], offset[2])
 
             if strength_model != 1.0:
                 weight *= strength_model
@@ -232,25 +377,33 @@ class ModelPatcher:
 
             if patch_type == "diff":
                 w1 = v[0]
-                if alpha != 0.0:
+                if strength != 0.0:
                     if w1.shape != weight.shape:
-                        print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
+                        logging.warning("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
                     else:
-                        weight += alpha * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype)
+                        weight += function(strength * comfy.model_management.cast_to_device(w1, weight.device, weight.dtype))
             elif patch_type == "lora": #lora/locon
                 mat1 = comfy.model_management.cast_to_device(v[0], weight.device, torch.float32)
                 mat2 = comfy.model_management.cast_to_device(v[1], weight.device, torch.float32)
+                dora_scale = v[4]
                 if v[2] is not None:
-                    alpha *= v[2] / mat2.shape[0]
+                    alpha = v[2] / mat2.shape[0]
+                else:
+                    alpha = 1.0
+
                 if v[3] is not None:
                     #locon mid weights, hopefully the math is fine because I didn't properly test it
                     mat3 = comfy.model_management.cast_to_device(v[3], weight.device, torch.float32)
                     final_shape = [mat2.shape[1], mat2.shape[0], mat3.shape[2], mat3.shape[3]]
                     mat2 = torch.mm(mat2.transpose(0, 1).flatten(start_dim=1), mat3.transpose(0, 1).flatten(start_dim=1)).reshape(final_shape).transpose(0, 1)
                 try:
-                    weight += (alpha * torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1))).reshape(weight.shape).type(weight.dtype)
+                    lora_diff = torch.mm(mat1.flatten(start_dim=1), mat2.flatten(start_dim=1)).reshape(weight.shape)
+                    if dora_scale is not None:
+                        weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength))
+                    else:
+                        weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
                 except Exception as e:
-                    print("ERROR", key, e)
+                    logging.error("ERROR {} {} {}".format(patch_type, key, e))
             elif patch_type == "lokr":
                 w1 = v[0]
                 w2 = v[1]
@@ -259,6 +412,7 @@ class ModelPatcher:
                 w2_a = v[5]
                 w2_b = v[6]
                 t2 = v[7]
+                dora_scale = v[8]
                 dim = None
 
                 if w1 is None:
@@ -284,19 +438,29 @@ class ModelPatcher:
                 if len(w2.shape) == 4:
                     w1 = w1.unsqueeze(2).unsqueeze(2)
                 if v[2] is not None and dim is not None:
-                    alpha *= v[2] / dim
+                    alpha = v[2] / dim
+                else:
+                    alpha = 1.0
 
                 try:
-                    weight += alpha * torch.kron(w1, w2).reshape(weight.shape).type(weight.dtype)
+                    lora_diff = torch.kron(w1, w2).reshape(weight.shape)
+                    if dora_scale is not None:
+                        weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength))
+                    else:
+                        weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
                 except Exception as e:
-                    print("ERROR", key, e)
+                    logging.error("ERROR {} {} {}".format(patch_type, key, e))
             elif patch_type == "loha":
                 w1a = v[0]
                 w1b = v[1]
                 if v[2] is not None:
-                    alpha *= v[2] / w1b.shape[0]
+                    alpha = v[2] / w1b.shape[0]
+                else:
+                    alpha = 1.0
+
                 w2a = v[3]
                 w2b = v[4]
+                dora_scale = v[7]
                 if v[5] is not None: #cp decomposition
                     t1 = v[5]
                     t2 = v[6]
@@ -316,42 +480,72 @@ class ModelPatcher:
                                   comfy.model_management.cast_to_device(w2b, weight.device, torch.float32))
 
                 try:
-                    weight += (alpha * m1 * m2).reshape(weight.shape).type(weight.dtype)
+                    lora_diff = (m1 * m2).reshape(weight.shape)
+                    if dora_scale is not None:
+                        weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength))
+                    else:
+                        weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
                 except Exception as e:
-                    print("ERROR", key, e)
+                    logging.error("ERROR {} {} {}".format(patch_type, key, e))
             elif patch_type == "glora":
                 if v[4] is not None:
-                    alpha *= v[4] / v[0].shape[0]
+                    alpha = v[4] / v[0].shape[0]
+                else:
+                    alpha = 1.0
+
+                dora_scale = v[5]
 
                 a1 = comfy.model_management.cast_to_device(v[0].flatten(start_dim=1), weight.device, torch.float32)
                 a2 = comfy.model_management.cast_to_device(v[1].flatten(start_dim=1), weight.device, torch.float32)
                 b1 = comfy.model_management.cast_to_device(v[2].flatten(start_dim=1), weight.device, torch.float32)
                 b2 = comfy.model_management.cast_to_device(v[3].flatten(start_dim=1), weight.device, torch.float32)
 
-                weight += ((torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)) * alpha).reshape(weight.shape).type(weight.dtype)
+                try:
+                    lora_diff = (torch.mm(b2, b1) + torch.mm(torch.mm(weight.flatten(start_dim=1), a2), a1)).reshape(weight.shape)
+                    if dora_scale is not None:
+                        weight = function(weight_decompose(dora_scale, weight, lora_diff, alpha, strength))
+                    else:
+                        weight += function(((strength * alpha) * lora_diff).type(weight.dtype))
+                except Exception as e:
+                    logging.error("ERROR {} {} {}".format(patch_type, key, e))
             else:
-                print("patch type not recognized", patch_type, key)
+                logging.warning("patch type not recognized {} {}".format(patch_type, key))
+
+            if old_weight is not None:
+                weight = old_weight
 
         return weight
 
-    def unpatch_model(self, device_to=None):
-        keys = list(self.backup.keys())
+    def unpatch_model(self, device_to=None, unpatch_weights=True):
+        if unpatch_weights:
+            if self.model_lowvram:
+                for m in self.model.modules():
+                    if hasattr(m, "prev_comfy_cast_weights"):
+                        m.comfy_cast_weights = m.prev_comfy_cast_weights
+                        del m.prev_comfy_cast_weights
+                    m.weight_function = None
+                    m.bias_function = None
 
-        if self.weight_inplace_update:
-            for k in keys:
-                comfy.utils.copy_to_param(self.model, k, self.backup[k])
-        else:
-            for k in keys:
-                comfy.utils.set_attr(self.model, k, self.backup[k])
+                self.model_lowvram = False
+                self.lowvram_patch_counter = 0
 
-        self.backup = {}
+            keys = list(self.backup.keys())
 
-        if device_to is not None:
-            self.model.to(device_to)
-            self.current_device = device_to
+            if self.weight_inplace_update:
+                for k in keys:
+                    comfy.utils.copy_to_param(self.model, k, self.backup[k])
+            else:
+                for k in keys:
+                    comfy.utils.set_attr_param(self.model, k, self.backup[k])
+
+            self.backup.clear()
+
+            if device_to is not None:
+                self.model.to(device_to)
+                self.current_device = device_to
 
         keys = list(self.object_patches_backup.keys())
         for k in keys:
-            setattr(self.model, k, self.object_patches_backup[k])
+            comfy.utils.set_attr(self.model, k, self.object_patches_backup[k])
 
-        self.object_patches_backup = {}
+        self.object_patches_backup.clear()
diff --git a/comfy/model_sampling.py b/comfy/model_sampling.py
index d5870027..2d95a83d 100644
--- a/comfy/model_sampling.py
+++ b/comfy/model_sampling.py
@@ -11,12 +11,41 @@ class EPS:
         sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
         return model_input - model_output * sigma
 
+    def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
+        if max_denoise:
+            noise = noise * torch.sqrt(1.0 + sigma ** 2.0)
+        else:
+            noise = noise * sigma
+
+        noise += latent_image
+        return noise
+
+    def inverse_noise_scaling(self, sigma, latent):
+        return latent
 
 class V_PREDICTION(EPS):
     def calculate_denoised(self, sigma, model_output, model_input):
         sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
         return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
 
+class EDM(V_PREDICTION):
+    def calculate_denoised(self, sigma, model_output, model_input):
+        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+        return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
+
+class CONST:
+    def calculate_input(self, sigma, noise):
+        return noise
+
+    def calculate_denoised(self, sigma, model_output, model_input):
+        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+        return model_input - model_output * sigma
+
+    def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
+        return sigma * noise + (1.0 - sigma) * latent_image
+
+    def inverse_noise_scaling(self, sigma, latent):
+        return latent / (1.0 - sigma)
 
 class ModelSamplingDiscrete(torch.nn.Module):
     def __init__(self, model_config=None):
@@ -88,12 +117,16 @@ class ModelSamplingDiscrete(torch.nn.Module):
         percent = 1.0 - percent
         return self.sigma(torch.tensor(percent * 999.0)).item()
 
+class ModelSamplingDiscreteEDM(ModelSamplingDiscrete):
+    def timestep(self, sigma):
+        return 0.25 * sigma.log()
+
+    def sigma(self, timestep):
+        return (timestep / 0.25).exp()
 
 class ModelSamplingContinuousEDM(torch.nn.Module):
     def __init__(self, model_config=None):
         super().__init__()
-        self.sigma_data = 1.0
-
         if model_config is not None:
             sampling_settings = model_config.sampling_settings
         else:
@@ -101,9 +134,11 @@ class ModelSamplingContinuousEDM(torch.nn.Module):
 
         sigma_min = sampling_settings.get("sigma_min", 0.002)
         sigma_max = sampling_settings.get("sigma_max", 120.0)
-        self.set_sigma_range(sigma_min, sigma_max)
+        sigma_data = sampling_settings.get("sigma_data", 1.0)
+        self.set_parameters(sigma_min, sigma_max, sigma_data)
 
-    def set_sigma_range(self, sigma_min, sigma_max):
+    def set_parameters(self, sigma_min, sigma_max, sigma_data):
+        self.sigma_data = sigma_data
         sigmas = torch.linspace(math.log(sigma_min), math.log(sigma_max), 1000).exp()
 
         self.register_buffer('sigmas', sigmas) #for compatibility with some schedulers
@@ -132,3 +167,107 @@ class ModelSamplingContinuousEDM(torch.nn.Module):
 
         log_sigma_min = math.log(self.sigma_min)
         return math.exp((math.log(self.sigma_max) - log_sigma_min) * percent + log_sigma_min)
+
+
+class ModelSamplingContinuousV(ModelSamplingContinuousEDM):
+    def timestep(self, sigma):
+        return sigma.atan() / math.pi * 2
+
+    def sigma(self, timestep):
+        return (timestep * math.pi / 2).tan()
+
+
+def time_snr_shift(alpha, t):
+    if alpha == 1.0:
+        return t
+    return alpha * t / (1 + (alpha - 1) * t)
+
+class ModelSamplingDiscreteFlow(torch.nn.Module):
+    def __init__(self, model_config=None):
+        super().__init__()
+        if model_config is not None:
+            sampling_settings = model_config.sampling_settings
+        else:
+            sampling_settings = {}
+
+        self.set_parameters(shift=sampling_settings.get("shift", 1.0), multiplier=sampling_settings.get("multiplier", 1000))
+
+    def set_parameters(self, shift=1.0, timesteps=1000, multiplier=1000):
+        self.shift = shift
+        self.multiplier = multiplier
+        ts = self.sigma((torch.arange(1, timesteps + 1, 1) / timesteps) * multiplier)
+        self.register_buffer('sigmas', ts)
+
+    @property
+    def sigma_min(self):
+        return self.sigmas[0]
+
+    @property
+    def sigma_max(self):
+        return self.sigmas[-1]
+
+    def timestep(self, sigma):
+        return sigma * self.multiplier
+
+    def sigma(self, timestep):
+        return time_snr_shift(self.shift, timestep / self.multiplier)
+
+    def percent_to_sigma(self, percent):
+        if percent <= 0.0:
+            return 1.0
+        if percent >= 1.0:
+            return 0.0
+        return 1.0 - percent
+
+class StableCascadeSampling(ModelSamplingDiscrete):
+    def __init__(self, model_config=None):
+        super().__init__()
+
+        if model_config is not None:
+            sampling_settings = model_config.sampling_settings
+        else:
+            sampling_settings = {}
+
+        self.set_parameters(sampling_settings.get("shift", 1.0))
+
+    def set_parameters(self, shift=1.0, cosine_s=8e-3):
+        self.shift = shift
+        self.cosine_s = torch.tensor(cosine_s)
+        self._init_alpha_cumprod = torch.cos(self.cosine_s / (1 + self.cosine_s) * torch.pi * 0.5) ** 2
+
+        #This part is just for compatibility with some schedulers in the codebase
+        self.num_timesteps = 10000
+        sigmas = torch.empty((self.num_timesteps), dtype=torch.float32)
+        for x in range(self.num_timesteps):
+            t = (x + 1) / self.num_timesteps
+            sigmas[x] = self.sigma(t)
+
+        self.set_sigmas(sigmas)
+
+    def sigma(self, timestep):
+        alpha_cumprod = (torch.cos((timestep + self.cosine_s) / (1 + self.cosine_s) * torch.pi * 0.5) ** 2 / self._init_alpha_cumprod)
+
+        if self.shift != 1.0:
+            var = alpha_cumprod
+            logSNR = (var/(1-var)).log()
+            logSNR += 2 * torch.log(1.0 / torch.tensor(self.shift))
+            alpha_cumprod = logSNR.sigmoid()
+
+        alpha_cumprod = alpha_cumprod.clamp(0.0001, 0.9999)
+        return ((1 - alpha_cumprod) / alpha_cumprod) ** 0.5
+
+    def timestep(self, sigma):
+        var = 1 / ((sigma * sigma) + 1)
+        var = var.clamp(0, 1.0)
+        s, min_var = self.cosine_s.to(var.device), self._init_alpha_cumprod.to(var.device)
+        t = (((var * min_var) ** 0.5).acos() / (torch.pi * 0.5)) * (1 + s) - s
+        return t
+
+    def percent_to_sigma(self, percent):
+        if percent <= 0.0:
+            return 999999999.9
+        if percent >= 1.0:
+            return 0.0
+
+        percent = 1.0 - percent
+        return self.sigma(torch.tensor(percent))
diff --git a/comfy/ops.py b/comfy/ops.py
index f674b47f..0f1ceb57 100644
--- a/comfy/ops.py
+++ b/comfy/ops.py
@@ -1,18 +1,43 @@
+"""
+    This file is part of ComfyUI.
+    Copyright (C) 2024 Stability AI
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+"""
+
 import torch
 import comfy.model_management
 
 def cast_bias_weight(s, input):
     bias = None
-    non_blocking = comfy.model_management.device_supports_non_blocking(input.device)
+    non_blocking = comfy.model_management.device_should_use_non_blocking(input.device)
     if s.bias is not None:
         bias = s.bias.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking)
+        if s.bias_function is not None:
+            bias = s.bias_function(bias)
     weight = s.weight.to(device=input.device, dtype=input.dtype, non_blocking=non_blocking)
+    if s.weight_function is not None:
+        weight = s.weight_function(weight)
     return weight, bias
 
+class CastWeightBiasOp:
+    comfy_cast_weights = False
+    weight_function = None
+    bias_function = None
 
 class disable_weight_init:
-    class Linear(torch.nn.Linear):
-        comfy_cast_weights = False
+    class Linear(torch.nn.Linear, CastWeightBiasOp):
         def reset_parameters(self):
             return None
 
@@ -26,8 +51,7 @@ class disable_weight_init:
             else:
                 return super().forward(*args, **kwargs)
 
-    class Conv2d(torch.nn.Conv2d):
-        comfy_cast_weights = False
+    class Conv1d(torch.nn.Conv1d, CastWeightBiasOp):
         def reset_parameters(self):
             return None
 
@@ -41,8 +65,7 @@ class disable_weight_init:
             else:
                 return super().forward(*args, **kwargs)
 
-    class Conv3d(torch.nn.Conv3d):
-        comfy_cast_weights = False
+    class Conv2d(torch.nn.Conv2d, CastWeightBiasOp):
         def reset_parameters(self):
             return None
 
@@ -56,8 +79,21 @@ class disable_weight_init:
             else:
                 return super().forward(*args, **kwargs)
 
-    class GroupNorm(torch.nn.GroupNorm):
-        comfy_cast_weights = False
+    class Conv3d(torch.nn.Conv3d, CastWeightBiasOp):
+        def reset_parameters(self):
+            return None
+
+        def forward_comfy_cast_weights(self, input):
+            weight, bias = cast_bias_weight(self, input)
+            return self._conv_forward(input, weight, bias)
+
+        def forward(self, *args, **kwargs):
+            if self.comfy_cast_weights:
+                return self.forward_comfy_cast_weights(*args, **kwargs)
+            else:
+                return super().forward(*args, **kwargs)
+
+    class GroupNorm(torch.nn.GroupNorm, CastWeightBiasOp):
         def reset_parameters(self):
             return None
 
@@ -72,13 +108,16 @@ class disable_weight_init:
                 return super().forward(*args, **kwargs)
 
 
-    class LayerNorm(torch.nn.LayerNorm):
-        comfy_cast_weights = False
+    class LayerNorm(torch.nn.LayerNorm, CastWeightBiasOp):
         def reset_parameters(self):
             return None
 
         def forward_comfy_cast_weights(self, input):
-            weight, bias = cast_bias_weight(self, input)
+            if self.weight is not None:
+                weight, bias = cast_bias_weight(self, input)
+            else:
+                weight = None
+                bias = None
             return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)
 
         def forward(self, *args, **kwargs):
@@ -87,6 +126,48 @@ class disable_weight_init:
             else:
                 return super().forward(*args, **kwargs)
 
+    class ConvTranspose2d(torch.nn.ConvTranspose2d, CastWeightBiasOp):
+        def reset_parameters(self):
+            return None
+
+        def forward_comfy_cast_weights(self, input, output_size=None):
+            num_spatial_dims = 2
+            output_padding = self._output_padding(
+                input, output_size, self.stride, self.padding, self.kernel_size,
+                num_spatial_dims, self.dilation)
+
+            weight, bias = cast_bias_weight(self, input)
+            return torch.nn.functional.conv_transpose2d(
+                input, weight, bias, self.stride, self.padding,
+                output_padding, self.groups, self.dilation)
+
+        def forward(self, *args, **kwargs):
+            if self.comfy_cast_weights:
+                return self.forward_comfy_cast_weights(*args, **kwargs)
+            else:
+                return super().forward(*args, **kwargs)
+
+    class ConvTranspose1d(torch.nn.ConvTranspose1d, CastWeightBiasOp):
+        def reset_parameters(self):
+            return None
+
+        def forward_comfy_cast_weights(self, input, output_size=None):
+            num_spatial_dims = 1
+            output_padding = self._output_padding(
+                input, output_size, self.stride, self.padding, self.kernel_size,
+                num_spatial_dims, self.dilation)
+
+            weight, bias = cast_bias_weight(self, input)
+            return torch.nn.functional.conv_transpose1d(
+                input, weight, bias, self.stride, self.padding,
+                output_padding, self.groups, self.dilation)
+
+        def forward(self, *args, **kwargs):
+            if self.comfy_cast_weights:
+                return self.forward_comfy_cast_weights(*args, **kwargs)
+            else:
+                return super().forward(*args, **kwargs)
+
     @classmethod
     def conv_nd(s, dims, *args, **kwargs):
         if dims == 2:
@@ -101,6 +182,9 @@ class manual_cast(disable_weight_init):
     class Linear(disable_weight_init.Linear):
         comfy_cast_weights = True
 
+    class Conv1d(disable_weight_init.Conv1d):
+        comfy_cast_weights = True
+
     class Conv2d(disable_weight_init.Conv2d):
         comfy_cast_weights = True
 
@@ -112,3 +196,9 @@ class manual_cast(disable_weight_init):
 
     class LayerNorm(disable_weight_init.LayerNorm):
         comfy_cast_weights = True
+
+    class ConvTranspose2d(disable_weight_init.ConvTranspose2d):
+        comfy_cast_weights = True
+
+    class ConvTranspose1d(disable_weight_init.ConvTranspose1d):
+        comfy_cast_weights = True
diff --git a/comfy/sa_t5.py b/comfy/sa_t5.py
new file mode 100644
index 00000000..acc302f6
--- /dev/null
+++ b/comfy/sa_t5.py
@@ -0,0 +1,22 @@
+from comfy import sd1_clip
+from transformers import T5TokenizerFast
+import comfy.t5
+import os
+
+class T5BaseModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_base.json")
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.t5.T5, enable_attention_masks=True, zero_out_masked=True)
+
+class T5BaseTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=768, embedding_key='t5base', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=128)
+
+class SAT5Tokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None):
+        super().__init__(embedding_directory=embedding_directory, clip_name="t5base", tokenizer=T5BaseTokenizer)
+
+class SAT5Model(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, **kwargs):
+        super().__init__(device=device, dtype=dtype, name="t5base", clip_model=T5BaseModel, **kwargs)
diff --git a/comfy/sample.py b/comfy/sample.py
index 5c8a7d13..98dcaca7 100644
--- a/comfy/sample.py
+++ b/comfy/sample.py
@@ -1,10 +1,9 @@
 import torch
 import comfy.model_management
 import comfy.samplers
-import comfy.conds
 import comfy.utils
-import math
 import numpy as np
+import logging
 
 def prepare_noise(latent_image, seed, noise_inds=None):
     """
@@ -25,94 +24,27 @@ def prepare_noise(latent_image, seed, noise_inds=None):
     noises = torch.cat(noises, axis=0)
     return noises
 
-def prepare_mask(noise_mask, shape, device):
-    """ensures noise mask is of proper dimensions"""
-    noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear")
-    noise_mask = torch.cat([noise_mask] * shape[1], dim=1)
-    noise_mask = comfy.utils.repeat_to_batch_size(noise_mask, shape[0])
-    noise_mask = noise_mask.to(device)
-    return noise_mask
-
-def get_models_from_cond(cond, model_type):
-    models = []
-    for c in cond:
-        if model_type in c:
-            models += [c[model_type]]
-    return models
-
-def convert_cond(cond):
-    out = []
-    for c in cond:
-        temp = c[1].copy()
-        model_conds = temp.get("model_conds", {})
-        if c[0] is not None:
-            model_conds["c_crossattn"] = comfy.conds.CONDCrossAttn(c[0]) #TODO: remove
-            temp["cross_attn"] = c[0]
-        temp["model_conds"] = model_conds
-        out.append(temp)
-    return out
-
-def get_additional_models(positive, negative, dtype):
-    """loads additional models in positive and negative conditioning"""
-    control_nets = set(get_models_from_cond(positive, "control") + get_models_from_cond(negative, "control"))
-
-    inference_memory = 0
-    control_models = []
-    for m in control_nets:
-        control_models += m.get_models()
-        inference_memory += m.inference_memory_requirements(dtype)
-
-    gligen = get_models_from_cond(positive, "gligen") + get_models_from_cond(negative, "gligen")
-    gligen = [x[1] for x in gligen]
-    models = control_models + gligen
-    return models, inference_memory
-
-def cleanup_additional_models(models):
-    """cleanup additional models that were loaded"""
-    for m in models:
-        if hasattr(m, 'cleanup'):
-            m.cleanup()
+def fix_empty_latent_channels(model, latent_image):
+    latent_channels = model.get_model_object("latent_format").latent_channels #Resize the empty latent image so it has the right number of channels
+    if latent_channels != latent_image.shape[1] and torch.count_nonzero(latent_image) == 0:
+        latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_channels, dim=1)
+    return latent_image
 
 def prepare_sampling(model, noise_shape, positive, negative, noise_mask):
-    device = model.load_device
-    positive = convert_cond(positive)
-    negative = convert_cond(negative)
-
-    if noise_mask is not None:
-        noise_mask = prepare_mask(noise_mask, noise_shape, device)
-
-    real_model = None
-    models, inference_memory = get_additional_models(positive, negative, model.model_dtype())
-    comfy.model_management.load_models_gpu([model] + models, model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory)
-    real_model = model.model
-
-    return real_model, positive, negative, noise_mask, models
+    logging.warning("Warning: comfy.sample.prepare_sampling isn't used anymore and can be removed")
+    return model, positive, negative, noise_mask, []
 
+def cleanup_additional_models(models):
+    logging.warning("Warning: comfy.sample.cleanup_additional_models isn't used anymore and can be removed")
 
 def sample(model, noise, steps, cfg, sampler_name, scheduler, positive, negative, latent_image, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False, noise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
-    real_model, positive_copy, negative_copy, noise_mask, models = prepare_sampling(model, noise.shape, positive, negative, noise_mask)
+    sampler = comfy.samplers.KSampler(model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
 
-    noise = noise.to(model.load_device)
-    latent_image = latent_image.to(model.load_device)
-
-    sampler = comfy.samplers.KSampler(real_model, steps=steps, device=model.load_device, sampler=sampler_name, scheduler=scheduler, denoise=denoise, model_options=model.model_options)
-
-    samples = sampler.sample(noise, positive_copy, negative_copy, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
+    samples = sampler.sample(noise, positive, negative, cfg=cfg, latent_image=latent_image, start_step=start_step, last_step=last_step, force_full_denoise=force_full_denoise, denoise_mask=noise_mask, sigmas=sigmas, callback=callback, disable_pbar=disable_pbar, seed=seed)
     samples = samples.to(comfy.model_management.intermediate_device())
-
-    cleanup_additional_models(models)
-    cleanup_additional_models(set(get_models_from_cond(positive_copy, "control") + get_models_from_cond(negative_copy, "control")))
     return samples
 
 def sample_custom(model, noise, cfg, sampler, sigmas, positive, negative, latent_image, noise_mask=None, callback=None, disable_pbar=False, seed=None):
-    real_model, positive_copy, negative_copy, noise_mask, models = prepare_sampling(model, noise.shape, positive, negative, noise_mask)
-    noise = noise.to(model.load_device)
-    latent_image = latent_image.to(model.load_device)
-    sigmas = sigmas.to(model.load_device)
-
-    samples = comfy.samplers.sample(real_model, noise, positive_copy, negative_copy, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
+    samples = comfy.samplers.sample(model, noise, positive, negative, cfg, model.load_device, sampler, sigmas, model_options=model.model_options, latent_image=latent_image, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=seed)
     samples = samples.to(comfy.model_management.intermediate_device())
-    cleanup_additional_models(models)
-    cleanup_additional_models(set(get_models_from_cond(positive_copy, "control") + get_models_from_cond(negative_copy, "control")))
     return samples
-
diff --git a/comfy/sampler_helpers.py b/comfy/sampler_helpers.py
new file mode 100644
index 00000000..a18abd9e
--- /dev/null
+++ b/comfy/sampler_helpers.py
@@ -0,0 +1,76 @@
+import torch
+import comfy.model_management
+import comfy.conds
+
+def prepare_mask(noise_mask, shape, device):
+    """ensures noise mask is of proper dimensions"""
+    noise_mask = torch.nn.functional.interpolate(noise_mask.reshape((-1, 1, noise_mask.shape[-2], noise_mask.shape[-1])), size=(shape[2], shape[3]), mode="bilinear")
+    noise_mask = torch.cat([noise_mask] * shape[1], dim=1)
+    noise_mask = comfy.utils.repeat_to_batch_size(noise_mask, shape[0])
+    noise_mask = noise_mask.to(device)
+    return noise_mask
+
+def get_models_from_cond(cond, model_type):
+    models = []
+    for c in cond:
+        if model_type in c:
+            models += [c[model_type]]
+    return models
+
+def convert_cond(cond):
+    out = []
+    for c in cond:
+        temp = c[1].copy()
+        model_conds = temp.get("model_conds", {})
+        if c[0] is not None:
+            model_conds["c_crossattn"] = comfy.conds.CONDCrossAttn(c[0]) #TODO: remove
+            temp["cross_attn"] = c[0]
+        temp["model_conds"] = model_conds
+        out.append(temp)
+    return out
+
+def get_additional_models(conds, dtype):
+    """loads additional models in conditioning"""
+    cnets = []
+    gligen = []
+
+    for k in conds:
+        cnets += get_models_from_cond(conds[k], "control")
+        gligen += get_models_from_cond(conds[k], "gligen")
+
+    control_nets = set(cnets)
+
+    inference_memory = 0
+    control_models = []
+    for m in control_nets:
+        control_models += m.get_models()
+        inference_memory += m.inference_memory_requirements(dtype)
+
+    gligen = [x[1] for x in gligen]
+    models = control_models + gligen
+    return models, inference_memory
+
+def cleanup_additional_models(models):
+    """cleanup additional models that were loaded"""
+    for m in models:
+        if hasattr(m, 'cleanup'):
+            m.cleanup()
+
+
+def prepare_sampling(model, noise_shape, conds):
+    device = model.load_device
+    real_model = None
+    models, inference_memory = get_additional_models(conds, model.model_dtype())
+    comfy.model_management.load_models_gpu([model] + models, model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory)
+    real_model = model.model
+
+    return real_model, conds, models
+
+def cleanup_models(conds, models):
+    cleanup_additional_models(models)
+
+    control_cleanup = []
+    for k in conds:
+        control_cleanup += get_models_from_cond(conds[k], "control")
+
+    cleanup_additional_models(set(control_cleanup))
diff --git a/comfy/samplers.py b/comfy/samplers.py
index f4c3e268..c0aa1291 100644
--- a/comfy/samplers.py
+++ b/comfy/samplers.py
@@ -4,9 +4,12 @@ import torch
 import collections
 from comfy import model_management
 import math
+import logging
+import comfy.sampler_helpers
 
 def get_area_and_mult(conds, x_in, timestep_in):
-    area = (x_in.shape[2], x_in.shape[3], 0, 0)
+    dims = tuple(x_in.shape[2:])
+    area = None
     strength = 1.0
 
     if 'timestep_start' in conds:
@@ -18,11 +21,16 @@ def get_area_and_mult(conds, x_in, timestep_in):
         if timestep_in[0] < timestep_end:
             return None
     if 'area' in conds:
-        area = conds['area']
+        area = list(conds['area'])
     if 'strength' in conds:
         strength = conds['strength']
 
-    input_x = x_in[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]]
+    input_x = x_in
+    if area is not None:
+        for i in range(len(dims)):
+            area[i] = min(input_x.shape[i + 2] - area[len(dims) + i], area[i])
+            input_x = input_x.narrow(i + 2, area[len(dims) + i], area[i])
+
     if 'mask' in conds:
         # Scale the mask to the size of the input
         # The mask should have been resized as we began the sampling process
@@ -30,28 +38,30 @@ def get_area_and_mult(conds, x_in, timestep_in):
         if "mask_strength" in conds:
             mask_strength = conds["mask_strength"]
         mask = conds['mask']
-        assert(mask.shape[1] == x_in.shape[2])
-        assert(mask.shape[2] == x_in.shape[3])
-        mask = mask[:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] * mask_strength
+        assert(mask.shape[1:] == x_in.shape[2:])
+
+        mask = mask[:input_x.shape[0]]
+        if area is not None:
+            for i in range(len(dims)):
+                mask = mask.narrow(i + 1, area[len(dims) + i], area[i])
+
+        mask = mask * mask_strength
         mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1)
     else:
         mask = torch.ones_like(input_x)
     mult = mask * strength
 
-    if 'mask' not in conds:
+    if 'mask' not in conds and area is not None:
         rr = 8
-        if area[2] != 0:
-            for t in range(rr):
-                mult[:,:,t:1+t,:] *= ((1.0/rr) * (t + 1))
-        if (area[0] + area[2]) < x_in.shape[2]:
-            for t in range(rr):
-                mult[:,:,area[0] - 1 - t:area[0] - t,:] *= ((1.0/rr) * (t + 1))
-        if area[3] != 0:
-            for t in range(rr):
-                mult[:,:,:,t:1+t] *= ((1.0/rr) * (t + 1))
-        if (area[1] + area[3]) < x_in.shape[3]:
-            for t in range(rr):
-                mult[:,:,:,area[1] - 1 - t:area[1] - t] *= ((1.0/rr) * (t + 1))
+        for i in range(len(dims)):
+            if area[len(dims) + i] != 0:
+                for t in range(rr):
+                    m = mult.narrow(i + 2, t, 1)
+                    m *= ((1.0/rr) * (t + 1))
+            if (area[i] + area[len(dims) + i]) < x_in.shape[i + 2]:
+                for t in range(rr):
+                    m = mult.narrow(i + 2, area[i] - 1 - t, 1)
+                    m *= ((1.0/rr) * (t + 1))
 
     conditioning = {}
     model_conds = conds["model_conds"]
@@ -126,30 +136,23 @@ def cond_cat(c_list):
 
     return out
 
-def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options):
-    out_cond = torch.zeros_like(x_in)
-    out_count = torch.ones_like(x_in) * 1e-37
-
-    out_uncond = torch.zeros_like(x_in)
-    out_uncond_count = torch.ones_like(x_in) * 1e-37
-
-    COND = 0
-    UNCOND = 1
-
+def calc_cond_batch(model, conds, x_in, timestep, model_options):
+    out_conds = []
+    out_counts = []
     to_run = []
-    for x in cond:
-        p = get_area_and_mult(x, x_in, timestep)
-        if p is None:
-            continue
 
-        to_run += [(p, COND)]
-    if uncond is not None:
-        for x in uncond:
-            p = get_area_and_mult(x, x_in, timestep)
-            if p is None:
-                continue
+    for i in range(len(conds)):
+        out_conds.append(torch.zeros_like(x_in))
+        out_counts.append(torch.ones_like(x_in) * 1e-37)
 
-            to_run += [(p, UNCOND)]
+        cond = conds[i]
+        if cond is not None:
+            for x in cond:
+                p = get_area_and_mult(x, x_in, timestep)
+                if p is None:
+                    continue
+
+                to_run += [(p, i)]
 
     while len(to_run) > 0:
         first = to_run[0]
@@ -208,6 +211,7 @@ def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options):
                         cur_patches[p] = cur_patches[p] + patches[p]
                     else:
                         cur_patches[p] = patches[p]
+                transformer_options["patches"] = cur_patches
             else:
                 transformer_options["patches"] = patches
 
@@ -220,71 +224,77 @@ def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options):
             output = model_options['model_function_wrapper'](model.apply_model, {"input": input_x, "timestep": timestep_, "c": c, "cond_or_uncond": cond_or_uncond}).chunk(batch_chunks)
         else:
             output = model.apply_model(input_x, timestep_, **c).chunk(batch_chunks)
-        del input_x
 
         for o in range(batch_chunks):
-            if cond_or_uncond[o] == COND:
-                out_cond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o]
-                out_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o]
+            cond_index = cond_or_uncond[o]
+            a = area[o]
+            if a is None:
+                out_conds[cond_index] += output[o] * mult[o]
+                out_counts[cond_index] += mult[o]
             else:
-                out_uncond[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += output[o] * mult[o]
-                out_uncond_count[:,:,area[o][2]:area[o][0] + area[o][2],area[o][3]:area[o][1] + area[o][3]] += mult[o]
-        del mult
+                out_c = out_conds[cond_index]
+                out_cts = out_counts[cond_index]
+                dims = len(a) // 2
+                for i in range(dims):
+                    out_c = out_c.narrow(i + 2, a[i + dims], a[i])
+                    out_cts = out_cts.narrow(i + 2, a[i + dims], a[i])
+                out_c += output[o] * mult[o]
+                out_cts += mult[o]
 
-    out_cond /= out_count
-    del out_count
-    out_uncond /= out_uncond_count
-    del out_uncond_count
-    return out_cond, out_uncond
+    for i in range(len(out_conds)):
+        out_conds[i] /= out_counts[i]
+
+    return out_conds
+
+def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options): #TODO: remove
+    logging.warning("WARNING: The comfy.samplers.calc_cond_uncond_batch function is deprecated please use the calc_cond_batch one instead.")
+    return tuple(calc_cond_batch(model, [cond, uncond], x_in, timestep, model_options))
+
+def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_options={}, cond=None, uncond=None):
+    if "sampler_cfg_function" in model_options:
+        args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep,
+                "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options}
+        cfg_result = x - model_options["sampler_cfg_function"](args)
+    else:
+        cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale
+
+    for fn in model_options.get("sampler_post_cfg_function", []):
+        args = {"denoised": cfg_result, "cond": cond, "uncond": uncond, "model": model, "uncond_denoised": uncond_pred, "cond_denoised": cond_pred,
+                "sigma": timestep, "model_options": model_options, "input": x}
+        cfg_result = fn(args)
+
+    return cfg_result
 
 #The main sampling function shared by all the samplers
 #Returns denoised
 def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_options={}, seed=None):
-        if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False:
-            uncond_ = None
-        else:
-            uncond_ = uncond
+    if math.isclose(cond_scale, 1.0) and model_options.get("disable_cfg1_optimization", False) == False:
+        uncond_ = None
+    else:
+        uncond_ = uncond
 
-        cond_pred, uncond_pred = calc_cond_uncond_batch(model, cond, uncond_, x, timestep, model_options)
-        if "sampler_cfg_function" in model_options:
-            args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep,
-                    "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options}
-            cfg_result = x - model_options["sampler_cfg_function"](args)
-        else:
-            cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale
+    conds = [cond, uncond_]
+    out = calc_cond_batch(model, conds, x, timestep, model_options)
+    return cfg_function(model, out[0], out[1], cond_scale, x, timestep, model_options=model_options, cond=cond, uncond=uncond_)
 
-        for fn in model_options.get("sampler_post_cfg_function", []):
-            args = {"denoised": cfg_result, "cond": cond, "uncond": uncond, "model": model, "uncond_denoised": uncond_pred, "cond_denoised": cond_pred,
-                    "sigma": timestep, "model_options": model_options, "input": x}
-            cfg_result = fn(args)
 
-        return cfg_result
-
-class CFGNoisePredictor(torch.nn.Module):
-    def __init__(self, model):
-        super().__init__()
+class KSamplerX0Inpaint:
+    def __init__(self, model, sigmas):
         self.inner_model = model
-    def apply_model(self, x, timestep, cond, uncond, cond_scale, model_options={}, seed=None):
-        out = sampling_function(self.inner_model, x, timestep, uncond, cond, cond_scale, model_options=model_options, seed=seed)
-        return out
-    def forward(self, *args, **kwargs):
-        return self.apply_model(*args, **kwargs)
-
-class KSamplerX0Inpaint(torch.nn.Module):
-    def __init__(self, model):
-        super().__init__()
-        self.inner_model = model
-    def forward(self, x, sigma, uncond, cond, cond_scale, denoise_mask, model_options={}, seed=None):
+        self.sigmas = sigmas
+    def __call__(self, x, sigma, denoise_mask, model_options={}, seed=None):
         if denoise_mask is not None:
+            if "denoise_mask_function" in model_options:
+                denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas})
             latent_mask = 1. - denoise_mask
-            x = x * denoise_mask + (self.latent_image + self.noise * sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1))) * latent_mask
-        out = self.inner_model(x, sigma, cond=cond, uncond=uncond, cond_scale=cond_scale, model_options=model_options, seed=seed)
+            x = x * denoise_mask + self.inner_model.inner_model.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1)), self.noise, self.latent_image) * latent_mask
+        out = self.inner_model(x, sigma, model_options=model_options, seed=seed)
         if denoise_mask is not None:
             out = out * denoise_mask + self.latent_image * latent_mask
         return out
 
-def simple_scheduler(model, steps):
-    s = model.model_sampling
+def simple_scheduler(model_sampling, steps):
+    s = model_sampling
     sigs = []
     ss = len(s.sigmas) / steps
     for x in range(steps):
@@ -292,10 +302,10 @@ def simple_scheduler(model, steps):
     sigs += [0.0]
     return torch.FloatTensor(sigs)
 
-def ddim_scheduler(model, steps):
-    s = model.model_sampling
+def ddim_scheduler(model_sampling, steps):
+    s = model_sampling
     sigs = []
-    ss = len(s.sigmas) // steps
+    ss = max(len(s.sigmas) // steps, 1)
     x = 1
     while x < len(s.sigmas):
         sigs += [float(s.sigmas[x])]
@@ -304,8 +314,8 @@ def ddim_scheduler(model, steps):
     sigs += [0.0]
     return torch.FloatTensor(sigs)
 
-def normal_scheduler(model, steps, sgm=False, floor=False):
-    s = model.model_sampling
+def normal_scheduler(model_sampling, steps, sgm=False, floor=False):
+    s = model_sampling
     start = s.timestep(s.sigma_max)
     end = s.timestep(s.sigma_min)
 
@@ -344,7 +354,7 @@ def get_mask_aabb(masks):
 
     return bounding_boxes, is_empty
 
-def resolve_areas_and_cond_masks(conditions, h, w, device):
+def resolve_areas_and_cond_masks_multidim(conditions, dims, device):
     # We need to decide on an area outside the sampling loop in order to properly generate opposite areas of equal sizes.
     # While we're doing this, we can also resolve the mask device and scaling for performance reasons
     for i in range(len(conditions)):
@@ -353,7 +363,14 @@ def resolve_areas_and_cond_masks(conditions, h, w, device):
             area = c['area']
             if area[0] == "percentage":
                 modified = c.copy()
-                area = (max(1, round(area[1] * h)), max(1, round(area[2] * w)), round(area[3] * h), round(area[4] * w))
+                a = area[1:]
+                a_len = len(a) // 2
+                area = ()
+                for d in range(len(dims)):
+                    area += (max(1, round(a[d] * dims[d])),)
+                for d in range(len(dims)):
+                    area += (round(a[d + a_len] * dims[d]),)
+
                 modified['area'] = area
                 c = modified
                 conditions[i] = c
@@ -362,12 +379,12 @@ def resolve_areas_and_cond_masks(conditions, h, w, device):
             mask = c['mask']
             mask = mask.to(device=device)
             modified = c.copy()
-            if len(mask.shape) == 2:
+            if len(mask.shape) == len(dims):
                 mask = mask.unsqueeze(0)
-            if mask.shape[1] != h or mask.shape[2] != w:
-                mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=(h, w), mode='bilinear', align_corners=False).squeeze(1)
+            if mask.shape[1:] != dims:
+                mask = torch.nn.functional.interpolate(mask.unsqueeze(1), size=dims, mode='bilinear', align_corners=False).squeeze(1)
 
-            if modified.get("set_area_to_bounds", False):
+            if modified.get("set_area_to_bounds", False): #TODO: handle dim != 2
                 bounds = torch.max(torch.abs(mask),dim=0).values.unsqueeze(0)
                 boxes, is_empty = get_mask_aabb(bounds)
                 if is_empty[0]:
@@ -384,7 +401,11 @@ def resolve_areas_and_cond_masks(conditions, h, w, device):
             modified['mask'] = mask
             conditions[i] = modified
 
-def create_cond_with_same_area_if_none(conds, c):
+def resolve_areas_and_cond_masks(conditions, h, w, device):
+    logging.warning("WARNING: The comfy.samplers.resolve_areas_and_cond_masks function is deprecated please use the resolve_areas_and_cond_masks_multidim one instead.")
+    return resolve_areas_and_cond_masks_multidim(conditions, [h, w], device)
+
+def create_cond_with_same_area_if_none(conds, c): #TODO: handle dim != 2
     if 'area' not in c:
         return
 
@@ -488,7 +509,10 @@ def encode_model_conds(model_function, conds, noise, device, prompt_type, **kwar
         params = x.copy()
         params["device"] = device
         params["noise"] = noise
-        params["width"] = params.get("width", noise.shape[3] * 8)
+        default_width = None
+        if len(noise.shape) >= 4: #TODO: 8 multiple should be set by the model
+            default_width = noise.shape[3] * 8
+        params["width"] = params.get("width", default_width)
         params["height"] = params.get("height", noise.shape[2] * 8)
         params["prompt_type"] = params.get("prompt_type", prompt_type)
         for k in kwargs:
@@ -513,17 +537,10 @@ class Sampler:
         sigma = float(sigmas[0])
         return math.isclose(max_sigma, sigma, rel_tol=1e-05) or sigma > max_sigma
 
-class UNIPC(Sampler):
-    def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
-        return uni_pc.sample_unipc(model_wrap, noise, latent_image, sigmas, max_denoise=self.max_denoise(model_wrap, sigmas), extra_args=extra_args, noise_mask=denoise_mask, callback=callback, disable=disable_pbar)
-
-class UNIPCBH2(Sampler):
-    def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
-        return uni_pc.sample_unipc(model_wrap, noise, latent_image, sigmas, max_denoise=self.max_denoise(model_wrap, sigmas), extra_args=extra_args, noise_mask=denoise_mask, callback=callback, variant='bh2', disable=disable_pbar)
-
-KSAMPLER_NAMES = ["euler", "euler_ancestral", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
+KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
                   "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_sde", "dpmpp_sde_gpu",
-                  "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm"]
+                  "dpmpp_2m", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
+                  "ipndm", "ipndm_v", "deis"]
 
 class KSAMPLER(Sampler):
     def __init__(self, sampler_function, extra_options={}, inpaint_options={}):
@@ -533,7 +550,7 @@ class KSAMPLER(Sampler):
 
     def sample(self, model_wrap, sigmas, extra_args, callback, noise, latent_image=None, denoise_mask=None, disable_pbar=False):
         extra_args["denoise_mask"] = denoise_mask
-        model_k = KSamplerX0Inpaint(model_wrap)
+        model_k = KSamplerX0Inpaint(model_wrap, sigmas)
         model_k.latent_image = latent_image
         if self.inpaint_options.get("random", False): #TODO: Should this be the default?
             generator = torch.manual_seed(extra_args.get("seed", 41) + 1)
@@ -541,26 +558,24 @@ class KSAMPLER(Sampler):
         else:
             model_k.noise = noise
 
-        if self.max_denoise(model_wrap, sigmas):
-            noise = noise * torch.sqrt(1.0 + sigmas[0] ** 2.0)
-        else:
-            noise = noise * sigmas[0]
+        noise = model_wrap.inner_model.model_sampling.noise_scaling(sigmas[0], noise, latent_image, self.max_denoise(model_wrap, sigmas))
 
         k_callback = None
         total_steps = len(sigmas) - 1
         if callback is not None:
             k_callback = lambda x: callback(x["i"], x["denoised"], x["x"], total_steps)
 
-        if latent_image is not None:
-            noise += latent_image
-
         samples = self.sampler_function(model_k, noise, sigmas, extra_args=extra_args, callback=k_callback, disable=disable_pbar, **self.extra_options)
+        samples = model_wrap.inner_model.model_sampling.inverse_noise_scaling(sigmas[-1], samples)
         return samples
 
 
 def ksampler(sampler_name, extra_options={}, inpaint_options={}):
     if sampler_name == "dpm_fast":
         def dpm_fast_function(model, noise, sigmas, extra_args, callback, disable):
+            if len(sigmas) <= 1:
+                return noise
+
             sigma_min = sigmas[-1]
             if sigma_min == 0:
                 sigma_min = sigmas[-2]
@@ -568,81 +583,145 @@ def ksampler(sampler_name, extra_options={}, inpaint_options={}):
             return k_diffusion_sampling.sample_dpm_fast(model, noise, sigma_min, sigmas[0], total_steps, extra_args=extra_args, callback=callback, disable=disable)
         sampler_function = dpm_fast_function
     elif sampler_name == "dpm_adaptive":
-        def dpm_adaptive_function(model, noise, sigmas, extra_args, callback, disable):
+        def dpm_adaptive_function(model, noise, sigmas, extra_args, callback, disable, **extra_options):
+            if len(sigmas) <= 1:
+                return noise
+
             sigma_min = sigmas[-1]
             if sigma_min == 0:
                 sigma_min = sigmas[-2]
-            return k_diffusion_sampling.sample_dpm_adaptive(model, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=callback, disable=disable)
+            return k_diffusion_sampling.sample_dpm_adaptive(model, noise, sigma_min, sigmas[0], extra_args=extra_args, callback=callback, disable=disable, **extra_options)
         sampler_function = dpm_adaptive_function
     else:
         sampler_function = getattr(k_diffusion_sampling, "sample_{}".format(sampler_name))
 
     return KSAMPLER(sampler_function, extra_options, inpaint_options)
 
-def wrap_model(model):
-    model_denoise = CFGNoisePredictor(model)
-    return model_denoise
 
-def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
-    positive = positive[:]
-    negative = negative[:]
+def process_conds(model, noise, conds, device, latent_image=None, denoise_mask=None, seed=None):
+    for k in conds:
+        conds[k] = conds[k][:]
+        resolve_areas_and_cond_masks_multidim(conds[k], noise.shape[2:], device)
 
-    resolve_areas_and_cond_masks(positive, noise.shape[2], noise.shape[3], device)
-    resolve_areas_and_cond_masks(negative, noise.shape[2], noise.shape[3], device)
-
-    model_wrap = wrap_model(model)
-
-    calculate_start_end_timesteps(model, negative)
-    calculate_start_end_timesteps(model, positive)
-
-    if latent_image is not None:
-        latent_image = model.process_latent_in(latent_image)
+    for k in conds:
+        calculate_start_end_timesteps(model, conds[k])
 
     if hasattr(model, 'extra_conds'):
-        positive = encode_model_conds(model.extra_conds, positive, noise, device, "positive", latent_image=latent_image, denoise_mask=denoise_mask, seed=seed)
-        negative = encode_model_conds(model.extra_conds, negative, noise, device, "negative", latent_image=latent_image, denoise_mask=denoise_mask, seed=seed)
+        for k in conds:
+            conds[k] = encode_model_conds(model.extra_conds, conds[k], noise, device, k, latent_image=latent_image, denoise_mask=denoise_mask, seed=seed)
 
     #make sure each cond area has an opposite one with the same area
-    for c in positive:
-        create_cond_with_same_area_if_none(negative, c)
-    for c in negative:
-        create_cond_with_same_area_if_none(positive, c)
+    for k in conds:
+        for c in conds[k]:
+            for kk in conds:
+                if k != kk:
+                    create_cond_with_same_area_if_none(conds[kk], c)
 
-    pre_run_control(model, negative + positive)
+    for k in conds:
+        pre_run_control(model, conds[k])
 
-    apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), negative, 'control', lambda cond_cnets, x: cond_cnets[x])
-    apply_empty_x_to_equal_area(positive, negative, 'gligen', lambda cond_cnets, x: cond_cnets[x])
+    if "positive" in conds:
+        positive = conds["positive"]
+        for k in conds:
+            if k != "positive":
+                apply_empty_x_to_equal_area(list(filter(lambda c: c.get('control_apply_to_uncond', False) == True, positive)), conds[k], 'control', lambda cond_cnets, x: cond_cnets[x])
+                apply_empty_x_to_equal_area(positive, conds[k], 'gligen', lambda cond_cnets, x: cond_cnets[x])
 
-    extra_args = {"cond":positive, "uncond":negative, "cond_scale": cfg, "model_options": model_options, "seed":seed}
+    return conds
+
+class CFGGuider:
+    def __init__(self, model_patcher):
+        self.model_patcher = model_patcher
+        self.model_options = model_patcher.model_options
+        self.original_conds = {}
+        self.cfg = 1.0
+
+    def set_conds(self, positive, negative):
+        self.inner_set_conds({"positive": positive, "negative": negative})
+
+    def set_cfg(self, cfg):
+        self.cfg = cfg
+
+    def inner_set_conds(self, conds):
+        for k in conds:
+            self.original_conds[k] = comfy.sampler_helpers.convert_cond(conds[k])
+
+    def __call__(self, *args, **kwargs):
+        return self.predict_noise(*args, **kwargs)
+
+    def predict_noise(self, x, timestep, model_options={}, seed=None):
+        return sampling_function(self.inner_model, x, timestep, self.conds.get("negative", None), self.conds.get("positive", None), self.cfg, model_options=model_options, seed=seed)
+
+    def inner_sample(self, noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed):
+        if latent_image is not None and torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image.
+            latent_image = self.inner_model.process_latent_in(latent_image)
+
+        self.conds = process_conds(self.inner_model, noise, self.conds, device, latent_image, denoise_mask, seed)
+
+        extra_args = {"model_options": self.model_options, "seed":seed}
+
+        samples = sampler.sample(self, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)
+        return self.inner_model.process_latent_out(samples.to(torch.float32))
+
+    def sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
+        if sigmas.shape[-1] == 0:
+            return latent_image
+
+        self.conds = {}
+        for k in self.original_conds:
+            self.conds[k] = list(map(lambda a: a.copy(), self.original_conds[k]))
+
+        self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds)
+        device = self.model_patcher.load_device
+
+        if denoise_mask is not None:
+            denoise_mask = comfy.sampler_helpers.prepare_mask(denoise_mask, noise.shape, device)
+
+        noise = noise.to(device)
+        latent_image = latent_image.to(device)
+        sigmas = sigmas.to(device)
+
+        output = self.inner_sample(noise, latent_image, device, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
+
+        comfy.sampler_helpers.cleanup_models(self.conds, self.loaded_models)
+        del self.inner_model
+        del self.conds
+        del self.loaded_models
+        return output
+
+
+def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model_options={}, latent_image=None, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
+    cfg_guider = CFGGuider(model)
+    cfg_guider.set_conds(positive, negative)
+    cfg_guider.set_cfg(cfg)
+    return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
 
-    samples = sampler.sample(model_wrap, sigmas, extra_args, callback, noise, latent_image, denoise_mask, disable_pbar)
-    return model.process_latent_out(samples.to(torch.float32))
 
 SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform"]
 SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"]
 
-def calculate_sigmas_scheduler(model, scheduler_name, steps):
+def calculate_sigmas(model_sampling, scheduler_name, steps):
     if scheduler_name == "karras":
-        sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max))
+        sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max))
     elif scheduler_name == "exponential":
-        sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model.model_sampling.sigma_min), sigma_max=float(model.model_sampling.sigma_max))
+        sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max))
     elif scheduler_name == "normal":
-        sigmas = normal_scheduler(model, steps)
+        sigmas = normal_scheduler(model_sampling, steps)
     elif scheduler_name == "simple":
-        sigmas = simple_scheduler(model, steps)
+        sigmas = simple_scheduler(model_sampling, steps)
     elif scheduler_name == "ddim_uniform":
-        sigmas = ddim_scheduler(model, steps)
+        sigmas = ddim_scheduler(model_sampling, steps)
     elif scheduler_name == "sgm_uniform":
-        sigmas = normal_scheduler(model, steps, sgm=True)
+        sigmas = normal_scheduler(model_sampling, steps, sgm=True)
     else:
-        print("error invalid scheduler", scheduler_name)
+        logging.error("error invalid scheduler {}".format(scheduler_name))
     return sigmas
 
 def sampler_object(name):
     if name == "uni_pc":
-        sampler = UNIPC()
+        sampler = KSAMPLER(uni_pc.sample_unipc)
     elif name == "uni_pc_bh2":
-        sampler = UNIPCBH2()
+        sampler = KSAMPLER(uni_pc.sample_unipc_bh2)
     elif name == "ddim":
         sampler = ksampler("euler", inpaint_options={"random": True})
     else:
@@ -652,6 +731,7 @@ def sampler_object(name):
 class KSampler:
     SCHEDULERS = SCHEDULER_NAMES
     SAMPLERS = SAMPLER_NAMES
+    DISCARD_PENULTIMATE_SIGMA_SAMPLERS = set(('dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2'))
 
     def __init__(self, model, steps, device, sampler=None, scheduler=None, denoise=None, model_options={}):
         self.model = model
@@ -670,11 +750,11 @@ class KSampler:
         sigmas = None
 
         discard_penultimate_sigma = False
-        if self.sampler in ['dpm_2', 'dpm_2_ancestral', 'uni_pc', 'uni_pc_bh2']:
+        if self.sampler in self.DISCARD_PENULTIMATE_SIGMA_SAMPLERS:
             steps += 1
             discard_penultimate_sigma = True
 
-        sigmas = calculate_sigmas_scheduler(self.model, self.scheduler, steps)
+        sigmas = calculate_sigmas(self.model.get_model_object("model_sampling"), self.scheduler, steps)
 
         if discard_penultimate_sigma:
             sigmas = torch.cat([sigmas[:-2], sigmas[-1:]])
@@ -685,9 +765,12 @@ class KSampler:
         if denoise is None or denoise > 0.9999:
             self.sigmas = self.calculate_sigmas(steps).to(self.device)
         else:
-            new_steps = int(steps/denoise)
-            sigmas = self.calculate_sigmas(new_steps).to(self.device)
-            self.sigmas = sigmas[-(steps + 1):]
+            if denoise <= 0.0:
+                self.sigmas = torch.FloatTensor([])
+            else:
+                new_steps = int(steps/denoise)
+                sigmas = self.calculate_sigmas(new_steps).to(self.device)
+                self.sigmas = sigmas[-(steps + 1):]
 
     def sample(self, noise, positive, negative, cfg, latent_image=None, start_step=None, last_step=None, force_full_denoise=False, denoise_mask=None, sigmas=None, callback=None, disable_pbar=False, seed=None):
         if sigmas is None:
diff --git a/comfy/sd.py b/comfy/sd.py
index c15d73fe..b39230bd 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -1,7 +1,12 @@
 import torch
+from enum import Enum
+import logging
 
 from comfy import model_management
 from .ldm.models.autoencoder import AutoencoderKL, AutoencodingEngine
+from .ldm.cascade.stage_a import StageA
+from .ldm.cascade.stage_c_coder import StageC_coder
+from .ldm.audio.autoencoder import AudioOobleckVAE
 import yaml
 
 import comfy.utils
@@ -9,12 +14,13 @@ import comfy.utils
 from . import clip_vision
 from . import gligen
 from . import diffusers_convert
-from . import model_base
 from . import model_detection
 
 from . import sd1_clip
 from . import sd2_clip
 from . import sdxl_clip
+from . import sd3_clip
+from . import sa_t5
 
 import comfy.model_patcher
 import comfy.lora
@@ -33,7 +39,7 @@ def load_model_weights(model, sd):
             w = sd.pop(x)
             del w
     if len(m) > 0:
-        print("missing", m)
+        logging.warning("missing {}".format(m))
     return model
 
 def load_clip_weights(model, sd):
@@ -48,7 +54,7 @@ def load_clip_weights(model, sd):
         if ids.dtype == torch.float32:
             sd['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round()
 
-    sd = comfy.utils.transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.text_model.", 24)
+    sd = comfy.utils.clip_text_transformers_convert(sd, "cond_stage_model.model.", "cond_stage_model.transformer.")
     return load_model_weights(model, sd)
 
 
@@ -77,7 +83,7 @@ def load_lora_for_models(model, clip, lora, strength_model, strength_clip):
     k1 = set(k1)
     for x in loaded:
         if (x not in k) and (x not in k1):
-            print("NOT LOADED", x)
+            logging.warning("NOT LOADED {}".format(x))
 
     return (new_modelpatcher, new_clip)
 
@@ -93,13 +99,19 @@ class CLIP:
         load_device = model_management.text_encoder_device()
         offload_device = model_management.text_encoder_offload_device()
         params['device'] = offload_device
-        params['dtype'] = model_management.text_encoder_dtype(load_device)
+        dtype = model_management.text_encoder_dtype(load_device)
+        params['dtype'] = dtype
 
         self.cond_stage_model = clip(**(params))
 
+        for dt in self.cond_stage_model.dtypes:
+            if not model_management.supports_cast(load_device, dt):
+                load_device = offload_device
+
         self.tokenizer = tokenizer(embedding_directory=embedding_directory)
         self.patcher = comfy.model_patcher.ModelPatcher(self.cond_stage_model, load_device=load_device, offload_device=offload_device)
         self.layer_idx = None
+        logging.debug("CLIP model load device: {}, offload device: {}".format(load_device, offload_device))
 
     def clone(self):
         n = CLIP(no_init=True)
@@ -119,10 +131,13 @@ class CLIP:
         return self.tokenizer.tokenize_with_weights(text, return_word_ids)
 
     def encode_from_tokens(self, tokens, return_pooled=False):
+        self.cond_stage_model.reset_clip_options()
+
         if self.layer_idx is not None:
-            self.cond_stage_model.clip_layer(self.layer_idx)
-        else:
-            self.cond_stage_model.reset_clip_layer()
+            self.cond_stage_model.set_clip_options({"layer": self.layer_idx})
+
+        if return_pooled == "unprojected":
+            self.cond_stage_model.set_clip_options({"projected_pooled": False})
 
         self.load_model()
         cond, pooled = self.cond_stage_model.encode_token_weights(tokens)
@@ -134,8 +149,11 @@ class CLIP:
         tokens = self.tokenize(text)
         return self.encode_from_tokens(tokens)
 
-    def load_sd(self, sd):
-        return self.cond_stage_model.load_sd(sd)
+    def load_sd(self, sd, full_model=False):
+        if full_model:
+            return self.cond_stage_model.load_state_dict(sd, strict=False)
+        else:
+            return self.cond_stage_model.load_sd(sd)
 
     def get_sd(self):
         return self.cond_stage_model.state_dict()
@@ -155,7 +173,12 @@ class VAE:
         self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) #These are for AutoencoderKL and need tweaking (should be lower)
         self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype)
         self.downscale_ratio = 8
+        self.upscale_ratio = 8
         self.latent_channels = 4
+        self.output_channels = 3
+        self.process_input = lambda image: image * 2.0 - 1.0
+        self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
+        self.working_dtypes = [torch.bfloat16, torch.float32]
 
         if config is None:
             if "decoder.mid.block_1.mix_factor" in sd:
@@ -167,38 +190,99 @@ class VAE:
                                                             encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': encoder_config},
                                                             decoder_config={'target': "comfy.ldm.modules.temporal_ae.VideoDecoder", 'params': decoder_config})
             elif "taesd_decoder.1.weight" in sd:
-                self.first_stage_model = comfy.taesd.taesd.TAESD()
-            else:
+                self.latent_channels = sd["taesd_decoder.1.weight"].shape[1]
+                self.first_stage_model = comfy.taesd.taesd.TAESD(latent_channels=self.latent_channels)
+            elif "vquantizer.codebook.weight" in sd: #VQGan: stage a of stable cascade
+                self.first_stage_model = StageA()
+                self.downscale_ratio = 4
+                self.upscale_ratio = 4
+                #TODO
+                #self.memory_used_encode
+                #self.memory_used_decode
+                self.process_input = lambda image: image
+                self.process_output = lambda image: image
+            elif "backbone.1.0.block.0.1.num_batches_tracked" in sd: #effnet: encoder for stage c latent of stable cascade
+                self.first_stage_model = StageC_coder()
+                self.downscale_ratio = 32
+                self.latent_channels = 16
+                new_sd = {}
+                for k in sd:
+                    new_sd["encoder.{}".format(k)] = sd[k]
+                sd = new_sd
+            elif "blocks.11.num_batches_tracked" in sd: #previewer: decoder for stage c latent of stable cascade
+                self.first_stage_model = StageC_coder()
+                self.latent_channels = 16
+                new_sd = {}
+                for k in sd:
+                    new_sd["previewer.{}".format(k)] = sd[k]
+                sd = new_sd
+            elif "encoder.backbone.1.0.block.0.1.num_batches_tracked" in sd: #combined effnet and previewer for stable cascade
+                self.first_stage_model = StageC_coder()
+                self.downscale_ratio = 32
+                self.latent_channels = 16
+            elif "decoder.conv_in.weight" in sd:
                 #default SD1.x/SD2.x VAE parameters
                 ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}
 
-                if 'encoder.down.2.downsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE
+                if 'encoder.down.2.downsample.conv.weight' not in sd and 'decoder.up.3.upsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE
                     ddconfig['ch_mult'] = [1, 2, 4]
                     self.downscale_ratio = 4
+                    self.upscale_ratio = 4
 
-                self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=4)
+                self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1]
+                if 'quant_conv.weight' in sd:
+                    self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=4)
+                else:
+                    self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
+                                                                encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig},
+                                                                decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig})
+            elif "decoder.layers.1.layers.0.beta" in sd:
+                self.first_stage_model = AudioOobleckVAE()
+                self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype)
+                self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * 2048) * model_management.dtype_size(dtype)
+                self.latent_channels = 64
+                self.output_channels = 2
+                self.upscale_ratio = 2048
+                self.downscale_ratio =  2048
+                self.process_output = lambda audio: audio
+                self.process_input = lambda audio: audio
+                self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+            else:
+                logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
+                self.first_stage_model = None
+                return
         else:
             self.first_stage_model = AutoencoderKL(**(config['params']))
         self.first_stage_model = self.first_stage_model.eval()
 
         m, u = self.first_stage_model.load_state_dict(sd, strict=False)
         if len(m) > 0:
-            print("Missing VAE keys", m)
+            logging.warning("Missing VAE keys {}".format(m))
 
         if len(u) > 0:
-            print("Leftover VAE keys", u)
+            logging.debug("Leftover VAE keys {}".format(u))
 
         if device is None:
             device = model_management.vae_device()
         self.device = device
         offload_device = model_management.vae_offload_device()
         if dtype is None:
-            dtype = model_management.vae_dtype()
+            dtype = model_management.vae_dtype(self.device, self.working_dtypes)
         self.vae_dtype = dtype
         self.first_stage_model.to(self.vae_dtype)
         self.output_device = model_management.intermediate_device()
 
         self.patcher = comfy.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device)
+        logging.debug("VAE load device: {}, offload device: {}, dtype: {}".format(self.device, offload_device, self.vae_dtype))
+
+    def vae_encode_crop_pixels(self, pixels):
+        dims = pixels.shape[1:-1]
+        for d in range(len(dims)):
+            x = (dims[d] // self.downscale_ratio) * self.downscale_ratio
+            x_offset = (dims[d] % self.downscale_ratio) // 2
+            if x != dims[d]:
+                pixels = pixels.narrow(d + 1, x_offset, x)
+        return pixels
 
     def decode_tiled_(self, samples, tile_x=64, tile_y=64, overlap = 16):
         steps = samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x, tile_y, overlap)
@@ -206,27 +290,35 @@ class VAE:
         steps += samples.shape[0] * comfy.utils.get_tiled_scale_steps(samples.shape[3], samples.shape[2], tile_x * 2, tile_y // 2, overlap)
         pbar = comfy.utils.ProgressBar(steps)
 
-        decode_fn = lambda a: (self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)) + 1.0).float()
-        output = torch.clamp((
-            (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar) +
-            comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar) +
-             comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.downscale_ratio, output_device=self.output_device, pbar = pbar))
-            / 3.0) / 2.0, min=0.0, max=1.0)
+        decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
+        output = self.process_output(
+            (comfy.utils.tiled_scale(samples, decode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) +
+            comfy.utils.tiled_scale(samples, decode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar) +
+             comfy.utils.tiled_scale(samples, decode_fn, tile_x, tile_y, overlap, upscale_amount = self.upscale_ratio, output_device=self.output_device, pbar = pbar))
+            / 3.0)
         return output
 
+    def decode_tiled_1d(self, samples, tile_x=128, overlap=32):
+        decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
+        return comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device)
+
     def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
         steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
         steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x // 2, tile_y * 2, overlap)
         steps += pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x * 2, tile_y // 2, overlap)
         pbar = comfy.utils.ProgressBar(steps)
 
-        encode_fn = lambda a: self.first_stage_model.encode((2. * a - 1.).to(self.vae_dtype).to(self.device)).float()
+        encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
         samples = comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x, tile_y, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
         samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x * 2, tile_y // 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
         samples += comfy.utils.tiled_scale(pixel_samples, encode_fn, tile_x // 2, tile_y * 2, overlap, upscale_amount = (1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device, pbar=pbar)
         samples /= 3.0
         return samples
 
+    def encode_tiled_1d(self, samples, tile_x=128 * 2048, overlap=32 * 2048):
+        encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
+        return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=(1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device)
+
     def decode(self, samples_in):
         try:
             memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype)
@@ -235,13 +327,16 @@ class VAE:
             batch_number = int(free_memory / memory_used)
             batch_number = max(1, batch_number)
 
-            pixel_samples = torch.empty((samples_in.shape[0], 3, round(samples_in.shape[2] * self.downscale_ratio), round(samples_in.shape[3] * self.downscale_ratio)), device=self.output_device)
+            pixel_samples = torch.empty((samples_in.shape[0], self.output_channels) + tuple(map(lambda a: a * self.upscale_ratio, samples_in.shape[2:])), device=self.output_device)
             for x in range(0, samples_in.shape[0], batch_number):
                 samples = samples_in[x:x+batch_number].to(self.vae_dtype).to(self.device)
-                pixel_samples[x:x+batch_number] = torch.clamp((self.first_stage_model.decode(samples).to(self.output_device).float() + 1.0) / 2.0, min=0.0, max=1.0)
+                pixel_samples[x:x+batch_number] = self.process_output(self.first_stage_model.decode(samples).to(self.output_device).float())
         except model_management.OOM_EXCEPTION as e:
-            print("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
-            pixel_samples = self.decode_tiled_(samples_in)
+            logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
+            if len(samples_in.shape) == 3:
+                pixel_samples = self.decode_tiled_1d(samples_in)
+            else:
+                pixel_samples = self.decode_tiled_(samples_in)
 
         pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1)
         return pixel_samples
@@ -252,6 +347,7 @@ class VAE:
         return output.movedim(1,-1)
 
     def encode(self, pixel_samples):
+        pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
         pixel_samples = pixel_samples.movedim(-1,1)
         try:
             memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype)
@@ -259,18 +355,22 @@ class VAE:
             free_memory = model_management.get_free_memory(self.device)
             batch_number = int(free_memory / memory_used)
             batch_number = max(1, batch_number)
-            samples = torch.empty((pixel_samples.shape[0], self.latent_channels, round(pixel_samples.shape[2] // self.downscale_ratio), round(pixel_samples.shape[3] // self.downscale_ratio)), device=self.output_device)
+            samples = torch.empty((pixel_samples.shape[0], self.latent_channels) + tuple(map(lambda a: a // self.downscale_ratio, pixel_samples.shape[2:])), device=self.output_device)
             for x in range(0, pixel_samples.shape[0], batch_number):
-                pixels_in = (2. * pixel_samples[x:x+batch_number] - 1.).to(self.vae_dtype).to(self.device)
+                pixels_in = self.process_input(pixel_samples[x:x+batch_number]).to(self.vae_dtype).to(self.device)
                 samples[x:x+batch_number] = self.first_stage_model.encode(pixels_in).to(self.output_device).float()
 
         except model_management.OOM_EXCEPTION as e:
-            print("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
-            samples = self.encode_tiled_(pixel_samples)
+            logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
+            if len(pixel_samples.shape) == 3:
+                samples = self.encode_tiled_1d(pixel_samples)
+            else:
+                samples = self.encode_tiled_(pixel_samples)
 
         return samples
 
     def encode_tiled(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
+        pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
         model_management.load_model_gpu(self.patcher)
         pixel_samples = pixel_samples.movedim(-1,1)
         samples = self.encode_tiled_(pixel_samples, tile_x=tile_x, tile_y=tile_y, overlap=overlap)
@@ -297,8 +397,13 @@ def load_style_model(ckpt_path):
     model.load_state_dict(model_data)
     return StyleModel(model)
 
+class CLIPType(Enum):
+    STABLE_DIFFUSION = 1
+    STABLE_CASCADE = 2
+    SD3 = 3
+    STABLE_AUDIO = 4
 
-def load_clip(ckpt_paths, embedding_directory=None):
+def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION):
     clip_data = []
     for p in ckpt_paths:
         clip_data.append(comfy.utils.load_torch_file(p, safe_load=True))
@@ -308,32 +413,55 @@ def load_clip(ckpt_paths, embedding_directory=None):
 
     for i in range(len(clip_data)):
         if "transformer.resblocks.0.ln_1.weight" in clip_data[i]:
-            clip_data[i] = comfy.utils.transformers_convert(clip_data[i], "", "text_model.", 32)
+            clip_data[i] = comfy.utils.clip_text_transformers_convert(clip_data[i], "", "")
+        else:
+            if "text_projection" in clip_data[i]:
+                clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) #old models saved with the CLIPSave node
 
     clip_target = EmptyClass()
     clip_target.params = {}
     if len(clip_data) == 1:
         if "text_model.encoder.layers.30.mlp.fc1.weight" in clip_data[0]:
-            clip_target.clip = sdxl_clip.SDXLRefinerClipModel
-            clip_target.tokenizer = sdxl_clip.SDXLTokenizer
+            if clip_type == CLIPType.STABLE_CASCADE:
+                clip_target.clip = sdxl_clip.StableCascadeClipModel
+                clip_target.tokenizer = sdxl_clip.StableCascadeTokenizer
+            else:
+                clip_target.clip = sdxl_clip.SDXLRefinerClipModel
+                clip_target.tokenizer = sdxl_clip.SDXLTokenizer
         elif "text_model.encoder.layers.22.mlp.fc1.weight" in clip_data[0]:
             clip_target.clip = sd2_clip.SD2ClipModel
             clip_target.tokenizer = sd2_clip.SD2Tokenizer
+        elif "encoder.block.23.layer.1.DenseReluDense.wi_1.weight" in clip_data[0]:
+            weight = clip_data[0]["encoder.block.23.layer.1.DenseReluDense.wi_1.weight"]
+            dtype_t5 = weight.dtype
+            if weight.shape[-1] == 4096:
+                clip_target.clip = sd3_clip.sd3_clip(clip_l=False, clip_g=False, t5=True, dtype_t5=dtype_t5)
+                clip_target.tokenizer = sd3_clip.SD3Tokenizer
+        elif "encoder.block.0.layer.0.SelfAttention.k.weight" in clip_data[0]:
+            clip_target.clip = sa_t5.SAT5Model
+            clip_target.tokenizer = sa_t5.SAT5Tokenizer
         else:
             clip_target.clip = sd1_clip.SD1ClipModel
             clip_target.tokenizer = sd1_clip.SD1Tokenizer
-    else:
-        clip_target.clip = sdxl_clip.SDXLClipModel
-        clip_target.tokenizer = sdxl_clip.SDXLTokenizer
+    elif len(clip_data) == 2:
+        if clip_type == CLIPType.SD3:
+            clip_target.clip = sd3_clip.sd3_clip(clip_l=True, clip_g=True, t5=False)
+            clip_target.tokenizer = sd3_clip.SD3Tokenizer
+        else:
+            clip_target.clip = sdxl_clip.SDXLClipModel
+            clip_target.tokenizer = sdxl_clip.SDXLTokenizer
+    elif len(clip_data) == 3:
+        clip_target.clip = sd3_clip.SD3ClipModel
+        clip_target.tokenizer = sd3_clip.SD3Tokenizer
 
     clip = CLIP(clip_target, embedding_directory=embedding_directory)
     for c in clip_data:
         m, u = clip.load_sd(c)
         if len(m) > 0:
-            print("clip missing:", m)
+            logging.warning("clip missing: {}".format(m))
 
         if len(u) > 0:
-            print("clip unexpected:", u)
+            logging.debug("clip unexpected: {}".format(u))
     return clip
 
 def load_gligen(ckpt_path):
@@ -344,6 +472,8 @@ def load_gligen(ckpt_path):
     return comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=model_management.unet_offload_device())
 
 def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_clip=True, embedding_directory=None, state_dict=None, config=None):
+    logging.warning("Warning: The load checkpoint with config function is deprecated and will eventually be removed, please use the other one.")
+    model, clip, vae, _ = load_checkpoint_guess_config(ckpt_path, output_vae=output_vae, output_clip=output_clip, output_clipvision=False, embedding_directory=embedding_directory, output_model=True)
     #TODO: this function is a mess and should be removed eventually
     if config is None:
         with open(config_path, 'r') as stream:
@@ -351,81 +481,20 @@ def load_checkpoint(config_path=None, ckpt_path=None, output_vae=True, output_cl
     model_config_params = config['model']['params']
     clip_config = model_config_params['cond_stage_config']
     scale_factor = model_config_params['scale_factor']
-    vae_config = model_config_params['first_stage_config']
-
-    fp16 = False
-    if "unet_config" in model_config_params:
-        if "params" in model_config_params["unet_config"]:
-            unet_config = model_config_params["unet_config"]["params"]
-            if "use_fp16" in unet_config:
-                fp16 = unet_config.pop("use_fp16")
-                if fp16:
-                    unet_config["dtype"] = torch.float16
-
-    noise_aug_config = None
-    if "noise_aug_config" in model_config_params:
-        noise_aug_config = model_config_params["noise_aug_config"]
-
-    model_type = model_base.ModelType.EPS
 
     if "parameterization" in model_config_params:
         if model_config_params["parameterization"] == "v":
-            model_type = model_base.ModelType.V_PREDICTION
+            m = model.clone()
+            class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingDiscrete, comfy.model_sampling.V_PREDICTION):
+                pass
+            m.add_object_patch("model_sampling", ModelSamplingAdvanced(model.model.model_config))
+            model = m
 
-    clip = None
-    vae = None
+    layer_idx = clip_config.get("params", {}).get("layer_idx", None)
+    if layer_idx is not None:
+        clip.clip_layer(layer_idx)
 
-    class WeightsLoader(torch.nn.Module):
-        pass
-
-    if state_dict is None:
-        state_dict = comfy.utils.load_torch_file(ckpt_path)
-
-    class EmptyClass:
-        pass
-
-    model_config = comfy.supported_models_base.BASE({})
-
-    from . import latent_formats
-    model_config.latent_format = latent_formats.SD15(scale_factor=scale_factor)
-    model_config.unet_config = model_detection.convert_config(unet_config)
-
-    if config['model']["target"].endswith("ImageEmbeddingConditionedLatentDiffusion"):
-        model = model_base.SD21UNCLIP(model_config, noise_aug_config["params"], model_type=model_type)
-    else:
-        model = model_base.BaseModel(model_config, model_type=model_type)
-
-    if config['model']["target"].endswith("LatentInpaintDiffusion"):
-        model.set_inpaint()
-
-    if fp16:
-        model = model.half()
-
-    offload_device = model_management.unet_offload_device()
-    model = model.to(offload_device)
-    model.load_model_weights(state_dict, "model.diffusion_model.")
-
-    if output_vae:
-        vae_sd = comfy.utils.state_dict_prefix_replace(state_dict, {"first_stage_model.": ""}, filter_keys=True)
-        vae = VAE(sd=vae_sd, config=vae_config)
-
-    if output_clip:
-        w = WeightsLoader()
-        clip_target = EmptyClass()
-        clip_target.params = clip_config.get("params", {})
-        if clip_config["target"].endswith("FrozenOpenCLIPEmbedder"):
-            clip_target.clip = sd2_clip.SD2ClipModel
-            clip_target.tokenizer = sd2_clip.SD2Tokenizer
-            clip = CLIP(clip_target, embedding_directory=embedding_directory)
-            w.cond_stage_model = clip.cond_stage_model.clip_h
-        elif clip_config["target"].endswith("FrozenCLIPEmbedder"):
-            clip_target.clip = sd1_clip.SD1ClipModel
-            clip_target.tokenizer = sd1_clip.SD1Tokenizer
-            clip = CLIP(clip_target, embedding_directory=embedding_directory)
-            w.cond_stage_model = clip.cond_stage_model.clip_l
-        load_clip_weights(w, state_dict)
-
-    return (comfy.model_patcher.ModelPatcher(model, load_device=model_management.get_torch_device(), offload_device=offload_device), clip, vae)
+    return (model, clip, vae)
 
 def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, output_clipvision=False, embedding_directory=None, output_model=True):
     sd = comfy.utils.load_torch_file(ckpt_path)
@@ -437,16 +506,14 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
     model_patcher = None
     clip_target = None
 
-    parameters = comfy.utils.calculate_parameters(sd, "model.diffusion_model.")
-    unet_dtype = model_management.unet_dtype(model_params=parameters)
+    diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd)
+    parameters = comfy.utils.calculate_parameters(sd, diffusion_model_prefix)
     load_device = model_management.get_torch_device()
-    manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device)
 
-    class WeightsLoader(torch.nn.Module):
-        pass
-
-    model_config = model_detection.model_config_from_unet(sd, "model.diffusion_model.", unet_dtype)
-    model_config.set_manual_cast(manual_cast_dtype)
+    model_config = model_detection.model_config_from_unet(sd, diffusion_model_prefix)
+    unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
+    manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
+    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
 
     if model_config is None:
         raise RuntimeError("ERROR: Could not detect model type of: {}".format(ckpt_path))
@@ -458,8 +525,8 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
     if output_model:
         inital_load_device = model_management.unet_inital_load_device(parameters, unet_dtype)
         offload_device = model_management.unet_offload_device()
-        model = model_config.get_model(sd, "model.diffusion_model.", device=inital_load_device)
-        model.load_model_weights(sd, "model.diffusion_model.")
+        model = model_config.get_model(sd, diffusion_model_prefix, device=inital_load_device)
+        model.load_model_weights(sd, diffusion_model_prefix)
 
     if output_vae:
         vae_sd = comfy.utils.state_dict_prefix_replace(sd, {k: "" for k in model_config.vae_key_prefix}, filter_keys=True)
@@ -467,41 +534,65 @@ def load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, o
         vae = VAE(sd=vae_sd)
 
     if output_clip:
-        w = WeightsLoader()
-        clip_target = model_config.clip_target()
+        clip_target = model_config.clip_target(state_dict=sd)
         if clip_target is not None:
-            clip = CLIP(clip_target, embedding_directory=embedding_directory)
-            w.cond_stage_model = clip.cond_stage_model
-            sd = model_config.process_clip_state_dict(sd)
-            load_model_weights(w, sd)
+            clip_sd = model_config.process_clip_state_dict(sd)
+            if len(clip_sd) > 0:
+                clip = CLIP(clip_target, embedding_directory=embedding_directory)
+                m, u = clip.load_sd(clip_sd, full_model=True)
+                if len(m) > 0:
+                    m_filter = list(filter(lambda a: ".logit_scale" not in a and ".transformer.text_projection.weight" not in a, m))
+                    if len(m_filter) > 0:
+                        logging.warning("clip missing: {}".format(m))
+                    else:
+                        logging.debug("clip missing: {}".format(m))
+
+                if len(u) > 0:
+                    logging.debug("clip unexpected {}:".format(u))
+            else:
+                logging.warning("no CLIP/text encoder weights in checkpoint, the text encoder model will not be loaded.")
 
     left_over = sd.keys()
     if len(left_over) > 0:
-        print("left over keys:", left_over)
+        logging.debug("left over keys: {}".format(left_over))
 
     if output_model:
         model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device(), current_device=inital_load_device)
         if inital_load_device != torch.device("cpu"):
-            print("loaded straight to GPU")
+            logging.info("loaded straight to GPU")
             model_management.load_model_gpu(model_patcher)
 
     return (model_patcher, clip, vae, clipvision)
 
 
-def load_unet_state_dict(sd): #load unet in diffusers format
+def load_unet_state_dict(sd): #load unet in diffusers or regular format
+
+    #Allow loading unets from checkpoint files
+    checkpoint = False
+    diffusion_model_prefix = model_detection.unet_prefix_from_state_dict(sd)
+    temp_sd = comfy.utils.state_dict_prefix_replace(sd, {diffusion_model_prefix: ""}, filter_keys=True)
+    if len(temp_sd) > 0:
+        sd = temp_sd
+        checkpoint = True
+
     parameters = comfy.utils.calculate_parameters(sd)
     unet_dtype = model_management.unet_dtype(model_params=parameters)
     load_device = model_management.get_torch_device()
-    manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device)
 
-    if "input_blocks.0.0.weight" in sd: #ldm
-        model_config = model_detection.model_config_from_unet(sd, "", unet_dtype)
+    if checkpoint or "input_blocks.0.0.weight" in sd or 'clf.1.weight' in sd: #ldm or stable cascade
+        model_config = model_detection.model_config_from_unet(sd, "")
         if model_config is None:
             return None
         new_sd = sd
-
+    elif 'transformer_blocks.0.attn.add_q_proj.weight' in sd: #MMDIT SD3
+        new_sd = model_detection.convert_diffusers_mmdit(sd, "")
+        if new_sd is None:
+            return None
+        model_config = model_detection.model_config_from_unet(new_sd, "")
+        if model_config is None:
+            return None
     else: #diffusers
-        model_config = model_detection.model_config_from_diffusers_unet(sd, unet_dtype)
+        model_config = model_detection.model_config_from_diffusers_unet(sd)
         if model_config is None:
             return None
 
@@ -512,33 +603,44 @@ def load_unet_state_dict(sd): #load unet in diffusers format
             if k in sd:
                 new_sd[diffusers_keys[k]] = sd.pop(k)
             else:
-                print(diffusers_keys[k], k)
+                logging.warning("{} {}".format(diffusers_keys[k], k))
+
     offload_device = model_management.unet_offload_device()
-    model_config.set_manual_cast(manual_cast_dtype)
+    unet_dtype = model_management.unet_dtype(model_params=parameters, supported_dtypes=model_config.supported_inference_dtypes)
+    manual_cast_dtype = model_management.unet_manual_cast(unet_dtype, load_device, model_config.supported_inference_dtypes)
+    model_config.set_inference_dtype(unet_dtype, manual_cast_dtype)
     model = model_config.get_model(new_sd, "")
     model = model.to(offload_device)
     model.load_model_weights(new_sd, "")
     left_over = sd.keys()
     if len(left_over) > 0:
-        print("left over keys in unet:", left_over)
+        logging.info("left over keys in unet: {}".format(left_over))
     return comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=offload_device)
 
 def load_unet(unet_path):
     sd = comfy.utils.load_torch_file(unet_path)
     model = load_unet_state_dict(sd)
     if model is None:
-        print("ERROR UNSUPPORTED UNET", unet_path)
+        logging.error("ERROR UNSUPPORTED UNET {}".format(unet_path))
         raise RuntimeError("ERROR: Could not detect model type of: {}".format(unet_path))
     return model
 
-def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, metadata=None):
+def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, metadata=None, extra_keys={}):
     clip_sd = None
     load_models = [model]
     if clip is not None:
         load_models.append(clip.load_model())
         clip_sd = clip.get_sd()
 
-    model_management.load_models_gpu(load_models)
+    model_management.load_models_gpu(load_models, force_patch_weights=True)
     clip_vision_sd = clip_vision.get_sd() if clip_vision is not None else None
     sd = model.model.state_dict_for_saving(clip_sd, vae.get_sd(), clip_vision_sd)
+    for k in extra_keys:
+        sd[k] = extra_keys[k]
+
+    for k in sd:
+        t = sd[k]
+        if not t.is_contiguous():
+            sd[k] = t.contiguous()
+
     comfy.utils.save_torch_file(sd, output_path, metadata=metadata)
diff --git a/comfy/sd1_clip.py b/comfy/sd1_clip.py
index 65ea909f..0fe1f1d1 100644
--- a/comfy/sd1_clip.py
+++ b/comfy/sd1_clip.py
@@ -8,6 +8,8 @@ import zipfile
 from . import model_management
 import comfy.clip_model
 import json
+import logging
+import numbers
 
 def gen_empty_tokens(special_tokens, length):
     start_token = special_tokens.get("start", None)
@@ -67,7 +69,8 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
     ]
     def __init__(self, version="openai/clip-vit-large-patch14", device="cpu", max_length=77,
                  freeze=True, layer="last", layer_idx=None, textmodel_json_config=None, dtype=None, model_class=comfy.clip_model.CLIPTextModel,
-                 special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True):  # clip-vit-base-patch32
+                 special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=True, enable_attention_masks=False, zero_out_masked=False,
+                 return_projected_pooled=True):  # clip-vit-base-patch32
         super().__init__()
         assert layer in self.LAYERS
 
@@ -86,16 +89,19 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         self.layer = layer
         self.layer_idx = None
         self.special_tokens = special_tokens
-        self.text_projection = torch.nn.Parameter(torch.eye(self.transformer.get_input_embeddings().weight.shape[1]))
+
         self.logit_scale = torch.nn.Parameter(torch.tensor(4.6055))
-        self.enable_attention_masks = False
+        self.enable_attention_masks = enable_attention_masks
+        self.zero_out_masked = zero_out_masked
 
         self.layer_norm_hidden_state = layer_norm_hidden_state
+        self.return_projected_pooled = return_projected_pooled
+
         if layer == "hidden":
             assert layer_idx is not None
             assert abs(layer_idx) < self.num_layers
-            self.clip_layer(layer_idx)
-        self.layer_default = (self.layer, self.layer_idx)
+            self.set_clip_options({"layer": layer_idx})
+        self.options_default = (self.layer, self.layer_idx, self.return_projected_pooled)
 
     def freeze(self):
         self.transformer = self.transformer.eval()
@@ -103,16 +109,19 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         for param in self.parameters():
             param.requires_grad = False
 
-    def clip_layer(self, layer_idx):
-        if abs(layer_idx) > self.num_layers:
+    def set_clip_options(self, options):
+        layer_idx = options.get("layer", self.layer_idx)
+        self.return_projected_pooled = options.get("projected_pooled", self.return_projected_pooled)
+        if layer_idx is None or abs(layer_idx) > self.num_layers:
             self.layer = "last"
         else:
             self.layer = "hidden"
             self.layer_idx = layer_idx
 
-    def reset_clip_layer(self):
-        self.layer = self.layer_default[0]
-        self.layer_idx = self.layer_default[1]
+    def reset_clip_options(self):
+        self.layer = self.options_default[0]
+        self.layer_idx = self.options_default[1]
+        self.return_projected_pooled = self.options_default[2]
 
     def set_up_textual_embeddings(self, tokens, current_embeds):
         out_tokens = []
@@ -122,17 +131,17 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         for x in tokens:
             tokens_temp = []
             for y in x:
-                if isinstance(y, int):
+                if isinstance(y, numbers.Integral):
                     if y == token_dict_size: #EOS token
                         y = -1
-                    tokens_temp += [y]
+                    tokens_temp += [int(y)]
                 else:
                     if y.shape[0] == current_embeds.weight.shape[1]:
                         embedding_weights += [y]
                         tokens_temp += [next_new_token]
                         next_new_token += 1
                     else:
-                        print("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored", y.shape[0], current_embeds.weight.shape[1])
+                        logging.warning("WARNING: shape mismatch when trying to apply embedding, embedding will be ignored {} != {}".format(y.shape[0], current_embeds.weight.shape[1]))
             while len(tokens_temp) < len(x):
                 tokens_temp += [self.special_tokens["pad"]]
             out_tokens += [tokens_temp]
@@ -160,40 +169,43 @@ class SDClipModel(torch.nn.Module, ClipTokenWeightEncoder):
         tokens = torch.LongTensor(tokens).to(device)
 
         attention_mask = None
-        if self.enable_attention_masks:
+        if self.enable_attention_masks or self.zero_out_masked:
             attention_mask = torch.zeros_like(tokens)
-            max_token = self.transformer.get_input_embeddings().weight.shape[0] - 1
+            end_token = self.special_tokens.get("end", -1)
             for x in range(attention_mask.shape[0]):
                 for y in range(attention_mask.shape[1]):
                     attention_mask[x, y] = 1
-                    if tokens[x, y] == max_token:
+                    if tokens[x, y] == end_token:
                         break
 
-        outputs = self.transformer(tokens, attention_mask, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state)
+        attention_mask_model = None
+        if self.enable_attention_masks:
+            attention_mask_model = attention_mask
+
+        outputs = self.transformer(tokens, attention_mask_model, intermediate_output=self.layer_idx, final_layer_norm_intermediate=self.layer_norm_hidden_state)
         self.transformer.set_input_embeddings(backup_embeds)
 
         if self.layer == "last":
-            z = outputs[0]
+            z = outputs[0].float()
         else:
-            z = outputs[1]
+            z = outputs[1].float()
 
-        if outputs[2] is not None:
-            pooled_output = outputs[2].float()
-        else:
-            pooled_output = None
+        if self.zero_out_masked:
+            z *= attention_mask.unsqueeze(-1).float()
 
-        if self.text_projection is not None and pooled_output is not None:
-            pooled_output = pooled_output.float().to(self.text_projection.device) @ self.text_projection.float()
-        return z.float(), pooled_output
+        pooled_output = None
+        if len(outputs) >= 3:
+            if not self.return_projected_pooled and len(outputs) >= 4 and outputs[3] is not None:
+                pooled_output = outputs[3].float()
+            elif outputs[2] is not None:
+                pooled_output = outputs[2].float()
+
+        return z, pooled_output
 
     def encode(self, tokens):
         return self(tokens)
 
     def load_sd(self, sd):
-        if "text_projection" in sd:
-            self.text_projection[:] = sd.pop("text_projection")
-        if "text_projection.weight" in sd:
-            self.text_projection[:] = sd.pop("text_projection.weight").transpose(0, 1)
         return self.transformer.load_state_dict(sd, strict=False)
 
 def parse_parentheses(string):
@@ -328,9 +340,7 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No
             else:
                 embed = torch.load(embed_path, map_location="cpu")
     except Exception as e:
-        print(traceback.format_exc())
-        print()
-        print("error loading embedding, skipping loading:", embedding_name)
+        logging.warning("{}\n\nerror loading embedding, skipping loading: {}".format(traceback.format_exc(), embedding_name))
         return None
 
     if embed_out is None:
@@ -354,11 +364,12 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No
     return embed_out
 
 class SDTokenizer:
-    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, pad_to_max_length=True):
+    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, pad_to_max_length=True, min_length=None, pad_token=None):
         if tokenizer_path is None:
             tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer")
         self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path)
         self.max_length = max_length
+        self.min_length = min_length
 
         empty = self.tokenizer('')["input_ids"]
         if has_start_token:
@@ -369,6 +380,14 @@ class SDTokenizer:
             self.tokens_start = 0
             self.start_token = None
             self.end_token = empty[0]
+
+        if pad_token is not None:
+            self.pad_token = pad_token
+        elif pad_with_end:
+            self.pad_token = self.end_token
+        else:
+            self.pad_token = 0
+
         self.pad_with_end = pad_with_end
         self.pad_to_max_length = pad_to_max_length
 
@@ -401,10 +420,6 @@ class SDTokenizer:
         Word id values are unique per word and embedding, where the id 0 is reserved for non word tokens.
         Returned list has the dimensions NxM where M is the input size of CLIP
         '''
-        if self.pad_with_end:
-            pad_token = self.end_token
-        else:
-            pad_token = 0
 
         text = escape_important(text)
         parsed_weights = token_weights(text, 1.0)
@@ -420,7 +435,7 @@ class SDTokenizer:
                     embedding_name = word[len(self.embedding_identifier):].strip('\n')
                     embed, leftover = self._try_get_embedding(embedding_name)
                     if embed is None:
-                        print(f"warning, embedding:{embedding_name} does not exist, ignoring")
+                        logging.warning(f"warning, embedding:{embedding_name} does not exist, ignoring")
                     else:
                         if len(embed.shape) == 1:
                             tokens.append([(embed, weight)])
@@ -456,7 +471,7 @@ class SDTokenizer:
                     else:
                         batch.append((self.end_token, 1.0, 0))
                         if self.pad_to_max_length:
-                            batch.extend([(pad_token, 1.0, 0)] * (remaining_length))
+                            batch.extend([(self.pad_token, 1.0, 0)] * (remaining_length))
                     #start new batch
                     batch = []
                     if self.start_token is not None:
@@ -469,7 +484,9 @@ class SDTokenizer:
         #fill last batch
         batch.append((self.end_token, 1.0, 0))
         if self.pad_to_max_length:
-            batch.extend([(pad_token, 1.0, 0)] * (self.max_length - len(batch)))
+            batch.extend([(self.pad_token, 1.0, 0)] * (self.max_length - len(batch)))
+        if self.min_length is not None and len(batch) < self.min_length:
+            batch.extend([(self.pad_token, 1.0, 0)] * (self.min_length - len(batch)))
 
         if not return_word_ids:
             batched_tokens = [[(t, w) for t, w,_ in x] for x in batched_tokens]
@@ -497,17 +514,27 @@ class SD1Tokenizer:
 
 
 class SD1ClipModel(torch.nn.Module):
-    def __init__(self, device="cpu", dtype=None, clip_name="l", clip_model=SDClipModel, **kwargs):
+    def __init__(self, device="cpu", dtype=None, clip_name="l", clip_model=SDClipModel, name=None, **kwargs):
         super().__init__()
-        self.clip_name = clip_name
-        self.clip = "clip_{}".format(self.clip_name)
+
+        if name is not None:
+            self.clip_name = name
+            self.clip = "{}".format(self.clip_name)
+        else:
+            self.clip_name = clip_name
+            self.clip = "clip_{}".format(self.clip_name)
+
         setattr(self, self.clip, clip_model(device=device, dtype=dtype, **kwargs))
 
-    def clip_layer(self, layer_idx):
-        getattr(self, self.clip).clip_layer(layer_idx)
+        self.dtypes = set()
+        if dtype is not None:
+            self.dtypes.add(dtype)
 
-    def reset_clip_layer(self):
-        getattr(self, self.clip).reset_clip_layer()
+    def set_clip_options(self, options):
+        getattr(self, self.clip).set_clip_options(options)
+
+    def reset_clip_options(self):
+        getattr(self, self.clip).reset_clip_options()
 
     def encode_token_weights(self, token_weight_pairs):
         token_weight_pairs = token_weight_pairs[self.clip_name]
diff --git a/comfy/sd2_clip.py b/comfy/sd2_clip.py
index 9c878d54..d14b4454 100644
--- a/comfy/sd2_clip.py
+++ b/comfy/sd2_clip.py
@@ -1,5 +1,4 @@
 from comfy import sd1_clip
-import torch
 import os
 
 class SD2ClipHModel(sd1_clip.SDClipModel):
diff --git a/comfy/sd3_clip.py b/comfy/sd3_clip.py
new file mode 100644
index 00000000..0713eb28
--- /dev/null
+++ b/comfy/sd3_clip.py
@@ -0,0 +1,150 @@
+from comfy import sd1_clip
+from comfy import sdxl_clip
+from transformers import T5TokenizerFast
+import comfy.t5
+import torch
+import os
+import comfy.model_management
+import logging
+
+class T5XXLModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None):
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_config_xxl.json")
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype, special_tokens={"end": 1, "pad": 0}, model_class=comfy.t5.T5)
+
+class T5XXLTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_size=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=77)
+
+class SDT5XXLTokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None):
+        super().__init__(embedding_directory=embedding_directory, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
+
+class SDT5XXLModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, **kwargs):
+        super().__init__(device=device, dtype=dtype, clip_name="t5xxl", clip_model=T5XXLModel, **kwargs)
+
+
+
+class SD3Tokenizer:
+    def __init__(self, embedding_directory=None):
+        self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory)
+        self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory)
+        self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory)
+
+    def tokenize_with_weights(self, text:str, return_word_ids=False):
+        out = {}
+        out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids)
+        out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
+        out["t5xxl"] = self.t5xxl.tokenize_with_weights(text, return_word_ids)
+        return out
+
+    def untokenize(self, token_weight_pair):
+        return self.clip_g.untokenize(token_weight_pair)
+
+class SD3ClipModel(torch.nn.Module):
+    def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, device="cpu", dtype=None):
+        super().__init__()
+        self.dtypes = set()
+        if clip_l:
+            self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False, return_projected_pooled=False)
+            self.dtypes.add(dtype)
+        else:
+            self.clip_l = None
+
+        if clip_g:
+            self.clip_g = sdxl_clip.SDXLClipG(device=device, dtype=dtype)
+            self.dtypes.add(dtype)
+        else:
+            self.clip_g = None
+
+        if t5:
+            if dtype_t5 is None:
+                dtype_t5 = dtype
+            elif comfy.model_management.dtype_size(dtype_t5) > comfy.model_management.dtype_size(dtype):
+                dtype_t5 = dtype
+
+            if not comfy.model_management.supports_cast(device, dtype_t5):
+                dtype_t5 = dtype
+
+            self.t5xxl = T5XXLModel(device=device, dtype=dtype_t5)
+            self.dtypes.add(dtype_t5)
+        else:
+            self.t5xxl = None
+
+        logging.debug("Created SD3 text encoder with: clip_l {}, clip_g {}, t5xxl {}:{}".format(clip_l, clip_g, t5, dtype_t5))
+
+    def set_clip_options(self, options):
+        if self.clip_l is not None:
+            self.clip_l.set_clip_options(options)
+        if self.clip_g is not None:
+            self.clip_g.set_clip_options(options)
+        if self.t5xxl is not None:
+            self.t5xxl.set_clip_options(options)
+
+    def reset_clip_options(self):
+        if self.clip_l is not None:
+            self.clip_l.reset_clip_options()
+        if self.clip_g is not None:
+            self.clip_g.reset_clip_options()
+        if self.t5xxl is not None:
+            self.t5xxl.reset_clip_options()
+
+    def encode_token_weights(self, token_weight_pairs):
+        token_weight_pairs_l = token_weight_pairs["l"]
+        token_weight_pairs_g = token_weight_pairs["g"]
+        token_weight_pars_t5 = token_weight_pairs["t5xxl"]
+        lg_out = None
+        pooled = None
+        out = None
+
+        if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0:
+            if self.clip_l is not None:
+                lg_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
+            else:
+                l_pooled = torch.zeros((1, 768), device=comfy.model_management.intermediate_device())
+
+            if self.clip_g is not None:
+                g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
+                if lg_out is not None:
+                    lg_out = torch.cat([lg_out, g_out], dim=-1)
+                else:
+                    lg_out = torch.nn.functional.pad(g_out, (768, 0))
+            else:
+                g_out = None
+                g_pooled = torch.zeros((1, 1280), device=comfy.model_management.intermediate_device())
+
+            if lg_out is not None:
+                lg_out = torch.nn.functional.pad(lg_out, (0, 4096 - lg_out.shape[-1]))
+                out = lg_out
+            pooled = torch.cat((l_pooled, g_pooled), dim=-1)
+
+        if self.t5xxl is not None:
+            t5_out, t5_pooled = self.t5xxl.encode_token_weights(token_weight_pars_t5)
+            if lg_out is not None:
+                out = torch.cat([lg_out, t5_out], dim=-2)
+            else:
+                out = t5_out
+
+        if out is None:
+            out = torch.zeros((1, 77, 4096), device=comfy.model_management.intermediate_device())
+
+        if pooled is None:
+            pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device())
+
+        return out, pooled
+
+    def load_sd(self, sd):
+        if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
+            return self.clip_g.load_sd(sd)
+        elif "text_model.encoder.layers.1.mlp.fc1.weight" in sd:
+            return self.clip_l.load_sd(sd)
+        else:
+            return self.t5xxl.load_sd(sd)
+
+def sd3_clip(clip_l=True, clip_g=True, t5=True, dtype_t5=None):
+    class SD3ClipModel_(SD3ClipModel):
+        def __init__(self, device="cpu", dtype=None):
+            super().__init__(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5, device=device, dtype=dtype)
+    return SD3ClipModel_
diff --git a/comfy/sdxl_clip.py b/comfy/sdxl_clip.py
index b35056bb..1257cba1 100644
--- a/comfy/sdxl_clip.py
+++ b/comfy/sdxl_clip.py
@@ -39,14 +39,15 @@ class SDXLClipModel(torch.nn.Module):
         super().__init__()
         self.clip_l = sd1_clip.SDClipModel(layer="hidden", layer_idx=-2, device=device, dtype=dtype, layer_norm_hidden_state=False)
         self.clip_g = SDXLClipG(device=device, dtype=dtype)
+        self.dtypes = set([dtype])
 
-    def clip_layer(self, layer_idx):
-        self.clip_l.clip_layer(layer_idx)
-        self.clip_g.clip_layer(layer_idx)
+    def set_clip_options(self, options):
+        self.clip_l.set_clip_options(options)
+        self.clip_g.set_clip_options(options)
 
-    def reset_clip_layer(self):
-        self.clip_g.reset_clip_layer()
-        self.clip_l.reset_clip_layer()
+    def reset_clip_options(self):
+        self.clip_g.reset_clip_options()
+        self.clip_l.reset_clip_options()
 
     def encode_token_weights(self, token_weight_pairs):
         token_weight_pairs_g = token_weight_pairs["g"]
@@ -64,3 +65,25 @@ class SDXLClipModel(torch.nn.Module):
 class SDXLRefinerClipModel(sd1_clip.SD1ClipModel):
     def __init__(self, device="cpu", dtype=None):
         super().__init__(device=device, dtype=dtype, clip_name="g", clip_model=SDXLClipG)
+
+
+class StableCascadeClipGTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, tokenizer_path=None, embedding_directory=None):
+        super().__init__(tokenizer_path, pad_with_end=True, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g')
+
+class StableCascadeTokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None):
+        super().__init__(embedding_directory=embedding_directory, clip_name="g", tokenizer=StableCascadeClipGTokenizer)
+
+class StableCascadeClipG(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", max_length=77, freeze=True, layer="hidden", layer_idx=-1, dtype=None):
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "clip_config_bigg.json")
+        super().__init__(device=device, freeze=freeze, layer=layer, layer_idx=layer_idx, textmodel_json_config=textmodel_json_config, dtype=dtype,
+                         special_tokens={"start": 49406, "end": 49407, "pad": 49407}, layer_norm_hidden_state=False, enable_attention_masks=True)
+
+    def load_sd(self, sd):
+        return super().load_sd(sd)
+
+class StableCascadeClipModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None):
+        super().__init__(device=device, dtype=dtype, clip_name="g", clip_model=StableCascadeClipG)
diff --git a/comfy/supported_models.py b/comfy/supported_models.py
index 1d442d4d..21fdb7ec 100644
--- a/comfy/supported_models.py
+++ b/comfy/supported_models.py
@@ -5,6 +5,8 @@ from . import utils
 from . import sd1_clip
 from . import sd2_clip
 from . import sdxl_clip
+from . import sd3_clip
+from . import sa_t5
 
 from . import supported_models_base
 from . import latent_formats
@@ -40,15 +42,20 @@ class SD15(supported_models_base.BASE):
                 state_dict['cond_stage_model.transformer.text_model.embeddings.position_ids'] = ids.round()
 
         replace_prefix = {}
-        replace_prefix["cond_stage_model."] = "cond_stage_model.clip_l."
-        state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix)
+        replace_prefix["cond_stage_model."] = "clip_l."
+        state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
         return state_dict
 
     def process_clip_state_dict_for_saving(self, state_dict):
+        pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"]
+        for p in pop_keys:
+            if p in state_dict:
+                state_dict.pop(p)
+
         replace_prefix = {"clip_l.": "cond_stage_model."}
         return utils.state_dict_prefix_replace(state_dict, replace_prefix)
 
-    def clip_target(self):
+    def clip_target(self, state_dict={}):
         return supported_models_base.ClipTarget(sd1_clip.SD1Tokenizer, sd1_clip.SD1ClipModel)
 
 class SD20(supported_models_base.BASE):
@@ -60,22 +67,28 @@ class SD20(supported_models_base.BASE):
         "use_temporal_attention": False,
     }
 
+    unet_extra_config = {
+        "num_heads": -1,
+        "num_head_channels": 64,
+        "attn_precision": torch.float32,
+    }
+
     latent_format = latent_formats.SD15
 
     def model_type(self, state_dict, prefix=""):
         if self.unet_config["in_channels"] == 4: #SD2.0 inpainting models are not v prediction
             k = "{}output_blocks.11.1.transformer_blocks.0.norm1.bias".format(prefix)
-            out = state_dict[k]
-            if torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out.
+            out = state_dict.get(k, None)
+            if out is not None and torch.std(out, unbiased=False) > 0.09: # not sure how well this will actually work. I guess we will find out.
                 return model_base.ModelType.V_PREDICTION
         return model_base.ModelType.EPS
 
     def process_clip_state_dict(self, state_dict):
         replace_prefix = {}
-        replace_prefix["conditioner.embedders.0.model."] = "cond_stage_model.model." #SD2 in sgm format
-        state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix)
-
-        state_dict = utils.transformers_convert(state_dict, "cond_stage_model.model.", "cond_stage_model.clip_h.transformer.text_model.", 24)
+        replace_prefix["conditioner.embedders.0.model."] = "clip_h." #SD2 in sgm format
+        replace_prefix["cond_stage_model.model."] = "clip_h."
+        state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
+        state_dict = utils.clip_text_transformers_convert(state_dict, "clip_h.", "clip_h.transformer.")
         return state_dict
 
     def process_clip_state_dict_for_saving(self, state_dict):
@@ -85,7 +98,7 @@ class SD20(supported_models_base.BASE):
         state_dict = diffusers_convert.convert_text_enc_state_dict_v20(state_dict)
         return state_dict
 
-    def clip_target(self):
+    def clip_target(self, state_dict={}):
         return supported_models_base.ClipTarget(sd2_clip.SD2Tokenizer, sd2_clip.SD2ClipModel)
 
 class SD21UnclipL(SD20):
@@ -131,11 +144,10 @@ class SDXLRefiner(supported_models_base.BASE):
     def process_clip_state_dict(self, state_dict):
         keys_to_replace = {}
         replace_prefix = {}
+        replace_prefix["conditioner.embedders.0.model."] = "clip_g."
+        state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
 
-        state_dict = utils.transformers_convert(state_dict, "conditioner.embedders.0.model.", "cond_stage_model.clip_g.transformer.text_model.", 32)
-        keys_to_replace["conditioner.embedders.0.model.text_projection"] = "cond_stage_model.clip_g.text_projection"
-        keys_to_replace["conditioner.embedders.0.model.logit_scale"] = "cond_stage_model.clip_g.logit_scale"
-
+        state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.")
         state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace)
         return state_dict
 
@@ -148,7 +160,7 @@ class SDXLRefiner(supported_models_base.BASE):
         state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix)
         return state_dict_g
 
-    def clip_target(self):
+    def clip_target(self, state_dict={}):
         return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLRefinerClipModel)
 
 class SDXL(supported_models_base.BASE):
@@ -164,7 +176,18 @@ class SDXL(supported_models_base.BASE):
     latent_format = latent_formats.SDXL
 
     def model_type(self, state_dict, prefix=""):
-        if "v_pred" in state_dict:
+        if 'edm_mean' in state_dict and 'edm_std' in state_dict: #Playground V2.5
+            self.latent_format = latent_formats.SDXL_Playground_2_5()
+            self.sampling_settings["sigma_data"] = 0.5
+            self.sampling_settings["sigma_max"] = 80.0
+            self.sampling_settings["sigma_min"] = 0.002
+            return model_base.ModelType.EDM
+        elif "edm_vpred.sigma_max" in state_dict:
+            self.sampling_settings["sigma_max"] = float(state_dict["edm_vpred.sigma_max"].item())
+            if "edm_vpred.sigma_min" in state_dict:
+                self.sampling_settings["sigma_min"] = float(state_dict["edm_vpred.sigma_min"].item())
+            return model_base.ModelType.V_PREDICTION_EDM
+        elif "v_pred" in state_dict:
             return model_base.ModelType.V_PREDICTION
         else:
             return model_base.ModelType.EPS
@@ -179,32 +202,34 @@ class SDXL(supported_models_base.BASE):
         keys_to_replace = {}
         replace_prefix = {}
 
-        replace_prefix["conditioner.embedders.0.transformer.text_model"] = "cond_stage_model.clip_l.transformer.text_model"
-        state_dict = utils.transformers_convert(state_dict, "conditioner.embedders.1.model.", "cond_stage_model.clip_g.transformer.text_model.", 32)
-        keys_to_replace["conditioner.embedders.1.model.text_projection"] = "cond_stage_model.clip_g.text_projection"
-        keys_to_replace["conditioner.embedders.1.model.text_projection.weight"] = "cond_stage_model.clip_g.text_projection"
-        keys_to_replace["conditioner.embedders.1.model.logit_scale"] = "cond_stage_model.clip_g.logit_scale"
+        replace_prefix["conditioner.embedders.0.transformer.text_model"] = "clip_l.transformer.text_model"
+        replace_prefix["conditioner.embedders.1.model."] = "clip_g."
+        state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix, filter_keys=True)
 
-        state_dict = utils.state_dict_prefix_replace(state_dict, replace_prefix)
         state_dict = utils.state_dict_key_replace(state_dict, keys_to_replace)
+        state_dict = utils.clip_text_transformers_convert(state_dict, "clip_g.", "clip_g.transformer.")
         return state_dict
 
     def process_clip_state_dict_for_saving(self, state_dict):
         replace_prefix = {}
         keys_to_replace = {}
         state_dict_g = diffusers_convert.convert_text_enc_state_dict_v20(state_dict, "clip_g")
-        if "clip_g.transformer.text_model.embeddings.position_ids" in state_dict_g:
-            state_dict_g.pop("clip_g.transformer.text_model.embeddings.position_ids")
         for k in state_dict:
             if k.startswith("clip_l"):
                 state_dict_g[k] = state_dict[k]
 
+        state_dict_g["clip_l.transformer.text_model.embeddings.position_ids"] = torch.arange(77).expand((1, -1))
+        pop_keys = ["clip_l.transformer.text_projection.weight", "clip_l.logit_scale"]
+        for p in pop_keys:
+            if p in state_dict_g:
+                state_dict_g.pop(p)
+
         replace_prefix["clip_g"] = "conditioner.embedders.1.model"
         replace_prefix["clip_l"] = "conditioner.embedders.0"
         state_dict_g = utils.state_dict_prefix_replace(state_dict_g, replace_prefix)
         return state_dict_g
 
-    def clip_target(self):
+    def clip_target(self, state_dict={}):
         return supported_models_base.ClipTarget(sdxl_clip.SDXLTokenizer, sdxl_clip.SDXLClipModel)
 
 class SSD1B(SDXL):
@@ -227,6 +252,26 @@ class Segmind_Vega(SDXL):
         "use_temporal_attention": False,
     }
 
+class KOALA_700M(SDXL):
+    unet_config = {
+        "model_channels": 320,
+        "use_linear_in_transformer": True,
+        "transformer_depth": [0, 2, 5],
+        "context_dim": 2048,
+        "adm_in_channels": 2816,
+        "use_temporal_attention": False,
+    }
+
+class KOALA_1B(SDXL):
+    unet_config = {
+        "model_channels": 320,
+        "use_linear_in_transformer": True,
+        "transformer_depth": [0, 2, 6],
+        "context_dim": 2048,
+        "adm_in_channels": 2816,
+        "use_temporal_attention": False,
+    }
+
 class SVD_img2vid(supported_models_base.BASE):
     unet_config = {
         "model_channels": 320,
@@ -239,6 +284,12 @@ class SVD_img2vid(supported_models_base.BASE):
         "use_temporal_resblock": True
     }
 
+    unet_extra_config = {
+        "num_heads": -1,
+        "num_head_channels": 64,
+        "attn_precision": torch.float32,
+    }
+
     clip_vision_prefix = "conditioner.embedders.0.open_clip.model.visual."
 
     latent_format = latent_formats.SD15
@@ -249,9 +300,44 @@ class SVD_img2vid(supported_models_base.BASE):
         out = model_base.SVD_img2vid(self, device=device)
         return out
 
-    def clip_target(self):
+    def clip_target(self, state_dict={}):
         return None
 
+class SV3D_u(SVD_img2vid):
+    unet_config = {
+        "model_channels": 320,
+        "in_channels": 8,
+        "use_linear_in_transformer": True,
+        "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0],
+        "context_dim": 1024,
+        "adm_in_channels": 256,
+        "use_temporal_attention": True,
+        "use_temporal_resblock": True
+    }
+
+    vae_key_prefix = ["conditioner.embedders.1.encoder."]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.SV3D_u(self, device=device)
+        return out
+
+class SV3D_p(SV3D_u):
+    unet_config = {
+        "model_channels": 320,
+        "in_channels": 8,
+        "use_linear_in_transformer": True,
+        "transformer_depth": [1, 1, 1, 1, 1, 1, 0, 0],
+        "context_dim": 1024,
+        "adm_in_channels": 1280,
+        "use_temporal_attention": True,
+        "use_temporal_resblock": True
+    }
+
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.SV3D_p(self, device=device)
+        return out
+
 class Stable_Zero123(supported_models_base.BASE):
     unet_config = {
         "context_dim": 768,
@@ -267,6 +353,11 @@ class Stable_Zero123(supported_models_base.BASE):
         "num_head_channels": -1,
     }
 
+    required_keys = {
+        "cc_projection.weight": None,
+        "cc_projection.bias": None,
+    }
+
     clip_vision_prefix = "cond_stage_model.model.visual."
 
     latent_format = latent_formats.SD15
@@ -275,7 +366,7 @@ class Stable_Zero123(supported_models_base.BASE):
         out = model_base.Stable_Zero123(self, device=device, cc_projection_weight=state_dict["cc_projection.weight"], cc_projection_bias=state_dict["cc_projection.bias"])
         return out
 
-    def clip_target(self):
+    def clip_target(self, state_dict={}):
         return None
 
 class SD_X4Upscaler(SD20):
@@ -306,5 +397,166 @@ class SD_X4Upscaler(SD20):
         out = model_base.SD_X4Upscaler(self, device=device)
         return out
 
-models = [Stable_Zero123, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXLRefiner, SDXL, SSD1B, Segmind_Vega, SD_X4Upscaler]
+class Stable_Cascade_C(supported_models_base.BASE):
+    unet_config = {
+        "stable_cascade_stage": 'c',
+    }
+
+    unet_extra_config = {}
+
+    latent_format = latent_formats.SC_Prior
+    supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+    sampling_settings = {
+        "shift": 2.0,
+    }
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoder."]
+    clip_vision_prefix = "clip_l_vision."
+
+    def process_unet_state_dict(self, state_dict):
+        key_list = list(state_dict.keys())
+        for y in ["weight", "bias"]:
+            suffix = "in_proj_{}".format(y)
+            keys = filter(lambda a: a.endswith(suffix), key_list)
+            for k_from in keys:
+                weights = state_dict.pop(k_from)
+                prefix = k_from[:-(len(suffix) + 1)]
+                shape_from = weights.shape[0] // 3
+                for x in range(3):
+                    p = ["to_q", "to_k", "to_v"]
+                    k_to = "{}.{}.{}".format(prefix, p[x], y)
+                    state_dict[k_to] = weights[shape_from*x:shape_from*(x + 1)]
+        return state_dict
+
+    def process_clip_state_dict(self, state_dict):
+        state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True)
+        if "clip_g.text_projection" in state_dict:
+            state_dict["clip_g.transformer.text_projection.weight"] = state_dict.pop("clip_g.text_projection").transpose(0, 1)
+        return state_dict
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.StableCascade_C(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        return supported_models_base.ClipTarget(sdxl_clip.StableCascadeTokenizer, sdxl_clip.StableCascadeClipModel)
+
+class Stable_Cascade_B(Stable_Cascade_C):
+    unet_config = {
+        "stable_cascade_stage": 'b',
+    }
+
+    unet_extra_config = {}
+
+    latent_format = latent_formats.SC_B
+    supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+
+    sampling_settings = {
+        "shift": 1.0,
+    }
+
+    clip_vision_prefix = None
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.StableCascade_B(self, device=device)
+        return out
+
+class SD15_instructpix2pix(SD15):
+    unet_config = {
+        "context_dim": 768,
+        "model_channels": 320,
+        "use_linear_in_transformer": False,
+        "adm_in_channels": None,
+        "use_temporal_attention": False,
+        "in_channels": 8,
+    }
+
+    def get_model(self, state_dict, prefix="", device=None):
+        return model_base.SD15_instructpix2pix(self, device=device)
+
+class SDXL_instructpix2pix(SDXL):
+    unet_config = {
+        "model_channels": 320,
+        "use_linear_in_transformer": True,
+        "transformer_depth": [0, 0, 2, 2, 10, 10],
+        "context_dim": 2048,
+        "adm_in_channels": 2816,
+        "use_temporal_attention": False,
+        "in_channels": 8,
+    }
+
+    def get_model(self, state_dict, prefix="", device=None):
+        return model_base.SDXL_instructpix2pix(self, model_type=self.model_type(state_dict, prefix), device=device)
+
+class SD3(supported_models_base.BASE):
+    unet_config = {
+        "in_channels": 16,
+        "pos_embed_scaling_factor": None,
+    }
+
+    sampling_settings = {
+        "shift": 3.0,
+    }
+
+    unet_extra_config = {}
+    latent_format = latent_formats.SD3
+    text_encoder_key_prefix = ["text_encoders."]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.SD3(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        clip_l = False
+        clip_g = False
+        t5 = False
+        dtype_t5 = None
+        pref = self.text_encoder_key_prefix[0]
+        if "{}clip_l.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict:
+            clip_l = True
+        if "{}clip_g.transformer.text_model.final_layer_norm.weight".format(pref) in state_dict:
+            clip_g = True
+        t5_key = "{}t5xxl.transformer.encoder.final_layer_norm.weight".format(pref)
+        if t5_key in state_dict:
+            t5 = True
+            dtype_t5 = state_dict[t5_key].dtype
+
+        return supported_models_base.ClipTarget(sd3_clip.SD3Tokenizer, sd3_clip.sd3_clip(clip_l=clip_l, clip_g=clip_g, t5=t5, dtype_t5=dtype_t5))
+
+class StableAudio(supported_models_base.BASE):
+    unet_config = {
+        "audio_model": "dit1.0",
+    }
+
+    sampling_settings = {"sigma_max": 500.0, "sigma_min": 0.03}
+
+    unet_extra_config = {}
+    latent_format = latent_formats.StableAudio1
+
+    text_encoder_key_prefix = ["text_encoders."]
+    vae_key_prefix = ["pretransform.model."]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        seconds_start_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_start.": ""}, filter_keys=True)
+        seconds_total_sd = utils.state_dict_prefix_replace(state_dict, {"conditioner.conditioners.seconds_total.": ""}, filter_keys=True)
+        return model_base.StableAudio1(self, seconds_start_embedder_weights=seconds_start_sd, seconds_total_embedder_weights=seconds_total_sd, device=device)
+
+    def process_unet_state_dict(self, state_dict):
+        for k in list(state_dict.keys()):
+            if k.endswith(".cross_attend_norm.beta") or k.endswith(".ff_norm.beta") or k.endswith(".pre_norm.beta"): #These weights are all zero
+                state_dict.pop(k)
+        return state_dict
+
+    def process_unet_state_dict_for_saving(self, state_dict):
+        replace_prefix = {"": "model.model."}
+        return utils.state_dict_prefix_replace(state_dict, replace_prefix)
+
+    def clip_target(self, state_dict={}):
+        return supported_models_base.ClipTarget(sa_t5.SAT5Tokenizer, sa_t5.SAT5Model)
+
+
+models = [Stable_Zero123, SD15_instructpix2pix, SD15, SD20, SD21UnclipL, SD21UnclipH, SDXL_instructpix2pix, SDXLRefiner, SDXL, SSD1B, KOALA_700M, KOALA_1B, Segmind_Vega, SD_X4Upscaler, Stable_Cascade_C, Stable_Cascade_B, SV3D_u, SV3D_p, SD3, StableAudio]
+
 models += [SVD_img2vid]
diff --git a/comfy/supported_models_base.py b/comfy/supported_models_base.py
index 58535a9f..cf7cdff3 100644
--- a/comfy/supported_models_base.py
+++ b/comfy/supported_models_base.py
@@ -16,20 +16,28 @@ class BASE:
         "num_head_channels": 64,
     }
 
+    required_keys = {}
+
     clip_prefix = []
     clip_vision_prefix = None
     noise_aug_config = None
     sampling_settings = {}
     latent_format = latent_formats.LatentFormat
     vae_key_prefix = ["first_stage_model."]
+    text_encoder_key_prefix = ["cond_stage_model."]
+    supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]
 
     manual_cast_dtype = None
 
     @classmethod
-    def matches(s, unet_config):
+    def matches(s, unet_config, state_dict=None):
         for k in s.unet_config:
-            if s.unet_config[k] != unet_config[k]:
+            if k not in unet_config or s.unet_config[k] != unet_config[k]:
                 return False
+        if state_dict is not None:
+            for k in s.required_keys:
+                if k not in state_dict:
+                    return False
         return True
 
     def model_type(self, state_dict, prefix=""):
@@ -39,7 +47,8 @@ class BASE:
         return self.unet_config["in_channels"] > 4
 
     def __init__(self, unet_config):
-        self.unet_config = unet_config
+        self.unet_config = unet_config.copy()
+        self.sampling_settings = self.sampling_settings.copy()
         self.latent_format = self.latent_format()
         for x in self.unet_extra_config:
             self.unet_config[x] = self.unet_extra_config[x]
@@ -54,6 +63,7 @@ class BASE:
         return out
 
     def process_clip_state_dict(self, state_dict):
+        state_dict = utils.state_dict_prefix_replace(state_dict, {k: "" for k in self.text_encoder_key_prefix}, filter_keys=True)
         return state_dict
 
     def process_unet_state_dict(self, state_dict):
@@ -63,7 +73,7 @@ class BASE:
         return state_dict
 
     def process_clip_state_dict_for_saving(self, state_dict):
-        replace_prefix = {"": "cond_stage_model."}
+        replace_prefix = {"": self.text_encoder_key_prefix[0]}
         return utils.state_dict_prefix_replace(state_dict, replace_prefix)
 
     def process_clip_vision_state_dict_for_saving(self, state_dict):
@@ -77,8 +87,9 @@ class BASE:
         return utils.state_dict_prefix_replace(state_dict, replace_prefix)
 
     def process_vae_state_dict_for_saving(self, state_dict):
-        replace_prefix = {"": "first_stage_model."}
+        replace_prefix = {"": self.vae_key_prefix[0]}
         return utils.state_dict_prefix_replace(state_dict, replace_prefix)
 
-    def set_manual_cast(self, manual_cast_dtype):
+    def set_inference_dtype(self, dtype, manual_cast_dtype):
+        self.unet_config['dtype'] = dtype
         self.manual_cast_dtype = manual_cast_dtype
diff --git a/comfy/t2i_adapter/adapter.py b/comfy/t2i_adapter/adapter.py
index e9a606b1..10ea18e3 100644
--- a/comfy/t2i_adapter/adapter.py
+++ b/comfy/t2i_adapter/adapter.py
@@ -153,7 +153,13 @@ class Adapter(nn.Module):
                 features.append(None)
             features.append(x)
 
-        return features
+        features = features[::-1]
+
+        if self.xl:
+            return {"input": features[1:], "middle": features[:1]}
+        else:
+            return {"input": features}
+
 
 
 class LayerNorm(nn.LayerNorm):
@@ -290,4 +296,4 @@ class Adapter_light(nn.Module):
             features.append(None)
             features.append(x)
 
-        return features
+        return {"input": features[::-1]}
diff --git a/comfy/t5.py b/comfy/t5.py
new file mode 100644
index 00000000..448c5aad
--- /dev/null
+++ b/comfy/t5.py
@@ -0,0 +1,238 @@
+import torch
+import math
+from comfy.ldm.modules.attention import optimized_attention_for_device
+
+class T5LayerNorm(torch.nn.Module):
+    def __init__(self, hidden_size, eps=1e-6, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.weight = torch.nn.Parameter(torch.empty(hidden_size, dtype=dtype, device=device))
+        self.variance_epsilon = eps
+
+    def forward(self, x):
+        variance = x.pow(2).mean(-1, keepdim=True)
+        x = x * torch.rsqrt(variance + self.variance_epsilon)
+        return self.weight.to(device=x.device, dtype=x.dtype) * x
+
+activations = {
+    "gelu_pytorch_tanh": lambda a: torch.nn.functional.gelu(a, approximate="tanh"),
+    "relu": torch.nn.functional.relu,
+}
+
+class T5DenseActDense(torch.nn.Module):
+    def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations):
+        super().__init__()
+        self.wi = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
+        self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)
+        # self.dropout = nn.Dropout(config.dropout_rate)
+        self.act = activations[ff_activation]
+
+    def forward(self, x):
+        x = self.act(self.wi(x))
+        # x = self.dropout(x)
+        x = self.wo(x)
+        return x
+
+class T5DenseGatedActDense(torch.nn.Module):
+    def __init__(self, model_dim, ff_dim, ff_activation, dtype, device, operations):
+        super().__init__()
+        self.wi_0 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
+        self.wi_1 = operations.Linear(model_dim, ff_dim, bias=False, dtype=dtype, device=device)
+        self.wo = operations.Linear(ff_dim, model_dim, bias=False, dtype=dtype, device=device)
+        # self.dropout = nn.Dropout(config.dropout_rate)
+        self.act = activations[ff_activation]
+
+    def forward(self, x):
+        hidden_gelu = self.act(self.wi_0(x))
+        hidden_linear = self.wi_1(x)
+        x = hidden_gelu * hidden_linear
+        # x = self.dropout(x)
+        x = self.wo(x)
+        return x
+
+class T5LayerFF(torch.nn.Module):
+    def __init__(self, model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations):
+        super().__init__()
+        if gated_act:
+            self.DenseReluDense = T5DenseGatedActDense(model_dim, ff_dim, ff_activation, dtype, device, operations)
+        else:
+            self.DenseReluDense = T5DenseActDense(model_dim, ff_dim, ff_activation, dtype, device, operations)
+
+        self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
+        # self.dropout = nn.Dropout(config.dropout_rate)
+
+    def forward(self, x):
+        forwarded_states = self.layer_norm(x)
+        forwarded_states = self.DenseReluDense(forwarded_states)
+        # x = x + self.dropout(forwarded_states)
+        x += forwarded_states
+        return x
+
+class T5Attention(torch.nn.Module):
+    def __init__(self, model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations):
+        super().__init__()
+
+        # Mesh TensorFlow initialization to avoid scaling before softmax
+        self.q = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.k = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.v = operations.Linear(model_dim, inner_dim, bias=False, dtype=dtype, device=device)
+        self.o = operations.Linear(inner_dim, model_dim, bias=False, dtype=dtype, device=device)
+        self.num_heads = num_heads
+
+        self.relative_attention_bias = None
+        if relative_attention_bias:
+            self.relative_attention_num_buckets = 32
+            self.relative_attention_max_distance = 128
+            self.relative_attention_bias = torch.nn.Embedding(self.relative_attention_num_buckets, self.num_heads, device=device)
+
+    @staticmethod
+    def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
+        """
+        Adapted from Mesh Tensorflow:
+        https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
+
+        Translate relative position to a bucket number for relative attention. The relative position is defined as
+        memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
+        position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
+        small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
+        positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
+        This should allow for more graceful generalization to longer sequences than the model has been trained on
+
+        Args:
+            relative_position: an int32 Tensor
+            bidirectional: a boolean - whether the attention is bidirectional
+            num_buckets: an integer
+            max_distance: an integer
+
+        Returns:
+            a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
+        """
+        relative_buckets = 0
+        if bidirectional:
+            num_buckets //= 2
+            relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
+            relative_position = torch.abs(relative_position)
+        else:
+            relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
+        # now relative_position is in the range [0, inf)
+
+        # half of the buckets are for exact increments in positions
+        max_exact = num_buckets // 2
+        is_small = relative_position < max_exact
+
+        # The other half of the buckets are for logarithmically bigger bins in positions up to max_distance
+        relative_position_if_large = max_exact + (
+            torch.log(relative_position.float() / max_exact)
+            / math.log(max_distance / max_exact)
+            * (num_buckets - max_exact)
+        ).to(torch.long)
+        relative_position_if_large = torch.min(
+            relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1)
+        )
+
+        relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
+        return relative_buckets
+
+    def compute_bias(self, query_length, key_length, device):
+        """Compute binned relative position bias"""
+        context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
+        memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
+        relative_position = memory_position - context_position  # shape (query_length, key_length)
+        relative_position_bucket = self._relative_position_bucket(
+            relative_position,  # shape (query_length, key_length)
+            bidirectional=True,
+            num_buckets=self.relative_attention_num_buckets,
+            max_distance=self.relative_attention_max_distance,
+        )
+        values = self.relative_attention_bias(relative_position_bucket)  # shape (query_length, key_length, num_heads)
+        values = values.permute([2, 0, 1]).unsqueeze(0)  # shape (1, num_heads, query_length, key_length)
+        return values
+
+    def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
+        q = self.q(x)
+        k = self.k(x)
+        v = self.v(x)
+        if self.relative_attention_bias is not None:
+            past_bias = self.compute_bias(x.shape[1], x.shape[1], x.device)
+
+        if past_bias is not None:
+            if mask is not None:
+                mask = mask + past_bias
+            else:
+                mask = past_bias
+
+        out = optimized_attention(q, k * ((k.shape[-1] / self.num_heads) ** 0.5), v, self.num_heads, mask)
+        return self.o(out), past_bias
+
+class T5LayerSelfAttention(torch.nn.Module):
+    def __init__(self, model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations):
+        super().__init__()
+        self.SelfAttention = T5Attention(model_dim, inner_dim, num_heads, relative_attention_bias, dtype, device, operations)
+        self.layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
+        # self.dropout = nn.Dropout(config.dropout_rate)
+
+    def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
+        normed_hidden_states = self.layer_norm(x)
+        output, past_bias = self.SelfAttention(self.layer_norm(x), mask=mask, past_bias=past_bias, optimized_attention=optimized_attention)
+        # x = x + self.dropout(attention_output)
+        x += output
+        return x, past_bias
+
+class T5Block(torch.nn.Module):
+    def __init__(self, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias, dtype, device, operations):
+        super().__init__()
+        self.layer = torch.nn.ModuleList()
+        self.layer.append(T5LayerSelfAttention(model_dim, inner_dim, ff_dim, num_heads, relative_attention_bias, dtype, device, operations))
+        self.layer.append(T5LayerFF(model_dim, ff_dim, ff_activation, gated_act, dtype, device, operations))
+
+    def forward(self, x, mask=None, past_bias=None, optimized_attention=None):
+        x, past_bias = self.layer[0](x, mask, past_bias, optimized_attention)
+        x = self.layer[-1](x)
+        return x, past_bias
+
+class T5Stack(torch.nn.Module):
+    def __init__(self, num_layers, model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention, dtype, device, operations):
+        super().__init__()
+
+        self.block = torch.nn.ModuleList(
+            [T5Block(model_dim, inner_dim, ff_dim, ff_activation, gated_act, num_heads, relative_attention_bias=((not relative_attention) or (i == 0)), dtype=dtype, device=device, operations=operations) for i in range(num_layers)]
+        )
+        self.final_layer_norm = T5LayerNorm(model_dim, dtype=dtype, device=device, operations=operations)
+        # self.dropout = nn.Dropout(config.dropout_rate)
+
+    def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True):
+        mask = None
+        if attention_mask is not None:
+            mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
+            mask = mask.masked_fill(mask.to(torch.bool), float("-inf"))
+
+        intermediate = None
+        optimized_attention = optimized_attention_for_device(x.device, mask=attention_mask is not None, small_input=True)
+        past_bias = None
+        for i, l in enumerate(self.block):
+            x, past_bias = l(x, mask, past_bias, optimized_attention)
+            if i == intermediate_output:
+                intermediate = x.clone()
+        x = self.final_layer_norm(x)
+        if intermediate is not None and final_layer_norm_intermediate:
+            intermediate = self.final_layer_norm(intermediate)
+        return x, intermediate
+
+class T5(torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        self.num_layers = config_dict["num_layers"]
+        model_dim = config_dict["d_model"]
+
+        self.encoder = T5Stack(self.num_layers, model_dim, model_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["is_gated_act"], config_dict["num_heads"], config_dict["model_type"] == "t5", dtype, device, operations)
+        self.dtype = dtype
+        self.shared = torch.nn.Embedding(config_dict["vocab_size"], model_dim, device=device)
+
+    def get_input_embeddings(self):
+        return self.shared
+
+    def set_input_embeddings(self, embeddings):
+        self.shared = embeddings
+
+    def forward(self, input_ids, *args, **kwargs):
+        x = self.shared(input_ids)
+        return self.encoder(x, *args, **kwargs)
diff --git a/comfy/t5_config_base.json b/comfy/t5_config_base.json
new file mode 100644
index 00000000..71f68327
--- /dev/null
+++ b/comfy/t5_config_base.json
@@ -0,0 +1,22 @@
+{
+  "d_ff": 3072,
+  "d_kv": 64,
+  "d_model": 768,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "dense_act_fn": "relu",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": false,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "num_decoder_layers": 12,
+  "num_heads": 12,
+  "num_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "vocab_size": 32128
+}
diff --git a/comfy/t5_config_xxl.json b/comfy/t5_config_xxl.json
new file mode 100644
index 00000000..28283b51
--- /dev/null
+++ b/comfy/t5_config_xxl.json
@@ -0,0 +1,22 @@
+{
+  "d_ff": 10240,
+  "d_kv": 64,
+  "d_model": 4096,
+  "decoder_start_token_id": 0,
+  "dropout_rate": 0.1,
+  "eos_token_id": 1,
+  "dense_act_fn": "gelu_pytorch_tanh",
+  "initializer_factor": 1.0,
+  "is_encoder_decoder": true,
+  "is_gated_act": true,
+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
+  "num_decoder_layers": 24,
+  "num_heads": 64,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "vocab_size": 32128
+}
diff --git a/comfy/t5_tokenizer/special_tokens_map.json b/comfy/t5_tokenizer/special_tokens_map.json
new file mode 100644
index 00000000..17ade346
--- /dev/null
+++ b/comfy/t5_tokenizer/special_tokens_map.json
@@ -0,0 +1,125 @@
+{
+  "additional_special_tokens": [
+    "<extra_id_0>",
+    "<extra_id_1>",
+    "<extra_id_2>",
+    "<extra_id_3>",
+    "<extra_id_4>",
+    "<extra_id_5>",
+    "<extra_id_6>",
+    "<extra_id_7>",
+    "<extra_id_8>",
+    "<extra_id_9>",
+    "<extra_id_10>",
+    "<extra_id_11>",
+    "<extra_id_12>",
+    "<extra_id_13>",
+    "<extra_id_14>",
+    "<extra_id_15>",
+    "<extra_id_16>",
+    "<extra_id_17>",
+    "<extra_id_18>",
+    "<extra_id_19>",
+    "<extra_id_20>",
+    "<extra_id_21>",
+    "<extra_id_22>",
+    "<extra_id_23>",
+    "<extra_id_24>",
+    "<extra_id_25>",
+    "<extra_id_26>",
+    "<extra_id_27>",
+    "<extra_id_28>",
+    "<extra_id_29>",
+    "<extra_id_30>",
+    "<extra_id_31>",
+    "<extra_id_32>",
+    "<extra_id_33>",
+    "<extra_id_34>",
+    "<extra_id_35>",
+    "<extra_id_36>",
+    "<extra_id_37>",
+    "<extra_id_38>",
+    "<extra_id_39>",
+    "<extra_id_40>",
+    "<extra_id_41>",
+    "<extra_id_42>",
+    "<extra_id_43>",
+    "<extra_id_44>",
+    "<extra_id_45>",
+    "<extra_id_46>",
+    "<extra_id_47>",
+    "<extra_id_48>",
+    "<extra_id_49>",
+    "<extra_id_50>",
+    "<extra_id_51>",
+    "<extra_id_52>",
+    "<extra_id_53>",
+    "<extra_id_54>",
+    "<extra_id_55>",
+    "<extra_id_56>",
+    "<extra_id_57>",
+    "<extra_id_58>",
+    "<extra_id_59>",
+    "<extra_id_60>",
+    "<extra_id_61>",
+    "<extra_id_62>",
+    "<extra_id_63>",
+    "<extra_id_64>",
+    "<extra_id_65>",
+    "<extra_id_66>",
+    "<extra_id_67>",
+    "<extra_id_68>",
+    "<extra_id_69>",
+    "<extra_id_70>",
+    "<extra_id_71>",
+    "<extra_id_72>",
+    "<extra_id_73>",
+    "<extra_id_74>",
+    "<extra_id_75>",
+    "<extra_id_76>",
+    "<extra_id_77>",
+    "<extra_id_78>",
+    "<extra_id_79>",
+    "<extra_id_80>",
+    "<extra_id_81>",
+    "<extra_id_82>",
+    "<extra_id_83>",
+    "<extra_id_84>",
+    "<extra_id_85>",
+    "<extra_id_86>",
+    "<extra_id_87>",
+    "<extra_id_88>",
+    "<extra_id_89>",
+    "<extra_id_90>",
+    "<extra_id_91>",
+    "<extra_id_92>",
+    "<extra_id_93>",
+    "<extra_id_94>",
+    "<extra_id_95>",
+    "<extra_id_96>",
+    "<extra_id_97>",
+    "<extra_id_98>",
+    "<extra_id_99>"
+  ],
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
diff --git a/comfy/t5_tokenizer/tokenizer.json b/comfy/t5_tokenizer/tokenizer.json
new file mode 100644
index 00000000..b11c92d7
--- /dev/null
+++ b/comfy/t5_tokenizer/tokenizer.json
@@ -0,0 +1,129428 @@
+{
+  "version": "1.0",
+  "truncation": null,
+  "padding": null,
+  "added_tokens": [
+    {
+      "id": 0,
+      "content": "<pad>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 1,
+      "content": "</s>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 2,
+      "content": "<unk>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32000,
+      "content": "<extra_id_99>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32001,
+      "content": "<extra_id_98>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 32002,
+      "content": "<extra_id_97>",
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+        0.0
+      ],
+      [
+        "<extra_id_7>",
+        0.0
+      ],
+      [
+        "<extra_id_6>",
+        0.0
+      ],
+      [
+        "<extra_id_5>",
+        0.0
+      ],
+      [
+        "<extra_id_4>",
+        0.0
+      ],
+      [
+        "<extra_id_3>",
+        0.0
+      ],
+      [
+        "<extra_id_2>",
+        0.0
+      ],
+      [
+        "<extra_id_1>",
+        0.0
+      ],
+      [
+        "<extra_id_0>",
+        0.0
+      ]
+    ],
+    "byte_fallback": false
+  }
+}
\ No newline at end of file
diff --git a/comfy/t5_tokenizer/tokenizer_config.json b/comfy/t5_tokenizer/tokenizer_config.json
new file mode 100644
index 00000000..02020eb6
--- /dev/null
+++ b/comfy/t5_tokenizer/tokenizer_config.json
@@ -0,0 +1,939 @@
+{
+  "added_tokens_decoder": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "special": true
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+    },
+    "32098": {
+      "content": "<extra_id_1>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32099": {
+      "content": "<extra_id_0>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<extra_id_0>",
+    "<extra_id_1>",
+    "<extra_id_2>",
+    "<extra_id_3>",
+    "<extra_id_4>",
+    "<extra_id_5>",
+    "<extra_id_6>",
+    "<extra_id_7>",
+    "<extra_id_8>",
+    "<extra_id_9>",
+    "<extra_id_10>",
+    "<extra_id_11>",
+    "<extra_id_12>",
+    "<extra_id_13>",
+    "<extra_id_14>",
+    "<extra_id_15>",
+    "<extra_id_16>",
+    "<extra_id_17>",
+    "<extra_id_18>",
+    "<extra_id_19>",
+    "<extra_id_20>",
+    "<extra_id_21>",
+    "<extra_id_22>",
+    "<extra_id_23>",
+    "<extra_id_24>",
+    "<extra_id_25>",
+    "<extra_id_26>",
+    "<extra_id_27>",
+    "<extra_id_28>",
+    "<extra_id_29>",
+    "<extra_id_30>",
+    "<extra_id_31>",
+    "<extra_id_32>",
+    "<extra_id_33>",
+    "<extra_id_34>",
+    "<extra_id_35>",
+    "<extra_id_36>",
+    "<extra_id_37>",
+    "<extra_id_38>",
+    "<extra_id_39>",
+    "<extra_id_40>",
+    "<extra_id_41>",
+    "<extra_id_42>",
+    "<extra_id_43>",
+    "<extra_id_44>",
+    "<extra_id_45>",
+    "<extra_id_46>",
+    "<extra_id_47>",
+    "<extra_id_48>",
+    "<extra_id_49>",
+    "<extra_id_50>",
+    "<extra_id_51>",
+    "<extra_id_52>",
+    "<extra_id_53>",
+    "<extra_id_54>",
+    "<extra_id_55>",
+    "<extra_id_56>",
+    "<extra_id_57>",
+    "<extra_id_58>",
+    "<extra_id_59>",
+    "<extra_id_60>",
+    "<extra_id_61>",
+    "<extra_id_62>",
+    "<extra_id_63>",
+    "<extra_id_64>",
+    "<extra_id_65>",
+    "<extra_id_66>",
+    "<extra_id_67>",
+    "<extra_id_68>",
+    "<extra_id_69>",
+    "<extra_id_70>",
+    "<extra_id_71>",
+    "<extra_id_72>",
+    "<extra_id_73>",
+    "<extra_id_74>",
+    "<extra_id_75>",
+    "<extra_id_76>",
+    "<extra_id_77>",
+    "<extra_id_78>",
+    "<extra_id_79>",
+    "<extra_id_80>",
+    "<extra_id_81>",
+    "<extra_id_82>",
+    "<extra_id_83>",
+    "<extra_id_84>",
+    "<extra_id_85>",
+    "<extra_id_86>",
+    "<extra_id_87>",
+    "<extra_id_88>",
+    "<extra_id_89>",
+    "<extra_id_90>",
+    "<extra_id_91>",
+    "<extra_id_92>",
+    "<extra_id_93>",
+    "<extra_id_94>",
+    "<extra_id_95>",
+    "<extra_id_96>",
+    "<extra_id_97>",
+    "<extra_id_98>",
+    "<extra_id_99>"
+  ],
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "</s>",
+  "extra_ids": 100,
+  "legacy": false,
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "T5Tokenizer",
+  "unk_token": "<unk>"
+}
diff --git a/comfy/taesd/taesd.py b/comfy/taesd/taesd.py
index 8f96c54e..ce36f1a8 100644
--- a/comfy/taesd/taesd.py
+++ b/comfy/taesd/taesd.py
@@ -25,18 +25,19 @@ class Block(nn.Module):
     def forward(self, x):
         return self.fuse(self.conv(x) + self.skip(x))
 
-def Encoder():
+def Encoder(latent_channels=4):
     return nn.Sequential(
         conv(3, 64), Block(64, 64),
         conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
         conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
         conv(64, 64, stride=2, bias=False), Block(64, 64), Block(64, 64), Block(64, 64),
-        conv(64, 4),
+        conv(64, latent_channels),
     )
 
-def Decoder():
+
+def Decoder(latent_channels=4):
     return nn.Sequential(
-        Clamp(), conv(4, 64), nn.ReLU(),
+        Clamp(), conv(latent_channels, 64), nn.ReLU(),
         Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
         Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
         Block(64, 64), Block(64, 64), Block(64, 64), nn.Upsample(scale_factor=2), conv(64, 64, bias=False),
@@ -47,12 +48,13 @@ class TAESD(nn.Module):
     latent_magnitude = 3
     latent_shift = 0.5
 
-    def __init__(self, encoder_path=None, decoder_path=None):
+    def __init__(self, encoder_path=None, decoder_path=None, latent_channels=4):
         """Initialize pretrained TAESD on the given device from the given checkpoints."""
         super().__init__()
-        self.taesd_encoder = Encoder()
-        self.taesd_decoder = Decoder()
+        self.taesd_encoder = Encoder(latent_channels=latent_channels)
+        self.taesd_decoder = Decoder(latent_channels=latent_channels)
         self.vae_scale = torch.nn.Parameter(torch.tensor(1.0))
+        self.vae_shift = torch.nn.Parameter(torch.tensor(0.0))
         if encoder_path is not None:
             self.taesd_encoder.load_state_dict(comfy.utils.load_torch_file(encoder_path, safe_load=True))
         if decoder_path is not None:
@@ -69,9 +71,9 @@ class TAESD(nn.Module):
         return x.sub(TAESD.latent_shift).mul(2 * TAESD.latent_magnitude)
 
     def decode(self, x):
-        x_sample = self.taesd_decoder(x * self.vae_scale)
+        x_sample = self.taesd_decoder((x - self.vae_shift) * self.vae_scale)
         x_sample = x_sample.sub(0.5).mul(2)
         return x_sample
 
     def encode(self, x):
-        return self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale
+        return (self.taesd_encoder(x * 0.5 + 0.5) / self.vae_scale) + self.vae_shift
diff --git a/comfy/types.py b/comfy/types.py
new file mode 100644
index 00000000..70cf4b15
--- /dev/null
+++ b/comfy/types.py
@@ -0,0 +1,32 @@
+import torch
+from typing import Callable, Protocol, TypedDict, Optional, List
+
+
+class UnetApplyFunction(Protocol):
+    """Function signature protocol on comfy.model_base.BaseModel.apply_model"""
+
+    def __call__(self, x: torch.Tensor, t: torch.Tensor, **kwargs) -> torch.Tensor:
+        pass
+
+
+class UnetApplyConds(TypedDict):
+    """Optional conditions for unet apply function."""
+
+    c_concat: Optional[torch.Tensor]
+    c_crossattn: Optional[torch.Tensor]
+    control: Optional[torch.Tensor]
+    transformer_options: Optional[dict]
+
+
+class UnetParams(TypedDict):
+    # Tensor of shape [B, C, H, W]
+    input: torch.Tensor
+    # Tensor of shape [B]
+    timestep: torch.Tensor
+    c: UnetApplyConds
+    # List of [0, 1], [0], [1], ...
+    # 0 means conditional, 1 means conditional unconditional
+    cond_or_uncond: List[int]
+
+
+UnetWrapperFunction = Callable[[UnetApplyFunction, UnetParams], torch.Tensor]
diff --git a/comfy/utils.py b/comfy/utils.py
index 1113bf0f..48618e07 100644
--- a/comfy/utils.py
+++ b/comfy/utils.py
@@ -5,6 +5,8 @@ import comfy.checkpoint_pickle
 import safetensors.torch
 import numpy as np
 from PIL import Image
+import logging
+import itertools
 
 def load_torch_file(ckpt, safe_load=False, device=None):
     if device is None:
@@ -14,14 +16,14 @@ def load_torch_file(ckpt, safe_load=False, device=None):
     else:
         if safe_load:
             if not 'weights_only' in torch.load.__code__.co_varnames:
-                print("Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely.")
+                logging.warning("Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely.")
                 safe_load = False
         if safe_load:
             pl_sd = torch.load(ckpt, map_location=device, weights_only=True)
         else:
             pl_sd = torch.load(ckpt, map_location=device, pickle_module=comfy.checkpoint_pickle)
         if "global_step" in pl_sd:
-            print(f"Global Step: {pl_sd['global_step']}")
+            logging.debug(f"Global Step: {pl_sd['global_step']}")
         if "state_dict" in pl_sd:
             sd = pl_sd["state_dict"]
         else:
@@ -98,8 +100,22 @@ def transformers_convert(sd, prefix_from, prefix_to, number):
                     p = ["self_attn.q_proj", "self_attn.k_proj", "self_attn.v_proj"]
                     k_to = "{}encoder.layers.{}.{}.{}".format(prefix_to, resblock, p[x], y)
                     sd[k_to] = weights[shape_from*x:shape_from*(x + 1)]
+
     return sd
 
+def clip_text_transformers_convert(sd, prefix_from, prefix_to):
+    sd = transformers_convert(sd, prefix_from, "{}text_model.".format(prefix_to), 32)
+
+    tp = "{}text_projection.weight".format(prefix_from)
+    if tp in sd:
+        sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp)
+
+    tp = "{}text_projection".format(prefix_from)
+    if tp in sd:
+        sd["{}text_projection.weight".format(prefix_to)] = sd.pop(tp).transpose(0, 1).contiguous()
+    return sd
+
+
 UNET_MAP_ATTENTIONS = {
     "proj_in.weight",
     "proj_in.bias",
@@ -169,6 +185,8 @@ UNET_MAP_BASIC = {
 }
 
 def unet_to_diffusers(unet_config):
+    if "num_res_blocks" not in unet_config:
+        return {}
     num_res_blocks = unet_config["num_res_blocks"]
     channel_mult = unet_config["channel_mult"]
     transformer_depth = unet_config["transformer_depth"][:]
@@ -232,11 +250,93 @@ def unet_to_diffusers(unet_config):
 
     return diffusers_unet_map
 
-def repeat_to_batch_size(tensor, batch_size):
-    if tensor.shape[0] > batch_size:
-        return tensor[:batch_size]
-    elif tensor.shape[0] < batch_size:
-        return tensor.repeat([math.ceil(batch_size / tensor.shape[0])] + [1] * (len(tensor.shape) - 1))[:batch_size]
+def swap_scale_shift(weight):
+    shift, scale = weight.chunk(2, dim=0)
+    new_weight = torch.cat([scale, shift], dim=0)
+    return new_weight
+
+MMDIT_MAP_BASIC = {
+    ("context_embedder.bias", "context_embedder.bias"),
+    ("context_embedder.weight", "context_embedder.weight"),
+    ("t_embedder.mlp.0.bias", "time_text_embed.timestep_embedder.linear_1.bias"),
+    ("t_embedder.mlp.0.weight", "time_text_embed.timestep_embedder.linear_1.weight"),
+    ("t_embedder.mlp.2.bias", "time_text_embed.timestep_embedder.linear_2.bias"),
+    ("t_embedder.mlp.2.weight", "time_text_embed.timestep_embedder.linear_2.weight"),
+    ("x_embedder.proj.bias", "pos_embed.proj.bias"),
+    ("x_embedder.proj.weight", "pos_embed.proj.weight"),
+    ("y_embedder.mlp.0.bias", "time_text_embed.text_embedder.linear_1.bias"),
+    ("y_embedder.mlp.0.weight", "time_text_embed.text_embedder.linear_1.weight"),
+    ("y_embedder.mlp.2.bias", "time_text_embed.text_embedder.linear_2.bias"),
+    ("y_embedder.mlp.2.weight", "time_text_embed.text_embedder.linear_2.weight"),
+    ("pos_embed", "pos_embed.pos_embed"),
+    ("final_layer.adaLN_modulation.1.bias", "norm_out.linear.bias", swap_scale_shift),
+    ("final_layer.adaLN_modulation.1.weight", "norm_out.linear.weight", swap_scale_shift),
+    ("final_layer.linear.bias", "proj_out.bias"),
+    ("final_layer.linear.weight", "proj_out.weight"),
+}
+
+MMDIT_MAP_BLOCK = {
+    ("context_block.adaLN_modulation.1.bias", "norm1_context.linear.bias"),
+    ("context_block.adaLN_modulation.1.weight", "norm1_context.linear.weight"),
+    ("context_block.attn.proj.bias", "attn.to_add_out.bias"),
+    ("context_block.attn.proj.weight", "attn.to_add_out.weight"),
+    ("context_block.mlp.fc1.bias", "ff_context.net.0.proj.bias"),
+    ("context_block.mlp.fc1.weight", "ff_context.net.0.proj.weight"),
+    ("context_block.mlp.fc2.bias", "ff_context.net.2.bias"),
+    ("context_block.mlp.fc2.weight", "ff_context.net.2.weight"),
+    ("x_block.adaLN_modulation.1.bias", "norm1.linear.bias"),
+    ("x_block.adaLN_modulation.1.weight", "norm1.linear.weight"),
+    ("x_block.attn.proj.bias", "attn.to_out.0.bias"),
+    ("x_block.attn.proj.weight", "attn.to_out.0.weight"),
+    ("x_block.mlp.fc1.bias", "ff.net.0.proj.bias"),
+    ("x_block.mlp.fc1.weight", "ff.net.0.proj.weight"),
+    ("x_block.mlp.fc2.bias", "ff.net.2.bias"),
+    ("x_block.mlp.fc2.weight", "ff.net.2.weight"),
+}
+
+def mmdit_to_diffusers(mmdit_config, output_prefix=""):
+    key_map = {}
+
+    depth = mmdit_config.get("depth", 0)
+    num_blocks = mmdit_config.get("num_blocks", depth)
+    for i in range(num_blocks):
+        block_from = "transformer_blocks.{}".format(i)
+        block_to = "{}joint_blocks.{}".format(output_prefix, i)
+
+        offset = depth * 64
+
+        for end in ("weight", "bias"):
+            k = "{}.attn.".format(block_from)
+            qkv = "{}.x_block.attn.qkv.{}".format(block_to, end)
+            key_map["{}to_q.{}".format(k, end)] = (qkv, (0, 0, offset))
+            key_map["{}to_k.{}".format(k, end)] = (qkv, (0, offset, offset))
+            key_map["{}to_v.{}".format(k, end)] = (qkv, (0, offset * 2, offset))
+
+            qkv = "{}.context_block.attn.qkv.{}".format(block_to, end)
+            key_map["{}add_q_proj.{}".format(k, end)] = (qkv, (0, 0, offset))
+            key_map["{}add_k_proj.{}".format(k, end)] = (qkv, (0, offset, offset))
+            key_map["{}add_v_proj.{}".format(k, end)] = (qkv, (0, offset * 2, offset))
+
+        for k in MMDIT_MAP_BLOCK:
+            key_map["{}.{}".format(block_from, k[1])] = "{}.{}".format(block_to, k[0])
+
+    map_basic = MMDIT_MAP_BASIC.copy()
+    map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.bias".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.bias".format(depth - 1), swap_scale_shift))
+    map_basic.add(("joint_blocks.{}.context_block.adaLN_modulation.1.weight".format(depth - 1), "transformer_blocks.{}.norm1_context.linear.weight".format(depth - 1), swap_scale_shift))
+
+    for k in map_basic:
+        if len(k) > 2:
+            key_map[k[1]] = ("{}{}".format(output_prefix, k[0]), None, k[2])
+        else:
+            key_map[k[1]] = "{}{}".format(output_prefix, k[0])
+
+    return key_map
+
+def repeat_to_batch_size(tensor, batch_size, dim=0):
+    if tensor.shape[dim] > batch_size:
+        return tensor.narrow(dim, 0, batch_size)
+    elif tensor.shape[dim] < batch_size:
+        return tensor.repeat(dim * [1] + [math.ceil(batch_size / tensor.shape[dim])] + [1] * (len(tensor.shape) - 1 - dim)).narrow(dim, 0, batch_size)
     return tensor
 
 def resize_to_batch_size(tensor, batch_size):
@@ -278,8 +378,11 @@ def set_attr(obj, attr, value):
     for name in attrs[:-1]:
         obj = getattr(obj, name)
     prev = getattr(obj, attrs[-1])
-    setattr(obj, attrs[-1], torch.nn.Parameter(value, requires_grad=False))
-    del prev
+    setattr(obj, attrs[-1], value)
+    return prev
+
+def set_attr_param(obj, attr, value):
+    return set_attr(obj, attr, torch.nn.Parameter(value, requires_grad=False))
 
 def copy_to_param(obj, attr, value):
     # inplace update tensor instead of replacing it
@@ -405,34 +508,52 @@ def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap):
     return math.ceil((height / (tile_y - overlap))) * math.ceil((width / (tile_x - overlap)))
 
 @torch.inference_mode()
-def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):
-    output = torch.empty((samples.shape[0], out_channels, round(samples.shape[2] * upscale_amount), round(samples.shape[3] * upscale_amount)), device=output_device)
+def tiled_scale_multidim(samples, function, tile=(64, 64), overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):
+    dims = len(tile)
+    output = torch.empty([samples.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), samples.shape[2:])), device=output_device)
+
     for b in range(samples.shape[0]):
         s = samples[b:b+1]
-        out = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device)
-        out_div = torch.zeros((s.shape[0], out_channels, round(s.shape[2] * upscale_amount), round(s.shape[3] * upscale_amount)), device=output_device)
-        for y in range(0, s.shape[2], tile_y - overlap):
-            for x in range(0, s.shape[3], tile_x - overlap):
-                x = max(0, min(s.shape[-1] - overlap, x))
-                y = max(0, min(s.shape[-2] - overlap, y))
-                s_in = s[:,:,y:y+tile_y,x:x+tile_x]
+        out = torch.zeros([s.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), s.shape[2:])), device=output_device)
+        out_div = torch.zeros([s.shape[0], out_channels] + list(map(lambda a: round(a * upscale_amount), s.shape[2:])), device=output_device)
 
-                ps = function(s_in).to(output_device)
-                mask = torch.ones_like(ps)
-                feather = round(overlap * upscale_amount)
-                for t in range(feather):
-                        mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1))
-                        mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1))
-                        mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1))
-                        mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1))
-                out[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += ps * mask
-                out_div[:,:,round(y*upscale_amount):round((y+tile_y)*upscale_amount),round(x*upscale_amount):round((x+tile_x)*upscale_amount)] += mask
-                if pbar is not None:
-                    pbar.update(1)
+        for it in itertools.product(*map(lambda a: range(0, a[0], a[1] - overlap), zip(s.shape[2:], tile))):
+            s_in = s
+            upscaled = []
+
+            for d in range(dims):
+                pos = max(0, min(s.shape[d + 2] - overlap, it[d]))
+                l = min(tile[d], s.shape[d + 2] - pos)
+                s_in = s_in.narrow(d + 2, pos, l)
+                upscaled.append(round(pos * upscale_amount))
+            ps = function(s_in).to(output_device)
+            mask = torch.ones_like(ps)
+            feather = round(overlap * upscale_amount)
+            for t in range(feather):
+                for d in range(2, dims + 2):
+                    m = mask.narrow(d, t, 1)
+                    m *= ((1.0/feather) * (t + 1))
+                    m = mask.narrow(d, mask.shape[d] -1 -t, 1)
+                    m *= ((1.0/feather) * (t + 1))
+
+            o = out
+            o_d = out_div
+            for d in range(dims):
+                o = o.narrow(d + 2, upscaled[d], mask.shape[d + 2])
+                o_d = o_d.narrow(d + 2, upscaled[d], mask.shape[d + 2])
+
+            o += ps * mask
+            o_d += mask
+
+            if pbar is not None:
+                pbar.update(1)
 
         output[b:b+1] = out/out_div
     return output
 
+def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):
+    return tiled_scale_multidim(samples, function, (tile_y, tile_x), overlap, upscale_amount, out_channels, output_device, pbar)
+
 PROGRESS_BAR_ENABLED = True
 def set_progress_bar_enabled(enabled):
     global PROGRESS_BAR_ENABLED
diff --git a/comfy_extras/chainner_models/__init__.py b/comfy_extras/chainner_models/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/comfy_extras/chainner_models/architecture/DAT.py b/comfy_extras/chainner_models/architecture/DAT.py
deleted file mode 100644
index 0bcc26ef..00000000
--- a/comfy_extras/chainner_models/architecture/DAT.py
+++ /dev/null
@@ -1,1182 +0,0 @@
-# pylint: skip-file
-import math
-import re
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.utils.checkpoint as checkpoint
-from einops import rearrange
-from einops.layers.torch import Rearrange
-from torch import Tensor
-from torch.nn import functional as F
-
-from .timm.drop import DropPath
-from .timm.weight_init import trunc_normal_
-
-
-def img2windows(img, H_sp, W_sp):
-    """
-    Input: Image (B, C, H, W)
-    Output: Window Partition (B', N, C)
-    """
-    B, C, H, W = img.shape
-    img_reshape = img.view(B, C, H // H_sp, H_sp, W // W_sp, W_sp)
-    img_perm = (
-        img_reshape.permute(0, 2, 4, 3, 5, 1).contiguous().reshape(-1, H_sp * W_sp, C)
-    )
-    return img_perm
-
-
-def windows2img(img_splits_hw, H_sp, W_sp, H, W):
-    """
-    Input: Window Partition (B', N, C)
-    Output: Image (B, H, W, C)
-    """
-    B = int(img_splits_hw.shape[0] / (H * W / H_sp / W_sp))
-
-    img = img_splits_hw.view(B, H // H_sp, W // W_sp, H_sp, W_sp, -1)
-    img = img.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return img
-
-
-class SpatialGate(nn.Module):
-    """Spatial-Gate.
-    Args:
-        dim (int): Half of input channels.
-    """
-
-    def __init__(self, dim):
-        super().__init__()
-        self.norm = nn.LayerNorm(dim)
-        self.conv = nn.Conv2d(
-            dim, dim, kernel_size=3, stride=1, padding=1, groups=dim
-        )  # DW Conv
-
-    def forward(self, x, H, W):
-        # Split
-        x1, x2 = x.chunk(2, dim=-1)
-        B, N, C = x.shape
-        x2 = (
-            self.conv(self.norm(x2).transpose(1, 2).contiguous().view(B, C // 2, H, W))
-            .flatten(2)
-            .transpose(-1, -2)
-            .contiguous()
-        )
-
-        return x1 * x2
-
-
-class SGFN(nn.Module):
-    """Spatial-Gate Feed-Forward Network.
-    Args:
-        in_features (int): Number of input channels.
-        hidden_features (int | None): Number of hidden channels. Default: None
-        out_features (int | None): Number of output channels. Default: None
-        act_layer (nn.Module): Activation layer. Default: nn.GELU
-        drop (float): Dropout rate. Default: 0.0
-    """
-
-    def __init__(
-        self,
-        in_features,
-        hidden_features=None,
-        out_features=None,
-        act_layer=nn.GELU,
-        drop=0.0,
-    ):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.sg = SpatialGate(hidden_features // 2)
-        self.fc2 = nn.Linear(hidden_features // 2, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x, H, W):
-        """
-        Input: x: (B, H*W, C), H, W
-        Output: x: (B, H*W, C)
-        """
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-
-        x = self.sg(x, H, W)
-        x = self.drop(x)
-
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-class DynamicPosBias(nn.Module):
-    # The implementation builds on Crossformer code https://github.com/cheerss/CrossFormer/blob/main/models/crossformer.py
-    """Dynamic Relative Position Bias.
-    Args:
-        dim (int): Number of input channels.
-        num_heads (int): Number of attention heads.
-        residual (bool):  If True, use residual strage to connect conv.
-    """
-
-    def __init__(self, dim, num_heads, residual):
-        super().__init__()
-        self.residual = residual
-        self.num_heads = num_heads
-        self.pos_dim = dim // 4
-        self.pos_proj = nn.Linear(2, self.pos_dim)
-        self.pos1 = nn.Sequential(
-            nn.LayerNorm(self.pos_dim),
-            nn.ReLU(inplace=True),
-            nn.Linear(self.pos_dim, self.pos_dim),
-        )
-        self.pos2 = nn.Sequential(
-            nn.LayerNorm(self.pos_dim),
-            nn.ReLU(inplace=True),
-            nn.Linear(self.pos_dim, self.pos_dim),
-        )
-        self.pos3 = nn.Sequential(
-            nn.LayerNorm(self.pos_dim),
-            nn.ReLU(inplace=True),
-            nn.Linear(self.pos_dim, self.num_heads),
-        )
-
-    def forward(self, biases):
-        if self.residual:
-            pos = self.pos_proj(biases)  # 2Gh-1 * 2Gw-1, heads
-            pos = pos + self.pos1(pos)
-            pos = pos + self.pos2(pos)
-            pos = self.pos3(pos)
-        else:
-            pos = self.pos3(self.pos2(self.pos1(self.pos_proj(biases))))
-        return pos
-
-
-class Spatial_Attention(nn.Module):
-    """Spatial Window Self-Attention.
-    It supports rectangle window (containing square window).
-    Args:
-        dim (int): Number of input channels.
-        idx (int): The indentix of window. (0/1)
-        split_size (tuple(int)): Height and Width of spatial window.
-        dim_out (int | None): The dimension of the attention output. Default: None
-        num_heads (int): Number of attention heads. Default: 6
-        attn_drop (float): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float): Dropout ratio of output. Default: 0.0
-        qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set
-        position_bias (bool): The dynamic relative position bias. Default: True
-    """
-
-    def __init__(
-        self,
-        dim,
-        idx,
-        split_size=[8, 8],
-        dim_out=None,
-        num_heads=6,
-        attn_drop=0.0,
-        proj_drop=0.0,
-        qk_scale=None,
-        position_bias=True,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.dim_out = dim_out or dim
-        self.split_size = split_size
-        self.num_heads = num_heads
-        self.idx = idx
-        self.position_bias = position_bias
-
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim**-0.5
-
-        if idx == 0:
-            H_sp, W_sp = self.split_size[0], self.split_size[1]
-        elif idx == 1:
-            W_sp, H_sp = self.split_size[0], self.split_size[1]
-        else:
-            print("ERROR MODE", idx)
-            exit(0)
-        self.H_sp = H_sp
-        self.W_sp = W_sp
-
-        if self.position_bias:
-            self.pos = DynamicPosBias(self.dim // 4, self.num_heads, residual=False)
-            # generate mother-set
-            position_bias_h = torch.arange(1 - self.H_sp, self.H_sp)
-            position_bias_w = torch.arange(1 - self.W_sp, self.W_sp)
-            biases = torch.stack(torch.meshgrid([position_bias_h, position_bias_w]))
-            biases = biases.flatten(1).transpose(0, 1).contiguous().float()
-            self.register_buffer("rpe_biases", biases)
-
-            # get pair-wise relative position index for each token inside the window
-            coords_h = torch.arange(self.H_sp)
-            coords_w = torch.arange(self.W_sp)
-            coords = torch.stack(torch.meshgrid([coords_h, coords_w]))
-            coords_flatten = torch.flatten(coords, 1)
-            relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]
-            relative_coords = relative_coords.permute(1, 2, 0).contiguous()
-            relative_coords[:, :, 0] += self.H_sp - 1
-            relative_coords[:, :, 1] += self.W_sp - 1
-            relative_coords[:, :, 0] *= 2 * self.W_sp - 1
-            relative_position_index = relative_coords.sum(-1)
-            self.register_buffer("relative_position_index", relative_position_index)
-
-        self.attn_drop = nn.Dropout(attn_drop)
-
-    def im2win(self, x, H, W):
-        B, N, C = x.shape
-        x = x.transpose(-2, -1).contiguous().view(B, C, H, W)
-        x = img2windows(x, self.H_sp, self.W_sp)
-        x = (
-            x.reshape(-1, self.H_sp * self.W_sp, self.num_heads, C // self.num_heads)
-            .permute(0, 2, 1, 3)
-            .contiguous()
-        )
-        return x
-
-    def forward(self, qkv, H, W, mask=None):
-        """
-        Input: qkv: (B, 3*L, C), H, W, mask: (B, N, N), N is the window size
-        Output: x (B, H, W, C)
-        """
-        q, k, v = qkv[0], qkv[1], qkv[2]
-
-        B, L, C = q.shape
-        assert L == H * W, "flatten img_tokens has wrong size"
-
-        # partition the q,k,v, image to window
-        q = self.im2win(q, H, W)
-        k = self.im2win(k, H, W)
-        v = self.im2win(v, H, W)
-
-        q = q * self.scale
-        attn = q @ k.transpose(-2, -1)  # B head N C @ B head C N --> B head N N
-
-        # calculate drpe
-        if self.position_bias:
-            pos = self.pos(self.rpe_biases)
-            # select position bias
-            relative_position_bias = pos[self.relative_position_index.view(-1)].view(
-                self.H_sp * self.W_sp, self.H_sp * self.W_sp, -1
-            )
-            relative_position_bias = relative_position_bias.permute(
-                2, 0, 1
-            ).contiguous()
-            attn = attn + relative_position_bias.unsqueeze(0)
-
-        N = attn.shape[3]
-
-        # use mask for shift window
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(
-                0
-            )
-            attn = attn.view(-1, self.num_heads, N, N)
-
-        attn = nn.functional.softmax(attn, dim=-1, dtype=attn.dtype)
-        attn = self.attn_drop(attn)
-
-        x = attn @ v
-        x = x.transpose(1, 2).reshape(
-            -1, self.H_sp * self.W_sp, C
-        )  # B head N N @ B head N C
-
-        # merge the window, window to image
-        x = windows2img(x, self.H_sp, self.W_sp, H, W)  # B H' W' C
-
-        return x
-
-
-class Adaptive_Spatial_Attention(nn.Module):
-    # The implementation builds on CAT code https://github.com/Zhengchen1999/CAT
-    """Adaptive Spatial Self-Attention
-    Args:
-        dim (int): Number of input channels.
-        num_heads (int): Number of attention heads. Default: 6
-        split_size (tuple(int)): Height and Width of spatial window.
-        shift_size (tuple(int)): Shift size for spatial window.
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float): Dropout rate. Default: 0.0
-        attn_drop (float): Attention dropout rate. Default: 0.0
-        rg_idx (int): The indentix of Residual Group (RG)
-        b_idx (int): The indentix of Block in each RG
-    """
-
-    def __init__(
-        self,
-        dim,
-        num_heads,
-        reso=64,
-        split_size=[8, 8],
-        shift_size=[1, 2],
-        qkv_bias=False,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        rg_idx=0,
-        b_idx=0,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.num_heads = num_heads
-        self.split_size = split_size
-        self.shift_size = shift_size
-        self.b_idx = b_idx
-        self.rg_idx = rg_idx
-        self.patches_resolution = reso
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-
-        assert (
-            0 <= self.shift_size[0] < self.split_size[0]
-        ), "shift_size must in 0-split_size0"
-        assert (
-            0 <= self.shift_size[1] < self.split_size[1]
-        ), "shift_size must in 0-split_size1"
-
-        self.branch_num = 2
-
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(drop)
-
-        self.attns = nn.ModuleList(
-            [
-                Spatial_Attention(
-                    dim // 2,
-                    idx=i,
-                    split_size=split_size,
-                    num_heads=num_heads // 2,
-                    dim_out=dim // 2,
-                    qk_scale=qk_scale,
-                    attn_drop=attn_drop,
-                    proj_drop=drop,
-                    position_bias=True,
-                )
-                for i in range(self.branch_num)
-            ]
-        )
-
-        if (self.rg_idx % 2 == 0 and self.b_idx > 0 and (self.b_idx - 2) % 4 == 0) or (
-            self.rg_idx % 2 != 0 and self.b_idx % 4 == 0
-        ):
-            attn_mask = self.calculate_mask(
-                self.patches_resolution, self.patches_resolution
-            )
-            self.register_buffer("attn_mask_0", attn_mask[0])
-            self.register_buffer("attn_mask_1", attn_mask[1])
-        else:
-            attn_mask = None
-            self.register_buffer("attn_mask_0", None)
-            self.register_buffer("attn_mask_1", None)
-
-        self.dwconv = nn.Sequential(
-            nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim),
-            nn.BatchNorm2d(dim),
-            nn.GELU(),
-        )
-        self.channel_interaction = nn.Sequential(
-            nn.AdaptiveAvgPool2d(1),
-            nn.Conv2d(dim, dim // 8, kernel_size=1),
-            nn.BatchNorm2d(dim // 8),
-            nn.GELU(),
-            nn.Conv2d(dim // 8, dim, kernel_size=1),
-        )
-        self.spatial_interaction = nn.Sequential(
-            nn.Conv2d(dim, dim // 16, kernel_size=1),
-            nn.BatchNorm2d(dim // 16),
-            nn.GELU(),
-            nn.Conv2d(dim // 16, 1, kernel_size=1),
-        )
-
-    def calculate_mask(self, H, W):
-        # The implementation builds on Swin Transformer code https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_transformer.py
-        # calculate attention mask for shift window
-        img_mask_0 = torch.zeros((1, H, W, 1))  # 1 H W 1 idx=0
-        img_mask_1 = torch.zeros((1, H, W, 1))  # 1 H W 1 idx=1
-        h_slices_0 = (
-            slice(0, -self.split_size[0]),
-            slice(-self.split_size[0], -self.shift_size[0]),
-            slice(-self.shift_size[0], None),
-        )
-        w_slices_0 = (
-            slice(0, -self.split_size[1]),
-            slice(-self.split_size[1], -self.shift_size[1]),
-            slice(-self.shift_size[1], None),
-        )
-
-        h_slices_1 = (
-            slice(0, -self.split_size[1]),
-            slice(-self.split_size[1], -self.shift_size[1]),
-            slice(-self.shift_size[1], None),
-        )
-        w_slices_1 = (
-            slice(0, -self.split_size[0]),
-            slice(-self.split_size[0], -self.shift_size[0]),
-            slice(-self.shift_size[0], None),
-        )
-        cnt = 0
-        for h in h_slices_0:
-            for w in w_slices_0:
-                img_mask_0[:, h, w, :] = cnt
-                cnt += 1
-        cnt = 0
-        for h in h_slices_1:
-            for w in w_slices_1:
-                img_mask_1[:, h, w, :] = cnt
-                cnt += 1
-
-        # calculate mask for window-0
-        img_mask_0 = img_mask_0.view(
-            1,
-            H // self.split_size[0],
-            self.split_size[0],
-            W // self.split_size[1],
-            self.split_size[1],
-            1,
-        )
-        img_mask_0 = (
-            img_mask_0.permute(0, 1, 3, 2, 4, 5)
-            .contiguous()
-            .view(-1, self.split_size[0], self.split_size[1], 1)
-        )  # nW, sw[0], sw[1], 1
-        mask_windows_0 = img_mask_0.view(-1, self.split_size[0] * self.split_size[1])
-        attn_mask_0 = mask_windows_0.unsqueeze(1) - mask_windows_0.unsqueeze(2)
-        attn_mask_0 = attn_mask_0.masked_fill(
-            attn_mask_0 != 0, float(-100.0)
-        ).masked_fill(attn_mask_0 == 0, float(0.0))
-
-        # calculate mask for window-1
-        img_mask_1 = img_mask_1.view(
-            1,
-            H // self.split_size[1],
-            self.split_size[1],
-            W // self.split_size[0],
-            self.split_size[0],
-            1,
-        )
-        img_mask_1 = (
-            img_mask_1.permute(0, 1, 3, 2, 4, 5)
-            .contiguous()
-            .view(-1, self.split_size[1], self.split_size[0], 1)
-        )  # nW, sw[1], sw[0], 1
-        mask_windows_1 = img_mask_1.view(-1, self.split_size[1] * self.split_size[0])
-        attn_mask_1 = mask_windows_1.unsqueeze(1) - mask_windows_1.unsqueeze(2)
-        attn_mask_1 = attn_mask_1.masked_fill(
-            attn_mask_1 != 0, float(-100.0)
-        ).masked_fill(attn_mask_1 == 0, float(0.0))
-
-        return attn_mask_0, attn_mask_1
-
-    def forward(self, x, H, W):
-        """
-        Input: x: (B, H*W, C), H, W
-        Output: x: (B, H*W, C)
-        """
-        B, L, C = x.shape
-        assert L == H * W, "flatten img_tokens has wrong size"
-
-        qkv = self.qkv(x).reshape(B, -1, 3, C).permute(2, 0, 1, 3)  # 3, B, HW, C
-        # V without partition
-        v = qkv[2].transpose(-2, -1).contiguous().view(B, C, H, W)
-
-        # image padding
-        max_split_size = max(self.split_size[0], self.split_size[1])
-        pad_l = pad_t = 0
-        pad_r = (max_split_size - W % max_split_size) % max_split_size
-        pad_b = (max_split_size - H % max_split_size) % max_split_size
-
-        qkv = qkv.reshape(3 * B, H, W, C).permute(0, 3, 1, 2)  # 3B C H W
-        qkv = (
-            F.pad(qkv, (pad_l, pad_r, pad_t, pad_b))
-            .reshape(3, B, C, -1)
-            .transpose(-2, -1)
-        )  # l r t b
-        _H = pad_b + H
-        _W = pad_r + W
-        _L = _H * _W
-
-        # window-0 and window-1 on split channels [C/2, C/2]; for square windows (e.g., 8x8), window-0 and window-1 can be merged
-        # shift in block: (0, 4, 8, ...), (2, 6, 10, ...), (0, 4, 8, ...), (2, 6, 10, ...), ...
-        if (self.rg_idx % 2 == 0 and self.b_idx > 0 and (self.b_idx - 2) % 4 == 0) or (
-            self.rg_idx % 2 != 0 and self.b_idx % 4 == 0
-        ):
-            qkv = qkv.view(3, B, _H, _W, C)
-            qkv_0 = torch.roll(
-                qkv[:, :, :, :, : C // 2],
-                shifts=(-self.shift_size[0], -self.shift_size[1]),
-                dims=(2, 3),
-            )
-            qkv_0 = qkv_0.view(3, B, _L, C // 2)
-            qkv_1 = torch.roll(
-                qkv[:, :, :, :, C // 2 :],
-                shifts=(-self.shift_size[1], -self.shift_size[0]),
-                dims=(2, 3),
-            )
-            qkv_1 = qkv_1.view(3, B, _L, C // 2)
-
-            if self.patches_resolution != _H or self.patches_resolution != _W:
-                mask_tmp = self.calculate_mask(_H, _W)
-                x1_shift = self.attns[0](qkv_0, _H, _W, mask=mask_tmp[0].to(x.device))
-                x2_shift = self.attns[1](qkv_1, _H, _W, mask=mask_tmp[1].to(x.device))
-            else:
-                x1_shift = self.attns[0](qkv_0, _H, _W, mask=self.attn_mask_0)
-                x2_shift = self.attns[1](qkv_1, _H, _W, mask=self.attn_mask_1)
-
-            x1 = torch.roll(
-                x1_shift, shifts=(self.shift_size[0], self.shift_size[1]), dims=(1, 2)
-            )
-            x2 = torch.roll(
-                x2_shift, shifts=(self.shift_size[1], self.shift_size[0]), dims=(1, 2)
-            )
-            x1 = x1[:, :H, :W, :].reshape(B, L, C // 2)
-            x2 = x2[:, :H, :W, :].reshape(B, L, C // 2)
-            # attention output
-            attened_x = torch.cat([x1, x2], dim=2)
-
-        else:
-            x1 = self.attns[0](qkv[:, :, :, : C // 2], _H, _W)[:, :H, :W, :].reshape(
-                B, L, C // 2
-            )
-            x2 = self.attns[1](qkv[:, :, :, C // 2 :], _H, _W)[:, :H, :W, :].reshape(
-                B, L, C // 2
-            )
-            # attention output
-            attened_x = torch.cat([x1, x2], dim=2)
-
-        # convolution output
-        conv_x = self.dwconv(v)
-
-        # Adaptive Interaction Module (AIM)
-        # C-Map (before sigmoid)
-        channel_map = (
-            self.channel_interaction(conv_x)
-            .permute(0, 2, 3, 1)
-            .contiguous()
-            .view(B, 1, C)
-        )
-        # S-Map (before sigmoid)
-        attention_reshape = attened_x.transpose(-2, -1).contiguous().view(B, C, H, W)
-        spatial_map = self.spatial_interaction(attention_reshape)
-
-        # C-I
-        attened_x = attened_x * torch.sigmoid(channel_map)
-        # S-I
-        conv_x = torch.sigmoid(spatial_map) * conv_x
-        conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(B, L, C)
-
-        x = attened_x + conv_x
-
-        x = self.proj(x)
-        x = self.proj_drop(x)
-
-        return x
-
-
-class Adaptive_Channel_Attention(nn.Module):
-    # The implementation builds on XCiT code https://github.com/facebookresearch/xcit
-    """Adaptive Channel Self-Attention
-    Args:
-        dim (int): Number of input channels.
-        num_heads (int): Number of attention heads. Default: 6
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set.
-        attn_drop (float): Attention dropout rate. Default: 0.0
-        drop_path (float): Stochastic depth rate. Default: 0.0
-    """
-
-    def __init__(
-        self,
-        dim,
-        num_heads=8,
-        qkv_bias=False,
-        qk_scale=None,
-        attn_drop=0.0,
-        proj_drop=0.0,
-    ):
-        super().__init__()
-        self.num_heads = num_heads
-        self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        self.dwconv = nn.Sequential(
-            nn.Conv2d(dim, dim, kernel_size=3, stride=1, padding=1, groups=dim),
-            nn.BatchNorm2d(dim),
-            nn.GELU(),
-        )
-        self.channel_interaction = nn.Sequential(
-            nn.AdaptiveAvgPool2d(1),
-            nn.Conv2d(dim, dim // 8, kernel_size=1),
-            nn.BatchNorm2d(dim // 8),
-            nn.GELU(),
-            nn.Conv2d(dim // 8, dim, kernel_size=1),
-        )
-        self.spatial_interaction = nn.Sequential(
-            nn.Conv2d(dim, dim // 16, kernel_size=1),
-            nn.BatchNorm2d(dim // 16),
-            nn.GELU(),
-            nn.Conv2d(dim // 16, 1, kernel_size=1),
-        )
-
-    def forward(self, x, H, W):
-        """
-        Input: x: (B, H*W, C), H, W
-        Output: x: (B, H*W, C)
-        """
-        B, N, C = x.shape
-        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads)
-        qkv = qkv.permute(2, 0, 3, 1, 4)
-        q, k, v = qkv[0], qkv[1], qkv[2]
-
-        q = q.transpose(-2, -1)
-        k = k.transpose(-2, -1)
-        v = v.transpose(-2, -1)
-
-        v_ = v.reshape(B, C, N).contiguous().view(B, C, H, W)
-
-        q = torch.nn.functional.normalize(q, dim=-1)
-        k = torch.nn.functional.normalize(k, dim=-1)
-
-        attn = (q @ k.transpose(-2, -1)) * self.temperature
-        attn = attn.softmax(dim=-1)
-        attn = self.attn_drop(attn)
-
-        # attention output
-        attened_x = (attn @ v).permute(0, 3, 1, 2).reshape(B, N, C)
-
-        # convolution output
-        conv_x = self.dwconv(v_)
-
-        # Adaptive Interaction Module (AIM)
-        # C-Map (before sigmoid)
-        attention_reshape = attened_x.transpose(-2, -1).contiguous().view(B, C, H, W)
-        channel_map = self.channel_interaction(attention_reshape)
-        # S-Map (before sigmoid)
-        spatial_map = (
-            self.spatial_interaction(conv_x)
-            .permute(0, 2, 3, 1)
-            .contiguous()
-            .view(B, N, 1)
-        )
-
-        # S-I
-        attened_x = attened_x * torch.sigmoid(spatial_map)
-        # C-I
-        conv_x = conv_x * torch.sigmoid(channel_map)
-        conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(B, N, C)
-
-        x = attened_x + conv_x
-
-        x = self.proj(x)
-        x = self.proj_drop(x)
-
-        return x
-
-
-class DATB(nn.Module):
-    def __init__(
-        self,
-        dim,
-        num_heads,
-        reso=64,
-        split_size=[2, 4],
-        shift_size=[1, 2],
-        expansion_factor=4.0,
-        qkv_bias=False,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        act_layer=nn.GELU,
-        norm_layer=nn.LayerNorm,
-        rg_idx=0,
-        b_idx=0,
-    ):
-        super().__init__()
-
-        self.norm1 = norm_layer(dim)
-
-        if b_idx % 2 == 0:
-            # DSTB
-            self.attn = Adaptive_Spatial_Attention(
-                dim,
-                num_heads=num_heads,
-                reso=reso,
-                split_size=split_size,
-                shift_size=shift_size,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                drop=drop,
-                attn_drop=attn_drop,
-                rg_idx=rg_idx,
-                b_idx=b_idx,
-            )
-        else:
-            # DCTB
-            self.attn = Adaptive_Channel_Attention(
-                dim,
-                num_heads=num_heads,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                attn_drop=attn_drop,
-                proj_drop=drop,
-            )
-        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
-
-        ffn_hidden_dim = int(dim * expansion_factor)
-        self.ffn = SGFN(
-            in_features=dim,
-            hidden_features=ffn_hidden_dim,
-            out_features=dim,
-            act_layer=act_layer,
-        )
-        self.norm2 = norm_layer(dim)
-
-    def forward(self, x, x_size):
-        """
-        Input: x: (B, H*W, C), x_size: (H, W)
-        Output: x: (B, H*W, C)
-        """
-        H, W = x_size
-        x = x + self.drop_path(self.attn(self.norm1(x), H, W))
-        x = x + self.drop_path(self.ffn(self.norm2(x), H, W))
-
-        return x
-
-
-class ResidualGroup(nn.Module):
-    """ResidualGroup
-    Args:
-        dim (int): Number of input channels.
-        reso (int): Input resolution.
-        num_heads (int): Number of attention heads.
-        split_size (tuple(int)): Height and Width of spatial window.
-        expansion_factor (float): Ratio of ffn hidden dim to embedding dim.
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. Default: None
-        drop (float): Dropout rate. Default: 0
-        attn_drop(float): Attention dropout rate. Default: 0
-        drop_paths (float | None): Stochastic depth rate.
-        act_layer (nn.Module): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm
-        depth (int): Number of dual aggregation Transformer blocks in residual group.
-        use_chk (bool): Whether to use checkpointing to save memory.
-        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
-    """
-
-    def __init__(
-        self,
-        dim,
-        reso,
-        num_heads,
-        split_size=[2, 4],
-        expansion_factor=4.0,
-        qkv_bias=False,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_paths=None,
-        act_layer=nn.GELU,
-        norm_layer=nn.LayerNorm,
-        depth=2,
-        use_chk=False,
-        resi_connection="1conv",
-        rg_idx=0,
-    ):
-        super().__init__()
-        self.use_chk = use_chk
-        self.reso = reso
-
-        self.blocks = nn.ModuleList(
-            [
-                DATB(
-                    dim=dim,
-                    num_heads=num_heads,
-                    reso=reso,
-                    split_size=split_size,
-                    shift_size=[split_size[0] // 2, split_size[1] // 2],
-                    expansion_factor=expansion_factor,
-                    qkv_bias=qkv_bias,
-                    qk_scale=qk_scale,
-                    drop=drop,
-                    attn_drop=attn_drop,
-                    drop_path=drop_paths[i],
-                    act_layer=act_layer,
-                    norm_layer=norm_layer,
-                    rg_idx=rg_idx,
-                    b_idx=i,
-                )
-                for i in range(depth)
-            ]
-        )
-
-        if resi_connection == "1conv":
-            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
-        elif resi_connection == "3conv":
-            self.conv = nn.Sequential(
-                nn.Conv2d(dim, dim // 4, 3, 1, 1),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(dim // 4, dim, 3, 1, 1),
-            )
-
-    def forward(self, x, x_size):
-        """
-        Input: x: (B, H*W, C), x_size: (H, W)
-        Output: x: (B, H*W, C)
-        """
-        H, W = x_size
-        res = x
-        for blk in self.blocks:
-            if self.use_chk:
-                x = checkpoint.checkpoint(blk, x, x_size)
-            else:
-                x = blk(x, x_size)
-        x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W)
-        x = self.conv(x)
-        x = rearrange(x, "b c h w -> b (h w) c")
-        x = res + x
-
-        return x
-
-
-class Upsample(nn.Sequential):
-    """Upsample module.
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(
-                f"scale {scale} is not supported. " "Supported scales: 2^n and 3."
-            )
-        super(Upsample, self).__init__(*m)
-
-
-class UpsampleOneStep(nn.Sequential):
-    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
-       Used in lightweight SR to save parameters.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-
-    """
-
-    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
-        self.num_feat = num_feat
-        self.input_resolution = input_resolution
-        m = []
-        m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1))
-        m.append(nn.PixelShuffle(scale))
-        super(UpsampleOneStep, self).__init__(*m)
-
-    def flops(self):
-        h, w = self.input_resolution
-        flops = h * w * self.num_feat * 3 * 9
-        return flops
-
-
-class DAT(nn.Module):
-    """Dual Aggregation Transformer
-    Args:
-        img_size (int): Input image size. Default: 64
-        in_chans (int): Number of input image channels. Default: 3
-        embed_dim (int): Patch embedding dimension. Default: 180
-        depths (tuple(int)): Depth of each residual group (number of DATB in each RG).
-        split_size (tuple(int)): Height and Width of spatial window.
-        num_heads (tuple(int)): Number of attention heads in different residual groups.
-        expansion_factor (float): Ratio of ffn hidden dim to embedding dim. Default: 4
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None): Override default qk scale of head_dim ** -0.5 if set. Default: None
-        drop_rate (float): Dropout rate. Default: 0
-        attn_drop_rate (float): Attention dropout rate. Default: 0
-        drop_path_rate (float): Stochastic depth rate. Default: 0.1
-        act_layer (nn.Module): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm
-        use_chk (bool): Whether to use checkpointing to save memory.
-        upscale: Upscale factor. 2/3/4 for image SR
-        img_range: Image range. 1. or 255.
-        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
-    """
-
-    def __init__(self, state_dict):
-        super().__init__()
-
-        # defaults
-        img_size = 64
-        in_chans = 3
-        embed_dim = 180
-        split_size = [2, 4]
-        depth = [2, 2, 2, 2]
-        num_heads = [2, 2, 2, 2]
-        expansion_factor = 4.0
-        qkv_bias = True
-        qk_scale = None
-        drop_rate = 0.0
-        attn_drop_rate = 0.0
-        drop_path_rate = 0.1
-        act_layer = nn.GELU
-        norm_layer = nn.LayerNorm
-        use_chk = False
-        upscale = 2
-        img_range = 1.0
-        resi_connection = "1conv"
-        upsampler = "pixelshuffle"
-
-        self.model_arch = "DAT"
-        self.sub_type = "SR"
-        self.state = state_dict
-
-        state_keys = state_dict.keys()
-        if "conv_before_upsample.0.weight" in state_keys:
-            if "conv_up1.weight" in state_keys:
-                upsampler = "nearest+conv"
-            else:
-                upsampler = "pixelshuffle"
-                supports_fp16 = False
-        elif "upsample.0.weight" in state_keys:
-            upsampler = "pixelshuffledirect"
-        else:
-            upsampler = ""
-
-        num_feat = (
-            state_dict.get("conv_before_upsample.0.weight", None).shape[1]
-            if state_dict.get("conv_before_upsample.weight", None)
-            else 64
-        )
-
-        num_in_ch = state_dict["conv_first.weight"].shape[1]
-        in_chans = num_in_ch
-        if "conv_last.weight" in state_keys:
-            num_out_ch = state_dict["conv_last.weight"].shape[0]
-        else:
-            num_out_ch = num_in_ch
-
-        upscale = 1
-        if upsampler == "nearest+conv":
-            upsample_keys = [
-                x for x in state_keys if "conv_up" in x and "bias" not in x
-            ]
-
-            for upsample_key in upsample_keys:
-                upscale *= 2
-        elif upsampler == "pixelshuffle":
-            upsample_keys = [
-                x
-                for x in state_keys
-                if "upsample" in x and "conv" not in x and "bias" not in x
-            ]
-            for upsample_key in upsample_keys:
-                shape = state_dict[upsample_key].shape[0]
-                upscale *= math.sqrt(shape // num_feat)
-            upscale = int(upscale)
-        elif upsampler == "pixelshuffledirect":
-            upscale = int(
-                math.sqrt(state_dict["upsample.0.bias"].shape[0] // num_out_ch)
-            )
-
-        max_layer_num = 0
-        max_block_num = 0
-        for key in state_keys:
-            result = re.match(r"layers.(\d*).blocks.(\d*).norm1.weight", key)
-            if result:
-                layer_num, block_num = result.groups()
-                max_layer_num = max(max_layer_num, int(layer_num))
-                max_block_num = max(max_block_num, int(block_num))
-
-        depth = [max_block_num + 1 for _ in range(max_layer_num + 1)]
-
-        if "layers.0.blocks.1.attn.temperature" in state_keys:
-            num_heads_num = state_dict["layers.0.blocks.1.attn.temperature"].shape[0]
-            num_heads = [num_heads_num for _ in range(max_layer_num + 1)]
-        else:
-            num_heads = depth
-
-        embed_dim = state_dict["conv_first.weight"].shape[0]
-        expansion_factor = float(
-            state_dict["layers.0.blocks.0.ffn.fc1.weight"].shape[0] / embed_dim
-        )
-
-        # TODO: could actually count the layers, but this should do
-        if "layers.0.conv.4.weight" in state_keys:
-            resi_connection = "3conv"
-        else:
-            resi_connection = "1conv"
-
-        if "layers.0.blocks.2.attn.attn_mask_0" in state_keys:
-            attn_mask_0_x, attn_mask_0_y, attn_mask_0_z = state_dict[
-                "layers.0.blocks.2.attn.attn_mask_0"
-            ].shape
-
-            img_size = int(math.sqrt(attn_mask_0_x * attn_mask_0_y))
-
-        if "layers.0.blocks.0.attn.attns.0.rpe_biases" in state_keys:
-            split_sizes = (
-                state_dict["layers.0.blocks.0.attn.attns.0.rpe_biases"][-1] + 1
-            )
-            split_size = [int(x) for x in split_sizes]
-
-        self.in_nc = num_in_ch
-        self.out_nc = num_out_ch
-        self.num_feat = num_feat
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.depth = depth
-        self.scale = upscale
-        self.upsampler = upsampler
-        self.img_size = img_size
-        self.img_range = img_range
-        self.expansion_factor = expansion_factor
-        self.resi_connection = resi_connection
-        self.split_size = split_size
-
-        self.supports_fp16 = False  # Too much weirdness to support this at the moment
-        self.supports_bfp16 = True
-        self.min_size_restriction = 16
-
-        num_in_ch = in_chans
-        num_out_ch = in_chans
-        num_feat = 64
-        self.img_range = img_range
-        if in_chans == 3:
-            rgb_mean = (0.4488, 0.4371, 0.4040)
-            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
-        else:
-            self.mean = torch.zeros(1, 1, 1, 1)
-        self.upscale = upscale
-        self.upsampler = upsampler
-
-        # ------------------------- 1, Shallow Feature Extraction ------------------------- #
-        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
-
-        # ------------------------- 2, Deep Feature Extraction ------------------------- #
-        self.num_layers = len(depth)
-        self.use_chk = use_chk
-        self.num_features = (
-            self.embed_dim
-        ) = embed_dim  # num_features for consistency with other models
-        heads = num_heads
-
-        self.before_RG = nn.Sequential(
-            Rearrange("b c h w -> b (h w) c"), nn.LayerNorm(embed_dim)
-        )
-
-        curr_dim = embed_dim
-        dpr = [
-            x.item() for x in torch.linspace(0, drop_path_rate, np.sum(depth))
-        ]  # stochastic depth decay rule
-
-        self.layers = nn.ModuleList()
-        for i in range(self.num_layers):
-            layer = ResidualGroup(
-                dim=embed_dim,
-                num_heads=heads[i],
-                reso=img_size,
-                split_size=split_size,
-                expansion_factor=expansion_factor,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                drop=drop_rate,
-                attn_drop=attn_drop_rate,
-                drop_paths=dpr[sum(depth[:i]) : sum(depth[: i + 1])],
-                act_layer=act_layer,
-                norm_layer=norm_layer,
-                depth=depth[i],
-                use_chk=use_chk,
-                resi_connection=resi_connection,
-                rg_idx=i,
-            )
-            self.layers.append(layer)
-
-        self.norm = norm_layer(curr_dim)
-        # build the last conv layer in deep feature extraction
-        if resi_connection == "1conv":
-            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
-        elif resi_connection == "3conv":
-            # to save parameters and memory
-            self.conv_after_body = nn.Sequential(
-                nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1),
-            )
-
-        # ------------------------- 3, Reconstruction ------------------------- #
-        if self.upsampler == "pixelshuffle":
-            # for classical SR
-            self.conv_before_upsample = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)
-            )
-            self.upsample = Upsample(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-        elif self.upsampler == "pixelshuffledirect":
-            # for lightweight SR (to save parameters)
-            self.upsample = UpsampleOneStep(
-                upscale, embed_dim, num_out_ch, (img_size, img_size)
-            )
-
-        self.apply(self._init_weights)
-        self.load_state_dict(state_dict, strict=True)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=0.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(
-            m, (nn.LayerNorm, nn.BatchNorm2d, nn.GroupNorm, nn.InstanceNorm2d)
-        ):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    def forward_features(self, x):
-        _, _, H, W = x.shape
-        x_size = [H, W]
-        x = self.before_RG(x)
-        for layer in self.layers:
-            x = layer(x, x_size)
-        x = self.norm(x)
-        x = rearrange(x, "b (h w) c -> b c h w", h=H, w=W)
-
-        return x
-
-    def forward(self, x):
-        """
-        Input: x: (B, C, H, W)
-        """
-        self.mean = self.mean.type_as(x)
-        x = (x - self.mean) * self.img_range
-
-        if self.upsampler == "pixelshuffle":
-            # for image SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.conv_last(self.upsample(x))
-        elif self.upsampler == "pixelshuffledirect":
-            # for lightweight SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.upsample(x)
-
-        x = x / self.img_range + self.mean
-        return x
diff --git a/comfy_extras/chainner_models/architecture/HAT.py b/comfy_extras/chainner_models/architecture/HAT.py
deleted file mode 100644
index 66947421..00000000
--- a/comfy_extras/chainner_models/architecture/HAT.py
+++ /dev/null
@@ -1,1277 +0,0 @@
-# pylint: skip-file
-# HAT from https://github.com/XPixelGroup/HAT/blob/main/hat/archs/hat_arch.py
-import math
-import re
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from einops import rearrange
-
-from .timm.helpers import to_2tuple
-from .timm.weight_init import trunc_normal_
-
-
-def drop_path(x, drop_prob: float = 0.0, training: bool = False):
-    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
-    From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py
-    """
-    if drop_prob == 0.0 or not training:
-        return x
-    keep_prob = 1 - drop_prob
-    shape = (x.shape[0],) + (1,) * (
-        x.ndim - 1
-    )  # work with diff dim tensors, not just 2D ConvNets
-    random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device)
-    random_tensor.floor_()  # binarize
-    output = x.div(keep_prob) * random_tensor
-    return output
-
-
-class DropPath(nn.Module):
-    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
-    From: https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/layers/drop.py
-    """
-
-    def __init__(self, drop_prob=None):
-        super(DropPath, self).__init__()
-        self.drop_prob = drop_prob
-
-    def forward(self, x):
-        return drop_path(x, self.drop_prob, self.training)  # type: ignore
-
-
-class ChannelAttention(nn.Module):
-    """Channel attention used in RCAN.
-    Args:
-        num_feat (int): Channel number of intermediate features.
-        squeeze_factor (int): Channel squeeze factor. Default: 16.
-    """
-
-    def __init__(self, num_feat, squeeze_factor=16):
-        super(ChannelAttention, self).__init__()
-        self.attention = nn.Sequential(
-            nn.AdaptiveAvgPool2d(1),
-            nn.Conv2d(num_feat, num_feat // squeeze_factor, 1, padding=0),
-            nn.ReLU(inplace=True),
-            nn.Conv2d(num_feat // squeeze_factor, num_feat, 1, padding=0),
-            nn.Sigmoid(),
-        )
-
-    def forward(self, x):
-        y = self.attention(x)
-        return x * y
-
-
-class CAB(nn.Module):
-    def __init__(self, num_feat, compress_ratio=3, squeeze_factor=30):
-        super(CAB, self).__init__()
-
-        self.cab = nn.Sequential(
-            nn.Conv2d(num_feat, num_feat // compress_ratio, 3, 1, 1),
-            nn.GELU(),
-            nn.Conv2d(num_feat // compress_ratio, num_feat, 3, 1, 1),
-            ChannelAttention(num_feat, squeeze_factor),
-        )
-
-    def forward(self, x):
-        return self.cab(x)
-
-
-class Mlp(nn.Module):
-    def __init__(
-        self,
-        in_features,
-        hidden_features=None,
-        out_features=None,
-        act_layer=nn.GELU,
-        drop=0.0,
-    ):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (b, h, w, c)
-        window_size (int): window size
-    Returns:
-        windows: (num_windows*b, window_size, window_size, c)
-    """
-    b, h, w, c = x.shape
-    x = x.view(b, h // window_size, window_size, w // window_size, window_size, c)
-    windows = (
-        x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, c)
-    )
-    return windows
-
-
-def window_reverse(windows, window_size, h, w):
-    """
-    Args:
-        windows: (num_windows*b, window_size, window_size, c)
-        window_size (int): Window size
-        h (int): Height of image
-        w (int): Width of image
-    Returns:
-        x: (b, h, w, c)
-    """
-    b = int(windows.shape[0] / (h * w / window_size / window_size))
-    x = windows.view(
-        b, h // window_size, w // window_size, window_size, window_size, -1
-    )
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(b, h, w, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    r"""Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-    """
-
-    def __init__(
-        self,
-        dim,
-        window_size,
-        num_heads,
-        qkv_bias=True,
-        qk_scale=None,
-        attn_drop=0.0,
-        proj_drop=0.0,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim**-0.5
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(  # type: ignore
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
-        )  # 2*Wh-1 * 2*Ww-1, nH
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        trunc_normal_(self.relative_position_bias_table, std=0.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, rpi, mask=None):
-        """
-        Args:
-            x: input features with shape of (num_windows*b, n, c)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        b_, n, c = x.shape
-        qkv = (
-            self.qkv(x)
-            .reshape(b_, n, 3, self.num_heads, c // self.num_heads)
-            .permute(2, 0, 3, 1, 4)
-        )
-        q, k, v = (
-            qkv[0],
-            qkv[1],
-            qkv[2],
-        )  # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-        attn = q @ k.transpose(-2, -1)
-
-        relative_position_bias = self.relative_position_bias_table[rpi.view(-1)].view(
-            self.window_size[0] * self.window_size[1],
-            self.window_size[0] * self.window_size[1],
-            -1,
-        )  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(
-            2, 0, 1
-        ).contiguous()  # nH, Wh*Ww, Wh*Ww
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nw = mask.shape[0]
-            attn = attn.view(b_ // nw, nw, self.num_heads, n, n) + mask.unsqueeze(
-                1
-            ).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, n, n)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(b_, n, c)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-
-class HAB(nn.Module):
-    r"""Hybrid Attention Block.
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(
-        self,
-        dim,
-        input_resolution,
-        num_heads,
-        window_size=7,
-        shift_size=0,
-        compress_ratio=3,
-        squeeze_factor=30,
-        conv_scale=0.01,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        act_layer=nn.GELU,
-        norm_layer=nn.LayerNorm,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        if min(self.input_resolution) <= self.window_size:
-            # if window size is larger than input resolution, we don't partition windows
-            self.shift_size = 0
-            self.window_size = min(self.input_resolution)
-        assert (
-            0 <= self.shift_size < self.window_size
-        ), "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim,
-            window_size=to_2tuple(self.window_size),
-            num_heads=num_heads,
-            qkv_bias=qkv_bias,
-            qk_scale=qk_scale,
-            attn_drop=attn_drop,
-            proj_drop=drop,
-        )
-
-        self.conv_scale = conv_scale
-        self.conv_block = CAB(
-            num_feat=dim, compress_ratio=compress_ratio, squeeze_factor=squeeze_factor
-        )
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(
-            in_features=dim,
-            hidden_features=mlp_hidden_dim,
-            act_layer=act_layer,
-            drop=drop,
-        )
-
-    def forward(self, x, x_size, rpi_sa, attn_mask):
-        h, w = x_size
-        b, _, c = x.shape
-        # assert seq_len == h * w, "input feature has wrong size"
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(b, h, w, c)
-
-        # Conv_X
-        conv_x = self.conv_block(x.permute(0, 3, 1, 2))
-        conv_x = conv_x.permute(0, 2, 3, 1).contiguous().view(b, h * w, c)
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(
-                x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)
-            )
-            attn_mask = attn_mask
-        else:
-            shifted_x = x
-            attn_mask = None
-
-        # partition windows
-        x_windows = window_partition(
-            shifted_x, self.window_size
-        )  # nw*b, window_size, window_size, c
-        x_windows = x_windows.view(
-            -1, self.window_size * self.window_size, c
-        )  # nw*b, window_size*window_size, c
-
-        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
-        attn_windows = self.attn(x_windows, rpi=rpi_sa, mask=attn_mask)
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, c)
-        shifted_x = window_reverse(attn_windows, self.window_size, h, w)  # b h' w' c
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            attn_x = torch.roll(
-                shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)
-            )
-        else:
-            attn_x = shifted_x
-        attn_x = attn_x.view(b, h * w, c)
-
-        # FFN
-        x = shortcut + self.drop_path(attn_x) + conv_x * self.conv_scale
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        return x
-
-
-class PatchMerging(nn.Module):
-    r"""Patch Merging Layer.
-    Args:
-        input_resolution (tuple[int]): Resolution of input feature.
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(4 * dim)
-
-    def forward(self, x):
-        """
-        x: b, h*w, c
-        """
-        h, w = self.input_resolution
-        b, seq_len, c = x.shape
-        assert seq_len == h * w, "input feature has wrong size"
-        assert h % 2 == 0 and w % 2 == 0, f"x size ({h}*{w}) are not even."
-
-        x = x.view(b, h, w, c)
-
-        x0 = x[:, 0::2, 0::2, :]  # b h/2 w/2 c
-        x1 = x[:, 1::2, 0::2, :]  # b h/2 w/2 c
-        x2 = x[:, 0::2, 1::2, :]  # b h/2 w/2 c
-        x3 = x[:, 1::2, 1::2, :]  # b h/2 w/2 c
-        x = torch.cat([x0, x1, x2, x3], -1)  # b h/2 w/2 4*c
-        x = x.view(b, -1, 4 * c)  # b h/2*w/2 4*c
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-
-class OCAB(nn.Module):
-    # overlapping cross-attention block
-
-    def __init__(
-        self,
-        dim,
-        input_resolution,
-        window_size,
-        overlap_ratio,
-        num_heads,
-        qkv_bias=True,
-        qk_scale=None,
-        mlp_ratio=2,
-        norm_layer=nn.LayerNorm,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.window_size = window_size
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim**-0.5
-        self.overlap_win_size = int(window_size * overlap_ratio) + window_size
-
-        self.norm1 = norm_layer(dim)
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.unfold = nn.Unfold(
-            kernel_size=(self.overlap_win_size, self.overlap_win_size),
-            stride=window_size,
-            padding=(self.overlap_win_size - window_size) // 2,
-        )
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(  # type: ignore
-            torch.zeros(
-                (window_size + self.overlap_win_size - 1)
-                * (window_size + self.overlap_win_size - 1),
-                num_heads,
-            )
-        )  # 2*Wh-1 * 2*Ww-1, nH
-
-        trunc_normal_(self.relative_position_bias_table, std=0.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-        self.proj = nn.Linear(dim, dim)
-
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(
-            in_features=dim, hidden_features=mlp_hidden_dim, act_layer=nn.GELU
-        )
-
-    def forward(self, x, x_size, rpi):
-        h, w = x_size
-        b, _, c = x.shape
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(b, h, w, c)
-
-        qkv = self.qkv(x).reshape(b, h, w, 3, c).permute(3, 0, 4, 1, 2)  # 3, b, c, h, w
-        q = qkv[0].permute(0, 2, 3, 1)  # b, h, w, c
-        kv = torch.cat((qkv[1], qkv[2]), dim=1)  # b, 2*c, h, w
-
-        # partition windows
-        q_windows = window_partition(
-            q, self.window_size
-        )  # nw*b, window_size, window_size, c
-        q_windows = q_windows.view(
-            -1, self.window_size * self.window_size, c
-        )  # nw*b, window_size*window_size, c
-
-        kv_windows = self.unfold(kv)  # b, c*w*w, nw
-        kv_windows = rearrange(
-            kv_windows,
-            "b (nc ch owh oww) nw -> nc (b nw) (owh oww) ch",
-            nc=2,
-            ch=c,
-            owh=self.overlap_win_size,
-            oww=self.overlap_win_size,
-        ).contiguous()  # 2, nw*b, ow*ow, c
-        # Do the above rearrangement without the rearrange function
-        # kv_windows = kv_windows.view(
-        #     2, b, self.overlap_win_size, self.overlap_win_size, c, -1
-        # )
-        # kv_windows = kv_windows.permute(0, 5, 1, 2, 3, 4).contiguous()
-        # kv_windows = kv_windows.view(
-        #     2, -1, self.overlap_win_size * self.overlap_win_size, c
-        # )
-
-        k_windows, v_windows = kv_windows[0], kv_windows[1]  # nw*b, ow*ow, c
-
-        b_, nq, _ = q_windows.shape
-        _, n, _ = k_windows.shape
-        d = self.dim // self.num_heads
-        q = q_windows.reshape(b_, nq, self.num_heads, d).permute(
-            0, 2, 1, 3
-        )  # nw*b, nH, nq, d
-        k = k_windows.reshape(b_, n, self.num_heads, d).permute(
-            0, 2, 1, 3
-        )  # nw*b, nH, n, d
-        v = v_windows.reshape(b_, n, self.num_heads, d).permute(
-            0, 2, 1, 3
-        )  # nw*b, nH, n, d
-
-        q = q * self.scale
-        attn = q @ k.transpose(-2, -1)
-
-        relative_position_bias = self.relative_position_bias_table[rpi.view(-1)].view(
-            self.window_size * self.window_size,
-            self.overlap_win_size * self.overlap_win_size,
-            -1,
-        )  # ws*ws, wse*wse, nH
-        relative_position_bias = relative_position_bias.permute(
-            2, 0, 1
-        ).contiguous()  # nH, ws*ws, wse*wse
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        attn = self.softmax(attn)
-        attn_windows = (attn @ v).transpose(1, 2).reshape(b_, nq, self.dim)
-
-        # merge windows
-        attn_windows = attn_windows.view(
-            -1, self.window_size, self.window_size, self.dim
-        )
-        x = window_reverse(attn_windows, self.window_size, h, w)  # b h w c
-        x = x.view(b, h * w, self.dim)
-
-        x = self.proj(x) + shortcut
-
-        x = x + self.mlp(self.norm2(x))
-        return x
-
-
-class AttenBlocks(nn.Module):
-    """A series of attention blocks for one RHAG.
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(
-        self,
-        dim,
-        input_resolution,
-        depth,
-        num_heads,
-        window_size,
-        compress_ratio,
-        squeeze_factor,
-        conv_scale,
-        overlap_ratio,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        norm_layer=nn.LayerNorm,
-        downsample=None,
-        use_checkpoint=False,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList(
-            [
-                HAB(
-                    dim=dim,
-                    input_resolution=input_resolution,
-                    num_heads=num_heads,
-                    window_size=window_size,
-                    shift_size=0 if (i % 2 == 0) else window_size // 2,
-                    compress_ratio=compress_ratio,
-                    squeeze_factor=squeeze_factor,
-                    conv_scale=conv_scale,
-                    mlp_ratio=mlp_ratio,
-                    qkv_bias=qkv_bias,
-                    qk_scale=qk_scale,
-                    drop=drop,
-                    attn_drop=attn_drop,
-                    drop_path=drop_path[i]
-                    if isinstance(drop_path, list)
-                    else drop_path,
-                    norm_layer=norm_layer,
-                )
-                for i in range(depth)
-            ]
-        )
-
-        # OCAB
-        self.overlap_attn = OCAB(
-            dim=dim,
-            input_resolution=input_resolution,
-            window_size=window_size,
-            overlap_ratio=overlap_ratio,
-            num_heads=num_heads,
-            qkv_bias=qkv_bias,
-            qk_scale=qk_scale,
-            mlp_ratio=mlp_ratio,  # type: ignore
-            norm_layer=norm_layer,
-        )
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(
-                input_resolution, dim=dim, norm_layer=norm_layer
-            )
-        else:
-            self.downsample = None
-
-    def forward(self, x, x_size, params):
-        for blk in self.blocks:
-            x = blk(x, x_size, params["rpi_sa"], params["attn_mask"])
-
-        x = self.overlap_attn(x, x_size, params["rpi_oca"])
-
-        if self.downsample is not None:
-            x = self.downsample(x)
-        return x
-
-
-class RHAG(nn.Module):
-    """Residual Hybrid Attention Group (RHAG).
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-        img_size: Input image size.
-        patch_size: Patch size.
-        resi_connection: The convolutional block before residual connection.
-    """
-
-    def __init__(
-        self,
-        dim,
-        input_resolution,
-        depth,
-        num_heads,
-        window_size,
-        compress_ratio,
-        squeeze_factor,
-        conv_scale,
-        overlap_ratio,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        norm_layer=nn.LayerNorm,
-        downsample=None,
-        use_checkpoint=False,
-        img_size=224,
-        patch_size=4,
-        resi_connection="1conv",
-    ):
-        super(RHAG, self).__init__()
-
-        self.dim = dim
-        self.input_resolution = input_resolution
-
-        self.residual_group = AttenBlocks(
-            dim=dim,
-            input_resolution=input_resolution,
-            depth=depth,
-            num_heads=num_heads,
-            window_size=window_size,
-            compress_ratio=compress_ratio,
-            squeeze_factor=squeeze_factor,
-            conv_scale=conv_scale,
-            overlap_ratio=overlap_ratio,
-            mlp_ratio=mlp_ratio,
-            qkv_bias=qkv_bias,
-            qk_scale=qk_scale,
-            drop=drop,
-            attn_drop=attn_drop,
-            drop_path=drop_path,
-            norm_layer=norm_layer,
-            downsample=downsample,
-            use_checkpoint=use_checkpoint,
-        )
-
-        if resi_connection == "1conv":
-            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
-        elif resi_connection == "identity":
-            self.conv = nn.Identity()
-
-        self.patch_embed = PatchEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=0,
-            embed_dim=dim,
-            norm_layer=None,
-        )
-
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=0,
-            embed_dim=dim,
-            norm_layer=None,
-        )
-
-    def forward(self, x, x_size, params):
-        return (
-            self.patch_embed(
-                self.conv(
-                    self.patch_unembed(self.residual_group(x, x_size, params), x_size)
-                )
-            )
-            + x
-        )
-
-
-class PatchEmbed(nn.Module):
-    r"""Image to Patch Embedding
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(
-        self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None
-    ):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [
-            img_size[0] // patch_size[0],  # type: ignore
-            img_size[1] // patch_size[1],  # type: ignore
-        ]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        x = x.flatten(2).transpose(1, 2)  # b Ph*Pw c
-        if self.norm is not None:
-            x = self.norm(x)
-        return x
-
-
-class PatchUnEmbed(nn.Module):
-    r"""Image to Patch Unembedding
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(
-        self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None
-    ):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [
-            img_size[0] // patch_size[0],  # type: ignore
-            img_size[1] // patch_size[1],  # type: ignore
-        ]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-    def forward(self, x, x_size):
-        x = (
-            x.transpose(1, 2)
-            .contiguous()
-            .view(x.shape[0], self.embed_dim, x_size[0], x_size[1])
-        )  # b Ph*Pw c
-        return x
-
-
-class Upsample(nn.Sequential):
-    """Upsample module.
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(
-                f"scale {scale} is not supported. " "Supported scales: 2^n and 3."
-            )
-        super(Upsample, self).__init__(*m)
-
-
-class HAT(nn.Module):
-    r"""Hybrid Attention Transformer
-        A PyTorch implementation of : `Activating More Pixels in Image Super-Resolution Transformer`.
-        Some codes are based on SwinIR.
-    Args:
-        img_size (int | tuple(int)): Input image size. Default 64
-        patch_size (int | tuple(int)): Patch size. Default: 1
-        in_chans (int): Number of input image channels. Default: 3
-        embed_dim (int): Patch embedding dimension. Default: 96
-        depths (tuple(int)): Depth of each Swin Transformer layer.
-        num_heads (tuple(int)): Number of attention heads in different layers.
-        window_size (int): Window size. Default: 7
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
-        drop_rate (float): Dropout rate. Default: 0
-        attn_drop_rate (float): Attention dropout rate. Default: 0
-        drop_path_rate (float): Stochastic depth rate. Default: 0.1
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
-        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
-        img_range: Image range. 1. or 255.
-        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
-        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
-    """
-
-    def __init__(
-        self,
-        state_dict,
-        **kwargs,
-    ):
-        super(HAT, self).__init__()
-
-        # Defaults
-        img_size = 64
-        patch_size = 1
-        in_chans = 3
-        embed_dim = 96
-        depths = (6, 6, 6, 6)
-        num_heads = (6, 6, 6, 6)
-        window_size = 7
-        compress_ratio = 3
-        squeeze_factor = 30
-        conv_scale = 0.01
-        overlap_ratio = 0.5
-        mlp_ratio = 4.0
-        qkv_bias = True
-        qk_scale = None
-        drop_rate = 0.0
-        attn_drop_rate = 0.0
-        drop_path_rate = 0.1
-        norm_layer = nn.LayerNorm
-        ape = False
-        patch_norm = True
-        use_checkpoint = False
-        upscale = 2
-        img_range = 1.0
-        upsampler = ""
-        resi_connection = "1conv"
-
-        self.state = state_dict
-        self.model_arch = "HAT"
-        self.sub_type = "SR"
-        self.supports_fp16 = False
-        self.support_bf16 = True
-        self.min_size_restriction = 16
-
-        state_keys = list(state_dict.keys())
-
-        num_feat = state_dict["conv_last.weight"].shape[1]
-        in_chans = state_dict["conv_first.weight"].shape[1]
-        num_out_ch = state_dict["conv_last.weight"].shape[0]
-        embed_dim = state_dict["conv_first.weight"].shape[0]
-
-        if "conv_before_upsample.0.weight" in state_keys:
-            if "conv_up1.weight" in state_keys:
-                upsampler = "nearest+conv"
-            else:
-                upsampler = "pixelshuffle"
-                supports_fp16 = False
-        elif "upsample.0.weight" in state_keys:
-            upsampler = "pixelshuffledirect"
-        else:
-            upsampler = ""
-        upscale = 1
-        if upsampler == "nearest+conv":
-            upsample_keys = [
-                x for x in state_keys if "conv_up" in x and "bias" not in x
-            ]
-
-            for upsample_key in upsample_keys:
-                upscale *= 2
-        elif upsampler == "pixelshuffle":
-            upsample_keys = [
-                x
-                for x in state_keys
-                if "upsample" in x and "conv" not in x and "bias" not in x
-            ]
-            for upsample_key in upsample_keys:
-                shape = self.state[upsample_key].shape[0]
-                upscale *= math.sqrt(shape // num_feat)
-            upscale = int(upscale)
-        elif upsampler == "pixelshuffledirect":
-            upscale = int(
-                math.sqrt(self.state["upsample.0.bias"].shape[0] // num_out_ch)
-            )
-
-        max_layer_num = 0
-        max_block_num = 0
-        for key in state_keys:
-            result = re.match(
-                r"layers.(\d*).residual_group.blocks.(\d*).conv_block.cab.0.weight", key
-            )
-            if result:
-                layer_num, block_num = result.groups()
-                max_layer_num = max(max_layer_num, int(layer_num))
-                max_block_num = max(max_block_num, int(block_num))
-
-        depths = [max_block_num + 1 for _ in range(max_layer_num + 1)]
-
-        if (
-            "layers.0.residual_group.blocks.0.attn.relative_position_bias_table"
-            in state_keys
-        ):
-            num_heads_num = self.state[
-                "layers.0.residual_group.blocks.0.attn.relative_position_bias_table"
-            ].shape[-1]
-            num_heads = [num_heads_num for _ in range(max_layer_num + 1)]
-        else:
-            num_heads = depths
-
-        mlp_ratio = float(
-            self.state["layers.0.residual_group.blocks.0.mlp.fc1.bias"].shape[0]
-            / embed_dim
-        )
-
-        # TODO: could actually count the layers, but this should do
-        if "layers.0.conv.4.weight" in state_keys:
-            resi_connection = "3conv"
-        else:
-            resi_connection = "1conv"
-
-        window_size = int(math.sqrt(self.state["relative_position_index_SA"].shape[0]))
-
-        # Not sure if this is needed or used at all anywhere in HAT's config
-        if "layers.0.residual_group.blocks.1.attn_mask" in state_keys:
-            img_size = int(
-                math.sqrt(
-                    self.state["layers.0.residual_group.blocks.1.attn_mask"].shape[0]
-                )
-                * window_size
-            )
-
-        self.window_size = window_size
-        self.shift_size = window_size // 2
-        self.overlap_ratio = overlap_ratio
-
-        self.in_nc = in_chans
-        self.out_nc = num_out_ch
-        self.num_feat = num_feat
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.depths = depths
-        self.window_size = window_size
-        self.mlp_ratio = mlp_ratio
-        self.scale = upscale
-        self.upsampler = upsampler
-        self.img_size = img_size
-        self.img_range = img_range
-        self.resi_connection = resi_connection
-
-        num_in_ch = in_chans
-        # num_out_ch = in_chans
-        # num_feat = 64
-        self.img_range = img_range
-        if in_chans == 3:
-            rgb_mean = (0.4488, 0.4371, 0.4040)
-            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
-        else:
-            self.mean = torch.zeros(1, 1, 1, 1)
-        self.upscale = upscale
-        self.upsampler = upsampler
-
-        # relative position index
-        relative_position_index_SA = self.calculate_rpi_sa()
-        relative_position_index_OCA = self.calculate_rpi_oca()
-        self.register_buffer("relative_position_index_SA", relative_position_index_SA)
-        self.register_buffer("relative_position_index_OCA", relative_position_index_OCA)
-
-        # ------------------------- 1, shallow feature extraction ------------------------- #
-        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
-
-        # ------------------------- 2, deep feature extraction ------------------------- #
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.num_features = embed_dim
-        self.mlp_ratio = mlp_ratio
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=embed_dim,
-            embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None,
-        )
-        num_patches = self.patch_embed.num_patches
-        patches_resolution = self.patch_embed.patches_resolution
-        self.patches_resolution = patches_resolution
-
-        # merge non-overlapping patches into image
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=embed_dim,
-            embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None,
-        )
-
-        # absolute position embedding
-        if self.ape:
-            self.absolute_pos_embed = nn.Parameter(  # type: ignore[arg-type]
-                torch.zeros(1, num_patches, embed_dim)
-            )
-            trunc_normal_(self.absolute_pos_embed, std=0.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [
-            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
-        ]  # stochastic depth decay rule
-
-        # build Residual Hybrid Attention Groups (RHAG)
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = RHAG(
-                dim=embed_dim,
-                input_resolution=(patches_resolution[0], patches_resolution[1]),
-                depth=depths[i_layer],
-                num_heads=num_heads[i_layer],
-                window_size=window_size,
-                compress_ratio=compress_ratio,
-                squeeze_factor=squeeze_factor,
-                conv_scale=conv_scale,
-                overlap_ratio=overlap_ratio,
-                mlp_ratio=self.mlp_ratio,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                drop=drop_rate,
-                attn_drop=attn_drop_rate,
-                drop_path=dpr[
-                    sum(depths[:i_layer]) : sum(depths[: i_layer + 1])  # type: ignore
-                ],  # no impact on SR results
-                norm_layer=norm_layer,
-                downsample=None,
-                use_checkpoint=use_checkpoint,
-                img_size=img_size,
-                patch_size=patch_size,
-                resi_connection=resi_connection,
-            )
-            self.layers.append(layer)
-        self.norm = norm_layer(self.num_features)
-
-        # build the last conv layer in deep feature extraction
-        if resi_connection == "1conv":
-            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
-        elif resi_connection == "identity":
-            self.conv_after_body = nn.Identity()
-
-        # ------------------------- 3, high quality image reconstruction ------------------------- #
-        if self.upsampler == "pixelshuffle":
-            # for classical SR
-            self.conv_before_upsample = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)
-            )
-            self.upsample = Upsample(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-
-        self.apply(self._init_weights)
-        self.load_state_dict(self.state, strict=False)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=0.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    def calculate_rpi_sa(self):
-        # calculate relative position index for SA
-        coords_h = torch.arange(self.window_size)
-        coords_w = torch.arange(self.window_size)
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = (
-            coords_flatten[:, :, None] - coords_flatten[:, None, :]
-        )  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(
-            1, 2, 0
-        ).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        return relative_position_index
-
-    def calculate_rpi_oca(self):
-        # calculate relative position index for OCA
-        window_size_ori = self.window_size
-        window_size_ext = self.window_size + int(self.overlap_ratio * self.window_size)
-
-        coords_h = torch.arange(window_size_ori)
-        coords_w = torch.arange(window_size_ori)
-        coords_ori = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, ws, ws
-        coords_ori_flatten = torch.flatten(coords_ori, 1)  # 2, ws*ws
-
-        coords_h = torch.arange(window_size_ext)
-        coords_w = torch.arange(window_size_ext)
-        coords_ext = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, wse, wse
-        coords_ext_flatten = torch.flatten(coords_ext, 1)  # 2, wse*wse
-
-        relative_coords = (
-            coords_ext_flatten[:, None, :] - coords_ori_flatten[:, :, None]
-        )  # 2, ws*ws, wse*wse
-
-        relative_coords = relative_coords.permute(
-            1, 2, 0
-        ).contiguous()  # ws*ws, wse*wse, 2
-        relative_coords[:, :, 0] += (
-            window_size_ori - window_size_ext + 1
-        )  # shift to start from 0
-        relative_coords[:, :, 1] += window_size_ori - window_size_ext + 1
-
-        relative_coords[:, :, 0] *= window_size_ori + window_size_ext - 1
-        relative_position_index = relative_coords.sum(-1)
-        return relative_position_index
-
-    def calculate_mask(self, x_size):
-        # calculate attention mask for SW-MSA
-        h, w = x_size
-        img_mask = torch.zeros((1, h, w, 1))  # 1 h w 1
-        h_slices = (
-            slice(0, -self.window_size),
-            slice(-self.window_size, -self.shift_size),
-            slice(-self.shift_size, None),
-        )
-        w_slices = (
-            slice(0, -self.window_size),
-            slice(-self.window_size, -self.shift_size),
-            slice(-self.shift_size, None),
-        )
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(
-            img_mask, self.window_size
-        )  # nw, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
-            attn_mask == 0, float(0.0)
-        )
-
-        return attn_mask
-
-    @torch.jit.ignore  # type: ignore
-    def no_weight_decay(self):
-        return {"absolute_pos_embed"}
-
-    @torch.jit.ignore  # type: ignore
-    def no_weight_decay_keywords(self):
-        return {"relative_position_bias_table"}
-
-    def check_image_size(self, x):
-        _, _, h, w = x.size()
-        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
-        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
-        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect")
-        return x
-
-    def forward_features(self, x):
-        x_size = (x.shape[2], x.shape[3])
-
-        # Calculate attention mask and relative position index in advance to speed up inference.
-        # The original code is very time-cosuming for large window size.
-        attn_mask = self.calculate_mask(x_size).to(x.device)
-        params = {
-            "attn_mask": attn_mask,
-            "rpi_sa": self.relative_position_index_SA,
-            "rpi_oca": self.relative_position_index_OCA,
-        }
-
-        x = self.patch_embed(x)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-
-        for layer in self.layers:
-            x = layer(x, x_size, params)
-
-        x = self.norm(x)  # b seq_len c
-        x = self.patch_unembed(x, x_size)
-
-        return x
-
-    def forward(self, x):
-        H, W = x.shape[2:]
-        self.mean = self.mean.type_as(x)
-        x = (x - self.mean) * self.img_range
-        x = self.check_image_size(x)
-
-        if self.upsampler == "pixelshuffle":
-            # for classical SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.conv_last(self.upsample(x))
-
-        x = x / self.img_range + self.mean
-
-        return x[:, :, : H * self.upscale, : W * self.upscale]
diff --git a/comfy_extras/chainner_models/architecture/LICENSE-DAT b/comfy_extras/chainner_models/architecture/LICENSE-DAT
deleted file mode 100644
index 261eeb9e..00000000
--- a/comfy_extras/chainner_models/architecture/LICENSE-DAT
+++ /dev/null
@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
-
-      "Derivative Works" shall mean any work, whether in Source or Object
-      form, that is based on (or derived from) the Work and for which the
-      editorial revisions, annotations, elaborations, or other modifications
-      represent, as a whole, an original work of authorship. For the purposes
-      of this License, Derivative Works shall not include works that remain
-      separable from, or merely link (or bind by name) to the interfaces of,
-      the Work and Derivative Works thereof.
-
-      "Contribution" shall mean any work of authorship, including
-      the original version of the Work and any modifications or additions
-      to that Work or Derivative Works thereof, that is intentionally
-      submitted to Licensor for inclusion in the Work by the copyright owner
-      or by an individual or Legal Entity authorized to submit on behalf of
-      the copyright owner. For the purposes of this definition, "submitted"
-      means any form of electronic, verbal, or written communication sent
-      to the Licensor or its representatives, including but not limited to
-      communication on electronic mailing lists, source code control systems,
-      and issue tracking systems that are managed by, or on behalf of, the
-      Licensor for the purpose of discussing and improving the Work, but
-      excluding communication that is conspicuously marked or otherwise
-      designated in writing by the copyright owner as "Not a Contribution."
-
-      "Contributor" shall mean Licensor and any individual or Legal Entity
-      on behalf of whom a Contribution has been received by Licensor and
-      subsequently incorporated within the Work.
-
-   2. Grant of Copyright License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      copyright license to reproduce, prepare Derivative Works of,
-      publicly display, publicly perform, sublicense, and distribute the
-      Work and such Derivative Works in Source or Object form.
-
-   3. Grant of Patent License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      (except as stated in this section) patent license to make, have made,
-      use, offer to sell, sell, import, and otherwise transfer the Work,
-      where such license applies only to those patent claims licensable
-      by such Contributor that are necessarily infringed by their
-      Contribution(s) alone or by combination of their Contribution(s)
-      with the Work to which such Contribution(s) was submitted. If You
-      institute patent litigation against any entity (including a
-      cross-claim or counterclaim in a lawsuit) alleging that the Work
-      or a Contribution incorporated within the Work constitutes direct
-      or contributory patent infringement, then any patent licenses
-      granted to You under this License for that Work shall terminate
-      as of the date such litigation is filed.
-
-   4. Redistribution. You may reproduce and distribute copies of the
-      Work or Derivative Works thereof in any medium, with or without
-      modifications, and in Source or Object form, provided that You
-      meet the following conditions:
-
-      (a) You must give any other recipients of the Work or
-          Derivative Works a copy of this License; and
-
-      (b) You must cause any modified files to carry prominent notices
-          stating that You changed the files; and
-
-      (c) You must retain, in the Source form of any Derivative Works
-          that You distribute, all copyright, patent, trademark, and
-          attribution notices from the Source form of the Work,
-          excluding those notices that do not pertain to any part of
-          the Derivative Works; and
-
-      (d) If the Work includes a "NOTICE" text file as part of its
-          distribution, then any Derivative Works that You distribute must
-          include a readable copy of the attribution notices contained
-          within such NOTICE file, excluding those notices that do not
-          pertain to any part of the Derivative Works, in at least one
-          of the following places: within a NOTICE text file distributed
-          as part of the Derivative Works; within the Source form or
-          documentation, if provided along with the Derivative Works; or,
-          within a display generated by the Derivative Works, if and
-          wherever such third-party notices normally appear. The contents
-          of the NOTICE file are for informational purposes only and
-          do not modify the License. You may add Your own attribution
-          notices within Derivative Works that You distribute, alongside
-          or as an addendum to the NOTICE text from the Work, provided
-          that such additional attribution notices cannot be construed
-          as modifying the License.
-
-      You may add Your own copyright statement to Your modifications and
-      may provide additional or different license terms and conditions
-      for use, reproduction, or distribution of Your modifications, or
-      for any such Derivative Works as a whole, provided Your use,
-      reproduction, and distribution of the Work otherwise complies with
-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-      implied, including, without limitation, any warranties or conditions
-      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
-      risks associated with Your exercise of permissions under this License.
-
-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
-      incidental, or consequential damages of any character arising as a
-      result of this License or out of the use or inability to use the
-      Work (including but not limited to damages for loss of goodwill,
-      work stoppage, computer failure or malfunction, or any and all
-      other commercial damages or losses), even if such Contributor
-      has been advised of the possibility of such damages.
-
-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
-      and charge a fee for, acceptance of support, warranty, indemnity,
-      or other liability obligations and/or rights consistent with this
-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "[]"
-      replaced with your own identifying information. (Don't include
-      the brackets!)  The text should be enclosed in the appropriate
-      comment syntax for the file format. We also recommend that a
-      file or class name and description of purpose be included on the
-      same "printed page" as the copyright notice for easier
-      identification within third-party archives.
-
-   Copyright [yyyy] [name of copyright owner]
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
diff --git a/comfy_extras/chainner_models/architecture/LICENSE-ESRGAN b/comfy_extras/chainner_models/architecture/LICENSE-ESRGAN
deleted file mode 100644
index 261eeb9e..00000000
--- a/comfy_extras/chainner_models/architecture/LICENSE-ESRGAN
+++ /dev/null
@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
-
-      "Derivative Works" shall mean any work, whether in Source or Object
-      form, that is based on (or derived from) the Work and for which the
-      editorial revisions, annotations, elaborations, or other modifications
-      represent, as a whole, an original work of authorship. For the purposes
-      of this License, Derivative Works shall not include works that remain
-      separable from, or merely link (or bind by name) to the interfaces of,
-      the Work and Derivative Works thereof.
-
-      "Contribution" shall mean any work of authorship, including
-      the original version of the Work and any modifications or additions
-      to that Work or Derivative Works thereof, that is intentionally
-      submitted to Licensor for inclusion in the Work by the copyright owner
-      or by an individual or Legal Entity authorized to submit on behalf of
-      the copyright owner. For the purposes of this definition, "submitted"
-      means any form of electronic, verbal, or written communication sent
-      to the Licensor or its representatives, including but not limited to
-      communication on electronic mailing lists, source code control systems,
-      and issue tracking systems that are managed by, or on behalf of, the
-      Licensor for the purpose of discussing and improving the Work, but
-      excluding communication that is conspicuously marked or otherwise
-      designated in writing by the copyright owner as "Not a Contribution."
-
-      "Contributor" shall mean Licensor and any individual or Legal Entity
-      on behalf of whom a Contribution has been received by Licensor and
-      subsequently incorporated within the Work.
-
-   2. Grant of Copyright License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      copyright license to reproduce, prepare Derivative Works of,
-      publicly display, publicly perform, sublicense, and distribute the
-      Work and such Derivative Works in Source or Object form.
-
-   3. Grant of Patent License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      (except as stated in this section) patent license to make, have made,
-      use, offer to sell, sell, import, and otherwise transfer the Work,
-      where such license applies only to those patent claims licensable
-      by such Contributor that are necessarily infringed by their
-      Contribution(s) alone or by combination of their Contribution(s)
-      with the Work to which such Contribution(s) was submitted. If You
-      institute patent litigation against any entity (including a
-      cross-claim or counterclaim in a lawsuit) alleging that the Work
-      or a Contribution incorporated within the Work constitutes direct
-      or contributory patent infringement, then any patent licenses
-      granted to You under this License for that Work shall terminate
-      as of the date such litigation is filed.
-
-   4. Redistribution. You may reproduce and distribute copies of the
-      Work or Derivative Works thereof in any medium, with or without
-      modifications, and in Source or Object form, provided that You
-      meet the following conditions:
-
-      (a) You must give any other recipients of the Work or
-          Derivative Works a copy of this License; and
-
-      (b) You must cause any modified files to carry prominent notices
-          stating that You changed the files; and
-
-      (c) You must retain, in the Source form of any Derivative Works
-          that You distribute, all copyright, patent, trademark, and
-          attribution notices from the Source form of the Work,
-          excluding those notices that do not pertain to any part of
-          the Derivative Works; and
-
-      (d) If the Work includes a "NOTICE" text file as part of its
-          distribution, then any Derivative Works that You distribute must
-          include a readable copy of the attribution notices contained
-          within such NOTICE file, excluding those notices that do not
-          pertain to any part of the Derivative Works, in at least one
-          of the following places: within a NOTICE text file distributed
-          as part of the Derivative Works; within the Source form or
-          documentation, if provided along with the Derivative Works; or,
-          within a display generated by the Derivative Works, if and
-          wherever such third-party notices normally appear. The contents
-          of the NOTICE file are for informational purposes only and
-          do not modify the License. You may add Your own attribution
-          notices within Derivative Works that You distribute, alongside
-          or as an addendum to the NOTICE text from the Work, provided
-          that such additional attribution notices cannot be construed
-          as modifying the License.
-
-      You may add Your own copyright statement to Your modifications and
-      may provide additional or different license terms and conditions
-      for use, reproduction, or distribution of Your modifications, or
-      for any such Derivative Works as a whole, provided Your use,
-      reproduction, and distribution of the Work otherwise complies with
-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-      implied, including, without limitation, any warranties or conditions
-      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
-      risks associated with Your exercise of permissions under this License.
-
-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
-      incidental, or consequential damages of any character arising as a
-      result of this License or out of the use or inability to use the
-      Work (including but not limited to damages for loss of goodwill,
-      work stoppage, computer failure or malfunction, or any and all
-      other commercial damages or losses), even if such Contributor
-      has been advised of the possibility of such damages.
-
-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
-      and charge a fee for, acceptance of support, warranty, indemnity,
-      or other liability obligations and/or rights consistent with this
-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "[]"
-      replaced with your own identifying information. (Don't include
-      the brackets!)  The text should be enclosed in the appropriate
-      comment syntax for the file format. We also recommend that a
-      file or class name and description of purpose be included on the
-      same "printed page" as the copyright notice for easier
-      identification within third-party archives.
-
-   Copyright [yyyy] [name of copyright owner]
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
diff --git a/comfy_extras/chainner_models/architecture/LICENSE-HAT b/comfy_extras/chainner_models/architecture/LICENSE-HAT
deleted file mode 100644
index 003e97e9..00000000
--- a/comfy_extras/chainner_models/architecture/LICENSE-HAT
+++ /dev/null
@@ -1,21 +0,0 @@
-MIT License
-
-Copyright (c) 2022 Xiangyu Chen
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
diff --git a/comfy_extras/chainner_models/architecture/LICENSE-RealESRGAN b/comfy_extras/chainner_models/architecture/LICENSE-RealESRGAN
deleted file mode 100644
index 552a1eea..00000000
--- a/comfy_extras/chainner_models/architecture/LICENSE-RealESRGAN
+++ /dev/null
@@ -1,29 +0,0 @@
-BSD 3-Clause License
-
-Copyright (c) 2021, Xintao Wang
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are met:
-
-1. Redistributions of source code must retain the above copyright notice, this
-   list of conditions and the following disclaimer.
-
-2. Redistributions in binary form must reproduce the above copyright notice,
-   this list of conditions and the following disclaimer in the documentation
-   and/or other materials provided with the distribution.
-
-3. Neither the name of the copyright holder nor the names of its
-   contributors may be used to endorse or promote products derived from
-   this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
-FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
-DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
-OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/comfy_extras/chainner_models/architecture/LICENSE-SCUNet b/comfy_extras/chainner_models/architecture/LICENSE-SCUNet
deleted file mode 100644
index ff75c988..00000000
--- a/comfy_extras/chainner_models/architecture/LICENSE-SCUNet
+++ /dev/null
@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
-
-      "Derivative Works" shall mean any work, whether in Source or Object
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diff --git a/comfy_extras/chainner_models/architecture/LICENSE-SPSR b/comfy_extras/chainner_models/architecture/LICENSE-SPSR
deleted file mode 100644
index 3245f3f9..00000000
--- a/comfy_extras/chainner_models/architecture/LICENSE-SPSR
+++ /dev/null
@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
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-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
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-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
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-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
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-      defend, and hold each Contributor harmless for any liability
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-   APPENDIX: How to apply the Apache License to your work.
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diff --git a/comfy_extras/chainner_models/architecture/LICENSE-SwiftSRGAN b/comfy_extras/chainner_models/architecture/LICENSE-SwiftSRGAN
deleted file mode 100644
index 0e259d42..00000000
--- a/comfy_extras/chainner_models/architecture/LICENSE-SwiftSRGAN
+++ /dev/null
@@ -1,121 +0,0 @@
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diff --git a/comfy_extras/chainner_models/architecture/LICENSE-Swin2SR b/comfy_extras/chainner_models/architecture/LICENSE-Swin2SR
deleted file mode 100644
index e5e4ee06..00000000
--- a/comfy_extras/chainner_models/architecture/LICENSE-Swin2SR
+++ /dev/null
@@ -1,201 +0,0 @@
-                                 Apache License
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-      the copyright owner. For the purposes of this definition, "submitted"
-      means any form of electronic, verbal, or written communication sent
-      to the Licensor or its representatives, including but not limited to
-      communication on electronic mailing lists, source code control systems,
-      and issue tracking systems that are managed by, or on behalf of, the
-      Licensor for the purpose of discussing and improving the Work, but
-      excluding communication that is conspicuously marked or otherwise
-      designated in writing by the copyright owner as "Not a Contribution."
-
-      "Contributor" shall mean Licensor and any individual or Legal Entity
-      on behalf of whom a Contribution has been received by Licensor and
-      subsequently incorporated within the Work.
-
-   2. Grant of Copyright License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      copyright license to reproduce, prepare Derivative Works of,
-      publicly display, publicly perform, sublicense, and distribute the
-      Work and such Derivative Works in Source or Object form.
-
-   3. Grant of Patent License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      (except as stated in this section) patent license to make, have made,
-      use, offer to sell, sell, import, and otherwise transfer the Work,
-      where such license applies only to those patent claims licensable
-      by such Contributor that are necessarily infringed by their
-      Contribution(s) alone or by combination of their Contribution(s)
-      with the Work to which such Contribution(s) was submitted. If You
-      institute patent litigation against any entity (including a
-      cross-claim or counterclaim in a lawsuit) alleging that the Work
-      or a Contribution incorporated within the Work constitutes direct
-      or contributory patent infringement, then any patent licenses
-      granted to You under this License for that Work shall terminate
-      as of the date such litigation is filed.
-
-   4. Redistribution. You may reproduce and distribute copies of the
-      Work or Derivative Works thereof in any medium, with or without
-      modifications, and in Source or Object form, provided that You
-      meet the following conditions:
-
-      (a) You must give any other recipients of the Work or
-          Derivative Works a copy of this License; and
-
-      (b) You must cause any modified files to carry prominent notices
-          stating that You changed the files; and
-
-      (c) You must retain, in the Source form of any Derivative Works
-          that You distribute, all copyright, patent, trademark, and
-          attribution notices from the Source form of the Work,
-          excluding those notices that do not pertain to any part of
-          the Derivative Works; and
-
-      (d) If the Work includes a "NOTICE" text file as part of its
-          distribution, then any Derivative Works that You distribute must
-          include a readable copy of the attribution notices contained
-          within such NOTICE file, excluding those notices that do not
-          pertain to any part of the Derivative Works, in at least one
-          of the following places: within a NOTICE text file distributed
-          as part of the Derivative Works; within the Source form or
-          documentation, if provided along with the Derivative Works; or,
-          within a display generated by the Derivative Works, if and
-          wherever such third-party notices normally appear. The contents
-          of the NOTICE file are for informational purposes only and
-          do not modify the License. You may add Your own attribution
-          notices within Derivative Works that You distribute, alongside
-          or as an addendum to the NOTICE text from the Work, provided
-          that such additional attribution notices cannot be construed
-          as modifying the License.
-
-      You may add Your own copyright statement to Your modifications and
-      may provide additional or different license terms and conditions
-      for use, reproduction, or distribution of Your modifications, or
-      for any such Derivative Works as a whole, provided Your use,
-      reproduction, and distribution of the Work otherwise complies with
-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-      implied, including, without limitation, any warranties or conditions
-      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
-      risks associated with Your exercise of permissions under this License.
-
-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
-      incidental, or consequential damages of any character arising as a
-      result of this License or out of the use or inability to use the
-      Work (including but not limited to damages for loss of goodwill,
-      work stoppage, computer failure or malfunction, or any and all
-      other commercial damages or losses), even if such Contributor
-      has been advised of the possibility of such damages.
-
-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
-      and charge a fee for, acceptance of support, warranty, indemnity,
-      or other liability obligations and/or rights consistent with this
-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "[]"
-      replaced with your own identifying information. (Don't include
-      the brackets!)  The text should be enclosed in the appropriate
-      comment syntax for the file format. We also recommend that a
-      file or class name and description of purpose be included on the
-      same "printed page" as the copyright notice for easier
-      identification within third-party archives.
-
-   Copyright [2021] [SwinIR Authors]
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
diff --git a/comfy_extras/chainner_models/architecture/LICENSE-SwinIR b/comfy_extras/chainner_models/architecture/LICENSE-SwinIR
deleted file mode 100644
index e5e4ee06..00000000
--- a/comfy_extras/chainner_models/architecture/LICENSE-SwinIR
+++ /dev/null
@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
-
-      "Derivative Works" shall mean any work, whether in Source or Object
-      form, that is based on (or derived from) the Work and for which the
-      editorial revisions, annotations, elaborations, or other modifications
-      represent, as a whole, an original work of authorship. For the purposes
-      of this License, Derivative Works shall not include works that remain
-      separable from, or merely link (or bind by name) to the interfaces of,
-      the Work and Derivative Works thereof.
-
-      "Contribution" shall mean any work of authorship, including
-      the original version of the Work and any modifications or additions
-      to that Work or Derivative Works thereof, that is intentionally
-      submitted to Licensor for inclusion in the Work by the copyright owner
-      or by an individual or Legal Entity authorized to submit on behalf of
-      the copyright owner. For the purposes of this definition, "submitted"
-      means any form of electronic, verbal, or written communication sent
-      to the Licensor or its representatives, including but not limited to
-      communication on electronic mailing lists, source code control systems,
-      and issue tracking systems that are managed by, or on behalf of, the
-      Licensor for the purpose of discussing and improving the Work, but
-      excluding communication that is conspicuously marked or otherwise
-      designated in writing by the copyright owner as "Not a Contribution."
-
-      "Contributor" shall mean Licensor and any individual or Legal Entity
-      on behalf of whom a Contribution has been received by Licensor and
-      subsequently incorporated within the Work.
-
-   2. Grant of Copyright License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      copyright license to reproduce, prepare Derivative Works of,
-      publicly display, publicly perform, sublicense, and distribute the
-      Work and such Derivative Works in Source or Object form.
-
-   3. Grant of Patent License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      (except as stated in this section) patent license to make, have made,
-      use, offer to sell, sell, import, and otherwise transfer the Work,
-      where such license applies only to those patent claims licensable
-      by such Contributor that are necessarily infringed by their
-      Contribution(s) alone or by combination of their Contribution(s)
-      with the Work to which such Contribution(s) was submitted. If You
-      institute patent litigation against any entity (including a
-      cross-claim or counterclaim in a lawsuit) alleging that the Work
-      or a Contribution incorporated within the Work constitutes direct
-      or contributory patent infringement, then any patent licenses
-      granted to You under this License for that Work shall terminate
-      as of the date such litigation is filed.
-
-   4. Redistribution. You may reproduce and distribute copies of the
-      Work or Derivative Works thereof in any medium, with or without
-      modifications, and in Source or Object form, provided that You
-      meet the following conditions:
-
-      (a) You must give any other recipients of the Work or
-          Derivative Works a copy of this License; and
-
-      (b) You must cause any modified files to carry prominent notices
-          stating that You changed the files; and
-
-      (c) You must retain, in the Source form of any Derivative Works
-          that You distribute, all copyright, patent, trademark, and
-          attribution notices from the Source form of the Work,
-          excluding those notices that do not pertain to any part of
-          the Derivative Works; and
-
-      (d) If the Work includes a "NOTICE" text file as part of its
-          distribution, then any Derivative Works that You distribute must
-          include a readable copy of the attribution notices contained
-          within such NOTICE file, excluding those notices that do not
-          pertain to any part of the Derivative Works, in at least one
-          of the following places: within a NOTICE text file distributed
-          as part of the Derivative Works; within the Source form or
-          documentation, if provided along with the Derivative Works; or,
-          within a display generated by the Derivative Works, if and
-          wherever such third-party notices normally appear. The contents
-          of the NOTICE file are for informational purposes only and
-          do not modify the License. You may add Your own attribution
-          notices within Derivative Works that You distribute, alongside
-          or as an addendum to the NOTICE text from the Work, provided
-          that such additional attribution notices cannot be construed
-          as modifying the License.
-
-      You may add Your own copyright statement to Your modifications and
-      may provide additional or different license terms and conditions
-      for use, reproduction, or distribution of Your modifications, or
-      for any such Derivative Works as a whole, provided Your use,
-      reproduction, and distribution of the Work otherwise complies with
-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-      implied, including, without limitation, any warranties or conditions
-      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
-      risks associated with Your exercise of permissions under this License.
-
-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
-      incidental, or consequential damages of any character arising as a
-      result of this License or out of the use or inability to use the
-      Work (including but not limited to damages for loss of goodwill,
-      work stoppage, computer failure or malfunction, or any and all
-      other commercial damages or losses), even if such Contributor
-      has been advised of the possibility of such damages.
-
-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
-      and charge a fee for, acceptance of support, warranty, indemnity,
-      or other liability obligations and/or rights consistent with this
-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "[]"
-      replaced with your own identifying information. (Don't include
-      the brackets!)  The text should be enclosed in the appropriate
-      comment syntax for the file format. We also recommend that a
-      file or class name and description of purpose be included on the
-      same "printed page" as the copyright notice for easier
-      identification within third-party archives.
-
-   Copyright [2021] [SwinIR Authors]
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
diff --git a/comfy_extras/chainner_models/architecture/LICENSE-lama b/comfy_extras/chainner_models/architecture/LICENSE-lama
deleted file mode 100644
index ca822bb5..00000000
--- a/comfy_extras/chainner_models/architecture/LICENSE-lama
+++ /dev/null
@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
-      and distribution as defined by Sections 1 through 9 of this document.
-
-      "Licensor" shall mean the copyright owner or entity authorized by
-      the copyright owner that is granting the License.
-
-      "Legal Entity" shall mean the union of the acting entity and all
-      other entities that control, are controlled by, or are under common
-      control with that entity. For the purposes of this definition,
-      "control" means (i) the power, direct or indirect, to cause the
-      direction or management of such entity, whether by contract or
-      otherwise, or (ii) ownership of fifty percent (50%) or more of the
-      outstanding shares, or (iii) beneficial ownership of such entity.
-
-      "You" (or "Your") shall mean an individual or Legal Entity
-      exercising permissions granted by this License.
-
-      "Source" form shall mean the preferred form for making modifications,
-      including but not limited to software source code, documentation
-      source, and configuration files.
-
-      "Object" form shall mean any form resulting from mechanical
-      transformation or translation of a Source form, including but
-      not limited to compiled object code, generated documentation,
-      and conversions to other media types.
-
-      "Work" shall mean the work of authorship, whether in Source or
-      Object form, made available under the License, as indicated by a
-      copyright notice that is included in or attached to the work
-      (an example is provided in the Appendix below).
-
-      "Derivative Works" shall mean any work, whether in Source or Object
-      form, that is based on (or derived from) the Work and for which the
-      editorial revisions, annotations, elaborations, or other modifications
-      represent, as a whole, an original work of authorship. For the purposes
-      of this License, Derivative Works shall not include works that remain
-      separable from, or merely link (or bind by name) to the interfaces of,
-      the Work and Derivative Works thereof.
-
-      "Contribution" shall mean any work of authorship, including
-      the original version of the Work and any modifications or additions
-      to that Work or Derivative Works thereof, that is intentionally
-      submitted to Licensor for inclusion in the Work by the copyright owner
-      or by an individual or Legal Entity authorized to submit on behalf of
-      the copyright owner. For the purposes of this definition, "submitted"
-      means any form of electronic, verbal, or written communication sent
-      to the Licensor or its representatives, including but not limited to
-      communication on electronic mailing lists, source code control systems,
-      and issue tracking systems that are managed by, or on behalf of, the
-      Licensor for the purpose of discussing and improving the Work, but
-      excluding communication that is conspicuously marked or otherwise
-      designated in writing by the copyright owner as "Not a Contribution."
-
-      "Contributor" shall mean Licensor and any individual or Legal Entity
-      on behalf of whom a Contribution has been received by Licensor and
-      subsequently incorporated within the Work.
-
-   2. Grant of Copyright License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      copyright license to reproduce, prepare Derivative Works of,
-      publicly display, publicly perform, sublicense, and distribute the
-      Work and such Derivative Works in Source or Object form.
-
-   3. Grant of Patent License. Subject to the terms and conditions of
-      this License, each Contributor hereby grants to You a perpetual,
-      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
-      (except as stated in this section) patent license to make, have made,
-      use, offer to sell, sell, import, and otherwise transfer the Work,
-      where such license applies only to those patent claims licensable
-      by such Contributor that are necessarily infringed by their
-      Contribution(s) alone or by combination of their Contribution(s)
-      with the Work to which such Contribution(s) was submitted. If You
-      institute patent litigation against any entity (including a
-      cross-claim or counterclaim in a lawsuit) alleging that the Work
-      or a Contribution incorporated within the Work constitutes direct
-      or contributory patent infringement, then any patent licenses
-      granted to You under this License for that Work shall terminate
-      as of the date such litigation is filed.
-
-   4. Redistribution. You may reproduce and distribute copies of the
-      Work or Derivative Works thereof in any medium, with or without
-      modifications, and in Source or Object form, provided that You
-      meet the following conditions:
-
-      (a) You must give any other recipients of the Work or
-          Derivative Works a copy of this License; and
-
-      (b) You must cause any modified files to carry prominent notices
-          stating that You changed the files; and
-
-      (c) You must retain, in the Source form of any Derivative Works
-          that You distribute, all copyright, patent, trademark, and
-          attribution notices from the Source form of the Work,
-          excluding those notices that do not pertain to any part of
-          the Derivative Works; and
-
-      (d) If the Work includes a "NOTICE" text file as part of its
-          distribution, then any Derivative Works that You distribute must
-          include a readable copy of the attribution notices contained
-          within such NOTICE file, excluding those notices that do not
-          pertain to any part of the Derivative Works, in at least one
-          of the following places: within a NOTICE text file distributed
-          as part of the Derivative Works; within the Source form or
-          documentation, if provided along with the Derivative Works; or,
-          within a display generated by the Derivative Works, if and
-          wherever such third-party notices normally appear. The contents
-          of the NOTICE file are for informational purposes only and
-          do not modify the License. You may add Your own attribution
-          notices within Derivative Works that You distribute, alongside
-          or as an addendum to the NOTICE text from the Work, provided
-          that such additional attribution notices cannot be construed
-          as modifying the License.
-
-      You may add Your own copyright statement to Your modifications and
-      may provide additional or different license terms and conditions
-      for use, reproduction, or distribution of Your modifications, or
-      for any such Derivative Works as a whole, provided Your use,
-      reproduction, and distribution of the Work otherwise complies with
-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
-      origin of the Work and reproducing the content of the NOTICE file.
-
-   7. Disclaimer of Warranty. Unless required by applicable law or
-      agreed to in writing, Licensor provides the Work (and each
-      Contributor provides its Contributions) on an "AS IS" BASIS,
-      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
-      implied, including, without limitation, any warranties or conditions
-      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
-      PARTICULAR PURPOSE. You are solely responsible for determining the
-      appropriateness of using or redistributing the Work and assume any
-      risks associated with Your exercise of permissions under this License.
-
-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
-      liable to You for damages, including any direct, indirect, special,
-      incidental, or consequential damages of any character arising as a
-      result of this License or out of the use or inability to use the
-      Work (including but not limited to damages for loss of goodwill,
-      work stoppage, computer failure or malfunction, or any and all
-      other commercial damages or losses), even if such Contributor
-      has been advised of the possibility of such damages.
-
-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
-      and charge a fee for, acceptance of support, warranty, indemnity,
-      or other liability obligations and/or rights consistent with this
-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "[]"
-      replaced with your own identifying information. (Don't include
-      the brackets!)  The text should be enclosed in the appropriate
-      comment syntax for the file format. We also recommend that a
-      file or class name and description of purpose be included on the
-      same "printed page" as the copyright notice for easier
-      identification within third-party archives.
-
-   Copyright [2021] Samsung Research
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
diff --git a/comfy_extras/chainner_models/architecture/LaMa.py b/comfy_extras/chainner_models/architecture/LaMa.py
deleted file mode 100644
index a781f3e4..00000000
--- a/comfy_extras/chainner_models/architecture/LaMa.py
+++ /dev/null
@@ -1,694 +0,0 @@
-# pylint: skip-file
-"""
-Model adapted from advimman's lama project: https://github.com/advimman/lama
-"""
-
-# Fast Fourier Convolution NeurIPS 2020
-# original implementation https://github.com/pkumivision/FFC/blob/main/model_zoo/ffc.py
-# paper https://proceedings.neurips.cc/paper/2020/file/2fd5d41ec6cfab47e32164d5624269b1-Paper.pdf
-
-from typing import List
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torchvision.transforms.functional import InterpolationMode, rotate
-
-
-class LearnableSpatialTransformWrapper(nn.Module):
-    def __init__(self, impl, pad_coef=0.5, angle_init_range=80, train_angle=True):
-        super().__init__()
-        self.impl = impl
-        self.angle = torch.rand(1) * angle_init_range
-        if train_angle:
-            self.angle = nn.Parameter(self.angle, requires_grad=True)
-        self.pad_coef = pad_coef
-
-    def forward(self, x):
-        if torch.is_tensor(x):
-            return self.inverse_transform(self.impl(self.transform(x)), x)
-        elif isinstance(x, tuple):
-            x_trans = tuple(self.transform(elem) for elem in x)
-            y_trans = self.impl(x_trans)
-            return tuple(
-                self.inverse_transform(elem, orig_x) for elem, orig_x in zip(y_trans, x)
-            )
-        else:
-            raise ValueError(f"Unexpected input type {type(x)}")
-
-    def transform(self, x):
-        height, width = x.shape[2:]
-        pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef)
-        x_padded = F.pad(x, [pad_w, pad_w, pad_h, pad_h], mode="reflect")
-        x_padded_rotated = rotate(
-            x_padded, self.angle.to(x_padded), InterpolationMode.BILINEAR, fill=0
-        )
-
-        return x_padded_rotated
-
-    def inverse_transform(self, y_padded_rotated, orig_x):
-        height, width = orig_x.shape[2:]
-        pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef)
-
-        y_padded = rotate(
-            y_padded_rotated,
-            -self.angle.to(y_padded_rotated),
-            InterpolationMode.BILINEAR,
-            fill=0,
-        )
-        y_height, y_width = y_padded.shape[2:]
-        y = y_padded[:, :, pad_h : y_height - pad_h, pad_w : y_width - pad_w]
-        return y
-
-
-class SELayer(nn.Module):
-    def __init__(self, channel, reduction=16):
-        super(SELayer, self).__init__()
-        self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        self.fc = nn.Sequential(
-            nn.Linear(channel, channel // reduction, bias=False),
-            nn.ReLU(inplace=True),
-            nn.Linear(channel // reduction, channel, bias=False),
-            nn.Sigmoid(),
-        )
-
-    def forward(self, x):
-        b, c, _, _ = x.size()
-        y = self.avg_pool(x).view(b, c)
-        y = self.fc(y).view(b, c, 1, 1)
-        res = x * y.expand_as(x)
-        return res
-
-
-class FourierUnit(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        groups=1,
-        spatial_scale_factor=None,
-        spatial_scale_mode="bilinear",
-        spectral_pos_encoding=False,
-        use_se=False,
-        se_kwargs=None,
-        ffc3d=False,
-        fft_norm="ortho",
-    ):
-        # bn_layer not used
-        super(FourierUnit, self).__init__()
-        self.groups = groups
-
-        self.conv_layer = torch.nn.Conv2d(
-            in_channels=in_channels * 2 + (2 if spectral_pos_encoding else 0),
-            out_channels=out_channels * 2,
-            kernel_size=1,
-            stride=1,
-            padding=0,
-            groups=self.groups,
-            bias=False,
-        )
-        self.bn = torch.nn.BatchNorm2d(out_channels * 2)
-        self.relu = torch.nn.ReLU(inplace=True)
-
-        # squeeze and excitation block
-        self.use_se = use_se
-        if use_se:
-            if se_kwargs is None:
-                se_kwargs = {}
-            self.se = SELayer(self.conv_layer.in_channels, **se_kwargs)
-
-        self.spatial_scale_factor = spatial_scale_factor
-        self.spatial_scale_mode = spatial_scale_mode
-        self.spectral_pos_encoding = spectral_pos_encoding
-        self.ffc3d = ffc3d
-        self.fft_norm = fft_norm
-
-    def forward(self, x):
-        half_check = False
-        if x.type() == "torch.cuda.HalfTensor":
-            # half only works on gpu anyway
-            half_check = True
-
-        batch = x.shape[0]
-
-        if self.spatial_scale_factor is not None:
-            orig_size = x.shape[-2:]
-            x = F.interpolate(
-                x,
-                scale_factor=self.spatial_scale_factor,
-                mode=self.spatial_scale_mode,
-                align_corners=False,
-            )
-
-        # (batch, c, h, w/2+1, 2)
-        fft_dim = (-3, -2, -1) if self.ffc3d else (-2, -1)
-        if half_check == True:
-            ffted = torch.fft.rfftn(
-                x.float(), dim=fft_dim, norm=self.fft_norm
-            )  # .type(torch.cuda.HalfTensor)
-        else:
-            ffted = torch.fft.rfftn(x, dim=fft_dim, norm=self.fft_norm)
-
-        ffted = torch.stack((ffted.real, ffted.imag), dim=-1)
-        ffted = ffted.permute(0, 1, 4, 2, 3).contiguous()  # (batch, c, 2, h, w/2+1)
-        ffted = ffted.view(
-            (
-                batch,
-                -1,
-            )
-            + ffted.size()[3:]
-        )
-
-        if self.spectral_pos_encoding:
-            height, width = ffted.shape[-2:]
-            coords_vert = (
-                torch.linspace(0, 1, height)[None, None, :, None]
-                .expand(batch, 1, height, width)
-                .to(ffted)
-            )
-            coords_hor = (
-                torch.linspace(0, 1, width)[None, None, None, :]
-                .expand(batch, 1, height, width)
-                .to(ffted)
-            )
-            ffted = torch.cat((coords_vert, coords_hor, ffted), dim=1)
-
-        if self.use_se:
-            ffted = self.se(ffted)
-
-        if half_check == True:
-            ffted = self.conv_layer(ffted.half())  # (batch, c*2, h, w/2+1)
-        else:
-            ffted = self.conv_layer(
-                ffted
-            )  # .type(torch.cuda.FloatTensor)  # (batch, c*2, h, w/2+1)
-
-        ffted = self.relu(self.bn(ffted))
-        # forcing to be always float
-        ffted = ffted.float()
-
-        ffted = (
-            ffted.view(
-                (
-                    batch,
-                    -1,
-                    2,
-                )
-                + ffted.size()[2:]
-            )
-            .permute(0, 1, 3, 4, 2)
-            .contiguous()
-        )  # (batch,c, t, h, w/2+1, 2)
-
-        ffted = torch.complex(ffted[..., 0], ffted[..., 1])
-
-        ifft_shape_slice = x.shape[-3:] if self.ffc3d else x.shape[-2:]
-        output = torch.fft.irfftn(
-            ffted, s=ifft_shape_slice, dim=fft_dim, norm=self.fft_norm
-        )
-
-        if half_check == True:
-            output = output.half()
-
-        if self.spatial_scale_factor is not None:
-            output = F.interpolate(
-                output,
-                size=orig_size,
-                mode=self.spatial_scale_mode,
-                align_corners=False,
-            )
-
-        return output
-
-
-class SpectralTransform(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        stride=1,
-        groups=1,
-        enable_lfu=True,
-        separable_fu=False,
-        **fu_kwargs,
-    ):
-        # bn_layer not used
-        super(SpectralTransform, self).__init__()
-        self.enable_lfu = enable_lfu
-        if stride == 2:
-            self.downsample = nn.AvgPool2d(kernel_size=(2, 2), stride=2)
-        else:
-            self.downsample = nn.Identity()
-
-        self.stride = stride
-        self.conv1 = nn.Sequential(
-            nn.Conv2d(
-                in_channels, out_channels // 2, kernel_size=1, groups=groups, bias=False
-            ),
-            nn.BatchNorm2d(out_channels // 2),
-            nn.ReLU(inplace=True),
-        )
-        fu_class = FourierUnit
-        self.fu = fu_class(out_channels // 2, out_channels // 2, groups, **fu_kwargs)
-        if self.enable_lfu:
-            self.lfu = fu_class(out_channels // 2, out_channels // 2, groups)
-        self.conv2 = torch.nn.Conv2d(
-            out_channels // 2, out_channels, kernel_size=1, groups=groups, bias=False
-        )
-
-    def forward(self, x):
-        x = self.downsample(x)
-        x = self.conv1(x)
-        output = self.fu(x)
-
-        if self.enable_lfu:
-            _, c, h, _ = x.shape
-            split_no = 2
-            split_s = h // split_no
-            xs = torch.cat(
-                torch.split(x[:, : c // 4], split_s, dim=-2), dim=1
-            ).contiguous()
-            xs = torch.cat(torch.split(xs, split_s, dim=-1), dim=1).contiguous()
-            xs = self.lfu(xs)
-            xs = xs.repeat(1, 1, split_no, split_no).contiguous()
-        else:
-            xs = 0
-
-        output = self.conv2(x + output + xs)
-
-        return output
-
-
-class FFC(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        ratio_gin,
-        ratio_gout,
-        stride=1,
-        padding=0,
-        dilation=1,
-        groups=1,
-        bias=False,
-        enable_lfu=True,
-        padding_type="reflect",
-        gated=False,
-        **spectral_kwargs,
-    ):
-        super(FFC, self).__init__()
-
-        assert stride == 1 or stride == 2, "Stride should be 1 or 2."
-        self.stride = stride
-
-        in_cg = int(in_channels * ratio_gin)
-        in_cl = in_channels - in_cg
-        out_cg = int(out_channels * ratio_gout)
-        out_cl = out_channels - out_cg
-        # groups_g = 1 if groups == 1 else int(groups * ratio_gout)
-        # groups_l = 1 if groups == 1 else groups - groups_g
-
-        self.ratio_gin = ratio_gin
-        self.ratio_gout = ratio_gout
-        self.global_in_num = in_cg
-
-        module = nn.Identity if in_cl == 0 or out_cl == 0 else nn.Conv2d
-        self.convl2l = module(
-            in_cl,
-            out_cl,
-            kernel_size,
-            stride,
-            padding,
-            dilation,
-            groups,
-            bias,
-            padding_mode=padding_type,
-        )
-        module = nn.Identity if in_cl == 0 or out_cg == 0 else nn.Conv2d
-        self.convl2g = module(
-            in_cl,
-            out_cg,
-            kernel_size,
-            stride,
-            padding,
-            dilation,
-            groups,
-            bias,
-            padding_mode=padding_type,
-        )
-        module = nn.Identity if in_cg == 0 or out_cl == 0 else nn.Conv2d
-        self.convg2l = module(
-            in_cg,
-            out_cl,
-            kernel_size,
-            stride,
-            padding,
-            dilation,
-            groups,
-            bias,
-            padding_mode=padding_type,
-        )
-        module = nn.Identity if in_cg == 0 or out_cg == 0 else SpectralTransform
-        self.convg2g = module(
-            in_cg,
-            out_cg,
-            stride,
-            1 if groups == 1 else groups // 2,
-            enable_lfu,
-            **spectral_kwargs,
-        )
-
-        self.gated = gated
-        module = (
-            nn.Identity if in_cg == 0 or out_cl == 0 or not self.gated else nn.Conv2d
-        )
-        self.gate = module(in_channels, 2, 1)
-
-    def forward(self, x):
-        x_l, x_g = x if type(x) is tuple else (x, 0)
-        out_xl, out_xg = 0, 0
-
-        if self.gated:
-            total_input_parts = [x_l]
-            if torch.is_tensor(x_g):
-                total_input_parts.append(x_g)
-            total_input = torch.cat(total_input_parts, dim=1)
-
-            gates = torch.sigmoid(self.gate(total_input))
-            g2l_gate, l2g_gate = gates.chunk(2, dim=1)
-        else:
-            g2l_gate, l2g_gate = 1, 1
-
-        if self.ratio_gout != 1:
-            out_xl = self.convl2l(x_l) + self.convg2l(x_g) * g2l_gate
-        if self.ratio_gout != 0:
-            out_xg = self.convl2g(x_l) * l2g_gate + self.convg2g(x_g)
-
-        return out_xl, out_xg
-
-
-class FFC_BN_ACT(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        ratio_gin,
-        ratio_gout,
-        stride=1,
-        padding=0,
-        dilation=1,
-        groups=1,
-        bias=False,
-        norm_layer=nn.BatchNorm2d,
-        activation_layer=nn.Identity,
-        padding_type="reflect",
-        enable_lfu=True,
-        **kwargs,
-    ):
-        super(FFC_BN_ACT, self).__init__()
-        self.ffc = FFC(
-            in_channels,
-            out_channels,
-            kernel_size,
-            ratio_gin,
-            ratio_gout,
-            stride,
-            padding,
-            dilation,
-            groups,
-            bias,
-            enable_lfu,
-            padding_type=padding_type,
-            **kwargs,
-        )
-        lnorm = nn.Identity if ratio_gout == 1 else norm_layer
-        gnorm = nn.Identity if ratio_gout == 0 else norm_layer
-        global_channels = int(out_channels * ratio_gout)
-        self.bn_l = lnorm(out_channels - global_channels)
-        self.bn_g = gnorm(global_channels)
-
-        lact = nn.Identity if ratio_gout == 1 else activation_layer
-        gact = nn.Identity if ratio_gout == 0 else activation_layer
-        self.act_l = lact(inplace=True)
-        self.act_g = gact(inplace=True)
-
-    def forward(self, x):
-        x_l, x_g = self.ffc(x)
-        x_l = self.act_l(self.bn_l(x_l))
-        x_g = self.act_g(self.bn_g(x_g))
-        return x_l, x_g
-
-
-class FFCResnetBlock(nn.Module):
-    def __init__(
-        self,
-        dim,
-        padding_type,
-        norm_layer,
-        activation_layer=nn.ReLU,
-        dilation=1,
-        spatial_transform_kwargs=None,
-        inline=False,
-        **conv_kwargs,
-    ):
-        super().__init__()
-        self.conv1 = FFC_BN_ACT(
-            dim,
-            dim,
-            kernel_size=3,
-            padding=dilation,
-            dilation=dilation,
-            norm_layer=norm_layer,
-            activation_layer=activation_layer,
-            padding_type=padding_type,
-            **conv_kwargs,
-        )
-        self.conv2 = FFC_BN_ACT(
-            dim,
-            dim,
-            kernel_size=3,
-            padding=dilation,
-            dilation=dilation,
-            norm_layer=norm_layer,
-            activation_layer=activation_layer,
-            padding_type=padding_type,
-            **conv_kwargs,
-        )
-        if spatial_transform_kwargs is not None:
-            self.conv1 = LearnableSpatialTransformWrapper(
-                self.conv1, **spatial_transform_kwargs
-            )
-            self.conv2 = LearnableSpatialTransformWrapper(
-                self.conv2, **spatial_transform_kwargs
-            )
-        self.inline = inline
-
-    def forward(self, x):
-        if self.inline:
-            x_l, x_g = (
-                x[:, : -self.conv1.ffc.global_in_num],
-                x[:, -self.conv1.ffc.global_in_num :],
-            )
-        else:
-            x_l, x_g = x if type(x) is tuple else (x, 0)
-
-        id_l, id_g = x_l, x_g
-
-        x_l, x_g = self.conv1((x_l, x_g))
-        x_l, x_g = self.conv2((x_l, x_g))
-
-        x_l, x_g = id_l + x_l, id_g + x_g
-        out = x_l, x_g
-        if self.inline:
-            out = torch.cat(out, dim=1)
-        return out
-
-
-class ConcatTupleLayer(nn.Module):
-    def forward(self, x):
-        assert isinstance(x, tuple)
-        x_l, x_g = x
-        assert torch.is_tensor(x_l) or torch.is_tensor(x_g)
-        if not torch.is_tensor(x_g):
-            return x_l
-        return torch.cat(x, dim=1)
-
-
-class FFCResNetGenerator(nn.Module):
-    def __init__(
-        self,
-        input_nc,
-        output_nc,
-        ngf=64,
-        n_downsampling=3,
-        n_blocks=18,
-        norm_layer=nn.BatchNorm2d,
-        padding_type="reflect",
-        activation_layer=nn.ReLU,
-        up_norm_layer=nn.BatchNorm2d,
-        up_activation=nn.ReLU(True),
-        init_conv_kwargs={},
-        downsample_conv_kwargs={},
-        resnet_conv_kwargs={},
-        spatial_transform_layers=None,
-        spatial_transform_kwargs={},
-        max_features=1024,
-        out_ffc=False,
-        out_ffc_kwargs={},
-    ):
-        assert n_blocks >= 0
-        super().__init__()
-        """
-        init_conv_kwargs = {'ratio_gin': 0, 'ratio_gout': 0, 'enable_lfu': False}
-        downsample_conv_kwargs = {'ratio_gin': '${generator.init_conv_kwargs.ratio_gout}', 'ratio_gout': '${generator.downsample_conv_kwargs.ratio_gin}', 'enable_lfu': False}
-        resnet_conv_kwargs = {'ratio_gin': 0.75, 'ratio_gout': '${generator.resnet_conv_kwargs.ratio_gin}', 'enable_lfu': False}
-        spatial_transform_kwargs = {}
-        out_ffc_kwargs = {}
-        """
-        """
-        print(input_nc, output_nc, ngf, n_downsampling, n_blocks, norm_layer,
-                padding_type, activation_layer,
-                up_norm_layer, up_activation,
-                spatial_transform_layers,
-                add_out_act, max_features, out_ffc, file=sys.stderr)
-
-        4 3 64 3 18 <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
-        reflect <class 'torch.nn.modules.activation.ReLU'>
-        <class 'torch.nn.modules.batchnorm.BatchNorm2d'>
-        ReLU(inplace=True)
-        None sigmoid 1024 False
-        """
-        init_conv_kwargs = {"ratio_gin": 0, "ratio_gout": 0, "enable_lfu": False}
-        downsample_conv_kwargs = {"ratio_gin": 0, "ratio_gout": 0, "enable_lfu": False}
-        resnet_conv_kwargs = {
-            "ratio_gin": 0.75,
-            "ratio_gout": 0.75,
-            "enable_lfu": False,
-        }
-        spatial_transform_kwargs = {}
-        out_ffc_kwargs = {}
-
-        model = [
-            nn.ReflectionPad2d(3),
-            FFC_BN_ACT(
-                input_nc,
-                ngf,
-                kernel_size=7,
-                padding=0,
-                norm_layer=norm_layer,
-                activation_layer=activation_layer,
-                **init_conv_kwargs,
-            ),
-        ]
-
-        ### downsample
-        for i in range(n_downsampling):
-            mult = 2**i
-            if i == n_downsampling - 1:
-                cur_conv_kwargs = dict(downsample_conv_kwargs)
-                cur_conv_kwargs["ratio_gout"] = resnet_conv_kwargs.get("ratio_gin", 0)
-            else:
-                cur_conv_kwargs = downsample_conv_kwargs
-            model += [
-                FFC_BN_ACT(
-                    min(max_features, ngf * mult),
-                    min(max_features, ngf * mult * 2),
-                    kernel_size=3,
-                    stride=2,
-                    padding=1,
-                    norm_layer=norm_layer,
-                    activation_layer=activation_layer,
-                    **cur_conv_kwargs,
-                )
-            ]
-
-        mult = 2**n_downsampling
-        feats_num_bottleneck = min(max_features, ngf * mult)
-
-        ### resnet blocks
-        for i in range(n_blocks):
-            cur_resblock = FFCResnetBlock(
-                feats_num_bottleneck,
-                padding_type=padding_type,
-                activation_layer=activation_layer,
-                norm_layer=norm_layer,
-                **resnet_conv_kwargs,
-            )
-            if spatial_transform_layers is not None and i in spatial_transform_layers:
-                cur_resblock = LearnableSpatialTransformWrapper(
-                    cur_resblock, **spatial_transform_kwargs
-                )
-            model += [cur_resblock]
-
-        model += [ConcatTupleLayer()]
-
-        ### upsample
-        for i in range(n_downsampling):
-            mult = 2 ** (n_downsampling - i)
-            model += [
-                nn.ConvTranspose2d(
-                    min(max_features, ngf * mult),
-                    min(max_features, int(ngf * mult / 2)),
-                    kernel_size=3,
-                    stride=2,
-                    padding=1,
-                    output_padding=1,
-                ),
-                up_norm_layer(min(max_features, int(ngf * mult / 2))),
-                up_activation,
-            ]
-
-        if out_ffc:
-            model += [
-                FFCResnetBlock(
-                    ngf,
-                    padding_type=padding_type,
-                    activation_layer=activation_layer,
-                    norm_layer=norm_layer,
-                    inline=True,
-                    **out_ffc_kwargs,
-                )
-            ]
-
-        model += [
-            nn.ReflectionPad2d(3),
-            nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0),
-        ]
-        model.append(nn.Sigmoid())
-        self.model = nn.Sequential(*model)
-
-    def forward(self, image, mask):
-        return self.model(torch.cat([image, mask], dim=1))
-
-
-class LaMa(nn.Module):
-    def __init__(self, state_dict) -> None:
-        super(LaMa, self).__init__()
-        self.model_arch = "LaMa"
-        self.sub_type = "Inpaint"
-        self.in_nc = 4
-        self.out_nc = 3
-        self.scale = 1
-
-        self.min_size = None
-        self.pad_mod = 8
-        self.pad_to_square = False
-
-        self.model = FFCResNetGenerator(self.in_nc, self.out_nc)
-        self.state = {
-            k.replace("generator.model", "model.model"): v
-            for k, v in state_dict.items()
-        }
-
-        self.supports_fp16 = False
-        self.support_bf16 = True
-
-        self.load_state_dict(self.state, strict=False)
-
-    def forward(self, img, mask):
-        masked_img = img * (1 - mask)
-        inpainted_mask = mask * self.model.forward(masked_img, mask)
-        result = inpainted_mask + (1 - mask) * img
-        return result
diff --git a/comfy_extras/chainner_models/architecture/OmniSR/ChannelAttention.py b/comfy_extras/chainner_models/architecture/OmniSR/ChannelAttention.py
deleted file mode 100644
index f4d52aa1..00000000
--- a/comfy_extras/chainner_models/architecture/OmniSR/ChannelAttention.py
+++ /dev/null
@@ -1,110 +0,0 @@
-import math
-
-import torch.nn as nn
-
-
-class CA_layer(nn.Module):
-    def __init__(self, channel, reduction=16):
-        super(CA_layer, self).__init__()
-        # global average pooling
-        self.gap = nn.AdaptiveAvgPool2d(1)
-        self.fc = nn.Sequential(
-            nn.Conv2d(channel, channel // reduction, kernel_size=(1, 1), bias=False),
-            nn.GELU(),
-            nn.Conv2d(channel // reduction, channel, kernel_size=(1, 1), bias=False),
-            # nn.Sigmoid()
-        )
-
-    def forward(self, x):
-        y = self.fc(self.gap(x))
-        return x * y.expand_as(x)
-
-
-class Simple_CA_layer(nn.Module):
-    def __init__(self, channel):
-        super(Simple_CA_layer, self).__init__()
-        self.gap = nn.AdaptiveAvgPool2d(1)
-        self.fc = nn.Conv2d(
-            in_channels=channel,
-            out_channels=channel,
-            kernel_size=1,
-            padding=0,
-            stride=1,
-            groups=1,
-            bias=True,
-        )
-
-    def forward(self, x):
-        return x * self.fc(self.gap(x))
-
-
-class ECA_layer(nn.Module):
-    """Constructs a ECA module.
-    Args:
-        channel: Number of channels of the input feature map
-        k_size: Adaptive selection of kernel size
-    """
-
-    def __init__(self, channel):
-        super(ECA_layer, self).__init__()
-
-        b = 1
-        gamma = 2
-        k_size = int(abs(math.log(channel, 2) + b) / gamma)
-        k_size = k_size if k_size % 2 else k_size + 1
-        self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        self.conv = nn.Conv1d(
-            1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False
-        )
-        # self.sigmoid = nn.Sigmoid()
-
-    def forward(self, x):
-        # x: input features with shape [b, c, h, w]
-        # b, c, h, w = x.size()
-
-        # feature descriptor on the global spatial information
-        y = self.avg_pool(x)
-
-        # Two different branches of ECA module
-        y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
-
-        # Multi-scale information fusion
-        # y = self.sigmoid(y)
-
-        return x * y.expand_as(x)
-
-
-class ECA_MaxPool_layer(nn.Module):
-    """Constructs a ECA module.
-    Args:
-        channel: Number of channels of the input feature map
-        k_size: Adaptive selection of kernel size
-    """
-
-    def __init__(self, channel):
-        super(ECA_MaxPool_layer, self).__init__()
-
-        b = 1
-        gamma = 2
-        k_size = int(abs(math.log(channel, 2) + b) / gamma)
-        k_size = k_size if k_size % 2 else k_size + 1
-        self.max_pool = nn.AdaptiveMaxPool2d(1)
-        self.conv = nn.Conv1d(
-            1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False
-        )
-        # self.sigmoid = nn.Sigmoid()
-
-    def forward(self, x):
-        # x: input features with shape [b, c, h, w]
-        # b, c, h, w = x.size()
-
-        # feature descriptor on the global spatial information
-        y = self.max_pool(x)
-
-        # Two different branches of ECA module
-        y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
-
-        # Multi-scale information fusion
-        # y = self.sigmoid(y)
-
-        return x * y.expand_as(x)
diff --git a/comfy_extras/chainner_models/architecture/OmniSR/LICENSE b/comfy_extras/chainner_models/architecture/OmniSR/LICENSE
deleted file mode 100644
index 261eeb9e..00000000
--- a/comfy_extras/chainner_models/architecture/OmniSR/LICENSE
+++ /dev/null
@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
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-      the conditions stated in this License.
-
-   5. Submission of Contributions. Unless You explicitly state otherwise,
-      any Contribution intentionally submitted for inclusion in the Work
-      by You to the Licensor shall be under the terms and conditions of
-      this License, without any additional terms or conditions.
-      Notwithstanding the above, nothing herein shall supersede or modify
-      the terms of any separate license agreement you may have executed
-      with Licensor regarding such Contributions.
-
-   6. Trademarks. This License does not grant permission to use the trade
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-      except as required for reasonable and customary use in describing the
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-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
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-   You may obtain a copy of the License at
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-       http://www.apache.org/licenses/LICENSE-2.0
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-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
diff --git a/comfy_extras/chainner_models/architecture/OmniSR/OSA.py b/comfy_extras/chainner_models/architecture/OmniSR/OSA.py
deleted file mode 100644
index d7a12969..00000000
--- a/comfy_extras/chainner_models/architecture/OmniSR/OSA.py
+++ /dev/null
@@ -1,577 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-#############################################################
-# File: OSA.py
-# Created Date: Tuesday April 28th 2022
-# Author: Chen Xuanhong
-# Email: chenxuanhongzju@outlook.com
-# Last Modified:  Sunday, 23rd April 2023 3:07:42 pm
-# Modified By: Chen Xuanhong
-# Copyright (c) 2020 Shanghai Jiao Tong University
-#############################################################
-
-import torch
-import torch.nn.functional as F
-from einops import rearrange, repeat
-from einops.layers.torch import Rearrange, Reduce
-from torch import einsum, nn
-
-from .layernorm import LayerNorm2d
-
-# helpers
-
-
-def exists(val):
-    return val is not None
-
-
-def default(val, d):
-    return val if exists(val) else d
-
-
-def cast_tuple(val, length=1):
-    return val if isinstance(val, tuple) else ((val,) * length)
-
-
-# helper classes
-
-
-class PreNormResidual(nn.Module):
-    def __init__(self, dim, fn):
-        super().__init__()
-        self.norm = nn.LayerNorm(dim)
-        self.fn = fn
-
-    def forward(self, x):
-        return self.fn(self.norm(x)) + x
-
-
-class Conv_PreNormResidual(nn.Module):
-    def __init__(self, dim, fn):
-        super().__init__()
-        self.norm = LayerNorm2d(dim)
-        self.fn = fn
-
-    def forward(self, x):
-        return self.fn(self.norm(x)) + x
-
-
-class FeedForward(nn.Module):
-    def __init__(self, dim, mult=2, dropout=0.0):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        self.net = nn.Sequential(
-            nn.Linear(dim, inner_dim),
-            nn.GELU(),
-            nn.Dropout(dropout),
-            nn.Linear(inner_dim, dim),
-            nn.Dropout(dropout),
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-
-class Conv_FeedForward(nn.Module):
-    def __init__(self, dim, mult=2, dropout=0.0):
-        super().__init__()
-        inner_dim = int(dim * mult)
-        self.net = nn.Sequential(
-            nn.Conv2d(dim, inner_dim, 1, 1, 0),
-            nn.GELU(),
-            nn.Dropout(dropout),
-            nn.Conv2d(inner_dim, dim, 1, 1, 0),
-            nn.Dropout(dropout),
-        )
-
-    def forward(self, x):
-        return self.net(x)
-
-
-class Gated_Conv_FeedForward(nn.Module):
-    def __init__(self, dim, mult=1, bias=False, dropout=0.0):
-        super().__init__()
-
-        hidden_features = int(dim * mult)
-
-        self.project_in = nn.Conv2d(dim, hidden_features * 2, kernel_size=1, bias=bias)
-
-        self.dwconv = nn.Conv2d(
-            hidden_features * 2,
-            hidden_features * 2,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            groups=hidden_features * 2,
-            bias=bias,
-        )
-
-        self.project_out = nn.Conv2d(hidden_features, dim, kernel_size=1, bias=bias)
-
-    def forward(self, x):
-        x = self.project_in(x)
-        x1, x2 = self.dwconv(x).chunk(2, dim=1)
-        x = F.gelu(x1) * x2
-        x = self.project_out(x)
-        return x
-
-
-# MBConv
-
-
-class SqueezeExcitation(nn.Module):
-    def __init__(self, dim, shrinkage_rate=0.25):
-        super().__init__()
-        hidden_dim = int(dim * shrinkage_rate)
-
-        self.gate = nn.Sequential(
-            Reduce("b c h w -> b c", "mean"),
-            nn.Linear(dim, hidden_dim, bias=False),
-            nn.SiLU(),
-            nn.Linear(hidden_dim, dim, bias=False),
-            nn.Sigmoid(),
-            Rearrange("b c -> b c 1 1"),
-        )
-
-    def forward(self, x):
-        return x * self.gate(x)
-
-
-class MBConvResidual(nn.Module):
-    def __init__(self, fn, dropout=0.0):
-        super().__init__()
-        self.fn = fn
-        self.dropsample = Dropsample(dropout)
-
-    def forward(self, x):
-        out = self.fn(x)
-        out = self.dropsample(out)
-        return out + x
-
-
-class Dropsample(nn.Module):
-    def __init__(self, prob=0):
-        super().__init__()
-        self.prob = prob
-
-    def forward(self, x):
-        device = x.device
-
-        if self.prob == 0.0 or (not self.training):
-            return x
-
-        keep_mask = (
-            torch.FloatTensor((x.shape[0], 1, 1, 1), device=device).uniform_()
-            > self.prob
-        )
-        return x * keep_mask / (1 - self.prob)
-
-
-def MBConv(
-    dim_in, dim_out, *, downsample, expansion_rate=4, shrinkage_rate=0.25, dropout=0.0
-):
-    hidden_dim = int(expansion_rate * dim_out)
-    stride = 2 if downsample else 1
-
-    net = nn.Sequential(
-        nn.Conv2d(dim_in, hidden_dim, 1),
-        # nn.BatchNorm2d(hidden_dim),
-        nn.GELU(),
-        nn.Conv2d(
-            hidden_dim, hidden_dim, 3, stride=stride, padding=1, groups=hidden_dim
-        ),
-        # nn.BatchNorm2d(hidden_dim),
-        nn.GELU(),
-        SqueezeExcitation(hidden_dim, shrinkage_rate=shrinkage_rate),
-        nn.Conv2d(hidden_dim, dim_out, 1),
-        # nn.BatchNorm2d(dim_out)
-    )
-
-    if dim_in == dim_out and not downsample:
-        net = MBConvResidual(net, dropout=dropout)
-
-    return net
-
-
-# attention related classes
-class Attention(nn.Module):
-    def __init__(
-        self,
-        dim,
-        dim_head=32,
-        dropout=0.0,
-        window_size=7,
-        with_pe=True,
-    ):
-        super().__init__()
-        assert (
-            dim % dim_head
-        ) == 0, "dimension should be divisible by dimension per head"
-
-        self.heads = dim // dim_head
-        self.scale = dim_head**-0.5
-        self.with_pe = with_pe
-
-        self.to_qkv = nn.Linear(dim, dim * 3, bias=False)
-
-        self.attend = nn.Sequential(nn.Softmax(dim=-1), nn.Dropout(dropout))
-
-        self.to_out = nn.Sequential(
-            nn.Linear(dim, dim, bias=False), nn.Dropout(dropout)
-        )
-
-        # relative positional bias
-        if self.with_pe:
-            self.rel_pos_bias = nn.Embedding((2 * window_size - 1) ** 2, self.heads)
-
-            pos = torch.arange(window_size)
-            grid = torch.stack(torch.meshgrid(pos, pos))
-            grid = rearrange(grid, "c i j -> (i j) c")
-            rel_pos = rearrange(grid, "i ... -> i 1 ...") - rearrange(
-                grid, "j ... -> 1 j ..."
-            )
-            rel_pos += window_size - 1
-            rel_pos_indices = (rel_pos * torch.tensor([2 * window_size - 1, 1])).sum(
-                dim=-1
-            )
-
-            self.register_buffer("rel_pos_indices", rel_pos_indices, persistent=False)
-
-    def forward(self, x):
-        batch, height, width, window_height, window_width, _, device, h = (
-            *x.shape,
-            x.device,
-            self.heads,
-        )
-
-        # flatten
-
-        x = rearrange(x, "b x y w1 w2 d -> (b x y) (w1 w2) d")
-
-        # project for queries, keys, values
-
-        q, k, v = self.to_qkv(x).chunk(3, dim=-1)
-
-        # split heads
-
-        q, k, v = map(lambda t: rearrange(t, "b n (h d ) -> b h n d", h=h), (q, k, v))
-
-        # scale
-
-        q = q * self.scale
-
-        # sim
-
-        sim = einsum("b h i d, b h j d -> b h i j", q, k)
-
-        # add positional bias
-        if self.with_pe:
-            bias = self.rel_pos_bias(self.rel_pos_indices)
-            sim = sim + rearrange(bias, "i j h -> h i j")
-
-        # attention
-
-        attn = self.attend(sim)
-
-        # aggregate
-
-        out = einsum("b h i j, b h j d -> b h i d", attn, v)
-
-        # merge heads
-
-        out = rearrange(
-            out, "b h (w1 w2) d -> b w1 w2 (h d)", w1=window_height, w2=window_width
-        )
-
-        # combine heads out
-
-        out = self.to_out(out)
-        return rearrange(out, "(b x y) ... -> b x y ...", x=height, y=width)
-
-
-class Block_Attention(nn.Module):
-    def __init__(
-        self,
-        dim,
-        dim_head=32,
-        bias=False,
-        dropout=0.0,
-        window_size=7,
-        with_pe=True,
-    ):
-        super().__init__()
-        assert (
-            dim % dim_head
-        ) == 0, "dimension should be divisible by dimension per head"
-
-        self.heads = dim // dim_head
-        self.ps = window_size
-        self.scale = dim_head**-0.5
-        self.with_pe = with_pe
-
-        self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias)
-        self.qkv_dwconv = nn.Conv2d(
-            dim * 3,
-            dim * 3,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            groups=dim * 3,
-            bias=bias,
-        )
-
-        self.attend = nn.Sequential(nn.Softmax(dim=-1), nn.Dropout(dropout))
-
-        self.to_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)
-
-    def forward(self, x):
-        # project for queries, keys, values
-        b, c, h, w = x.shape
-
-        qkv = self.qkv_dwconv(self.qkv(x))
-        q, k, v = qkv.chunk(3, dim=1)
-
-        # split heads
-
-        q, k, v = map(
-            lambda t: rearrange(
-                t,
-                "b (h d) (x w1) (y w2) -> (b x y) h (w1 w2) d",
-                h=self.heads,
-                w1=self.ps,
-                w2=self.ps,
-            ),
-            (q, k, v),
-        )
-
-        # scale
-
-        q = q * self.scale
-
-        # sim
-
-        sim = einsum("b h i d, b h j d -> b h i j", q, k)
-
-        # attention
-        attn = self.attend(sim)
-
-        # aggregate
-
-        out = einsum("b h i j, b h j d -> b h i d", attn, v)
-
-        # merge heads
-        out = rearrange(
-            out,
-            "(b x y) head (w1 w2) d -> b (head d) (x w1) (y w2)",
-            x=h // self.ps,
-            y=w // self.ps,
-            head=self.heads,
-            w1=self.ps,
-            w2=self.ps,
-        )
-
-        out = self.to_out(out)
-        return out
-
-
-class Channel_Attention(nn.Module):
-    def __init__(self, dim, heads, bias=False, dropout=0.0, window_size=7):
-        super(Channel_Attention, self).__init__()
-        self.heads = heads
-
-        self.temperature = nn.Parameter(torch.ones(heads, 1, 1))
-
-        self.ps = window_size
-
-        self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias)
-        self.qkv_dwconv = nn.Conv2d(
-            dim * 3,
-            dim * 3,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            groups=dim * 3,
-            bias=bias,
-        )
-        self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)
-
-    def forward(self, x):
-        b, c, h, w = x.shape
-
-        qkv = self.qkv_dwconv(self.qkv(x))
-        qkv = qkv.chunk(3, dim=1)
-
-        q, k, v = map(
-            lambda t: rearrange(
-                t,
-                "b (head d) (h ph) (w pw) -> b (h w) head d (ph pw)",
-                ph=self.ps,
-                pw=self.ps,
-                head=self.heads,
-            ),
-            qkv,
-        )
-
-        q = F.normalize(q, dim=-1)
-        k = F.normalize(k, dim=-1)
-
-        attn = (q @ k.transpose(-2, -1)) * self.temperature
-        attn = attn.softmax(dim=-1)
-        out = attn @ v
-
-        out = rearrange(
-            out,
-            "b (h w) head d (ph pw) -> b (head d) (h ph) (w pw)",
-            h=h // self.ps,
-            w=w // self.ps,
-            ph=self.ps,
-            pw=self.ps,
-            head=self.heads,
-        )
-
-        out = self.project_out(out)
-
-        return out
-
-
-class Channel_Attention_grid(nn.Module):
-    def __init__(self, dim, heads, bias=False, dropout=0.0, window_size=7):
-        super(Channel_Attention_grid, self).__init__()
-        self.heads = heads
-
-        self.temperature = nn.Parameter(torch.ones(heads, 1, 1))
-
-        self.ps = window_size
-
-        self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias)
-        self.qkv_dwconv = nn.Conv2d(
-            dim * 3,
-            dim * 3,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            groups=dim * 3,
-            bias=bias,
-        )
-        self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)
-
-    def forward(self, x):
-        b, c, h, w = x.shape
-
-        qkv = self.qkv_dwconv(self.qkv(x))
-        qkv = qkv.chunk(3, dim=1)
-
-        q, k, v = map(
-            lambda t: rearrange(
-                t,
-                "b (head d) (h ph) (w pw) -> b (ph pw) head d (h w)",
-                ph=self.ps,
-                pw=self.ps,
-                head=self.heads,
-            ),
-            qkv,
-        )
-
-        q = F.normalize(q, dim=-1)
-        k = F.normalize(k, dim=-1)
-
-        attn = (q @ k.transpose(-2, -1)) * self.temperature
-        attn = attn.softmax(dim=-1)
-        out = attn @ v
-
-        out = rearrange(
-            out,
-            "b (ph pw) head d (h w) -> b (head d) (h ph) (w pw)",
-            h=h // self.ps,
-            w=w // self.ps,
-            ph=self.ps,
-            pw=self.ps,
-            head=self.heads,
-        )
-
-        out = self.project_out(out)
-
-        return out
-
-
-class OSA_Block(nn.Module):
-    def __init__(
-        self,
-        channel_num=64,
-        bias=True,
-        ffn_bias=True,
-        window_size=8,
-        with_pe=False,
-        dropout=0.0,
-    ):
-        super(OSA_Block, self).__init__()
-
-        w = window_size
-
-        self.layer = nn.Sequential(
-            MBConv(
-                channel_num,
-                channel_num,
-                downsample=False,
-                expansion_rate=1,
-                shrinkage_rate=0.25,
-            ),
-            Rearrange(
-                "b d (x w1) (y w2) -> b x y w1 w2 d", w1=w, w2=w
-            ),  # block-like attention
-            PreNormResidual(
-                channel_num,
-                Attention(
-                    dim=channel_num,
-                    dim_head=channel_num // 4,
-                    dropout=dropout,
-                    window_size=window_size,
-                    with_pe=with_pe,
-                ),
-            ),
-            Rearrange("b x y w1 w2 d -> b d (x w1) (y w2)"),
-            Conv_PreNormResidual(
-                channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout)
-            ),
-            # channel-like attention
-            Conv_PreNormResidual(
-                channel_num,
-                Channel_Attention(
-                    dim=channel_num, heads=4, dropout=dropout, window_size=window_size
-                ),
-            ),
-            Conv_PreNormResidual(
-                channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout)
-            ),
-            Rearrange(
-                "b d (w1 x) (w2 y) -> b x y w1 w2 d", w1=w, w2=w
-            ),  # grid-like attention
-            PreNormResidual(
-                channel_num,
-                Attention(
-                    dim=channel_num,
-                    dim_head=channel_num // 4,
-                    dropout=dropout,
-                    window_size=window_size,
-                    with_pe=with_pe,
-                ),
-            ),
-            Rearrange("b x y w1 w2 d -> b d (w1 x) (w2 y)"),
-            Conv_PreNormResidual(
-                channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout)
-            ),
-            # channel-like attention
-            Conv_PreNormResidual(
-                channel_num,
-                Channel_Attention_grid(
-                    dim=channel_num, heads=4, dropout=dropout, window_size=window_size
-                ),
-            ),
-            Conv_PreNormResidual(
-                channel_num, Gated_Conv_FeedForward(dim=channel_num, dropout=dropout)
-            ),
-        )
-
-    def forward(self, x):
-        out = self.layer(x)
-        return out
diff --git a/comfy_extras/chainner_models/architecture/OmniSR/OSAG.py b/comfy_extras/chainner_models/architecture/OmniSR/OSAG.py
deleted file mode 100644
index 477e81f9..00000000
--- a/comfy_extras/chainner_models/architecture/OmniSR/OSAG.py
+++ /dev/null
@@ -1,60 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-#############################################################
-# File: OSAG.py
-# Created Date: Tuesday April 28th 2022
-# Author: Chen Xuanhong
-# Email: chenxuanhongzju@outlook.com
-# Last Modified:  Sunday, 23rd April 2023 3:08:49 pm
-# Modified By: Chen Xuanhong
-# Copyright (c) 2020 Shanghai Jiao Tong University
-#############################################################
-
-
-import torch.nn as nn
-
-from .esa import ESA
-from .OSA import OSA_Block
-
-
-class OSAG(nn.Module):
-    def __init__(
-        self,
-        channel_num=64,
-        bias=True,
-        block_num=4,
-        ffn_bias=False,
-        window_size=0,
-        pe=False,
-    ):
-        super(OSAG, self).__init__()
-
-        # print("window_size: %d" % (window_size))
-        # print("with_pe", pe)
-        # print("ffn_bias: %d" % (ffn_bias))
-
-        # block_script_name = kwargs.get("block_script_name", "OSA")
-        # block_class_name = kwargs.get("block_class_name", "OSA_Block")
-
-        # script_name = "." + block_script_name
-        # package = __import__(script_name, fromlist=True)
-        block_class = OSA_Block  # getattr(package, block_class_name)
-        group_list = []
-        for _ in range(block_num):
-            temp_res = block_class(
-                channel_num,
-                bias,
-                ffn_bias=ffn_bias,
-                window_size=window_size,
-                with_pe=pe,
-            )
-            group_list.append(temp_res)
-        group_list.append(nn.Conv2d(channel_num, channel_num, 1, 1, 0, bias=bias))
-        self.residual_layer = nn.Sequential(*group_list)
-        esa_channel = max(channel_num // 4, 16)
-        self.esa = ESA(esa_channel, channel_num)
-
-    def forward(self, x):
-        out = self.residual_layer(x)
-        out = out + x
-        return self.esa(out)
diff --git a/comfy_extras/chainner_models/architecture/OmniSR/OmniSR.py b/comfy_extras/chainner_models/architecture/OmniSR/OmniSR.py
deleted file mode 100644
index 1e1c3f35..00000000
--- a/comfy_extras/chainner_models/architecture/OmniSR/OmniSR.py
+++ /dev/null
@@ -1,143 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-#############################################################
-# File: OmniSR.py
-# Created Date: Tuesday April 28th 2022
-# Author: Chen Xuanhong
-# Email: chenxuanhongzju@outlook.com
-# Last Modified:  Sunday, 23rd April 2023 3:06:36 pm
-# Modified By: Chen Xuanhong
-# Copyright (c) 2020 Shanghai Jiao Tong University
-#############################################################
-
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .OSAG import OSAG
-from .pixelshuffle import pixelshuffle_block
-
-
-class OmniSR(nn.Module):
-    def __init__(
-        self,
-        state_dict,
-        **kwargs,
-    ):
-        super(OmniSR, self).__init__()
-        self.state = state_dict
-
-        bias = True  # Fine to assume this for now
-        block_num = 1  # Fine to assume this for now
-        ffn_bias = True
-        pe = True
-
-        num_feat = state_dict["input.weight"].shape[0] or 64
-        num_in_ch = state_dict["input.weight"].shape[1] or 3
-        num_out_ch = num_in_ch  # we can just assume this for now. pixelshuffle smh
-
-        pixelshuffle_shape = state_dict["up.0.weight"].shape[0]
-        up_scale = math.sqrt(pixelshuffle_shape / num_out_ch)
-        if up_scale - int(up_scale) > 0:
-            print(
-                "out_nc is probably different than in_nc, scale calculation might be wrong"
-            )
-        up_scale = int(up_scale)
-        res_num = 0
-        for key in state_dict.keys():
-            if "residual_layer" in key:
-                temp_res_num = int(key.split(".")[1])
-                if temp_res_num > res_num:
-                    res_num = temp_res_num
-        res_num = res_num + 1  # zero-indexed
-
-        residual_layer = []
-        self.res_num = res_num
-
-        if (
-            "residual_layer.0.residual_layer.0.layer.2.fn.rel_pos_bias.weight"
-            in state_dict.keys()
-        ):
-            rel_pos_bias_weight = state_dict[
-                "residual_layer.0.residual_layer.0.layer.2.fn.rel_pos_bias.weight"
-            ].shape[0]
-            self.window_size = int((math.sqrt(rel_pos_bias_weight) + 1) / 2)
-        else:
-            self.window_size = 8
-
-        self.up_scale = up_scale
-
-        for _ in range(res_num):
-            temp_res = OSAG(
-                channel_num=num_feat,
-                bias=bias,
-                block_num=block_num,
-                ffn_bias=ffn_bias,
-                window_size=self.window_size,
-                pe=pe,
-            )
-            residual_layer.append(temp_res)
-        self.residual_layer = nn.Sequential(*residual_layer)
-        self.input = nn.Conv2d(
-            in_channels=num_in_ch,
-            out_channels=num_feat,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            bias=bias,
-        )
-        self.output = nn.Conv2d(
-            in_channels=num_feat,
-            out_channels=num_feat,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            bias=bias,
-        )
-        self.up = pixelshuffle_block(num_feat, num_out_ch, up_scale, bias=bias)
-
-        # self.tail   = pixelshuffle_block(num_feat,num_out_ch,up_scale,bias=bias)
-
-        # for m in self.modules():
-        #     if isinstance(m, nn.Conv2d):
-        #         n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
-        #         m.weight.data.normal_(0, sqrt(2. / n))
-
-        # chaiNNer specific stuff
-        self.model_arch = "OmniSR"
-        self.sub_type = "SR"
-        self.in_nc = num_in_ch
-        self.out_nc = num_out_ch
-        self.num_feat = num_feat
-        self.scale = up_scale
-
-        self.supports_fp16 = True  # TODO: Test this
-        self.supports_bfp16 = True
-        self.min_size_restriction = 16
-
-        self.load_state_dict(state_dict, strict=False)
-
-    def check_image_size(self, x):
-        _, _, h, w = x.size()
-        # import pdb; pdb.set_trace()
-        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
-        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
-        # x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), 'reflect')
-        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "constant", 0)
-        return x
-
-    def forward(self, x):
-        H, W = x.shape[2:]
-        x = self.check_image_size(x)
-
-        residual = self.input(x)
-        out = self.residual_layer(residual)
-
-        # origin
-        out = torch.add(self.output(out), residual)
-        out = self.up(out)
-
-        out = out[:, :, : H * self.up_scale, : W * self.up_scale]
-        return out
diff --git a/comfy_extras/chainner_models/architecture/OmniSR/esa.py b/comfy_extras/chainner_models/architecture/OmniSR/esa.py
deleted file mode 100644
index f9ce7f7a..00000000
--- a/comfy_extras/chainner_models/architecture/OmniSR/esa.py
+++ /dev/null
@@ -1,294 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-#############################################################
-# File: esa.py
-# Created Date: Tuesday April 28th 2022
-# Author: Chen Xuanhong
-# Email: chenxuanhongzju@outlook.com
-# Last Modified:  Thursday, 20th April 2023 9:28:06 am
-# Modified By: Chen Xuanhong
-# Copyright (c) 2020 Shanghai Jiao Tong University
-#############################################################
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .layernorm import LayerNorm2d
-
-
-def moment(x, dim=(2, 3), k=2):
-    assert len(x.size()) == 4
-    mean = torch.mean(x, dim=dim).unsqueeze(-1).unsqueeze(-1)
-    mk = (1 / (x.size(2) * x.size(3))) * torch.sum(torch.pow(x - mean, k), dim=dim)
-    return mk
-
-
-class ESA(nn.Module):
-    """
-    Modification of Enhanced Spatial Attention (ESA), which is proposed by
-    `Residual Feature Aggregation Network for Image Super-Resolution`
-    Note: `conv_max` and `conv3_` are NOT used here, so the corresponding codes
-    are deleted.
-    """
-
-    def __init__(self, esa_channels, n_feats, conv=nn.Conv2d):
-        super(ESA, self).__init__()
-        f = esa_channels
-        self.conv1 = conv(n_feats, f, kernel_size=1)
-        self.conv_f = conv(f, f, kernel_size=1)
-        self.conv2 = conv(f, f, kernel_size=3, stride=2, padding=0)
-        self.conv3 = conv(f, f, kernel_size=3, padding=1)
-        self.conv4 = conv(f, n_feats, kernel_size=1)
-        self.sigmoid = nn.Sigmoid()
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        c1_ = self.conv1(x)
-        c1 = self.conv2(c1_)
-        v_max = F.max_pool2d(c1, kernel_size=7, stride=3)
-        c3 = self.conv3(v_max)
-        c3 = F.interpolate(
-            c3, (x.size(2), x.size(3)), mode="bilinear", align_corners=False
-        )
-        cf = self.conv_f(c1_)
-        c4 = self.conv4(c3 + cf)
-        m = self.sigmoid(c4)
-        return x * m
-
-
-class LK_ESA(nn.Module):
-    def __init__(
-        self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True
-    ):
-        super(LK_ESA, self).__init__()
-        f = esa_channels
-        self.conv1 = conv(n_feats, f, kernel_size=1)
-        self.conv_f = conv(f, f, kernel_size=1)
-
-        kernel_size = 17
-        kernel_expand = kernel_expand
-        padding = kernel_size // 2
-
-        self.vec_conv = nn.Conv2d(
-            in_channels=f * kernel_expand,
-            out_channels=f * kernel_expand,
-            kernel_size=(1, kernel_size),
-            padding=(0, padding),
-            groups=2,
-            bias=bias,
-        )
-        self.vec_conv3x1 = nn.Conv2d(
-            in_channels=f * kernel_expand,
-            out_channels=f * kernel_expand,
-            kernel_size=(1, 3),
-            padding=(0, 1),
-            groups=2,
-            bias=bias,
-        )
-
-        self.hor_conv = nn.Conv2d(
-            in_channels=f * kernel_expand,
-            out_channels=f * kernel_expand,
-            kernel_size=(kernel_size, 1),
-            padding=(padding, 0),
-            groups=2,
-            bias=bias,
-        )
-        self.hor_conv1x3 = nn.Conv2d(
-            in_channels=f * kernel_expand,
-            out_channels=f * kernel_expand,
-            kernel_size=(3, 1),
-            padding=(1, 0),
-            groups=2,
-            bias=bias,
-        )
-
-        self.conv4 = conv(f, n_feats, kernel_size=1)
-        self.sigmoid = nn.Sigmoid()
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        c1_ = self.conv1(x)
-
-        res = self.vec_conv(c1_) + self.vec_conv3x1(c1_)
-        res = self.hor_conv(res) + self.hor_conv1x3(res)
-
-        cf = self.conv_f(c1_)
-        c4 = self.conv4(res + cf)
-        m = self.sigmoid(c4)
-        return x * m
-
-
-class LK_ESA_LN(nn.Module):
-    def __init__(
-        self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True
-    ):
-        super(LK_ESA_LN, self).__init__()
-        f = esa_channels
-        self.conv1 = conv(n_feats, f, kernel_size=1)
-        self.conv_f = conv(f, f, kernel_size=1)
-
-        kernel_size = 17
-        kernel_expand = kernel_expand
-        padding = kernel_size // 2
-
-        self.norm = LayerNorm2d(n_feats)
-
-        self.vec_conv = nn.Conv2d(
-            in_channels=f * kernel_expand,
-            out_channels=f * kernel_expand,
-            kernel_size=(1, kernel_size),
-            padding=(0, padding),
-            groups=2,
-            bias=bias,
-        )
-        self.vec_conv3x1 = nn.Conv2d(
-            in_channels=f * kernel_expand,
-            out_channels=f * kernel_expand,
-            kernel_size=(1, 3),
-            padding=(0, 1),
-            groups=2,
-            bias=bias,
-        )
-
-        self.hor_conv = nn.Conv2d(
-            in_channels=f * kernel_expand,
-            out_channels=f * kernel_expand,
-            kernel_size=(kernel_size, 1),
-            padding=(padding, 0),
-            groups=2,
-            bias=bias,
-        )
-        self.hor_conv1x3 = nn.Conv2d(
-            in_channels=f * kernel_expand,
-            out_channels=f * kernel_expand,
-            kernel_size=(3, 1),
-            padding=(1, 0),
-            groups=2,
-            bias=bias,
-        )
-
-        self.conv4 = conv(f, n_feats, kernel_size=1)
-        self.sigmoid = nn.Sigmoid()
-        self.relu = nn.ReLU(inplace=True)
-
-    def forward(self, x):
-        c1_ = self.norm(x)
-        c1_ = self.conv1(c1_)
-
-        res = self.vec_conv(c1_) + self.vec_conv3x1(c1_)
-        res = self.hor_conv(res) + self.hor_conv1x3(res)
-
-        cf = self.conv_f(c1_)
-        c4 = self.conv4(res + cf)
-        m = self.sigmoid(c4)
-        return x * m
-
-
-class AdaGuidedFilter(nn.Module):
-    def __init__(
-        self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True
-    ):
-        super(AdaGuidedFilter, self).__init__()
-
-        self.gap = nn.AdaptiveAvgPool2d(1)
-        self.fc = nn.Conv2d(
-            in_channels=n_feats,
-            out_channels=1,
-            kernel_size=1,
-            padding=0,
-            stride=1,
-            groups=1,
-            bias=True,
-        )
-
-        self.r = 5
-
-    def box_filter(self, x, r):
-        channel = x.shape[1]
-        kernel_size = 2 * r + 1
-        weight = 1.0 / (kernel_size**2)
-        box_kernel = weight * torch.ones(
-            (channel, 1, kernel_size, kernel_size), dtype=torch.float32, device=x.device
-        )
-        output = F.conv2d(x, weight=box_kernel, stride=1, padding=r, groups=channel)
-        return output
-
-    def forward(self, x):
-        _, _, H, W = x.shape
-        N = self.box_filter(
-            torch.ones((1, 1, H, W), dtype=x.dtype, device=x.device), self.r
-        )
-
-        # epsilon = self.fc(self.gap(x))
-        # epsilon = torch.pow(epsilon, 2)
-        epsilon = 1e-2
-
-        mean_x = self.box_filter(x, self.r) / N
-        var_x = self.box_filter(x * x, self.r) / N - mean_x * mean_x
-
-        A = var_x / (var_x + epsilon)
-        b = (1 - A) * mean_x
-        m = A * x + b
-
-        # mean_A = self.box_filter(A, self.r) / N
-        # mean_b = self.box_filter(b, self.r) / N
-        # m = mean_A * x + mean_b
-        return x * m
-
-
-class AdaConvGuidedFilter(nn.Module):
-    def __init__(
-        self, esa_channels, n_feats, conv=nn.Conv2d, kernel_expand=1, bias=True
-    ):
-        super(AdaConvGuidedFilter, self).__init__()
-        f = esa_channels
-
-        self.conv_f = conv(f, f, kernel_size=1)
-
-        kernel_size = 17
-        kernel_expand = kernel_expand
-        padding = kernel_size // 2
-
-        self.vec_conv = nn.Conv2d(
-            in_channels=f,
-            out_channels=f,
-            kernel_size=(1, kernel_size),
-            padding=(0, padding),
-            groups=f,
-            bias=bias,
-        )
-
-        self.hor_conv = nn.Conv2d(
-            in_channels=f,
-            out_channels=f,
-            kernel_size=(kernel_size, 1),
-            padding=(padding, 0),
-            groups=f,
-            bias=bias,
-        )
-
-        self.gap = nn.AdaptiveAvgPool2d(1)
-        self.fc = nn.Conv2d(
-            in_channels=f,
-            out_channels=f,
-            kernel_size=1,
-            padding=0,
-            stride=1,
-            groups=1,
-            bias=True,
-        )
-
-    def forward(self, x):
-        y = self.vec_conv(x)
-        y = self.hor_conv(y)
-
-        sigma = torch.pow(y, 2)
-        epsilon = self.fc(self.gap(y))
-
-        weight = sigma / (sigma + epsilon)
-
-        m = weight * x + (1 - weight)
-
-        return x * m
diff --git a/comfy_extras/chainner_models/architecture/OmniSR/layernorm.py b/comfy_extras/chainner_models/architecture/OmniSR/layernorm.py
deleted file mode 100644
index 731a25f7..00000000
--- a/comfy_extras/chainner_models/architecture/OmniSR/layernorm.py
+++ /dev/null
@@ -1,70 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-#############################################################
-# File: layernorm.py
-# Created Date: Tuesday April 28th 2022
-# Author: Chen Xuanhong
-# Email: chenxuanhongzju@outlook.com
-# Last Modified:  Thursday, 20th April 2023 9:28:20 am
-# Modified By: Chen Xuanhong
-# Copyright (c) 2020 Shanghai Jiao Tong University
-#############################################################
-
-import torch
-import torch.nn as nn
-
-
-class LayerNormFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, x, weight, bias, eps):
-        ctx.eps = eps
-        N, C, H, W = x.size()
-        mu = x.mean(1, keepdim=True)
-        var = (x - mu).pow(2).mean(1, keepdim=True)
-        y = (x - mu) / (var + eps).sqrt()
-        ctx.save_for_backward(y, var, weight)
-        y = weight.view(1, C, 1, 1) * y + bias.view(1, C, 1, 1)
-        return y
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        eps = ctx.eps
-
-        N, C, H, W = grad_output.size()
-        y, var, weight = ctx.saved_variables
-        g = grad_output * weight.view(1, C, 1, 1)
-        mean_g = g.mean(dim=1, keepdim=True)
-
-        mean_gy = (g * y).mean(dim=1, keepdim=True)
-        gx = 1.0 / torch.sqrt(var + eps) * (g - y * mean_gy - mean_g)
-        return (
-            gx,
-            (grad_output * y).sum(dim=3).sum(dim=2).sum(dim=0),
-            grad_output.sum(dim=3).sum(dim=2).sum(dim=0),
-            None,
-        )
-
-
-class LayerNorm2d(nn.Module):
-    def __init__(self, channels, eps=1e-6):
-        super(LayerNorm2d, self).__init__()
-        self.register_parameter("weight", nn.Parameter(torch.ones(channels)))
-        self.register_parameter("bias", nn.Parameter(torch.zeros(channels)))
-        self.eps = eps
-
-    def forward(self, x):
-        return LayerNormFunction.apply(x, self.weight, self.bias, self.eps)
-
-
-class GRN(nn.Module):
-    """GRN (Global Response Normalization) layer"""
-
-    def __init__(self, dim):
-        super().__init__()
-        self.gamma = nn.Parameter(torch.zeros(1, dim, 1, 1))
-        self.beta = nn.Parameter(torch.zeros(1, dim, 1, 1))
-
-    def forward(self, x):
-        Gx = torch.norm(x, p=2, dim=(2, 3), keepdim=True)
-        Nx = Gx / (Gx.mean(dim=1, keepdim=True) + 1e-6)
-        return self.gamma * (x * Nx) + self.beta + x
diff --git a/comfy_extras/chainner_models/architecture/OmniSR/pixelshuffle.py b/comfy_extras/chainner_models/architecture/OmniSR/pixelshuffle.py
deleted file mode 100644
index 4260fb7c..00000000
--- a/comfy_extras/chainner_models/architecture/OmniSR/pixelshuffle.py
+++ /dev/null
@@ -1,31 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding:utf-8 -*-
-#############################################################
-# File: pixelshuffle.py
-# Created Date: Friday July 1st 2022
-# Author: Chen Xuanhong
-# Email: chenxuanhongzju@outlook.com
-# Last Modified:  Friday, 1st July 2022 10:18:39 am
-# Modified By: Chen Xuanhong
-# Copyright (c) 2022 Shanghai Jiao Tong University
-#############################################################
-
-import torch.nn as nn
-
-
-def pixelshuffle_block(
-    in_channels, out_channels, upscale_factor=2, kernel_size=3, bias=False
-):
-    """
-    Upsample features according to `upscale_factor`.
-    """
-    padding = kernel_size // 2
-    conv = nn.Conv2d(
-        in_channels,
-        out_channels * (upscale_factor**2),
-        kernel_size,
-        padding=1,
-        bias=bias,
-    )
-    pixel_shuffle = nn.PixelShuffle(upscale_factor)
-    return nn.Sequential(*[conv, pixel_shuffle])
diff --git a/comfy_extras/chainner_models/architecture/RRDB.py b/comfy_extras/chainner_models/architecture/RRDB.py
deleted file mode 100644
index b50db7c2..00000000
--- a/comfy_extras/chainner_models/architecture/RRDB.py
+++ /dev/null
@@ -1,296 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-import functools
-import math
-import re
-from collections import OrderedDict
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from . import block as B
-
-
-# Borrowed from https://github.com/rlaphoenix/VSGAN/blob/master/vsgan/archs/ESRGAN.py
-# Which enhanced stuff that was already here
-class RRDBNet(nn.Module):
-    def __init__(
-        self,
-        state_dict,
-        norm=None,
-        act: str = "leakyrelu",
-        upsampler: str = "upconv",
-        mode: B.ConvMode = "CNA",
-    ) -> None:
-        """
-        ESRGAN - Enhanced Super-Resolution Generative Adversarial Networks.
-        By Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Yu Qiao,
-        and Chen Change Loy.
-        This is old-arch Residual in Residual Dense Block Network and is not
-        the newest revision that's available at github.com/xinntao/ESRGAN.
-        This is on purpose, the newest Network has severely limited the
-        potential use of the Network with no benefits.
-        This network supports model files from both new and old-arch.
-        Args:
-            norm: Normalization layer
-            act: Activation layer
-            upsampler: Upsample layer. upconv, pixel_shuffle
-            mode: Convolution mode
-        """
-        super(RRDBNet, self).__init__()
-        self.model_arch = "ESRGAN"
-        self.sub_type = "SR"
-
-        self.state = state_dict
-        self.norm = norm
-        self.act = act
-        self.upsampler = upsampler
-        self.mode = mode
-
-        self.state_map = {
-            # currently supports old, new, and newer RRDBNet arch models
-            # ESRGAN, BSRGAN/RealSR, Real-ESRGAN
-            "model.0.weight": ("conv_first.weight",),
-            "model.0.bias": ("conv_first.bias",),
-            "model.1.sub./NB/.weight": ("trunk_conv.weight", "conv_body.weight"),
-            "model.1.sub./NB/.bias": ("trunk_conv.bias", "conv_body.bias"),
-            r"model.1.sub.\1.RDB\2.conv\3.0.\4": (
-                r"RRDB_trunk\.(\d+)\.RDB(\d)\.conv(\d+)\.(weight|bias)",
-                r"body\.(\d+)\.rdb(\d)\.conv(\d+)\.(weight|bias)",
-            ),
-        }
-        if "params_ema" in self.state:
-            self.state = self.state["params_ema"]
-            # self.model_arch = "RealESRGAN"
-        self.num_blocks = self.get_num_blocks()
-        self.plus = any("conv1x1" in k for k in self.state.keys())
-        if self.plus:
-            self.model_arch = "ESRGAN+"
-
-        self.state = self.new_to_old_arch(self.state)
-
-        self.key_arr = list(self.state.keys())
-
-        self.in_nc: int = self.state[self.key_arr[0]].shape[1]
-        self.out_nc: int = self.state[self.key_arr[-1]].shape[0]
-
-        self.scale: int = self.get_scale()
-        self.num_filters: int = self.state[self.key_arr[0]].shape[0]
-
-        c2x2 = False
-        if self.state["model.0.weight"].shape[-2] == 2:
-            c2x2 = True
-            self.scale = round(math.sqrt(self.scale / 4))
-            self.model_arch = "ESRGAN-2c2"
-
-        self.supports_fp16 = True
-        self.supports_bfp16 = True
-        self.min_size_restriction = None
-
-        # Detect if pixelunshuffle was used (Real-ESRGAN)
-        if self.in_nc in (self.out_nc * 4, self.out_nc * 16) and self.out_nc in (
-            self.in_nc / 4,
-            self.in_nc / 16,
-        ):
-            self.shuffle_factor = int(math.sqrt(self.in_nc / self.out_nc))
-        else:
-            self.shuffle_factor = None
-
-        upsample_block = {
-            "upconv": B.upconv_block,
-            "pixel_shuffle": B.pixelshuffle_block,
-        }.get(self.upsampler)
-        if upsample_block is None:
-            raise NotImplementedError(f"Upsample mode [{self.upsampler}] is not found")
-
-        if self.scale == 3:
-            upsample_blocks = upsample_block(
-                in_nc=self.num_filters,
-                out_nc=self.num_filters,
-                upscale_factor=3,
-                act_type=self.act,
-                c2x2=c2x2,
-            )
-        else:
-            upsample_blocks = [
-                upsample_block(
-                    in_nc=self.num_filters,
-                    out_nc=self.num_filters,
-                    act_type=self.act,
-                    c2x2=c2x2,
-                )
-                for _ in range(int(math.log(self.scale, 2)))
-            ]
-
-        self.model = B.sequential(
-            # fea conv
-            B.conv_block(
-                in_nc=self.in_nc,
-                out_nc=self.num_filters,
-                kernel_size=3,
-                norm_type=None,
-                act_type=None,
-                c2x2=c2x2,
-            ),
-            B.ShortcutBlock(
-                B.sequential(
-                    # rrdb blocks
-                    *[
-                        B.RRDB(
-                            nf=self.num_filters,
-                            kernel_size=3,
-                            gc=32,
-                            stride=1,
-                            bias=True,
-                            pad_type="zero",
-                            norm_type=self.norm,
-                            act_type=self.act,
-                            mode="CNA",
-                            plus=self.plus,
-                            c2x2=c2x2,
-                        )
-                        for _ in range(self.num_blocks)
-                    ],
-                    # lr conv
-                    B.conv_block(
-                        in_nc=self.num_filters,
-                        out_nc=self.num_filters,
-                        kernel_size=3,
-                        norm_type=self.norm,
-                        act_type=None,
-                        mode=self.mode,
-                        c2x2=c2x2,
-                    ),
-                )
-            ),
-            *upsample_blocks,
-            # hr_conv0
-            B.conv_block(
-                in_nc=self.num_filters,
-                out_nc=self.num_filters,
-                kernel_size=3,
-                norm_type=None,
-                act_type=self.act,
-                c2x2=c2x2,
-            ),
-            # hr_conv1
-            B.conv_block(
-                in_nc=self.num_filters,
-                out_nc=self.out_nc,
-                kernel_size=3,
-                norm_type=None,
-                act_type=None,
-                c2x2=c2x2,
-            ),
-        )
-
-        # Adjust these properties for calculations outside of the model
-        if self.shuffle_factor:
-            self.in_nc //= self.shuffle_factor**2
-            self.scale //= self.shuffle_factor
-
-        self.load_state_dict(self.state, strict=False)
-
-    def new_to_old_arch(self, state):
-        """Convert a new-arch model state dictionary to an old-arch dictionary."""
-        if "params_ema" in state:
-            state = state["params_ema"]
-
-        if "conv_first.weight" not in state:
-            # model is already old arch, this is a loose check, but should be sufficient
-            return state
-
-        # add nb to state keys
-        for kind in ("weight", "bias"):
-            self.state_map[f"model.1.sub.{self.num_blocks}.{kind}"] = self.state_map[
-                f"model.1.sub./NB/.{kind}"
-            ]
-            del self.state_map[f"model.1.sub./NB/.{kind}"]
-
-        old_state = OrderedDict()
-        for old_key, new_keys in self.state_map.items():
-            for new_key in new_keys:
-                if r"\1" in old_key:
-                    for k, v in state.items():
-                        sub = re.sub(new_key, old_key, k)
-                        if sub != k:
-                            old_state[sub] = v
-                else:
-                    if new_key in state:
-                        old_state[old_key] = state[new_key]
-
-        # upconv layers
-        max_upconv = 0
-        for key in state.keys():
-            match = re.match(r"(upconv|conv_up)(\d)\.(weight|bias)", key)
-            if match is not None:
-                _, key_num, key_type = match.groups()
-                old_state[f"model.{int(key_num) * 3}.{key_type}"] = state[key]
-                max_upconv = max(max_upconv, int(key_num) * 3)
-
-        # final layers
-        for key in state.keys():
-            if key in ("HRconv.weight", "conv_hr.weight"):
-                old_state[f"model.{max_upconv + 2}.weight"] = state[key]
-            elif key in ("HRconv.bias", "conv_hr.bias"):
-                old_state[f"model.{max_upconv + 2}.bias"] = state[key]
-            elif key in ("conv_last.weight",):
-                old_state[f"model.{max_upconv + 4}.weight"] = state[key]
-            elif key in ("conv_last.bias",):
-                old_state[f"model.{max_upconv + 4}.bias"] = state[key]
-
-        # Sort by first numeric value of each layer
-        def compare(item1, item2):
-            parts1 = item1.split(".")
-            parts2 = item2.split(".")
-            int1 = int(parts1[1])
-            int2 = int(parts2[1])
-            return int1 - int2
-
-        sorted_keys = sorted(old_state.keys(), key=functools.cmp_to_key(compare))
-
-        # Rebuild the output dict in the right order
-        out_dict = OrderedDict((k, old_state[k]) for k in sorted_keys)
-
-        return out_dict
-
-    def get_scale(self, min_part: int = 6) -> int:
-        n = 0
-        for part in list(self.state):
-            parts = part.split(".")[1:]
-            if len(parts) == 2:
-                part_num = int(parts[0])
-                if part_num > min_part and parts[1] == "weight":
-                    n += 1
-        return 2**n
-
-    def get_num_blocks(self) -> int:
-        nbs = []
-        state_keys = self.state_map[r"model.1.sub.\1.RDB\2.conv\3.0.\4"] + (
-            r"model\.\d+\.sub\.(\d+)\.RDB(\d+)\.conv(\d+)\.0\.(weight|bias)",
-        )
-        for state_key in state_keys:
-            for k in self.state:
-                m = re.search(state_key, k)
-                if m:
-                    nbs.append(int(m.group(1)))
-            if nbs:
-                break
-        return max(*nbs) + 1
-
-    def forward(self, x):
-        if self.shuffle_factor:
-            _, _, h, w = x.size()
-            mod_pad_h = (
-                self.shuffle_factor - h % self.shuffle_factor
-            ) % self.shuffle_factor
-            mod_pad_w = (
-                self.shuffle_factor - w % self.shuffle_factor
-            ) % self.shuffle_factor
-            x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect")
-            x = torch.pixel_unshuffle(x, downscale_factor=self.shuffle_factor)
-            x = self.model(x)
-            return x[:, :, : h * self.scale, : w * self.scale]
-        return self.model(x)
diff --git a/comfy_extras/chainner_models/architecture/SCUNet.py b/comfy_extras/chainner_models/architecture/SCUNet.py
deleted file mode 100644
index b8354a87..00000000
--- a/comfy_extras/chainner_models/architecture/SCUNet.py
+++ /dev/null
@@ -1,455 +0,0 @@
-# pylint: skip-file
-# -----------------------------------------------------------------------------------
-# SCUNet: Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis, https://arxiv.org/abs/2203.13278
-# Zhang, Kai and Li, Yawei and Liang, Jingyun and Cao, Jiezhang and Zhang, Yulun and Tang, Hao and Timofte, Radu and Van Gool, Luc
-# -----------------------------------------------------------------------------------
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from einops import rearrange
-from einops.layers.torch import Rearrange
-
-from .timm.drop import DropPath
-from .timm.weight_init import trunc_normal_
-
-
-# Borrowed from https://github.com/cszn/SCUNet/blob/main/models/network_scunet.py
-class WMSA(nn.Module):
-    """Self-attention module in Swin Transformer"""
-
-    def __init__(self, input_dim, output_dim, head_dim, window_size, type):
-        super(WMSA, self).__init__()
-        self.input_dim = input_dim
-        self.output_dim = output_dim
-        self.head_dim = head_dim
-        self.scale = self.head_dim**-0.5
-        self.n_heads = input_dim // head_dim
-        self.window_size = window_size
-        self.type = type
-        self.embedding_layer = nn.Linear(self.input_dim, 3 * self.input_dim, bias=True)
-
-        self.relative_position_params = nn.Parameter(
-            torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads)
-        )
-        # TODO recover
-        # self.relative_position_params = nn.Parameter(torch.zeros(self.n_heads, 2 * window_size - 1, 2 * window_size -1))
-        self.relative_position_params = nn.Parameter(
-            torch.zeros((2 * window_size - 1) * (2 * window_size - 1), self.n_heads)
-        )
-
-        self.linear = nn.Linear(self.input_dim, self.output_dim)
-
-        trunc_normal_(self.relative_position_params, std=0.02)
-        self.relative_position_params = torch.nn.Parameter(
-            self.relative_position_params.view(
-                2 * window_size - 1, 2 * window_size - 1, self.n_heads
-            )
-            .transpose(1, 2)
-            .transpose(0, 1)
-        )
-
-    def generate_mask(self, h, w, p, shift):
-        """generating the mask of SW-MSA
-        Args:
-            shift: shift parameters in CyclicShift.
-        Returns:
-            attn_mask: should be (1 1 w p p),
-        """
-        # supporting square.
-        attn_mask = torch.zeros(
-            h,
-            w,
-            p,
-            p,
-            p,
-            p,
-            dtype=torch.bool,
-            device=self.relative_position_params.device,
-        )
-        if self.type == "W":
-            return attn_mask
-
-        s = p - shift
-        attn_mask[-1, :, :s, :, s:, :] = True
-        attn_mask[-1, :, s:, :, :s, :] = True
-        attn_mask[:, -1, :, :s, :, s:] = True
-        attn_mask[:, -1, :, s:, :, :s] = True
-        attn_mask = rearrange(
-            attn_mask, "w1 w2 p1 p2 p3 p4 -> 1 1 (w1 w2) (p1 p2) (p3 p4)"
-        )
-        return attn_mask
-
-    def forward(self, x):
-        """Forward pass of Window Multi-head Self-attention module.
-        Args:
-            x: input tensor with shape of [b h w c];
-            attn_mask: attention mask, fill -inf where the value is True;
-        Returns:
-            output: tensor shape [b h w c]
-        """
-        if self.type != "W":
-            x = torch.roll(
-                x,
-                shifts=(-(self.window_size // 2), -(self.window_size // 2)),
-                dims=(1, 2),
-            )
-
-        x = rearrange(
-            x,
-            "b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c",
-            p1=self.window_size,
-            p2=self.window_size,
-        )
-        h_windows = x.size(1)
-        w_windows = x.size(2)
-        # square validation
-        # assert h_windows == w_windows
-
-        x = rearrange(
-            x,
-            "b w1 w2 p1 p2 c -> b (w1 w2) (p1 p2) c",
-            p1=self.window_size,
-            p2=self.window_size,
-        )
-        qkv = self.embedding_layer(x)
-        q, k, v = rearrange(
-            qkv, "b nw np (threeh c) -> threeh b nw np c", c=self.head_dim
-        ).chunk(3, dim=0)
-        sim = torch.einsum("hbwpc,hbwqc->hbwpq", q, k) * self.scale
-        # Adding learnable relative embedding
-        sim = sim + rearrange(self.relative_embedding(), "h p q -> h 1 1 p q")
-        # Using Attn Mask to distinguish different subwindows.
-        if self.type != "W":
-            attn_mask = self.generate_mask(
-                h_windows, w_windows, self.window_size, shift=self.window_size // 2
-            )
-            sim = sim.masked_fill_(attn_mask, float("-inf"))
-
-        probs = nn.functional.softmax(sim, dim=-1)
-        output = torch.einsum("hbwij,hbwjc->hbwic", probs, v)
-        output = rearrange(output, "h b w p c -> b w p (h c)")
-        output = self.linear(output)
-        output = rearrange(
-            output,
-            "b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c",
-            w1=h_windows,
-            p1=self.window_size,
-        )
-
-        if self.type != "W":
-            output = torch.roll(
-                output,
-                shifts=(self.window_size // 2, self.window_size // 2),
-                dims=(1, 2),
-            )
-
-        return output
-
-    def relative_embedding(self):
-        cord = torch.tensor(
-            np.array(
-                [
-                    [i, j]
-                    for i in range(self.window_size)
-                    for j in range(self.window_size)
-                ]
-            )
-        )
-        relation = cord[:, None, :] - cord[None, :, :] + self.window_size - 1
-        # negative is allowed
-        return self.relative_position_params[
-            :, relation[:, :, 0].long(), relation[:, :, 1].long()
-        ]
-
-
-class Block(nn.Module):
-    def __init__(
-        self,
-        input_dim,
-        output_dim,
-        head_dim,
-        window_size,
-        drop_path,
-        type="W",
-        input_resolution=None,
-    ):
-        """SwinTransformer Block"""
-        super(Block, self).__init__()
-        self.input_dim = input_dim
-        self.output_dim = output_dim
-        assert type in ["W", "SW"]
-        self.type = type
-        if input_resolution <= window_size:
-            self.type = "W"
-
-        self.ln1 = nn.LayerNorm(input_dim)
-        self.msa = WMSA(input_dim, input_dim, head_dim, window_size, self.type)
-        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
-        self.ln2 = nn.LayerNorm(input_dim)
-        self.mlp = nn.Sequential(
-            nn.Linear(input_dim, 4 * input_dim),
-            nn.GELU(),
-            nn.Linear(4 * input_dim, output_dim),
-        )
-
-    def forward(self, x):
-        x = x + self.drop_path(self.msa(self.ln1(x)))
-        x = x + self.drop_path(self.mlp(self.ln2(x)))
-        return x
-
-
-class ConvTransBlock(nn.Module):
-    def __init__(
-        self,
-        conv_dim,
-        trans_dim,
-        head_dim,
-        window_size,
-        drop_path,
-        type="W",
-        input_resolution=None,
-    ):
-        """SwinTransformer and Conv Block"""
-        super(ConvTransBlock, self).__init__()
-        self.conv_dim = conv_dim
-        self.trans_dim = trans_dim
-        self.head_dim = head_dim
-        self.window_size = window_size
-        self.drop_path = drop_path
-        self.type = type
-        self.input_resolution = input_resolution
-
-        assert self.type in ["W", "SW"]
-        if self.input_resolution <= self.window_size:
-            self.type = "W"
-
-        self.trans_block = Block(
-            self.trans_dim,
-            self.trans_dim,
-            self.head_dim,
-            self.window_size,
-            self.drop_path,
-            self.type,
-            self.input_resolution,
-        )
-        self.conv1_1 = nn.Conv2d(
-            self.conv_dim + self.trans_dim,
-            self.conv_dim + self.trans_dim,
-            1,
-            1,
-            0,
-            bias=True,
-        )
-        self.conv1_2 = nn.Conv2d(
-            self.conv_dim + self.trans_dim,
-            self.conv_dim + self.trans_dim,
-            1,
-            1,
-            0,
-            bias=True,
-        )
-
-        self.conv_block = nn.Sequential(
-            nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
-            nn.ReLU(True),
-            nn.Conv2d(self.conv_dim, self.conv_dim, 3, 1, 1, bias=False),
-        )
-
-    def forward(self, x):
-        conv_x, trans_x = torch.split(
-            self.conv1_1(x), (self.conv_dim, self.trans_dim), dim=1
-        )
-        conv_x = self.conv_block(conv_x) + conv_x
-        trans_x = Rearrange("b c h w -> b h w c")(trans_x)
-        trans_x = self.trans_block(trans_x)
-        trans_x = Rearrange("b h w c -> b c h w")(trans_x)
-        res = self.conv1_2(torch.cat((conv_x, trans_x), dim=1))
-        x = x + res
-
-        return x
-
-
-class SCUNet(nn.Module):
-    def __init__(
-        self,
-        state_dict,
-        in_nc=3,
-        config=[4, 4, 4, 4, 4, 4, 4],
-        dim=64,
-        drop_path_rate=0.0,
-        input_resolution=256,
-    ):
-        super(SCUNet, self).__init__()
-        self.model_arch = "SCUNet"
-        self.sub_type = "SR"
-
-        self.num_filters: int = 0
-
-        self.state = state_dict
-        self.config = config
-        self.dim = dim
-        self.head_dim = 32
-        self.window_size = 8
-
-        self.in_nc = in_nc
-        self.out_nc = self.in_nc
-        self.scale = 1
-        self.supports_fp16 = True
-
-        # drop path rate for each layer
-        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(config))]
-
-        self.m_head = [nn.Conv2d(in_nc, dim, 3, 1, 1, bias=False)]
-
-        begin = 0
-        self.m_down1 = [
-            ConvTransBlock(
-                dim // 2,
-                dim // 2,
-                self.head_dim,
-                self.window_size,
-                dpr[i + begin],
-                "W" if not i % 2 else "SW",
-                input_resolution,
-            )
-            for i in range(config[0])
-        ] + [nn.Conv2d(dim, 2 * dim, 2, 2, 0, bias=False)]
-
-        begin += config[0]
-        self.m_down2 = [
-            ConvTransBlock(
-                dim,
-                dim,
-                self.head_dim,
-                self.window_size,
-                dpr[i + begin],
-                "W" if not i % 2 else "SW",
-                input_resolution // 2,
-            )
-            for i in range(config[1])
-        ] + [nn.Conv2d(2 * dim, 4 * dim, 2, 2, 0, bias=False)]
-
-        begin += config[1]
-        self.m_down3 = [
-            ConvTransBlock(
-                2 * dim,
-                2 * dim,
-                self.head_dim,
-                self.window_size,
-                dpr[i + begin],
-                "W" if not i % 2 else "SW",
-                input_resolution // 4,
-            )
-            for i in range(config[2])
-        ] + [nn.Conv2d(4 * dim, 8 * dim, 2, 2, 0, bias=False)]
-
-        begin += config[2]
-        self.m_body = [
-            ConvTransBlock(
-                4 * dim,
-                4 * dim,
-                self.head_dim,
-                self.window_size,
-                dpr[i + begin],
-                "W" if not i % 2 else "SW",
-                input_resolution // 8,
-            )
-            for i in range(config[3])
-        ]
-
-        begin += config[3]
-        self.m_up3 = [
-            nn.ConvTranspose2d(8 * dim, 4 * dim, 2, 2, 0, bias=False),
-        ] + [
-            ConvTransBlock(
-                2 * dim,
-                2 * dim,
-                self.head_dim,
-                self.window_size,
-                dpr[i + begin],
-                "W" if not i % 2 else "SW",
-                input_resolution // 4,
-            )
-            for i in range(config[4])
-        ]
-
-        begin += config[4]
-        self.m_up2 = [
-            nn.ConvTranspose2d(4 * dim, 2 * dim, 2, 2, 0, bias=False),
-        ] + [
-            ConvTransBlock(
-                dim,
-                dim,
-                self.head_dim,
-                self.window_size,
-                dpr[i + begin],
-                "W" if not i % 2 else "SW",
-                input_resolution // 2,
-            )
-            for i in range(config[5])
-        ]
-
-        begin += config[5]
-        self.m_up1 = [
-            nn.ConvTranspose2d(2 * dim, dim, 2, 2, 0, bias=False),
-        ] + [
-            ConvTransBlock(
-                dim // 2,
-                dim // 2,
-                self.head_dim,
-                self.window_size,
-                dpr[i + begin],
-                "W" if not i % 2 else "SW",
-                input_resolution,
-            )
-            for i in range(config[6])
-        ]
-
-        self.m_tail = [nn.Conv2d(dim, in_nc, 3, 1, 1, bias=False)]
-
-        self.m_head = nn.Sequential(*self.m_head)
-        self.m_down1 = nn.Sequential(*self.m_down1)
-        self.m_down2 = nn.Sequential(*self.m_down2)
-        self.m_down3 = nn.Sequential(*self.m_down3)
-        self.m_body = nn.Sequential(*self.m_body)
-        self.m_up3 = nn.Sequential(*self.m_up3)
-        self.m_up2 = nn.Sequential(*self.m_up2)
-        self.m_up1 = nn.Sequential(*self.m_up1)
-        self.m_tail = nn.Sequential(*self.m_tail)
-        # self.apply(self._init_weights)
-        self.load_state_dict(state_dict, strict=True)
-
-    def check_image_size(self, x):
-        _, _, h, w = x.size()
-        mod_pad_h = (64 - h % 64) % 64
-        mod_pad_w = (64 - w % 64) % 64
-        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect")
-        return x
-
-    def forward(self, x0):
-        h, w = x0.size()[-2:]
-        x0 = self.check_image_size(x0)
-
-        x1 = self.m_head(x0)
-        x2 = self.m_down1(x1)
-        x3 = self.m_down2(x2)
-        x4 = self.m_down3(x3)
-        x = self.m_body(x4)
-        x = self.m_up3(x + x4)
-        x = self.m_up2(x + x3)
-        x = self.m_up1(x + x2)
-        x = self.m_tail(x + x1)
-
-        x = x[:, :, :h, :w]
-        return x
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=0.02)
-            if m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
diff --git a/comfy_extras/chainner_models/architecture/SPSR.py b/comfy_extras/chainner_models/architecture/SPSR.py
deleted file mode 100644
index c3cefff1..00000000
--- a/comfy_extras/chainner_models/architecture/SPSR.py
+++ /dev/null
@@ -1,383 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-import math
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from . import block as B
-
-
-class Get_gradient_nopadding(nn.Module):
-    def __init__(self):
-        super(Get_gradient_nopadding, self).__init__()
-        kernel_v = [[0, -1, 0], [0, 0, 0], [0, 1, 0]]
-        kernel_h = [[0, 0, 0], [-1, 0, 1], [0, 0, 0]]
-        kernel_h = torch.FloatTensor(kernel_h).unsqueeze(0).unsqueeze(0)
-        kernel_v = torch.FloatTensor(kernel_v).unsqueeze(0).unsqueeze(0)
-        self.weight_h = nn.Parameter(data=kernel_h, requires_grad=False)  # type: ignore
-
-        self.weight_v = nn.Parameter(data=kernel_v, requires_grad=False)  # type: ignore
-
-    def forward(self, x):
-        x_list = []
-        for i in range(x.shape[1]):
-            x_i = x[:, i]
-            x_i_v = F.conv2d(x_i.unsqueeze(1), self.weight_v, padding=1)
-            x_i_h = F.conv2d(x_i.unsqueeze(1), self.weight_h, padding=1)
-            x_i = torch.sqrt(torch.pow(x_i_v, 2) + torch.pow(x_i_h, 2) + 1e-6)
-            x_list.append(x_i)
-
-        x = torch.cat(x_list, dim=1)
-
-        return x
-
-
-class SPSRNet(nn.Module):
-    def __init__(
-        self,
-        state_dict,
-        norm=None,
-        act: str = "leakyrelu",
-        upsampler: str = "upconv",
-        mode: B.ConvMode = "CNA",
-    ):
-        super(SPSRNet, self).__init__()
-        self.model_arch = "SPSR"
-        self.sub_type = "SR"
-
-        self.state = state_dict
-        self.norm = norm
-        self.act = act
-        self.upsampler = upsampler
-        self.mode = mode
-
-        self.num_blocks = self.get_num_blocks()
-
-        self.in_nc: int = self.state["model.0.weight"].shape[1]
-        self.out_nc: int = self.state["f_HR_conv1.0.bias"].shape[0]
-
-        self.scale = self.get_scale(4)
-        self.num_filters: int = self.state["model.0.weight"].shape[0]
-
-        self.supports_fp16 = True
-        self.supports_bfp16 = True
-        self.min_size_restriction = None
-
-        n_upscale = int(math.log(self.scale, 2))
-        if self.scale == 3:
-            n_upscale = 1
-
-        fea_conv = B.conv_block(
-            self.in_nc, self.num_filters, kernel_size=3, norm_type=None, act_type=None
-        )
-        rb_blocks = [
-            B.RRDB(
-                self.num_filters,
-                kernel_size=3,
-                gc=32,
-                stride=1,
-                bias=True,
-                pad_type="zero",
-                norm_type=norm,
-                act_type=act,
-                mode="CNA",
-            )
-            for _ in range(self.num_blocks)
-        ]
-        LR_conv = B.conv_block(
-            self.num_filters,
-            self.num_filters,
-            kernel_size=3,
-            norm_type=norm,
-            act_type=None,
-            mode=mode,
-        )
-
-        if upsampler == "upconv":
-            upsample_block = B.upconv_block
-        elif upsampler == "pixelshuffle":
-            upsample_block = B.pixelshuffle_block
-        else:
-            raise NotImplementedError(f"upsample mode [{upsampler}] is not found")
-        if self.scale == 3:
-            a_upsampler = upsample_block(
-                self.num_filters, self.num_filters, 3, act_type=act
-            )
-        else:
-            a_upsampler = [
-                upsample_block(self.num_filters, self.num_filters, act_type=act)
-                for _ in range(n_upscale)
-            ]
-        self.HR_conv0_new = B.conv_block(
-            self.num_filters,
-            self.num_filters,
-            kernel_size=3,
-            norm_type=None,
-            act_type=act,
-        )
-        self.HR_conv1_new = B.conv_block(
-            self.num_filters,
-            self.num_filters,
-            kernel_size=3,
-            norm_type=None,
-            act_type=None,
-        )
-
-        self.model = B.sequential(
-            fea_conv,
-            B.ShortcutBlockSPSR(B.sequential(*rb_blocks, LR_conv)),
-            *a_upsampler,
-            self.HR_conv0_new,
-        )
-
-        self.get_g_nopadding = Get_gradient_nopadding()
-
-        self.b_fea_conv = B.conv_block(
-            self.in_nc, self.num_filters, kernel_size=3, norm_type=None, act_type=None
-        )
-
-        self.b_concat_1 = B.conv_block(
-            2 * self.num_filters,
-            self.num_filters,
-            kernel_size=3,
-            norm_type=None,
-            act_type=None,
-        )
-        self.b_block_1 = B.RRDB(
-            self.num_filters * 2,
-            kernel_size=3,
-            gc=32,
-            stride=1,
-            bias=True,
-            pad_type="zero",
-            norm_type=norm,
-            act_type=act,
-            mode="CNA",
-        )
-
-        self.b_concat_2 = B.conv_block(
-            2 * self.num_filters,
-            self.num_filters,
-            kernel_size=3,
-            norm_type=None,
-            act_type=None,
-        )
-        self.b_block_2 = B.RRDB(
-            self.num_filters * 2,
-            kernel_size=3,
-            gc=32,
-            stride=1,
-            bias=True,
-            pad_type="zero",
-            norm_type=norm,
-            act_type=act,
-            mode="CNA",
-        )
-
-        self.b_concat_3 = B.conv_block(
-            2 * self.num_filters,
-            self.num_filters,
-            kernel_size=3,
-            norm_type=None,
-            act_type=None,
-        )
-        self.b_block_3 = B.RRDB(
-            self.num_filters * 2,
-            kernel_size=3,
-            gc=32,
-            stride=1,
-            bias=True,
-            pad_type="zero",
-            norm_type=norm,
-            act_type=act,
-            mode="CNA",
-        )
-
-        self.b_concat_4 = B.conv_block(
-            2 * self.num_filters,
-            self.num_filters,
-            kernel_size=3,
-            norm_type=None,
-            act_type=None,
-        )
-        self.b_block_4 = B.RRDB(
-            self.num_filters * 2,
-            kernel_size=3,
-            gc=32,
-            stride=1,
-            bias=True,
-            pad_type="zero",
-            norm_type=norm,
-            act_type=act,
-            mode="CNA",
-        )
-
-        self.b_LR_conv = B.conv_block(
-            self.num_filters,
-            self.num_filters,
-            kernel_size=3,
-            norm_type=norm,
-            act_type=None,
-            mode=mode,
-        )
-
-        if upsampler == "upconv":
-            upsample_block = B.upconv_block
-        elif upsampler == "pixelshuffle":
-            upsample_block = B.pixelshuffle_block
-        else:
-            raise NotImplementedError(f"upsample mode [{upsampler}] is not found")
-        if self.scale == 3:
-            b_upsampler = upsample_block(
-                self.num_filters, self.num_filters, 3, act_type=act
-            )
-        else:
-            b_upsampler = [
-                upsample_block(self.num_filters, self.num_filters, act_type=act)
-                for _ in range(n_upscale)
-            ]
-
-        b_HR_conv0 = B.conv_block(
-            self.num_filters,
-            self.num_filters,
-            kernel_size=3,
-            norm_type=None,
-            act_type=act,
-        )
-        b_HR_conv1 = B.conv_block(
-            self.num_filters,
-            self.num_filters,
-            kernel_size=3,
-            norm_type=None,
-            act_type=None,
-        )
-
-        self.b_module = B.sequential(*b_upsampler, b_HR_conv0, b_HR_conv1)
-
-        self.conv_w = B.conv_block(
-            self.num_filters, self.out_nc, kernel_size=1, norm_type=None, act_type=None
-        )
-
-        self.f_concat = B.conv_block(
-            self.num_filters * 2,
-            self.num_filters,
-            kernel_size=3,
-            norm_type=None,
-            act_type=None,
-        )
-
-        self.f_block = B.RRDB(
-            self.num_filters * 2,
-            kernel_size=3,
-            gc=32,
-            stride=1,
-            bias=True,
-            pad_type="zero",
-            norm_type=norm,
-            act_type=act,
-            mode="CNA",
-        )
-
-        self.f_HR_conv0 = B.conv_block(
-            self.num_filters,
-            self.num_filters,
-            kernel_size=3,
-            norm_type=None,
-            act_type=act,
-        )
-        self.f_HR_conv1 = B.conv_block(
-            self.num_filters, self.out_nc, kernel_size=3, norm_type=None, act_type=None
-        )
-
-        self.load_state_dict(self.state, strict=False)
-
-    def get_scale(self, min_part: int = 4) -> int:
-        n = 0
-        for part in list(self.state):
-            parts = part.split(".")
-            if len(parts) == 3:
-                part_num = int(parts[1])
-                if part_num > min_part and parts[0] == "model" and parts[2] == "weight":
-                    n += 1
-        return 2**n
-
-    def get_num_blocks(self) -> int:
-        nb = 0
-        for part in list(self.state):
-            parts = part.split(".")
-            n_parts = len(parts)
-            if n_parts == 5 and parts[2] == "sub":
-                nb = int(parts[3])
-        return nb
-
-    def forward(self, x):
-        x_grad = self.get_g_nopadding(x)
-        x = self.model[0](x)
-
-        x, block_list = self.model[1](x)
-
-        x_ori = x
-        for i in range(5):
-            x = block_list[i](x)
-        x_fea1 = x
-
-        for i in range(5):
-            x = block_list[i + 5](x)
-        x_fea2 = x
-
-        for i in range(5):
-            x = block_list[i + 10](x)
-        x_fea3 = x
-
-        for i in range(5):
-            x = block_list[i + 15](x)
-        x_fea4 = x
-
-        x = block_list[20:](x)
-        # short cut
-        x = x_ori + x
-        x = self.model[2:](x)
-        x = self.HR_conv1_new(x)
-
-        x_b_fea = self.b_fea_conv(x_grad)
-        x_cat_1 = torch.cat([x_b_fea, x_fea1], dim=1)
-
-        x_cat_1 = self.b_block_1(x_cat_1)
-        x_cat_1 = self.b_concat_1(x_cat_1)
-
-        x_cat_2 = torch.cat([x_cat_1, x_fea2], dim=1)
-
-        x_cat_2 = self.b_block_2(x_cat_2)
-        x_cat_2 = self.b_concat_2(x_cat_2)
-
-        x_cat_3 = torch.cat([x_cat_2, x_fea3], dim=1)
-
-        x_cat_3 = self.b_block_3(x_cat_3)
-        x_cat_3 = self.b_concat_3(x_cat_3)
-
-        x_cat_4 = torch.cat([x_cat_3, x_fea4], dim=1)
-
-        x_cat_4 = self.b_block_4(x_cat_4)
-        x_cat_4 = self.b_concat_4(x_cat_4)
-
-        x_cat_4 = self.b_LR_conv(x_cat_4)
-
-        # short cut
-        x_cat_4 = x_cat_4 + x_b_fea
-        x_branch = self.b_module(x_cat_4)
-
-        # x_out_branch = self.conv_w(x_branch)
-        ########
-        x_branch_d = x_branch
-        x_f_cat = torch.cat([x_branch_d, x], dim=1)
-        x_f_cat = self.f_block(x_f_cat)
-        x_out = self.f_concat(x_f_cat)
-        x_out = self.f_HR_conv0(x_out)
-        x_out = self.f_HR_conv1(x_out)
-
-        #########
-        # return x_out_branch, x_out, x_grad
-        return x_out
diff --git a/comfy_extras/chainner_models/architecture/SRVGG.py b/comfy_extras/chainner_models/architecture/SRVGG.py
deleted file mode 100644
index 7a8ec37a..00000000
--- a/comfy_extras/chainner_models/architecture/SRVGG.py
+++ /dev/null
@@ -1,114 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-import math
-
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class SRVGGNetCompact(nn.Module):
-    """A compact VGG-style network structure for super-resolution.
-    It is a compact network structure, which performs upsampling in the last layer and no convolution is
-    conducted on the HR feature space.
-    Args:
-        num_in_ch (int): Channel number of inputs. Default: 3.
-        num_out_ch (int): Channel number of outputs. Default: 3.
-        num_feat (int): Channel number of intermediate features. Default: 64.
-        num_conv (int): Number of convolution layers in the body network. Default: 16.
-        upscale (int): Upsampling factor. Default: 4.
-        act_type (str): Activation type, options: 'relu', 'prelu', 'leakyrelu'. Default: prelu.
-    """
-
-    def __init__(
-        self,
-        state_dict,
-        act_type: str = "prelu",
-    ):
-        super(SRVGGNetCompact, self).__init__()
-        self.model_arch = "SRVGG (RealESRGAN)"
-        self.sub_type = "SR"
-
-        self.act_type = act_type
-
-        self.state = state_dict
-
-        if "params" in self.state:
-            self.state = self.state["params"]
-
-        self.key_arr = list(self.state.keys())
-
-        self.in_nc = self.get_in_nc()
-        self.num_feat = self.get_num_feats()
-        self.num_conv = self.get_num_conv()
-        self.out_nc = self.in_nc  # :(
-        self.pixelshuffle_shape = None  # Defined in get_scale()
-        self.scale = self.get_scale()
-
-        self.supports_fp16 = True
-        self.supports_bfp16 = True
-        self.min_size_restriction = None
-
-        self.body = nn.ModuleList()
-        # the first conv
-        self.body.append(nn.Conv2d(self.in_nc, self.num_feat, 3, 1, 1))
-        # the first activation
-        if act_type == "relu":
-            activation = nn.ReLU(inplace=True)
-        elif act_type == "prelu":
-            activation = nn.PReLU(num_parameters=self.num_feat)
-        elif act_type == "leakyrelu":
-            activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
-        self.body.append(activation)  # type: ignore
-
-        # the body structure
-        for _ in range(self.num_conv):
-            self.body.append(nn.Conv2d(self.num_feat, self.num_feat, 3, 1, 1))
-            # activation
-            if act_type == "relu":
-                activation = nn.ReLU(inplace=True)
-            elif act_type == "prelu":
-                activation = nn.PReLU(num_parameters=self.num_feat)
-            elif act_type == "leakyrelu":
-                activation = nn.LeakyReLU(negative_slope=0.1, inplace=True)
-            self.body.append(activation)  # type: ignore
-
-        # the last conv
-        self.body.append(nn.Conv2d(self.num_feat, self.pixelshuffle_shape, 3, 1, 1))  # type: ignore
-        # upsample
-        self.upsampler = nn.PixelShuffle(self.scale)
-
-        self.load_state_dict(self.state, strict=False)
-
-    def get_num_conv(self) -> int:
-        return (int(self.key_arr[-1].split(".")[1]) - 2) // 2
-
-    def get_num_feats(self) -> int:
-        return self.state[self.key_arr[0]].shape[0]
-
-    def get_in_nc(self) -> int:
-        return self.state[self.key_arr[0]].shape[1]
-
-    def get_scale(self) -> int:
-        self.pixelshuffle_shape = self.state[self.key_arr[-1]].shape[0]
-        # Assume out_nc is the same as in_nc
-        # I cant think of a better way to do that
-        self.out_nc = self.in_nc
-        scale = math.sqrt(self.pixelshuffle_shape / self.out_nc)
-        if scale - int(scale) > 0:
-            print(
-                "out_nc is probably different than in_nc, scale calculation might be wrong"
-            )
-        scale = int(scale)
-        return scale
-
-    def forward(self, x):
-        out = x
-        for i in range(0, len(self.body)):
-            out = self.body[i](out)
-
-        out = self.upsampler(out)
-        # add the nearest upsampled image, so that the network learns the residual
-        base = F.interpolate(x, scale_factor=self.scale, mode="nearest")
-        out += base
-        return out
diff --git a/comfy_extras/chainner_models/architecture/SwiftSRGAN.py b/comfy_extras/chainner_models/architecture/SwiftSRGAN.py
deleted file mode 100644
index dbb7725b..00000000
--- a/comfy_extras/chainner_models/architecture/SwiftSRGAN.py
+++ /dev/null
@@ -1,161 +0,0 @@
-# From https://github.com/Koushik0901/Swift-SRGAN/blob/master/swift-srgan/models.py
-
-import torch
-from torch import nn
-
-
-class SeperableConv2d(nn.Module):
-    def __init__(
-        self, in_channels, out_channels, kernel_size, stride=1, padding=1, bias=True
-    ):
-        super(SeperableConv2d, self).__init__()
-        self.depthwise = nn.Conv2d(
-            in_channels,
-            in_channels,
-            kernel_size=kernel_size,
-            stride=stride,
-            groups=in_channels,
-            bias=bias,
-            padding=padding,
-        )
-        self.pointwise = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=bias)
-
-    def forward(self, x):
-        return self.pointwise(self.depthwise(x))
-
-
-class ConvBlock(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        use_act=True,
-        use_bn=True,
-        discriminator=False,
-        **kwargs,
-    ):
-        super(ConvBlock, self).__init__()
-
-        self.use_act = use_act
-        self.cnn = SeperableConv2d(in_channels, out_channels, **kwargs, bias=not use_bn)
-        self.bn = nn.BatchNorm2d(out_channels) if use_bn else nn.Identity()
-        self.act = (
-            nn.LeakyReLU(0.2, inplace=True)
-            if discriminator
-            else nn.PReLU(num_parameters=out_channels)
-        )
-
-    def forward(self, x):
-        return self.act(self.bn(self.cnn(x))) if self.use_act else self.bn(self.cnn(x))
-
-
-class UpsampleBlock(nn.Module):
-    def __init__(self, in_channels, scale_factor):
-        super(UpsampleBlock, self).__init__()
-
-        self.conv = SeperableConv2d(
-            in_channels,
-            in_channels * scale_factor**2,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-        )
-        self.ps = nn.PixelShuffle(
-            scale_factor
-        )  # (in_channels * 4, H, W) -> (in_channels, H*2, W*2)
-        self.act = nn.PReLU(num_parameters=in_channels)
-
-    def forward(self, x):
-        return self.act(self.ps(self.conv(x)))
-
-
-class ResidualBlock(nn.Module):
-    def __init__(self, in_channels):
-        super(ResidualBlock, self).__init__()
-
-        self.block1 = ConvBlock(
-            in_channels, in_channels, kernel_size=3, stride=1, padding=1
-        )
-        self.block2 = ConvBlock(
-            in_channels, in_channels, kernel_size=3, stride=1, padding=1, use_act=False
-        )
-
-    def forward(self, x):
-        out = self.block1(x)
-        out = self.block2(out)
-        return out + x
-
-
-class Generator(nn.Module):
-    """Swift-SRGAN Generator
-    Args:
-        in_channels (int): number of input image channels.
-        num_channels (int): number of hidden channels.
-        num_blocks (int): number of residual blocks.
-        upscale_factor (int): factor to upscale the image [2x, 4x, 8x].
-    Returns:
-        torch.Tensor: super resolution image
-    """
-
-    def __init__(
-        self,
-        state_dict,
-    ):
-        super(Generator, self).__init__()
-        self.model_arch = "Swift-SRGAN"
-        self.sub_type = "SR"
-        self.state = state_dict
-        if "model" in self.state:
-            self.state = self.state["model"]
-
-        self.in_nc: int = self.state["initial.cnn.depthwise.weight"].shape[0]
-        self.out_nc: int = self.state["final_conv.pointwise.weight"].shape[0]
-        self.num_filters: int = self.state["initial.cnn.pointwise.weight"].shape[0]
-        self.num_blocks = len(
-            set([x.split(".")[1] for x in self.state.keys() if "residual" in x])
-        )
-        self.scale: int = 2 ** len(
-            set([x.split(".")[1] for x in self.state.keys() if "upsampler" in x])
-        )
-
-        in_channels = self.in_nc
-        num_channels = self.num_filters
-        num_blocks = self.num_blocks
-        upscale_factor = self.scale
-
-        self.supports_fp16 = True
-        self.supports_bfp16 = True
-        self.min_size_restriction = None
-
-        self.initial = ConvBlock(
-            in_channels, num_channels, kernel_size=9, stride=1, padding=4, use_bn=False
-        )
-        self.residual = nn.Sequential(
-            *[ResidualBlock(num_channels) for _ in range(num_blocks)]
-        )
-        self.convblock = ConvBlock(
-            num_channels,
-            num_channels,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-            use_act=False,
-        )
-        self.upsampler = nn.Sequential(
-            *[
-                UpsampleBlock(num_channels, scale_factor=2)
-                for _ in range(upscale_factor // 2)
-            ]
-        )
-        self.final_conv = SeperableConv2d(
-            num_channels, in_channels, kernel_size=9, stride=1, padding=4
-        )
-
-        self.load_state_dict(self.state, strict=False)
-
-    def forward(self, x):
-        initial = self.initial(x)
-        x = self.residual(initial)
-        x = self.convblock(x) + initial
-        x = self.upsampler(x)
-        return (torch.tanh(self.final_conv(x)) + 1) / 2
diff --git a/comfy_extras/chainner_models/architecture/Swin2SR.py b/comfy_extras/chainner_models/architecture/Swin2SR.py
deleted file mode 100644
index cb57ecfc..00000000
--- a/comfy_extras/chainner_models/architecture/Swin2SR.py
+++ /dev/null
@@ -1,1377 +0,0 @@
-# pylint: skip-file
-# -----------------------------------------------------------------------------------
-# Swin2SR: Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration, https://arxiv.org/abs/2209.11345
-# Written by Conde and Choi et al.
-# From: https://raw.githubusercontent.com/mv-lab/swin2sr/main/models/network_swin2sr.py
-# -----------------------------------------------------------------------------------
-
-import math
-import re
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-
-# Originally from the timm package
-from .timm.drop import DropPath
-from .timm.helpers import to_2tuple
-from .timm.weight_init import trunc_normal_
-
-
-class Mlp(nn.Module):
-    def __init__(
-        self,
-        in_features,
-        hidden_features=None,
-        out_features=None,
-        act_layer=nn.GELU,
-        drop=0.0,
-    ):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = (
-        x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    )
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(
-        B, H // window_size, W // window_size, window_size, window_size, -1
-    )
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    r"""Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-        pretrained_window_size (tuple[int]): The height and width of the window in pre-training.
-    """
-
-    def __init__(
-        self,
-        dim,
-        window_size,
-        num_heads,
-        qkv_bias=True,
-        attn_drop=0.0,
-        proj_drop=0.0,
-        pretrained_window_size=[0, 0],
-    ):
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.pretrained_window_size = pretrained_window_size
-        self.num_heads = num_heads
-
-        self.logit_scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True)  # type: ignore
-
-        # mlp to generate continuous relative position bias
-        self.cpb_mlp = nn.Sequential(
-            nn.Linear(2, 512, bias=True),
-            nn.ReLU(inplace=True),
-            nn.Linear(512, num_heads, bias=False),
-        )
-
-        # get relative_coords_table
-        relative_coords_h = torch.arange(
-            -(self.window_size[0] - 1), self.window_size[0], dtype=torch.float32
-        )
-        relative_coords_w = torch.arange(
-            -(self.window_size[1] - 1), self.window_size[1], dtype=torch.float32
-        )
-        relative_coords_table = (
-            torch.stack(torch.meshgrid([relative_coords_h, relative_coords_w]))
-            .permute(1, 2, 0)
-            .contiguous()
-            .unsqueeze(0)
-        )  # 1, 2*Wh-1, 2*Ww-1, 2
-        if pretrained_window_size[0] > 0:
-            relative_coords_table[:, :, :, 0] /= pretrained_window_size[0] - 1
-            relative_coords_table[:, :, :, 1] /= pretrained_window_size[1] - 1
-        else:
-            relative_coords_table[:, :, :, 0] /= self.window_size[0] - 1
-            relative_coords_table[:, :, :, 1] /= self.window_size[1] - 1
-        relative_coords_table *= 8  # normalize to -8, 8
-        relative_coords_table = (
-            torch.sign(relative_coords_table)
-            * torch.log2(torch.abs(relative_coords_table) + 1.0)
-            / np.log2(8)
-        )
-
-        self.register_buffer("relative_coords_table", relative_coords_table)
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = (
-            coords_flatten[:, :, None] - coords_flatten[:, None, :]
-        )  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(
-            1, 2, 0
-        ).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=False)
-        if qkv_bias:
-            self.q_bias = nn.Parameter(torch.zeros(dim))  # type: ignore
-            self.v_bias = nn.Parameter(torch.zeros(dim))  # type: ignore
-        else:
-            self.q_bias = None
-            self.v_bias = None
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-        self.proj_drop = nn.Dropout(proj_drop)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv_bias = None
-        if self.q_bias is not None:
-            qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias))  # type: ignore
-        qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)
-        qkv = qkv.reshape(B_, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
-        q, k, v = (
-            qkv[0],
-            qkv[1],
-            qkv[2],
-        )  # make torchscript happy (cannot use tensor as tuple)
-
-        # cosine attention
-        attn = F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1)
-        logit_scale = torch.clamp(
-            self.logit_scale,
-            max=torch.log(torch.tensor(1.0 / 0.01)).to(self.logit_scale.device),
-        ).exp()
-        attn = attn * logit_scale
-
-        relative_position_bias_table = self.cpb_mlp(self.relative_coords_table).view(
-            -1, self.num_heads
-        )
-        relative_position_bias = relative_position_bias_table[self.relative_position_index.view(-1)].view(  # type: ignore
-            self.window_size[0] * self.window_size[1],
-            self.window_size[0] * self.window_size[1],
-            -1,
-        )  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(
-            2, 0, 1
-        ).contiguous()  # nH, Wh*Ww, Wh*Ww
-        relative_position_bias = 16 * torch.sigmoid(relative_position_bias)
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(
-                1
-            ).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return (
-            f"dim={self.dim}, window_size={self.window_size}, "
-            f"pretrained_window_size={self.pretrained_window_size}, num_heads={self.num_heads}"
-        )
-
-    def flops(self, N):
-        # calculate flops for 1 window with token length of N
-        flops = 0
-        # qkv = self.qkv(x)
-        flops += N * self.dim * 3 * self.dim
-        # attn = (q @ k.transpose(-2, -1))
-        flops += self.num_heads * N * (self.dim // self.num_heads) * N
-        #  x = (attn @ v)
-        flops += self.num_heads * N * N * (self.dim // self.num_heads)
-        # x = self.proj(x)
-        flops += N * self.dim * self.dim
-        return flops
-
-
-class SwinTransformerBlock(nn.Module):
-    r"""Swin Transformer Block.
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resulotion.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-        pretrained_window_size (int): Window size in pre-training.
-    """
-
-    def __init__(
-        self,
-        dim,
-        input_resolution,
-        num_heads,
-        window_size=7,
-        shift_size=0,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        act_layer=nn.GELU,
-        norm_layer=nn.LayerNorm,
-        pretrained_window_size=0,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        if min(self.input_resolution) <= self.window_size:
-            # if window size is larger than input resolution, we don't partition windows
-            self.shift_size = 0
-            self.window_size = min(self.input_resolution)
-        assert (
-            0 <= self.shift_size < self.window_size
-        ), "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim,
-            window_size=to_2tuple(self.window_size),
-            num_heads=num_heads,
-            qkv_bias=qkv_bias,
-            attn_drop=attn_drop,
-            proj_drop=drop,
-            pretrained_window_size=to_2tuple(pretrained_window_size),
-        )
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(
-            in_features=dim,
-            hidden_features=mlp_hidden_dim,
-            act_layer=act_layer,
-            drop=drop,
-        )
-
-        if self.shift_size > 0:
-            attn_mask = self.calculate_mask(self.input_resolution)
-        else:
-            attn_mask = None
-
-        self.register_buffer("attn_mask", attn_mask)
-
-    def calculate_mask(self, x_size):
-        # calculate attention mask for SW-MSA
-        H, W = x_size
-        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
-        h_slices = (
-            slice(0, -self.window_size),
-            slice(-self.window_size, -self.shift_size),
-            slice(-self.shift_size, None),
-        )
-        w_slices = (
-            slice(0, -self.window_size),
-            slice(-self.window_size, -self.shift_size),
-            slice(-self.shift_size, None),
-        )
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(
-            img_mask, self.window_size
-        )  # nW, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
-            attn_mask == 0, float(0.0)
-        )
-
-        return attn_mask
-
-    def forward(self, x, x_size):
-        H, W = x_size
-        B, L, C = x.shape
-        # assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = x.view(B, H, W, C)
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(
-                x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)
-            )
-        else:
-            shifted_x = x
-
-        # partition windows
-        x_windows = window_partition(
-            shifted_x, self.window_size
-        )  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(
-            -1, self.window_size * self.window_size, C
-        )  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
-        if self.input_resolution == x_size:
-            attn_windows = self.attn(
-                x_windows, mask=self.attn_mask
-            )  # nW*B, window_size*window_size, C
-        else:
-            attn_windows = self.attn(
-                x_windows, mask=self.calculate_mask(x_size).to(x.device)
-            )
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(
-                shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)
-            )
-        else:
-            x = shifted_x
-        x = x.view(B, H * W, C)
-        x = shortcut + self.drop_path(self.norm1(x))
-
-        # FFN
-        x = x + self.drop_path(self.norm2(self.mlp(x)))
-
-        return x
-
-    def extra_repr(self) -> str:
-        return (
-            f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, "
-            f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
-        )
-
-    def flops(self):
-        flops = 0
-        H, W = self.input_resolution
-        # norm1
-        flops += self.dim * H * W
-        # W-MSA/SW-MSA
-        nW = H * W / self.window_size / self.window_size
-        flops += nW * self.attn.flops(self.window_size * self.window_size)
-        # mlp
-        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
-        # norm2
-        flops += self.dim * H * W
-        return flops
-
-
-class PatchMerging(nn.Module):
-    r"""Patch Merging Layer.
-    Args:
-        input_resolution (tuple[int]): Resolution of input feature.
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(2 * dim)
-
-    def forward(self, x):
-        """
-        x: B, H*W, C
-        """
-        H, W = self.input_resolution
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
-
-        x = x.view(B, H, W, C)
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.reduction(x)
-        x = self.norm(x)
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"input_resolution={self.input_resolution}, dim={self.dim}"
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
-        flops += H * W * self.dim // 2
-        return flops
-
-
-class BasicLayer(nn.Module):
-    """A basic Swin Transformer layer for one stage.
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-        pretrained_window_size (int): Local window size in pre-training.
-    """
-
-    def __init__(
-        self,
-        dim,
-        input_resolution,
-        depth,
-        num_heads,
-        window_size,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        norm_layer=nn.LayerNorm,
-        downsample=None,
-        use_checkpoint=False,
-        pretrained_window_size=0,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList(
-            [
-                SwinTransformerBlock(
-                    dim=dim,
-                    input_resolution=input_resolution,
-                    num_heads=num_heads,
-                    window_size=window_size,
-                    shift_size=0 if (i % 2 == 0) else window_size // 2,
-                    mlp_ratio=mlp_ratio,
-                    qkv_bias=qkv_bias,
-                    drop=drop,
-                    attn_drop=attn_drop,
-                    drop_path=drop_path[i]
-                    if isinstance(drop_path, list)
-                    else drop_path,
-                    norm_layer=norm_layer,
-                    pretrained_window_size=pretrained_window_size,
-                )
-                for i in range(depth)
-            ]
-        )
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(
-                input_resolution, dim=dim, norm_layer=norm_layer
-            )
-        else:
-            self.downsample = None
-
-    def forward(self, x, x_size):
-        for blk in self.blocks:
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x, x_size)
-            else:
-                x = blk(x, x_size)
-        if self.downsample is not None:
-            x = self.downsample(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
-
-    def flops(self):
-        flops = 0
-        for blk in self.blocks:
-            flops += blk.flops()  # type: ignore
-        if self.downsample is not None:
-            flops += self.downsample.flops()
-        return flops
-
-    def _init_respostnorm(self):
-        for blk in self.blocks:
-            nn.init.constant_(blk.norm1.bias, 0)  # type: ignore
-            nn.init.constant_(blk.norm1.weight, 0)  # type: ignore
-            nn.init.constant_(blk.norm2.bias, 0)  # type: ignore
-            nn.init.constant_(blk.norm2.weight, 0)  # type: ignore
-
-
-class PatchEmbed(nn.Module):
-    r"""Image to Patch Embedding
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(
-        self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None
-    ):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]  # type: ignore
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        self.proj = nn.Conv2d(
-            in_chans, embed_dim, kernel_size=patch_size, stride=patch_size  # type: ignore
-        )
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        B, C, H, W = x.shape
-        # FIXME look at relaxing size constraints
-        # assert H == self.img_size[0] and W == self.img_size[1],
-        #     f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})."
-        x = self.proj(x).flatten(2).transpose(1, 2)  # B Ph*Pw C
-        if self.norm is not None:
-            x = self.norm(x)
-        return x
-
-    def flops(self):
-        Ho, Wo = self.patches_resolution
-        flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])  # type: ignore
-        if self.norm is not None:
-            flops += Ho * Wo * self.embed_dim
-        return flops
-
-
-class RSTB(nn.Module):
-    """Residual Swin Transformer Block (RSTB).
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-        img_size: Input image size.
-        patch_size: Patch size.
-        resi_connection: The convolutional block before residual connection.
-    """
-
-    def __init__(
-        self,
-        dim,
-        input_resolution,
-        depth,
-        num_heads,
-        window_size,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        norm_layer=nn.LayerNorm,
-        downsample=None,
-        use_checkpoint=False,
-        img_size=224,
-        patch_size=4,
-        resi_connection="1conv",
-    ):
-        super(RSTB, self).__init__()
-
-        self.dim = dim
-        self.input_resolution = input_resolution
-
-        self.residual_group = BasicLayer(
-            dim=dim,
-            input_resolution=input_resolution,
-            depth=depth,
-            num_heads=num_heads,
-            window_size=window_size,
-            mlp_ratio=mlp_ratio,
-            qkv_bias=qkv_bias,
-            drop=drop,
-            attn_drop=attn_drop,
-            drop_path=drop_path,
-            norm_layer=norm_layer,
-            downsample=downsample,
-            use_checkpoint=use_checkpoint,
-        )
-
-        if resi_connection == "1conv":
-            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
-        elif resi_connection == "3conv":
-            # to save parameters and memory
-            self.conv = nn.Sequential(
-                nn.Conv2d(dim, dim // 4, 3, 1, 1),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(dim // 4, dim, 3, 1, 1),
-            )
-
-        self.patch_embed = PatchEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=dim,
-            embed_dim=dim,
-            norm_layer=None,
-        )
-
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=dim,
-            embed_dim=dim,
-            norm_layer=None,
-        )
-
-    def forward(self, x, x_size):
-        return (
-            self.patch_embed(
-                self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))
-            )
-            + x
-        )
-
-    def flops(self):
-        flops = 0
-        flops += self.residual_group.flops()
-        H, W = self.input_resolution
-        flops += H * W * self.dim * self.dim * 9
-        flops += self.patch_embed.flops()
-        flops += self.patch_unembed.flops()
-
-        return flops
-
-
-class PatchUnEmbed(nn.Module):
-    r"""Image to Patch Unembedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(
-        self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None
-    ):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [img_size[0] // patch_size[0], img_size[1] // patch_size[1]]  # type: ignore
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-    def forward(self, x, x_size):
-        B, HW, C = x.shape
-        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
-        return x
-
-    def flops(self):
-        flops = 0
-        return flops
-
-
-class Upsample(nn.Sequential):
-    """Upsample module.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(
-                f"scale {scale} is not supported. " "Supported scales: 2^n and 3."
-            )
-        super(Upsample, self).__init__(*m)
-
-
-class Upsample_hf(nn.Sequential):
-    """Upsample module.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(
-                f"scale {scale} is not supported. " "Supported scales: 2^n and 3."
-            )
-        super(Upsample_hf, self).__init__(*m)
-
-
-class UpsampleOneStep(nn.Sequential):
-    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
-       Used in lightweight SR to save parameters.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-
-    """
-
-    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
-        self.num_feat = num_feat
-        self.input_resolution = input_resolution
-        m = []
-        m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1))
-        m.append(nn.PixelShuffle(scale))
-        super(UpsampleOneStep, self).__init__(*m)
-
-    def flops(self):
-        H, W = self.input_resolution  # type: ignore
-        flops = H * W * self.num_feat * 3 * 9
-        return flops
-
-
-class Swin2SR(nn.Module):
-    r"""Swin2SR
-        A PyTorch impl of : `Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration`.
-
-    Args:
-        img_size (int | tuple(int)): Input image size. Default 64
-        patch_size (int | tuple(int)): Patch size. Default: 1
-        in_chans (int): Number of input image channels. Default: 3
-        embed_dim (int): Patch embedding dimension. Default: 96
-        depths (tuple(int)): Depth of each Swin Transformer layer.
-        num_heads (tuple(int)): Number of attention heads in different layers.
-        window_size (int): Window size. Default: 7
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        drop_rate (float): Dropout rate. Default: 0
-        attn_drop_rate (float): Attention dropout rate. Default: 0
-        drop_path_rate (float): Stochastic depth rate. Default: 0.1
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
-        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
-        img_range: Image range. 1. or 255.
-        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
-        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
-    """
-
-    def __init__(
-        self,
-        state_dict,
-        **kwargs,
-    ):
-        super(Swin2SR, self).__init__()
-
-        # Defaults
-        img_size = 128
-        patch_size = 1
-        in_chans = 3
-        embed_dim = 96
-        depths = [6, 6, 6, 6]
-        num_heads = [6, 6, 6, 6]
-        window_size = 7
-        mlp_ratio = 4.0
-        qkv_bias = True
-        drop_rate = 0.0
-        attn_drop_rate = 0.0
-        drop_path_rate = 0.1
-        norm_layer = nn.LayerNorm
-        ape = False
-        patch_norm = True
-        use_checkpoint = False
-        upscale = 2
-        img_range = 1.0
-        upsampler = ""
-        resi_connection = "1conv"
-        num_in_ch = in_chans
-        num_out_ch = in_chans
-        num_feat = 64
-
-        self.model_arch = "Swin2SR"
-        self.sub_type = "SR"
-        self.state = state_dict
-        if "params_ema" in self.state:
-            self.state = self.state["params_ema"]
-        elif "params" in self.state:
-            self.state = self.state["params"]
-
-        state_keys = self.state.keys()
-
-        if "conv_before_upsample.0.weight" in state_keys:
-            if "conv_aux.weight" in state_keys:
-                upsampler = "pixelshuffle_aux"
-            elif "conv_up1.weight" in state_keys:
-                upsampler = "nearest+conv"
-            else:
-                upsampler = "pixelshuffle"
-                supports_fp16 = False
-        elif "upsample.0.weight" in state_keys:
-            upsampler = "pixelshuffledirect"
-        else:
-            upsampler = ""
-
-        num_feat = (
-            self.state.get("conv_before_upsample.0.weight", None).shape[1]
-            if self.state.get("conv_before_upsample.weight", None)
-            else 64
-        )
-
-        num_in_ch = self.state["conv_first.weight"].shape[1]
-        in_chans = num_in_ch
-        if "conv_last.weight" in state_keys:
-            num_out_ch = self.state["conv_last.weight"].shape[0]
-        else:
-            num_out_ch = num_in_ch
-
-        upscale = 1
-        if upsampler == "nearest+conv":
-            upsample_keys = [
-                x for x in state_keys if "conv_up" in x and "bias" not in x
-            ]
-
-            for upsample_key in upsample_keys:
-                upscale *= 2
-        elif upsampler == "pixelshuffle" or upsampler == "pixelshuffle_aux":
-            upsample_keys = [
-                x
-                for x in state_keys
-                if "upsample" in x and "conv" not in x and "bias" not in x
-            ]
-            for upsample_key in upsample_keys:
-                shape = self.state[upsample_key].shape[0]
-                upscale *= math.sqrt(shape // num_feat)
-            upscale = int(upscale)
-        elif upsampler == "pixelshuffledirect":
-            upscale = int(
-                math.sqrt(self.state["upsample.0.bias"].shape[0] // num_out_ch)
-            )
-
-        max_layer_num = 0
-        max_block_num = 0
-        for key in state_keys:
-            result = re.match(
-                r"layers.(\d*).residual_group.blocks.(\d*).norm1.weight", key
-            )
-            if result:
-                layer_num, block_num = result.groups()
-                max_layer_num = max(max_layer_num, int(layer_num))
-                max_block_num = max(max_block_num, int(block_num))
-
-        depths = [max_block_num + 1 for _ in range(max_layer_num + 1)]
-
-        if (
-            "layers.0.residual_group.blocks.0.attn.relative_position_bias_table"
-            in state_keys
-        ):
-            num_heads_num = self.state[
-                "layers.0.residual_group.blocks.0.attn.relative_position_bias_table"
-            ].shape[-1]
-            num_heads = [num_heads_num for _ in range(max_layer_num + 1)]
-        else:
-            num_heads = depths
-
-        embed_dim = self.state["conv_first.weight"].shape[0]
-
-        mlp_ratio = float(
-            self.state["layers.0.residual_group.blocks.0.mlp.fc1.bias"].shape[0]
-            / embed_dim
-        )
-
-        # TODO: could actually count the layers, but this should do
-        if "layers.0.conv.4.weight" in state_keys:
-            resi_connection = "3conv"
-        else:
-            resi_connection = "1conv"
-
-        window_size = int(
-            math.sqrt(
-                self.state[
-                    "layers.0.residual_group.blocks.0.attn.relative_position_index"
-                ].shape[0]
-            )
-        )
-
-        if "layers.0.residual_group.blocks.1.attn_mask" in state_keys:
-            img_size = int(
-                math.sqrt(
-                    self.state["layers.0.residual_group.blocks.1.attn_mask"].shape[0]
-                )
-                * window_size
-            )
-
-        # The JPEG models are the only ones with window-size 7, and they also use this range
-        img_range = 255.0 if window_size == 7 else 1.0
-
-        self.in_nc = num_in_ch
-        self.out_nc = num_out_ch
-        self.num_feat = num_feat
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.depths = depths
-        self.window_size = window_size
-        self.mlp_ratio = mlp_ratio
-        self.scale = upscale
-        self.upsampler = upsampler
-        self.img_size = img_size
-        self.img_range = img_range
-        self.resi_connection = resi_connection
-
-        self.supports_fp16 = False  # Too much weirdness to support this at the moment
-        self.supports_bfp16 = True
-        self.min_size_restriction = 16
-
-        ## END AUTO DETECTION
-
-        if in_chans == 3:
-            rgb_mean = (0.4488, 0.4371, 0.4040)
-            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
-        else:
-            self.mean = torch.zeros(1, 1, 1, 1)
-        self.upscale = upscale
-        self.upsampler = upsampler
-        self.window_size = window_size
-
-        #####################################################################################################
-        ################################### 1, shallow feature extraction ###################################
-        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
-
-        #####################################################################################################
-        ################################### 2, deep feature extraction ######################################
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.num_features = embed_dim
-        self.mlp_ratio = mlp_ratio
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=embed_dim,
-            embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None,
-        )
-        num_patches = self.patch_embed.num_patches
-        patches_resolution = self.patch_embed.patches_resolution
-        self.patches_resolution = patches_resolution
-
-        # merge non-overlapping patches into image
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=embed_dim,
-            embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None,
-        )
-
-        # absolute position embedding
-        if self.ape:
-            self.absolute_pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim))  # type: ignore
-            trunc_normal_(self.absolute_pos_embed, std=0.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [
-            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
-        ]  # stochastic depth decay rule
-
-        # build Residual Swin Transformer blocks (RSTB)
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = RSTB(
-                dim=embed_dim,
-                input_resolution=(patches_resolution[0], patches_resolution[1]),
-                depth=depths[i_layer],
-                num_heads=num_heads[i_layer],
-                window_size=window_size,
-                mlp_ratio=self.mlp_ratio,
-                qkv_bias=qkv_bias,
-                drop=drop_rate,
-                attn_drop=attn_drop_rate,
-                drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],  # type: ignore    # no impact on SR results
-                norm_layer=norm_layer,
-                downsample=None,
-                use_checkpoint=use_checkpoint,
-                img_size=img_size,
-                patch_size=patch_size,
-                resi_connection=resi_connection,
-            )
-            self.layers.append(layer)
-
-        if self.upsampler == "pixelshuffle_hf":
-            self.layers_hf = nn.ModuleList()
-            for i_layer in range(self.num_layers):
-                layer = RSTB(
-                    dim=embed_dim,
-                    input_resolution=(patches_resolution[0], patches_resolution[1]),
-                    depth=depths[i_layer],
-                    num_heads=num_heads[i_layer],
-                    window_size=window_size,
-                    mlp_ratio=self.mlp_ratio,
-                    qkv_bias=qkv_bias,
-                    drop=drop_rate,
-                    attn_drop=attn_drop_rate,
-                    drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],  # type: ignore    # no impact on SR results # type: ignore
-                    norm_layer=norm_layer,
-                    downsample=None,
-                    use_checkpoint=use_checkpoint,
-                    img_size=img_size,
-                    patch_size=patch_size,
-                    resi_connection=resi_connection,
-                )
-                self.layers_hf.append(layer)
-
-        self.norm = norm_layer(self.num_features)
-
-        # build the last conv layer in deep feature extraction
-        if resi_connection == "1conv":
-            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
-        elif resi_connection == "3conv":
-            # to save parameters and memory
-            self.conv_after_body = nn.Sequential(
-                nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1),
-            )
-
-        #####################################################################################################
-        ################################ 3, high quality image reconstruction ################################
-        if self.upsampler == "pixelshuffle":
-            # for classical SR
-            self.conv_before_upsample = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)
-            )
-            self.upsample = Upsample(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-        elif self.upsampler == "pixelshuffle_aux":
-            self.conv_bicubic = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
-            self.conv_before_upsample = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)
-            )
-            self.conv_aux = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            self.conv_after_aux = nn.Sequential(
-                nn.Conv2d(3, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)
-            )
-            self.upsample = Upsample(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-
-        elif self.upsampler == "pixelshuffle_hf":
-            self.conv_before_upsample = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)
-            )
-            self.upsample = Upsample(upscale, num_feat)
-            self.upsample_hf = Upsample_hf(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            self.conv_first_hf = nn.Sequential(
-                nn.Conv2d(num_feat, embed_dim, 3, 1, 1), nn.LeakyReLU(inplace=True)
-            )
-            self.conv_after_body_hf = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
-            self.conv_before_upsample_hf = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)
-            )
-            self.conv_last_hf = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-
-        elif self.upsampler == "pixelshuffledirect":
-            # for lightweight SR (to save parameters)
-            self.upsample = UpsampleOneStep(
-                upscale,
-                embed_dim,
-                num_out_ch,
-                (patches_resolution[0], patches_resolution[1]),
-            )
-        elif self.upsampler == "nearest+conv":
-            # for real-world SR (less artifacts)
-            assert self.upscale == 4, "only support x4 now."
-            self.conv_before_upsample = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)
-            )
-            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
-
-        self.apply(self._init_weights)
-
-        self.load_state_dict(state_dict)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=0.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore  # type: ignore
-    def no_weight_decay(self):
-        return {"absolute_pos_embed"}
-
-    @torch.jit.ignore  # type: ignore
-    def no_weight_decay_keywords(self):
-        return {"relative_position_bias_table"}
-
-    def check_image_size(self, x):
-        _, _, h, w = x.size()
-        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
-        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
-        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect")
-        return x
-
-    def forward_features(self, x):
-        x_size = (x.shape[2], x.shape[3])
-        x = self.patch_embed(x)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-
-        for layer in self.layers:
-            x = layer(x, x_size)
-
-        x = self.norm(x)  # B L C
-        x = self.patch_unembed(x, x_size)
-
-        return x
-
-    def forward_features_hf(self, x):
-        x_size = (x.shape[2], x.shape[3])
-        x = self.patch_embed(x)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-
-        for layer in self.layers_hf:
-            x = layer(x, x_size)
-
-        x = self.norm(x)  # B L C
-        x = self.patch_unembed(x, x_size)
-
-        return x
-
-    def forward(self, x):
-        H, W = x.shape[2:]
-        x = self.check_image_size(x)
-
-        self.mean = self.mean.type_as(x)
-        x = (x - self.mean) * self.img_range
-
-        if self.upsampler == "pixelshuffle":
-            # for classical SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.conv_last(self.upsample(x))
-        elif self.upsampler == "pixelshuffle_aux":
-            bicubic = F.interpolate(
-                x,
-                size=(H * self.upscale, W * self.upscale),
-                mode="bicubic",
-                align_corners=False,
-            )
-            bicubic = self.conv_bicubic(bicubic)
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            aux = self.conv_aux(x)  # b, 3, LR_H, LR_W
-            x = self.conv_after_aux(aux)
-            x = (
-                self.upsample(x)[:, :, : H * self.upscale, : W * self.upscale]
-                + bicubic[:, :, : H * self.upscale, : W * self.upscale]
-            )
-            x = self.conv_last(x)
-            aux = aux / self.img_range + self.mean
-        elif self.upsampler == "pixelshuffle_hf":
-            # for classical SR with HF
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x_before = self.conv_before_upsample(x)
-            x_out = self.conv_last(self.upsample(x_before))
-
-            x_hf = self.conv_first_hf(x_before)
-            x_hf = self.conv_after_body_hf(self.forward_features_hf(x_hf)) + x_hf
-            x_hf = self.conv_before_upsample_hf(x_hf)
-            x_hf = self.conv_last_hf(self.upsample_hf(x_hf))
-            x = x_out + x_hf
-            x_hf = x_hf / self.img_range + self.mean
-
-        elif self.upsampler == "pixelshuffledirect":
-            # for lightweight SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.upsample(x)
-        elif self.upsampler == "nearest+conv":
-            # for real-world SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.lrelu(
-                self.conv_up1(
-                    torch.nn.functional.interpolate(x, scale_factor=2, mode="nearest")
-                )
-            )
-            x = self.lrelu(
-                self.conv_up2(
-                    torch.nn.functional.interpolate(x, scale_factor=2, mode="nearest")
-                )
-            )
-            x = self.conv_last(self.lrelu(self.conv_hr(x)))
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            x_first = self.conv_first(x)
-            res = self.conv_after_body(self.forward_features(x_first)) + x_first
-            x = x + self.conv_last(res)
-
-        x = x / self.img_range + self.mean
-        if self.upsampler == "pixelshuffle_aux":
-            # NOTE: I removed an "aux" output here. not sure what that was for
-            return x[:, :, : H * self.upscale, : W * self.upscale]  # type: ignore
-
-        elif self.upsampler == "pixelshuffle_hf":
-            x_out = x_out / self.img_range + self.mean  # type: ignore
-            return x_out[:, :, : H * self.upscale, : W * self.upscale], x[:, :, : H * self.upscale, : W * self.upscale], x_hf[:, :, : H * self.upscale, : W * self.upscale]  # type: ignore
-
-        else:
-            return x[:, :, : H * self.upscale, : W * self.upscale]
-
-    def flops(self):
-        flops = 0
-        H, W = self.patches_resolution
-        flops += H * W * 3 * self.embed_dim * 9
-        flops += self.patch_embed.flops()
-        for i, layer in enumerate(self.layers):
-            flops += layer.flops()  # type: ignore
-        flops += H * W * 3 * self.embed_dim * self.embed_dim
-        flops += self.upsample.flops()  # type: ignore
-        return flops
diff --git a/comfy_extras/chainner_models/architecture/SwinIR.py b/comfy_extras/chainner_models/architecture/SwinIR.py
deleted file mode 100644
index 439dcbcb..00000000
--- a/comfy_extras/chainner_models/architecture/SwinIR.py
+++ /dev/null
@@ -1,1224 +0,0 @@
-# pylint: skip-file
-# -----------------------------------------------------------------------------------
-# SwinIR: Image Restoration Using Swin Transformer, https://arxiv.org/abs/2108.10257
-# Originally Written by Ze Liu, Modified by Jingyun Liang.
-# -----------------------------------------------------------------------------------
-
-import math
-import re
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torch.utils.checkpoint as checkpoint
-
-# Originally from the timm package
-from .timm.drop import DropPath
-from .timm.helpers import to_2tuple
-from .timm.weight_init import trunc_normal_
-
-
-class Mlp(nn.Module):
-    def __init__(
-        self,
-        in_features,
-        hidden_features=None,
-        out_features=None,
-        act_layer=nn.GELU,
-        drop=0.0,
-    ):
-        super().__init__()
-        out_features = out_features or in_features
-        hidden_features = hidden_features or in_features
-        self.fc1 = nn.Linear(in_features, hidden_features)
-        self.act = act_layer()
-        self.fc2 = nn.Linear(hidden_features, out_features)
-        self.drop = nn.Dropout(drop)
-
-    def forward(self, x):
-        x = self.fc1(x)
-        x = self.act(x)
-        x = self.drop(x)
-        x = self.fc2(x)
-        x = self.drop(x)
-        return x
-
-
-def window_partition(x, window_size):
-    """
-    Args:
-        x: (B, H, W, C)
-        window_size (int): window size
-
-    Returns:
-        windows: (num_windows*B, window_size, window_size, C)
-    """
-    B, H, W, C = x.shape
-    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
-    windows = (
-        x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
-    )
-    return windows
-
-
-def window_reverse(windows, window_size, H, W):
-    """
-    Args:
-        windows: (num_windows*B, window_size, window_size, C)
-        window_size (int): Window size
-        H (int): Height of image
-        W (int): Width of image
-
-    Returns:
-        x: (B, H, W, C)
-    """
-    B = int(windows.shape[0] / (H * W / window_size / window_size))
-    x = windows.view(
-        B, H // window_size, W // window_size, window_size, window_size, -1
-    )
-    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
-    return x
-
-
-class WindowAttention(nn.Module):
-    r"""Window based multi-head self attention (W-MSA) module with relative position bias.
-    It supports both of shifted and non-shifted window.
-
-    Args:
-        dim (int): Number of input channels.
-        window_size (tuple[int]): The height and width of the window.
-        num_heads (int): Number of attention heads.
-        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
-        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
-        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
-    """
-
-    def __init__(
-        self,
-        dim,
-        window_size,
-        num_heads,
-        qkv_bias=True,
-        qk_scale=None,
-        attn_drop=0.0,
-        proj_drop=0.0,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.window_size = window_size  # Wh, Ww
-        self.num_heads = num_heads
-        head_dim = dim // num_heads
-        self.scale = qk_scale or head_dim**-0.5
-
-        # define a parameter table of relative position bias
-        self.relative_position_bias_table = nn.Parameter(  # type: ignore
-            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
-        )  # 2*Wh-1 * 2*Ww-1, nH
-
-        # get pair-wise relative position index for each token inside the window
-        coords_h = torch.arange(self.window_size[0])
-        coords_w = torch.arange(self.window_size[1])
-        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
-        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
-        relative_coords = (
-            coords_flatten[:, :, None] - coords_flatten[:, None, :]
-        )  # 2, Wh*Ww, Wh*Ww
-        relative_coords = relative_coords.permute(
-            1, 2, 0
-        ).contiguous()  # Wh*Ww, Wh*Ww, 2
-        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
-        relative_coords[:, :, 1] += self.window_size[1] - 1
-        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
-        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
-        self.register_buffer("relative_position_index", relative_position_index)
-
-        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
-        self.attn_drop = nn.Dropout(attn_drop)
-        self.proj = nn.Linear(dim, dim)
-
-        self.proj_drop = nn.Dropout(proj_drop)
-
-        trunc_normal_(self.relative_position_bias_table, std=0.02)
-        self.softmax = nn.Softmax(dim=-1)
-
-    def forward(self, x, mask=None):
-        """
-        Args:
-            x: input features with shape of (num_windows*B, N, C)
-            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
-        """
-        B_, N, C = x.shape
-        qkv = (
-            self.qkv(x)
-            .reshape(B_, N, 3, self.num_heads, C // self.num_heads)
-            .permute(2, 0, 3, 1, 4)
-        )
-        q, k, v = (
-            qkv[0],
-            qkv[1],
-            qkv[2],
-        )  # make torchscript happy (cannot use tensor as tuple)
-
-        q = q * self.scale
-        attn = q @ k.transpose(-2, -1)
-
-        relative_position_bias = self.relative_position_bias_table[
-            self.relative_position_index.view(-1)  # type: ignore
-        ].view(
-            self.window_size[0] * self.window_size[1],
-            self.window_size[0] * self.window_size[1],
-            -1,
-        )  # Wh*Ww,Wh*Ww,nH
-        relative_position_bias = relative_position_bias.permute(
-            2, 0, 1
-        ).contiguous()  # nH, Wh*Ww, Wh*Ww
-        attn = attn + relative_position_bias.unsqueeze(0)
-
-        if mask is not None:
-            nW = mask.shape[0]
-            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(
-                1
-            ).unsqueeze(0)
-            attn = attn.view(-1, self.num_heads, N, N)
-            attn = self.softmax(attn)
-        else:
-            attn = self.softmax(attn)
-
-        attn = self.attn_drop(attn)
-
-        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
-        x = self.proj(x)
-        x = self.proj_drop(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, window_size={self.window_size}, num_heads={self.num_heads}"
-
-    def flops(self, N):
-        # calculate flops for 1 window with token length of N
-        flops = 0
-        # qkv = self.qkv(x)
-        flops += N * self.dim * 3 * self.dim
-        # attn = (q @ k.transpose(-2, -1))
-        flops += self.num_heads * N * (self.dim // self.num_heads) * N
-        #  x = (attn @ v)
-        flops += self.num_heads * N * N * (self.dim // self.num_heads)
-        # x = self.proj(x)
-        flops += N * self.dim * self.dim
-        return flops
-
-
-class SwinTransformerBlock(nn.Module):
-    r"""Swin Transformer Block.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resulotion.
-        num_heads (int): Number of attention heads.
-        window_size (int): Window size.
-        shift_size (int): Shift size for SW-MSA.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float, optional): Stochastic depth rate. Default: 0.0
-        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(
-        self,
-        dim,
-        input_resolution,
-        num_heads,
-        window_size=7,
-        shift_size=0,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        act_layer=nn.GELU,
-        norm_layer=nn.LayerNorm,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.num_heads = num_heads
-        self.window_size = window_size
-        self.shift_size = shift_size
-        self.mlp_ratio = mlp_ratio
-        if min(self.input_resolution) <= self.window_size:
-            # if window size is larger than input resolution, we don't partition windows
-            self.shift_size = 0
-            self.window_size = min(self.input_resolution)
-        assert (
-            0 <= self.shift_size < self.window_size
-        ), "shift_size must in 0-window_size"
-
-        self.norm1 = norm_layer(dim)
-        self.attn = WindowAttention(
-            dim,
-            window_size=to_2tuple(self.window_size),
-            num_heads=num_heads,
-            qkv_bias=qkv_bias,
-            qk_scale=qk_scale,
-            attn_drop=attn_drop,
-            proj_drop=drop,
-        )
-
-        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
-        self.norm2 = norm_layer(dim)
-        mlp_hidden_dim = int(dim * mlp_ratio)
-        self.mlp = Mlp(
-            in_features=dim,
-            hidden_features=mlp_hidden_dim,
-            act_layer=act_layer,
-            drop=drop,
-        )
-
-        if self.shift_size > 0:
-            attn_mask = self.calculate_mask(self.input_resolution)
-        else:
-            attn_mask = None
-
-        self.register_buffer("attn_mask", attn_mask)
-
-    def calculate_mask(self, x_size):
-        # calculate attention mask for SW-MSA
-        H, W = x_size
-        img_mask = torch.zeros((1, H, W, 1))  # 1 H W 1
-        h_slices = (
-            slice(0, -self.window_size),
-            slice(-self.window_size, -self.shift_size),
-            slice(-self.shift_size, None),
-        )
-        w_slices = (
-            slice(0, -self.window_size),
-            slice(-self.window_size, -self.shift_size),
-            slice(-self.shift_size, None),
-        )
-        cnt = 0
-        for h in h_slices:
-            for w in w_slices:
-                img_mask[:, h, w, :] = cnt
-                cnt += 1
-
-        mask_windows = window_partition(
-            img_mask, self.window_size
-        )  # nW, window_size, window_size, 1
-        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
-        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
-        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
-            attn_mask == 0, float(0.0)
-        )
-
-        return attn_mask
-
-    def forward(self, x, x_size):
-        H, W = x_size
-        B, L, C = x.shape
-        # assert L == H * W, "input feature has wrong size"
-
-        shortcut = x
-        x = self.norm1(x)
-        x = x.view(B, H, W, C)
-
-        # cyclic shift
-        if self.shift_size > 0:
-            shifted_x = torch.roll(
-                x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2)
-            )
-        else:
-            shifted_x = x
-
-        # partition windows
-        x_windows = window_partition(
-            shifted_x, self.window_size
-        )  # nW*B, window_size, window_size, C
-        x_windows = x_windows.view(
-            -1, self.window_size * self.window_size, C
-        )  # nW*B, window_size*window_size, C
-
-        # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
-        if self.input_resolution == x_size:
-            attn_windows = self.attn(
-                x_windows, mask=self.attn_mask
-            )  # nW*B, window_size*window_size, C
-        else:
-            attn_windows = self.attn(
-                x_windows, mask=self.calculate_mask(x_size).to(x.device)
-            )
-
-        # merge windows
-        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
-        shifted_x = window_reverse(attn_windows, self.window_size, H, W)  # B H' W' C
-
-        # reverse cyclic shift
-        if self.shift_size > 0:
-            x = torch.roll(
-                shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2)
-            )
-        else:
-            x = shifted_x
-        x = x.view(B, H * W, C)
-
-        # FFN
-        x = shortcut + self.drop_path(x)
-        x = x + self.drop_path(self.mlp(self.norm2(x)))
-
-        return x
-
-    def extra_repr(self) -> str:
-        return (
-            f"dim={self.dim}, input_resolution={self.input_resolution}, num_heads={self.num_heads}, "
-            f"window_size={self.window_size}, shift_size={self.shift_size}, mlp_ratio={self.mlp_ratio}"
-        )
-
-    def flops(self):
-        flops = 0
-        H, W = self.input_resolution
-        # norm1
-        flops += self.dim * H * W
-        # W-MSA/SW-MSA
-        nW = H * W / self.window_size / self.window_size
-        flops += nW * self.attn.flops(self.window_size * self.window_size)
-        # mlp
-        flops += 2 * H * W * self.dim * self.dim * self.mlp_ratio
-        # norm2
-        flops += self.dim * H * W
-        return flops
-
-
-class PatchMerging(nn.Module):
-    r"""Patch Merging Layer.
-
-    Args:
-        input_resolution (tuple[int]): Resolution of input feature.
-        dim (int): Number of input channels.
-        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
-    """
-
-    def __init__(self, input_resolution, dim, norm_layer=nn.LayerNorm):
-        super().__init__()
-        self.input_resolution = input_resolution
-        self.dim = dim
-        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
-        self.norm = norm_layer(4 * dim)
-
-    def forward(self, x):
-        """
-        x: B, H*W, C
-        """
-        H, W = self.input_resolution
-        B, L, C = x.shape
-        assert L == H * W, "input feature has wrong size"
-        assert H % 2 == 0 and W % 2 == 0, f"x size ({H}*{W}) are not even."
-
-        x = x.view(B, H, W, C)
-
-        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
-        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
-        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
-        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
-        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
-        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
-
-        x = self.norm(x)
-        x = self.reduction(x)
-
-        return x
-
-    def extra_repr(self) -> str:
-        return f"input_resolution={self.input_resolution}, dim={self.dim}"
-
-    def flops(self):
-        H, W = self.input_resolution
-        flops = H * W * self.dim
-        flops += (H // 2) * (W // 2) * 4 * self.dim * 2 * self.dim
-        return flops
-
-
-class BasicLayer(nn.Module):
-    """A basic Swin Transformer layer for one stage.
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-    """
-
-    def __init__(
-        self,
-        dim,
-        input_resolution,
-        depth,
-        num_heads,
-        window_size,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        norm_layer=nn.LayerNorm,
-        downsample=None,
-        use_checkpoint=False,
-    ):
-        super().__init__()
-        self.dim = dim
-        self.input_resolution = input_resolution
-        self.depth = depth
-        self.use_checkpoint = use_checkpoint
-
-        # build blocks
-        self.blocks = nn.ModuleList(
-            [
-                SwinTransformerBlock(
-                    dim=dim,
-                    input_resolution=input_resolution,
-                    num_heads=num_heads,
-                    window_size=window_size,
-                    shift_size=0 if (i % 2 == 0) else window_size // 2,
-                    mlp_ratio=mlp_ratio,
-                    qkv_bias=qkv_bias,
-                    qk_scale=qk_scale,
-                    drop=drop,
-                    attn_drop=attn_drop,
-                    drop_path=drop_path[i]
-                    if isinstance(drop_path, list)
-                    else drop_path,
-                    norm_layer=norm_layer,
-                )
-                for i in range(depth)
-            ]
-        )
-
-        # patch merging layer
-        if downsample is not None:
-            self.downsample = downsample(
-                input_resolution, dim=dim, norm_layer=norm_layer
-            )
-        else:
-            self.downsample = None
-
-    def forward(self, x, x_size):
-        for blk in self.blocks:
-            if self.use_checkpoint:
-                x = checkpoint.checkpoint(blk, x, x_size)
-            else:
-                x = blk(x, x_size)
-        if self.downsample is not None:
-            x = self.downsample(x)
-        return x
-
-    def extra_repr(self) -> str:
-        return f"dim={self.dim}, input_resolution={self.input_resolution}, depth={self.depth}"
-
-    def flops(self):
-        flops = 0
-        for blk in self.blocks:
-            flops += blk.flops()  # type: ignore
-        if self.downsample is not None:
-            flops += self.downsample.flops()
-        return flops
-
-
-class RSTB(nn.Module):
-    """Residual Swin Transformer Block (RSTB).
-
-    Args:
-        dim (int): Number of input channels.
-        input_resolution (tuple[int]): Input resolution.
-        depth (int): Number of blocks.
-        num_heads (int): Number of attention heads.
-        window_size (int): Local window size.
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
-        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
-        drop (float, optional): Dropout rate. Default: 0.0
-        attn_drop (float, optional): Attention dropout rate. Default: 0.0
-        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
-        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
-        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
-        img_size: Input image size.
-        patch_size: Patch size.
-        resi_connection: The convolutional block before residual connection.
-    """
-
-    def __init__(
-        self,
-        dim,
-        input_resolution,
-        depth,
-        num_heads,
-        window_size,
-        mlp_ratio=4.0,
-        qkv_bias=True,
-        qk_scale=None,
-        drop=0.0,
-        attn_drop=0.0,
-        drop_path=0.0,
-        norm_layer=nn.LayerNorm,
-        downsample=None,
-        use_checkpoint=False,
-        img_size=224,
-        patch_size=4,
-        resi_connection="1conv",
-    ):
-        super(RSTB, self).__init__()
-
-        self.dim = dim
-        self.input_resolution = input_resolution
-
-        self.residual_group = BasicLayer(
-            dim=dim,
-            input_resolution=input_resolution,
-            depth=depth,
-            num_heads=num_heads,
-            window_size=window_size,
-            mlp_ratio=mlp_ratio,
-            qkv_bias=qkv_bias,
-            qk_scale=qk_scale,
-            drop=drop,
-            attn_drop=attn_drop,
-            drop_path=drop_path,
-            norm_layer=norm_layer,
-            downsample=downsample,
-            use_checkpoint=use_checkpoint,
-        )
-
-        if resi_connection == "1conv":
-            self.conv = nn.Conv2d(dim, dim, 3, 1, 1)
-        elif resi_connection == "3conv":
-            # to save parameters and memory
-            self.conv = nn.Sequential(
-                nn.Conv2d(dim, dim // 4, 3, 1, 1),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(dim // 4, dim // 4, 1, 1, 0),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(dim // 4, dim, 3, 1, 1),
-            )
-
-        self.patch_embed = PatchEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=0,
-            embed_dim=dim,
-            norm_layer=None,
-        )
-
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=0,
-            embed_dim=dim,
-            norm_layer=None,
-        )
-
-    def forward(self, x, x_size):
-        return (
-            self.patch_embed(
-                self.conv(self.patch_unembed(self.residual_group(x, x_size), x_size))
-            )
-            + x
-        )
-
-    def flops(self):
-        flops = 0
-        flops += self.residual_group.flops()
-        H, W = self.input_resolution
-        flops += H * W * self.dim * self.dim * 9
-        flops += self.patch_embed.flops()
-        flops += self.patch_unembed.flops()
-
-        return flops
-
-
-class PatchEmbed(nn.Module):
-    r"""Image to Patch Embedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(
-        self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None
-    ):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [
-            img_size[0] // patch_size[0],  # type: ignore
-            img_size[1] // patch_size[1],  # type: ignore
-        ]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-        if norm_layer is not None:
-            self.norm = norm_layer(embed_dim)
-        else:
-            self.norm = None
-
-    def forward(self, x):
-        x = x.flatten(2).transpose(1, 2)  # B Ph*Pw C
-        if self.norm is not None:
-            x = self.norm(x)
-        return x
-
-    def flops(self):
-        flops = 0
-        H, W = self.img_size
-        if self.norm is not None:
-            flops += H * W * self.embed_dim  # type: ignore
-        return flops
-
-
-class PatchUnEmbed(nn.Module):
-    r"""Image to Patch Unembedding
-
-    Args:
-        img_size (int): Image size.  Default: 224.
-        patch_size (int): Patch token size. Default: 4.
-        in_chans (int): Number of input image channels. Default: 3.
-        embed_dim (int): Number of linear projection output channels. Default: 96.
-        norm_layer (nn.Module, optional): Normalization layer. Default: None
-    """
-
-    def __init__(
-        self, img_size=224, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None
-    ):
-        super().__init__()
-        img_size = to_2tuple(img_size)
-        patch_size = to_2tuple(patch_size)
-        patches_resolution = [
-            img_size[0] // patch_size[0],  # type: ignore
-            img_size[1] // patch_size[1],  # type: ignore
-        ]
-        self.img_size = img_size
-        self.patch_size = patch_size
-        self.patches_resolution = patches_resolution
-        self.num_patches = patches_resolution[0] * patches_resolution[1]
-
-        self.in_chans = in_chans
-        self.embed_dim = embed_dim
-
-    def forward(self, x, x_size):
-        B, HW, C = x.shape
-        x = x.transpose(1, 2).view(B, self.embed_dim, x_size[0], x_size[1])  # B Ph*Pw C
-        return x
-
-    def flops(self):
-        flops = 0
-        return flops
-
-
-class Upsample(nn.Sequential):
-    """Upsample module.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-    """
-
-    def __init__(self, scale, num_feat):
-        m = []
-        if (scale & (scale - 1)) == 0:  # scale = 2^n
-            for _ in range(int(math.log(scale, 2))):
-                m.append(nn.Conv2d(num_feat, 4 * num_feat, 3, 1, 1))
-                m.append(nn.PixelShuffle(2))
-        elif scale == 3:
-            m.append(nn.Conv2d(num_feat, 9 * num_feat, 3, 1, 1))
-            m.append(nn.PixelShuffle(3))
-        else:
-            raise ValueError(
-                f"scale {scale} is not supported. " "Supported scales: 2^n and 3."
-            )
-        super(Upsample, self).__init__(*m)
-
-
-class UpsampleOneStep(nn.Sequential):
-    """UpsampleOneStep module (the difference with Upsample is that it always only has 1conv + 1pixelshuffle)
-       Used in lightweight SR to save parameters.
-
-    Args:
-        scale (int): Scale factor. Supported scales: 2^n and 3.
-        num_feat (int): Channel number of intermediate features.
-
-    """
-
-    def __init__(self, scale, num_feat, num_out_ch, input_resolution=None):
-        self.num_feat = num_feat
-        self.input_resolution = input_resolution
-        m = []
-        m.append(nn.Conv2d(num_feat, (scale**2) * num_out_ch, 3, 1, 1))
-        m.append(nn.PixelShuffle(scale))
-        super(UpsampleOneStep, self).__init__(*m)
-
-    def flops(self):
-        H, W = self.input_resolution  # type: ignore
-        flops = H * W * self.num_feat * 3 * 9
-        return flops
-
-
-class SwinIR(nn.Module):
-    r"""SwinIR
-        A PyTorch impl of : `SwinIR: Image Restoration Using Swin Transformer`, based on Swin Transformer.
-
-    Args:
-        img_size (int | tuple(int)): Input image size. Default 64
-        patch_size (int | tuple(int)): Patch size. Default: 1
-        in_chans (int): Number of input image channels. Default: 3
-        embed_dim (int): Patch embedding dimension. Default: 96
-        depths (tuple(int)): Depth of each Swin Transformer layer.
-        num_heads (tuple(int)): Number of attention heads in different layers.
-        window_size (int): Window size. Default: 7
-        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
-        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
-        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
-        drop_rate (float): Dropout rate. Default: 0
-        attn_drop_rate (float): Attention dropout rate. Default: 0
-        drop_path_rate (float): Stochastic depth rate. Default: 0.1
-        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
-        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False
-        patch_norm (bool): If True, add normalization after patch embedding. Default: True
-        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False
-        upscale: Upscale factor. 2/3/4/8 for image SR, 1 for denoising and compress artifact reduction
-        img_range: Image range. 1. or 255.
-        upsampler: The reconstruction reconstruction module. 'pixelshuffle'/'pixelshuffledirect'/'nearest+conv'/None
-        resi_connection: The convolutional block before residual connection. '1conv'/'3conv'
-    """
-
-    def __init__(
-        self,
-        state_dict,
-        **kwargs,
-    ):
-        super(SwinIR, self).__init__()
-
-        # Defaults
-        img_size = 64
-        patch_size = 1
-        in_chans = 3
-        embed_dim = 96
-        depths = [6, 6, 6, 6]
-        num_heads = [6, 6, 6, 6]
-        window_size = 7
-        mlp_ratio = 4.0
-        qkv_bias = True
-        qk_scale = None
-        drop_rate = 0.0
-        attn_drop_rate = 0.0
-        drop_path_rate = 0.1
-        norm_layer = nn.LayerNorm
-        ape = False
-        patch_norm = True
-        use_checkpoint = False
-        upscale = 2
-        img_range = 1.0
-        upsampler = ""
-        resi_connection = "1conv"
-        num_feat = 64
-        num_in_ch = in_chans
-        num_out_ch = in_chans
-        supports_fp16 = True
-        self.start_unshuffle = 1
-
-        self.model_arch = "SwinIR"
-        self.sub_type = "SR"
-        self.state = state_dict
-        if "params_ema" in self.state:
-            self.state = self.state["params_ema"]
-        elif "params" in self.state:
-            self.state = self.state["params"]
-
-        state_keys = self.state.keys()
-
-        if "conv_before_upsample.0.weight" in state_keys:
-            if "conv_up1.weight" in state_keys:
-                upsampler = "nearest+conv"
-            else:
-                upsampler = "pixelshuffle"
-                supports_fp16 = False
-        elif "upsample.0.weight" in state_keys:
-            upsampler = "pixelshuffledirect"
-        else:
-            upsampler = ""
-
-        num_feat = (
-            self.state.get("conv_before_upsample.0.weight", None).shape[1]
-            if self.state.get("conv_before_upsample.weight", None)
-            else 64
-        )
-
-        if "conv_first.1.weight" in self.state:
-            self.state["conv_first.weight"] = self.state.pop("conv_first.1.weight")
-            self.state["conv_first.bias"] = self.state.pop("conv_first.1.bias")
-            self.start_unshuffle = round(math.sqrt(self.state["conv_first.weight"].shape[1] // 3))
-
-        num_in_ch = self.state["conv_first.weight"].shape[1]
-        in_chans = num_in_ch
-        if "conv_last.weight" in state_keys:
-            num_out_ch = self.state["conv_last.weight"].shape[0]
-        else:
-            num_out_ch = num_in_ch
-
-        upscale = 1
-        if upsampler == "nearest+conv":
-            upsample_keys = [
-                x for x in state_keys if "conv_up" in x and "bias" not in x
-            ]
-
-            for upsample_key in upsample_keys:
-                upscale *= 2
-        elif upsampler == "pixelshuffle":
-            upsample_keys = [
-                x
-                for x in state_keys
-                if "upsample" in x and "conv" not in x and "bias" not in x
-            ]
-            for upsample_key in upsample_keys:
-                shape = self.state[upsample_key].shape[0]
-                upscale *= math.sqrt(shape // num_feat)
-            upscale = int(upscale)
-        elif upsampler == "pixelshuffledirect":
-            upscale = int(
-                math.sqrt(self.state["upsample.0.bias"].shape[0] // num_out_ch)
-            )
-
-        max_layer_num = 0
-        max_block_num = 0
-        for key in state_keys:
-            result = re.match(
-                r"layers.(\d*).residual_group.blocks.(\d*).norm1.weight", key
-            )
-            if result:
-                layer_num, block_num = result.groups()
-                max_layer_num = max(max_layer_num, int(layer_num))
-                max_block_num = max(max_block_num, int(block_num))
-
-        depths = [max_block_num + 1 for _ in range(max_layer_num + 1)]
-
-        if (
-            "layers.0.residual_group.blocks.0.attn.relative_position_bias_table"
-            in state_keys
-        ):
-            num_heads_num = self.state[
-                "layers.0.residual_group.blocks.0.attn.relative_position_bias_table"
-            ].shape[-1]
-            num_heads = [num_heads_num for _ in range(max_layer_num + 1)]
-        else:
-            num_heads = depths
-
-        embed_dim = self.state["conv_first.weight"].shape[0]
-
-        mlp_ratio = float(
-            self.state["layers.0.residual_group.blocks.0.mlp.fc1.bias"].shape[0]
-            / embed_dim
-        )
-
-        # TODO: could actually count the layers, but this should do
-        if "layers.0.conv.4.weight" in state_keys:
-            resi_connection = "3conv"
-        else:
-            resi_connection = "1conv"
-
-        window_size = int(
-            math.sqrt(
-                self.state[
-                    "layers.0.residual_group.blocks.0.attn.relative_position_index"
-                ].shape[0]
-            )
-        )
-
-        if "layers.0.residual_group.blocks.1.attn_mask" in state_keys:
-            img_size = int(
-                math.sqrt(
-                    self.state["layers.0.residual_group.blocks.1.attn_mask"].shape[0]
-                )
-                * window_size
-            )
-
-        # The JPEG models are the only ones with window-size 7, and they also use this range
-        img_range = 255.0 if window_size == 7 else 1.0
-
-        self.in_nc = num_in_ch
-        self.out_nc = num_out_ch
-        self.num_feat = num_feat
-        self.embed_dim = embed_dim
-        self.num_heads = num_heads
-        self.depths = depths
-        self.window_size = window_size
-        self.mlp_ratio = mlp_ratio
-        self.scale = upscale / self.start_unshuffle
-        self.upsampler = upsampler
-        self.img_size = img_size
-        self.img_range = img_range
-        self.resi_connection = resi_connection
-
-        self.supports_fp16 = False  # Too much weirdness to support this at the moment
-        self.supports_bfp16 = True
-        self.min_size_restriction = 16
-
-        self.img_range = img_range
-        if in_chans == 3:
-            rgb_mean = (0.4488, 0.4371, 0.4040)
-            self.mean = torch.Tensor(rgb_mean).view(1, 3, 1, 1)
-        else:
-            self.mean = torch.zeros(1, 1, 1, 1)
-        self.upscale = upscale
-        self.upsampler = upsampler
-        self.window_size = window_size
-
-        #####################################################################################################
-        ################################### 1, shallow feature extraction ###################################
-        self.conv_first = nn.Conv2d(num_in_ch, embed_dim, 3, 1, 1)
-
-        #####################################################################################################
-        ################################### 2, deep feature extraction ######################################
-        self.num_layers = len(depths)
-        self.embed_dim = embed_dim
-        self.ape = ape
-        self.patch_norm = patch_norm
-        self.num_features = embed_dim
-        self.mlp_ratio = mlp_ratio
-
-        # split image into non-overlapping patches
-        self.patch_embed = PatchEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=embed_dim,
-            embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None,
-        )
-        num_patches = self.patch_embed.num_patches
-        patches_resolution = self.patch_embed.patches_resolution
-        self.patches_resolution = patches_resolution
-
-        # merge non-overlapping patches into image
-        self.patch_unembed = PatchUnEmbed(
-            img_size=img_size,
-            patch_size=patch_size,
-            in_chans=embed_dim,
-            embed_dim=embed_dim,
-            norm_layer=norm_layer if self.patch_norm else None,
-        )
-
-        # absolute position embedding
-        if self.ape:
-            self.absolute_pos_embed = nn.Parameter(  # type: ignore
-                torch.zeros(1, num_patches, embed_dim)
-            )
-            trunc_normal_(self.absolute_pos_embed, std=0.02)
-
-        self.pos_drop = nn.Dropout(p=drop_rate)
-
-        # stochastic depth
-        dpr = [
-            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
-        ]  # stochastic depth decay rule
-
-        # build Residual Swin Transformer blocks (RSTB)
-        self.layers = nn.ModuleList()
-        for i_layer in range(self.num_layers):
-            layer = RSTB(
-                dim=embed_dim,
-                input_resolution=(patches_resolution[0], patches_resolution[1]),
-                depth=depths[i_layer],
-                num_heads=num_heads[i_layer],
-                window_size=window_size,
-                mlp_ratio=self.mlp_ratio,
-                qkv_bias=qkv_bias,
-                qk_scale=qk_scale,
-                drop=drop_rate,
-                attn_drop=attn_drop_rate,
-                drop_path=dpr[
-                    sum(depths[:i_layer]) : sum(depths[: i_layer + 1])  # type: ignore
-                ],  # no impact on SR results
-                norm_layer=norm_layer,
-                downsample=None,
-                use_checkpoint=use_checkpoint,
-                img_size=img_size,
-                patch_size=patch_size,
-                resi_connection=resi_connection,
-            )
-            self.layers.append(layer)
-        self.norm = norm_layer(self.num_features)
-
-        # build the last conv layer in deep feature extraction
-        if resi_connection == "1conv":
-            self.conv_after_body = nn.Conv2d(embed_dim, embed_dim, 3, 1, 1)
-        elif resi_connection == "3conv":
-            # to save parameters and memory
-            self.conv_after_body = nn.Sequential(
-                nn.Conv2d(embed_dim, embed_dim // 4, 3, 1, 1),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(embed_dim // 4, embed_dim // 4, 1, 1, 0),
-                nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                nn.Conv2d(embed_dim // 4, embed_dim, 3, 1, 1),
-            )
-
-        #####################################################################################################
-        ################################ 3, high quality image reconstruction ################################
-        if self.upsampler == "pixelshuffle":
-            # for classical SR
-            self.conv_before_upsample = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)
-            )
-            self.upsample = Upsample(upscale, num_feat)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-        elif self.upsampler == "pixelshuffledirect":
-            # for lightweight SR (to save parameters)
-            self.upsample = UpsampleOneStep(
-                upscale,
-                embed_dim,
-                num_out_ch,
-                (patches_resolution[0], patches_resolution[1]),
-            )
-        elif self.upsampler == "nearest+conv":
-            # for real-world SR (less artifacts)
-            self.conv_before_upsample = nn.Sequential(
-                nn.Conv2d(embed_dim, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True)
-            )
-            self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            if self.upscale == 4:
-                self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            elif self.upscale == 8:
-                self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-                self.conv_up3 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
-            self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
-            self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            self.conv_last = nn.Conv2d(embed_dim, num_out_ch, 3, 1, 1)
-
-        self.apply(self._init_weights)
-        self.load_state_dict(self.state, strict=False)
-
-    def _init_weights(self, m):
-        if isinstance(m, nn.Linear):
-            trunc_normal_(m.weight, std=0.02)
-            if isinstance(m, nn.Linear) and m.bias is not None:
-                nn.init.constant_(m.bias, 0)
-        elif isinstance(m, nn.LayerNorm):
-            nn.init.constant_(m.bias, 0)
-            nn.init.constant_(m.weight, 1.0)
-
-    @torch.jit.ignore  # type: ignore
-    def no_weight_decay(self):
-        return {"absolute_pos_embed"}
-
-    @torch.jit.ignore  # type: ignore
-    def no_weight_decay_keywords(self):
-        return {"relative_position_bias_table"}
-
-    def check_image_size(self, x):
-        _, _, h, w = x.size()
-        mod_pad_h = (self.window_size - h % self.window_size) % self.window_size
-        mod_pad_w = (self.window_size - w % self.window_size) % self.window_size
-        x = F.pad(x, (0, mod_pad_w, 0, mod_pad_h), "reflect")
-        return x
-
-    def forward_features(self, x):
-        x_size = (x.shape[2], x.shape[3])
-        x = self.patch_embed(x)
-        if self.ape:
-            x = x + self.absolute_pos_embed
-        x = self.pos_drop(x)
-
-        for layer in self.layers:
-            x = layer(x, x_size)
-
-        x = self.norm(x)  # B L C
-        x = self.patch_unembed(x, x_size)
-
-        return x
-
-    def forward(self, x):
-        H, W = x.shape[2:]
-        x = self.check_image_size(x)
-
-        self.mean = self.mean.type_as(x)
-        x = (x - self.mean) * self.img_range
-
-        if self.start_unshuffle > 1:
-            x = torch.nn.functional.pixel_unshuffle(x, self.start_unshuffle)
-
-        if self.upsampler == "pixelshuffle":
-            # for classical SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.conv_last(self.upsample(x))
-        elif self.upsampler == "pixelshuffledirect":
-            # for lightweight SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.upsample(x)
-        elif self.upsampler == "nearest+conv":
-            # for real-world SR
-            x = self.conv_first(x)
-            x = self.conv_after_body(self.forward_features(x)) + x
-            x = self.conv_before_upsample(x)
-            x = self.lrelu(
-                self.conv_up1(
-                    torch.nn.functional.interpolate(x, scale_factor=2, mode="nearest")  # type: ignore
-                )
-            )
-            if self.upscale == 4:
-                x = self.lrelu(
-                    self.conv_up2(
-                        torch.nn.functional.interpolate(  # type: ignore
-                            x, scale_factor=2, mode="nearest"
-                        )
-                    )
-                )
-            elif self.upscale == 8:
-                x = self.lrelu(self.conv_up2(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-                x = self.lrelu(self.conv_up3(torch.nn.functional.interpolate(x, scale_factor=2, mode='nearest')))
-            x = self.conv_last(self.lrelu(self.conv_hr(x)))
-        else:
-            # for image denoising and JPEG compression artifact reduction
-            x_first = self.conv_first(x)
-            res = self.conv_after_body(self.forward_features(x_first)) + x_first
-            x = x + self.conv_last(res)
-
-        x = x / self.img_range + self.mean
-
-        return x[:, :, : H * self.upscale, : W * self.upscale]
-
-    def flops(self):
-        flops = 0
-        H, W = self.patches_resolution
-        flops += H * W * 3 * self.embed_dim * 9
-        flops += self.patch_embed.flops()
-        for i, layer in enumerate(self.layers):
-            flops += layer.flops()  # type: ignore
-        flops += H * W * 3 * self.embed_dim * self.embed_dim
-        flops += self.upsample.flops()  # type: ignore
-        return flops
diff --git a/comfy_extras/chainner_models/architecture/__init__.py b/comfy_extras/chainner_models/architecture/__init__.py
deleted file mode 100644
index e69de29b..00000000
diff --git a/comfy_extras/chainner_models/architecture/block.py b/comfy_extras/chainner_models/architecture/block.py
deleted file mode 100644
index d7bc5d22..00000000
--- a/comfy_extras/chainner_models/architecture/block.py
+++ /dev/null
@@ -1,546 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-from __future__ import annotations
-
-from collections import OrderedDict
-try:
-    from typing import Literal
-except ImportError:
-    from typing_extensions import Literal
-
-import torch
-import torch.nn as nn
-
-####################
-# Basic blocks
-####################
-
-
-def act(act_type: str, inplace=True, neg_slope=0.2, n_prelu=1):
-    # helper selecting activation
-    # neg_slope: for leakyrelu and init of prelu
-    # n_prelu: for p_relu num_parameters
-    act_type = act_type.lower()
-    if act_type == "relu":
-        layer = nn.ReLU(inplace)
-    elif act_type == "leakyrelu":
-        layer = nn.LeakyReLU(neg_slope, inplace)
-    elif act_type == "prelu":
-        layer = nn.PReLU(num_parameters=n_prelu, init=neg_slope)
-    else:
-        raise NotImplementedError(
-            "activation layer [{:s}] is not found".format(act_type)
-        )
-    return layer
-
-
-def norm(norm_type: str, nc: int):
-    # helper selecting normalization layer
-    norm_type = norm_type.lower()
-    if norm_type == "batch":
-        layer = nn.BatchNorm2d(nc, affine=True)
-    elif norm_type == "instance":
-        layer = nn.InstanceNorm2d(nc, affine=False)
-    else:
-        raise NotImplementedError(
-            "normalization layer [{:s}] is not found".format(norm_type)
-        )
-    return layer
-
-
-def pad(pad_type: str, padding):
-    # helper selecting padding layer
-    # if padding is 'zero', do by conv layers
-    pad_type = pad_type.lower()
-    if padding == 0:
-        return None
-    if pad_type == "reflect":
-        layer = nn.ReflectionPad2d(padding)
-    elif pad_type == "replicate":
-        layer = nn.ReplicationPad2d(padding)
-    else:
-        raise NotImplementedError(
-            "padding layer [{:s}] is not implemented".format(pad_type)
-        )
-    return layer
-
-
-def get_valid_padding(kernel_size, dilation):
-    kernel_size = kernel_size + (kernel_size - 1) * (dilation - 1)
-    padding = (kernel_size - 1) // 2
-    return padding
-
-
-class ConcatBlock(nn.Module):
-    # Concat the output of a submodule to its input
-    def __init__(self, submodule):
-        super(ConcatBlock, self).__init__()
-        self.sub = submodule
-
-    def forward(self, x):
-        output = torch.cat((x, self.sub(x)), dim=1)
-        return output
-
-    def __repr__(self):
-        tmpstr = "Identity .. \n|"
-        modstr = self.sub.__repr__().replace("\n", "\n|")
-        tmpstr = tmpstr + modstr
-        return tmpstr
-
-
-class ShortcutBlock(nn.Module):
-    # Elementwise sum the output of a submodule to its input
-    def __init__(self, submodule):
-        super(ShortcutBlock, self).__init__()
-        self.sub = submodule
-
-    def forward(self, x):
-        output = x + self.sub(x)
-        return output
-
-    def __repr__(self):
-        tmpstr = "Identity + \n|"
-        modstr = self.sub.__repr__().replace("\n", "\n|")
-        tmpstr = tmpstr + modstr
-        return tmpstr
-
-
-class ShortcutBlockSPSR(nn.Module):
-    # Elementwise sum the output of a submodule to its input
-    def __init__(self, submodule):
-        super(ShortcutBlockSPSR, self).__init__()
-        self.sub = submodule
-
-    def forward(self, x):
-        return x, self.sub
-
-    def __repr__(self):
-        tmpstr = "Identity + \n|"
-        modstr = self.sub.__repr__().replace("\n", "\n|")
-        tmpstr = tmpstr + modstr
-        return tmpstr
-
-
-def sequential(*args):
-    # Flatten Sequential. It unwraps nn.Sequential.
-    if len(args) == 1:
-        if isinstance(args[0], OrderedDict):
-            raise NotImplementedError("sequential does not support OrderedDict input.")
-        return args[0]  # No sequential is needed.
-    modules = []
-    for module in args:
-        if isinstance(module, nn.Sequential):
-            for submodule in module.children():
-                modules.append(submodule)
-        elif isinstance(module, nn.Module):
-            modules.append(module)
-    return nn.Sequential(*modules)
-
-
-ConvMode = Literal["CNA", "NAC", "CNAC"]
-
-
-# 2x2x2 Conv Block
-def conv_block_2c2(
-    in_nc,
-    out_nc,
-    act_type="relu",
-):
-    return sequential(
-        nn.Conv2d(in_nc, out_nc, kernel_size=2, padding=1),
-        nn.Conv2d(out_nc, out_nc, kernel_size=2, padding=0),
-        act(act_type) if act_type else None,
-    )
-
-
-def conv_block(
-    in_nc: int,
-    out_nc: int,
-    kernel_size,
-    stride=1,
-    dilation=1,
-    groups=1,
-    bias=True,
-    pad_type="zero",
-    norm_type: str | None = None,
-    act_type: str | None = "relu",
-    mode: ConvMode = "CNA",
-    c2x2=False,
-):
-    """
-    Conv layer with padding, normalization, activation
-    mode: CNA --> Conv -> Norm -> Act
-        NAC --> Norm -> Act --> Conv (Identity Mappings in Deep Residual Networks, ECCV16)
-    """
-
-    if c2x2:
-        return conv_block_2c2(in_nc, out_nc, act_type=act_type)
-
-    assert mode in ("CNA", "NAC", "CNAC"), "Wrong conv mode [{:s}]".format(mode)
-    padding = get_valid_padding(kernel_size, dilation)
-    p = pad(pad_type, padding) if pad_type and pad_type != "zero" else None
-    padding = padding if pad_type == "zero" else 0
-
-    c = nn.Conv2d(
-        in_nc,
-        out_nc,
-        kernel_size=kernel_size,
-        stride=stride,
-        padding=padding,
-        dilation=dilation,
-        bias=bias,
-        groups=groups,
-    )
-    a = act(act_type) if act_type else None
-    if mode in ("CNA", "CNAC"):
-        n = norm(norm_type, out_nc) if norm_type else None
-        return sequential(p, c, n, a)
-    elif mode == "NAC":
-        if norm_type is None and act_type is not None:
-            a = act(act_type, inplace=False)
-            # Important!
-            # input----ReLU(inplace)----Conv--+----output
-            #        |________________________|
-            # inplace ReLU will modify the input, therefore wrong output
-        n = norm(norm_type, in_nc) if norm_type else None
-        return sequential(n, a, p, c)
-    else:
-        assert False, f"Invalid conv mode {mode}"
-
-
-####################
-# Useful blocks
-####################
-
-
-class ResNetBlock(nn.Module):
-    """
-    ResNet Block, 3-3 style
-    with extra residual scaling used in EDSR
-    (Enhanced Deep Residual Networks for Single Image Super-Resolution, CVPRW 17)
-    """
-
-    def __init__(
-        self,
-        in_nc,
-        mid_nc,
-        out_nc,
-        kernel_size=3,
-        stride=1,
-        dilation=1,
-        groups=1,
-        bias=True,
-        pad_type="zero",
-        norm_type=None,
-        act_type="relu",
-        mode: ConvMode = "CNA",
-        res_scale=1,
-    ):
-        super(ResNetBlock, self).__init__()
-        conv0 = conv_block(
-            in_nc,
-            mid_nc,
-            kernel_size,
-            stride,
-            dilation,
-            groups,
-            bias,
-            pad_type,
-            norm_type,
-            act_type,
-            mode,
-        )
-        if mode == "CNA":
-            act_type = None
-        if mode == "CNAC":  # Residual path: |-CNAC-|
-            act_type = None
-            norm_type = None
-        conv1 = conv_block(
-            mid_nc,
-            out_nc,
-            kernel_size,
-            stride,
-            dilation,
-            groups,
-            bias,
-            pad_type,
-            norm_type,
-            act_type,
-            mode,
-        )
-        # if in_nc != out_nc:
-        #     self.project = conv_block(in_nc, out_nc, 1, stride, dilation, 1, bias, pad_type, \
-        #         None, None)
-        #     print('Need a projecter in ResNetBlock.')
-        # else:
-        #     self.project = lambda x:x
-        self.res = sequential(conv0, conv1)
-        self.res_scale = res_scale
-
-    def forward(self, x):
-        res = self.res(x).mul(self.res_scale)
-        return x + res
-
-
-class RRDB(nn.Module):
-    """
-    Residual in Residual Dense Block
-    (ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks)
-    """
-
-    def __init__(
-        self,
-        nf,
-        kernel_size=3,
-        gc=32,
-        stride=1,
-        bias: bool = True,
-        pad_type="zero",
-        norm_type=None,
-        act_type="leakyrelu",
-        mode: ConvMode = "CNA",
-        _convtype="Conv2D",
-        _spectral_norm=False,
-        plus=False,
-        c2x2=False,
-    ):
-        super(RRDB, self).__init__()
-        self.RDB1 = ResidualDenseBlock_5C(
-            nf,
-            kernel_size,
-            gc,
-            stride,
-            bias,
-            pad_type,
-            norm_type,
-            act_type,
-            mode,
-            plus=plus,
-            c2x2=c2x2,
-        )
-        self.RDB2 = ResidualDenseBlock_5C(
-            nf,
-            kernel_size,
-            gc,
-            stride,
-            bias,
-            pad_type,
-            norm_type,
-            act_type,
-            mode,
-            plus=plus,
-            c2x2=c2x2,
-        )
-        self.RDB3 = ResidualDenseBlock_5C(
-            nf,
-            kernel_size,
-            gc,
-            stride,
-            bias,
-            pad_type,
-            norm_type,
-            act_type,
-            mode,
-            plus=plus,
-            c2x2=c2x2,
-        )
-
-    def forward(self, x):
-        out = self.RDB1(x)
-        out = self.RDB2(out)
-        out = self.RDB3(out)
-        return out * 0.2 + x
-
-
-class ResidualDenseBlock_5C(nn.Module):
-    """
-    Residual Dense Block
-    style: 5 convs
-    The core module of paper: (Residual Dense Network for Image Super-Resolution, CVPR 18)
-    Modified options that can be used:
-        - "Partial Convolution based Padding" arXiv:1811.11718
-        - "Spectral normalization" arXiv:1802.05957
-        - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C.
-            {Rakotonirina} and A. {Rasoanaivo}
-
-    Args:
-        nf (int): Channel number of intermediate features (num_feat).
-        gc (int): Channels for each growth (num_grow_ch: growth channel,
-            i.e. intermediate channels).
-        convtype (str): the type of convolution to use. Default: 'Conv2D'
-        gaussian_noise (bool): enable the ESRGAN+ gaussian noise (no new
-            trainable parameters)
-        plus (bool): enable the additional residual paths from ESRGAN+
-            (adds trainable parameters)
-    """
-
-    def __init__(
-        self,
-        nf=64,
-        kernel_size=3,
-        gc=32,
-        stride=1,
-        bias: bool = True,
-        pad_type="zero",
-        norm_type=None,
-        act_type="leakyrelu",
-        mode: ConvMode = "CNA",
-        plus=False,
-        c2x2=False,
-    ):
-        super(ResidualDenseBlock_5C, self).__init__()
-
-        ## +
-        self.conv1x1 = conv1x1(nf, gc) if plus else None
-        ## +
-
-        self.conv1 = conv_block(
-            nf,
-            gc,
-            kernel_size,
-            stride,
-            bias=bias,
-            pad_type=pad_type,
-            norm_type=norm_type,
-            act_type=act_type,
-            mode=mode,
-            c2x2=c2x2,
-        )
-        self.conv2 = conv_block(
-            nf + gc,
-            gc,
-            kernel_size,
-            stride,
-            bias=bias,
-            pad_type=pad_type,
-            norm_type=norm_type,
-            act_type=act_type,
-            mode=mode,
-            c2x2=c2x2,
-        )
-        self.conv3 = conv_block(
-            nf + 2 * gc,
-            gc,
-            kernel_size,
-            stride,
-            bias=bias,
-            pad_type=pad_type,
-            norm_type=norm_type,
-            act_type=act_type,
-            mode=mode,
-            c2x2=c2x2,
-        )
-        self.conv4 = conv_block(
-            nf + 3 * gc,
-            gc,
-            kernel_size,
-            stride,
-            bias=bias,
-            pad_type=pad_type,
-            norm_type=norm_type,
-            act_type=act_type,
-            mode=mode,
-            c2x2=c2x2,
-        )
-        if mode == "CNA":
-            last_act = None
-        else:
-            last_act = act_type
-        self.conv5 = conv_block(
-            nf + 4 * gc,
-            nf,
-            3,
-            stride,
-            bias=bias,
-            pad_type=pad_type,
-            norm_type=norm_type,
-            act_type=last_act,
-            mode=mode,
-            c2x2=c2x2,
-        )
-
-    def forward(self, x):
-        x1 = self.conv1(x)
-        x2 = self.conv2(torch.cat((x, x1), 1))
-        if self.conv1x1:
-            # pylint: disable=not-callable
-            x2 = x2 + self.conv1x1(x)  # +
-        x3 = self.conv3(torch.cat((x, x1, x2), 1))
-        x4 = self.conv4(torch.cat((x, x1, x2, x3), 1))
-        if self.conv1x1:
-            x4 = x4 + x2  # +
-        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
-        return x5 * 0.2 + x
-
-
-def conv1x1(in_planes, out_planes, stride=1):
-    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
-
-
-####################
-# Upsampler
-####################
-
-
-def pixelshuffle_block(
-    in_nc: int,
-    out_nc: int,
-    upscale_factor=2,
-    kernel_size=3,
-    stride=1,
-    bias=True,
-    pad_type="zero",
-    norm_type: str | None = None,
-    act_type="relu",
-):
-    """
-    Pixel shuffle layer
-    (Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional
-    Neural Network, CVPR17)
-    """
-    conv = conv_block(
-        in_nc,
-        out_nc * (upscale_factor**2),
-        kernel_size,
-        stride,
-        bias=bias,
-        pad_type=pad_type,
-        norm_type=None,
-        act_type=None,
-    )
-    pixel_shuffle = nn.PixelShuffle(upscale_factor)
-
-    n = norm(norm_type, out_nc) if norm_type else None
-    a = act(act_type) if act_type else None
-    return sequential(conv, pixel_shuffle, n, a)
-
-
-def upconv_block(
-    in_nc: int,
-    out_nc: int,
-    upscale_factor=2,
-    kernel_size=3,
-    stride=1,
-    bias=True,
-    pad_type="zero",
-    norm_type: str | None = None,
-    act_type="relu",
-    mode="nearest",
-    c2x2=False,
-):
-    # Up conv
-    # described in https://distill.pub/2016/deconv-checkerboard/
-    upsample = nn.Upsample(scale_factor=upscale_factor, mode=mode)
-    conv = conv_block(
-        in_nc,
-        out_nc,
-        kernel_size,
-        stride,
-        bias=bias,
-        pad_type=pad_type,
-        norm_type=norm_type,
-        act_type=act_type,
-        c2x2=c2x2,
-    )
-    return sequential(upsample, conv)
diff --git a/comfy_extras/chainner_models/architecture/face/LICENSE-GFPGAN b/comfy_extras/chainner_models/architecture/face/LICENSE-GFPGAN
deleted file mode 100644
index 5ac273fd..00000000
--- a/comfy_extras/chainner_models/architecture/face/LICENSE-GFPGAN
+++ /dev/null
@@ -1,351 +0,0 @@
-Tencent is pleased to support the open source community by making GFPGAN available.
-
-Copyright (C) 2021 THL A29 Limited, a Tencent company.  All rights reserved.
-
-GFPGAN is licensed under the Apache License Version 2.0 except for the third-party components listed below.
-
-
-Terms of the Apache License Version 2.0:
----------------------------------------------
-Apache License
-
-Version 2.0, January 2004
-
-http://www.apache.org/licenses/
-
-TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-1. Definitions.
-
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-
-“Licensor” shall mean the copyright owner or entity authorized by the copyright owner that is granting the License.
-
-“Legal Entity” shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, “control” means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity.
-
-“You” (or “Your”) shall mean an individual or Legal Entity exercising permissions granted by this License.
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-
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-
-“Work” shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below).
-
-“Derivative Works” shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof.
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-Other  dependencies and licenses:
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-Open Source Software licensed under the Apache 2.0 license and Other Licenses of the Third-Party Components therein:
----------------------------------------------
-1. basicsr
-Copyright 2018-2020 BasicSR Authors
-
-
-This BasicSR project is released under the Apache 2.0 license.
-
-A copy of Apache 2.0 is included in this file.
-
-StyleGAN2
-The codes are modified from the repository stylegan2-pytorch. Many thanks to the author - Kim Seonghyeon 😊 for translating from the official TensorFlow codes to PyTorch ones. Here is the license of stylegan2-pytorch.
-The official repository is https://github.com/NVlabs/stylegan2, and here is the NVIDIA license.
-DFDNet
-The codes are largely modified from the repository DFDNet. Their license is Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
-
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-MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR
-NON-INFRINGEMENT. YOU BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER
-THIS LICENSE.
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-5. Limitation of Liability.
-
-EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL
-THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE
-SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT,
-INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF
-OR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK
-(INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION,
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-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
-
-
-
-Open Source Software licensed under the BSD 3-Clause license:
----------------------------------------------
-1. torchvision
-Copyright (c) Soumith Chintala 2016,
-All rights reserved.
-
-2. torch
-Copyright (c) 2016-     Facebook, Inc            (Adam Paszke)
-Copyright (c) 2014-     Facebook, Inc            (Soumith Chintala)
-Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
-Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
-Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
-Copyright (c) 2011-2013 NYU                      (Clement Farabet)
-Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
-Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
-Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
-
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----------------------------------------------
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-Open Source Software licensed under the BSD 3-Clause License and Other Licenses of the Third-Party Components therein:
----------------------------------------------
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-Copyright (c) 2005-2020, NumPy Developers.
-All rights reserved.
-
-A copy of BSD 3-Clause License is included in this file.
-
-The NumPy repository and source distributions bundle several libraries that are
-compatibly licensed.  We list these here.
-
-Name: Numpydoc
-Files: doc/sphinxext/numpydoc/*
-License: BSD-2-Clause
-  For details, see doc/sphinxext/LICENSE.txt
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-Name: scipy-sphinx-theme
-Files: doc/scipy-sphinx-theme/*
-License: BSD-3-Clause AND PSF-2.0 AND Apache-2.0
-  For details, see doc/scipy-sphinx-theme/LICENSE.txt
-
-Name: lapack-lite
-Files: numpy/linalg/lapack_lite/*
-License: BSD-3-Clause
-  For details, see numpy/linalg/lapack_lite/LICENSE.txt
-
-Name: tempita
-Files: tools/npy_tempita/*
-License: MIT
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-
-Name: dragon4
-Files: numpy/core/src/multiarray/dragon4.c
-License: MIT
-  For license text, see numpy/core/src/multiarray/dragon4.c
-
-
-
-Open Source Software licensed under the MIT license:
----------------------------------------------
-1. facexlib
-Copyright (c) 2020 Xintao Wang
-
-2. opencv-python
-Copyright (c) Olli-Pekka Heinisuo
-Please note that only files in cv2 package are used.
-
-
-Terms of the MIT License:
----------------------------------------------
-Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
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-Open Source Software licensed under the MIT license and Other Licenses of the Third-Party Components therein:
----------------------------------------------
-1. tqdm
-Copyright (c) 2013 noamraph
-
-`tqdm` is a product of collaborative work.
-Unless otherwise stated, all authors (see commit logs) retain copyright
-for their respective work, and release the work under the MIT licence
-(text below).
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-Exceptions or notable authors are listed below
-in reverse chronological order:
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-* files: *
-  MPLv2.0 2015-2020 (c) Casper da Costa-Luis
-  [casperdcl](https://github.com/casperdcl).
-* files: tqdm/_tqdm.py
-  MIT 2016 (c) [PR #96] on behalf of Google Inc.
-* files: tqdm/_tqdm.py setup.py README.rst MANIFEST.in .gitignore
-  MIT 2013 (c) Noam Yorav-Raphael, original author.
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-[PR #96]: https://github.com/tqdm/tqdm/pull/96
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-Mozilla Public Licence (MPL) v. 2.0 - Exhibit A
------------------------------------------------
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-This Source Code Form is subject to the terms of the
-Mozilla Public License, v. 2.0.
-If a copy of the MPL was not distributed with this file,
-You can obtain one at https://mozilla.org/MPL/2.0/.
-
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-MIT License (MIT)
------------------
-
-Copyright (c) 2013 noamraph
-
-Permission is hereby granted, free of charge, to any person obtaining a copy of
-this software and associated documentation files (the "Software"), to deal in
-the Software without restriction, including without limitation the rights to
-use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
-the Software, and to permit persons to whom the Software is furnished to do so,
-subject to the following conditions:
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-COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
-IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
-CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
\ No newline at end of file
diff --git a/comfy_extras/chainner_models/architecture/face/LICENSE-RestoreFormer b/comfy_extras/chainner_models/architecture/face/LICENSE-RestoreFormer
deleted file mode 100644
index 5ac273fd..00000000
--- a/comfy_extras/chainner_models/architecture/face/LICENSE-RestoreFormer
+++ /dev/null
@@ -1,351 +0,0 @@
-Tencent is pleased to support the open source community by making GFPGAN available.
-
-Copyright (C) 2021 THL A29 Limited, a Tencent company.  All rights reserved.
-
-GFPGAN is licensed under the Apache License Version 2.0 except for the third-party components listed below.
-
-
-Terms of the Apache License Version 2.0:
----------------------------------------------
-Apache License
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-StyleGAN2
-The codes are modified from the repository stylegan2-pytorch. Many thanks to the author - Kim Seonghyeon 😊 for translating from the official TensorFlow codes to PyTorch ones. Here is the license of stylegan2-pytorch.
-The official repository is https://github.com/NVlabs/stylegan2, and here is the NVIDIA license.
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-COMMERCIAL DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF
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-SOFTWARE.
-
-
-
-Open Source Software licensed under the BSD 3-Clause license:
----------------------------------------------
-1. torchvision
-Copyright (c) Soumith Chintala 2016,
-All rights reserved.
-
-2. torch
-Copyright (c) 2016-     Facebook, Inc            (Adam Paszke)
-Copyright (c) 2014-     Facebook, Inc            (Soumith Chintala)
-Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
-Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
-Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
-Copyright (c) 2011-2013 NYU                      (Clement Farabet)
-Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
-Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
-Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
-
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-Terms of the BSD 3-Clause License:
----------------------------------------------
-Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
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-Files: doc/sphinxext/numpydoc/*
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-License: MIT
-  For license text, see numpy/core/src/multiarray/dragon4.c
-
-
-
-Open Source Software licensed under the MIT license:
----------------------------------------------
-1. facexlib
-Copyright (c) 2020 Xintao Wang
-
-2. opencv-python
-Copyright (c) Olli-Pekka Heinisuo
-Please note that only files in cv2 package are used.
-
-
-Terms of the MIT License:
----------------------------------------------
-Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
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-The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
-
-
-
-Open Source Software licensed under the MIT license and Other Licenses of the Third-Party Components therein:
----------------------------------------------
-1. tqdm
-Copyright (c) 2013 noamraph
-
-`tqdm` is a product of collaborative work.
-Unless otherwise stated, all authors (see commit logs) retain copyright
-for their respective work, and release the work under the MIT licence
-(text below).
-
-Exceptions or notable authors are listed below
-in reverse chronological order:
-
-* files: *
-  MPLv2.0 2015-2020 (c) Casper da Costa-Luis
-  [casperdcl](https://github.com/casperdcl).
-* files: tqdm/_tqdm.py
-  MIT 2016 (c) [PR #96] on behalf of Google Inc.
-* files: tqdm/_tqdm.py setup.py README.rst MANIFEST.in .gitignore
-  MIT 2013 (c) Noam Yorav-Raphael, original author.
-
-[PR #96]: https://github.com/tqdm/tqdm/pull/96
-
-
-Mozilla Public Licence (MPL) v. 2.0 - Exhibit A
------------------------------------------------
-
-This Source Code Form is subject to the terms of the
-Mozilla Public License, v. 2.0.
-If a copy of the MPL was not distributed with this file,
-You can obtain one at https://mozilla.org/MPL/2.0/.
-
-
-MIT License (MIT)
------------------
-
-Copyright (c) 2013 noamraph
-
-Permission is hereby granted, free of charge, to any person obtaining a copy of
-this software and associated documentation files (the "Software"), to deal in
-the Software without restriction, including without limitation the rights to
-use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
-the Software, and to permit persons to whom the Software is furnished to do so,
-subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
-FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
-COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
-IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
-CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
\ No newline at end of file
diff --git a/comfy_extras/chainner_models/architecture/face/LICENSE-codeformer b/comfy_extras/chainner_models/architecture/face/LICENSE-codeformer
deleted file mode 100644
index be6c4ed8..00000000
--- a/comfy_extras/chainner_models/architecture/face/LICENSE-codeformer
+++ /dev/null
@@ -1,35 +0,0 @@
-S-Lab License 1.0
-
-Copyright 2022 S-Lab
-
-Redistribution and use for non-commercial purpose in source and
-binary forms, with or without modification, are permitted provided
-that the following conditions are met:
-
-1. Redistributions of source code must retain the above copyright
-   notice, this list of conditions and the following disclaimer.
-
-2. Redistributions in binary form must reproduce the above copyright
-   notice, this list of conditions and the following disclaimer in
-   the documentation and/or other materials provided with the
-   distribution.
-
-3. Neither the name of the copyright holder nor the names of its
-   contributors may be used to endorse or promote products derived
-   from this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
-"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
-LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
-A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
-HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
-SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
-LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
-DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
-THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
-
-In the event that redistribution and/or use for commercial purpose in
-source or binary forms, with or without modification is required,
-please contact the contributor(s) of the work.
diff --git a/comfy_extras/chainner_models/architecture/face/arcface_arch.py b/comfy_extras/chainner_models/architecture/face/arcface_arch.py
deleted file mode 100644
index b548af05..00000000
--- a/comfy_extras/chainner_models/architecture/face/arcface_arch.py
+++ /dev/null
@@ -1,265 +0,0 @@
-import torch.nn as nn
-
-
-def conv3x3(inplanes, outplanes, stride=1):
-    """A simple wrapper for 3x3 convolution with padding.
-
-    Args:
-        inplanes (int): Channel number of inputs.
-        outplanes (int): Channel number of outputs.
-        stride (int): Stride in convolution. Default: 1.
-    """
-    return nn.Conv2d(
-        inplanes, outplanes, kernel_size=3, stride=stride, padding=1, bias=False
-    )
-
-
-class BasicBlock(nn.Module):
-    """Basic residual block used in the ResNetArcFace architecture.
-
-    Args:
-        inplanes (int): Channel number of inputs.
-        planes (int): Channel number of outputs.
-        stride (int): Stride in convolution. Default: 1.
-        downsample (nn.Module): The downsample module. Default: None.
-    """
-
-    expansion = 1  # output channel expansion ratio
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None):
-        super(BasicBlock, self).__init__()
-        self.conv1 = conv3x3(inplanes, planes, stride)
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.relu = nn.ReLU(inplace=True)
-        self.conv2 = conv3x3(planes, planes)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class IRBlock(nn.Module):
-    """Improved residual block (IR Block) used in the ResNetArcFace architecture.
-
-    Args:
-        inplanes (int): Channel number of inputs.
-        planes (int): Channel number of outputs.
-        stride (int): Stride in convolution. Default: 1.
-        downsample (nn.Module): The downsample module. Default: None.
-        use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True.
-    """
-
-    expansion = 1  # output channel expansion ratio
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None, use_se=True):
-        super(IRBlock, self).__init__()
-        self.bn0 = nn.BatchNorm2d(inplanes)
-        self.conv1 = conv3x3(inplanes, inplanes)
-        self.bn1 = nn.BatchNorm2d(inplanes)
-        self.prelu = nn.PReLU()
-        self.conv2 = conv3x3(inplanes, planes, stride)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.downsample = downsample
-        self.stride = stride
-        self.use_se = use_se
-        if self.use_se:
-            self.se = SEBlock(planes)
-
-    def forward(self, x):
-        residual = x
-        out = self.bn0(x)
-        out = self.conv1(out)
-        out = self.bn1(out)
-        out = self.prelu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        if self.use_se:
-            out = self.se(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.prelu(out)
-
-        return out
-
-
-class Bottleneck(nn.Module):
-    """Bottleneck block used in the ResNetArcFace architecture.
-
-    Args:
-        inplanes (int): Channel number of inputs.
-        planes (int): Channel number of outputs.
-        stride (int): Stride in convolution. Default: 1.
-        downsample (nn.Module): The downsample module. Default: None.
-    """
-
-    expansion = 4  # output channel expansion ratio
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None):
-        super(Bottleneck, self).__init__()
-        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.conv2 = nn.Conv2d(
-            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False
-        )
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.conv3 = nn.Conv2d(
-            planes, planes * self.expansion, kernel_size=1, bias=False
-        )
-        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-
-        return out
-
-
-class SEBlock(nn.Module):
-    """The squeeze-and-excitation block (SEBlock) used in the IRBlock.
-
-    Args:
-        channel (int): Channel number of inputs.
-        reduction (int): Channel reduction ration. Default: 16.
-    """
-
-    def __init__(self, channel, reduction=16):
-        super(SEBlock, self).__init__()
-        self.avg_pool = nn.AdaptiveAvgPool2d(
-            1
-        )  # pool to 1x1 without spatial information
-        self.fc = nn.Sequential(
-            nn.Linear(channel, channel // reduction),
-            nn.PReLU(),
-            nn.Linear(channel // reduction, channel),
-            nn.Sigmoid(),
-        )
-
-    def forward(self, x):
-        b, c, _, _ = x.size()
-        y = self.avg_pool(x).view(b, c)
-        y = self.fc(y).view(b, c, 1, 1)
-        return x * y
-
-
-class ResNetArcFace(nn.Module):
-    """ArcFace with ResNet architectures.
-
-    Ref: ArcFace: Additive Angular Margin Loss for Deep Face Recognition.
-
-    Args:
-        block (str): Block used in the ArcFace architecture.
-        layers (tuple(int)): Block numbers in each layer.
-        use_se (bool): Whether use the SEBlock (squeeze and excitation block). Default: True.
-    """
-
-    def __init__(self, block, layers, use_se=True):
-        if block == "IRBlock":
-            block = IRBlock
-        self.inplanes = 64
-        self.use_se = use_se
-        super(ResNetArcFace, self).__init__()
-
-        self.conv1 = nn.Conv2d(1, 64, kernel_size=3, padding=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(64)
-        self.prelu = nn.PReLU()
-        self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2)
-        self.layer1 = self._make_layer(block, 64, layers[0])
-        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
-        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
-        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
-        self.bn4 = nn.BatchNorm2d(512)
-        self.dropout = nn.Dropout()
-        self.fc5 = nn.Linear(512 * 8 * 8, 512)
-        self.bn5 = nn.BatchNorm1d(512)
-
-        # initialization
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.xavier_normal_(m.weight)
-            elif isinstance(m, nn.BatchNorm2d) or isinstance(m, nn.BatchNorm1d):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-            elif isinstance(m, nn.Linear):
-                nn.init.xavier_normal_(m.weight)
-                nn.init.constant_(m.bias, 0)
-
-    def _make_layer(self, block, planes, num_blocks, stride=1):
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(
-                    self.inplanes,
-                    planes * block.expansion,
-                    kernel_size=1,
-                    stride=stride,
-                    bias=False,
-                ),
-                nn.BatchNorm2d(planes * block.expansion),
-            )
-        layers = []
-        layers.append(
-            block(self.inplanes, planes, stride, downsample, use_se=self.use_se)
-        )
-        self.inplanes = planes
-        for _ in range(1, num_blocks):
-            layers.append(block(self.inplanes, planes, use_se=self.use_se))
-
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.bn1(x)
-        x = self.prelu(x)
-        x = self.maxpool(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-        x = self.bn4(x)
-        x = self.dropout(x)
-        x = x.view(x.size(0), -1)
-        x = self.fc5(x)
-        x = self.bn5(x)
-
-        return x
diff --git a/comfy_extras/chainner_models/architecture/face/codeformer.py b/comfy_extras/chainner_models/architecture/face/codeformer.py
deleted file mode 100644
index 06614007..00000000
--- a/comfy_extras/chainner_models/architecture/face/codeformer.py
+++ /dev/null
@@ -1,790 +0,0 @@
-"""
-Modified from https://github.com/sczhou/CodeFormer
-VQGAN code, adapted from the original created by the Unleashing Transformers authors:
-https://github.com/samb-t/unleashing-transformers/blob/master/models/vqgan.py
-This verison of the arch specifically was gathered from an old version of GFPGAN. If this is a problem, please contact me.
-"""
-import math
-from typing import Optional
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import logging as logger
-from torch import Tensor
-
-
-class VectorQuantizer(nn.Module):
-    def __init__(self, codebook_size, emb_dim, beta):
-        super(VectorQuantizer, self).__init__()
-        self.codebook_size = codebook_size  # number of embeddings
-        self.emb_dim = emb_dim  # dimension of embedding
-        self.beta = beta  # commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
-        self.embedding = nn.Embedding(self.codebook_size, self.emb_dim)
-        self.embedding.weight.data.uniform_(
-            -1.0 / self.codebook_size, 1.0 / self.codebook_size
-        )
-
-    def forward(self, z):
-        # reshape z -> (batch, height, width, channel) and flatten
-        z = z.permute(0, 2, 3, 1).contiguous()
-        z_flattened = z.view(-1, self.emb_dim)
-
-        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
-        d = (
-            (z_flattened**2).sum(dim=1, keepdim=True)
-            + (self.embedding.weight**2).sum(1)
-            - 2 * torch.matmul(z_flattened, self.embedding.weight.t())
-        )
-
-        mean_distance = torch.mean(d)
-        # find closest encodings
-        # min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)
-        min_encoding_scores, min_encoding_indices = torch.topk(
-            d, 1, dim=1, largest=False
-        )
-        # [0-1], higher score, higher confidence
-        min_encoding_scores = torch.exp(-min_encoding_scores / 10)
-
-        min_encodings = torch.zeros(
-            min_encoding_indices.shape[0], self.codebook_size
-        ).to(z)
-        min_encodings.scatter_(1, min_encoding_indices, 1)
-
-        # get quantized latent vectors
-        z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
-        # compute loss for embedding
-        loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean(
-            (z_q - z.detach()) ** 2
-        )
-        # preserve gradients
-        z_q = z + (z_q - z).detach()
-
-        # perplexity
-        e_mean = torch.mean(min_encodings, dim=0)
-        perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
-        # reshape back to match original input shape
-        z_q = z_q.permute(0, 3, 1, 2).contiguous()
-
-        return (
-            z_q,
-            loss,
-            {
-                "perplexity": perplexity,
-                "min_encodings": min_encodings,
-                "min_encoding_indices": min_encoding_indices,
-                "min_encoding_scores": min_encoding_scores,
-                "mean_distance": mean_distance,
-            },
-        )
-
-    def get_codebook_feat(self, indices, shape):
-        # input indices: batch*token_num -> (batch*token_num)*1
-        # shape: batch, height, width, channel
-        indices = indices.view(-1, 1)
-        min_encodings = torch.zeros(indices.shape[0], self.codebook_size).to(indices)
-        min_encodings.scatter_(1, indices, 1)
-        # get quantized latent vectors
-        z_q = torch.matmul(min_encodings.float(), self.embedding.weight)
-
-        if shape is not None:  # reshape back to match original input shape
-            z_q = z_q.view(shape).permute(0, 3, 1, 2).contiguous()
-
-        return z_q
-
-
-class GumbelQuantizer(nn.Module):
-    def __init__(
-        self,
-        codebook_size,
-        emb_dim,
-        num_hiddens,
-        straight_through=False,
-        kl_weight=5e-4,
-        temp_init=1.0,
-    ):
-        super().__init__()
-        self.codebook_size = codebook_size  # number of embeddings
-        self.emb_dim = emb_dim  # dimension of embedding
-        self.straight_through = straight_through
-        self.temperature = temp_init
-        self.kl_weight = kl_weight
-        self.proj = nn.Conv2d(
-            num_hiddens, codebook_size, 1
-        )  # projects last encoder layer to quantized logits
-        self.embed = nn.Embedding(codebook_size, emb_dim)
-
-    def forward(self, z):
-        hard = self.straight_through if self.training else True
-
-        logits = self.proj(z)
-
-        soft_one_hot = F.gumbel_softmax(logits, tau=self.temperature, dim=1, hard=hard)
-
-        z_q = torch.einsum("b n h w, n d -> b d h w", soft_one_hot, self.embed.weight)
-
-        # + kl divergence to the prior loss
-        qy = F.softmax(logits, dim=1)
-        diff = (
-            self.kl_weight
-            * torch.sum(qy * torch.log(qy * self.codebook_size + 1e-10), dim=1).mean()
-        )
-        min_encoding_indices = soft_one_hot.argmax(dim=1)
-
-        return z_q, diff, {"min_encoding_indices": min_encoding_indices}
-
-
-class Downsample(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.conv = torch.nn.Conv2d(
-            in_channels, in_channels, kernel_size=3, stride=2, padding=0
-        )
-
-    def forward(self, x):
-        pad = (0, 1, 0, 1)
-        x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
-        x = self.conv(x)
-        return x
-
-
-class Upsample(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.conv = nn.Conv2d(
-            in_channels, in_channels, kernel_size=3, stride=1, padding=1
-        )
-
-    def forward(self, x):
-        x = F.interpolate(x, scale_factor=2.0, mode="nearest")
-        x = self.conv(x)
-
-        return x
-
-
-class AttnBlock(nn.Module):
-    def __init__(self, in_channels):
-        super().__init__()
-        self.in_channels = in_channels
-
-        self.norm = normalize(in_channels)
-        self.q = torch.nn.Conv2d(
-            in_channels, in_channels, kernel_size=1, stride=1, padding=0
-        )
-        self.k = torch.nn.Conv2d(
-            in_channels, in_channels, kernel_size=1, stride=1, padding=0
-        )
-        self.v = torch.nn.Conv2d(
-            in_channels, in_channels, kernel_size=1, stride=1, padding=0
-        )
-        self.proj_out = torch.nn.Conv2d(
-            in_channels, in_channels, kernel_size=1, stride=1, padding=0
-        )
-
-    def forward(self, x):
-        h_ = x
-        h_ = self.norm(h_)
-        q = self.q(h_)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b, c, h, w = q.shape
-        q = q.reshape(b, c, h * w)
-        q = q.permute(0, 2, 1)
-        k = k.reshape(b, c, h * w)
-        w_ = torch.bmm(q, k)
-        w_ = w_ * (int(c) ** (-0.5))
-        w_ = F.softmax(w_, dim=2)
-
-        # attend to values
-        v = v.reshape(b, c, h * w)
-        w_ = w_.permute(0, 2, 1)
-        h_ = torch.bmm(v, w_)
-        h_ = h_.reshape(b, c, h, w)
-
-        h_ = self.proj_out(h_)
-
-        return x + h_
-
-
-class Encoder(nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        nf,
-        out_channels,
-        ch_mult,
-        num_res_blocks,
-        resolution,
-        attn_resolutions,
-    ):
-        super().__init__()
-        self.nf = nf
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.attn_resolutions = attn_resolutions
-
-        curr_res = self.resolution
-        in_ch_mult = (1,) + tuple(ch_mult)
-
-        blocks = []
-        # initial convultion
-        blocks.append(nn.Conv2d(in_channels, nf, kernel_size=3, stride=1, padding=1))
-
-        # residual and downsampling blocks, with attention on smaller res (16x16)
-        for i in range(self.num_resolutions):
-            block_in_ch = nf * in_ch_mult[i]
-            block_out_ch = nf * ch_mult[i]
-            for _ in range(self.num_res_blocks):
-                blocks.append(ResBlock(block_in_ch, block_out_ch))
-                block_in_ch = block_out_ch
-                if curr_res in attn_resolutions:
-                    blocks.append(AttnBlock(block_in_ch))
-
-            if i != self.num_resolutions - 1:
-                blocks.append(Downsample(block_in_ch))
-                curr_res = curr_res // 2
-
-        # non-local attention block
-        blocks.append(ResBlock(block_in_ch, block_in_ch))  # type: ignore
-        blocks.append(AttnBlock(block_in_ch))  # type: ignore
-        blocks.append(ResBlock(block_in_ch, block_in_ch))  # type: ignore
-
-        # normalise and convert to latent size
-        blocks.append(normalize(block_in_ch))  # type: ignore
-        blocks.append(
-            nn.Conv2d(block_in_ch, out_channels, kernel_size=3, stride=1, padding=1)  # type: ignore
-        )
-        self.blocks = nn.ModuleList(blocks)
-
-    def forward(self, x):
-        for block in self.blocks:
-            x = block(x)
-
-        return x
-
-
-class Generator(nn.Module):
-    def __init__(self, nf, ch_mult, res_blocks, img_size, attn_resolutions, emb_dim):
-        super().__init__()
-        self.nf = nf
-        self.ch_mult = ch_mult
-        self.num_resolutions = len(self.ch_mult)
-        self.num_res_blocks = res_blocks
-        self.resolution = img_size
-        self.attn_resolutions = attn_resolutions
-        self.in_channels = emb_dim
-        self.out_channels = 3
-        block_in_ch = self.nf * self.ch_mult[-1]
-        curr_res = self.resolution // 2 ** (self.num_resolutions - 1)
-
-        blocks = []
-        # initial conv
-        blocks.append(
-            nn.Conv2d(self.in_channels, block_in_ch, kernel_size=3, stride=1, padding=1)
-        )
-
-        # non-local attention block
-        blocks.append(ResBlock(block_in_ch, block_in_ch))
-        blocks.append(AttnBlock(block_in_ch))
-        blocks.append(ResBlock(block_in_ch, block_in_ch))
-
-        for i in reversed(range(self.num_resolutions)):
-            block_out_ch = self.nf * self.ch_mult[i]
-
-            for _ in range(self.num_res_blocks):
-                blocks.append(ResBlock(block_in_ch, block_out_ch))
-                block_in_ch = block_out_ch
-
-                if curr_res in self.attn_resolutions:
-                    blocks.append(AttnBlock(block_in_ch))
-
-            if i != 0:
-                blocks.append(Upsample(block_in_ch))
-                curr_res = curr_res * 2
-
-        blocks.append(normalize(block_in_ch))
-        blocks.append(
-            nn.Conv2d(
-                block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1
-            )
-        )
-
-        self.blocks = nn.ModuleList(blocks)
-
-    def forward(self, x):
-        for block in self.blocks:
-            x = block(x)
-
-        return x
-
-
-class VQAutoEncoder(nn.Module):
-    def __init__(
-        self,
-        img_size,
-        nf,
-        ch_mult,
-        quantizer="nearest",
-        res_blocks=2,
-        attn_resolutions=[16],
-        codebook_size=1024,
-        emb_dim=256,
-        beta=0.25,
-        gumbel_straight_through=False,
-        gumbel_kl_weight=1e-8,
-        model_path=None,
-    ):
-        super().__init__()
-        self.in_channels = 3
-        self.nf = nf
-        self.n_blocks = res_blocks
-        self.codebook_size = codebook_size
-        self.embed_dim = emb_dim
-        self.ch_mult = ch_mult
-        self.resolution = img_size
-        self.attn_resolutions = attn_resolutions
-        self.quantizer_type = quantizer
-        self.encoder = Encoder(
-            self.in_channels,
-            self.nf,
-            self.embed_dim,
-            self.ch_mult,
-            self.n_blocks,
-            self.resolution,
-            self.attn_resolutions,
-        )
-        if self.quantizer_type == "nearest":
-            self.beta = beta  # 0.25
-            self.quantize = VectorQuantizer(
-                self.codebook_size, self.embed_dim, self.beta
-            )
-        elif self.quantizer_type == "gumbel":
-            self.gumbel_num_hiddens = emb_dim
-            self.straight_through = gumbel_straight_through
-            self.kl_weight = gumbel_kl_weight
-            self.quantize = GumbelQuantizer(
-                self.codebook_size,
-                self.embed_dim,
-                self.gumbel_num_hiddens,
-                self.straight_through,
-                self.kl_weight,
-            )
-        self.generator = Generator(
-            nf, ch_mult, res_blocks, img_size, attn_resolutions, emb_dim
-        )
-
-        if model_path is not None:
-            chkpt = torch.load(model_path, map_location="cpu")
-            if "params_ema" in chkpt:
-                self.load_state_dict(
-                    torch.load(model_path, map_location="cpu")["params_ema"]
-                )
-                logger.info(f"vqgan is loaded from: {model_path} [params_ema]")
-            elif "params" in chkpt:
-                self.load_state_dict(
-                    torch.load(model_path, map_location="cpu")["params"]
-                )
-                logger.info(f"vqgan is loaded from: {model_path} [params]")
-            else:
-                raise ValueError("Wrong params!")
-
-    def forward(self, x):
-        x = self.encoder(x)
-        quant, codebook_loss, quant_stats = self.quantize(x)
-        x = self.generator(quant)
-        return x, codebook_loss, quant_stats
-
-
-def calc_mean_std(feat, eps=1e-5):
-    """Calculate mean and std for adaptive_instance_normalization.
-    Args:
-        feat (Tensor): 4D tensor.
-        eps (float): A small value added to the variance to avoid
-            divide-by-zero. Default: 1e-5.
-    """
-    size = feat.size()
-    assert len(size) == 4, "The input feature should be 4D tensor."
-    b, c = size[:2]
-    feat_var = feat.view(b, c, -1).var(dim=2) + eps
-    feat_std = feat_var.sqrt().view(b, c, 1, 1)
-    feat_mean = feat.view(b, c, -1).mean(dim=2).view(b, c, 1, 1)
-    return feat_mean, feat_std
-
-
-def adaptive_instance_normalization(content_feat, style_feat):
-    """Adaptive instance normalization.
-    Adjust the reference features to have the similar color and illuminations
-    as those in the degradate features.
-    Args:
-        content_feat (Tensor): The reference feature.
-        style_feat (Tensor): The degradate features.
-    """
-    size = content_feat.size()
-    style_mean, style_std = calc_mean_std(style_feat)
-    content_mean, content_std = calc_mean_std(content_feat)
-    normalized_feat = (content_feat - content_mean.expand(size)) / content_std.expand(
-        size
-    )
-    return normalized_feat * style_std.expand(size) + style_mean.expand(size)
-
-
-class PositionEmbeddingSine(nn.Module):
-    """
-    This is a more standard version of the position embedding, very similar to the one
-    used by the Attention is all you need paper, generalized to work on images.
-    """
-
-    def __init__(
-        self, num_pos_feats=64, temperature=10000, normalize=False, scale=None
-    ):
-        super().__init__()
-        self.num_pos_feats = num_pos_feats
-        self.temperature = temperature
-        self.normalize = normalize
-        if scale is not None and normalize is False:
-            raise ValueError("normalize should be True if scale is passed")
-        if scale is None:
-            scale = 2 * math.pi
-        self.scale = scale
-
-    def forward(self, x, mask=None):
-        if mask is None:
-            mask = torch.zeros(
-                (x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool
-            )
-        not_mask = ~mask  # pylint: disable=invalid-unary-operand-type
-        y_embed = not_mask.cumsum(1, dtype=torch.float32)
-        x_embed = not_mask.cumsum(2, dtype=torch.float32)
-        if self.normalize:
-            eps = 1e-6
-            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
-            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
-
-        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
-        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
-
-        pos_x = x_embed[:, :, :, None] / dim_t
-        pos_y = y_embed[:, :, :, None] / dim_t
-        pos_x = torch.stack(
-            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos_y = torch.stack(
-            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
-        ).flatten(3)
-        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
-        return pos
-
-
-def _get_activation_fn(activation):
-    """Return an activation function given a string"""
-    if activation == "relu":
-        return F.relu
-    if activation == "gelu":
-        return F.gelu
-    if activation == "glu":
-        return F.glu
-    raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
-
-
-class TransformerSALayer(nn.Module):
-    def __init__(
-        self, embed_dim, nhead=8, dim_mlp=2048, dropout=0.0, activation="gelu"
-    ):
-        super().__init__()
-        self.self_attn = nn.MultiheadAttention(embed_dim, nhead, dropout=dropout)
-        # Implementation of Feedforward model - MLP
-        self.linear1 = nn.Linear(embed_dim, dim_mlp)
-        self.dropout = nn.Dropout(dropout)
-        self.linear2 = nn.Linear(dim_mlp, embed_dim)
-
-        self.norm1 = nn.LayerNorm(embed_dim)
-        self.norm2 = nn.LayerNorm(embed_dim)
-        self.dropout1 = nn.Dropout(dropout)
-        self.dropout2 = nn.Dropout(dropout)
-
-        self.activation = _get_activation_fn(activation)
-
-    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
-        return tensor if pos is None else tensor + pos
-
-    def forward(
-        self,
-        tgt,
-        tgt_mask: Optional[Tensor] = None,
-        tgt_key_padding_mask: Optional[Tensor] = None,
-        query_pos: Optional[Tensor] = None,
-    ):
-        # self attention
-        tgt2 = self.norm1(tgt)
-        q = k = self.with_pos_embed(tgt2, query_pos)
-        tgt2 = self.self_attn(
-            q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask
-        )[0]
-        tgt = tgt + self.dropout1(tgt2)
-
-        # ffn
-        tgt2 = self.norm2(tgt)
-        tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
-        tgt = tgt + self.dropout2(tgt2)
-        return tgt
-
-
-def normalize(in_channels):
-    return torch.nn.GroupNorm(
-        num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
-    )
-
-
-@torch.jit.script  # type: ignore
-def swish(x):
-    return x * torch.sigmoid(x)
-
-
-class ResBlock(nn.Module):
-    def __init__(self, in_channels, out_channels=None):
-        super(ResBlock, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = in_channels if out_channels is None else out_channels
-        self.norm1 = normalize(in_channels)
-        self.conv1 = nn.Conv2d(
-            in_channels, out_channels, kernel_size=3, stride=1, padding=1  # type: ignore
-        )
-        self.norm2 = normalize(out_channels)
-        self.conv2 = nn.Conv2d(
-            out_channels, out_channels, kernel_size=3, stride=1, padding=1  # type: ignore
-        )
-        if self.in_channels != self.out_channels:
-            self.conv_out = nn.Conv2d(
-                in_channels, out_channels, kernel_size=1, stride=1, padding=0  # type: ignore
-            )
-
-    def forward(self, x_in):
-        x = x_in
-        x = self.norm1(x)
-        x = swish(x)
-        x = self.conv1(x)
-        x = self.norm2(x)
-        x = swish(x)
-        x = self.conv2(x)
-        if self.in_channels != self.out_channels:
-            x_in = self.conv_out(x_in)
-
-        return x + x_in
-
-
-class Fuse_sft_block(nn.Module):
-    def __init__(self, in_ch, out_ch):
-        super().__init__()
-        self.encode_enc = ResBlock(2 * in_ch, out_ch)
-
-        self.scale = nn.Sequential(
-            nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
-            nn.LeakyReLU(0.2, True),
-            nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1),
-        )
-
-        self.shift = nn.Sequential(
-            nn.Conv2d(in_ch, out_ch, kernel_size=3, padding=1),
-            nn.LeakyReLU(0.2, True),
-            nn.Conv2d(out_ch, out_ch, kernel_size=3, padding=1),
-        )
-
-    def forward(self, enc_feat, dec_feat, w=1):
-        enc_feat = self.encode_enc(torch.cat([enc_feat, dec_feat], dim=1))
-        scale = self.scale(enc_feat)
-        shift = self.shift(enc_feat)
-        residual = w * (dec_feat * scale + shift)
-        out = dec_feat + residual
-        return out
-
-
-class CodeFormer(VQAutoEncoder):
-    def __init__(self, state_dict):
-        dim_embd = 512
-        n_head = 8
-        n_layers = 9
-        codebook_size = 1024
-        latent_size = 256
-        connect_list = ["32", "64", "128", "256"]
-        fix_modules = ["quantize", "generator"]
-
-        # This is just a guess as I only have one model to look at
-        position_emb = state_dict["position_emb"]
-        dim_embd = position_emb.shape[1]
-        latent_size = position_emb.shape[0]
-
-        try:
-            n_layers = len(
-                set([x.split(".")[1] for x in state_dict.keys() if "ft_layers" in x])
-            )
-        except:
-            pass
-
-        codebook_size = state_dict["quantize.embedding.weight"].shape[0]
-
-        # This is also just another guess
-        n_head_exp = (
-            state_dict["ft_layers.0.self_attn.in_proj_weight"].shape[0] // dim_embd
-        )
-        n_head = 2**n_head_exp
-
-        in_nc = state_dict["encoder.blocks.0.weight"].shape[1]
-
-        self.model_arch = "CodeFormer"
-        self.sub_type = "Face SR"
-        self.scale = 8
-        self.in_nc = in_nc
-        self.out_nc = in_nc
-
-        self.state = state_dict
-
-        self.supports_fp16 = False
-        self.supports_bf16 = True
-        self.min_size_restriction = 16
-
-        super(CodeFormer, self).__init__(
-            512, 64, [1, 2, 2, 4, 4, 8], "nearest", 2, [16], codebook_size
-        )
-
-        if fix_modules is not None:
-            for module in fix_modules:
-                for param in getattr(self, module).parameters():
-                    param.requires_grad = False
-
-        self.connect_list = connect_list
-        self.n_layers = n_layers
-        self.dim_embd = dim_embd
-        self.dim_mlp = dim_embd * 2
-
-        self.position_emb = nn.Parameter(torch.zeros(latent_size, self.dim_embd))  # type: ignore
-        self.feat_emb = nn.Linear(256, self.dim_embd)
-
-        # transformer
-        self.ft_layers = nn.Sequential(
-            *[
-                TransformerSALayer(
-                    embed_dim=dim_embd, nhead=n_head, dim_mlp=self.dim_mlp, dropout=0.0
-                )
-                for _ in range(self.n_layers)
-            ]
-        )
-
-        # logits_predict head
-        self.idx_pred_layer = nn.Sequential(
-            nn.LayerNorm(dim_embd), nn.Linear(dim_embd, codebook_size, bias=False)
-        )
-
-        self.channels = {
-            "16": 512,
-            "32": 256,
-            "64": 256,
-            "128": 128,
-            "256": 128,
-            "512": 64,
-        }
-
-        # after second residual block for > 16, before attn layer for ==16
-        self.fuse_encoder_block = {
-            "512": 2,
-            "256": 5,
-            "128": 8,
-            "64": 11,
-            "32": 14,
-            "16": 18,
-        }
-        # after first residual block for > 16, before attn layer for ==16
-        self.fuse_generator_block = {
-            "16": 6,
-            "32": 9,
-            "64": 12,
-            "128": 15,
-            "256": 18,
-            "512": 21,
-        }
-
-        # fuse_convs_dict
-        self.fuse_convs_dict = nn.ModuleDict()
-        for f_size in self.connect_list:
-            in_ch = self.channels[f_size]
-            self.fuse_convs_dict[f_size] = Fuse_sft_block(in_ch, in_ch)
-
-        self.load_state_dict(state_dict)
-
-    def _init_weights(self, module):
-        if isinstance(module, (nn.Linear, nn.Embedding)):
-            module.weight.data.normal_(mean=0.0, std=0.02)
-            if isinstance(module, nn.Linear) and module.bias is not None:
-                module.bias.data.zero_()
-        elif isinstance(module, nn.LayerNorm):
-            module.bias.data.zero_()
-            module.weight.data.fill_(1.0)
-
-    def forward(self, x, weight=0.5, **kwargs):
-        detach_16 = True
-        code_only = False
-        adain = True
-        # ################### Encoder #####################
-        enc_feat_dict = {}
-        out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list]
-        for i, block in enumerate(self.encoder.blocks):
-            x = block(x)
-            if i in out_list:
-                enc_feat_dict[str(x.shape[-1])] = x.clone()
-
-        lq_feat = x
-        # ################# Transformer ###################
-        # quant_feat, codebook_loss, quant_stats = self.quantize(lq_feat)
-        pos_emb = self.position_emb.unsqueeze(1).repeat(1, x.shape[0], 1)
-        # BCHW -> BC(HW) -> (HW)BC
-        feat_emb = self.feat_emb(lq_feat.flatten(2).permute(2, 0, 1))
-        query_emb = feat_emb
-        # Transformer encoder
-        for layer in self.ft_layers:
-            query_emb = layer(query_emb, query_pos=pos_emb)
-
-        # output logits
-        logits = self.idx_pred_layer(query_emb)  # (hw)bn
-        logits = logits.permute(1, 0, 2)  # (hw)bn -> b(hw)n
-
-        if code_only:  # for training stage II
-            # logits doesn't need softmax before cross_entropy loss
-            return logits, lq_feat
-
-        # ################# Quantization ###################
-        # if self.training:
-        #     quant_feat = torch.einsum('btn,nc->btc', [soft_one_hot, self.quantize.embedding.weight])
-        #     # b(hw)c -> bc(hw) -> bchw
-        #     quant_feat = quant_feat.permute(0,2,1).view(lq_feat.shape)
-        # ------------
-        soft_one_hot = F.softmax(logits, dim=2)
-        _, top_idx = torch.topk(soft_one_hot, 1, dim=2)
-        quant_feat = self.quantize.get_codebook_feat(
-            top_idx, shape=[x.shape[0], 16, 16, 256]  # type: ignore
-        )
-        # preserve gradients
-        # quant_feat = lq_feat + (quant_feat - lq_feat).detach()
-
-        if detach_16:
-            quant_feat = quant_feat.detach()  # for training stage III
-        if adain:
-            quant_feat = adaptive_instance_normalization(quant_feat, lq_feat)
-
-        # ################## Generator ####################
-        x = quant_feat
-        fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list]
-
-        for i, block in enumerate(self.generator.blocks):
-            x = block(x)
-            if i in fuse_list:  # fuse after i-th block
-                f_size = str(x.shape[-1])
-                if weight > 0:
-                    x = self.fuse_convs_dict[f_size](
-                        enc_feat_dict[f_size].detach(), x, weight
-                    )
-        out = x
-        # logits doesn't need softmax before cross_entropy loss
-        # return out, logits, lq_feat
-        return out, logits
diff --git a/comfy_extras/chainner_models/architecture/face/fused_act.py b/comfy_extras/chainner_models/architecture/face/fused_act.py
deleted file mode 100644
index 7ed52654..00000000
--- a/comfy_extras/chainner_models/architecture/face/fused_act.py
+++ /dev/null
@@ -1,81 +0,0 @@
-# pylint: skip-file
-# type: ignore
-# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/fused_act.py # noqa:E501
-
-import torch
-from torch import nn
-from torch.autograd import Function
-
-fused_act_ext = None
-
-
-class FusedLeakyReLUFunctionBackward(Function):
-    @staticmethod
-    def forward(ctx, grad_output, out, negative_slope, scale):
-        ctx.save_for_backward(out)
-        ctx.negative_slope = negative_slope
-        ctx.scale = scale
-
-        empty = grad_output.new_empty(0)
-
-        grad_input = fused_act_ext.fused_bias_act(
-            grad_output, empty, out, 3, 1, negative_slope, scale
-        )
-
-        dim = [0]
-
-        if grad_input.ndim > 2:
-            dim += list(range(2, grad_input.ndim))
-
-        grad_bias = grad_input.sum(dim).detach()
-
-        return grad_input, grad_bias
-
-    @staticmethod
-    def backward(ctx, gradgrad_input, gradgrad_bias):
-        (out,) = ctx.saved_tensors
-        gradgrad_out = fused_act_ext.fused_bias_act(
-            gradgrad_input, gradgrad_bias, out, 3, 1, ctx.negative_slope, ctx.scale
-        )
-
-        return gradgrad_out, None, None, None
-
-
-class FusedLeakyReLUFunction(Function):
-    @staticmethod
-    def forward(ctx, input, bias, negative_slope, scale):
-        empty = input.new_empty(0)
-        out = fused_act_ext.fused_bias_act(
-            input, bias, empty, 3, 0, negative_slope, scale
-        )
-        ctx.save_for_backward(out)
-        ctx.negative_slope = negative_slope
-        ctx.scale = scale
-
-        return out
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        (out,) = ctx.saved_tensors
-
-        grad_input, grad_bias = FusedLeakyReLUFunctionBackward.apply(
-            grad_output, out, ctx.negative_slope, ctx.scale
-        )
-
-        return grad_input, grad_bias, None, None
-
-
-class FusedLeakyReLU(nn.Module):
-    def __init__(self, channel, negative_slope=0.2, scale=2**0.5):
-        super().__init__()
-
-        self.bias = nn.Parameter(torch.zeros(channel))
-        self.negative_slope = negative_slope
-        self.scale = scale
-
-    def forward(self, input):
-        return fused_leaky_relu(input, self.bias, self.negative_slope, self.scale)
-
-
-def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2**0.5):
-    return FusedLeakyReLUFunction.apply(input, bias, negative_slope, scale)
diff --git a/comfy_extras/chainner_models/architecture/face/gfpgan_bilinear_arch.py b/comfy_extras/chainner_models/architecture/face/gfpgan_bilinear_arch.py
deleted file mode 100644
index b6e820e0..00000000
--- a/comfy_extras/chainner_models/architecture/face/gfpgan_bilinear_arch.py
+++ /dev/null
@@ -1,389 +0,0 @@
-# pylint: skip-file
-# type: ignore
-import math
-import random
-
-import torch
-from torch import nn
-
-from .gfpganv1_arch import ResUpBlock
-from .stylegan2_bilinear_arch import (
-    ConvLayer,
-    EqualConv2d,
-    EqualLinear,
-    ResBlock,
-    ScaledLeakyReLU,
-    StyleGAN2GeneratorBilinear,
-)
-
-
-class StyleGAN2GeneratorBilinearSFT(StyleGAN2GeneratorBilinear):
-    """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform).
-    It is the bilinear version. It does not use the complicated UpFirDnSmooth function that is not friendly for
-    deployment. It can be easily converted to the clean version: StyleGAN2GeneratorCSFT.
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.
-        narrow (float): The narrow ratio for channels. Default: 1.
-        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
-    """
-
-    def __init__(
-        self,
-        out_size,
-        num_style_feat=512,
-        num_mlp=8,
-        channel_multiplier=2,
-        lr_mlp=0.01,
-        narrow=1,
-        sft_half=False,
-    ):
-        super(StyleGAN2GeneratorBilinearSFT, self).__init__(
-            out_size,
-            num_style_feat=num_style_feat,
-            num_mlp=num_mlp,
-            channel_multiplier=channel_multiplier,
-            lr_mlp=lr_mlp,
-            narrow=narrow,
-        )
-        self.sft_half = sft_half
-
-    def forward(
-        self,
-        styles,
-        conditions,
-        input_is_latent=False,
-        noise=None,
-        randomize_noise=True,
-        truncation=1,
-        truncation_latent=None,
-        inject_index=None,
-        return_latents=False,
-    ):
-        """Forward function for StyleGAN2GeneratorBilinearSFT.
-        Args:
-            styles (list[Tensor]): Sample codes of styles.
-            conditions (list[Tensor]): SFT conditions to generators.
-            input_is_latent (bool): Whether input is latent style. Default: False.
-            noise (Tensor | None): Input noise or None. Default: None.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-            truncation (float): The truncation ratio. Default: 1.
-            truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
-            inject_index (int | None): The injection index for mixing noise. Default: None.
-            return_latents (bool): Whether to return style latents. Default: False.
-        """
-        # style codes -> latents with Style MLP layer
-        if not input_is_latent:
-            styles = [self.style_mlp(s) for s in styles]
-        # noises
-        if noise is None:
-            if randomize_noise:
-                noise = [None] * self.num_layers  # for each style conv layer
-            else:  # use the stored noise
-                noise = [
-                    getattr(self.noises, f"noise{i}") for i in range(self.num_layers)
-                ]
-        # style truncation
-        if truncation < 1:
-            style_truncation = []
-            for style in styles:
-                style_truncation.append(
-                    truncation_latent + truncation * (style - truncation_latent)
-                )
-            styles = style_truncation
-        # get style latents with injection
-        if len(styles) == 1:
-            inject_index = self.num_latent
-
-            if styles[0].ndim < 3:
-                # repeat latent code for all the layers
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            else:  # used for encoder with different latent code for each layer
-                latent = styles[0]
-        elif len(styles) == 2:  # mixing noises
-            if inject_index is None:
-                inject_index = random.randint(1, self.num_latent - 1)
-            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            latent2 = (
-                styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
-            )
-            latent = torch.cat([latent1, latent2], 1)
-
-        # main generation
-        out = self.constant_input(latent.shape[0])
-        out = self.style_conv1(out, latent[:, 0], noise=noise[0])
-        skip = self.to_rgb1(out, latent[:, 1])
-
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(
-            self.style_convs[::2],
-            self.style_convs[1::2],
-            noise[1::2],
-            noise[2::2],
-            self.to_rgbs,
-        ):
-            out = conv1(out, latent[:, i], noise=noise1)
-
-            # the conditions may have fewer levels
-            if i < len(conditions):
-                # SFT part to combine the conditions
-                if self.sft_half:  # only apply SFT to half of the channels
-                    out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1)
-                    out_sft = out_sft * conditions[i - 1] + conditions[i]
-                    out = torch.cat([out_same, out_sft], dim=1)
-                else:  # apply SFT to all the channels
-                    out = out * conditions[i - 1] + conditions[i]
-
-            out = conv2(out, latent[:, i + 1], noise=noise2)
-            skip = to_rgb(out, latent[:, i + 2], skip)  # feature back to the rgb space
-            i += 2
-
-        image = skip
-
-        if return_latents:
-            return image, latent
-        else:
-            return image, None
-
-
-class GFPGANBilinear(nn.Module):
-    """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT.
-    It is the bilinear version and it does not use the complicated UpFirDnSmooth function that is not friendly for
-    deployment. It can be easily converted to the clean version: GFPGANv1Clean.
-    Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior.
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None.
-        fix_decoder (bool): Whether to fix the decoder. Default: True.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.
-        input_is_latent (bool): Whether input is latent style. Default: False.
-        different_w (bool): Whether to use different latent w for different layers. Default: False.
-        narrow (float): The narrow ratio for channels. Default: 1.
-        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
-    """
-
-    def __init__(
-        self,
-        out_size,
-        num_style_feat=512,
-        channel_multiplier=1,
-        decoder_load_path=None,
-        fix_decoder=True,
-        # for stylegan decoder
-        num_mlp=8,
-        lr_mlp=0.01,
-        input_is_latent=False,
-        different_w=False,
-        narrow=1,
-        sft_half=False,
-    ):
-        super(GFPGANBilinear, self).__init__()
-        self.input_is_latent = input_is_latent
-        self.different_w = different_w
-        self.num_style_feat = num_style_feat
-        self.min_size_restriction = 512
-
-        unet_narrow = narrow * 0.5  # by default, use a half of input channels
-        channels = {
-            "4": int(512 * unet_narrow),
-            "8": int(512 * unet_narrow),
-            "16": int(512 * unet_narrow),
-            "32": int(512 * unet_narrow),
-            "64": int(256 * channel_multiplier * unet_narrow),
-            "128": int(128 * channel_multiplier * unet_narrow),
-            "256": int(64 * channel_multiplier * unet_narrow),
-            "512": int(32 * channel_multiplier * unet_narrow),
-            "1024": int(16 * channel_multiplier * unet_narrow),
-        }
-
-        self.log_size = int(math.log(out_size, 2))
-        first_out_size = 2 ** (int(math.log(out_size, 2)))
-
-        self.conv_body_first = ConvLayer(
-            3, channels[f"{first_out_size}"], 1, bias=True, activate=True
-        )
-
-        # downsample
-        in_channels = channels[f"{first_out_size}"]
-        self.conv_body_down = nn.ModuleList()
-        for i in range(self.log_size, 2, -1):
-            out_channels = channels[f"{2**(i - 1)}"]
-            self.conv_body_down.append(ResBlock(in_channels, out_channels))
-            in_channels = out_channels
-
-        self.final_conv = ConvLayer(
-            in_channels, channels["4"], 3, bias=True, activate=True
-        )
-
-        # upsample
-        in_channels = channels["4"]
-        self.conv_body_up = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f"{2**i}"]
-            self.conv_body_up.append(ResUpBlock(in_channels, out_channels))
-            in_channels = out_channels
-
-        # to RGB
-        self.toRGB = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            self.toRGB.append(
-                EqualConv2d(
-                    channels[f"{2**i}"],
-                    3,
-                    1,
-                    stride=1,
-                    padding=0,
-                    bias=True,
-                    bias_init_val=0,
-                )
-            )
-
-        if different_w:
-            linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat
-        else:
-            linear_out_channel = num_style_feat
-
-        self.final_linear = EqualLinear(
-            channels["4"] * 4 * 4,
-            linear_out_channel,
-            bias=True,
-            bias_init_val=0,
-            lr_mul=1,
-            activation=None,
-        )
-
-        # the decoder: stylegan2 generator with SFT modulations
-        self.stylegan_decoder = StyleGAN2GeneratorBilinearSFT(
-            out_size=out_size,
-            num_style_feat=num_style_feat,
-            num_mlp=num_mlp,
-            channel_multiplier=channel_multiplier,
-            lr_mlp=lr_mlp,
-            narrow=narrow,
-            sft_half=sft_half,
-        )
-
-        # load pre-trained stylegan2 model if necessary
-        if decoder_load_path:
-            self.stylegan_decoder.load_state_dict(
-                torch.load(
-                    decoder_load_path, map_location=lambda storage, loc: storage
-                )["params_ema"]
-            )
-        # fix decoder without updating params
-        if fix_decoder:
-            for _, param in self.stylegan_decoder.named_parameters():
-                param.requires_grad = False
-
-        # for SFT modulations (scale and shift)
-        self.condition_scale = nn.ModuleList()
-        self.condition_shift = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f"{2**i}"]
-            if sft_half:
-                sft_out_channels = out_channels
-            else:
-                sft_out_channels = out_channels * 2
-            self.condition_scale.append(
-                nn.Sequential(
-                    EqualConv2d(
-                        out_channels,
-                        out_channels,
-                        3,
-                        stride=1,
-                        padding=1,
-                        bias=True,
-                        bias_init_val=0,
-                    ),
-                    ScaledLeakyReLU(0.2),
-                    EqualConv2d(
-                        out_channels,
-                        sft_out_channels,
-                        3,
-                        stride=1,
-                        padding=1,
-                        bias=True,
-                        bias_init_val=1,
-                    ),
-                )
-            )
-            self.condition_shift.append(
-                nn.Sequential(
-                    EqualConv2d(
-                        out_channels,
-                        out_channels,
-                        3,
-                        stride=1,
-                        padding=1,
-                        bias=True,
-                        bias_init_val=0,
-                    ),
-                    ScaledLeakyReLU(0.2),
-                    EqualConv2d(
-                        out_channels,
-                        sft_out_channels,
-                        3,
-                        stride=1,
-                        padding=1,
-                        bias=True,
-                        bias_init_val=0,
-                    ),
-                )
-            )
-
-    def forward(self, x, return_latents=False, return_rgb=True, randomize_noise=True):
-        """Forward function for GFPGANBilinear.
-        Args:
-            x (Tensor): Input images.
-            return_latents (bool): Whether to return style latents. Default: False.
-            return_rgb (bool): Whether return intermediate rgb images. Default: True.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-        """
-        conditions = []
-        unet_skips = []
-        out_rgbs = []
-
-        # encoder
-        feat = self.conv_body_first(x)
-        for i in range(self.log_size - 2):
-            feat = self.conv_body_down[i](feat)
-            unet_skips.insert(0, feat)
-
-        feat = self.final_conv(feat)
-
-        # style code
-        style_code = self.final_linear(feat.view(feat.size(0), -1))
-        if self.different_w:
-            style_code = style_code.view(style_code.size(0), -1, self.num_style_feat)
-
-        # decode
-        for i in range(self.log_size - 2):
-            # add unet skip
-            feat = feat + unet_skips[i]
-            # ResUpLayer
-            feat = self.conv_body_up[i](feat)
-            # generate scale and shift for SFT layers
-            scale = self.condition_scale[i](feat)
-            conditions.append(scale.clone())
-            shift = self.condition_shift[i](feat)
-            conditions.append(shift.clone())
-            # generate rgb images
-            if return_rgb:
-                out_rgbs.append(self.toRGB[i](feat))
-
-        # decoder
-        image, _ = self.stylegan_decoder(
-            [style_code],
-            conditions,
-            return_latents=return_latents,
-            input_is_latent=self.input_is_latent,
-            randomize_noise=randomize_noise,
-        )
-
-        return image, out_rgbs
diff --git a/comfy_extras/chainner_models/architecture/face/gfpganv1_arch.py b/comfy_extras/chainner_models/architecture/face/gfpganv1_arch.py
deleted file mode 100644
index 72d72fc8..00000000
--- a/comfy_extras/chainner_models/architecture/face/gfpganv1_arch.py
+++ /dev/null
@@ -1,566 +0,0 @@
-# pylint: skip-file
-# type: ignore
-import math
-import random
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from .fused_act import FusedLeakyReLU
-from .stylegan2_arch import (
-    ConvLayer,
-    EqualConv2d,
-    EqualLinear,
-    ResBlock,
-    ScaledLeakyReLU,
-    StyleGAN2Generator,
-)
-
-
-class StyleGAN2GeneratorSFT(StyleGAN2Generator):
-    """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform).
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be
-            applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1).
-        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.
-        narrow (float): The narrow ratio for channels. Default: 1.
-        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
-    """
-
-    def __init__(
-        self,
-        out_size,
-        num_style_feat=512,
-        num_mlp=8,
-        channel_multiplier=2,
-        resample_kernel=(1, 3, 3, 1),
-        lr_mlp=0.01,
-        narrow=1,
-        sft_half=False,
-    ):
-        super(StyleGAN2GeneratorSFT, self).__init__(
-            out_size,
-            num_style_feat=num_style_feat,
-            num_mlp=num_mlp,
-            channel_multiplier=channel_multiplier,
-            resample_kernel=resample_kernel,
-            lr_mlp=lr_mlp,
-            narrow=narrow,
-        )
-        self.sft_half = sft_half
-
-    def forward(
-        self,
-        styles,
-        conditions,
-        input_is_latent=False,
-        noise=None,
-        randomize_noise=True,
-        truncation=1,
-        truncation_latent=None,
-        inject_index=None,
-        return_latents=False,
-    ):
-        """Forward function for StyleGAN2GeneratorSFT.
-        Args:
-            styles (list[Tensor]): Sample codes of styles.
-            conditions (list[Tensor]): SFT conditions to generators.
-            input_is_latent (bool): Whether input is latent style. Default: False.
-            noise (Tensor | None): Input noise or None. Default: None.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-            truncation (float): The truncation ratio. Default: 1.
-            truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
-            inject_index (int | None): The injection index for mixing noise. Default: None.
-            return_latents (bool): Whether to return style latents. Default: False.
-        """
-        # style codes -> latents with Style MLP layer
-        if not input_is_latent:
-            styles = [self.style_mlp(s) for s in styles]
-        # noises
-        if noise is None:
-            if randomize_noise:
-                noise = [None] * self.num_layers  # for each style conv layer
-            else:  # use the stored noise
-                noise = [
-                    getattr(self.noises, f"noise{i}") for i in range(self.num_layers)
-                ]
-        # style truncation
-        if truncation < 1:
-            style_truncation = []
-            for style in styles:
-                style_truncation.append(
-                    truncation_latent + truncation * (style - truncation_latent)
-                )
-            styles = style_truncation
-        # get style latents with injection
-        if len(styles) == 1:
-            inject_index = self.num_latent
-
-            if styles[0].ndim < 3:
-                # repeat latent code for all the layers
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            else:  # used for encoder with different latent code for each layer
-                latent = styles[0]
-        elif len(styles) == 2:  # mixing noises
-            if inject_index is None:
-                inject_index = random.randint(1, self.num_latent - 1)
-            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            latent2 = (
-                styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
-            )
-            latent = torch.cat([latent1, latent2], 1)
-
-        # main generation
-        out = self.constant_input(latent.shape[0])
-        out = self.style_conv1(out, latent[:, 0], noise=noise[0])
-        skip = self.to_rgb1(out, latent[:, 1])
-
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(
-            self.style_convs[::2],
-            self.style_convs[1::2],
-            noise[1::2],
-            noise[2::2],
-            self.to_rgbs,
-        ):
-            out = conv1(out, latent[:, i], noise=noise1)
-
-            # the conditions may have fewer levels
-            if i < len(conditions):
-                # SFT part to combine the conditions
-                if self.sft_half:  # only apply SFT to half of the channels
-                    out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1)
-                    out_sft = out_sft * conditions[i - 1] + conditions[i]
-                    out = torch.cat([out_same, out_sft], dim=1)
-                else:  # apply SFT to all the channels
-                    out = out * conditions[i - 1] + conditions[i]
-
-            out = conv2(out, latent[:, i + 1], noise=noise2)
-            skip = to_rgb(out, latent[:, i + 2], skip)  # feature back to the rgb space
-            i += 2
-
-        image = skip
-
-        if return_latents:
-            return image, latent
-        else:
-            return image, None
-
-
-class ConvUpLayer(nn.Module):
-    """Convolutional upsampling layer. It uses bilinear upsampler + Conv.
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        stride (int): Stride of the convolution. Default: 1
-        padding (int): Zero-padding added to both sides of the input. Default: 0.
-        bias (bool): If ``True``, adds a learnable bias to the output. Default: ``True``.
-        bias_init_val (float): Bias initialized value. Default: 0.
-        activate (bool): Whether use activateion. Default: True.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        stride=1,
-        padding=0,
-        bias=True,
-        bias_init_val=0,
-        activate=True,
-    ):
-        super(ConvUpLayer, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.stride = stride
-        self.padding = padding
-        # self.scale is used to scale the convolution weights, which is related to the common initializations.
-        self.scale = 1 / math.sqrt(in_channels * kernel_size**2)
-
-        self.weight = nn.Parameter(
-            torch.randn(out_channels, in_channels, kernel_size, kernel_size)
-        )
-
-        if bias and not activate:
-            self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val))
-        else:
-            self.register_parameter("bias", None)
-
-        # activation
-        if activate:
-            if bias:
-                self.activation = FusedLeakyReLU(out_channels)
-            else:
-                self.activation = ScaledLeakyReLU(0.2)
-        else:
-            self.activation = None
-
-    def forward(self, x):
-        # bilinear upsample
-        out = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=False)
-        # conv
-        out = F.conv2d(
-            out,
-            self.weight * self.scale,
-            bias=self.bias,
-            stride=self.stride,
-            padding=self.padding,
-        )
-        # activation
-        if self.activation is not None:
-            out = self.activation(out)
-        return out
-
-
-class ResUpBlock(nn.Module):
-    """Residual block with upsampling.
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-    """
-
-    def __init__(self, in_channels, out_channels):
-        super(ResUpBlock, self).__init__()
-
-        self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True)
-        self.conv2 = ConvUpLayer(
-            in_channels, out_channels, 3, stride=1, padding=1, bias=True, activate=True
-        )
-        self.skip = ConvUpLayer(
-            in_channels, out_channels, 1, bias=False, activate=False
-        )
-
-    def forward(self, x):
-        out = self.conv1(x)
-        out = self.conv2(out)
-        skip = self.skip(x)
-        out = (out + skip) / math.sqrt(2)
-        return out
-
-
-class GFPGANv1(nn.Module):
-    """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT.
-    Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior.
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        resample_kernel (list[int]): A list indicating the 1D resample kernel magnitude. A cross production will be
-            applied to extent 1D resample kernel to 2D resample kernel. Default: (1, 3, 3, 1).
-        decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None.
-        fix_decoder (bool): Whether to fix the decoder. Default: True.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.
-        input_is_latent (bool): Whether input is latent style. Default: False.
-        different_w (bool): Whether to use different latent w for different layers. Default: False.
-        narrow (float): The narrow ratio for channels. Default: 1.
-        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
-    """
-
-    def __init__(
-        self,
-        out_size,
-        num_style_feat=512,
-        channel_multiplier=1,
-        resample_kernel=(1, 3, 3, 1),
-        decoder_load_path=None,
-        fix_decoder=True,
-        # for stylegan decoder
-        num_mlp=8,
-        lr_mlp=0.01,
-        input_is_latent=False,
-        different_w=False,
-        narrow=1,
-        sft_half=False,
-    ):
-        super(GFPGANv1, self).__init__()
-        self.input_is_latent = input_is_latent
-        self.different_w = different_w
-        self.num_style_feat = num_style_feat
-
-        unet_narrow = narrow * 0.5  # by default, use a half of input channels
-        channels = {
-            "4": int(512 * unet_narrow),
-            "8": int(512 * unet_narrow),
-            "16": int(512 * unet_narrow),
-            "32": int(512 * unet_narrow),
-            "64": int(256 * channel_multiplier * unet_narrow),
-            "128": int(128 * channel_multiplier * unet_narrow),
-            "256": int(64 * channel_multiplier * unet_narrow),
-            "512": int(32 * channel_multiplier * unet_narrow),
-            "1024": int(16 * channel_multiplier * unet_narrow),
-        }
-
-        self.log_size = int(math.log(out_size, 2))
-        first_out_size = 2 ** (int(math.log(out_size, 2)))
-
-        self.conv_body_first = ConvLayer(
-            3, channels[f"{first_out_size}"], 1, bias=True, activate=True
-        )
-
-        # downsample
-        in_channels = channels[f"{first_out_size}"]
-        self.conv_body_down = nn.ModuleList()
-        for i in range(self.log_size, 2, -1):
-            out_channels = channels[f"{2**(i - 1)}"]
-            self.conv_body_down.append(
-                ResBlock(in_channels, out_channels, resample_kernel)
-            )
-            in_channels = out_channels
-
-        self.final_conv = ConvLayer(
-            in_channels, channels["4"], 3, bias=True, activate=True
-        )
-
-        # upsample
-        in_channels = channels["4"]
-        self.conv_body_up = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f"{2**i}"]
-            self.conv_body_up.append(ResUpBlock(in_channels, out_channels))
-            in_channels = out_channels
-
-        # to RGB
-        self.toRGB = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            self.toRGB.append(
-                EqualConv2d(
-                    channels[f"{2**i}"],
-                    3,
-                    1,
-                    stride=1,
-                    padding=0,
-                    bias=True,
-                    bias_init_val=0,
-                )
-            )
-
-        if different_w:
-            linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat
-        else:
-            linear_out_channel = num_style_feat
-
-        self.final_linear = EqualLinear(
-            channels["4"] * 4 * 4,
-            linear_out_channel,
-            bias=True,
-            bias_init_val=0,
-            lr_mul=1,
-            activation=None,
-        )
-
-        # the decoder: stylegan2 generator with SFT modulations
-        self.stylegan_decoder = StyleGAN2GeneratorSFT(
-            out_size=out_size,
-            num_style_feat=num_style_feat,
-            num_mlp=num_mlp,
-            channel_multiplier=channel_multiplier,
-            resample_kernel=resample_kernel,
-            lr_mlp=lr_mlp,
-            narrow=narrow,
-            sft_half=sft_half,
-        )
-
-        # load pre-trained stylegan2 model if necessary
-        if decoder_load_path:
-            self.stylegan_decoder.load_state_dict(
-                torch.load(
-                    decoder_load_path, map_location=lambda storage, loc: storage
-                )["params_ema"]
-            )
-        # fix decoder without updating params
-        if fix_decoder:
-            for _, param in self.stylegan_decoder.named_parameters():
-                param.requires_grad = False
-
-        # for SFT modulations (scale and shift)
-        self.condition_scale = nn.ModuleList()
-        self.condition_shift = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f"{2**i}"]
-            if sft_half:
-                sft_out_channels = out_channels
-            else:
-                sft_out_channels = out_channels * 2
-            self.condition_scale.append(
-                nn.Sequential(
-                    EqualConv2d(
-                        out_channels,
-                        out_channels,
-                        3,
-                        stride=1,
-                        padding=1,
-                        bias=True,
-                        bias_init_val=0,
-                    ),
-                    ScaledLeakyReLU(0.2),
-                    EqualConv2d(
-                        out_channels,
-                        sft_out_channels,
-                        3,
-                        stride=1,
-                        padding=1,
-                        bias=True,
-                        bias_init_val=1,
-                    ),
-                )
-            )
-            self.condition_shift.append(
-                nn.Sequential(
-                    EqualConv2d(
-                        out_channels,
-                        out_channels,
-                        3,
-                        stride=1,
-                        padding=1,
-                        bias=True,
-                        bias_init_val=0,
-                    ),
-                    ScaledLeakyReLU(0.2),
-                    EqualConv2d(
-                        out_channels,
-                        sft_out_channels,
-                        3,
-                        stride=1,
-                        padding=1,
-                        bias=True,
-                        bias_init_val=0,
-                    ),
-                )
-            )
-
-    def forward(
-        self, x, return_latents=False, return_rgb=True, randomize_noise=True, **kwargs
-    ):
-        """Forward function for GFPGANv1.
-        Args:
-            x (Tensor): Input images.
-            return_latents (bool): Whether to return style latents. Default: False.
-            return_rgb (bool): Whether return intermediate rgb images. Default: True.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-        """
-        conditions = []
-        unet_skips = []
-        out_rgbs = []
-
-        # encoder
-        feat = self.conv_body_first(x)
-        for i in range(self.log_size - 2):
-            feat = self.conv_body_down[i](feat)
-            unet_skips.insert(0, feat)
-
-        feat = self.final_conv(feat)
-
-        # style code
-        style_code = self.final_linear(feat.view(feat.size(0), -1))
-        if self.different_w:
-            style_code = style_code.view(style_code.size(0), -1, self.num_style_feat)
-
-        # decode
-        for i in range(self.log_size - 2):
-            # add unet skip
-            feat = feat + unet_skips[i]
-            # ResUpLayer
-            feat = self.conv_body_up[i](feat)
-            # generate scale and shift for SFT layers
-            scale = self.condition_scale[i](feat)
-            conditions.append(scale.clone())
-            shift = self.condition_shift[i](feat)
-            conditions.append(shift.clone())
-            # generate rgb images
-            if return_rgb:
-                out_rgbs.append(self.toRGB[i](feat))
-
-        # decoder
-        image, _ = self.stylegan_decoder(
-            [style_code],
-            conditions,
-            return_latents=return_latents,
-            input_is_latent=self.input_is_latent,
-            randomize_noise=randomize_noise,
-        )
-
-        return image, out_rgbs
-
-
-class FacialComponentDiscriminator(nn.Module):
-    """Facial component (eyes, mouth, noise) discriminator used in GFPGAN."""
-
-    def __init__(self):
-        super(FacialComponentDiscriminator, self).__init__()
-        # It now uses a VGG-style architectrue with fixed model size
-        self.conv1 = ConvLayer(
-            3,
-            64,
-            3,
-            downsample=False,
-            resample_kernel=(1, 3, 3, 1),
-            bias=True,
-            activate=True,
-        )
-        self.conv2 = ConvLayer(
-            64,
-            128,
-            3,
-            downsample=True,
-            resample_kernel=(1, 3, 3, 1),
-            bias=True,
-            activate=True,
-        )
-        self.conv3 = ConvLayer(
-            128,
-            128,
-            3,
-            downsample=False,
-            resample_kernel=(1, 3, 3, 1),
-            bias=True,
-            activate=True,
-        )
-        self.conv4 = ConvLayer(
-            128,
-            256,
-            3,
-            downsample=True,
-            resample_kernel=(1, 3, 3, 1),
-            bias=True,
-            activate=True,
-        )
-        self.conv5 = ConvLayer(
-            256,
-            256,
-            3,
-            downsample=False,
-            resample_kernel=(1, 3, 3, 1),
-            bias=True,
-            activate=True,
-        )
-        self.final_conv = ConvLayer(256, 1, 3, bias=True, activate=False)
-
-    def forward(self, x, return_feats=False, **kwargs):
-        """Forward function for FacialComponentDiscriminator.
-        Args:
-            x (Tensor): Input images.
-            return_feats (bool): Whether to return intermediate features. Default: False.
-        """
-        feat = self.conv1(x)
-        feat = self.conv3(self.conv2(feat))
-        rlt_feats = []
-        if return_feats:
-            rlt_feats.append(feat.clone())
-        feat = self.conv5(self.conv4(feat))
-        if return_feats:
-            rlt_feats.append(feat.clone())
-        out = self.final_conv(feat)
-
-        if return_feats:
-            return out, rlt_feats
-        else:
-            return out, None
diff --git a/comfy_extras/chainner_models/architecture/face/gfpganv1_clean_arch.py b/comfy_extras/chainner_models/architecture/face/gfpganv1_clean_arch.py
deleted file mode 100644
index 16470d63..00000000
--- a/comfy_extras/chainner_models/architecture/face/gfpganv1_clean_arch.py
+++ /dev/null
@@ -1,370 +0,0 @@
-# pylint: skip-file
-# type: ignore
-import math
-import random
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from .stylegan2_clean_arch import StyleGAN2GeneratorClean
-
-
-class StyleGAN2GeneratorCSFT(StyleGAN2GeneratorClean):
-    """StyleGAN2 Generator with SFT modulation (Spatial Feature Transform).
-    It is the clean version without custom compiled CUDA extensions used in StyleGAN2.
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        narrow (float): The narrow ratio for channels. Default: 1.
-        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
-    """
-
-    def __init__(
-        self,
-        out_size,
-        num_style_feat=512,
-        num_mlp=8,
-        channel_multiplier=2,
-        narrow=1,
-        sft_half=False,
-    ):
-        super(StyleGAN2GeneratorCSFT, self).__init__(
-            out_size,
-            num_style_feat=num_style_feat,
-            num_mlp=num_mlp,
-            channel_multiplier=channel_multiplier,
-            narrow=narrow,
-        )
-        self.sft_half = sft_half
-
-    def forward(
-        self,
-        styles,
-        conditions,
-        input_is_latent=False,
-        noise=None,
-        randomize_noise=True,
-        truncation=1,
-        truncation_latent=None,
-        inject_index=None,
-        return_latents=False,
-    ):
-        """Forward function for StyleGAN2GeneratorCSFT.
-        Args:
-            styles (list[Tensor]): Sample codes of styles.
-            conditions (list[Tensor]): SFT conditions to generators.
-            input_is_latent (bool): Whether input is latent style. Default: False.
-            noise (Tensor | None): Input noise or None. Default: None.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-            truncation (float): The truncation ratio. Default: 1.
-            truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
-            inject_index (int | None): The injection index for mixing noise. Default: None.
-            return_latents (bool): Whether to return style latents. Default: False.
-        """
-        # style codes -> latents with Style MLP layer
-        if not input_is_latent:
-            styles = [self.style_mlp(s) for s in styles]
-        # noises
-        if noise is None:
-            if randomize_noise:
-                noise = [None] * self.num_layers  # for each style conv layer
-            else:  # use the stored noise
-                noise = [
-                    getattr(self.noises, f"noise{i}") for i in range(self.num_layers)
-                ]
-        # style truncation
-        if truncation < 1:
-            style_truncation = []
-            for style in styles:
-                style_truncation.append(
-                    truncation_latent + truncation * (style - truncation_latent)
-                )
-            styles = style_truncation
-        # get style latents with injection
-        if len(styles) == 1:
-            inject_index = self.num_latent
-
-            if styles[0].ndim < 3:
-                # repeat latent code for all the layers
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            else:  # used for encoder with different latent code for each layer
-                latent = styles[0]
-        elif len(styles) == 2:  # mixing noises
-            if inject_index is None:
-                inject_index = random.randint(1, self.num_latent - 1)
-            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            latent2 = (
-                styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
-            )
-            latent = torch.cat([latent1, latent2], 1)
-
-        # main generation
-        out = self.constant_input(latent.shape[0])
-        out = self.style_conv1(out, latent[:, 0], noise=noise[0])
-        skip = self.to_rgb1(out, latent[:, 1])
-
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(
-            self.style_convs[::2],
-            self.style_convs[1::2],
-            noise[1::2],
-            noise[2::2],
-            self.to_rgbs,
-        ):
-            out = conv1(out, latent[:, i], noise=noise1)
-
-            # the conditions may have fewer levels
-            if i < len(conditions):
-                # SFT part to combine the conditions
-                if self.sft_half:  # only apply SFT to half of the channels
-                    out_same, out_sft = torch.split(out, int(out.size(1) // 2), dim=1)
-                    out_sft = out_sft * conditions[i - 1] + conditions[i]
-                    out = torch.cat([out_same, out_sft], dim=1)
-                else:  # apply SFT to all the channels
-                    out = out * conditions[i - 1] + conditions[i]
-
-            out = conv2(out, latent[:, i + 1], noise=noise2)
-            skip = to_rgb(out, latent[:, i + 2], skip)  # feature back to the rgb space
-            i += 2
-
-        image = skip
-
-        if return_latents:
-            return image, latent
-        else:
-            return image, None
-
-
-class ResBlock(nn.Module):
-    """Residual block with bilinear upsampling/downsampling.
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        mode (str): Upsampling/downsampling mode. Options: down | up. Default: down.
-    """
-
-    def __init__(self, in_channels, out_channels, mode="down"):
-        super(ResBlock, self).__init__()
-
-        self.conv1 = nn.Conv2d(in_channels, in_channels, 3, 1, 1)
-        self.conv2 = nn.Conv2d(in_channels, out_channels, 3, 1, 1)
-        self.skip = nn.Conv2d(in_channels, out_channels, 1, bias=False)
-        if mode == "down":
-            self.scale_factor = 0.5
-        elif mode == "up":
-            self.scale_factor = 2
-
-    def forward(self, x):
-        out = F.leaky_relu_(self.conv1(x), negative_slope=0.2)
-        # upsample/downsample
-        out = F.interpolate(
-            out, scale_factor=self.scale_factor, mode="bilinear", align_corners=False
-        )
-        out = F.leaky_relu_(self.conv2(out), negative_slope=0.2)
-        # skip
-        x = F.interpolate(
-            x, scale_factor=self.scale_factor, mode="bilinear", align_corners=False
-        )
-        skip = self.skip(x)
-        out = out + skip
-        return out
-
-
-class GFPGANv1Clean(nn.Module):
-    """The GFPGAN architecture: Unet + StyleGAN2 decoder with SFT.
-    It is the clean version without custom compiled CUDA extensions used in StyleGAN2.
-    Ref: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior.
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        decoder_load_path (str): The path to the pre-trained decoder model (usually, the StyleGAN2). Default: None.
-        fix_decoder (bool): Whether to fix the decoder. Default: True.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        input_is_latent (bool): Whether input is latent style. Default: False.
-        different_w (bool): Whether to use different latent w for different layers. Default: False.
-        narrow (float): The narrow ratio for channels. Default: 1.
-        sft_half (bool): Whether to apply SFT on half of the input channels. Default: False.
-    """
-
-    def __init__(
-        self,
-        state_dict,
-    ):
-        super(GFPGANv1Clean, self).__init__()
-
-        out_size = 512
-        num_style_feat = 512
-        channel_multiplier = 2
-        decoder_load_path = None
-        fix_decoder = False
-        num_mlp = 8
-        input_is_latent = True
-        different_w = True
-        narrow = 1
-        sft_half = True
-
-        self.model_arch = "GFPGAN"
-        self.sub_type = "Face SR"
-        self.scale = 8
-        self.in_nc = 3
-        self.out_nc = 3
-        self.state = state_dict
-
-        self.supports_fp16 = False
-        self.supports_bf16 = True
-        self.min_size_restriction = 512
-
-        self.input_is_latent = input_is_latent
-        self.different_w = different_w
-        self.num_style_feat = num_style_feat
-
-        unet_narrow = narrow * 0.5  # by default, use a half of input channels
-        channels = {
-            "4": int(512 * unet_narrow),
-            "8": int(512 * unet_narrow),
-            "16": int(512 * unet_narrow),
-            "32": int(512 * unet_narrow),
-            "64": int(256 * channel_multiplier * unet_narrow),
-            "128": int(128 * channel_multiplier * unet_narrow),
-            "256": int(64 * channel_multiplier * unet_narrow),
-            "512": int(32 * channel_multiplier * unet_narrow),
-            "1024": int(16 * channel_multiplier * unet_narrow),
-        }
-
-        self.log_size = int(math.log(out_size, 2))
-        first_out_size = 2 ** (int(math.log(out_size, 2)))
-
-        self.conv_body_first = nn.Conv2d(3, channels[f"{first_out_size}"], 1)
-
-        # downsample
-        in_channels = channels[f"{first_out_size}"]
-        self.conv_body_down = nn.ModuleList()
-        for i in range(self.log_size, 2, -1):
-            out_channels = channels[f"{2**(i - 1)}"]
-            self.conv_body_down.append(ResBlock(in_channels, out_channels, mode="down"))
-            in_channels = out_channels
-
-        self.final_conv = nn.Conv2d(in_channels, channels["4"], 3, 1, 1)
-
-        # upsample
-        in_channels = channels["4"]
-        self.conv_body_up = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f"{2**i}"]
-            self.conv_body_up.append(ResBlock(in_channels, out_channels, mode="up"))
-            in_channels = out_channels
-
-        # to RGB
-        self.toRGB = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            self.toRGB.append(nn.Conv2d(channels[f"{2**i}"], 3, 1))
-
-        if different_w:
-            linear_out_channel = (int(math.log(out_size, 2)) * 2 - 2) * num_style_feat
-        else:
-            linear_out_channel = num_style_feat
-
-        self.final_linear = nn.Linear(channels["4"] * 4 * 4, linear_out_channel)
-
-        # the decoder: stylegan2 generator with SFT modulations
-        self.stylegan_decoder = StyleGAN2GeneratorCSFT(
-            out_size=out_size,
-            num_style_feat=num_style_feat,
-            num_mlp=num_mlp,
-            channel_multiplier=channel_multiplier,
-            narrow=narrow,
-            sft_half=sft_half,
-        )
-
-        # load pre-trained stylegan2 model if necessary
-        if decoder_load_path:
-            self.stylegan_decoder.load_state_dict(
-                torch.load(
-                    decoder_load_path, map_location=lambda storage, loc: storage
-                )["params_ema"]
-            )
-        # fix decoder without updating params
-        if fix_decoder:
-            for _, param in self.stylegan_decoder.named_parameters():
-                param.requires_grad = False
-
-        # for SFT modulations (scale and shift)
-        self.condition_scale = nn.ModuleList()
-        self.condition_shift = nn.ModuleList()
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f"{2**i}"]
-            if sft_half:
-                sft_out_channels = out_channels
-            else:
-                sft_out_channels = out_channels * 2
-            self.condition_scale.append(
-                nn.Sequential(
-                    nn.Conv2d(out_channels, out_channels, 3, 1, 1),
-                    nn.LeakyReLU(0.2, True),
-                    nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1),
-                )
-            )
-            self.condition_shift.append(
-                nn.Sequential(
-                    nn.Conv2d(out_channels, out_channels, 3, 1, 1),
-                    nn.LeakyReLU(0.2, True),
-                    nn.Conv2d(out_channels, sft_out_channels, 3, 1, 1),
-                )
-            )
-        self.load_state_dict(state_dict)
-
-    def forward(
-        self, x, return_latents=False, return_rgb=True, randomize_noise=True, **kwargs
-    ):
-        """Forward function for GFPGANv1Clean.
-        Args:
-            x (Tensor): Input images.
-            return_latents (bool): Whether to return style latents. Default: False.
-            return_rgb (bool): Whether return intermediate rgb images. Default: True.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-        """
-        conditions = []
-        unet_skips = []
-        out_rgbs = []
-
-        # encoder
-        feat = F.leaky_relu_(self.conv_body_first(x), negative_slope=0.2)
-        for i in range(self.log_size - 2):
-            feat = self.conv_body_down[i](feat)
-            unet_skips.insert(0, feat)
-        feat = F.leaky_relu_(self.final_conv(feat), negative_slope=0.2)
-
-        # style code
-        style_code = self.final_linear(feat.view(feat.size(0), -1))
-        if self.different_w:
-            style_code = style_code.view(style_code.size(0), -1, self.num_style_feat)
-
-        # decode
-        for i in range(self.log_size - 2):
-            # add unet skip
-            feat = feat + unet_skips[i]
-            # ResUpLayer
-            feat = self.conv_body_up[i](feat)
-            # generate scale and shift for SFT layers
-            scale = self.condition_scale[i](feat)
-            conditions.append(scale.clone())
-            shift = self.condition_shift[i](feat)
-            conditions.append(shift.clone())
-            # generate rgb images
-            if return_rgb:
-                out_rgbs.append(self.toRGB[i](feat))
-
-        # decoder
-        image, _ = self.stylegan_decoder(
-            [style_code],
-            conditions,
-            return_latents=return_latents,
-            input_is_latent=self.input_is_latent,
-            randomize_noise=randomize_noise,
-        )
-
-        return image, out_rgbs
diff --git a/comfy_extras/chainner_models/architecture/face/restoreformer_arch.py b/comfy_extras/chainner_models/architecture/face/restoreformer_arch.py
deleted file mode 100644
index 44922602..00000000
--- a/comfy_extras/chainner_models/architecture/face/restoreformer_arch.py
+++ /dev/null
@@ -1,776 +0,0 @@
-# pylint: skip-file
-# type: ignore
-"""Modified from https://github.com/wzhouxiff/RestoreFormer
-"""
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class VectorQuantizer(nn.Module):
-    """
-    see https://github.com/MishaLaskin/vqvae/blob/d761a999e2267766400dc646d82d3ac3657771d4/models/quantizer.py
-    ____________________________________________
-    Discretization bottleneck part of the VQ-VAE.
-    Inputs:
-    - n_e : number of embeddings
-    - e_dim : dimension of embedding
-    - beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2
-    _____________________________________________
-    """
-
-    def __init__(self, n_e, e_dim, beta):
-        super(VectorQuantizer, self).__init__()
-        self.n_e = n_e
-        self.e_dim = e_dim
-        self.beta = beta
-
-        self.embedding = nn.Embedding(self.n_e, self.e_dim)
-        self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)
-
-    def forward(self, z):
-        """
-        Inputs the output of the encoder network z and maps it to a discrete
-        one-hot vector that is the index of the closest embedding vector e_j
-        z (continuous) -> z_q (discrete)
-        z.shape = (batch, channel, height, width)
-        quantization pipeline:
-            1. get encoder input (B,C,H,W)
-            2. flatten input to (B*H*W,C)
-        """
-        # reshape z -> (batch, height, width, channel) and flatten
-        z = z.permute(0, 2, 3, 1).contiguous()
-        z_flattened = z.view(-1, self.e_dim)
-        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
-
-        d = (
-            torch.sum(z_flattened**2, dim=1, keepdim=True)
-            + torch.sum(self.embedding.weight**2, dim=1)
-            - 2 * torch.matmul(z_flattened, self.embedding.weight.t())
-        )
-
-        # could possible replace this here
-        # #\start...
-        # find closest encodings
-
-        min_value, min_encoding_indices = torch.min(d, dim=1)
-
-        min_encoding_indices = min_encoding_indices.unsqueeze(1)
-
-        min_encodings = torch.zeros(min_encoding_indices.shape[0], self.n_e).to(z)
-        min_encodings.scatter_(1, min_encoding_indices, 1)
-
-        # dtype min encodings: torch.float32
-        # min_encodings shape: torch.Size([2048, 512])
-        # min_encoding_indices.shape: torch.Size([2048, 1])
-
-        # get quantized latent vectors
-        z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)
-        # .........\end
-
-        # with:
-        # .........\start
-        # min_encoding_indices = torch.argmin(d, dim=1)
-        # z_q = self.embedding(min_encoding_indices)
-        # ......\end......... (TODO)
-
-        # compute loss for embedding
-        loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean(
-            (z_q - z.detach()) ** 2
-        )
-
-        # preserve gradients
-        z_q = z + (z_q - z).detach()
-
-        # perplexity
-
-        e_mean = torch.mean(min_encodings, dim=0)
-        perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))
-
-        # reshape back to match original input shape
-        z_q = z_q.permute(0, 3, 1, 2).contiguous()
-
-        return z_q, loss, (perplexity, min_encodings, min_encoding_indices, d)
-
-    def get_codebook_entry(self, indices, shape):
-        # shape specifying (batch, height, width, channel)
-        # TODO: check for more easy handling with nn.Embedding
-        min_encodings = torch.zeros(indices.shape[0], self.n_e).to(indices)
-        min_encodings.scatter_(1, indices[:, None], 1)
-
-        # get quantized latent vectors
-        z_q = torch.matmul(min_encodings.float(), self.embedding.weight)
-
-        if shape is not None:
-            z_q = z_q.view(shape)
-
-            # reshape back to match original input shape
-            z_q = z_q.permute(0, 3, 1, 2).contiguous()
-
-        return z_q
-
-
-# pytorch_diffusion + derived encoder decoder
-def nonlinearity(x):
-    # swish
-    return x * torch.sigmoid(x)
-
-
-def Normalize(in_channels):
-    return torch.nn.GroupNorm(
-        num_groups=32, num_channels=in_channels, eps=1e-6, affine=True
-    )
-
-
-class Upsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            self.conv = torch.nn.Conv2d(
-                in_channels, in_channels, kernel_size=3, stride=1, padding=1
-            )
-
-    def forward(self, x):
-        x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
-        if self.with_conv:
-            x = self.conv(x)
-        return x
-
-
-class Downsample(nn.Module):
-    def __init__(self, in_channels, with_conv):
-        super().__init__()
-        self.with_conv = with_conv
-        if self.with_conv:
-            # no asymmetric padding in torch conv, must do it ourselves
-            self.conv = torch.nn.Conv2d(
-                in_channels, in_channels, kernel_size=3, stride=2, padding=0
-            )
-
-    def forward(self, x):
-        if self.with_conv:
-            pad = (0, 1, 0, 1)
-            x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
-            x = self.conv(x)
-        else:
-            x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
-        return x
-
-
-class ResnetBlock(nn.Module):
-    def __init__(
-        self,
-        *,
-        in_channels,
-        out_channels=None,
-        conv_shortcut=False,
-        dropout,
-        temb_channels=512
-    ):
-        super().__init__()
-        self.in_channels = in_channels
-        out_channels = in_channels if out_channels is None else out_channels
-        self.out_channels = out_channels
-        self.use_conv_shortcut = conv_shortcut
-
-        self.norm1 = Normalize(in_channels)
-        self.conv1 = torch.nn.Conv2d(
-            in_channels, out_channels, kernel_size=3, stride=1, padding=1
-        )
-        if temb_channels > 0:
-            self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
-        self.norm2 = Normalize(out_channels)
-        self.dropout = torch.nn.Dropout(dropout)
-        self.conv2 = torch.nn.Conv2d(
-            out_channels, out_channels, kernel_size=3, stride=1, padding=1
-        )
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                self.conv_shortcut = torch.nn.Conv2d(
-                    in_channels, out_channels, kernel_size=3, stride=1, padding=1
-                )
-            else:
-                self.nin_shortcut = torch.nn.Conv2d(
-                    in_channels, out_channels, kernel_size=1, stride=1, padding=0
-                )
-
-    def forward(self, x, temb):
-        h = x
-        h = self.norm1(h)
-        h = nonlinearity(h)
-        h = self.conv1(h)
-
-        if temb is not None:
-            h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None]
-
-        h = self.norm2(h)
-        h = nonlinearity(h)
-        h = self.dropout(h)
-        h = self.conv2(h)
-
-        if self.in_channels != self.out_channels:
-            if self.use_conv_shortcut:
-                x = self.conv_shortcut(x)
-            else:
-                x = self.nin_shortcut(x)
-
-        return x + h
-
-
-class MultiHeadAttnBlock(nn.Module):
-    def __init__(self, in_channels, head_size=1):
-        super().__init__()
-        self.in_channels = in_channels
-        self.head_size = head_size
-        self.att_size = in_channels // head_size
-        assert (
-            in_channels % head_size == 0
-        ), "The size of head should be divided by the number of channels."
-
-        self.norm1 = Normalize(in_channels)
-        self.norm2 = Normalize(in_channels)
-
-        self.q = torch.nn.Conv2d(
-            in_channels, in_channels, kernel_size=1, stride=1, padding=0
-        )
-        self.k = torch.nn.Conv2d(
-            in_channels, in_channels, kernel_size=1, stride=1, padding=0
-        )
-        self.v = torch.nn.Conv2d(
-            in_channels, in_channels, kernel_size=1, stride=1, padding=0
-        )
-        self.proj_out = torch.nn.Conv2d(
-            in_channels, in_channels, kernel_size=1, stride=1, padding=0
-        )
-        self.num = 0
-
-    def forward(self, x, y=None):
-        h_ = x
-        h_ = self.norm1(h_)
-        if y is None:
-            y = h_
-        else:
-            y = self.norm2(y)
-
-        q = self.q(y)
-        k = self.k(h_)
-        v = self.v(h_)
-
-        # compute attention
-        b, c, h, w = q.shape
-        q = q.reshape(b, self.head_size, self.att_size, h * w)
-        q = q.permute(0, 3, 1, 2)  # b, hw, head, att
-
-        k = k.reshape(b, self.head_size, self.att_size, h * w)
-        k = k.permute(0, 3, 1, 2)
-
-        v = v.reshape(b, self.head_size, self.att_size, h * w)
-        v = v.permute(0, 3, 1, 2)
-
-        q = q.transpose(1, 2)
-        v = v.transpose(1, 2)
-        k = k.transpose(1, 2).transpose(2, 3)
-
-        scale = int(self.att_size) ** (-0.5)
-        q.mul_(scale)
-        w_ = torch.matmul(q, k)
-        w_ = F.softmax(w_, dim=3)
-
-        w_ = w_.matmul(v)
-
-        w_ = w_.transpose(1, 2).contiguous()  # [b, h*w, head, att]
-        w_ = w_.view(b, h, w, -1)
-        w_ = w_.permute(0, 3, 1, 2)
-
-        w_ = self.proj_out(w_)
-
-        return x + w_
-
-
-class MultiHeadEncoder(nn.Module):
-    def __init__(
-        self,
-        ch,
-        out_ch,
-        ch_mult=(1, 2, 4, 8),
-        num_res_blocks=2,
-        attn_resolutions=(16,),
-        dropout=0.0,
-        resamp_with_conv=True,
-        in_channels=3,
-        resolution=512,
-        z_channels=256,
-        double_z=True,
-        enable_mid=True,
-        head_size=1,
-        **ignore_kwargs
-    ):
-        super().__init__()
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-        self.enable_mid = enable_mid
-
-        # downsampling
-        self.conv_in = torch.nn.Conv2d(
-            in_channels, self.ch, kernel_size=3, stride=1, padding=1
-        )
-
-        curr_res = resolution
-        in_ch_mult = (1,) + tuple(ch_mult)
-        self.down = nn.ModuleList()
-        for i_level in range(self.num_resolutions):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_in = ch * in_ch_mult[i_level]
-            block_out = ch * ch_mult[i_level]
-            for i_block in range(self.num_res_blocks):
-                block.append(
-                    ResnetBlock(
-                        in_channels=block_in,
-                        out_channels=block_out,
-                        temb_channels=self.temb_ch,
-                        dropout=dropout,
-                    )
-                )
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(MultiHeadAttnBlock(block_in, head_size))
-            down = nn.Module()
-            down.block = block
-            down.attn = attn
-            if i_level != self.num_resolutions - 1:
-                down.downsample = Downsample(block_in, resamp_with_conv)
-                curr_res = curr_res // 2
-            self.down.append(down)
-
-        # middle
-        if self.enable_mid:
-            self.mid = nn.Module()
-            self.mid.block_1 = ResnetBlock(
-                in_channels=block_in,
-                out_channels=block_in,
-                temb_channels=self.temb_ch,
-                dropout=dropout,
-            )
-            self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size)
-            self.mid.block_2 = ResnetBlock(
-                in_channels=block_in,
-                out_channels=block_in,
-                temb_channels=self.temb_ch,
-                dropout=dropout,
-            )
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(
-            block_in,
-            2 * z_channels if double_z else z_channels,
-            kernel_size=3,
-            stride=1,
-            padding=1,
-        )
-
-    def forward(self, x):
-        hs = {}
-        # timestep embedding
-        temb = None
-
-        # downsampling
-        h = self.conv_in(x)
-        hs["in"] = h
-        for i_level in range(self.num_resolutions):
-            for i_block in range(self.num_res_blocks):
-                h = self.down[i_level].block[i_block](h, temb)
-                if len(self.down[i_level].attn) > 0:
-                    h = self.down[i_level].attn[i_block](h)
-
-            if i_level != self.num_resolutions - 1:
-                # hs.append(h)
-                hs["block_" + str(i_level)] = h
-                h = self.down[i_level].downsample(h)
-
-        # middle
-        # h = hs[-1]
-        if self.enable_mid:
-            h = self.mid.block_1(h, temb)
-            hs["block_" + str(i_level) + "_atten"] = h
-            h = self.mid.attn_1(h)
-            h = self.mid.block_2(h, temb)
-            hs["mid_atten"] = h
-
-        # end
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        # hs.append(h)
-        hs["out"] = h
-
-        return hs
-
-
-class MultiHeadDecoder(nn.Module):
-    def __init__(
-        self,
-        ch,
-        out_ch,
-        ch_mult=(1, 2, 4, 8),
-        num_res_blocks=2,
-        attn_resolutions=(16,),
-        dropout=0.0,
-        resamp_with_conv=True,
-        in_channels=3,
-        resolution=512,
-        z_channels=256,
-        give_pre_end=False,
-        enable_mid=True,
-        head_size=1,
-        **ignorekwargs
-    ):
-        super().__init__()
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-        self.give_pre_end = give_pre_end
-        self.enable_mid = enable_mid
-
-        # compute in_ch_mult, block_in and curr_res at lowest res
-        block_in = ch * ch_mult[self.num_resolutions - 1]
-        curr_res = resolution // 2 ** (self.num_resolutions - 1)
-        self.z_shape = (1, z_channels, curr_res, curr_res)
-        print(
-            "Working with z of shape {} = {} dimensions.".format(
-                self.z_shape, np.prod(self.z_shape)
-            )
-        )
-
-        # z to block_in
-        self.conv_in = torch.nn.Conv2d(
-            z_channels, block_in, kernel_size=3, stride=1, padding=1
-        )
-
-        # middle
-        if self.enable_mid:
-            self.mid = nn.Module()
-            self.mid.block_1 = ResnetBlock(
-                in_channels=block_in,
-                out_channels=block_in,
-                temb_channels=self.temb_ch,
-                dropout=dropout,
-            )
-            self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size)
-            self.mid.block_2 = ResnetBlock(
-                in_channels=block_in,
-                out_channels=block_in,
-                temb_channels=self.temb_ch,
-                dropout=dropout,
-            )
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch * ch_mult[i_level]
-            for i_block in range(self.num_res_blocks + 1):
-                block.append(
-                    ResnetBlock(
-                        in_channels=block_in,
-                        out_channels=block_out,
-                        temb_channels=self.temb_ch,
-                        dropout=dropout,
-                    )
-                )
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(MultiHeadAttnBlock(block_in, head_size))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up)  # prepend to get consistent order
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(
-            block_in, out_ch, kernel_size=3, stride=1, padding=1
-        )
-
-    def forward(self, z):
-        # assert z.shape[1:] == self.z_shape[1:]
-        self.last_z_shape = z.shape
-
-        # timestep embedding
-        temb = None
-
-        # z to block_in
-        h = self.conv_in(z)
-
-        # middle
-        if self.enable_mid:
-            h = self.mid.block_1(h, temb)
-            h = self.mid.attn_1(h)
-            h = self.mid.block_2(h, temb)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks + 1):
-                h = self.up[i_level].block[i_block](h, temb)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](h)
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        if self.give_pre_end:
-            return h
-
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class MultiHeadDecoderTransformer(nn.Module):
-    def __init__(
-        self,
-        ch,
-        out_ch,
-        ch_mult=(1, 2, 4, 8),
-        num_res_blocks=2,
-        attn_resolutions=(16,),
-        dropout=0.0,
-        resamp_with_conv=True,
-        in_channels=3,
-        resolution=512,
-        z_channels=256,
-        give_pre_end=False,
-        enable_mid=True,
-        head_size=1,
-        **ignorekwargs
-    ):
-        super().__init__()
-        self.ch = ch
-        self.temb_ch = 0
-        self.num_resolutions = len(ch_mult)
-        self.num_res_blocks = num_res_blocks
-        self.resolution = resolution
-        self.in_channels = in_channels
-        self.give_pre_end = give_pre_end
-        self.enable_mid = enable_mid
-
-        # compute in_ch_mult, block_in and curr_res at lowest res
-        block_in = ch * ch_mult[self.num_resolutions - 1]
-        curr_res = resolution // 2 ** (self.num_resolutions - 1)
-        self.z_shape = (1, z_channels, curr_res, curr_res)
-        print(
-            "Working with z of shape {} = {} dimensions.".format(
-                self.z_shape, np.prod(self.z_shape)
-            )
-        )
-
-        # z to block_in
-        self.conv_in = torch.nn.Conv2d(
-            z_channels, block_in, kernel_size=3, stride=1, padding=1
-        )
-
-        # middle
-        if self.enable_mid:
-            self.mid = nn.Module()
-            self.mid.block_1 = ResnetBlock(
-                in_channels=block_in,
-                out_channels=block_in,
-                temb_channels=self.temb_ch,
-                dropout=dropout,
-            )
-            self.mid.attn_1 = MultiHeadAttnBlock(block_in, head_size)
-            self.mid.block_2 = ResnetBlock(
-                in_channels=block_in,
-                out_channels=block_in,
-                temb_channels=self.temb_ch,
-                dropout=dropout,
-            )
-
-        # upsampling
-        self.up = nn.ModuleList()
-        for i_level in reversed(range(self.num_resolutions)):
-            block = nn.ModuleList()
-            attn = nn.ModuleList()
-            block_out = ch * ch_mult[i_level]
-            for i_block in range(self.num_res_blocks + 1):
-                block.append(
-                    ResnetBlock(
-                        in_channels=block_in,
-                        out_channels=block_out,
-                        temb_channels=self.temb_ch,
-                        dropout=dropout,
-                    )
-                )
-                block_in = block_out
-                if curr_res in attn_resolutions:
-                    attn.append(MultiHeadAttnBlock(block_in, head_size))
-            up = nn.Module()
-            up.block = block
-            up.attn = attn
-            if i_level != 0:
-                up.upsample = Upsample(block_in, resamp_with_conv)
-                curr_res = curr_res * 2
-            self.up.insert(0, up)  # prepend to get consistent order
-
-        # end
-        self.norm_out = Normalize(block_in)
-        self.conv_out = torch.nn.Conv2d(
-            block_in, out_ch, kernel_size=3, stride=1, padding=1
-        )
-
-    def forward(self, z, hs):
-        # assert z.shape[1:] == self.z_shape[1:]
-        # self.last_z_shape = z.shape
-
-        # timestep embedding
-        temb = None
-
-        # z to block_in
-        h = self.conv_in(z)
-
-        # middle
-        if self.enable_mid:
-            h = self.mid.block_1(h, temb)
-            h = self.mid.attn_1(h, hs["mid_atten"])
-            h = self.mid.block_2(h, temb)
-
-        # upsampling
-        for i_level in reversed(range(self.num_resolutions)):
-            for i_block in range(self.num_res_blocks + 1):
-                h = self.up[i_level].block[i_block](h, temb)
-                if len(self.up[i_level].attn) > 0:
-                    h = self.up[i_level].attn[i_block](
-                        h, hs["block_" + str(i_level) + "_atten"]
-                    )
-                    # hfeature = h.clone()
-            if i_level != 0:
-                h = self.up[i_level].upsample(h)
-
-        # end
-        if self.give_pre_end:
-            return h
-
-        h = self.norm_out(h)
-        h = nonlinearity(h)
-        h = self.conv_out(h)
-        return h
-
-
-class RestoreFormer(nn.Module):
-    def __init__(
-        self,
-        state_dict,
-    ):
-        super(RestoreFormer, self).__init__()
-
-        n_embed = 1024
-        embed_dim = 256
-        ch = 64
-        out_ch = 3
-        ch_mult = (1, 2, 2, 4, 4, 8)
-        num_res_blocks = 2
-        attn_resolutions = (16,)
-        dropout = 0.0
-        in_channels = 3
-        resolution = 512
-        z_channels = 256
-        double_z = False
-        enable_mid = True
-        fix_decoder = False
-        fix_codebook = True
-        fix_encoder = False
-        head_size = 8
-
-        self.model_arch = "RestoreFormer"
-        self.sub_type = "Face SR"
-        self.scale = 8
-        self.in_nc = 3
-        self.out_nc = out_ch
-        self.state = state_dict
-
-        self.supports_fp16 = False
-        self.supports_bf16 = True
-        self.min_size_restriction = 16
-
-        self.encoder = MultiHeadEncoder(
-            ch=ch,
-            out_ch=out_ch,
-            ch_mult=ch_mult,
-            num_res_blocks=num_res_blocks,
-            attn_resolutions=attn_resolutions,
-            dropout=dropout,
-            in_channels=in_channels,
-            resolution=resolution,
-            z_channels=z_channels,
-            double_z=double_z,
-            enable_mid=enable_mid,
-            head_size=head_size,
-        )
-        self.decoder = MultiHeadDecoderTransformer(
-            ch=ch,
-            out_ch=out_ch,
-            ch_mult=ch_mult,
-            num_res_blocks=num_res_blocks,
-            attn_resolutions=attn_resolutions,
-            dropout=dropout,
-            in_channels=in_channels,
-            resolution=resolution,
-            z_channels=z_channels,
-            enable_mid=enable_mid,
-            head_size=head_size,
-        )
-
-        self.quantize = VectorQuantizer(n_embed, embed_dim, beta=0.25)
-
-        self.quant_conv = torch.nn.Conv2d(z_channels, embed_dim, 1)
-        self.post_quant_conv = torch.nn.Conv2d(embed_dim, z_channels, 1)
-
-        if fix_decoder:
-            for _, param in self.decoder.named_parameters():
-                param.requires_grad = False
-            for _, param in self.post_quant_conv.named_parameters():
-                param.requires_grad = False
-            for _, param in self.quantize.named_parameters():
-                param.requires_grad = False
-        elif fix_codebook:
-            for _, param in self.quantize.named_parameters():
-                param.requires_grad = False
-
-        if fix_encoder:
-            for _, param in self.encoder.named_parameters():
-                param.requires_grad = False
-
-        self.load_state_dict(state_dict)
-
-    def encode(self, x):
-        hs = self.encoder(x)
-        h = self.quant_conv(hs["out"])
-        quant, emb_loss, info = self.quantize(h)
-        return quant, emb_loss, info, hs
-
-    def decode(self, quant, hs):
-        quant = self.post_quant_conv(quant)
-        dec = self.decoder(quant, hs)
-
-        return dec
-
-    def forward(self, input, **kwargs):
-        quant, diff, info, hs = self.encode(input)
-        dec = self.decode(quant, hs)
-
-        return dec, None
diff --git a/comfy_extras/chainner_models/architecture/face/stylegan2_arch.py b/comfy_extras/chainner_models/architecture/face/stylegan2_arch.py
deleted file mode 100644
index 1eb0e9f1..00000000
--- a/comfy_extras/chainner_models/architecture/face/stylegan2_arch.py
+++ /dev/null
@@ -1,865 +0,0 @@
-# pylint: skip-file
-# type: ignore
-import math
-import random
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from .fused_act import FusedLeakyReLU, fused_leaky_relu
-from .upfirdn2d import upfirdn2d
-
-
-class NormStyleCode(nn.Module):
-    def forward(self, x):
-        """Normalize the style codes.
-
-        Args:
-            x (Tensor): Style codes with shape (b, c).
-
-        Returns:
-            Tensor: Normalized tensor.
-        """
-        return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8)
-
-
-def make_resample_kernel(k):
-    """Make resampling kernel for UpFirDn.
-
-    Args:
-        k (list[int]): A list indicating the 1D resample kernel magnitude.
-
-    Returns:
-        Tensor: 2D resampled kernel.
-    """
-    k = torch.tensor(k, dtype=torch.float32)
-    if k.ndim == 1:
-        k = k[None, :] * k[:, None]  # to 2D kernel, outer product
-    # normalize
-    k /= k.sum()
-    return k
-
-
-class UpFirDnUpsample(nn.Module):
-    """Upsample, FIR filter, and downsample (upsampole version).
-
-    References:
-    1. https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.upfirdn.html  # noqa: E501
-    2. http://www.ece.northwestern.edu/local-apps/matlabhelp/toolbox/signal/upfirdn.html  # noqa: E501
-
-    Args:
-        resample_kernel (list[int]): A list indicating the 1D resample kernel
-            magnitude.
-        factor (int): Upsampling scale factor. Default: 2.
-    """
-
-    def __init__(self, resample_kernel, factor=2):
-        super(UpFirDnUpsample, self).__init__()
-        self.kernel = make_resample_kernel(resample_kernel) * (factor**2)
-        self.factor = factor
-
-        pad = self.kernel.shape[0] - factor
-        self.pad = ((pad + 1) // 2 + factor - 1, pad // 2)
-
-    def forward(self, x):
-        out = upfirdn2d(x, self.kernel.type_as(x), up=self.factor, down=1, pad=self.pad)
-        return out
-
-    def __repr__(self):
-        return f"{self.__class__.__name__}(factor={self.factor})"
-
-
-class UpFirDnDownsample(nn.Module):
-    """Upsample, FIR filter, and downsample (downsampole version).
-
-    Args:
-        resample_kernel (list[int]): A list indicating the 1D resample kernel
-            magnitude.
-        factor (int): Downsampling scale factor. Default: 2.
-    """
-
-    def __init__(self, resample_kernel, factor=2):
-        super(UpFirDnDownsample, self).__init__()
-        self.kernel = make_resample_kernel(resample_kernel)
-        self.factor = factor
-
-        pad = self.kernel.shape[0] - factor
-        self.pad = ((pad + 1) // 2, pad // 2)
-
-    def forward(self, x):
-        out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=self.factor, pad=self.pad)
-        return out
-
-    def __repr__(self):
-        return f"{self.__class__.__name__}(factor={self.factor})"
-
-
-class UpFirDnSmooth(nn.Module):
-    """Upsample, FIR filter, and downsample (smooth version).
-
-    Args:
-        resample_kernel (list[int]): A list indicating the 1D resample kernel
-            magnitude.
-        upsample_factor (int): Upsampling scale factor. Default: 1.
-        downsample_factor (int): Downsampling scale factor. Default: 1.
-        kernel_size (int): Kernel size: Default: 1.
-    """
-
-    def __init__(
-        self, resample_kernel, upsample_factor=1, downsample_factor=1, kernel_size=1
-    ):
-        super(UpFirDnSmooth, self).__init__()
-        self.upsample_factor = upsample_factor
-        self.downsample_factor = downsample_factor
-        self.kernel = make_resample_kernel(resample_kernel)
-        if upsample_factor > 1:
-            self.kernel = self.kernel * (upsample_factor**2)
-
-        if upsample_factor > 1:
-            pad = (self.kernel.shape[0] - upsample_factor) - (kernel_size - 1)
-            self.pad = ((pad + 1) // 2 + upsample_factor - 1, pad // 2 + 1)
-        elif downsample_factor > 1:
-            pad = (self.kernel.shape[0] - downsample_factor) + (kernel_size - 1)
-            self.pad = ((pad + 1) // 2, pad // 2)
-        else:
-            raise NotImplementedError
-
-    def forward(self, x):
-        out = upfirdn2d(x, self.kernel.type_as(x), up=1, down=1, pad=self.pad)
-        return out
-
-    def __repr__(self):
-        return (
-            f"{self.__class__.__name__}(upsample_factor={self.upsample_factor}"
-            f", downsample_factor={self.downsample_factor})"
-        )
-
-
-class EqualLinear(nn.Module):
-    """Equalized Linear as StyleGAN2.
-
-    Args:
-        in_channels (int): Size of each sample.
-        out_channels (int): Size of each output sample.
-        bias (bool): If set to ``False``, the layer will not learn an additive
-            bias. Default: ``True``.
-        bias_init_val (float): Bias initialized value. Default: 0.
-        lr_mul (float): Learning rate multiplier. Default: 1.
-        activation (None | str): The activation after ``linear`` operation.
-            Supported: 'fused_lrelu', None. Default: None.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        bias=True,
-        bias_init_val=0,
-        lr_mul=1,
-        activation=None,
-    ):
-        super(EqualLinear, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.lr_mul = lr_mul
-        self.activation = activation
-        if self.activation not in ["fused_lrelu", None]:
-            raise ValueError(
-                f"Wrong activation value in EqualLinear: {activation}"
-                "Supported ones are: ['fused_lrelu', None]."
-            )
-        self.scale = (1 / math.sqrt(in_channels)) * lr_mul
-
-        self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul))
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val))
-        else:
-            self.register_parameter("bias", None)
-
-    def forward(self, x):
-        if self.bias is None:
-            bias = None
-        else:
-            bias = self.bias * self.lr_mul
-        if self.activation == "fused_lrelu":
-            out = F.linear(x, self.weight * self.scale)
-            out = fused_leaky_relu(out, bias)
-        else:
-            out = F.linear(x, self.weight * self.scale, bias=bias)
-        return out
-
-    def __repr__(self):
-        return (
-            f"{self.__class__.__name__}(in_channels={self.in_channels}, "
-            f"out_channels={self.out_channels}, bias={self.bias is not None})"
-        )
-
-
-class ModulatedConv2d(nn.Module):
-    """Modulated Conv2d used in StyleGAN2.
-
-    There is no bias in ModulatedConv2d.
-
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        num_style_feat (int): Channel number of style features.
-        demodulate (bool): Whether to demodulate in the conv layer.
-            Default: True.
-        sample_mode (str | None): Indicating 'upsample', 'downsample' or None.
-            Default: None.
-        resample_kernel (list[int]): A list indicating the 1D resample kernel
-            magnitude. Default: (1, 3, 3, 1).
-        eps (float): A value added to the denominator for numerical stability.
-            Default: 1e-8.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        num_style_feat,
-        demodulate=True,
-        sample_mode=None,
-        resample_kernel=(1, 3, 3, 1),
-        eps=1e-8,
-    ):
-        super(ModulatedConv2d, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.demodulate = demodulate
-        self.sample_mode = sample_mode
-        self.eps = eps
-
-        if self.sample_mode == "upsample":
-            self.smooth = UpFirDnSmooth(
-                resample_kernel,
-                upsample_factor=2,
-                downsample_factor=1,
-                kernel_size=kernel_size,
-            )
-        elif self.sample_mode == "downsample":
-            self.smooth = UpFirDnSmooth(
-                resample_kernel,
-                upsample_factor=1,
-                downsample_factor=2,
-                kernel_size=kernel_size,
-            )
-        elif self.sample_mode is None:
-            pass
-        else:
-            raise ValueError(
-                f"Wrong sample mode {self.sample_mode}, "
-                "supported ones are ['upsample', 'downsample', None]."
-            )
-
-        self.scale = 1 / math.sqrt(in_channels * kernel_size**2)
-        # modulation inside each modulated conv
-        self.modulation = EqualLinear(
-            num_style_feat,
-            in_channels,
-            bias=True,
-            bias_init_val=1,
-            lr_mul=1,
-            activation=None,
-        )
-
-        self.weight = nn.Parameter(
-            torch.randn(1, out_channels, in_channels, kernel_size, kernel_size)
-        )
-        self.padding = kernel_size // 2
-
-    def forward(self, x, style):
-        """Forward function.
-
-        Args:
-            x (Tensor): Tensor with shape (b, c, h, w).
-            style (Tensor): Tensor with shape (b, num_style_feat).
-
-        Returns:
-            Tensor: Modulated tensor after convolution.
-        """
-        b, c, h, w = x.shape  # c = c_in
-        # weight modulation
-        style = self.modulation(style).view(b, 1, c, 1, 1)
-        # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1)
-        weight = self.scale * self.weight * style  # (b, c_out, c_in, k, k)
-
-        if self.demodulate:
-            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps)
-            weight = weight * demod.view(b, self.out_channels, 1, 1, 1)
-
-        weight = weight.view(
-            b * self.out_channels, c, self.kernel_size, self.kernel_size
-        )
-
-        if self.sample_mode == "upsample":
-            x = x.view(1, b * c, h, w)
-            weight = weight.view(
-                b, self.out_channels, c, self.kernel_size, self.kernel_size
-            )
-            weight = weight.transpose(1, 2).reshape(
-                b * c, self.out_channels, self.kernel_size, self.kernel_size
-            )
-            out = F.conv_transpose2d(x, weight, padding=0, stride=2, groups=b)
-            out = out.view(b, self.out_channels, *out.shape[2:4])
-            out = self.smooth(out)
-        elif self.sample_mode == "downsample":
-            x = self.smooth(x)
-            x = x.view(1, b * c, *x.shape[2:4])
-            out = F.conv2d(x, weight, padding=0, stride=2, groups=b)
-            out = out.view(b, self.out_channels, *out.shape[2:4])
-        else:
-            x = x.view(1, b * c, h, w)
-            # weight: (b*c_out, c_in, k, k), groups=b
-            out = F.conv2d(x, weight, padding=self.padding, groups=b)
-            out = out.view(b, self.out_channels, *out.shape[2:4])
-
-        return out
-
-    def __repr__(self):
-        return (
-            f"{self.__class__.__name__}(in_channels={self.in_channels}, "
-            f"out_channels={self.out_channels}, "
-            f"kernel_size={self.kernel_size}, "
-            f"demodulate={self.demodulate}, sample_mode={self.sample_mode})"
-        )
-
-
-class StyleConv(nn.Module):
-    """Style conv.
-
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        num_style_feat (int): Channel number of style features.
-        demodulate (bool): Whether demodulate in the conv layer. Default: True.
-        sample_mode (str | None): Indicating 'upsample', 'downsample' or None.
-            Default: None.
-        resample_kernel (list[int]): A list indicating the 1D resample kernel
-            magnitude. Default: (1, 3, 3, 1).
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        num_style_feat,
-        demodulate=True,
-        sample_mode=None,
-        resample_kernel=(1, 3, 3, 1),
-    ):
-        super(StyleConv, self).__init__()
-        self.modulated_conv = ModulatedConv2d(
-            in_channels,
-            out_channels,
-            kernel_size,
-            num_style_feat,
-            demodulate=demodulate,
-            sample_mode=sample_mode,
-            resample_kernel=resample_kernel,
-        )
-        self.weight = nn.Parameter(torch.zeros(1))  # for noise injection
-        self.activate = FusedLeakyReLU(out_channels)
-
-    def forward(self, x, style, noise=None):
-        # modulate
-        out = self.modulated_conv(x, style)
-        # noise injection
-        if noise is None:
-            b, _, h, w = out.shape
-            noise = out.new_empty(b, 1, h, w).normal_()
-        out = out + self.weight * noise
-        # activation (with bias)
-        out = self.activate(out)
-        return out
-
-
-class ToRGB(nn.Module):
-    """To RGB from features.
-
-    Args:
-        in_channels (int): Channel number of input.
-        num_style_feat (int): Channel number of style features.
-        upsample (bool): Whether to upsample. Default: True.
-        resample_kernel (list[int]): A list indicating the 1D resample kernel
-            magnitude. Default: (1, 3, 3, 1).
-    """
-
-    def __init__(
-        self, in_channels, num_style_feat, upsample=True, resample_kernel=(1, 3, 3, 1)
-    ):
-        super(ToRGB, self).__init__()
-        if upsample:
-            self.upsample = UpFirDnUpsample(resample_kernel, factor=2)
-        else:
-            self.upsample = None
-        self.modulated_conv = ModulatedConv2d(
-            in_channels,
-            3,
-            kernel_size=1,
-            num_style_feat=num_style_feat,
-            demodulate=False,
-            sample_mode=None,
-        )
-        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
-
-    def forward(self, x, style, skip=None):
-        """Forward function.
-
-        Args:
-            x (Tensor): Feature tensor with shape (b, c, h, w).
-            style (Tensor): Tensor with shape (b, num_style_feat).
-            skip (Tensor): Base/skip tensor. Default: None.
-
-        Returns:
-            Tensor: RGB images.
-        """
-        out = self.modulated_conv(x, style)
-        out = out + self.bias
-        if skip is not None:
-            if self.upsample:
-                skip = self.upsample(skip)
-            out = out + skip
-        return out
-
-
-class ConstantInput(nn.Module):
-    """Constant input.
-
-    Args:
-        num_channel (int): Channel number of constant input.
-        size (int): Spatial size of constant input.
-    """
-
-    def __init__(self, num_channel, size):
-        super(ConstantInput, self).__init__()
-        self.weight = nn.Parameter(torch.randn(1, num_channel, size, size))
-
-    def forward(self, batch):
-        out = self.weight.repeat(batch, 1, 1, 1)
-        return out
-
-
-class StyleGAN2Generator(nn.Module):
-    """StyleGAN2 Generator.
-
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        channel_multiplier (int): Channel multiplier for large networks of
-            StyleGAN2. Default: 2.
-        resample_kernel (list[int]): A list indicating the 1D resample kernel
-            magnitude. A cross production will be applied to extent 1D resample
-            kernel to 2D resample kernel. Default: (1, 3, 3, 1).
-        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.
-        narrow (float): Narrow ratio for channels. Default: 1.0.
-    """
-
-    def __init__(
-        self,
-        out_size,
-        num_style_feat=512,
-        num_mlp=8,
-        channel_multiplier=2,
-        resample_kernel=(1, 3, 3, 1),
-        lr_mlp=0.01,
-        narrow=1,
-    ):
-        super(StyleGAN2Generator, self).__init__()
-        # Style MLP layers
-        self.num_style_feat = num_style_feat
-        style_mlp_layers = [NormStyleCode()]
-        for i in range(num_mlp):
-            style_mlp_layers.append(
-                EqualLinear(
-                    num_style_feat,
-                    num_style_feat,
-                    bias=True,
-                    bias_init_val=0,
-                    lr_mul=lr_mlp,
-                    activation="fused_lrelu",
-                )
-            )
-        self.style_mlp = nn.Sequential(*style_mlp_layers)
-
-        channels = {
-            "4": int(512 * narrow),
-            "8": int(512 * narrow),
-            "16": int(512 * narrow),
-            "32": int(512 * narrow),
-            "64": int(256 * channel_multiplier * narrow),
-            "128": int(128 * channel_multiplier * narrow),
-            "256": int(64 * channel_multiplier * narrow),
-            "512": int(32 * channel_multiplier * narrow),
-            "1024": int(16 * channel_multiplier * narrow),
-        }
-        self.channels = channels
-
-        self.constant_input = ConstantInput(channels["4"], size=4)
-        self.style_conv1 = StyleConv(
-            channels["4"],
-            channels["4"],
-            kernel_size=3,
-            num_style_feat=num_style_feat,
-            demodulate=True,
-            sample_mode=None,
-            resample_kernel=resample_kernel,
-        )
-        self.to_rgb1 = ToRGB(
-            channels["4"],
-            num_style_feat,
-            upsample=False,
-            resample_kernel=resample_kernel,
-        )
-
-        self.log_size = int(math.log(out_size, 2))
-        self.num_layers = (self.log_size - 2) * 2 + 1
-        self.num_latent = self.log_size * 2 - 2
-
-        self.style_convs = nn.ModuleList()
-        self.to_rgbs = nn.ModuleList()
-        self.noises = nn.Module()
-
-        in_channels = channels["4"]
-        # noise
-        for layer_idx in range(self.num_layers):
-            resolution = 2 ** ((layer_idx + 5) // 2)
-            shape = [1, 1, resolution, resolution]
-            self.noises.register_buffer(f"noise{layer_idx}", torch.randn(*shape))
-        # style convs and to_rgbs
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f"{2**i}"]
-            self.style_convs.append(
-                StyleConv(
-                    in_channels,
-                    out_channels,
-                    kernel_size=3,
-                    num_style_feat=num_style_feat,
-                    demodulate=True,
-                    sample_mode="upsample",
-                    resample_kernel=resample_kernel,
-                )
-            )
-            self.style_convs.append(
-                StyleConv(
-                    out_channels,
-                    out_channels,
-                    kernel_size=3,
-                    num_style_feat=num_style_feat,
-                    demodulate=True,
-                    sample_mode=None,
-                    resample_kernel=resample_kernel,
-                )
-            )
-            self.to_rgbs.append(
-                ToRGB(
-                    out_channels,
-                    num_style_feat,
-                    upsample=True,
-                    resample_kernel=resample_kernel,
-                )
-            )
-            in_channels = out_channels
-
-    def make_noise(self):
-        """Make noise for noise injection."""
-        device = self.constant_input.weight.device
-        noises = [torch.randn(1, 1, 4, 4, device=device)]
-
-        for i in range(3, self.log_size + 1):
-            for _ in range(2):
-                noises.append(torch.randn(1, 1, 2**i, 2**i, device=device))
-
-        return noises
-
-    def get_latent(self, x):
-        return self.style_mlp(x)
-
-    def mean_latent(self, num_latent):
-        latent_in = torch.randn(
-            num_latent, self.num_style_feat, device=self.constant_input.weight.device
-        )
-        latent = self.style_mlp(latent_in).mean(0, keepdim=True)
-        return latent
-
-    def forward(
-        self,
-        styles,
-        input_is_latent=False,
-        noise=None,
-        randomize_noise=True,
-        truncation=1,
-        truncation_latent=None,
-        inject_index=None,
-        return_latents=False,
-    ):
-        """Forward function for StyleGAN2Generator.
-
-        Args:
-            styles (list[Tensor]): Sample codes of styles.
-            input_is_latent (bool): Whether input is latent style.
-                Default: False.
-            noise (Tensor | None): Input noise or None. Default: None.
-            randomize_noise (bool): Randomize noise, used when 'noise' is
-                False. Default: True.
-            truncation (float): TODO. Default: 1.
-            truncation_latent (Tensor | None): TODO. Default: None.
-            inject_index (int | None): The injection index for mixing noise.
-                Default: None.
-            return_latents (bool): Whether to return style latents.
-                Default: False.
-        """
-        # style codes -> latents with Style MLP layer
-        if not input_is_latent:
-            styles = [self.style_mlp(s) for s in styles]
-        # noises
-        if noise is None:
-            if randomize_noise:
-                noise = [None] * self.num_layers  # for each style conv layer
-            else:  # use the stored noise
-                noise = [
-                    getattr(self.noises, f"noise{i}") for i in range(self.num_layers)
-                ]
-        # style truncation
-        if truncation < 1:
-            style_truncation = []
-            for style in styles:
-                style_truncation.append(
-                    truncation_latent + truncation * (style - truncation_latent)
-                )
-            styles = style_truncation
-        # get style latent with injection
-        if len(styles) == 1:
-            inject_index = self.num_latent
-
-            if styles[0].ndim < 3:
-                # repeat latent code for all the layers
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            else:  # used for encoder with different latent code for each layer
-                latent = styles[0]
-        elif len(styles) == 2:  # mixing noises
-            if inject_index is None:
-                inject_index = random.randint(1, self.num_latent - 1)
-            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            latent2 = (
-                styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
-            )
-            latent = torch.cat([latent1, latent2], 1)
-
-        # main generation
-        out = self.constant_input(latent.shape[0])
-        out = self.style_conv1(out, latent[:, 0], noise=noise[0])
-        skip = self.to_rgb1(out, latent[:, 1])
-
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(
-            self.style_convs[::2],
-            self.style_convs[1::2],
-            noise[1::2],
-            noise[2::2],
-            self.to_rgbs,
-        ):
-            out = conv1(out, latent[:, i], noise=noise1)
-            out = conv2(out, latent[:, i + 1], noise=noise2)
-            skip = to_rgb(out, latent[:, i + 2], skip)
-            i += 2
-
-        image = skip
-
-        if return_latents:
-            return image, latent
-        else:
-            return image, None
-
-
-class ScaledLeakyReLU(nn.Module):
-    """Scaled LeakyReLU.
-
-    Args:
-        negative_slope (float): Negative slope. Default: 0.2.
-    """
-
-    def __init__(self, negative_slope=0.2):
-        super(ScaledLeakyReLU, self).__init__()
-        self.negative_slope = negative_slope
-
-    def forward(self, x):
-        out = F.leaky_relu(x, negative_slope=self.negative_slope)
-        return out * math.sqrt(2)
-
-
-class EqualConv2d(nn.Module):
-    """Equalized Linear as StyleGAN2.
-
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        stride (int): Stride of the convolution. Default: 1
-        padding (int): Zero-padding added to both sides of the input.
-            Default: 0.
-        bias (bool): If ``True``, adds a learnable bias to the output.
-            Default: ``True``.
-        bias_init_val (float): Bias initialized value. Default: 0.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        stride=1,
-        padding=0,
-        bias=True,
-        bias_init_val=0,
-    ):
-        super(EqualConv2d, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.stride = stride
-        self.padding = padding
-        self.scale = 1 / math.sqrt(in_channels * kernel_size**2)
-
-        self.weight = nn.Parameter(
-            torch.randn(out_channels, in_channels, kernel_size, kernel_size)
-        )
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val))
-        else:
-            self.register_parameter("bias", None)
-
-    def forward(self, x):
-        out = F.conv2d(
-            x,
-            self.weight * self.scale,
-            bias=self.bias,
-            stride=self.stride,
-            padding=self.padding,
-        )
-
-        return out
-
-    def __repr__(self):
-        return (
-            f"{self.__class__.__name__}(in_channels={self.in_channels}, "
-            f"out_channels={self.out_channels}, "
-            f"kernel_size={self.kernel_size},"
-            f" stride={self.stride}, padding={self.padding}, "
-            f"bias={self.bias is not None})"
-        )
-
-
-class ConvLayer(nn.Sequential):
-    """Conv Layer used in StyleGAN2 Discriminator.
-
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Kernel size.
-        downsample (bool): Whether downsample by a factor of 2.
-            Default: False.
-        resample_kernel (list[int]): A list indicating the 1D resample
-            kernel magnitude. A cross production will be applied to
-            extent 1D resample kernel to 2D resample kernel.
-            Default: (1, 3, 3, 1).
-        bias (bool): Whether with bias. Default: True.
-        activate (bool): Whether use activateion. Default: True.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        downsample=False,
-        resample_kernel=(1, 3, 3, 1),
-        bias=True,
-        activate=True,
-    ):
-        layers = []
-        # downsample
-        if downsample:
-            layers.append(
-                UpFirDnSmooth(
-                    resample_kernel,
-                    upsample_factor=1,
-                    downsample_factor=2,
-                    kernel_size=kernel_size,
-                )
-            )
-            stride = 2
-            self.padding = 0
-        else:
-            stride = 1
-            self.padding = kernel_size // 2
-        # conv
-        layers.append(
-            EqualConv2d(
-                in_channels,
-                out_channels,
-                kernel_size,
-                stride=stride,
-                padding=self.padding,
-                bias=bias and not activate,
-            )
-        )
-        # activation
-        if activate:
-            if bias:
-                layers.append(FusedLeakyReLU(out_channels))
-            else:
-                layers.append(ScaledLeakyReLU(0.2))
-
-        super(ConvLayer, self).__init__(*layers)
-
-
-class ResBlock(nn.Module):
-    """Residual block used in StyleGAN2 Discriminator.
-
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        resample_kernel (list[int]): A list indicating the 1D resample
-            kernel magnitude. A cross production will be applied to
-            extent 1D resample kernel to 2D resample kernel.
-            Default: (1, 3, 3, 1).
-    """
-
-    def __init__(self, in_channels, out_channels, resample_kernel=(1, 3, 3, 1)):
-        super(ResBlock, self).__init__()
-
-        self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True)
-        self.conv2 = ConvLayer(
-            in_channels,
-            out_channels,
-            3,
-            downsample=True,
-            resample_kernel=resample_kernel,
-            bias=True,
-            activate=True,
-        )
-        self.skip = ConvLayer(
-            in_channels,
-            out_channels,
-            1,
-            downsample=True,
-            resample_kernel=resample_kernel,
-            bias=False,
-            activate=False,
-        )
-
-    def forward(self, x):
-        out = self.conv1(x)
-        out = self.conv2(out)
-        skip = self.skip(x)
-        out = (out + skip) / math.sqrt(2)
-        return out
diff --git a/comfy_extras/chainner_models/architecture/face/stylegan2_bilinear_arch.py b/comfy_extras/chainner_models/architecture/face/stylegan2_bilinear_arch.py
deleted file mode 100644
index 601f8cc4..00000000
--- a/comfy_extras/chainner_models/architecture/face/stylegan2_bilinear_arch.py
+++ /dev/null
@@ -1,709 +0,0 @@
-# pylint: skip-file
-# type: ignore
-import math
-import random
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-
-from .fused_act import FusedLeakyReLU, fused_leaky_relu
-
-
-class NormStyleCode(nn.Module):
-    def forward(self, x):
-        """Normalize the style codes.
-        Args:
-            x (Tensor): Style codes with shape (b, c).
-        Returns:
-            Tensor: Normalized tensor.
-        """
-        return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8)
-
-
-class EqualLinear(nn.Module):
-    """Equalized Linear as StyleGAN2.
-    Args:
-        in_channels (int): Size of each sample.
-        out_channels (int): Size of each output sample.
-        bias (bool): If set to ``False``, the layer will not learn an additive
-            bias. Default: ``True``.
-        bias_init_val (float): Bias initialized value. Default: 0.
-        lr_mul (float): Learning rate multiplier. Default: 1.
-        activation (None | str): The activation after ``linear`` operation.
-            Supported: 'fused_lrelu', None. Default: None.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        bias=True,
-        bias_init_val=0,
-        lr_mul=1,
-        activation=None,
-    ):
-        super(EqualLinear, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.lr_mul = lr_mul
-        self.activation = activation
-        if self.activation not in ["fused_lrelu", None]:
-            raise ValueError(
-                f"Wrong activation value in EqualLinear: {activation}"
-                "Supported ones are: ['fused_lrelu', None]."
-            )
-        self.scale = (1 / math.sqrt(in_channels)) * lr_mul
-
-        self.weight = nn.Parameter(torch.randn(out_channels, in_channels).div_(lr_mul))
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val))
-        else:
-            self.register_parameter("bias", None)
-
-    def forward(self, x):
-        if self.bias is None:
-            bias = None
-        else:
-            bias = self.bias * self.lr_mul
-        if self.activation == "fused_lrelu":
-            out = F.linear(x, self.weight * self.scale)
-            out = fused_leaky_relu(out, bias)
-        else:
-            out = F.linear(x, self.weight * self.scale, bias=bias)
-        return out
-
-    def __repr__(self):
-        return (
-            f"{self.__class__.__name__}(in_channels={self.in_channels}, "
-            f"out_channels={self.out_channels}, bias={self.bias is not None})"
-        )
-
-
-class ModulatedConv2d(nn.Module):
-    """Modulated Conv2d used in StyleGAN2.
-    There is no bias in ModulatedConv2d.
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        num_style_feat (int): Channel number of style features.
-        demodulate (bool): Whether to demodulate in the conv layer.
-            Default: True.
-        sample_mode (str | None): Indicating 'upsample', 'downsample' or None.
-            Default: None.
-        eps (float): A value added to the denominator for numerical stability.
-            Default: 1e-8.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        num_style_feat,
-        demodulate=True,
-        sample_mode=None,
-        eps=1e-8,
-        interpolation_mode="bilinear",
-    ):
-        super(ModulatedConv2d, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.demodulate = demodulate
-        self.sample_mode = sample_mode
-        self.eps = eps
-        self.interpolation_mode = interpolation_mode
-        if self.interpolation_mode == "nearest":
-            self.align_corners = None
-        else:
-            self.align_corners = False
-
-        self.scale = 1 / math.sqrt(in_channels * kernel_size**2)
-        # modulation inside each modulated conv
-        self.modulation = EqualLinear(
-            num_style_feat,
-            in_channels,
-            bias=True,
-            bias_init_val=1,
-            lr_mul=1,
-            activation=None,
-        )
-
-        self.weight = nn.Parameter(
-            torch.randn(1, out_channels, in_channels, kernel_size, kernel_size)
-        )
-        self.padding = kernel_size // 2
-
-    def forward(self, x, style):
-        """Forward function.
-        Args:
-            x (Tensor): Tensor with shape (b, c, h, w).
-            style (Tensor): Tensor with shape (b, num_style_feat).
-        Returns:
-            Tensor: Modulated tensor after convolution.
-        """
-        b, c, h, w = x.shape  # c = c_in
-        # weight modulation
-        style = self.modulation(style).view(b, 1, c, 1, 1)
-        # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1)
-        weight = self.scale * self.weight * style  # (b, c_out, c_in, k, k)
-
-        if self.demodulate:
-            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps)
-            weight = weight * demod.view(b, self.out_channels, 1, 1, 1)
-
-        weight = weight.view(
-            b * self.out_channels, c, self.kernel_size, self.kernel_size
-        )
-
-        if self.sample_mode == "upsample":
-            x = F.interpolate(
-                x,
-                scale_factor=2,
-                mode=self.interpolation_mode,
-                align_corners=self.align_corners,
-            )
-        elif self.sample_mode == "downsample":
-            x = F.interpolate(
-                x,
-                scale_factor=0.5,
-                mode=self.interpolation_mode,
-                align_corners=self.align_corners,
-            )
-
-        b, c, h, w = x.shape
-        x = x.view(1, b * c, h, w)
-        # weight: (b*c_out, c_in, k, k), groups=b
-        out = F.conv2d(x, weight, padding=self.padding, groups=b)
-        out = out.view(b, self.out_channels, *out.shape[2:4])
-
-        return out
-
-    def __repr__(self):
-        return (
-            f"{self.__class__.__name__}(in_channels={self.in_channels}, "
-            f"out_channels={self.out_channels}, "
-            f"kernel_size={self.kernel_size}, "
-            f"demodulate={self.demodulate}, sample_mode={self.sample_mode})"
-        )
-
-
-class StyleConv(nn.Module):
-    """Style conv.
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        num_style_feat (int): Channel number of style features.
-        demodulate (bool): Whether demodulate in the conv layer. Default: True.
-        sample_mode (str | None): Indicating 'upsample', 'downsample' or None.
-            Default: None.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        num_style_feat,
-        demodulate=True,
-        sample_mode=None,
-        interpolation_mode="bilinear",
-    ):
-        super(StyleConv, self).__init__()
-        self.modulated_conv = ModulatedConv2d(
-            in_channels,
-            out_channels,
-            kernel_size,
-            num_style_feat,
-            demodulate=demodulate,
-            sample_mode=sample_mode,
-            interpolation_mode=interpolation_mode,
-        )
-        self.weight = nn.Parameter(torch.zeros(1))  # for noise injection
-        self.activate = FusedLeakyReLU(out_channels)
-
-    def forward(self, x, style, noise=None):
-        # modulate
-        out = self.modulated_conv(x, style)
-        # noise injection
-        if noise is None:
-            b, _, h, w = out.shape
-            noise = out.new_empty(b, 1, h, w).normal_()
-        out = out + self.weight * noise
-        # activation (with bias)
-        out = self.activate(out)
-        return out
-
-
-class ToRGB(nn.Module):
-    """To RGB from features.
-    Args:
-        in_channels (int): Channel number of input.
-        num_style_feat (int): Channel number of style features.
-        upsample (bool): Whether to upsample. Default: True.
-    """
-
-    def __init__(
-        self, in_channels, num_style_feat, upsample=True, interpolation_mode="bilinear"
-    ):
-        super(ToRGB, self).__init__()
-        self.upsample = upsample
-        self.interpolation_mode = interpolation_mode
-        if self.interpolation_mode == "nearest":
-            self.align_corners = None
-        else:
-            self.align_corners = False
-        self.modulated_conv = ModulatedConv2d(
-            in_channels,
-            3,
-            kernel_size=1,
-            num_style_feat=num_style_feat,
-            demodulate=False,
-            sample_mode=None,
-            interpolation_mode=interpolation_mode,
-        )
-        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
-
-    def forward(self, x, style, skip=None):
-        """Forward function.
-        Args:
-            x (Tensor): Feature tensor with shape (b, c, h, w).
-            style (Tensor): Tensor with shape (b, num_style_feat).
-            skip (Tensor): Base/skip tensor. Default: None.
-        Returns:
-            Tensor: RGB images.
-        """
-        out = self.modulated_conv(x, style)
-        out = out + self.bias
-        if skip is not None:
-            if self.upsample:
-                skip = F.interpolate(
-                    skip,
-                    scale_factor=2,
-                    mode=self.interpolation_mode,
-                    align_corners=self.align_corners,
-                )
-            out = out + skip
-        return out
-
-
-class ConstantInput(nn.Module):
-    """Constant input.
-    Args:
-        num_channel (int): Channel number of constant input.
-        size (int): Spatial size of constant input.
-    """
-
-    def __init__(self, num_channel, size):
-        super(ConstantInput, self).__init__()
-        self.weight = nn.Parameter(torch.randn(1, num_channel, size, size))
-
-    def forward(self, batch):
-        out = self.weight.repeat(batch, 1, 1, 1)
-        return out
-
-
-class StyleGAN2GeneratorBilinear(nn.Module):
-    """StyleGAN2 Generator.
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        channel_multiplier (int): Channel multiplier for large networks of
-            StyleGAN2. Default: 2.
-        lr_mlp (float): Learning rate multiplier for mlp layers. Default: 0.01.
-        narrow (float): Narrow ratio for channels. Default: 1.0.
-    """
-
-    def __init__(
-        self,
-        out_size,
-        num_style_feat=512,
-        num_mlp=8,
-        channel_multiplier=2,
-        lr_mlp=0.01,
-        narrow=1,
-        interpolation_mode="bilinear",
-    ):
-        super(StyleGAN2GeneratorBilinear, self).__init__()
-        # Style MLP layers
-        self.num_style_feat = num_style_feat
-        style_mlp_layers = [NormStyleCode()]
-        for i in range(num_mlp):
-            style_mlp_layers.append(
-                EqualLinear(
-                    num_style_feat,
-                    num_style_feat,
-                    bias=True,
-                    bias_init_val=0,
-                    lr_mul=lr_mlp,
-                    activation="fused_lrelu",
-                )
-            )
-        self.style_mlp = nn.Sequential(*style_mlp_layers)
-
-        channels = {
-            "4": int(512 * narrow),
-            "8": int(512 * narrow),
-            "16": int(512 * narrow),
-            "32": int(512 * narrow),
-            "64": int(256 * channel_multiplier * narrow),
-            "128": int(128 * channel_multiplier * narrow),
-            "256": int(64 * channel_multiplier * narrow),
-            "512": int(32 * channel_multiplier * narrow),
-            "1024": int(16 * channel_multiplier * narrow),
-        }
-        self.channels = channels
-
-        self.constant_input = ConstantInput(channels["4"], size=4)
-        self.style_conv1 = StyleConv(
-            channels["4"],
-            channels["4"],
-            kernel_size=3,
-            num_style_feat=num_style_feat,
-            demodulate=True,
-            sample_mode=None,
-            interpolation_mode=interpolation_mode,
-        )
-        self.to_rgb1 = ToRGB(
-            channels["4"],
-            num_style_feat,
-            upsample=False,
-            interpolation_mode=interpolation_mode,
-        )
-
-        self.log_size = int(math.log(out_size, 2))
-        self.num_layers = (self.log_size - 2) * 2 + 1
-        self.num_latent = self.log_size * 2 - 2
-
-        self.style_convs = nn.ModuleList()
-        self.to_rgbs = nn.ModuleList()
-        self.noises = nn.Module()
-
-        in_channels = channels["4"]
-        # noise
-        for layer_idx in range(self.num_layers):
-            resolution = 2 ** ((layer_idx + 5) // 2)
-            shape = [1, 1, resolution, resolution]
-            self.noises.register_buffer(f"noise{layer_idx}", torch.randn(*shape))
-        # style convs and to_rgbs
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f"{2**i}"]
-            self.style_convs.append(
-                StyleConv(
-                    in_channels,
-                    out_channels,
-                    kernel_size=3,
-                    num_style_feat=num_style_feat,
-                    demodulate=True,
-                    sample_mode="upsample",
-                    interpolation_mode=interpolation_mode,
-                )
-            )
-            self.style_convs.append(
-                StyleConv(
-                    out_channels,
-                    out_channels,
-                    kernel_size=3,
-                    num_style_feat=num_style_feat,
-                    demodulate=True,
-                    sample_mode=None,
-                    interpolation_mode=interpolation_mode,
-                )
-            )
-            self.to_rgbs.append(
-                ToRGB(
-                    out_channels,
-                    num_style_feat,
-                    upsample=True,
-                    interpolation_mode=interpolation_mode,
-                )
-            )
-            in_channels = out_channels
-
-    def make_noise(self):
-        """Make noise for noise injection."""
-        device = self.constant_input.weight.device
-        noises = [torch.randn(1, 1, 4, 4, device=device)]
-
-        for i in range(3, self.log_size + 1):
-            for _ in range(2):
-                noises.append(torch.randn(1, 1, 2**i, 2**i, device=device))
-
-        return noises
-
-    def get_latent(self, x):
-        return self.style_mlp(x)
-
-    def mean_latent(self, num_latent):
-        latent_in = torch.randn(
-            num_latent, self.num_style_feat, device=self.constant_input.weight.device
-        )
-        latent = self.style_mlp(latent_in).mean(0, keepdim=True)
-        return latent
-
-    def forward(
-        self,
-        styles,
-        input_is_latent=False,
-        noise=None,
-        randomize_noise=True,
-        truncation=1,
-        truncation_latent=None,
-        inject_index=None,
-        return_latents=False,
-    ):
-        """Forward function for StyleGAN2Generator.
-        Args:
-            styles (list[Tensor]): Sample codes of styles.
-            input_is_latent (bool): Whether input is latent style.
-                Default: False.
-            noise (Tensor | None): Input noise or None. Default: None.
-            randomize_noise (bool): Randomize noise, used when 'noise' is
-                False. Default: True.
-            truncation (float): TODO. Default: 1.
-            truncation_latent (Tensor | None): TODO. Default: None.
-            inject_index (int | None): The injection index for mixing noise.
-                Default: None.
-            return_latents (bool): Whether to return style latents.
-                Default: False.
-        """
-        # style codes -> latents with Style MLP layer
-        if not input_is_latent:
-            styles = [self.style_mlp(s) for s in styles]
-        # noises
-        if noise is None:
-            if randomize_noise:
-                noise = [None] * self.num_layers  # for each style conv layer
-            else:  # use the stored noise
-                noise = [
-                    getattr(self.noises, f"noise{i}") for i in range(self.num_layers)
-                ]
-        # style truncation
-        if truncation < 1:
-            style_truncation = []
-            for style in styles:
-                style_truncation.append(
-                    truncation_latent + truncation * (style - truncation_latent)
-                )
-            styles = style_truncation
-        # get style latent with injection
-        if len(styles) == 1:
-            inject_index = self.num_latent
-
-            if styles[0].ndim < 3:
-                # repeat latent code for all the layers
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            else:  # used for encoder with different latent code for each layer
-                latent = styles[0]
-        elif len(styles) == 2:  # mixing noises
-            if inject_index is None:
-                inject_index = random.randint(1, self.num_latent - 1)
-            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            latent2 = (
-                styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
-            )
-            latent = torch.cat([latent1, latent2], 1)
-
-        # main generation
-        out = self.constant_input(latent.shape[0])
-        out = self.style_conv1(out, latent[:, 0], noise=noise[0])
-        skip = self.to_rgb1(out, latent[:, 1])
-
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(
-            self.style_convs[::2],
-            self.style_convs[1::2],
-            noise[1::2],
-            noise[2::2],
-            self.to_rgbs,
-        ):
-            out = conv1(out, latent[:, i], noise=noise1)
-            out = conv2(out, latent[:, i + 1], noise=noise2)
-            skip = to_rgb(out, latent[:, i + 2], skip)
-            i += 2
-
-        image = skip
-
-        if return_latents:
-            return image, latent
-        else:
-            return image, None
-
-
-class ScaledLeakyReLU(nn.Module):
-    """Scaled LeakyReLU.
-    Args:
-        negative_slope (float): Negative slope. Default: 0.2.
-    """
-
-    def __init__(self, negative_slope=0.2):
-        super(ScaledLeakyReLU, self).__init__()
-        self.negative_slope = negative_slope
-
-    def forward(self, x):
-        out = F.leaky_relu(x, negative_slope=self.negative_slope)
-        return out * math.sqrt(2)
-
-
-class EqualConv2d(nn.Module):
-    """Equalized Linear as StyleGAN2.
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        stride (int): Stride of the convolution. Default: 1
-        padding (int): Zero-padding added to both sides of the input.
-            Default: 0.
-        bias (bool): If ``True``, adds a learnable bias to the output.
-            Default: ``True``.
-        bias_init_val (float): Bias initialized value. Default: 0.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        stride=1,
-        padding=0,
-        bias=True,
-        bias_init_val=0,
-    ):
-        super(EqualConv2d, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.stride = stride
-        self.padding = padding
-        self.scale = 1 / math.sqrt(in_channels * kernel_size**2)
-
-        self.weight = nn.Parameter(
-            torch.randn(out_channels, in_channels, kernel_size, kernel_size)
-        )
-        if bias:
-            self.bias = nn.Parameter(torch.zeros(out_channels).fill_(bias_init_val))
-        else:
-            self.register_parameter("bias", None)
-
-    def forward(self, x):
-        out = F.conv2d(
-            x,
-            self.weight * self.scale,
-            bias=self.bias,
-            stride=self.stride,
-            padding=self.padding,
-        )
-
-        return out
-
-    def __repr__(self):
-        return (
-            f"{self.__class__.__name__}(in_channels={self.in_channels}, "
-            f"out_channels={self.out_channels}, "
-            f"kernel_size={self.kernel_size},"
-            f" stride={self.stride}, padding={self.padding}, "
-            f"bias={self.bias is not None})"
-        )
-
-
-class ConvLayer(nn.Sequential):
-    """Conv Layer used in StyleGAN2 Discriminator.
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Kernel size.
-        downsample (bool): Whether downsample by a factor of 2.
-            Default: False.
-        bias (bool): Whether with bias. Default: True.
-        activate (bool): Whether use activateion. Default: True.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        downsample=False,
-        bias=True,
-        activate=True,
-        interpolation_mode="bilinear",
-    ):
-        layers = []
-        self.interpolation_mode = interpolation_mode
-        # downsample
-        if downsample:
-            if self.interpolation_mode == "nearest":
-                self.align_corners = None
-            else:
-                self.align_corners = False
-
-            layers.append(
-                torch.nn.Upsample(
-                    scale_factor=0.5,
-                    mode=interpolation_mode,
-                    align_corners=self.align_corners,
-                )
-            )
-        stride = 1
-        self.padding = kernel_size // 2
-        # conv
-        layers.append(
-            EqualConv2d(
-                in_channels,
-                out_channels,
-                kernel_size,
-                stride=stride,
-                padding=self.padding,
-                bias=bias and not activate,
-            )
-        )
-        # activation
-        if activate:
-            if bias:
-                layers.append(FusedLeakyReLU(out_channels))
-            else:
-                layers.append(ScaledLeakyReLU(0.2))
-
-        super(ConvLayer, self).__init__(*layers)
-
-
-class ResBlock(nn.Module):
-    """Residual block used in StyleGAN2 Discriminator.
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-    """
-
-    def __init__(self, in_channels, out_channels, interpolation_mode="bilinear"):
-        super(ResBlock, self).__init__()
-
-        self.conv1 = ConvLayer(in_channels, in_channels, 3, bias=True, activate=True)
-        self.conv2 = ConvLayer(
-            in_channels,
-            out_channels,
-            3,
-            downsample=True,
-            interpolation_mode=interpolation_mode,
-            bias=True,
-            activate=True,
-        )
-        self.skip = ConvLayer(
-            in_channels,
-            out_channels,
-            1,
-            downsample=True,
-            interpolation_mode=interpolation_mode,
-            bias=False,
-            activate=False,
-        )
-
-    def forward(self, x):
-        out = self.conv1(x)
-        out = self.conv2(out)
-        skip = self.skip(x)
-        out = (out + skip) / math.sqrt(2)
-        return out
diff --git a/comfy_extras/chainner_models/architecture/face/stylegan2_clean_arch.py b/comfy_extras/chainner_models/architecture/face/stylegan2_clean_arch.py
deleted file mode 100644
index c48de9af..00000000
--- a/comfy_extras/chainner_models/architecture/face/stylegan2_clean_arch.py
+++ /dev/null
@@ -1,453 +0,0 @@
-# pylint: skip-file
-# type: ignore
-import math
-
-import torch
-from torch import nn
-from torch.nn import functional as F
-from torch.nn import init
-from torch.nn.modules.batchnorm import _BatchNorm
-
-
-@torch.no_grad()
-def default_init_weights(module_list, scale=1, bias_fill=0, **kwargs):
-    """Initialize network weights.
-    Args:
-        module_list (list[nn.Module] | nn.Module): Modules to be initialized.
-        scale (float): Scale initialized weights, especially for residual
-            blocks. Default: 1.
-        bias_fill (float): The value to fill bias. Default: 0
-        kwargs (dict): Other arguments for initialization function.
-    """
-    if not isinstance(module_list, list):
-        module_list = [module_list]
-    for module in module_list:
-        for m in module.modules():
-            if isinstance(m, nn.Conv2d):
-                init.kaiming_normal_(m.weight, **kwargs)
-                m.weight.data *= scale
-                if m.bias is not None:
-                    m.bias.data.fill_(bias_fill)
-            elif isinstance(m, nn.Linear):
-                init.kaiming_normal_(m.weight, **kwargs)
-                m.weight.data *= scale
-                if m.bias is not None:
-                    m.bias.data.fill_(bias_fill)
-            elif isinstance(m, _BatchNorm):
-                init.constant_(m.weight, 1)
-                if m.bias is not None:
-                    m.bias.data.fill_(bias_fill)
-
-
-class NormStyleCode(nn.Module):
-    def forward(self, x):
-        """Normalize the style codes.
-        Args:
-            x (Tensor): Style codes with shape (b, c).
-        Returns:
-            Tensor: Normalized tensor.
-        """
-        return x * torch.rsqrt(torch.mean(x**2, dim=1, keepdim=True) + 1e-8)
-
-
-class ModulatedConv2d(nn.Module):
-    """Modulated Conv2d used in StyleGAN2.
-    There is no bias in ModulatedConv2d.
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        num_style_feat (int): Channel number of style features.
-        demodulate (bool): Whether to demodulate in the conv layer. Default: True.
-        sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None.
-        eps (float): A value added to the denominator for numerical stability. Default: 1e-8.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        num_style_feat,
-        demodulate=True,
-        sample_mode=None,
-        eps=1e-8,
-    ):
-        super(ModulatedConv2d, self).__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.demodulate = demodulate
-        self.sample_mode = sample_mode
-        self.eps = eps
-
-        # modulation inside each modulated conv
-        self.modulation = nn.Linear(num_style_feat, in_channels, bias=True)
-        # initialization
-        default_init_weights(
-            self.modulation,
-            scale=1,
-            bias_fill=1,
-            a=0,
-            mode="fan_in",
-            nonlinearity="linear",
-        )
-
-        self.weight = nn.Parameter(
-            torch.randn(1, out_channels, in_channels, kernel_size, kernel_size)
-            / math.sqrt(in_channels * kernel_size**2)
-        )
-        self.padding = kernel_size // 2
-
-    def forward(self, x, style):
-        """Forward function.
-        Args:
-            x (Tensor): Tensor with shape (b, c, h, w).
-            style (Tensor): Tensor with shape (b, num_style_feat).
-        Returns:
-            Tensor: Modulated tensor after convolution.
-        """
-        b, c, h, w = x.shape  # c = c_in
-        # weight modulation
-        style = self.modulation(style).view(b, 1, c, 1, 1)
-        # self.weight: (1, c_out, c_in, k, k); style: (b, 1, c, 1, 1)
-        weight = self.weight * style  # (b, c_out, c_in, k, k)
-
-        if self.demodulate:
-            demod = torch.rsqrt(weight.pow(2).sum([2, 3, 4]) + self.eps)
-            weight = weight * demod.view(b, self.out_channels, 1, 1, 1)
-
-        weight = weight.view(
-            b * self.out_channels, c, self.kernel_size, self.kernel_size
-        )
-
-        # upsample or downsample if necessary
-        if self.sample_mode == "upsample":
-            x = F.interpolate(x, scale_factor=2, mode="bilinear", align_corners=False)
-        elif self.sample_mode == "downsample":
-            x = F.interpolate(x, scale_factor=0.5, mode="bilinear", align_corners=False)
-
-        b, c, h, w = x.shape
-        x = x.view(1, b * c, h, w)
-        # weight: (b*c_out, c_in, k, k), groups=b
-        out = F.conv2d(x, weight, padding=self.padding, groups=b)
-        out = out.view(b, self.out_channels, *out.shape[2:4])
-
-        return out
-
-    def __repr__(self):
-        return (
-            f"{self.__class__.__name__}(in_channels={self.in_channels}, out_channels={self.out_channels}, "
-            f"kernel_size={self.kernel_size}, demodulate={self.demodulate}, sample_mode={self.sample_mode})"
-        )
-
-
-class StyleConv(nn.Module):
-    """Style conv used in StyleGAN2.
-    Args:
-        in_channels (int): Channel number of the input.
-        out_channels (int): Channel number of the output.
-        kernel_size (int): Size of the convolving kernel.
-        num_style_feat (int): Channel number of style features.
-        demodulate (bool): Whether demodulate in the conv layer. Default: True.
-        sample_mode (str | None): Indicating 'upsample', 'downsample' or None. Default: None.
-    """
-
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        kernel_size,
-        num_style_feat,
-        demodulate=True,
-        sample_mode=None,
-    ):
-        super(StyleConv, self).__init__()
-        self.modulated_conv = ModulatedConv2d(
-            in_channels,
-            out_channels,
-            kernel_size,
-            num_style_feat,
-            demodulate=demodulate,
-            sample_mode=sample_mode,
-        )
-        self.weight = nn.Parameter(torch.zeros(1))  # for noise injection
-        self.bias = nn.Parameter(torch.zeros(1, out_channels, 1, 1))
-        self.activate = nn.LeakyReLU(negative_slope=0.2, inplace=True)
-
-    def forward(self, x, style, noise=None):
-        # modulate
-        out = self.modulated_conv(x, style) * 2**0.5  # for conversion
-        # noise injection
-        if noise is None:
-            b, _, h, w = out.shape
-            noise = out.new_empty(b, 1, h, w).normal_()
-        out = out + self.weight * noise
-        # add bias
-        out = out + self.bias
-        # activation
-        out = self.activate(out)
-        return out
-
-
-class ToRGB(nn.Module):
-    """To RGB (image space) from features.
-    Args:
-        in_channels (int): Channel number of input.
-        num_style_feat (int): Channel number of style features.
-        upsample (bool): Whether to upsample. Default: True.
-    """
-
-    def __init__(self, in_channels, num_style_feat, upsample=True):
-        super(ToRGB, self).__init__()
-        self.upsample = upsample
-        self.modulated_conv = ModulatedConv2d(
-            in_channels,
-            3,
-            kernel_size=1,
-            num_style_feat=num_style_feat,
-            demodulate=False,
-            sample_mode=None,
-        )
-        self.bias = nn.Parameter(torch.zeros(1, 3, 1, 1))
-
-    def forward(self, x, style, skip=None):
-        """Forward function.
-        Args:
-            x (Tensor): Feature tensor with shape (b, c, h, w).
-            style (Tensor): Tensor with shape (b, num_style_feat).
-            skip (Tensor): Base/skip tensor. Default: None.
-        Returns:
-            Tensor: RGB images.
-        """
-        out = self.modulated_conv(x, style)
-        out = out + self.bias
-        if skip is not None:
-            if self.upsample:
-                skip = F.interpolate(
-                    skip, scale_factor=2, mode="bilinear", align_corners=False
-                )
-            out = out + skip
-        return out
-
-
-class ConstantInput(nn.Module):
-    """Constant input.
-    Args:
-        num_channel (int): Channel number of constant input.
-        size (int): Spatial size of constant input.
-    """
-
-    def __init__(self, num_channel, size):
-        super(ConstantInput, self).__init__()
-        self.weight = nn.Parameter(torch.randn(1, num_channel, size, size))
-
-    def forward(self, batch):
-        out = self.weight.repeat(batch, 1, 1, 1)
-        return out
-
-
-class StyleGAN2GeneratorClean(nn.Module):
-    """Clean version of StyleGAN2 Generator.
-    Args:
-        out_size (int): The spatial size of outputs.
-        num_style_feat (int): Channel number of style features. Default: 512.
-        num_mlp (int): Layer number of MLP style layers. Default: 8.
-        channel_multiplier (int): Channel multiplier for large networks of StyleGAN2. Default: 2.
-        narrow (float): Narrow ratio for channels. Default: 1.0.
-    """
-
-    def __init__(
-        self, out_size, num_style_feat=512, num_mlp=8, channel_multiplier=2, narrow=1
-    ):
-        super(StyleGAN2GeneratorClean, self).__init__()
-        # Style MLP layers
-        self.num_style_feat = num_style_feat
-        style_mlp_layers = [NormStyleCode()]
-        for i in range(num_mlp):
-            style_mlp_layers.extend(
-                [
-                    nn.Linear(num_style_feat, num_style_feat, bias=True),
-                    nn.LeakyReLU(negative_slope=0.2, inplace=True),
-                ]
-            )
-        self.style_mlp = nn.Sequential(*style_mlp_layers)
-        # initialization
-        default_init_weights(
-            self.style_mlp,
-            scale=1,
-            bias_fill=0,
-            a=0.2,
-            mode="fan_in",
-            nonlinearity="leaky_relu",
-        )
-
-        # channel list
-        channels = {
-            "4": int(512 * narrow),
-            "8": int(512 * narrow),
-            "16": int(512 * narrow),
-            "32": int(512 * narrow),
-            "64": int(256 * channel_multiplier * narrow),
-            "128": int(128 * channel_multiplier * narrow),
-            "256": int(64 * channel_multiplier * narrow),
-            "512": int(32 * channel_multiplier * narrow),
-            "1024": int(16 * channel_multiplier * narrow),
-        }
-        self.channels = channels
-
-        self.constant_input = ConstantInput(channels["4"], size=4)
-        self.style_conv1 = StyleConv(
-            channels["4"],
-            channels["4"],
-            kernel_size=3,
-            num_style_feat=num_style_feat,
-            demodulate=True,
-            sample_mode=None,
-        )
-        self.to_rgb1 = ToRGB(channels["4"], num_style_feat, upsample=False)
-
-        self.log_size = int(math.log(out_size, 2))
-        self.num_layers = (self.log_size - 2) * 2 + 1
-        self.num_latent = self.log_size * 2 - 2
-
-        self.style_convs = nn.ModuleList()
-        self.to_rgbs = nn.ModuleList()
-        self.noises = nn.Module()
-
-        in_channels = channels["4"]
-        # noise
-        for layer_idx in range(self.num_layers):
-            resolution = 2 ** ((layer_idx + 5) // 2)
-            shape = [1, 1, resolution, resolution]
-            self.noises.register_buffer(f"noise{layer_idx}", torch.randn(*shape))
-        # style convs and to_rgbs
-        for i in range(3, self.log_size + 1):
-            out_channels = channels[f"{2**i}"]
-            self.style_convs.append(
-                StyleConv(
-                    in_channels,
-                    out_channels,
-                    kernel_size=3,
-                    num_style_feat=num_style_feat,
-                    demodulate=True,
-                    sample_mode="upsample",
-                )
-            )
-            self.style_convs.append(
-                StyleConv(
-                    out_channels,
-                    out_channels,
-                    kernel_size=3,
-                    num_style_feat=num_style_feat,
-                    demodulate=True,
-                    sample_mode=None,
-                )
-            )
-            self.to_rgbs.append(ToRGB(out_channels, num_style_feat, upsample=True))
-            in_channels = out_channels
-
-    def make_noise(self):
-        """Make noise for noise injection."""
-        device = self.constant_input.weight.device
-        noises = [torch.randn(1, 1, 4, 4, device=device)]
-
-        for i in range(3, self.log_size + 1):
-            for _ in range(2):
-                noises.append(torch.randn(1, 1, 2**i, 2**i, device=device))
-
-        return noises
-
-    def get_latent(self, x):
-        return self.style_mlp(x)
-
-    def mean_latent(self, num_latent):
-        latent_in = torch.randn(
-            num_latent, self.num_style_feat, device=self.constant_input.weight.device
-        )
-        latent = self.style_mlp(latent_in).mean(0, keepdim=True)
-        return latent
-
-    def forward(
-        self,
-        styles,
-        input_is_latent=False,
-        noise=None,
-        randomize_noise=True,
-        truncation=1,
-        truncation_latent=None,
-        inject_index=None,
-        return_latents=False,
-    ):
-        """Forward function for StyleGAN2GeneratorClean.
-        Args:
-            styles (list[Tensor]): Sample codes of styles.
-            input_is_latent (bool): Whether input is latent style. Default: False.
-            noise (Tensor | None): Input noise or None. Default: None.
-            randomize_noise (bool): Randomize noise, used when 'noise' is False. Default: True.
-            truncation (float): The truncation ratio. Default: 1.
-            truncation_latent (Tensor | None): The truncation latent tensor. Default: None.
-            inject_index (int | None): The injection index for mixing noise. Default: None.
-            return_latents (bool): Whether to return style latents. Default: False.
-        """
-        # style codes -> latents with Style MLP layer
-        if not input_is_latent:
-            styles = [self.style_mlp(s) for s in styles]
-        # noises
-        if noise is None:
-            if randomize_noise:
-                noise = [None] * self.num_layers  # for each style conv layer
-            else:  # use the stored noise
-                noise = [
-                    getattr(self.noises, f"noise{i}") for i in range(self.num_layers)
-                ]
-        # style truncation
-        if truncation < 1:
-            style_truncation = []
-            for style in styles:
-                style_truncation.append(
-                    truncation_latent + truncation * (style - truncation_latent)
-                )
-            styles = style_truncation
-        # get style latents with injection
-        if len(styles) == 1:
-            inject_index = self.num_latent
-
-            if styles[0].ndim < 3:
-                # repeat latent code for all the layers
-                latent = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            else:  # used for encoder with different latent code for each layer
-                latent = styles[0]
-        elif len(styles) == 2:  # mixing noises
-            if inject_index is None:
-                inject_index = random.randint(1, self.num_latent - 1)
-            latent1 = styles[0].unsqueeze(1).repeat(1, inject_index, 1)
-            latent2 = (
-                styles[1].unsqueeze(1).repeat(1, self.num_latent - inject_index, 1)
-            )
-            latent = torch.cat([latent1, latent2], 1)
-
-        # main generation
-        out = self.constant_input(latent.shape[0])
-        out = self.style_conv1(out, latent[:, 0], noise=noise[0])
-        skip = self.to_rgb1(out, latent[:, 1])
-
-        i = 1
-        for conv1, conv2, noise1, noise2, to_rgb in zip(
-            self.style_convs[::2],
-            self.style_convs[1::2],
-            noise[1::2],
-            noise[2::2],
-            self.to_rgbs,
-        ):
-            out = conv1(out, latent[:, i], noise=noise1)
-            out = conv2(out, latent[:, i + 1], noise=noise2)
-            skip = to_rgb(out, latent[:, i + 2], skip)  # feature back to the rgb space
-            i += 2
-
-        image = skip
-
-        if return_latents:
-            return image, latent
-        else:
-            return image, None
diff --git a/comfy_extras/chainner_models/architecture/face/upfirdn2d.py b/comfy_extras/chainner_models/architecture/face/upfirdn2d.py
deleted file mode 100644
index 4ea45415..00000000
--- a/comfy_extras/chainner_models/architecture/face/upfirdn2d.py
+++ /dev/null
@@ -1,194 +0,0 @@
-# pylint: skip-file
-# type: ignore
-# modify from https://github.com/rosinality/stylegan2-pytorch/blob/master/op/upfirdn2d.py  # noqa:E501
-
-import os
-
-import torch
-from torch.autograd import Function
-from torch.nn import functional as F
-
-upfirdn2d_ext = None
-
-
-class UpFirDn2dBackward(Function):
-    @staticmethod
-    def forward(
-        ctx, grad_output, kernel, grad_kernel, up, down, pad, g_pad, in_size, out_size
-    ):
-        up_x, up_y = up
-        down_x, down_y = down
-        g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1 = g_pad
-
-        grad_output = grad_output.reshape(-1, out_size[0], out_size[1], 1)
-
-        grad_input = upfirdn2d_ext.upfirdn2d(
-            grad_output,
-            grad_kernel,
-            down_x,
-            down_y,
-            up_x,
-            up_y,
-            g_pad_x0,
-            g_pad_x1,
-            g_pad_y0,
-            g_pad_y1,
-        )
-        grad_input = grad_input.view(in_size[0], in_size[1], in_size[2], in_size[3])
-
-        ctx.save_for_backward(kernel)
-
-        pad_x0, pad_x1, pad_y0, pad_y1 = pad
-
-        ctx.up_x = up_x
-        ctx.up_y = up_y
-        ctx.down_x = down_x
-        ctx.down_y = down_y
-        ctx.pad_x0 = pad_x0
-        ctx.pad_x1 = pad_x1
-        ctx.pad_y0 = pad_y0
-        ctx.pad_y1 = pad_y1
-        ctx.in_size = in_size
-        ctx.out_size = out_size
-
-        return grad_input
-
-    @staticmethod
-    def backward(ctx, gradgrad_input):
-        (kernel,) = ctx.saved_tensors
-
-        gradgrad_input = gradgrad_input.reshape(-1, ctx.in_size[2], ctx.in_size[3], 1)
-
-        gradgrad_out = upfirdn2d_ext.upfirdn2d(
-            gradgrad_input,
-            kernel,
-            ctx.up_x,
-            ctx.up_y,
-            ctx.down_x,
-            ctx.down_y,
-            ctx.pad_x0,
-            ctx.pad_x1,
-            ctx.pad_y0,
-            ctx.pad_y1,
-        )
-        # gradgrad_out = gradgrad_out.view(ctx.in_size[0], ctx.out_size[0],
-        #                                  ctx.out_size[1], ctx.in_size[3])
-        gradgrad_out = gradgrad_out.view(
-            ctx.in_size[0], ctx.in_size[1], ctx.out_size[0], ctx.out_size[1]
-        )
-
-        return gradgrad_out, None, None, None, None, None, None, None, None
-
-
-class UpFirDn2d(Function):
-    @staticmethod
-    def forward(ctx, input, kernel, up, down, pad):
-        up_x, up_y = up
-        down_x, down_y = down
-        pad_x0, pad_x1, pad_y0, pad_y1 = pad
-
-        kernel_h, kernel_w = kernel.shape
-        _, channel, in_h, in_w = input.shape
-        ctx.in_size = input.shape
-
-        input = input.reshape(-1, in_h, in_w, 1)
-
-        ctx.save_for_backward(kernel, torch.flip(kernel, [0, 1]))
-
-        out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
-        out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
-        ctx.out_size = (out_h, out_w)
-
-        ctx.up = (up_x, up_y)
-        ctx.down = (down_x, down_y)
-        ctx.pad = (pad_x0, pad_x1, pad_y0, pad_y1)
-
-        g_pad_x0 = kernel_w - pad_x0 - 1
-        g_pad_y0 = kernel_h - pad_y0 - 1
-        g_pad_x1 = in_w * up_x - out_w * down_x + pad_x0 - up_x + 1
-        g_pad_y1 = in_h * up_y - out_h * down_y + pad_y0 - up_y + 1
-
-        ctx.g_pad = (g_pad_x0, g_pad_x1, g_pad_y0, g_pad_y1)
-
-        out = upfirdn2d_ext.upfirdn2d(
-            input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
-        )
-        # out = out.view(major, out_h, out_w, minor)
-        out = out.view(-1, channel, out_h, out_w)
-
-        return out
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        kernel, grad_kernel = ctx.saved_tensors
-
-        grad_input = UpFirDn2dBackward.apply(
-            grad_output,
-            kernel,
-            grad_kernel,
-            ctx.up,
-            ctx.down,
-            ctx.pad,
-            ctx.g_pad,
-            ctx.in_size,
-            ctx.out_size,
-        )
-
-        return grad_input, None, None, None, None
-
-
-def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
-    if input.device.type == "cpu":
-        out = upfirdn2d_native(
-            input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1]
-        )
-    else:
-        out = UpFirDn2d.apply(
-            input, kernel, (up, up), (down, down), (pad[0], pad[1], pad[0], pad[1])
-        )
-
-    return out
-
-
-def upfirdn2d_native(
-    input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1
-):
-    _, channel, in_h, in_w = input.shape
-    input = input.reshape(-1, in_h, in_w, 1)
-
-    _, in_h, in_w, minor = input.shape
-    kernel_h, kernel_w = kernel.shape
-
-    out = input.view(-1, in_h, 1, in_w, 1, minor)
-    out = F.pad(out, [0, 0, 0, up_x - 1, 0, 0, 0, up_y - 1])
-    out = out.view(-1, in_h * up_y, in_w * up_x, minor)
-
-    out = F.pad(
-        out, [0, 0, max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)]
-    )
-    out = out[
-        :,
-        max(-pad_y0, 0) : out.shape[1] - max(-pad_y1, 0),
-        max(-pad_x0, 0) : out.shape[2] - max(-pad_x1, 0),
-        :,
-    ]
-
-    out = out.permute(0, 3, 1, 2)
-    out = out.reshape(
-        [-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1]
-    )
-    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
-    out = F.conv2d(out, w)
-    out = out.reshape(
-        -1,
-        minor,
-        in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1,
-        in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1,
-    )
-    out = out.permute(0, 2, 3, 1)
-    out = out[:, ::down_y, ::down_x, :]
-
-    out_h = (in_h * up_y + pad_y0 + pad_y1 - kernel_h) // down_y + 1
-    out_w = (in_w * up_x + pad_x0 + pad_x1 - kernel_w) // down_x + 1
-
-    return out.view(-1, channel, out_h, out_w)
diff --git a/comfy_extras/chainner_models/architecture/timm/LICENSE b/comfy_extras/chainner_models/architecture/timm/LICENSE
deleted file mode 100644
index b4e9438b..00000000
--- a/comfy_extras/chainner_models/architecture/timm/LICENSE
+++ /dev/null
@@ -1,201 +0,0 @@
-                                 Apache License
-                           Version 2.0, January 2004
-                        http://www.apache.org/licenses/
-
-   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
-
-   1. Definitions.
-
-      "License" shall mean the terms and conditions for use, reproduction,
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-      "Legal Entity" shall mean the union of the acting entity and all
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-      outstanding shares, or (iii) beneficial ownership of such entity.
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-   5. Submission of Contributions. Unless You explicitly state otherwise,
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-   6. Trademarks. This License does not grant permission to use the trade
-      names, trademarks, service marks, or product names of the Licensor,
-      except as required for reasonable and customary use in describing the
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-   7. Disclaimer of Warranty. Unless required by applicable law or
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-   8. Limitation of Liability. In no event and under no legal theory,
-      whether in tort (including negligence), contract, or otherwise,
-      unless required by applicable law (such as deliberate and grossly
-      negligent acts) or agreed to in writing, shall any Contributor be
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-   9. Accepting Warranty or Additional Liability. While redistributing
-      the Work or Derivative Works thereof, You may choose to offer,
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-      License. However, in accepting such obligations, You may act only
-      on Your own behalf and on Your sole responsibility, not on behalf
-      of any other Contributor, and only if You agree to indemnify,
-      defend, and hold each Contributor harmless for any liability
-      incurred by, or claims asserted against, such Contributor by reason
-      of your accepting any such warranty or additional liability.
-
-   END OF TERMS AND CONDITIONS
-
-   APPENDIX: How to apply the Apache License to your work.
-
-      To apply the Apache License to your work, attach the following
-      boilerplate notice, with the fields enclosed by brackets "{}"
-      replaced with your own identifying information. (Don't include
-      the brackets!)  The text should be enclosed in the appropriate
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-      file or class name and description of purpose be included on the
-      same "printed page" as the copyright notice for easier
-      identification within third-party archives.
-
-   Copyright 2019 Ross Wightman
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
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diff --git a/comfy_extras/chainner_models/architecture/timm/drop.py b/comfy_extras/chainner_models/architecture/timm/drop.py
deleted file mode 100644
index 14f0da91..00000000
--- a/comfy_extras/chainner_models/architecture/timm/drop.py
+++ /dev/null
@@ -1,223 +0,0 @@
-""" DropBlock, DropPath
-
-PyTorch implementations of DropBlock and DropPath (Stochastic Depth) regularization layers.
-
-Papers:
-DropBlock: A regularization method for convolutional networks (https://arxiv.org/abs/1810.12890)
-
-Deep Networks with Stochastic Depth (https://arxiv.org/abs/1603.09382)
-
-Code:
-DropBlock impl inspired by two Tensorflow impl that I liked:
- - https://github.com/tensorflow/tpu/blob/master/models/official/resnet/resnet_model.py#L74
- - https://github.com/clovaai/assembled-cnn/blob/master/nets/blocks.py
-
-Hacked together by / Copyright 2020 Ross Wightman
-"""
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-def drop_block_2d(
-    x,
-    drop_prob: float = 0.1,
-    block_size: int = 7,
-    gamma_scale: float = 1.0,
-    with_noise: bool = False,
-    inplace: bool = False,
-    batchwise: bool = False,
-):
-    """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
-
-    DropBlock with an experimental gaussian noise option. This layer has been tested on a few training
-    runs with success, but needs further validation and possibly optimization for lower runtime impact.
-    """
-    _, C, H, W = x.shape
-    total_size = W * H
-    clipped_block_size = min(block_size, min(W, H))
-    # seed_drop_rate, the gamma parameter
-    gamma = (
-        gamma_scale
-        * drop_prob
-        * total_size
-        / clipped_block_size**2
-        / ((W - block_size + 1) * (H - block_size + 1))
-    )
-
-    # Forces the block to be inside the feature map.
-    w_i, h_i = torch.meshgrid(
-        torch.arange(W).to(x.device), torch.arange(H).to(x.device)
-    )
-    valid_block = (
-        (w_i >= clipped_block_size // 2) & (w_i < W - (clipped_block_size - 1) // 2)
-    ) & ((h_i >= clipped_block_size // 2) & (h_i < H - (clipped_block_size - 1) // 2))
-    valid_block = torch.reshape(valid_block, (1, 1, H, W)).to(dtype=x.dtype)
-
-    if batchwise:
-        # one mask for whole batch, quite a bit faster
-        uniform_noise = torch.rand((1, C, H, W), dtype=x.dtype, device=x.device)
-    else:
-        uniform_noise = torch.rand_like(x)
-    block_mask = ((2 - gamma - valid_block + uniform_noise) >= 1).to(dtype=x.dtype)
-    block_mask = -F.max_pool2d(
-        -block_mask,
-        kernel_size=clipped_block_size,  # block_size,
-        stride=1,
-        padding=clipped_block_size // 2,
-    )
-
-    if with_noise:
-        normal_noise = (
-            torch.randn((1, C, H, W), dtype=x.dtype, device=x.device)
-            if batchwise
-            else torch.randn_like(x)
-        )
-        if inplace:
-            x.mul_(block_mask).add_(normal_noise * (1 - block_mask))
-        else:
-            x = x * block_mask + normal_noise * (1 - block_mask)
-    else:
-        normalize_scale = (
-            block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-7)
-        ).to(x.dtype)
-        if inplace:
-            x.mul_(block_mask * normalize_scale)
-        else:
-            x = x * block_mask * normalize_scale
-    return x
-
-
-def drop_block_fast_2d(
-    x: torch.Tensor,
-    drop_prob: float = 0.1,
-    block_size: int = 7,
-    gamma_scale: float = 1.0,
-    with_noise: bool = False,
-    inplace: bool = False,
-):
-    """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf
-
-    DropBlock with an experimental gaussian noise option. Simplied from above without concern for valid
-    block mask at edges.
-    """
-    _, _, H, W = x.shape
-    total_size = W * H
-    clipped_block_size = min(block_size, min(W, H))
-    gamma = (
-        gamma_scale
-        * drop_prob
-        * total_size
-        / clipped_block_size**2
-        / ((W - block_size + 1) * (H - block_size + 1))
-    )
-
-    block_mask = torch.empty_like(x).bernoulli_(gamma)
-    block_mask = F.max_pool2d(
-        block_mask.to(x.dtype),
-        kernel_size=clipped_block_size,
-        stride=1,
-        padding=clipped_block_size // 2,
-    )
-
-    if with_noise:
-        normal_noise = torch.empty_like(x).normal_()
-        if inplace:
-            x.mul_(1.0 - block_mask).add_(normal_noise * block_mask)
-        else:
-            x = x * (1.0 - block_mask) + normal_noise * block_mask
-    else:
-        block_mask = 1 - block_mask
-        normalize_scale = (
-            block_mask.numel() / block_mask.to(dtype=torch.float32).sum().add(1e-6)
-        ).to(dtype=x.dtype)
-        if inplace:
-            x.mul_(block_mask * normalize_scale)
-        else:
-            x = x * block_mask * normalize_scale
-    return x
-
-
-class DropBlock2d(nn.Module):
-    """DropBlock. See https://arxiv.org/pdf/1810.12890.pdf"""
-
-    def __init__(
-        self,
-        drop_prob: float = 0.1,
-        block_size: int = 7,
-        gamma_scale: float = 1.0,
-        with_noise: bool = False,
-        inplace: bool = False,
-        batchwise: bool = False,
-        fast: bool = True,
-    ):
-        super(DropBlock2d, self).__init__()
-        self.drop_prob = drop_prob
-        self.gamma_scale = gamma_scale
-        self.block_size = block_size
-        self.with_noise = with_noise
-        self.inplace = inplace
-        self.batchwise = batchwise
-        self.fast = fast  # FIXME finish comparisons of fast vs not
-
-    def forward(self, x):
-        if not self.training or not self.drop_prob:
-            return x
-        if self.fast:
-            return drop_block_fast_2d(
-                x,
-                self.drop_prob,
-                self.block_size,
-                self.gamma_scale,
-                self.with_noise,
-                self.inplace,
-            )
-        else:
-            return drop_block_2d(
-                x,
-                self.drop_prob,
-                self.block_size,
-                self.gamma_scale,
-                self.with_noise,
-                self.inplace,
-                self.batchwise,
-            )
-
-
-def drop_path(
-    x, drop_prob: float = 0.0, training: bool = False, scale_by_keep: bool = True
-):
-    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
-
-    This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,
-    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
-    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for
-    changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use
-    'survival rate' as the argument.
-
-    """
-    if drop_prob == 0.0 or not training:
-        return x
-    keep_prob = 1 - drop_prob
-    shape = (x.shape[0],) + (1,) * (
-        x.ndim - 1
-    )  # work with diff dim tensors, not just 2D ConvNets
-    random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
-    if keep_prob > 0.0 and scale_by_keep:
-        random_tensor.div_(keep_prob)
-    return x * random_tensor
-
-
-class DropPath(nn.Module):
-    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks)."""
-
-    def __init__(self, drop_prob: float = 0.0, scale_by_keep: bool = True):
-        super(DropPath, self).__init__()
-        self.drop_prob = drop_prob
-        self.scale_by_keep = scale_by_keep
-
-    def forward(self, x):
-        return drop_path(x, self.drop_prob, self.training, self.scale_by_keep)
-
-    def extra_repr(self):
-        return f"drop_prob={round(self.drop_prob,3):0.3f}"
diff --git a/comfy_extras/chainner_models/architecture/timm/helpers.py b/comfy_extras/chainner_models/architecture/timm/helpers.py
deleted file mode 100644
index cdafee07..00000000
--- a/comfy_extras/chainner_models/architecture/timm/helpers.py
+++ /dev/null
@@ -1,31 +0,0 @@
-""" Layer/Module Helpers
-Hacked together by / Copyright 2020 Ross Wightman
-"""
-import collections.abc
-from itertools import repeat
-
-
-# From PyTorch internals
-def _ntuple(n):
-    def parse(x):
-        if isinstance(x, collections.abc.Iterable) and not isinstance(x, str):
-            return x
-        return tuple(repeat(x, n))
-
-    return parse
-
-
-to_1tuple = _ntuple(1)
-to_2tuple = _ntuple(2)
-to_3tuple = _ntuple(3)
-to_4tuple = _ntuple(4)
-to_ntuple = _ntuple
-
-
-def make_divisible(v, divisor=8, min_value=None, round_limit=0.9):
-    min_value = min_value or divisor
-    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
-    # Make sure that round down does not go down by more than 10%.
-    if new_v < round_limit * v:
-        new_v += divisor
-    return new_v
diff --git a/comfy_extras/chainner_models/architecture/timm/weight_init.py b/comfy_extras/chainner_models/architecture/timm/weight_init.py
deleted file mode 100644
index b0169774..00000000
--- a/comfy_extras/chainner_models/architecture/timm/weight_init.py
+++ /dev/null
@@ -1,128 +0,0 @@
-import math
-import warnings
-
-import torch
-from torch.nn.init import _calculate_fan_in_and_fan_out
-
-
-def _no_grad_trunc_normal_(tensor, mean, std, a, b):
-    # Cut & paste from PyTorch official master until it's in a few official releases - RW
-    # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
-    def norm_cdf(x):
-        # Computes standard normal cumulative distribution function
-        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
-
-    if (mean < a - 2 * std) or (mean > b + 2 * std):
-        warnings.warn(
-            "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
-            "The distribution of values may be incorrect.",
-            stacklevel=2,
-        )
-
-    with torch.no_grad():
-        # Values are generated by using a truncated uniform distribution and
-        # then using the inverse CDF for the normal distribution.
-        # Get upper and lower cdf values
-        l = norm_cdf((a - mean) / std)
-        u = norm_cdf((b - mean) / std)
-
-        # Uniformly fill tensor with values from [l, u], then translate to
-        # [2l-1, 2u-1].
-        tensor.uniform_(2 * l - 1, 2 * u - 1)
-
-        # Use inverse cdf transform for normal distribution to get truncated
-        # standard normal
-        tensor.erfinv_()
-
-        # Transform to proper mean, std
-        tensor.mul_(std * math.sqrt(2.0))
-        tensor.add_(mean)
-
-        # Clamp to ensure it's in the proper range
-        tensor.clamp_(min=a, max=b)
-        return tensor
-
-
-def trunc_normal_(
-    tensor: torch.Tensor, mean=0.0, std=1.0, a=-2.0, b=2.0
-) -> torch.Tensor:
-    r"""Fills the input Tensor with values drawn from a truncated
-    normal distribution. The values are effectively drawn from the
-    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
-    with values outside :math:`[a, b]` redrawn until they are within
-    the bounds. The method used for generating the random values works
-    best when :math:`a \leq \text{mean} \leq b`.
-
-    NOTE: this impl is similar to the PyTorch trunc_normal_, the bounds [a, b] are
-    applied while sampling the normal with mean/std applied, therefore a, b args
-    should be adjusted to match the range of mean, std args.
-
-    Args:
-        tensor: an n-dimensional `torch.Tensor`
-        mean: the mean of the normal distribution
-        std: the standard deviation of the normal distribution
-        a: the minimum cutoff value
-        b: the maximum cutoff value
-    Examples:
-        >>> w = torch.empty(3, 5)
-        >>> nn.init.trunc_normal_(w)
-    """
-    return _no_grad_trunc_normal_(tensor, mean, std, a, b)
-
-
-def trunc_normal_tf_(
-    tensor: torch.Tensor, mean=0.0, std=1.0, a=-2.0, b=2.0
-) -> torch.Tensor:
-    r"""Fills the input Tensor with values drawn from a truncated
-    normal distribution. The values are effectively drawn from the
-    normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
-    with values outside :math:`[a, b]` redrawn until they are within
-    the bounds. The method used for generating the random values works
-    best when :math:`a \leq \text{mean} \leq b`.
-
-    NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the
-    bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0
-    and the result is subsquently scaled and shifted by the mean and std args.
-
-    Args:
-        tensor: an n-dimensional `torch.Tensor`
-        mean: the mean of the normal distribution
-        std: the standard deviation of the normal distribution
-        a: the minimum cutoff value
-        b: the maximum cutoff value
-    Examples:
-        >>> w = torch.empty(3, 5)
-        >>> nn.init.trunc_normal_(w)
-    """
-    _no_grad_trunc_normal_(tensor, 0, 1.0, a, b)
-    with torch.no_grad():
-        tensor.mul_(std).add_(mean)
-    return tensor
-
-
-def variance_scaling_(tensor, scale=1.0, mode="fan_in", distribution="normal"):
-    fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor)
-    if mode == "fan_in":
-        denom = fan_in
-    elif mode == "fan_out":
-        denom = fan_out
-    elif mode == "fan_avg":
-        denom = (fan_in + fan_out) / 2
-
-    variance = scale / denom  # type: ignore
-
-    if distribution == "truncated_normal":
-        # constant is stddev of standard normal truncated to (-2, 2)
-        trunc_normal_tf_(tensor, std=math.sqrt(variance) / 0.87962566103423978)
-    elif distribution == "normal":
-        tensor.normal_(std=math.sqrt(variance))
-    elif distribution == "uniform":
-        bound = math.sqrt(3 * variance)
-        # pylint: disable=invalid-unary-operand-type
-        tensor.uniform_(-bound, bound)
-    else:
-        raise ValueError(f"invalid distribution {distribution}")
-
-
-def lecun_normal_(tensor):
-    variance_scaling_(tensor, mode="fan_in", distribution="truncated_normal")
diff --git a/comfy_extras/chainner_models/model_loading.py b/comfy_extras/chainner_models/model_loading.py
index e000871c..d48bc238 100644
--- a/comfy_extras/chainner_models/model_loading.py
+++ b/comfy_extras/chainner_models/model_loading.py
@@ -1,99 +1,5 @@
-import logging as logger
+from spandrel import ModelLoader
 
-from .architecture.DAT import DAT
-from .architecture.face.codeformer import CodeFormer
-from .architecture.face.gfpganv1_clean_arch import GFPGANv1Clean
-from .architecture.face.restoreformer_arch import RestoreFormer
-from .architecture.HAT import HAT
-from .architecture.LaMa import LaMa
-from .architecture.OmniSR.OmniSR import OmniSR
-from .architecture.RRDB import RRDBNet as ESRGAN
-from .architecture.SCUNet import SCUNet
-from .architecture.SPSR import SPSRNet as SPSR
-from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2
-from .architecture.SwiftSRGAN import Generator as SwiftSRGAN
-from .architecture.Swin2SR import Swin2SR
-from .architecture.SwinIR import SwinIR
-from .types import PyTorchModel
-
-
-class UnsupportedModel(Exception):
-    pass
-
-
-def load_state_dict(state_dict) -> PyTorchModel:
-    logger.debug(f"Loading state dict into pytorch model arch")
-
-    state_dict_keys = list(state_dict.keys())
-
-    if "params_ema" in state_dict_keys:
-        state_dict = state_dict["params_ema"]
-    elif "params-ema" in state_dict_keys:
-        state_dict = state_dict["params-ema"]
-    elif "params" in state_dict_keys:
-        state_dict = state_dict["params"]
-
-    state_dict_keys = list(state_dict.keys())
-    # SRVGGNet Real-ESRGAN (v2)
-    if "body.0.weight" in state_dict_keys and "body.1.weight" in state_dict_keys:
-        model = RealESRGANv2(state_dict)
-    # SPSR (ESRGAN with lots of extra layers)
-    elif "f_HR_conv1.0.weight" in state_dict:
-        model = SPSR(state_dict)
-    # Swift-SRGAN
-    elif (
-        "model" in state_dict_keys
-        and "initial.cnn.depthwise.weight" in state_dict["model"].keys()
-    ):
-        model = SwiftSRGAN(state_dict)
-    # SwinIR, Swin2SR, HAT
-    elif "layers.0.residual_group.blocks.0.norm1.weight" in state_dict_keys:
-        if (
-            "layers.0.residual_group.blocks.0.conv_block.cab.0.weight"
-            in state_dict_keys
-        ):
-            model = HAT(state_dict)
-        elif "patch_embed.proj.weight" in state_dict_keys:
-            model = Swin2SR(state_dict)
-        else:
-            model = SwinIR(state_dict)
-    # GFPGAN
-    elif (
-        "toRGB.0.weight" in state_dict_keys
-        and "stylegan_decoder.style_mlp.1.weight" in state_dict_keys
-    ):
-        model = GFPGANv1Clean(state_dict)
-    # RestoreFormer
-    elif (
-        "encoder.conv_in.weight" in state_dict_keys
-        and "encoder.down.0.block.0.norm1.weight" in state_dict_keys
-    ):
-        model = RestoreFormer(state_dict)
-    elif (
-        "encoder.blocks.0.weight" in state_dict_keys
-        and "quantize.embedding.weight" in state_dict_keys
-    ):
-        model = CodeFormer(state_dict)
-    # LaMa
-    elif (
-        "model.model.1.bn_l.running_mean" in state_dict_keys
-        or "generator.model.1.bn_l.running_mean" in state_dict_keys
-    ):
-        model = LaMa(state_dict)
-    # Omni-SR
-    elif "residual_layer.0.residual_layer.0.layer.0.fn.0.weight" in state_dict_keys:
-        model = OmniSR(state_dict)
-    # SCUNet
-    elif "m_head.0.weight" in state_dict_keys and "m_tail.0.weight" in state_dict_keys:
-        model = SCUNet(state_dict)
-    # DAT
-    elif "layers.0.blocks.2.attn.attn_mask_0" in state_dict_keys:
-        model = DAT(state_dict)
-    # Regular ESRGAN, "new-arch" ESRGAN, Real-ESRGAN v1
-    else:
-        try:
-            model = ESRGAN(state_dict)
-        except:
-            # pylint: disable=raise-missing-from
-            raise UnsupportedModel
-    return model
+def load_state_dict(state_dict):
+    print("WARNING: comfy_extras.chainner_models is deprecated and has been replaced by the spandrel library.")
+    return ModelLoader().load_from_state_dict(state_dict).eval()
diff --git a/comfy_extras/chainner_models/types.py b/comfy_extras/chainner_models/types.py
deleted file mode 100644
index 193333b9..00000000
--- a/comfy_extras/chainner_models/types.py
+++ /dev/null
@@ -1,69 +0,0 @@
-from typing import Union
-
-from .architecture.DAT import DAT
-from .architecture.face.codeformer import CodeFormer
-from .architecture.face.gfpganv1_clean_arch import GFPGANv1Clean
-from .architecture.face.restoreformer_arch import RestoreFormer
-from .architecture.HAT import HAT
-from .architecture.LaMa import LaMa
-from .architecture.OmniSR.OmniSR import OmniSR
-from .architecture.RRDB import RRDBNet as ESRGAN
-from .architecture.SCUNet import SCUNet
-from .architecture.SPSR import SPSRNet as SPSR
-from .architecture.SRVGG import SRVGGNetCompact as RealESRGANv2
-from .architecture.SwiftSRGAN import Generator as SwiftSRGAN
-from .architecture.Swin2SR import Swin2SR
-from .architecture.SwinIR import SwinIR
-
-PyTorchSRModels = (
-    RealESRGANv2,
-    SPSR,
-    SwiftSRGAN,
-    ESRGAN,
-    SwinIR,
-    Swin2SR,
-    HAT,
-    OmniSR,
-    SCUNet,
-    DAT,
-)
-PyTorchSRModel = Union[
-    RealESRGANv2,
-    SPSR,
-    SwiftSRGAN,
-    ESRGAN,
-    SwinIR,
-    Swin2SR,
-    HAT,
-    OmniSR,
-    SCUNet,
-    DAT,
-]
-
-
-def is_pytorch_sr_model(model: object):
-    return isinstance(model, PyTorchSRModels)
-
-
-PyTorchFaceModels = (GFPGANv1Clean, RestoreFormer, CodeFormer)
-PyTorchFaceModel = Union[GFPGANv1Clean, RestoreFormer, CodeFormer]
-
-
-def is_pytorch_face_model(model: object):
-    return isinstance(model, PyTorchFaceModels)
-
-
-PyTorchInpaintModels = (LaMa,)
-PyTorchInpaintModel = Union[LaMa]
-
-
-def is_pytorch_inpaint_model(model: object):
-    return isinstance(model, PyTorchInpaintModels)
-
-
-PyTorchModels = (*PyTorchSRModels, *PyTorchFaceModels, *PyTorchInpaintModels)
-PyTorchModel = Union[PyTorchSRModel, PyTorchFaceModel, PyTorchInpaintModel]
-
-
-def is_pytorch_model(model: object):
-    return isinstance(model, PyTorchModels)
diff --git a/comfy_extras/nodes_advanced_samplers.py b/comfy_extras/nodes_advanced_samplers.py
new file mode 100644
index 00000000..820c250e
--- /dev/null
+++ b/comfy_extras/nodes_advanced_samplers.py
@@ -0,0 +1,112 @@
+import comfy.samplers
+import comfy.utils
+import torch
+import numpy as np
+from tqdm.auto import trange, tqdm
+import math
+
+
+@torch.no_grad()
+def sample_lcm_upscale(model, x, sigmas, extra_args=None, callback=None, disable=None, total_upscale=2.0, upscale_method="bislerp", upscale_steps=None):
+    extra_args = {} if extra_args is None else extra_args
+
+    if upscale_steps is None:
+        upscale_steps = max(len(sigmas) // 2 + 1, 2)
+    else:
+        upscale_steps += 1
+        upscale_steps = min(upscale_steps, len(sigmas) + 1)
+
+    upscales = np.linspace(1.0, total_upscale, upscale_steps)[1:]
+
+    orig_shape = x.size()
+    s_in = x.new_ones([x.shape[0]])
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+
+        x = denoised
+        if i < len(upscales):
+            x = comfy.utils.common_upscale(x, round(orig_shape[-1] * upscales[i]), round(orig_shape[-2] * upscales[i]), upscale_method, "disabled")
+
+        if sigmas[i + 1] > 0:
+            x += sigmas[i + 1] * torch.randn_like(x)
+    return x
+
+
+class SamplerLCMUpscale:
+    upscale_methods = ["bislerp", "nearest-exact", "bilinear", "area", "bicubic"]
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"scale_ratio": ("FLOAT", {"default": 1.0, "min": 0.1, "max": 20.0, "step": 0.01}),
+                     "scale_steps": ("INT", {"default": -1, "min": -1, "max": 1000, "step": 1}),
+                     "upscale_method": (s.upscale_methods,),
+                      }
+               }
+    RETURN_TYPES = ("SAMPLER",)
+    CATEGORY = "sampling/custom_sampling/samplers"
+
+    FUNCTION = "get_sampler"
+
+    def get_sampler(self, scale_ratio, scale_steps, upscale_method):
+        if scale_steps < 0:
+            scale_steps = None
+        sampler = comfy.samplers.KSAMPLER(sample_lcm_upscale, extra_options={"total_upscale": scale_ratio, "upscale_steps": scale_steps, "upscale_method": upscale_method})
+        return (sampler, )
+
+from comfy.k_diffusion.sampling import to_d
+import comfy.model_patcher
+
+@torch.no_grad()
+def sample_euler_pp(model, x, sigmas, extra_args=None, callback=None, disable=None):
+    extra_args = {} if extra_args is None else extra_args
+
+    temp = [0]
+    def post_cfg_function(args):
+        temp[0] = args["uncond_denoised"]
+        return args["denoised"]
+
+    model_options = extra_args.get("model_options", {}).copy()
+    extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+    s_in = x.new_ones([x.shape[0]])
+    for i in trange(len(sigmas) - 1, disable=disable):
+        sigma_hat = sigmas[i]
+        denoised = model(x, sigma_hat * s_in, **extra_args)
+        d = to_d(x - denoised + temp[0], sigmas[i], denoised)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigma_hat, 'denoised': denoised})
+        dt = sigmas[i + 1] - sigma_hat
+        x = x + d * dt
+    return x
+
+
+class SamplerEulerCFGpp:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"version": (["regular", "alternative"],),}
+               }
+    RETURN_TYPES = ("SAMPLER",)
+    # CATEGORY = "sampling/custom_sampling/samplers"
+    CATEGORY = "_for_testing"
+
+    FUNCTION = "get_sampler"
+
+    def get_sampler(self, version):
+        if version == "alternative":
+            sampler = comfy.samplers.KSAMPLER(sample_euler_pp)
+        else:
+            sampler = comfy.samplers.ksampler("euler_cfg_pp")
+        return (sampler, )
+
+NODE_CLASS_MAPPINGS = {
+    "SamplerLCMUpscale": SamplerLCMUpscale,
+    "SamplerEulerCFGpp": SamplerEulerCFGpp,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "SamplerEulerCFGpp": "SamplerEulerCFG++",
+}
diff --git a/comfy_extras/nodes_align_your_steps.py b/comfy_extras/nodes_align_your_steps.py
new file mode 100644
index 00000000..3ffe5318
--- /dev/null
+++ b/comfy_extras/nodes_align_your_steps.py
@@ -0,0 +1,53 @@
+#from: https://research.nvidia.com/labs/toronto-ai/AlignYourSteps/howto.html
+import numpy as np
+import torch
+
+def loglinear_interp(t_steps, num_steps):
+    """
+    Performs log-linear interpolation of a given array of decreasing numbers.
+    """
+    xs = np.linspace(0, 1, len(t_steps))
+    ys = np.log(t_steps[::-1])
+
+    new_xs = np.linspace(0, 1, num_steps)
+    new_ys = np.interp(new_xs, xs, ys)
+
+    interped_ys = np.exp(new_ys)[::-1].copy()
+    return interped_ys
+
+NOISE_LEVELS = {"SD1": [14.6146412293, 6.4745760956,  3.8636745985,  2.6946151520, 1.8841921177,  1.3943805092,  0.9642583904,  0.6523686016, 0.3977456272,  0.1515232662,  0.0291671582],
+                "SDXL":[14.6146412293, 6.3184485287,  3.7681790315,  2.1811480769, 1.3405244945,  0.8620721141,  0.5550693289,  0.3798540708, 0.2332364134,  0.1114188177,  0.0291671582],
+                "SVD": [700.00, 54.5, 15.886, 7.977, 4.248, 1.789, 0.981, 0.403, 0.173, 0.034, 0.002]}
+
+class AlignYourStepsScheduler:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"model_type": (["SD1", "SDXL", "SVD"], ),
+                     "steps": ("INT", {"default": 10, "min": 10, "max": 10000}),
+                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                      }
+               }
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "sampling/custom_sampling/schedulers"
+
+    FUNCTION = "get_sigmas"
+
+    def get_sigmas(self, model_type, steps, denoise):
+        total_steps = steps
+        if denoise < 1.0:
+            if denoise <= 0.0:
+                return (torch.FloatTensor([]),)
+            total_steps = round(steps * denoise)
+
+        sigmas = NOISE_LEVELS[model_type][:]
+        if (steps + 1) != len(sigmas):
+            sigmas = loglinear_interp(sigmas, steps + 1)
+
+        sigmas = sigmas[-(total_steps + 1):]
+        sigmas[-1] = 0
+        return (torch.FloatTensor(sigmas), )
+
+NODE_CLASS_MAPPINGS = {
+    "AlignYourStepsScheduler": AlignYourStepsScheduler,
+}
diff --git a/comfy_extras/nodes_attention_multiply.py b/comfy_extras/nodes_attention_multiply.py
new file mode 100644
index 00000000..4747eb39
--- /dev/null
+++ b/comfy_extras/nodes_attention_multiply.py
@@ -0,0 +1,120 @@
+
+def attention_multiply(attn, model, q, k, v, out):
+    m = model.clone()
+    sd = model.model_state_dict()
+
+    for key in sd:
+        if key.endswith("{}.to_q.bias".format(attn)) or key.endswith("{}.to_q.weight".format(attn)):
+            m.add_patches({key: (None,)}, 0.0, q)
+        if key.endswith("{}.to_k.bias".format(attn)) or key.endswith("{}.to_k.weight".format(attn)):
+            m.add_patches({key: (None,)}, 0.0, k)
+        if key.endswith("{}.to_v.bias".format(attn)) or key.endswith("{}.to_v.weight".format(attn)):
+            m.add_patches({key: (None,)}, 0.0, v)
+        if key.endswith("{}.to_out.0.bias".format(attn)) or key.endswith("{}.to_out.0.weight".format(attn)):
+            m.add_patches({key: (None,)}, 0.0, out)
+
+    return m
+
+
+class UNetSelfAttentionMultiply:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "q": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                              "k": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                              "v": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                              "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "_for_testing/attention_experiments"
+
+    def patch(self, model, q, k, v, out):
+        m = attention_multiply("attn1", model, q, k, v, out)
+        return (m, )
+
+class UNetCrossAttentionMultiply:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "q": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                              "k": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                              "v": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                              "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "_for_testing/attention_experiments"
+
+    def patch(self, model, q, k, v, out):
+        m = attention_multiply("attn2", model, q, k, v, out)
+        return (m, )
+
+class CLIPAttentionMultiply:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "clip": ("CLIP",),
+                              "q": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                              "k": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                              "v": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                              "out": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                              }}
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "patch"
+
+    CATEGORY = "_for_testing/attention_experiments"
+
+    def patch(self, clip, q, k, v, out):
+        m = clip.clone()
+        sd = m.patcher.model_state_dict()
+
+        for key in sd:
+            if key.endswith("self_attn.q_proj.weight") or key.endswith("self_attn.q_proj.bias"):
+                m.add_patches({key: (None,)}, 0.0, q)
+            if key.endswith("self_attn.k_proj.weight") or key.endswith("self_attn.k_proj.bias"):
+                m.add_patches({key: (None,)}, 0.0, k)
+            if key.endswith("self_attn.v_proj.weight") or key.endswith("self_attn.v_proj.bias"):
+                m.add_patches({key: (None,)}, 0.0, v)
+            if key.endswith("self_attn.out_proj.weight") or key.endswith("self_attn.out_proj.bias"):
+                m.add_patches({key: (None,)}, 0.0, out)
+        return (m, )
+
+class UNetTemporalAttentionMultiply:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "self_structural": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                              "self_temporal": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                              "cross_structural": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                              "cross_temporal": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "_for_testing/attention_experiments"
+
+    def patch(self, model, self_structural, self_temporal, cross_structural, cross_temporal):
+        m = model.clone()
+        sd = model.model_state_dict()
+
+        for k in sd:
+            if (k.endswith("attn1.to_out.0.bias") or k.endswith("attn1.to_out.0.weight")):
+                if '.time_stack.' in k:
+                    m.add_patches({k: (None,)}, 0.0, self_temporal)
+                else:
+                    m.add_patches({k: (None,)}, 0.0, self_structural)
+            elif (k.endswith("attn2.to_out.0.bias") or k.endswith("attn2.to_out.0.weight")):
+                if '.time_stack.' in k:
+                    m.add_patches({k: (None,)}, 0.0, cross_temporal)
+                else:
+                    m.add_patches({k: (None,)}, 0.0, cross_structural)
+        return (m, )
+
+NODE_CLASS_MAPPINGS = {
+    "UNetSelfAttentionMultiply": UNetSelfAttentionMultiply,
+    "UNetCrossAttentionMultiply": UNetCrossAttentionMultiply,
+    "CLIPAttentionMultiply": CLIPAttentionMultiply,
+    "UNetTemporalAttentionMultiply": UNetTemporalAttentionMultiply,
+}
diff --git a/comfy_extras/nodes_audio.py b/comfy_extras/nodes_audio.py
new file mode 100644
index 00000000..64d241e9
--- /dev/null
+++ b/comfy_extras/nodes_audio.py
@@ -0,0 +1,230 @@
+import torchaudio
+import torch
+import comfy.model_management
+import folder_paths
+import os
+import io
+import json
+import struct
+import random
+from comfy.cli_args import args
+
+class EmptyLatentAudio:
+    def __init__(self):
+        self.device = comfy.model_management.intermediate_device()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"seconds": ("FLOAT", {"default": 47.6, "min": 1.0, "max": 1000.0, "step": 0.1})}}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "generate"
+
+    CATEGORY = "latent/audio"
+
+    def generate(self, seconds):
+        batch_size = 1
+        length = round((seconds * 44100 / 2048) / 2) * 2
+        latent = torch.zeros([batch_size, 64, length], device=self.device)
+        return ({"samples":latent, "type": "audio"}, )
+
+class VAEEncodeAudio:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "audio": ("AUDIO", ), "vae": ("VAE", )}}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "encode"
+
+    CATEGORY = "latent/audio"
+
+    def encode(self, vae, audio):
+        sample_rate = audio["sample_rate"]
+        if 44100 != sample_rate:
+            waveform = torchaudio.functional.resample(audio["waveform"], sample_rate, 44100)
+        else:
+            waveform = audio["waveform"]
+
+        t = vae.encode(waveform.movedim(1, -1))
+        return ({"samples":t}, )
+
+class VAEDecodeAudio:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "samples": ("LATENT", ), "vae": ("VAE", )}}
+    RETURN_TYPES = ("AUDIO",)
+    FUNCTION = "decode"
+
+    CATEGORY = "latent/audio"
+
+    def decode(self, vae, samples):
+        audio = vae.decode(samples["samples"]).movedim(-1, 1)
+        return ({"waveform": audio, "sample_rate": 44100}, )
+
+
+def create_vorbis_comment_block(comment_dict, last_block):
+    vendor_string = b'ComfyUI'
+    vendor_length = len(vendor_string)
+
+    comments = []
+    for key, value in comment_dict.items():
+        comment = f"{key}={value}".encode('utf-8')
+        comments.append(struct.pack('<I', len(comment)) + comment)
+
+    user_comment_list_length = len(comments)
+    user_comments = b''.join(comments)
+
+    comment_data = struct.pack('<I', vendor_length) + vendor_string + struct.pack('<I', user_comment_list_length) + user_comments
+    if last_block:
+        id = b'\x84'
+    else:
+        id = b'\x04'
+    comment_block = id + struct.pack('>I', len(comment_data))[1:] + comment_data
+
+    return comment_block
+
+def insert_or_replace_vorbis_comment(flac_io, comment_dict):
+    if len(comment_dict) == 0:
+        return flac_io
+
+    flac_io.seek(4)
+
+    blocks = []
+    last_block = False
+
+    while not last_block:
+        header = flac_io.read(4)
+        last_block = (header[0] & 0x80) != 0
+        block_type = header[0] & 0x7F
+        block_length = struct.unpack('>I', b'\x00' + header[1:])[0]
+        block_data = flac_io.read(block_length)
+
+        if block_type == 4 or block_type == 1:
+            pass
+        else:
+            header = bytes([(header[0] & (~0x80))]) + header[1:]
+            blocks.append(header + block_data)
+
+    blocks.append(create_vorbis_comment_block(comment_dict, last_block=True))
+
+    new_flac_io = io.BytesIO()
+    new_flac_io.write(b'fLaC')
+    for block in blocks:
+        new_flac_io.write(block)
+
+    new_flac_io.write(flac_io.read())
+    return new_flac_io
+
+
+class SaveAudio:
+    def __init__(self):
+        self.output_dir = folder_paths.get_output_directory()
+        self.type = "output"
+        self.prefix_append = ""
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "audio": ("AUDIO", ),
+                              "filename_prefix": ("STRING", {"default": "audio/ComfyUI"})},
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save_audio"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "audio"
+
+    def save_audio(self, audio, filename_prefix="ComfyUI", prompt=None, extra_pnginfo=None):
+        filename_prefix += self.prefix_append
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+        results = list()
+
+        metadata = {}
+        if not args.disable_metadata:
+            if prompt is not None:
+                metadata["prompt"] = json.dumps(prompt)
+            if extra_pnginfo is not None:
+                for x in extra_pnginfo:
+                    metadata[x] = json.dumps(extra_pnginfo[x])
+
+        for (batch_number, waveform) in enumerate(audio["waveform"]):
+            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+            file = f"{filename_with_batch_num}_{counter:05}_.flac"
+
+            buff = io.BytesIO()
+            torchaudio.save(buff, waveform, audio["sample_rate"], format="FLAC")
+
+            buff = insert_or_replace_vorbis_comment(buff, metadata)
+
+            with open(os.path.join(full_output_folder, file), 'wb') as f:
+                f.write(buff.getbuffer())
+
+            results.append({
+                "filename": file,
+                "subfolder": subfolder,
+                "type": self.type
+            })
+            counter += 1
+
+        return { "ui": { "audio": results } }
+
+class PreviewAudio(SaveAudio):
+    def __init__(self):
+        self.output_dir = folder_paths.get_temp_directory()
+        self.type = "temp"
+        self.prefix_append = "_temp_" + ''.join(random.choice("abcdefghijklmnopqrstupvxyz") for x in range(5))
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"audio": ("AUDIO", ), },
+                "hidden": {"prompt": "PROMPT", "extra_pnginfo": "EXTRA_PNGINFO"},
+                }
+
+class LoadAudio:
+    SUPPORTED_FORMATS = ('.wav', '.mp3', '.ogg', '.flac', '.aiff', '.aif')
+
+    @classmethod
+    def INPUT_TYPES(s):
+        input_dir = folder_paths.get_input_directory()
+        files = [
+            f for f in os.listdir(input_dir)
+            if (os.path.isfile(os.path.join(input_dir, f))
+                and f.endswith(LoadAudio.SUPPORTED_FORMATS)
+            )
+        ]
+        return {"required": {"audio": (sorted(files), {"audio_upload": True})}}
+
+    CATEGORY = "audio"
+
+    RETURN_TYPES = ("AUDIO", )
+    FUNCTION = "load"
+
+    def load(self, audio):
+        audio_path = folder_paths.get_annotated_filepath(audio)
+        waveform, sample_rate = torchaudio.load(audio_path)
+        audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
+        return (audio, )
+
+    @classmethod
+    def IS_CHANGED(s, audio):
+        image_path = folder_paths.get_annotated_filepath(audio)
+        m = hashlib.sha256()
+        with open(image_path, 'rb') as f:
+            m.update(f.read())
+        return m.digest().hex()
+
+    @classmethod
+    def VALIDATE_INPUTS(s, audio):
+        if not folder_paths.exists_annotated_filepath(audio):
+            return "Invalid audio file: {}".format(audio)
+        return True
+
+NODE_CLASS_MAPPINGS = {
+    "EmptyLatentAudio": EmptyLatentAudio,
+    "VAEEncodeAudio": VAEEncodeAudio,
+    "VAEDecodeAudio": VAEDecodeAudio,
+    "SaveAudio": SaveAudio,
+    "LoadAudio": LoadAudio,
+    "PreviewAudio": PreviewAudio,
+}
diff --git a/comfy_extras/nodes_canny.py b/comfy_extras/nodes_canny.py
index 730dded5..d85e6b85 100644
--- a/comfy_extras/nodes_canny.py
+++ b/comfy_extras/nodes_canny.py
@@ -1,280 +1,6 @@
-#From https://github.com/kornia/kornia
-import math
-
-import torch
-import torch.nn.functional as F
+from kornia.filters import canny
 import comfy.model_management
 
-def get_canny_nms_kernel(device=None, dtype=None):
-    """Utility function that returns 3x3 kernels for the Canny Non-maximal suppression."""
-    return torch.tensor(
-        [
-            [[[0.0, 0.0, 0.0], [0.0, 1.0, -1.0], [0.0, 0.0, 0.0]]],
-            [[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, -1.0]]],
-            [[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, -1.0, 0.0]]],
-            [[[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [-1.0, 0.0, 0.0]]],
-            [[[0.0, 0.0, 0.0], [-1.0, 1.0, 0.0], [0.0, 0.0, 0.0]]],
-            [[[-1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]],
-            [[[0.0, -1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]],
-            [[[0.0, 0.0, -1.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]]],
-        ],
-        device=device,
-        dtype=dtype,
-    )
-
-
-def get_hysteresis_kernel(device=None, dtype=None):
-    """Utility function that returns the 3x3 kernels for the Canny hysteresis."""
-    return torch.tensor(
-        [
-            [[[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 0.0]]],
-            [[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0]]],
-            [[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]]],
-            [[[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [1.0, 0.0, 0.0]]],
-            [[[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 0.0]]],
-            [[[1.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]],
-            [[[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]],
-            [[[0.0, 0.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]],
-        ],
-        device=device,
-        dtype=dtype,
-    )
-
-def gaussian_blur_2d(img, kernel_size, sigma):
-    ksize_half = (kernel_size - 1) * 0.5
-
-    x = torch.linspace(-ksize_half, ksize_half, steps=kernel_size)
-
-    pdf = torch.exp(-0.5 * (x / sigma).pow(2))
-
-    x_kernel = pdf / pdf.sum()
-    x_kernel = x_kernel.to(device=img.device, dtype=img.dtype)
-
-    kernel2d = torch.mm(x_kernel[:, None], x_kernel[None, :])
-    kernel2d = kernel2d.expand(img.shape[-3], 1, kernel2d.shape[0], kernel2d.shape[1])
-
-    padding = [kernel_size // 2, kernel_size // 2, kernel_size // 2, kernel_size // 2]
-
-    img = torch.nn.functional.pad(img, padding, mode="reflect")
-    img = torch.nn.functional.conv2d(img, kernel2d, groups=img.shape[-3])
-
-    return img
-
-def get_sobel_kernel2d(device=None, dtype=None):
-    kernel_x = torch.tensor([[-1.0, 0.0, 1.0], [-2.0, 0.0, 2.0], [-1.0, 0.0, 1.0]], device=device, dtype=dtype)
-    kernel_y = kernel_x.transpose(0, 1)
-    return torch.stack([kernel_x, kernel_y])
-
-def spatial_gradient(input, normalized: bool = True):
-    r"""Compute the first order image derivative in both x and y using a Sobel operator.
-    .. image:: _static/img/spatial_gradient.png
-    Args:
-        input: input image tensor with shape :math:`(B, C, H, W)`.
-        mode: derivatives modality, can be: `sobel` or `diff`.
-        order: the order of the derivatives.
-        normalized: whether the output is normalized.
-    Return:
-        the derivatives of the input feature map. with shape :math:`(B, C, 2, H, W)`.
-    .. note::
-       See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/
-       filtering_edges.html>`__.
-    Examples:
-        >>> input = torch.rand(1, 3, 4, 4)
-        >>> output = spatial_gradient(input)  # 1x3x2x4x4
-        >>> output.shape
-        torch.Size([1, 3, 2, 4, 4])
-    """
-    # KORNIA_CHECK_IS_TENSOR(input)
-    # KORNIA_CHECK_SHAPE(input, ['B', 'C', 'H', 'W'])
-
-    # allocate kernel
-    kernel = get_sobel_kernel2d(device=input.device, dtype=input.dtype)
-    if normalized:
-        kernel = normalize_kernel2d(kernel)
-
-    # prepare kernel
-    b, c, h, w = input.shape
-    tmp_kernel = kernel[:, None, ...]
-
-    # Pad with "replicate for spatial dims, but with zeros for channel
-    spatial_pad = [kernel.size(1) // 2, kernel.size(1) // 2, kernel.size(2) // 2, kernel.size(2) // 2]
-    out_channels: int = 2
-    padded_inp = torch.nn.functional.pad(input.reshape(b * c, 1, h, w), spatial_pad, 'replicate')
-    out = F.conv2d(padded_inp, tmp_kernel, groups=1, padding=0, stride=1)
-    return out.reshape(b, c, out_channels, h, w)
-
-def rgb_to_grayscale(image, rgb_weights = None):
-    r"""Convert a RGB image to grayscale version of image.
-
-    .. image:: _static/img/rgb_to_grayscale.png
-
-    The image data is assumed to be in the range of (0, 1).
-
-    Args:
-        image: RGB image to be converted to grayscale with shape :math:`(*,3,H,W)`.
-        rgb_weights: Weights that will be applied on each channel (RGB).
-            The sum of the weights should add up to one.
-    Returns:
-        grayscale version of the image with shape :math:`(*,1,H,W)`.
-
-    .. note::
-       See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/
-       color_conversions.html>`__.
-
-    Example:
-        >>> input = torch.rand(2, 3, 4, 5)
-        >>> gray = rgb_to_grayscale(input) # 2x1x4x5
-    """
-
-    if len(image.shape) < 3 or image.shape[-3] != 3:
-        raise ValueError(f"Input size must have a shape of (*, 3, H, W). Got {image.shape}")
-
-    if rgb_weights is None:
-        # 8 bit images
-        if image.dtype == torch.uint8:
-            rgb_weights = torch.tensor([76, 150, 29], device=image.device, dtype=torch.uint8)
-        # floating point images
-        elif image.dtype in (torch.float16, torch.float32, torch.float64):
-            rgb_weights = torch.tensor([0.299, 0.587, 0.114], device=image.device, dtype=image.dtype)
-        else:
-            raise TypeError(f"Unknown data type: {image.dtype}")
-    else:
-        # is tensor that we make sure is in the same device/dtype
-        rgb_weights = rgb_weights.to(image)
-
-    # unpack the color image channels with RGB order
-    r: Tensor = image[..., 0:1, :, :]
-    g: Tensor = image[..., 1:2, :, :]
-    b: Tensor = image[..., 2:3, :, :]
-
-    w_r, w_g, w_b = rgb_weights.unbind()
-    return w_r * r + w_g * g + w_b * b
-
-def canny(
-    input,
-    low_threshold = 0.1,
-    high_threshold = 0.2,
-    kernel_size  = 5,
-    sigma = 1,
-    hysteresis = True,
-    eps = 1e-6,
-):
-    r"""Find edges of the input image and filters them using the Canny algorithm.
-    .. image:: _static/img/canny.png
-    Args:
-        input: input image tensor with shape :math:`(B,C,H,W)`.
-        low_threshold: lower threshold for the hysteresis procedure.
-        high_threshold: upper threshold for the hysteresis procedure.
-        kernel_size: the size of the kernel for the gaussian blur.
-        sigma: the standard deviation of the kernel for the gaussian blur.
-        hysteresis: if True, applies the hysteresis edge tracking.
-            Otherwise, the edges are divided between weak (0.5) and strong (1) edges.
-        eps: regularization number to avoid NaN during backprop.
-    Returns:
-        - the canny edge magnitudes map, shape of :math:`(B,1,H,W)`.
-        - the canny edge detection filtered by thresholds and hysteresis, shape of :math:`(B,1,H,W)`.
-    .. note::
-       See a working example `here <https://kornia-tutorials.readthedocs.io/en/latest/
-       canny.html>`__.
-    Example:
-        >>> input = torch.rand(5, 3, 4, 4)
-        >>> magnitude, edges = canny(input)  # 5x3x4x4
-        >>> magnitude.shape
-        torch.Size([5, 1, 4, 4])
-        >>> edges.shape
-        torch.Size([5, 1, 4, 4])
-    """
-    # KORNIA_CHECK_IS_TENSOR(input)
-    # KORNIA_CHECK_SHAPE(input, ['B', 'C', 'H', 'W'])
-    # KORNIA_CHECK(
-    #     low_threshold <= high_threshold,
-    #     "Invalid input thresholds. low_threshold should be smaller than the high_threshold. Got: "
-    #     f"{low_threshold}>{high_threshold}",
-    # )
-    # KORNIA_CHECK(0 < low_threshold < 1, f'Invalid low threshold. Should be in range (0, 1). Got: {low_threshold}')
-    # KORNIA_CHECK(0 < high_threshold < 1, f'Invalid high threshold. Should be in range (0, 1). Got: {high_threshold}')
-
-    device = input.device
-    dtype = input.dtype
-
-    # To Grayscale
-    if input.shape[1] == 3:
-        input = rgb_to_grayscale(input)
-
-    # Gaussian filter
-    blurred: Tensor = gaussian_blur_2d(input, kernel_size, sigma)
-
-    # Compute the gradients
-    gradients: Tensor = spatial_gradient(blurred, normalized=False)
-
-    # Unpack the edges
-    gx: Tensor = gradients[:, :, 0]
-    gy: Tensor = gradients[:, :, 1]
-
-    # Compute gradient magnitude and angle
-    magnitude: Tensor = torch.sqrt(gx * gx + gy * gy + eps)
-    angle: Tensor = torch.atan2(gy, gx)
-
-    # Radians to Degrees
-    angle = 180.0 * angle / math.pi
-
-    # Round angle to the nearest 45 degree
-    angle = torch.round(angle / 45) * 45
-
-    # Non-maximal suppression
-    nms_kernels: Tensor = get_canny_nms_kernel(device, dtype)
-    nms_magnitude: Tensor = F.conv2d(magnitude, nms_kernels, padding=nms_kernels.shape[-1] // 2)
-
-    # Get the indices for both directions
-    positive_idx: Tensor = (angle / 45) % 8
-    positive_idx = positive_idx.long()
-
-    negative_idx: Tensor = ((angle / 45) + 4) % 8
-    negative_idx = negative_idx.long()
-
-    # Apply the non-maximum suppression to the different directions
-    channel_select_filtered_positive: Tensor = torch.gather(nms_magnitude, 1, positive_idx)
-    channel_select_filtered_negative: Tensor = torch.gather(nms_magnitude, 1, negative_idx)
-
-    channel_select_filtered: Tensor = torch.stack(
-        [channel_select_filtered_positive, channel_select_filtered_negative], 1
-    )
-
-    is_max: Tensor = channel_select_filtered.min(dim=1)[0] > 0.0
-
-    magnitude = magnitude * is_max
-
-    # Threshold
-    edges: Tensor = F.threshold(magnitude, low_threshold, 0.0)
-
-    low: Tensor = magnitude > low_threshold
-    high: Tensor = magnitude > high_threshold
-
-    edges = low * 0.5 + high * 0.5
-    edges = edges.to(dtype)
-
-    # Hysteresis
-    if hysteresis:
-        edges_old: Tensor = -torch.ones(edges.shape, device=edges.device, dtype=dtype)
-        hysteresis_kernels: Tensor = get_hysteresis_kernel(device, dtype)
-
-        while ((edges_old - edges).abs() != 0).any():
-            weak: Tensor = (edges == 0.5).float()
-            strong: Tensor = (edges == 1).float()
-
-            hysteresis_magnitude: Tensor = F.conv2d(
-                edges, hysteresis_kernels, padding=hysteresis_kernels.shape[-1] // 2
-            )
-            hysteresis_magnitude = (hysteresis_magnitude == 1).any(1, keepdim=True).to(dtype)
-            hysteresis_magnitude = hysteresis_magnitude * weak + strong
-
-            edges_old = edges.clone()
-            edges = hysteresis_magnitude + (hysteresis_magnitude == 0) * weak * 0.5
-
-        edges = hysteresis_magnitude
-
-    return magnitude, edges
-
 
 class Canny:
     @classmethod
diff --git a/comfy_extras/nodes_clip_sdxl.py b/comfy_extras/nodes_clip_sdxl.py
index dcf8859f..3087b917 100644
--- a/comfy_extras/nodes_clip_sdxl.py
+++ b/comfy_extras/nodes_clip_sdxl.py
@@ -8,7 +8,7 @@ class CLIPTextEncodeSDXLRefiner:
             "ascore": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
             "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
             "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
-            "text": ("STRING", {"multiline": True}), "clip": ("CLIP", ),
+            "text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ),
             }}
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "encode"
@@ -30,8 +30,8 @@ class CLIPTextEncodeSDXL:
             "crop_h": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}),
             "target_width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
             "target_height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
-            "text_g": ("STRING", {"multiline": True, "default": "CLIP_G"}), "clip": ("CLIP", ),
-            "text_l": ("STRING", {"multiline": True, "default": "CLIP_L"}), "clip": ("CLIP", ),
+            "text_g": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ),
+            "text_l": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ),
             }}
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "encode"
diff --git a/comfy_extras/nodes_compositing.py b/comfy_extras/nodes_compositing.py
index 181b36ed..48fe5e3d 100644
--- a/comfy_extras/nodes_compositing.py
+++ b/comfy_extras/nodes_compositing.py
@@ -28,6 +28,14 @@ class PorterDuffMode(Enum):
 
 
 def porter_duff_composite(src_image: torch.Tensor, src_alpha: torch.Tensor, dst_image: torch.Tensor, dst_alpha: torch.Tensor, mode: PorterDuffMode):
+    # convert mask to alpha
+    src_alpha = 1 - src_alpha
+    dst_alpha = 1 - dst_alpha
+    # premultiply alpha
+    src_image = src_image * src_alpha
+    dst_image = dst_image * dst_alpha
+
+    # composite ops below assume alpha-premultiplied images
     if mode == PorterDuffMode.ADD:
         out_alpha = torch.clamp(src_alpha + dst_alpha, 0, 1)
         out_image = torch.clamp(src_image + dst_image, 0, 1)
@@ -35,7 +43,7 @@ def porter_duff_composite(src_image: torch.Tensor, src_alpha: torch.Tensor, dst_
         out_alpha = torch.zeros_like(dst_alpha)
         out_image = torch.zeros_like(dst_image)
     elif mode == PorterDuffMode.DARKEN:
-        out_alpha = src_alpha + dst_alpha  - src_alpha * dst_alpha
+        out_alpha = src_alpha + dst_alpha - src_alpha * dst_alpha
         out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image + torch.min(src_image, dst_image)
     elif mode == PorterDuffMode.DST:
         out_alpha = dst_alpha
@@ -84,8 +92,13 @@ def porter_duff_composite(src_image: torch.Tensor, src_alpha: torch.Tensor, dst_
         out_alpha = (1 - dst_alpha) * src_alpha + (1 - src_alpha) * dst_alpha
         out_image = (1 - dst_alpha) * src_image + (1 - src_alpha) * dst_image
     else:
-        out_alpha = None
-        out_image = None
+        return None, None
+
+    # back to non-premultiplied alpha
+    out_image = torch.where(out_alpha > 1e-5, out_image / out_alpha, torch.zeros_like(out_image))
+    out_image = torch.clamp(out_image, 0, 1)
+    # convert alpha to mask
+    out_alpha = 1 - out_alpha
     return out_image, out_alpha
 
 
diff --git a/comfy_extras/nodes_cond.py b/comfy_extras/nodes_cond.py
new file mode 100644
index 00000000..4c3a1d5b
--- /dev/null
+++ b/comfy_extras/nodes_cond.py
@@ -0,0 +1,25 @@
+
+
+class CLIPTextEncodeControlnet:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"clip": ("CLIP", ), "conditioning": ("CONDITIONING", ), "text": ("STRING", {"multiline": True, "dynamicPrompts": True})}}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "encode"
+
+    CATEGORY = "_for_testing/conditioning"
+
+    def encode(self, clip, conditioning, text):
+        tokens = clip.tokenize(text)
+        cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
+        c = []
+        for t in conditioning:
+            n = [t[0], t[1].copy()]
+            n[1]['cross_attn_controlnet'] = cond
+            n[1]['pooled_output_controlnet'] = pooled
+            c.append(n)
+        return (c, )
+
+NODE_CLASS_MAPPINGS = {
+    "CLIPTextEncodeControlnet": CLIPTextEncodeControlnet
+}
diff --git a/comfy_extras/nodes_custom_sampler.py b/comfy_extras/nodes_custom_sampler.py
index 99f9ea7d..64a8c063 100644
--- a/comfy_extras/nodes_custom_sampler.py
+++ b/comfy_extras/nodes_custom_sampler.py
@@ -4,6 +4,7 @@ from comfy.k_diffusion import sampling as k_diffusion_sampling
 import latent_preview
 import torch
 import comfy.utils
+import node_helpers
 
 
 class BasicScheduler:
@@ -24,10 +25,11 @@ class BasicScheduler:
     def get_sigmas(self, model, scheduler, steps, denoise):
         total_steps = steps
         if denoise < 1.0:
+            if denoise <= 0.0:
+                return (torch.FloatTensor([]),)
             total_steps = int(steps/denoise)
 
-        comfy.model_management.load_models_gpu([model])
-        sigmas = comfy.samplers.calculate_sigmas_scheduler(model.model, scheduler, total_steps).cpu()
+        sigmas = comfy.samplers.calculate_sigmas(model.get_model_object("model_sampling"), scheduler, total_steps).cpu()
         sigmas = sigmas[-(steps + 1):]
         return (sigmas, )
 
@@ -37,8 +39,8 @@ class KarrasScheduler:
     def INPUT_TYPES(s):
         return {"required":
                     {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                     "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}),
-                     "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}),
+                     "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
+                     "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
                      "rho": ("FLOAT", {"default": 7.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
                     }
                }
@@ -56,8 +58,8 @@ class ExponentialScheduler:
     def INPUT_TYPES(s):
         return {"required":
                     {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                     "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}),
-                     "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}),
+                     "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
+                     "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
                     }
                }
     RETURN_TYPES = ("SIGMAS",)
@@ -74,8 +76,8 @@ class PolyexponentialScheduler:
     def INPUT_TYPES(s):
         return {"required":
                     {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                     "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}),
-                     "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}),
+                     "sigma_max": ("FLOAT", {"default": 14.614642, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
+                     "sigma_min": ("FLOAT", {"default": 0.0291675, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
                      "rho": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
                     }
                }
@@ -105,8 +107,7 @@ class SDTurboScheduler:
     def get_sigmas(self, model, steps, denoise):
         start_step = 10 - int(10 * denoise)
         timesteps = torch.flip(torch.arange(1, 11) * 100 - 1, (0,))[start_step:start_step + steps]
-        comfy.model_management.load_models_gpu([model])
-        sigmas = model.model.model_sampling.sigma(timesteps)
+        sigmas = model.get_model_object("model_sampling").sigma(timesteps)
         sigmas = torch.cat([sigmas, sigmas.new_zeros([1])])
         return (sigmas, )
 
@@ -115,8 +116,8 @@ class VPScheduler:
     def INPUT_TYPES(s):
         return {"required":
                     {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                     "beta_d": ("FLOAT", {"default": 19.9, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}), #TODO: fix default values
-                     "beta_min": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 1000.0, "step":0.01, "round": False}),
+                     "beta_d": ("FLOAT", {"default": 19.9, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}), #TODO: fix default values
+                     "beta_min": ("FLOAT", {"default": 0.1, "min": 0.0, "max": 5000.0, "step":0.01, "round": False}),
                      "eps_s": ("FLOAT", {"default": 0.001, "min": 0.0, "max": 1.0, "step":0.0001, "round": False}),
                     }
                }
@@ -138,6 +139,7 @@ class SplitSigmas:
                      }
                 }
     RETURN_TYPES = ("SIGMAS","SIGMAS")
+    RETURN_NAMES = ("high_sigmas", "low_sigmas")
     CATEGORY = "sampling/custom_sampling/sigmas"
 
     FUNCTION = "get_sigmas"
@@ -147,6 +149,27 @@ class SplitSigmas:
         sigmas2 = sigmas[step:]
         return (sigmas1, sigmas2)
 
+class SplitSigmasDenoise:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"sigmas": ("SIGMAS", ),
+                    "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                     }
+                }
+    RETURN_TYPES = ("SIGMAS","SIGMAS")
+    RETURN_NAMES = ("high_sigmas", "low_sigmas")
+    CATEGORY = "sampling/custom_sampling/sigmas"
+
+    FUNCTION = "get_sigmas"
+
+    def get_sigmas(self, sigmas, denoise):
+        steps = max(sigmas.shape[-1] - 1, 0)
+        total_steps = round(steps * denoise)
+        sigmas1 = sigmas[:-(total_steps)]
+        sigmas2 = sigmas[-(total_steps + 1):]
+        return (sigmas1, sigmas2)
+
 class FlipSigmas:
     @classmethod
     def INPUT_TYPES(s):
@@ -160,6 +183,9 @@ class FlipSigmas:
     FUNCTION = "get_sigmas"
 
     def get_sigmas(self, sigmas):
+        if len(sigmas) == 0:
+            return (sigmas,)
+
         sigmas = sigmas.flip(0)
         if sigmas[0] == 0:
             sigmas[0] = 0.0001
@@ -181,6 +207,28 @@ class KSamplerSelect:
         sampler = comfy.samplers.sampler_object(sampler_name)
         return (sampler, )
 
+class SamplerDPMPP_3M_SDE:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+                     "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+                     "noise_device": (['gpu', 'cpu'], ),
+                      }
+               }
+    RETURN_TYPES = ("SAMPLER",)
+    CATEGORY = "sampling/custom_sampling/samplers"
+
+    FUNCTION = "get_sampler"
+
+    def get_sampler(self, eta, s_noise, noise_device):
+        if noise_device == 'cpu':
+            sampler_name = "dpmpp_3m_sde"
+        else:
+            sampler_name = "dpmpp_3m_sde_gpu"
+        sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise})
+        return (sampler, )
+
 class SamplerDPMPP_2M_SDE:
     @classmethod
     def INPUT_TYPES(s):
@@ -228,6 +276,103 @@ class SamplerDPMPP_SDE:
         sampler = comfy.samplers.ksampler(sampler_name, {"eta": eta, "s_noise": s_noise, "r": r})
         return (sampler, )
 
+class SamplerEulerAncestral:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+                     "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+                      }
+               }
+    RETURN_TYPES = ("SAMPLER",)
+    CATEGORY = "sampling/custom_sampling/samplers"
+
+    FUNCTION = "get_sampler"
+
+    def get_sampler(self, eta, s_noise):
+        sampler = comfy.samplers.ksampler("euler_ancestral", {"eta": eta, "s_noise": s_noise})
+        return (sampler, )
+
+class SamplerEulerAncestralCFGPP:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "eta": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step":0.01, "round": False}),
+                "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step":0.01, "round": False}),
+            }}
+    RETURN_TYPES = ("SAMPLER",)
+    CATEGORY = "sampling/custom_sampling/samplers"
+
+    FUNCTION = "get_sampler"
+
+    def get_sampler(self, eta, s_noise):
+        sampler = comfy.samplers.ksampler(
+            "euler_ancestral_cfg_pp",
+            {"eta": eta, "s_noise": s_noise})
+        return (sampler, )
+
+class SamplerLMS:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"order": ("INT", {"default": 4, "min": 1, "max": 100}),
+                      }
+               }
+    RETURN_TYPES = ("SAMPLER",)
+    CATEGORY = "sampling/custom_sampling/samplers"
+
+    FUNCTION = "get_sampler"
+
+    def get_sampler(self, order):
+        sampler = comfy.samplers.ksampler("lms", {"order": order})
+        return (sampler, )
+
+class SamplerDPMAdaptative:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"order": ("INT", {"default": 3, "min": 2, "max": 3}),
+                     "rtol": ("FLOAT", {"default": 0.05, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+                     "atol": ("FLOAT", {"default": 0.0078, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+                     "h_init": ("FLOAT", {"default": 0.05, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+                     "pcoeff": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+                     "icoeff": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+                     "dcoeff": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+                     "accept_safety": ("FLOAT", {"default": 0.81, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+                     "eta": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+                     "s_noise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.01, "round": False}),
+                      }
+               }
+    RETURN_TYPES = ("SAMPLER",)
+    CATEGORY = "sampling/custom_sampling/samplers"
+
+    FUNCTION = "get_sampler"
+
+    def get_sampler(self, order, rtol, atol, h_init, pcoeff, icoeff, dcoeff, accept_safety, eta, s_noise):
+        sampler = comfy.samplers.ksampler("dpm_adaptive", {"order": order, "rtol": rtol, "atol": atol, "h_init": h_init, "pcoeff": pcoeff,
+                                                              "icoeff": icoeff, "dcoeff": dcoeff, "accept_safety": accept_safety, "eta": eta,
+                                                              "s_noise":s_noise })
+        return (sampler, )
+
+class Noise_EmptyNoise:
+    def __init__(self):
+        self.seed = 0
+
+    def generate_noise(self, input_latent):
+        latent_image = input_latent["samples"]
+        return torch.zeros(latent_image.shape, dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
+
+
+class Noise_RandomNoise:
+    def __init__(self, seed):
+        self.seed = seed
+
+    def generate_noise(self, input_latent):
+        latent_image = input_latent["samples"]
+        batch_inds = input_latent["batch_index"] if "batch_index" in input_latent else None
+        return comfy.sample.prepare_noise(latent_image, self.seed, batch_inds)
+
 class SamplerCustom:
     @classmethod
     def INPUT_TYPES(s):
@@ -254,11 +399,14 @@ class SamplerCustom:
     def sample(self, model, add_noise, noise_seed, cfg, positive, negative, sampler, sigmas, latent_image):
         latent = latent_image
         latent_image = latent["samples"]
+        latent = latent.copy()
+        latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
+        latent["samples"] = latent_image
+
         if not add_noise:
-            noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
+            noise = Noise_EmptyNoise().generate_noise(latent)
         else:
-            batch_inds = latent["batch_index"] if "batch_index" in latent else None
-            noise = comfy.sample.prepare_noise(latent_image, noise_seed, batch_inds)
+            noise = Noise_RandomNoise(noise_seed).generate_noise(latent)
 
         noise_mask = None
         if "noise_mask" in latent:
@@ -279,6 +427,210 @@ class SamplerCustom:
             out_denoised = out
         return (out, out_denoised)
 
+class Guider_Basic(comfy.samplers.CFGGuider):
+    def set_conds(self, positive):
+        self.inner_set_conds({"positive": positive})
+
+class BasicGuider:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"model": ("MODEL",),
+                    "conditioning": ("CONDITIONING", ),
+                     }
+                }
+
+    RETURN_TYPES = ("GUIDER",)
+
+    FUNCTION = "get_guider"
+    CATEGORY = "sampling/custom_sampling/guiders"
+
+    def get_guider(self, model, conditioning):
+        guider = Guider_Basic(model)
+        guider.set_conds(conditioning)
+        return (guider,)
+
+class CFGGuider:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"model": ("MODEL",),
+                    "positive": ("CONDITIONING", ),
+                    "negative": ("CONDITIONING", ),
+                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
+                     }
+                }
+
+    RETURN_TYPES = ("GUIDER",)
+
+    FUNCTION = "get_guider"
+    CATEGORY = "sampling/custom_sampling/guiders"
+
+    def get_guider(self, model, positive, negative, cfg):
+        guider = comfy.samplers.CFGGuider(model)
+        guider.set_conds(positive, negative)
+        guider.set_cfg(cfg)
+        return (guider,)
+
+class Guider_DualCFG(comfy.samplers.CFGGuider):
+    def set_cfg(self, cfg1, cfg2):
+        self.cfg1 = cfg1
+        self.cfg2 = cfg2
+
+    def set_conds(self, positive, middle, negative):
+        middle = node_helpers.conditioning_set_values(middle, {"prompt_type": "negative"})
+        self.inner_set_conds({"positive": positive, "middle": middle, "negative": negative})
+
+    def predict_noise(self, x, timestep, model_options={}, seed=None):
+        negative_cond = self.conds.get("negative", None)
+        middle_cond = self.conds.get("middle", None)
+
+        out = comfy.samplers.calc_cond_batch(self.inner_model, [negative_cond, middle_cond, self.conds.get("positive", None)], x, timestep, model_options)
+        return comfy.samplers.cfg_function(self.inner_model, out[1], out[0], self.cfg2, x, timestep, model_options=model_options, cond=middle_cond, uncond=negative_cond) + (out[2] - out[1]) * self.cfg1
+
+class DualCFGGuider:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"model": ("MODEL",),
+                    "cond1": ("CONDITIONING", ),
+                    "cond2": ("CONDITIONING", ),
+                    "negative": ("CONDITIONING", ),
+                    "cfg_conds": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
+                    "cfg_cond2_negative": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
+                     }
+                }
+
+    RETURN_TYPES = ("GUIDER",)
+
+    FUNCTION = "get_guider"
+    CATEGORY = "sampling/custom_sampling/guiders"
+
+    def get_guider(self, model, cond1, cond2, negative, cfg_conds, cfg_cond2_negative):
+        guider = Guider_DualCFG(model)
+        guider.set_conds(cond1, cond2, negative)
+        guider.set_cfg(cfg_conds, cfg_cond2_negative)
+        return (guider,)
+
+class DisableNoise:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":{
+                     }
+                }
+
+    RETURN_TYPES = ("NOISE",)
+    FUNCTION = "get_noise"
+    CATEGORY = "sampling/custom_sampling/noise"
+
+    def get_noise(self):
+        return (Noise_EmptyNoise(),)
+
+
+class RandomNoise(DisableNoise):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":{
+                    "noise_seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
+                     }
+                }
+
+    def get_noise(self, noise_seed):
+        return (Noise_RandomNoise(noise_seed),)
+
+
+class SamplerCustomAdvanced:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"noise": ("NOISE", ),
+                    "guider": ("GUIDER", ),
+                    "sampler": ("SAMPLER", ),
+                    "sigmas": ("SIGMAS", ),
+                    "latent_image": ("LATENT", ),
+                     }
+                }
+
+    RETURN_TYPES = ("LATENT","LATENT")
+    RETURN_NAMES = ("output", "denoised_output")
+
+    FUNCTION = "sample"
+
+    CATEGORY = "sampling/custom_sampling"
+
+    def sample(self, noise, guider, sampler, sigmas, latent_image):
+        latent = latent_image
+        latent_image = latent["samples"]
+        latent = latent.copy()
+        latent_image = comfy.sample.fix_empty_latent_channels(guider.model_patcher, latent_image)
+        latent["samples"] = latent_image
+
+        noise_mask = None
+        if "noise_mask" in latent:
+            noise_mask = latent["noise_mask"]
+
+        x0_output = {}
+        callback = latent_preview.prepare_callback(guider.model_patcher, sigmas.shape[-1] - 1, x0_output)
+
+        disable_pbar = not comfy.utils.PROGRESS_BAR_ENABLED
+        samples = guider.sample(noise.generate_noise(latent), latent_image, sampler, sigmas, denoise_mask=noise_mask, callback=callback, disable_pbar=disable_pbar, seed=noise.seed)
+        samples = samples.to(comfy.model_management.intermediate_device())
+
+        out = latent.copy()
+        out["samples"] = samples
+        if "x0" in x0_output:
+            out_denoised = latent.copy()
+            out_denoised["samples"] = guider.model_patcher.model.process_latent_out(x0_output["x0"].cpu())
+        else:
+            out_denoised = out
+        return (out, out_denoised)
+
+class AddNoise:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"model": ("MODEL",),
+                     "noise": ("NOISE", ),
+                     "sigmas": ("SIGMAS", ),
+                     "latent_image": ("LATENT", ),
+                     }
+                }
+
+    RETURN_TYPES = ("LATENT",)
+
+    FUNCTION = "add_noise"
+
+    CATEGORY = "_for_testing/custom_sampling/noise"
+
+    def add_noise(self, model, noise, sigmas, latent_image):
+        if len(sigmas) == 0:
+            return latent_image
+
+        latent = latent_image
+        latent_image = latent["samples"]
+
+        noisy = noise.generate_noise(latent)
+
+        model_sampling = model.get_model_object("model_sampling")
+        process_latent_out = model.get_model_object("process_latent_out")
+        process_latent_in = model.get_model_object("process_latent_in")
+
+        if len(sigmas) > 1:
+            scale = torch.abs(sigmas[0] - sigmas[-1])
+        else:
+            scale = sigmas[0]
+
+        if torch.count_nonzero(latent_image) > 0: #Don't shift the empty latent image.
+            latent_image = process_latent_in(latent_image)
+        noisy = model_sampling.noise_scaling(scale, noisy, latent_image)
+        noisy = process_latent_out(noisy)
+        noisy = torch.nan_to_num(noisy, nan=0.0, posinf=0.0, neginf=0.0)
+
+        out = latent.copy()
+        out["samples"] = noisy
+        return (out,)
+
+
 NODE_CLASS_MAPPINGS = {
     "SamplerCustom": SamplerCustom,
     "BasicScheduler": BasicScheduler,
@@ -288,8 +640,26 @@ NODE_CLASS_MAPPINGS = {
     "VPScheduler": VPScheduler,
     "SDTurboScheduler": SDTurboScheduler,
     "KSamplerSelect": KSamplerSelect,
+    "SamplerEulerAncestral": SamplerEulerAncestral,
+    "SamplerEulerAncestralCFGPP": SamplerEulerAncestralCFGPP,
+    "SamplerLMS": SamplerLMS,
+    "SamplerDPMPP_3M_SDE": SamplerDPMPP_3M_SDE,
     "SamplerDPMPP_2M_SDE": SamplerDPMPP_2M_SDE,
     "SamplerDPMPP_SDE": SamplerDPMPP_SDE,
+    "SamplerDPMAdaptative": SamplerDPMAdaptative,
     "SplitSigmas": SplitSigmas,
+    "SplitSigmasDenoise": SplitSigmasDenoise,
     "FlipSigmas": FlipSigmas,
+
+    "CFGGuider": CFGGuider,
+    "DualCFGGuider": DualCFGGuider,
+    "BasicGuider": BasicGuider,
+    "RandomNoise": RandomNoise,
+    "DisableNoise": DisableNoise,
+    "AddNoise": AddNoise,
+    "SamplerCustomAdvanced": SamplerCustomAdvanced,
 }
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "SamplerEulerAncestralCFGPP": "SamplerEulerAncestralCFG++",
+}
\ No newline at end of file
diff --git a/comfy_extras/nodes_differential_diffusion.py b/comfy_extras/nodes_differential_diffusion.py
new file mode 100644
index 00000000..98dbbf10
--- /dev/null
+++ b/comfy_extras/nodes_differential_diffusion.py
@@ -0,0 +1,42 @@
+# code adapted from https://github.com/exx8/differential-diffusion
+
+import torch
+
+class DifferentialDiffusion():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"model": ("MODEL", ),
+                            }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "apply"
+    CATEGORY = "_for_testing"
+    INIT = False
+
+    def apply(self, model):
+        model = model.clone()
+        model.set_model_denoise_mask_function(self.forward)
+        return (model,)
+
+    def forward(self, sigma: torch.Tensor, denoise_mask: torch.Tensor, extra_options: dict):
+        model = extra_options["model"]
+        step_sigmas = extra_options["sigmas"]
+        sigma_to = model.inner_model.model_sampling.sigma_min
+        if step_sigmas[-1] > sigma_to:
+            sigma_to = step_sigmas[-1]
+        sigma_from = step_sigmas[0]
+
+        ts_from = model.inner_model.model_sampling.timestep(sigma_from)
+        ts_to = model.inner_model.model_sampling.timestep(sigma_to)
+        current_ts = model.inner_model.model_sampling.timestep(sigma[0])
+
+        threshold = (current_ts - ts_to) / (ts_from - ts_to)
+
+        return (denoise_mask >= threshold).to(denoise_mask.dtype)
+
+
+NODE_CLASS_MAPPINGS = {
+    "DifferentialDiffusion": DifferentialDiffusion,
+}
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "DifferentialDiffusion": "Differential Diffusion",
+}
diff --git a/comfy_extras/nodes_freelunch.py b/comfy_extras/nodes_freelunch.py
index 7764aa0b..c5ebcf26 100644
--- a/comfy_extras/nodes_freelunch.py
+++ b/comfy_extras/nodes_freelunch.py
@@ -1,7 +1,7 @@
 #code originally taken from: https://github.com/ChenyangSi/FreeU (under MIT License)
 
 import torch
-
+import logging
 
 def Fourier_filter(x, threshold, scale):
     # FFT
@@ -42,14 +42,14 @@ class FreeU:
         on_cpu_devices = {}
 
         def output_block_patch(h, hsp, transformer_options):
-            scale = scale_dict.get(h.shape[1], None)
+            scale = scale_dict.get(int(h.shape[1]), None)
             if scale is not None:
                 h[:,:h.shape[1] // 2] = h[:,:h.shape[1] // 2] * scale[0]
                 if hsp.device not in on_cpu_devices:
                     try:
                         hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
                     except:
-                        print("Device", hsp.device, "does not support the torch.fft functions used in the FreeU node, switching to CPU.")
+                        logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device))
                         on_cpu_devices[hsp.device] = True
                         hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
                 else:
@@ -81,7 +81,7 @@ class FreeU_V2:
         on_cpu_devices = {}
 
         def output_block_patch(h, hsp, transformer_options):
-            scale = scale_dict.get(h.shape[1], None)
+            scale = scale_dict.get(int(h.shape[1]), None)
             if scale is not None:
                 hidden_mean = h.mean(1).unsqueeze(1)
                 B = hidden_mean.shape[0]
@@ -95,7 +95,7 @@ class FreeU_V2:
                     try:
                         hsp = Fourier_filter(hsp, threshold=1, scale=scale[1])
                     except:
-                        print("Device", hsp.device, "does not support the torch.fft functions used in the FreeU node, switching to CPU.")
+                        logging.warning("Device {} does not support the torch.fft functions used in the FreeU node, switching to CPU.".format(hsp.device))
                         on_cpu_devices[hsp.device] = True
                         hsp = Fourier_filter(hsp.cpu(), threshold=1, scale=scale[1]).to(hsp.device)
                 else:
diff --git a/comfy_extras/nodes_gits.py b/comfy_extras/nodes_gits.py
new file mode 100644
index 00000000..7bfae4ce
--- /dev/null
+++ b/comfy_extras/nodes_gits.py
@@ -0,0 +1,369 @@
+# from https://github.com/zju-pi/diff-sampler/tree/main/gits-main
+import numpy as np
+import torch
+
+def loglinear_interp(t_steps, num_steps):
+    """
+    Performs log-linear interpolation of a given array of decreasing numbers.
+    """
+    xs = np.linspace(0, 1, len(t_steps))
+    ys = np.log(t_steps[::-1])
+
+    new_xs = np.linspace(0, 1, num_steps)
+    new_ys = np.interp(new_xs, xs, ys)
+
+    interped_ys = np.exp(new_ys)[::-1].copy()
+    return interped_ys
+
+NOISE_LEVELS = {
+    0.80: [
+        [14.61464119, 7.49001646, 0.02916753],
+        [14.61464119, 11.54541874, 6.77309084, 0.02916753],
+        [14.61464119, 11.54541874, 7.49001646, 3.07277966, 0.02916753],
+        [14.61464119, 11.54541874, 7.49001646, 5.85520077, 2.05039096, 0.02916753],
+        [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 2.05039096, 0.02916753],
+        [14.61464119, 12.2308979, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 12.96784878, 11.54541874, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 13.76078796, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 8.75849152, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 5.85520077, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 10.90732002, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.07277966, 1.56271636, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.1956799, 1.98035145, 0.86115354, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.14220476, 4.86714602, 3.1956799, 1.98035145, 0.86115354, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.07277966, 1.84880662, 0.83188516, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.65472794, 3.07277966, 1.84880662, 0.83188516, 0.02916753],
+        [14.61464119, 13.76078796, 12.96784878, 12.2308979, 11.54541874, 10.90732002, 10.31284904, 9.75859547, 9.24142551, 8.75849152, 8.30717278, 7.88507891, 7.49001646, 6.77309084, 5.85520077, 4.86714602, 3.75677586, 2.84484982, 1.78698075, 0.803307, 0.02916753],
+    ],
+    0.85: [
+        [14.61464119, 7.49001646, 0.02916753],
+        [14.61464119, 7.49001646, 1.84880662, 0.02916753],
+        [14.61464119, 11.54541874, 6.77309084, 1.56271636, 0.02916753],
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+        [14.61464119, 3.07277966, 1.61558151, 1.01931262, 0.72133851, 0.52423614, 0.41087446, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 3.07277966, 1.61558151, 1.05362725, 0.74807048, 0.54755926, 0.43325692, 0.34370604, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 3.07277966, 1.72759056, 1.12534678, 0.803307, 0.59516323, 0.45573691, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 3.07277966, 1.72759056, 1.12534678, 0.803307, 0.59516323, 0.4783645, 0.38853383, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 5.85520077, 2.45070267, 1.51179266, 1.01931262, 0.74807048, 0.57119018, 0.45573691, 0.36617002, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 5.85520077, 2.6383388, 1.61558151, 1.08895338, 0.803307, 0.61951244, 0.50118381, 0.41087446, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 5.85520077, 2.6383388, 1.61558151, 1.08895338, 0.803307, 0.64427125, 0.52423614, 0.43325692, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 5.85520077, 2.6383388, 1.61558151, 1.08895338, 0.803307, 0.64427125, 0.52423614, 0.45573691, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+    ],
+    1.40: [
+        [14.61464119, 0.59516323, 0.02916753],
+        [14.61464119, 0.95350921, 0.34370604, 0.02916753],
+        [14.61464119, 1.08895338, 0.43325692, 0.13792117, 0.02916753],
+        [14.61464119, 1.56271636, 0.64427125, 0.27464288, 0.09824532, 0.02916753],
+        [14.61464119, 1.61558151, 0.803307, 0.43325692, 0.22545385, 0.09824532, 0.02916753],
+        [14.61464119, 2.05039096, 0.95350921, 0.54755926, 0.34370604, 0.19894916, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.24153244, 0.72133851, 0.43325692, 0.27464288, 0.17026083, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.24153244, 0.74807048, 0.50118381, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.28281462, 0.803307, 0.52423614, 0.36617002, 0.27464288, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.28281462, 0.803307, 0.54755926, 0.38853383, 0.29807833, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.41535246, 0.86115354, 0.59516323, 0.43325692, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.64427125, 0.45573691, 0.34370604, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.64427125, 0.4783645, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.56271636, 0.98595673, 0.69515091, 0.52423614, 0.41087446, 0.34370604, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.72133851, 0.54755926, 0.43325692, 0.36617002, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.61558151, 1.05362725, 0.74807048, 0.57119018, 0.45573691, 0.38853383, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.61951244, 0.50118381, 0.41087446, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.61951244, 0.50118381, 0.43325692, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.61558151, 1.08895338, 0.803307, 0.64427125, 0.52423614, 0.45573691, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+    ],
+    1.45: [
+        [14.61464119, 0.59516323, 0.02916753],
+        [14.61464119, 0.803307, 0.25053367, 0.02916753],
+        [14.61464119, 0.95350921, 0.34370604, 0.09824532, 0.02916753],
+        [14.61464119, 1.24153244, 0.54755926, 0.25053367, 0.09824532, 0.02916753],
+        [14.61464119, 1.56271636, 0.72133851, 0.36617002, 0.19894916, 0.09824532, 0.02916753],
+        [14.61464119, 1.61558151, 0.803307, 0.45573691, 0.27464288, 0.17026083, 0.09824532, 0.02916753],
+        [14.61464119, 1.91321158, 0.95350921, 0.57119018, 0.36617002, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
+        [14.61464119, 2.19988537, 1.08895338, 0.64427125, 0.41087446, 0.27464288, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.24153244, 0.74807048, 0.50118381, 0.34370604, 0.25053367, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.24153244, 0.74807048, 0.50118381, 0.36617002, 0.27464288, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.28281462, 0.803307, 0.54755926, 0.41087446, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.28281462, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.28281462, 0.83188516, 0.59516323, 0.45573691, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.28281462, 0.83188516, 0.59516323, 0.45573691, 0.36617002, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.69515091, 0.52423614, 0.41087446, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.51179266, 0.95350921, 0.69515091, 0.52423614, 0.43325692, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.56271636, 0.98595673, 0.72133851, 0.54755926, 0.45573691, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.74807048, 0.57119018, 0.4783645, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.84484982, 1.56271636, 1.01931262, 0.74807048, 0.59516323, 0.50118381, 0.43325692, 0.38853383, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+    ],
+    1.50: [
+        [14.61464119, 0.54755926, 0.02916753],
+        [14.61464119, 0.803307, 0.25053367, 0.02916753],
+        [14.61464119, 0.86115354, 0.32104823, 0.09824532, 0.02916753],
+        [14.61464119, 1.24153244, 0.54755926, 0.25053367, 0.09824532, 0.02916753],
+        [14.61464119, 1.56271636, 0.72133851, 0.36617002, 0.19894916, 0.09824532, 0.02916753],
+        [14.61464119, 1.61558151, 0.803307, 0.45573691, 0.27464288, 0.17026083, 0.09824532, 0.02916753],
+        [14.61464119, 1.61558151, 0.83188516, 0.52423614, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
+        [14.61464119, 1.84880662, 0.95350921, 0.59516323, 0.38853383, 0.27464288, 0.19894916, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 1.84880662, 0.95350921, 0.59516323, 0.41087446, 0.29807833, 0.22545385, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 1.84880662, 0.95350921, 0.61951244, 0.43325692, 0.32104823, 0.25053367, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.19988537, 1.12534678, 0.72133851, 0.50118381, 0.36617002, 0.27464288, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.19988537, 1.12534678, 0.72133851, 0.50118381, 0.36617002, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.57119018, 0.43325692, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.36326075, 1.24153244, 0.803307, 0.59516323, 0.45573691, 0.38853383, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.32549286, 0.86115354, 0.64427125, 0.50118381, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.36964464, 0.92192322, 0.69515091, 0.54755926, 0.45573691, 0.41087446, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+        [14.61464119, 2.45070267, 1.41535246, 0.95350921, 0.72133851, 0.57119018, 0.4783645, 0.43325692, 0.38853383, 0.36617002, 0.34370604, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
+    ],
+}
+
+class GITSScheduler:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"coeff": ("FLOAT", {"default": 1.20, "min": 0.80, "max": 1.50, "step": 0.05}),
+                     "steps": ("INT", {"default": 10, "min": 2, "max": 1000}),
+                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                      }
+               }
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "sampling/custom_sampling/schedulers"
+
+    FUNCTION = "get_sigmas"
+
+    def get_sigmas(self, coeff, steps, denoise):
+        total_steps = steps
+        if denoise < 1.0:
+            if denoise <= 0.0:
+                return (torch.FloatTensor([]),)
+            total_steps = round(steps * denoise)
+
+        if steps <= 20:
+            sigmas = NOISE_LEVELS[round(coeff, 2)][steps-2][:]
+        else:
+            sigmas = NOISE_LEVELS[round(coeff, 2)][-1][:]
+            sigmas = loglinear_interp(sigmas, steps + 1)
+
+        sigmas = sigmas[-(total_steps + 1):]
+        sigmas[-1] = 0
+        return (torch.FloatTensor(sigmas), )
+
+NODE_CLASS_MAPPINGS = {
+    "GITSScheduler": GITSScheduler,
+}
diff --git a/comfy_extras/nodes_hypernetwork.py b/comfy_extras/nodes_hypernetwork.py
index f692945a..cafafa6a 100644
--- a/comfy_extras/nodes_hypernetwork.py
+++ b/comfy_extras/nodes_hypernetwork.py
@@ -1,6 +1,7 @@
 import comfy.utils
 import folder_paths
 import torch
+import logging
 
 def load_hypernetwork_patch(path, strength):
     sd = comfy.utils.load_torch_file(path, safe_load=True)
@@ -23,7 +24,7 @@ def load_hypernetwork_patch(path, strength):
     }
 
     if activation_func not in valid_activation:
-        print("Unsupported Hypernetwork format, if you report it I might implement it.", path, " ", activation_func, is_layer_norm, use_dropout, activate_output, last_layer_dropout)
+        logging.error("Unsupported Hypernetwork format, if you report it I might implement it. {}   {} {} {} {} {}".format(path, activation_func, is_layer_norm, use_dropout, activate_output, last_layer_dropout))
         return None
 
     out = {}
diff --git a/comfy_extras/nodes_images.py b/comfy_extras/nodes_images.py
index aa80f526..af37666b 100644
--- a/comfy_extras/nodes_images.py
+++ b/comfy_extras/nodes_images.py
@@ -37,7 +37,7 @@ class RepeatImageBatch:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "image": ("IMAGE",),
-                              "amount": ("INT", {"default": 1, "min": 1, "max": 64}),
+                              "amount": ("INT", {"default": 1, "min": 1, "max": 4096}),
                               }}
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "repeat"
@@ -48,6 +48,25 @@ class RepeatImageBatch:
         s = image.repeat((amount, 1,1,1))
         return (s,)
 
+class ImageFromBatch:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "image": ("IMAGE",),
+                              "batch_index": ("INT", {"default": 0, "min": 0, "max": 4095}),
+                              "length": ("INT", {"default": 1, "min": 1, "max": 4096}),
+                              }}
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "frombatch"
+
+    CATEGORY = "image/batch"
+
+    def frombatch(self, image, batch_index, length):
+        s_in = image
+        batch_index = min(s_in.shape[0] - 1, batch_index)
+        length = min(s_in.shape[0] - batch_index, length)
+        s = s_in[batch_index:batch_index + length].clone()
+        return (s,)
+
 class SaveAnimatedWEBP:
     def __init__(self):
         self.output_dir = folder_paths.get_output_directory()
@@ -170,6 +189,7 @@ class SaveAnimatedPNG:
 NODE_CLASS_MAPPINGS = {
     "ImageCrop": ImageCrop,
     "RepeatImageBatch": RepeatImageBatch,
+    "ImageFromBatch": ImageFromBatch,
     "SaveAnimatedWEBP": SaveAnimatedWEBP,
     "SaveAnimatedPNG": SaveAnimatedPNG,
 }
diff --git a/comfy_extras/nodes_ip2p.py b/comfy_extras/nodes_ip2p.py
new file mode 100644
index 00000000..c2e70a84
--- /dev/null
+++ b/comfy_extras/nodes_ip2p.py
@@ -0,0 +1,45 @@
+import torch
+
+class InstructPixToPixConditioning:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"positive": ("CONDITIONING", ),
+                             "negative": ("CONDITIONING", ),
+                             "vae": ("VAE", ),
+                             "pixels": ("IMAGE", ),
+                             }}
+
+    RETURN_TYPES = ("CONDITIONING","CONDITIONING","LATENT")
+    RETURN_NAMES = ("positive", "negative", "latent")
+    FUNCTION = "encode"
+
+    CATEGORY = "conditioning/instructpix2pix"
+
+    def encode(self, positive, negative, pixels, vae):
+        x = (pixels.shape[1] // 8) * 8
+        y = (pixels.shape[2] // 8) * 8
+
+        if pixels.shape[1] != x or pixels.shape[2] != y:
+            x_offset = (pixels.shape[1] % 8) // 2
+            y_offset = (pixels.shape[2] % 8) // 2
+            pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:]
+
+        concat_latent = vae.encode(pixels)
+
+        out_latent = {}
+        out_latent["samples"] = torch.zeros_like(concat_latent)
+
+        out = []
+        for conditioning in [positive, negative]:
+            c = []
+            for t in conditioning:
+                d = t[1].copy()
+                d["concat_latent_image"] = concat_latent
+                n = [t[0], d]
+                c.append(n)
+            out.append(c)
+        return (out[0], out[1], out_latent)
+
+NODE_CLASS_MAPPINGS = {
+    "InstructPixToPixConditioning": InstructPixToPixConditioning,
+}
diff --git a/comfy_extras/nodes_mask.py b/comfy_extras/nodes_mask.py
index a7d164bf..29589b4a 100644
--- a/comfy_extras/nodes_mask.py
+++ b/comfy_extras/nodes_mask.py
@@ -341,6 +341,24 @@ class GrowMask:
             out.append(output)
         return (torch.stack(out, dim=0),)
 
+class ThresholdMask:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+                "required": {
+                    "mask": ("MASK",),
+                    "value": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.01}),
+                }
+        }
+
+    CATEGORY = "mask"
+
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "image_to_mask"
+
+    def image_to_mask(self, mask, value):
+        mask = (mask > value).float()
+        return (mask,)
 
 
 NODE_CLASS_MAPPINGS = {
@@ -355,6 +373,7 @@ NODE_CLASS_MAPPINGS = {
     "MaskComposite": MaskComposite,
     "FeatherMask": FeatherMask,
     "GrowMask": GrowMask,
+    "ThresholdMask": ThresholdMask,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
diff --git a/comfy_extras/nodes_model_advanced.py b/comfy_extras/nodes_model_advanced.py
index 541ce8fa..97559cf5 100644
--- a/comfy_extras/nodes_model_advanced.py
+++ b/comfy_extras/nodes_model_advanced.py
@@ -1,6 +1,7 @@
 import folder_paths
 import comfy.sd
 import comfy.model_sampling
+import comfy.latent_formats
 import torch
 
 class LCM(comfy.model_sampling.EPS):
@@ -17,6 +18,10 @@ class LCM(comfy.model_sampling.EPS):
 
         return c_out * x0 + c_skip * model_input
 
+class X0(comfy.model_sampling.EPS):
+    def calculate_denoised(self, sigma, model_output, model_input):
+        return model_output
+
 class ModelSamplingDiscreteDistilled(comfy.model_sampling.ModelSamplingDiscrete):
     original_timesteps = 50
 
@@ -68,7 +73,7 @@ class ModelSamplingDiscrete:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "sampling": (["eps", "v_prediction", "lcm"],),
+                              "sampling": (["eps", "v_prediction", "lcm", "x0"],),
                               "zsnr": ("BOOLEAN", {"default": False}),
                               }}
 
@@ -88,6 +93,8 @@ class ModelSamplingDiscrete:
         elif sampling == "lcm":
             sampling_type = LCM
             sampling_base = ModelSamplingDiscreteDistilled
+        elif sampling == "x0":
+            sampling_type = X0
 
         class ModelSamplingAdvanced(sampling_base, sampling_type):
             pass
@@ -99,11 +106,63 @@ class ModelSamplingDiscrete:
         m.add_object_patch("model_sampling", model_sampling)
         return (m, )
 
+class ModelSamplingStableCascade:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "shift": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 100.0, "step":0.01}),
+                              }}
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/model"
+
+    def patch(self, model, shift):
+        m = model.clone()
+
+        sampling_base = comfy.model_sampling.StableCascadeSampling
+        sampling_type = comfy.model_sampling.EPS
+
+        class ModelSamplingAdvanced(sampling_base, sampling_type):
+            pass
+
+        model_sampling = ModelSamplingAdvanced(model.model.model_config)
+        model_sampling.set_parameters(shift)
+        m.add_object_patch("model_sampling", model_sampling)
+        return (m, )
+
+class ModelSamplingSD3:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "shift": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step":0.01}),
+                              }}
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/model"
+
+    def patch(self, model, shift):
+        m = model.clone()
+
+        sampling_base = comfy.model_sampling.ModelSamplingDiscreteFlow
+        sampling_type = comfy.model_sampling.CONST
+
+        class ModelSamplingAdvanced(sampling_base, sampling_type):
+            pass
+
+        model_sampling = ModelSamplingAdvanced(model.model.model_config)
+        model_sampling.set_parameters(shift=shift)
+        m.add_object_patch("model_sampling", model_sampling)
+        return (m, )
+
 class ModelSamplingContinuousEDM:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "sampling": (["v_prediction", "eps"],),
+                              "sampling": (["v_prediction", "edm_playground_v2.5", "eps"],),
                               "sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
                               "sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
                               }}
@@ -116,16 +175,54 @@ class ModelSamplingContinuousEDM:
     def patch(self, model, sampling, sigma_max, sigma_min):
         m = model.clone()
 
+        latent_format = None
+        sigma_data = 1.0
         if sampling == "eps":
             sampling_type = comfy.model_sampling.EPS
         elif sampling == "v_prediction":
             sampling_type = comfy.model_sampling.V_PREDICTION
+        elif sampling == "edm_playground_v2.5":
+            sampling_type = comfy.model_sampling.EDM
+            sigma_data = 0.5
+            latent_format = comfy.latent_formats.SDXL_Playground_2_5()
 
         class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingContinuousEDM, sampling_type):
             pass
 
         model_sampling = ModelSamplingAdvanced(model.model.model_config)
-        model_sampling.set_sigma_range(sigma_min, sigma_max)
+        model_sampling.set_parameters(sigma_min, sigma_max, sigma_data)
+        m.add_object_patch("model_sampling", model_sampling)
+        if latent_format is not None:
+            m.add_object_patch("latent_format", latent_format)
+        return (m, )
+
+class ModelSamplingContinuousV:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "sampling": (["v_prediction"],),
+                              "sigma_max": ("FLOAT", {"default": 500.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
+                              "sigma_min": ("FLOAT", {"default": 0.03, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
+                              }}
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/model"
+
+    def patch(self, model, sampling, sigma_max, sigma_min):
+        m = model.clone()
+
+        latent_format = None
+        sigma_data = 1.0
+        if sampling == "v_prediction":
+            sampling_type = comfy.model_sampling.V_PREDICTION
+
+        class ModelSamplingAdvanced(comfy.model_sampling.ModelSamplingContinuousV, sampling_type):
+            pass
+
+        model_sampling = ModelSamplingAdvanced(model.model.model_config)
+        model_sampling.set_parameters(sigma_min, sigma_max, sigma_data)
         m.add_object_patch("model_sampling", model_sampling)
         return (m, )
 
@@ -171,5 +268,8 @@ class RescaleCFG:
 NODE_CLASS_MAPPINGS = {
     "ModelSamplingDiscrete": ModelSamplingDiscrete,
     "ModelSamplingContinuousEDM": ModelSamplingContinuousEDM,
+    "ModelSamplingContinuousV": ModelSamplingContinuousV,
+    "ModelSamplingStableCascade": ModelSamplingStableCascade,
+    "ModelSamplingSD3": ModelSamplingSD3,
     "RescaleCFG": RescaleCFG,
 }
diff --git a/comfy_extras/nodes_model_downscale.py b/comfy_extras/nodes_model_downscale.py
index 48bcc689..58b5073e 100644
--- a/comfy_extras/nodes_model_downscale.py
+++ b/comfy_extras/nodes_model_downscale.py
@@ -20,8 +20,9 @@ class PatchModelAddDownscale:
     CATEGORY = "_for_testing"
 
     def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method):
-        sigma_start = model.model.model_sampling.percent_to_sigma(start_percent)
-        sigma_end = model.model.model_sampling.percent_to_sigma(end_percent)
+        model_sampling = model.get_model_object("model_sampling")
+        sigma_start = model_sampling.percent_to_sigma(start_percent)
+        sigma_end = model_sampling.percent_to_sigma(end_percent)
 
         def input_block_patch(h, transformer_options):
             if transformer_options["block"][1] == block_number:
diff --git a/comfy_extras/nodes_model_merging.py b/comfy_extras/nodes_model_merging.py
index d594cf49..b0d149c6 100644
--- a/comfy_extras/nodes_model_merging.py
+++ b/comfy_extras/nodes_model_merging.py
@@ -2,7 +2,9 @@ import comfy.sd
 import comfy.utils
 import comfy.model_base
 import comfy.model_management
+import comfy.model_sampling
 
+import torch
 import folder_paths
 import json
 import os
@@ -87,6 +89,50 @@ class CLIPMergeSimple:
             m.add_patches({k: kp[k]}, 1.0 - ratio, ratio)
         return (m, )
 
+
+class CLIPSubtract:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "clip1": ("CLIP",),
+                              "clip2": ("CLIP",),
+                              "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
+                              }}
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "merge"
+
+    CATEGORY = "advanced/model_merging"
+
+    def merge(self, clip1, clip2, multiplier):
+        m = clip1.clone()
+        kp = clip2.get_key_patches()
+        for k in kp:
+            if k.endswith(".position_ids") or k.endswith(".logit_scale"):
+                continue
+            m.add_patches({k: kp[k]}, - multiplier, multiplier)
+        return (m, )
+
+
+class CLIPAdd:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "clip1": ("CLIP",),
+                              "clip2": ("CLIP",),
+                              }}
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "merge"
+
+    CATEGORY = "advanced/model_merging"
+
+    def merge(self, clip1, clip2):
+        m = clip1.clone()
+        kp = clip2.get_key_patches()
+        for k in kp:
+            if k.endswith(".position_ids") or k.endswith(".logit_scale"):
+                continue
+            m.add_patches({k: kp[k]}, 1.0, 1.0)
+        return (m, )
+
+
 class ModelMergeBlocks:
     @classmethod
     def INPUT_TYPES(s):
@@ -129,9 +175,16 @@ def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefi
 
     enable_modelspec = True
     if isinstance(model.model, comfy.model_base.SDXL):
-        metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-base"
+        if isinstance(model.model, comfy.model_base.SDXL_instructpix2pix):
+            metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-edit"
+        else:
+            metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-base"
     elif isinstance(model.model, comfy.model_base.SDXLRefiner):
         metadata["modelspec.architecture"] = "stable-diffusion-xl-v1-refiner"
+    elif isinstance(model.model, comfy.model_base.SVD_img2vid):
+        metadata["modelspec.architecture"] = "stable-video-diffusion-img2vid-v1"
+    elif isinstance(model.model, comfy.model_base.SD3):
+        metadata["modelspec.architecture"] = "stable-diffusion-v3-medium" #TODO: other SD3 variants
     else:
         enable_modelspec = False
 
@@ -145,6 +198,13 @@ def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefi
     # "stable-diffusion-v2-768-v", "stable-diffusion-v2-unclip-l", "stable-diffusion-v2-unclip-h",
     # "v2-inpainting"
 
+    extra_keys = {}
+    model_sampling = model.get_model_object("model_sampling")
+    if isinstance(model_sampling, comfy.model_sampling.ModelSamplingContinuousEDM):
+        if isinstance(model_sampling, comfy.model_sampling.V_PREDICTION):
+            extra_keys["edm_vpred.sigma_max"] = torch.tensor(model_sampling.sigma_max).float()
+            extra_keys["edm_vpred.sigma_min"] = torch.tensor(model_sampling.sigma_min).float()
+
     if model.model.model_type == comfy.model_base.ModelType.EPS:
         metadata["modelspec.predict_key"] = "epsilon"
     elif model.model.model_type == comfy.model_base.ModelType.V_PREDICTION:
@@ -159,7 +219,7 @@ def save_checkpoint(model, clip=None, vae=None, clip_vision=None, filename_prefi
     output_checkpoint = f"{filename}_{counter:05}_.safetensors"
     output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
 
-    comfy.sd.save_checkpoint(output_checkpoint, model, clip, vae, clip_vision, metadata=metadata)
+    comfy.sd.save_checkpoint(output_checkpoint, model, clip, vae, clip_vision, metadata=metadata, extra_keys=extra_keys)
 
 class CheckpointSave:
     def __init__(self):
@@ -209,7 +269,7 @@ class CLIPSave:
                 for x in extra_pnginfo:
                     metadata[x] = json.dumps(extra_pnginfo[x])
 
-        comfy.model_management.load_models_gpu([clip.load_model()])
+        comfy.model_management.load_models_gpu([clip.load_model()], force_patch_weights=True)
         clip_sd = clip.get_sd()
 
         for prefix in ["clip_l.", "clip_g.", ""]:
@@ -279,6 +339,8 @@ NODE_CLASS_MAPPINGS = {
     "ModelMergeAdd": ModelAdd,
     "CheckpointSave": CheckpointSave,
     "CLIPMergeSimple": CLIPMergeSimple,
+    "CLIPMergeSubtract": CLIPSubtract,
+    "CLIPMergeAdd": CLIPAdd,
     "CLIPSave": CLIPSave,
     "VAESave": VAESave,
 }
diff --git a/comfy_extras/nodes_model_merging_model_specific.py b/comfy_extras/nodes_model_merging_model_specific.py
new file mode 100644
index 00000000..df111bd6
--- /dev/null
+++ b/comfy_extras/nodes_model_merging_model_specific.py
@@ -0,0 +1,83 @@
+import comfy_extras.nodes_model_merging
+
+class ModelMergeSD1(comfy_extras.nodes_model_merging.ModelMergeBlocks):
+    CATEGORY = "advanced/model_merging/model_specific"
+    @classmethod
+    def INPUT_TYPES(s):
+        arg_dict = { "model1": ("MODEL",),
+                              "model2": ("MODEL",)}
+
+        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+
+        arg_dict["time_embed."] = argument
+        arg_dict["label_emb."] = argument
+
+        for i in range(12):
+            arg_dict["input_blocks.{}.".format(i)] = argument
+
+        for i in range(3):
+            arg_dict["middle_block.{}.".format(i)] = argument
+
+        for i in range(12):
+            arg_dict["output_blocks.{}.".format(i)] = argument
+
+        arg_dict["out."] = argument
+
+        return {"required": arg_dict}
+
+
+class ModelMergeSDXL(comfy_extras.nodes_model_merging.ModelMergeBlocks):
+    CATEGORY = "advanced/model_merging/model_specific"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        arg_dict = { "model1": ("MODEL",),
+                              "model2": ("MODEL",)}
+
+        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+
+        arg_dict["time_embed."] = argument
+        arg_dict["label_emb."] = argument
+
+        for i in range(9):
+            arg_dict["input_blocks.{}".format(i)] = argument
+
+        for i in range(3):
+            arg_dict["middle_block.{}".format(i)] = argument
+
+        for i in range(9):
+            arg_dict["output_blocks.{}".format(i)] = argument
+
+        arg_dict["out."] = argument
+
+        return {"required": arg_dict}
+
+class ModelMergeSD3_2B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
+    CATEGORY = "advanced/model_merging/model_specific"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        arg_dict = { "model1": ("MODEL",),
+                              "model2": ("MODEL",)}
+
+        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+
+        arg_dict["pos_embed."] = argument
+        arg_dict["x_embedder."] = argument
+        arg_dict["context_embedder."] = argument
+        arg_dict["y_embedder."] = argument
+        arg_dict["t_embedder."] = argument
+
+        for i in range(24):
+            arg_dict["joint_blocks.{}.".format(i)] = argument
+
+        arg_dict["final_layer."] = argument
+
+        return {"required": arg_dict}
+
+NODE_CLASS_MAPPINGS = {
+    "ModelMergeSD1": ModelMergeSD1,
+    "ModelMergeSD2": ModelMergeSD1, #SD1 and SD2 have the same blocks
+    "ModelMergeSDXL": ModelMergeSDXL,
+    "ModelMergeSD3_2B": ModelMergeSD3_2B,
+}
diff --git a/comfy_extras/nodes_morphology.py b/comfy_extras/nodes_morphology.py
new file mode 100644
index 00000000..071521d8
--- /dev/null
+++ b/comfy_extras/nodes_morphology.py
@@ -0,0 +1,49 @@
+import torch
+import comfy.model_management
+
+from kornia.morphology import dilation, erosion, opening, closing, gradient, top_hat, bottom_hat
+
+
+class Morphology:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"image": ("IMAGE",),
+                                "operation": (["erode",  "dilate", "open", "close", "gradient", "bottom_hat", "top_hat"],),
+                                "kernel_size": ("INT", {"default": 3, "min": 3, "max": 999, "step": 1}),
+                                }}
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "process"
+
+    CATEGORY = "image/postprocessing"
+
+    def process(self, image, operation, kernel_size):
+        device = comfy.model_management.get_torch_device()
+        kernel = torch.ones(kernel_size, kernel_size, device=device)
+        image_k = image.to(device).movedim(-1, 1)
+        if operation == "erode":
+            output = erosion(image_k, kernel)
+        elif operation == "dilate":
+            output = dilation(image_k, kernel)
+        elif operation == "open":
+            output = opening(image_k, kernel)
+        elif operation == "close":
+            output = closing(image_k, kernel)
+        elif operation == "gradient":
+            output = gradient(image_k, kernel)
+        elif operation == "top_hat":
+            output = top_hat(image_k, kernel)
+        elif operation == "bottom_hat":
+            output = bottom_hat(image_k, kernel)
+        else:
+            raise ValueError(f"Invalid operation {operation} for morphology. Must be one of 'erode', 'dilate', 'open', 'close', 'gradient', 'tophat', 'bottomhat'")
+        img_out = output.to(comfy.model_management.intermediate_device()).movedim(1, -1)
+        return (img_out,)
+
+NODE_CLASS_MAPPINGS = {
+    "Morphology": Morphology,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "Morphology": "ImageMorphology",
+}
\ No newline at end of file
diff --git a/comfy_extras/nodes_pag.py b/comfy_extras/nodes_pag.py
new file mode 100644
index 00000000..63f43fd6
--- /dev/null
+++ b/comfy_extras/nodes_pag.py
@@ -0,0 +1,56 @@
+#Modified/simplified version of the node from: https://github.com/pamparamm/sd-perturbed-attention
+#If you want the one with more options see the above repo.
+
+#My modified one here is more basic but has less chances of breaking with ComfyUI updates.
+
+import comfy.model_patcher
+import comfy.samplers
+
+class PerturbedAttentionGuidance:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "model": ("MODEL",),
+                "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step": 0.1, "round": 0.01}),
+            }
+        }
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "_for_testing"
+
+    def patch(self, model, scale):
+        unet_block = "middle"
+        unet_block_id = 0
+        m = model.clone()
+
+        def perturbed_attention(q, k, v, extra_options, mask=None):
+            return v
+
+        def post_cfg_function(args):
+            model = args["model"]
+            cond_pred = args["cond_denoised"]
+            cond = args["cond"]
+            cfg_result = args["denoised"]
+            sigma = args["sigma"]
+            model_options = args["model_options"].copy()
+            x = args["input"]
+
+            if scale == 0:
+                return cfg_result
+
+            # Replace Self-attention with PAG
+            model_options = comfy.model_patcher.set_model_options_patch_replace(model_options, perturbed_attention, "attn1", unet_block, unet_block_id)
+            (pag,) = comfy.samplers.calc_cond_batch(model, [cond], x, sigma, model_options)
+
+            return cfg_result + (cond_pred - pag) * scale
+
+        m.set_model_sampler_post_cfg_function(post_cfg_function)
+
+        return (m,)
+
+NODE_CLASS_MAPPINGS = {
+    "PerturbedAttentionGuidance": PerturbedAttentionGuidance,
+}
diff --git a/comfy_extras/nodes_perpneg.py b/comfy_extras/nodes_perpneg.py
index 45e4d418..546276aa 100644
--- a/comfy_extras/nodes_perpneg.py
+++ b/comfy_extras/nodes_perpneg.py
@@ -1,16 +1,26 @@
 import torch
 import comfy.model_management
-import comfy.sample
+import comfy.sampler_helpers
 import comfy.samplers
 import comfy.utils
+import node_helpers
 
+def perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_nocond, neg_scale, cond_scale):
+    pos = noise_pred_pos - noise_pred_nocond
+    neg = noise_pred_neg - noise_pred_nocond
 
+    perp = neg - ((torch.mul(neg, pos).sum())/(torch.norm(pos)**2)) * pos
+    perp_neg = perp * neg_scale
+    cfg_result = noise_pred_nocond + cond_scale*(pos - perp_neg)
+    return cfg_result
+
+#TODO: This node should be removed, it has been replaced with PerpNegGuider
 class PerpNeg:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {"model": ("MODEL", ),
                              "empty_conditioning": ("CONDITIONING", ),
-                             "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0}),
+                             "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
                             }}
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
@@ -19,7 +29,7 @@ class PerpNeg:
 
     def patch(self, model, empty_conditioning, neg_scale):
         m = model.clone()
-        nocond = comfy.sample.convert_cond(empty_conditioning)
+        nocond = comfy.sampler_helpers.convert_cond(empty_conditioning)
 
         def cfg_function(args):
             model = args["model"]
@@ -31,14 +41,9 @@ class PerpNeg:
             model_options = args["model_options"]
             nocond_processed = comfy.samplers.encode_model_conds(model.extra_conds, nocond, x, x.device, "negative")
 
-            (noise_pred_nocond, _) = comfy.samplers.calc_cond_uncond_batch(model, nocond_processed, None, x, sigma, model_options)
+            (noise_pred_nocond,) = comfy.samplers.calc_cond_batch(model, [nocond_processed], x, sigma, model_options)
 
-            pos = noise_pred_pos - noise_pred_nocond
-            neg = noise_pred_neg - noise_pred_nocond
-            perp = ((torch.mul(pos, neg).sum())/(torch.norm(neg)**2)) * neg
-            perp_neg = perp * neg_scale
-            cfg_result = noise_pred_nocond + cond_scale*(pos - perp_neg)
-            cfg_result = x - cfg_result
+            cfg_result = x - perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_nocond, neg_scale, cond_scale)
             return cfg_result
 
         m.set_model_sampler_cfg_function(cfg_function)
@@ -46,10 +51,78 @@ class PerpNeg:
         return (m, )
 
 
+class Guider_PerpNeg(comfy.samplers.CFGGuider):
+    def set_conds(self, positive, negative, empty_negative_prompt):
+        empty_negative_prompt = node_helpers.conditioning_set_values(empty_negative_prompt, {"prompt_type": "negative"})
+        self.inner_set_conds({"positive": positive, "empty_negative_prompt": empty_negative_prompt, "negative": negative})
+
+    def set_cfg(self, cfg, neg_scale):
+        self.cfg = cfg
+        self.neg_scale = neg_scale
+
+    def predict_noise(self, x, timestep, model_options={}, seed=None):
+        # in CFGGuider.predict_noise, we call sampling_function(), which uses cfg_function() to compute pos & neg
+        # but we'd rather do a single batch of sampling pos, neg, and empty, so we call calc_cond_batch([pos,neg,empty]) directly
+        
+        positive_cond = self.conds.get("positive", None)
+        negative_cond = self.conds.get("negative", None)
+        empty_cond = self.conds.get("empty_negative_prompt", None)
+
+        (noise_pred_pos, noise_pred_neg, noise_pred_empty) = \
+            comfy.samplers.calc_cond_batch(self.inner_model, [positive_cond, negative_cond, empty_cond], x, timestep, model_options)
+        cfg_result = perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_empty, self.neg_scale, self.cfg)
+
+        # normally this would be done in cfg_function, but we skipped 
+        # that for efficiency: we can compute the noise predictions in
+        # a single call to calc_cond_batch() (rather than two)
+        # so we replicate the hook here
+        for fn in model_options.get("sampler_post_cfg_function", []):
+            args = {
+                "denoised": cfg_result,
+                "cond": positive_cond,
+                "uncond": negative_cond,
+                "model": self.inner_model,
+                "uncond_denoised": noise_pred_neg,
+                "cond_denoised": noise_pred_pos,
+                "sigma": timestep,
+                "model_options": model_options,
+                "input": x,
+                # not in the original call in samplers.py:cfg_function, but made available for future hooks
+                "empty_cond": empty_cond,
+                "empty_cond_denoised": noise_pred_empty,}
+            cfg_result = fn(args)
+
+        return cfg_result
+
+class PerpNegGuider:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"model": ("MODEL",),
+                    "positive": ("CONDITIONING", ),
+                    "negative": ("CONDITIONING", ),
+                    "empty_conditioning": ("CONDITIONING", ),
+                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
+                    "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
+                     }
+                }
+
+    RETURN_TYPES = ("GUIDER",)
+
+    FUNCTION = "get_guider"
+    CATEGORY = "_for_testing"
+
+    def get_guider(self, model, positive, negative, empty_conditioning, cfg, neg_scale):
+        guider = Guider_PerpNeg(model)
+        guider.set_conds(positive, negative, empty_conditioning)
+        guider.set_cfg(cfg, neg_scale)
+        return (guider,)
+
 NODE_CLASS_MAPPINGS = {
     "PerpNeg": PerpNeg,
+    "PerpNegGuider": PerpNegGuider,
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
-    "PerpNeg": "Perp-Neg",
+    "PerpNeg": "Perp-Neg (DEPRECATED by PerpNegGuider)",
 }
diff --git a/comfy_extras/nodes_photomaker.py b/comfy_extras/nodes_photomaker.py
index 90130142..29d127d7 100644
--- a/comfy_extras/nodes_photomaker.py
+++ b/comfy_extras/nodes_photomaker.py
@@ -141,7 +141,7 @@ class PhotoMakerEncode:
         return {"required": { "photomaker": ("PHOTOMAKER",),
                               "image": ("IMAGE",),
                               "clip": ("CLIP", ),
-                              "text": ("STRING", {"multiline": True, "default": "photograph of photomaker"}),
+                              "text": ("STRING", {"multiline": True, "dynamicPrompts": True, "default": "photograph of photomaker"}),
                              }}
 
     RETURN_TYPES = ("CONDITIONING",)
diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py
index cb5c7d22..68f6ef51 100644
--- a/comfy_extras/nodes_post_processing.py
+++ b/comfy_extras/nodes_post_processing.py
@@ -5,6 +5,7 @@ from PIL import Image
 import math
 
 import comfy.utils
+import comfy.model_management
 
 
 class Blend:
@@ -102,6 +103,7 @@ class Blur:
         if blur_radius == 0:
             return (image,)
 
+        image = image.to(comfy.model_management.get_torch_device())
         batch_size, height, width, channels = image.shape
 
         kernel_size = blur_radius * 2 + 1
@@ -112,7 +114,7 @@ class Blur:
         blurred = F.conv2d(padded_image, kernel, padding=kernel_size // 2, groups=channels)[:,:,blur_radius:-blur_radius, blur_radius:-blur_radius]
         blurred = blurred.permute(0, 2, 3, 1)
 
-        return (blurred,)
+        return (blurred.to(comfy.model_management.intermediate_device()),)
 
 class Quantize:
     def __init__(self):
@@ -204,13 +206,13 @@ class Sharpen:
                     "default": 1.0,
                     "min": 0.1,
                     "max": 10.0,
-                    "step": 0.1
+                    "step": 0.01
                 }),
                 "alpha": ("FLOAT", {
                     "default": 1.0,
                     "min": 0.0,
                     "max": 5.0,
-                    "step": 0.1
+                    "step": 0.01
                 }),
             },
         }
@@ -225,6 +227,7 @@ class Sharpen:
             return (image,)
 
         batch_size, height, width, channels = image.shape
+        image = image.to(comfy.model_management.get_torch_device())
 
         kernel_size = sharpen_radius * 2 + 1
         kernel = gaussian_kernel(kernel_size, sigma, device=image.device) * -(alpha*10)
@@ -239,7 +242,7 @@ class Sharpen:
 
         result = torch.clamp(sharpened, 0, 1)
 
-        return (result,)
+        return (result.to(comfy.model_management.intermediate_device()),)
 
 class ImageScaleToTotalPixels:
     upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
diff --git a/comfy_extras/nodes_sag.py b/comfy_extras/nodes_sag.py
index bbd38080..010e9974 100644
--- a/comfy_extras/nodes_sag.py
+++ b/comfy_extras/nodes_sag.py
@@ -4,13 +4,12 @@ import torch.nn.functional as F
 import math
 
 from einops import rearrange, repeat
-import os
-from comfy.ldm.modules.attention import optimized_attention, _ATTN_PRECISION
+from comfy.ldm.modules.attention import optimized_attention
 import comfy.samplers
 
 # from comfy/ldm/modules/attention.py
 # but modified to return attention scores as well as output
-def attention_basic_with_sim(q, k, v, heads, mask=None):
+def attention_basic_with_sim(q, k, v, heads, mask=None, attn_precision=None):
     b, _, dim_head = q.shape
     dim_head //= heads
     scale = dim_head ** -0.5
@@ -26,7 +25,7 @@ def attention_basic_with_sim(q, k, v, heads, mask=None):
     )
 
     # force cast to fp32 to avoid overflowing
-    if _ATTN_PRECISION =="fp32":
+    if attn_precision == torch.float32:
         sim = einsum('b i d, b j d -> b i j', q.float(), k.float()) * scale
     else:
         sim = einsum('b i d, b j d -> b i j', q, k) * scale
@@ -121,13 +120,13 @@ class SelfAttentionGuidance:
             if 1 in cond_or_uncond:
                 uncond_index = cond_or_uncond.index(1)
                 # do the entire attention operation, but save the attention scores to attn_scores
-                (out, sim) = attention_basic_with_sim(q, k, v, heads=heads)
+                (out, sim) = attention_basic_with_sim(q, k, v, heads=heads, attn_precision=extra_options["attn_precision"])
                 # when using a higher batch size, I BELIEVE the result batch dimension is [uc1, ... ucn, c1, ... cn]
                 n_slices = heads * b
                 attn_scores = sim[n_slices * uncond_index:n_slices * (uncond_index+1)]
                 return out
             else:
-                return optimized_attention(q, k, v, heads=heads)
+                return optimized_attention(q, k, v, heads=heads, attn_precision=extra_options["attn_precision"])
 
         def post_cfg_function(args):
             nonlocal attn_scores
@@ -150,7 +149,7 @@ class SelfAttentionGuidance:
             degraded = create_blur_map(uncond_pred, uncond_attn, sag_sigma, sag_threshold)
             degraded_noised = degraded + x - uncond_pred
             # call into the UNet
-            (sag, _) = comfy.samplers.calc_cond_uncond_batch(model, uncond, None, degraded_noised, sigma, model_options)
+            (sag,) = comfy.samplers.calc_cond_batch(model, [uncond], degraded_noised, sigma, model_options)
             return cfg_result + (degraded - sag) * sag_scale
 
         m.set_model_sampler_post_cfg_function(post_cfg_function, disable_cfg1_optimization=True)
diff --git a/comfy_extras/nodes_sd3.py b/comfy_extras/nodes_sd3.py
new file mode 100644
index 00000000..548b1ad6
--- /dev/null
+++ b/comfy_extras/nodes_sd3.py
@@ -0,0 +1,102 @@
+import folder_paths
+import comfy.sd
+import comfy.model_management
+import nodes
+import torch
+
+class TripleCLIPLoader:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), "clip_name2": (folder_paths.get_filename_list("clip"), ), "clip_name3": (folder_paths.get_filename_list("clip"), )
+                             }}
+    RETURN_TYPES = ("CLIP",)
+    FUNCTION = "load_clip"
+
+    CATEGORY = "advanced/loaders"
+
+    def load_clip(self, clip_name1, clip_name2, clip_name3):
+        clip_path1 = folder_paths.get_full_path("clip", clip_name1)
+        clip_path2 = folder_paths.get_full_path("clip", clip_name2)
+        clip_path3 = folder_paths.get_full_path("clip", clip_name3)
+        clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2, clip_path3], embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        return (clip,)
+
+class EmptySD3LatentImage:
+    def __init__(self):
+        self.device = comfy.model_management.intermediate_device()
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "width": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
+                              "height": ("INT", {"default": 1024, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
+                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "generate"
+
+    CATEGORY = "latent/sd3"
+
+    def generate(self, width, height, batch_size=1):
+        latent = torch.ones([batch_size, 16, height // 8, width // 8], device=self.device) * 0.0609
+        return ({"samples":latent}, )
+
+class CLIPTextEncodeSD3:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "clip": ("CLIP", ),
+            "clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+            "clip_g": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+            "t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+            "empty_padding": (["none", "empty_prompt"], )
+            }}
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "encode"
+
+    CATEGORY = "advanced/conditioning"
+
+    def encode(self, clip, clip_l, clip_g, t5xxl, empty_padding):
+        no_padding = empty_padding == "none"
+
+        tokens = clip.tokenize(clip_g)
+        if len(clip_g) == 0 and no_padding:
+            tokens["g"] = []
+
+        if len(clip_l) == 0 and no_padding:
+            tokens["l"] = []
+        else:
+            tokens["l"] = clip.tokenize(clip_l)["l"]
+
+        if len(t5xxl) == 0 and no_padding:
+            tokens["t5xxl"] =  []
+        else:
+            tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"]
+        if len(tokens["l"]) != len(tokens["g"]):
+            empty = clip.tokenize("")
+            while len(tokens["l"]) < len(tokens["g"]):
+                tokens["l"] += empty["l"]
+            while len(tokens["l"]) > len(tokens["g"]):
+                tokens["g"] += empty["g"]
+        cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
+        return ([[cond, {"pooled_output": pooled}]], )
+
+
+class ControlNetApplySD3(nodes.ControlNetApplyAdvanced):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"positive": ("CONDITIONING", ),
+                             "negative": ("CONDITIONING", ),
+                             "control_net": ("CONTROL_NET", ),
+                             "vae": ("VAE", ),
+                             "image": ("IMAGE", ),
+                             "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+                             "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                             "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
+                             }}
+    CATEGORY = "_for_testing/sd3"
+
+NODE_CLASS_MAPPINGS = {
+    "TripleCLIPLoader": TripleCLIPLoader,
+    "EmptySD3LatentImage": EmptySD3LatentImage,
+    "CLIPTextEncodeSD3": CLIPTextEncodeSD3,
+    "ControlNetApplySD3": ControlNetApplySD3,
+}
diff --git a/comfy_extras/nodes_sdupscale.py b/comfy_extras/nodes_sdupscale.py
index 28c1cb0f..bba67e8d 100644
--- a/comfy_extras/nodes_sdupscale.py
+++ b/comfy_extras/nodes_sdupscale.py
@@ -1,5 +1,4 @@
 import torch
-import nodes
 import comfy.utils
 
 class SD_4XUpscale_Conditioning:
diff --git a/comfy_extras/nodes_stable3d.py b/comfy_extras/nodes_stable3d.py
index 4375d8f9..be2e34c2 100644
--- a/comfy_extras/nodes_stable3d.py
+++ b/comfy_extras/nodes_stable3d.py
@@ -29,8 +29,8 @@ class StableZero123_Conditioning:
                               "width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
                               "height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
                               "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
-                              "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
-                              "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
+                              "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
+                              "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
                              }}
     RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
     RETURN_NAMES = ("positive", "negative", "latent")
@@ -62,10 +62,10 @@ class StableZero123_Conditioning_Batched:
                               "width": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
                               "height": ("INT", {"default": 256, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
                               "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
-                              "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
-                              "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
-                              "elevation_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
-                              "azimuth_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0}),
+                              "elevation": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
+                              "azimuth": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
+                              "elevation_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
+                              "azimuth_batch_increment": ("FLOAT", {"default": 0.0, "min": -180.0, "max": 180.0, "step": 0.1, "round": False}),
                              }}
     RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
     RETURN_NAMES = ("positive", "negative", "latent")
@@ -95,8 +95,49 @@ class StableZero123_Conditioning_Batched:
         latent = torch.zeros([batch_size, 4, height // 8, width // 8])
         return (positive, negative, {"samples":latent, "batch_index": [0] * batch_size})
 
+class SV3D_Conditioning:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "clip_vision": ("CLIP_VISION",),
+                              "init_image": ("IMAGE",),
+                              "vae": ("VAE",),
+                              "width": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
+                              "height": ("INT", {"default": 576, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 8}),
+                              "video_frames": ("INT", {"default": 21, "min": 1, "max": 4096}),
+                              "elevation": ("FLOAT", {"default": 0.0, "min": -90.0, "max": 90.0, "step": 0.1, "round": False}),
+                             }}
+    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
+    RETURN_NAMES = ("positive", "negative", "latent")
+
+    FUNCTION = "encode"
+
+    CATEGORY = "conditioning/3d_models"
+
+    def encode(self, clip_vision, init_image, vae, width, height, video_frames, elevation):
+        output = clip_vision.encode_image(init_image)
+        pooled = output.image_embeds.unsqueeze(0)
+        pixels = comfy.utils.common_upscale(init_image.movedim(-1,1), width, height, "bilinear", "center").movedim(1,-1)
+        encode_pixels = pixels[:,:,:,:3]
+        t = vae.encode(encode_pixels)
+
+        azimuth = 0
+        azimuth_increment = 360 / (max(video_frames, 2) - 1)
+
+        elevations = []
+        azimuths = []
+        for i in range(video_frames):
+            elevations.append(elevation)
+            azimuths.append(azimuth)
+            azimuth += azimuth_increment
+
+        positive = [[pooled, {"concat_latent_image": t, "elevation": elevations, "azimuth": azimuths}]]
+        negative = [[torch.zeros_like(pooled), {"concat_latent_image": torch.zeros_like(t), "elevation": elevations, "azimuth": azimuths}]]
+        latent = torch.zeros([video_frames, 4, height // 8, width // 8])
+        return (positive, negative, {"samples":latent})
+
 
 NODE_CLASS_MAPPINGS = {
     "StableZero123_Conditioning": StableZero123_Conditioning,
     "StableZero123_Conditioning_Batched": StableZero123_Conditioning_Batched,
+    "SV3D_Conditioning": SV3D_Conditioning,
 }
diff --git a/comfy_extras/nodes_stable_cascade.py b/comfy_extras/nodes_stable_cascade.py
new file mode 100644
index 00000000..fcbbeb27
--- /dev/null
+++ b/comfy_extras/nodes_stable_cascade.py
@@ -0,0 +1,140 @@
+"""
+    This file is part of ComfyUI.
+    Copyright (C) 2024 Stability AI
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+"""
+
+import torch
+import nodes
+import comfy.utils
+
+
+class StableCascade_EmptyLatentImage:
+    def __init__(self, device="cpu"):
+        self.device = device
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "width": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}),
+            "height": ("INT", {"default": 1024, "min": 256, "max": nodes.MAX_RESOLUTION, "step": 8}),
+            "compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}),
+            "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})
+        }}
+    RETURN_TYPES = ("LATENT", "LATENT")
+    RETURN_NAMES = ("stage_c", "stage_b")
+    FUNCTION = "generate"
+
+    CATEGORY = "latent/stable_cascade"
+
+    def generate(self, width, height, compression, batch_size=1):
+        c_latent = torch.zeros([batch_size, 16, height // compression, width // compression])
+        b_latent = torch.zeros([batch_size, 4, height // 4, width // 4])
+        return ({
+            "samples": c_latent,
+        }, {
+            "samples": b_latent,
+        })
+
+class StableCascade_StageC_VAEEncode:
+    def __init__(self, device="cpu"):
+        self.device = device
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "image": ("IMAGE",),
+            "vae": ("VAE", ),
+            "compression": ("INT", {"default": 42, "min": 4, "max": 128, "step": 1}),
+        }}
+    RETURN_TYPES = ("LATENT", "LATENT")
+    RETURN_NAMES = ("stage_c", "stage_b")
+    FUNCTION = "generate"
+
+    CATEGORY = "latent/stable_cascade"
+
+    def generate(self, image, vae, compression):
+        width = image.shape[-2]
+        height = image.shape[-3]
+        out_width = (width // compression) * vae.downscale_ratio
+        out_height = (height // compression) * vae.downscale_ratio
+
+        s = comfy.utils.common_upscale(image.movedim(-1,1), out_width, out_height, "bicubic", "center").movedim(1,-1)
+
+        c_latent = vae.encode(s[:,:,:,:3])
+        b_latent = torch.zeros([c_latent.shape[0], 4, (height // 8) * 2, (width // 8) * 2])
+        return ({
+            "samples": c_latent,
+        }, {
+            "samples": b_latent,
+        })
+
+class StableCascade_StageB_Conditioning:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "conditioning": ("CONDITIONING",),
+                              "stage_c": ("LATENT",),
+                             }}
+    RETURN_TYPES = ("CONDITIONING",)
+
+    FUNCTION = "set_prior"
+
+    CATEGORY = "conditioning/stable_cascade"
+
+    def set_prior(self, conditioning, stage_c):
+        c = []
+        for t in conditioning:
+            d = t[1].copy()
+            d['stable_cascade_prior'] = stage_c['samples']
+            n = [t[0], d]
+            c.append(n)
+        return (c, )
+
+class StableCascade_SuperResolutionControlnet:
+    def __init__(self, device="cpu"):
+        self.device = device
+
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "image": ("IMAGE",),
+            "vae": ("VAE", ),
+        }}
+    RETURN_TYPES = ("IMAGE", "LATENT", "LATENT")
+    RETURN_NAMES = ("controlnet_input", "stage_c", "stage_b")
+    FUNCTION = "generate"
+
+    CATEGORY = "_for_testing/stable_cascade"
+
+    def generate(self, image, vae):
+        width = image.shape[-2]
+        height = image.shape[-3]
+        batch_size = image.shape[0]
+        controlnet_input = vae.encode(image[:,:,:,:3]).movedim(1, -1)
+
+        c_latent = torch.zeros([batch_size, 16, height // 16, width // 16])
+        b_latent = torch.zeros([batch_size, 4, height // 2, width // 2])
+        return (controlnet_input, {
+            "samples": c_latent,
+        }, {
+            "samples": b_latent,
+        })
+
+NODE_CLASS_MAPPINGS = {
+    "StableCascade_EmptyLatentImage": StableCascade_EmptyLatentImage,
+    "StableCascade_StageB_Conditioning": StableCascade_StageB_Conditioning,
+    "StableCascade_StageC_VAEEncode": StableCascade_StageC_VAEEncode,
+    "StableCascade_SuperResolutionControlnet": StableCascade_SuperResolutionControlnet,
+}
diff --git a/comfy_extras/nodes_upscale_model.py b/comfy_extras/nodes_upscale_model.py
index 2b5e49a5..bca79ef2 100644
--- a/comfy_extras/nodes_upscale_model.py
+++ b/comfy_extras/nodes_upscale_model.py
@@ -1,10 +1,19 @@
 import os
-from comfy_extras.chainner_models import model_loading
+import logging
+from spandrel import ModelLoader, ImageModelDescriptor
 from comfy import model_management
 import torch
 import comfy.utils
 import folder_paths
 
+try:
+    from spandrel_extra_arches import EXTRA_REGISTRY
+    from spandrel import MAIN_REGISTRY
+    MAIN_REGISTRY.add(*EXTRA_REGISTRY)
+    logging.info("Successfully imported spandrel_extra_arches: support for non commercial upscale models.")
+except:
+    pass
+
 class UpscaleModelLoader:
     @classmethod
     def INPUT_TYPES(s):
@@ -20,7 +29,11 @@ class UpscaleModelLoader:
         sd = comfy.utils.load_torch_file(model_path, safe_load=True)
         if "module.layers.0.residual_group.blocks.0.norm1.weight" in sd:
             sd = comfy.utils.state_dict_prefix_replace(sd, {"module.":""})
-        out = model_loading.load_state_dict(sd).eval()
+        out = ModelLoader().load_from_state_dict(sd).eval()
+
+        if not isinstance(out, ImageModelDescriptor):
+            raise Exception("Upscale model must be a single-image model.")
+
         return (out, )
 
 
@@ -37,9 +50,14 @@ class ImageUpscaleWithModel:
 
     def upscale(self, upscale_model, image):
         device = model_management.get_torch_device()
+
+        memory_required = model_management.module_size(upscale_model.model)
+        memory_required += (512 * 512 * 3) * image.element_size() * max(upscale_model.scale, 1.0) * 384.0 #The 384.0 is an estimate of how much some of these models take, TODO: make it more accurate
+        memory_required += image.nelement() * image.element_size()
+        model_management.free_memory(memory_required, device)
+
         upscale_model.to(device)
         in_img = image.movedim(-1,-3).to(device)
-        free_memory = model_management.get_free_memory(device)
 
         tile = 512
         overlap = 32
@@ -56,7 +74,7 @@ class ImageUpscaleWithModel:
                 if tile < 128:
                     raise e
 
-        upscale_model.cpu()
+        upscale_model.to("cpu")
         s = torch.clamp(s.movedim(-3,-1), min=0, max=1.0)
         return (s,)
 
diff --git a/comfy_extras/nodes_video_model.py b/comfy_extras/nodes_video_model.py
index a5262565..1a0189ed 100644
--- a/comfy_extras/nodes_video_model.py
+++ b/comfy_extras/nodes_video_model.py
@@ -79,6 +79,33 @@ class VideoLinearCFGGuidance:
         m.set_model_sampler_cfg_function(linear_cfg)
         return (m, )
 
+class VideoTriangleCFGGuidance:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "min_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step":0.5, "round": 0.01}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "sampling/video_models"
+
+    def patch(self, model, min_cfg):
+        def linear_cfg(args):
+            cond = args["cond"]
+            uncond = args["uncond"]
+            cond_scale = args["cond_scale"]
+            period = 1.0
+            values = torch.linspace(0, 1, cond.shape[0], device=cond.device)
+            values = 2 * (values / period - torch.floor(values / period + 0.5)).abs()
+            scale = (values * (cond_scale - min_cfg) + min_cfg).reshape((cond.shape[0], 1, 1, 1))
+
+            return uncond + scale * (cond - uncond)
+
+        m = model.clone()
+        m.set_model_sampler_cfg_function(linear_cfg)
+        return (m, )
+
 class ImageOnlyCheckpointSave(comfy_extras.nodes_model_merging.CheckpointSave):
     CATEGORY = "_for_testing"
 
@@ -98,6 +125,7 @@ NODE_CLASS_MAPPINGS = {
     "ImageOnlyCheckpointLoader": ImageOnlyCheckpointLoader,
     "SVD_img2vid_Conditioning": SVD_img2vid_Conditioning,
     "VideoLinearCFGGuidance": VideoLinearCFGGuidance,
+    "VideoTriangleCFGGuidance": VideoTriangleCFGGuidance,
     "ImageOnlyCheckpointSave": ImageOnlyCheckpointSave,
 }
 
diff --git a/comfy_extras/nodes_webcam.py b/comfy_extras/nodes_webcam.py
new file mode 100644
index 00000000..32a0ba2f
--- /dev/null
+++ b/comfy_extras/nodes_webcam.py
@@ -0,0 +1,33 @@
+import nodes
+import folder_paths
+
+MAX_RESOLUTION = nodes.MAX_RESOLUTION
+
+
+class WebcamCapture(nodes.LoadImage):
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "image": ("WEBCAM", {}),
+                "width": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                "height": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                "capture_on_queue": ("BOOLEAN", {"default": True}),
+            }
+        }
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "load_capture"
+
+    CATEGORY = "image"
+
+    def load_capture(s, image, **kwargs):
+        return super().load_image(folder_paths.get_annotated_filepath(image))
+
+
+NODE_CLASS_MAPPINGS = {
+    "WebcamCapture": WebcamCapture,
+}
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "WebcamCapture": "Webcam Capture",
+}
\ No newline at end of file
diff --git a/cuda_malloc.py b/cuda_malloc.py
index 144cdacd..eb2857c5 100644
--- a/cuda_malloc.py
+++ b/cuda_malloc.py
@@ -1,6 +1,7 @@
 import os
 import importlib.util
 from comfy.cli_args import args
+import subprocess
 
 #Can't use pytorch to get the GPU names because the cuda malloc has to be set before the first import.
 def get_gpu_names():
@@ -34,14 +35,19 @@ def get_gpu_names():
             return gpu_names
         return enum_display_devices()
     else:
-        return set()
+        gpu_names = set()
+        out = subprocess.check_output(['nvidia-smi', '-L'])
+        for l in out.split(b'\n'):
+            if len(l) > 0:
+                gpu_names.add(l.decode('utf-8').split(' (UUID')[0])
+        return gpu_names
 
 blacklist = {"GeForce GTX TITAN X", "GeForce GTX 980", "GeForce GTX 970", "GeForce GTX 960", "GeForce GTX 950", "GeForce 945M",
                 "GeForce 940M", "GeForce 930M", "GeForce 920M", "GeForce 910M", "GeForce GTX 750", "GeForce GTX 745", "Quadro K620",
                 "Quadro K1200", "Quadro K2200", "Quadro M500", "Quadro M520", "Quadro M600", "Quadro M620", "Quadro M1000",
                 "Quadro M1200", "Quadro M2000", "Quadro M2200", "Quadro M3000", "Quadro M4000", "Quadro M5000", "Quadro M5500", "Quadro M6000",
                 "GeForce MX110", "GeForce MX130", "GeForce 830M", "GeForce 840M", "GeForce GTX 850M", "GeForce GTX 860M",
-                "GeForce GTX 1650", "GeForce GTX 1630"
+                "GeForce GTX 1650", "GeForce GTX 1630", "Tesla M4", "Tesla M6", "Tesla M10", "Tesla M40", "Tesla M60"
                 }
 
 def cuda_malloc_supported():
diff --git a/custom_nodes/example_node.py.example b/custom_nodes/example_node.py.example
index 7ce271ec..72ca3688 100644
--- a/custom_nodes/example_node.py.example
+++ b/custom_nodes/example_node.py.example
@@ -12,9 +12,9 @@ class Example:
     Attributes
     ----------
     RETURN_TYPES (`tuple`): 
-        The type of each element in the output tulple.
+        The type of each element in the output tuple.
     RETURN_NAMES (`tuple`):
-        Optional: The name of each output in the output tulple.
+        Optional: The name of each output in the output tuple.
     FUNCTION (`str`):
         The name of the entry-point method. For example, if `FUNCTION = "execute"` then it will run Example().execute()
     OUTPUT_NODE ([`bool`]):
@@ -44,7 +44,7 @@ class Example:
                     * Key field_name (`string`): Name of a entry-point method's argument
                     * Value field_config (`tuple`):
                         + First value is a string indicate the type of field or a list for selection.
-                        + Secound value is a config for type "INT", "STRING" or "FLOAT".
+                        + Second value is a config for type "INT", "STRING" or "FLOAT".
         """
         return {
             "required": {
@@ -61,7 +61,7 @@ class Example:
                     "min": 0.0,
                     "max": 10.0,
                     "step": 0.01,
-                    "round": 0.001, #The value represeting the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
+                    "round": 0.001, #The value representing the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
                     "display": "number"}),
                 "print_to_screen": (["enable", "disable"],),
                 "string_field": ("STRING", {
@@ -103,6 +103,19 @@ class Example:
     #def IS_CHANGED(s, image, string_field, int_field, float_field, print_to_screen):
     #    return ""
 
+# Set the web directory, any .js file in that directory will be loaded by the frontend as a frontend extension
+# WEB_DIRECTORY = "./somejs"
+
+
+# Add custom API routes, using router
+from aiohttp import web
+from server import PromptServer
+
+@PromptServer.instance.routes.get("/hello")
+async def get_hello(request):
+    return web.json_response("hello")
+
+
 # A dictionary that contains all nodes you want to export with their names
 # NOTE: names should be globally unique
 NODE_CLASS_MAPPINGS = {
diff --git a/custom_nodes/websocket_image_save.py b/custom_nodes/websocket_image_save.py
new file mode 100644
index 00000000..5aa57364
--- /dev/null
+++ b/custom_nodes/websocket_image_save.py
@@ -0,0 +1,45 @@
+from PIL import Image, ImageOps
+from io import BytesIO
+import numpy as np
+import struct
+import comfy.utils
+import time
+
+#You can use this node to save full size images through the websocket, the
+#images will be sent in exactly the same format as the image previews: as
+#binary images on the websocket with a 8 byte header indicating the type
+#of binary message (first 4 bytes) and the image format (next 4 bytes).
+
+#Note that no metadata will be put in the images saved with this node.
+
+class SaveImageWebsocket:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"images": ("IMAGE", ),}
+                }
+
+    RETURN_TYPES = ()
+    FUNCTION = "save_images"
+
+    OUTPUT_NODE = True
+
+    CATEGORY = "api/image"
+
+    def save_images(self, images):
+        pbar = comfy.utils.ProgressBar(images.shape[0])
+        step = 0
+        for image in images:
+            i = 255. * image.cpu().numpy()
+            img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
+            pbar.update_absolute(step, images.shape[0], ("PNG", img, None))
+            step += 1
+
+        return {}
+
+    def IS_CHANGED(s, images):
+        return time.time()
+
+NODE_CLASS_MAPPINGS = {
+    "SaveImageWebsocket": SaveImageWebsocket,
+}
diff --git a/execution.py b/execution.py
index 00908ead..76225a96 100644
--- a/execution.py
+++ b/execution.py
@@ -35,8 +35,7 @@ def get_input_data(inputs, class_def, unique_id, outputs={}, prompt={}, extra_da
             if h[x] == "PROMPT":
                 input_data_all[x] = [prompt]
             if h[x] == "EXTRA_PNGINFO":
-                if "extra_pnginfo" in extra_data:
-                    input_data_all[x] = [extra_data['extra_pnginfo']]
+                input_data_all[x] = [extra_data.get('extra_pnginfo', None)]
             if h[x] == "UNIQUE_ID":
                 input_data_all[x] = [unique_id]
     return input_data_all
@@ -177,7 +176,7 @@ def recursive_execute(server, prompt, outputs, current_item, extra_data, execute
         for node_id, node_outputs in outputs.items():
             output_data_formatted[node_id] = [[format_value(x) for x in l] for l in node_outputs]
 
-        logging.error("!!! Exception during processing !!!")
+        logging.error(f"!!! Exception during processing!!! {ex}")
         logging.error(traceback.format_exc())
 
         error_details = {
@@ -194,8 +193,12 @@ def recursive_execute(server, prompt, outputs, current_item, extra_data, execute
 
     return (True, None, None)
 
-def recursive_will_execute(prompt, outputs, current_item):
+def recursive_will_execute(prompt, outputs, current_item, memo={}):
     unique_id = current_item
+
+    if unique_id in memo:
+        return memo[unique_id]
+
     inputs = prompt[unique_id]['inputs']
     will_execute = []
     if unique_id in outputs:
@@ -207,9 +210,10 @@ def recursive_will_execute(prompt, outputs, current_item):
             input_unique_id = input_data[0]
             output_index = input_data[1]
             if input_unique_id not in outputs:
-                will_execute += recursive_will_execute(prompt, outputs, input_unique_id)
+                will_execute += recursive_will_execute(prompt, outputs, input_unique_id, memo)
 
-    return will_execute + [unique_id]
+    memo[unique_id] = will_execute + [unique_id]
+    return memo[unique_id]
 
 def recursive_output_delete_if_changed(prompt, old_prompt, outputs, current_item):
     unique_id = current_item
@@ -364,7 +368,7 @@ class PromptExecutor:
                     d = self.outputs_ui.pop(x)
                     del d
 
-            comfy.model_management.cleanup_models()
+            comfy.model_management.cleanup_models(keep_clone_weights_loaded=True)
             self.add_message("execution_cached",
                           { "nodes": list(current_outputs) , "prompt_id": prompt_id},
                           broadcast=False)
@@ -377,7 +381,8 @@ class PromptExecutor:
 
             while len(to_execute) > 0:
                 #always execute the output that depends on the least amount of unexecuted nodes first
-                to_execute = sorted(list(map(lambda a: (len(recursive_will_execute(prompt, self.outputs, a[-1])), a[-1]), to_execute)))
+                memo = {}
+                to_execute = sorted(list(map(lambda a: (len(recursive_will_execute(prompt, self.outputs, a[-1], memo)), a[-1]), to_execute)))
                 output_node_id = to_execute.pop(0)[-1]
 
                 # This call shouldn't raise anything if there's an error deep in
@@ -617,8 +622,27 @@ def full_type_name(klass):
 def validate_prompt(prompt):
     outputs = set()
     for x in prompt:
-        class_ = nodes.NODE_CLASS_MAPPINGS[prompt[x]['class_type']]
-        if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE == True:
+        if 'class_type' not in prompt[x]:
+            error = {
+                "type": "invalid_prompt",
+                "message": f"Cannot execute because a node is missing the class_type property.",
+                "details": f"Node ID '#{x}'",
+                "extra_info": {}
+            }
+            return (False, error, [], [])
+
+        class_type = prompt[x]['class_type']
+        class_ = nodes.NODE_CLASS_MAPPINGS.get(class_type, None)
+        if class_ is None:
+            error = {
+                "type": "invalid_prompt",
+                "message": f"Cannot execute because node {class_type} does not exist.",
+                "details": f"Node ID '#{x}'",
+                "extra_info": {}
+            }
+            return (False, error, [], [])
+
+        if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE is True:
             outputs.add(x)
 
     if len(outputs) == 0:
diff --git a/folder_paths.py b/folder_paths.py
index f1bf40f8..2baf8ce1 100644
--- a/folder_paths.py
+++ b/folder_paths.py
@@ -1,9 +1,13 @@
 import os
 import time
+import logging
+from typing import Set, List, Dict, Tuple
 
-supported_pt_extensions = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors'])
+supported_pt_extensions: Set[str] = set(['.ckpt', '.pt', '.bin', '.pth', '.safetensors', '.pkl'])
 
-folder_names_and_paths = {}
+SupportedFileExtensionsType = Set[str]
+ScanPathType = List[str]
+folder_names_and_paths: Dict[str, Tuple[ScanPathType, SupportedFileExtensionsType]] = {}
 
 base_path = os.path.dirname(os.path.realpath(__file__))
 models_dir = os.path.join(base_path, "models")
@@ -25,7 +29,7 @@ folder_names_and_paths["gligen"] = ([os.path.join(models_dir, "gligen")], suppor
 
 folder_names_and_paths["upscale_models"] = ([os.path.join(models_dir, "upscale_models")], supported_pt_extensions)
 
-folder_names_and_paths["custom_nodes"] = ([os.path.join(base_path, "custom_nodes")], [])
+folder_names_and_paths["custom_nodes"] = ([os.path.join(base_path, "custom_nodes")], set())
 
 folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetworks")], supported_pt_extensions)
 
@@ -44,7 +48,7 @@ if not os.path.exists(input_directory):
     try:
         os.makedirs(input_directory)
     except:
-        print("Failed to create input directory")
+        logging.error("Failed to create input directory")
 
 def set_output_directory(output_dir):
     global output_directory
@@ -146,21 +150,23 @@ def recursive_search(directory, excluded_dir_names=None):
     try:
         dirs[directory] = os.path.getmtime(directory)
     except FileNotFoundError:
-        print(f"Warning: Unable to access {directory}. Skipping this path.")
-        
+        logging.warning(f"Warning: Unable to access {directory}. Skipping this path.")
+
+    logging.debug("recursive file list on directory {}".format(directory))
     for dirpath, subdirs, filenames in os.walk(directory, followlinks=True, topdown=True):
         subdirs[:] = [d for d in subdirs if d not in excluded_dir_names]
         for file_name in filenames:
             relative_path = os.path.relpath(os.path.join(dirpath, file_name), directory)
             result.append(relative_path)
-        
+
         for d in subdirs:
             path = os.path.join(dirpath, d)
             try:
                 dirs[path] = os.path.getmtime(path)
             except FileNotFoundError:
-                print(f"Warning: Unable to access {path}. Skipping this path.")
+                logging.warning(f"Warning: Unable to access {path}. Skipping this path.")
                 continue
+    logging.debug("found {} files".format(len(result)))
     return result, dirs
 
 def filter_files_extensions(files, extensions):
@@ -178,6 +184,8 @@ def get_full_path(folder_name, filename):
         full_path = os.path.join(x, filename)
         if os.path.isfile(full_path):
             return full_path
+        elif os.path.islink(full_path):
+            logging.warning("WARNING path {} exists but doesn't link anywhere, skipping.".format(full_path))
 
     return None
 
@@ -248,12 +256,12 @@ def get_save_image_path(filename_prefix, output_dir, image_width=0, image_height
         err = "**** ERROR: Saving image outside the output folder is not allowed." + \
               "\n full_output_folder: " + os.path.abspath(full_output_folder) + \
               "\n         output_dir: " + output_dir + \
-              "\n         commonpath: " + os.path.commonpath((output_dir, os.path.abspath(full_output_folder))) 
-        print(err)
+              "\n         commonpath: " + os.path.commonpath((output_dir, os.path.abspath(full_output_folder)))
+        logging.error(err)
         raise Exception(err)
 
     try:
-        counter = max(filter(lambda a: a[1][:-1] == filename and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1
+        counter = max(filter(lambda a: os.path.normcase(a[1][:-1]) == os.path.normcase(filename) and a[1][-1] == "_", map(map_filename, os.listdir(full_output_folder))))[0] + 1
     except ValueError:
         counter = 1
     except FileNotFoundError:
diff --git a/latent_preview.py b/latent_preview.py
index 61754751..ae6c106e 100644
--- a/latent_preview.py
+++ b/latent_preview.py
@@ -4,11 +4,20 @@ import struct
 import numpy as np
 from comfy.cli_args import args, LatentPreviewMethod
 from comfy.taesd.taesd import TAESD
+import comfy.model_management
 import folder_paths
 import comfy.utils
+import logging
 
 MAX_PREVIEW_RESOLUTION = 512
 
+def preview_to_image(latent_image):
+        latents_ubyte = (((latent_image + 1.0) / 2.0).clamp(0, 1)  # change scale from -1..1 to 0..1
+                            .mul(0xFF)  # to 0..255
+                            ).to(device="cpu", dtype=torch.uint8, non_blocking=comfy.model_management.device_supports_non_blocking(latent_image.device))
+
+        return Image.fromarray(latents_ubyte.numpy())
+
 class LatentPreviewer:
     def decode_latent_to_preview(self, x0):
         pass
@@ -22,13 +31,8 @@ class TAESDPreviewerImpl(LatentPreviewer):
         self.taesd = taesd
 
     def decode_latent_to_preview(self, x0):
-        x_sample = self.taesd.decode(x0[:1])[0].detach()
-        x_sample = torch.clamp((x_sample + 1.0) / 2.0, min=0.0, max=1.0)
-        x_sample = 255. * np.moveaxis(x_sample.cpu().numpy(), 0, 2)
-        x_sample = x_sample.astype(np.uint8)
-
-        preview_image = Image.fromarray(x_sample)
-        return preview_image
+        x_sample = self.taesd.decode(x0[:1])[0].movedim(0, 2)
+        return preview_to_image(x_sample)
 
 
 class Latent2RGBPreviewer(LatentPreviewer):
@@ -36,14 +40,9 @@ class Latent2RGBPreviewer(LatentPreviewer):
         self.latent_rgb_factors = torch.tensor(latent_rgb_factors, device="cpu")
 
     def decode_latent_to_preview(self, x0):
-        latent_image = x0[0].permute(1, 2, 0).cpu() @ self.latent_rgb_factors
-
-        latents_ubyte = (((latent_image + 1) / 2)
-                            .clamp(0, 1)  # change scale from -1..1 to 0..1
-                            .mul(0xFF)  # to 0..255
-                            .byte()).cpu()
-
-        return Image.fromarray(latents_ubyte.numpy())
+        self.latent_rgb_factors = self.latent_rgb_factors.to(dtype=x0.dtype, device=x0.device)
+        latent_image = x0[0].permute(1, 2, 0) @ self.latent_rgb_factors
+        return preview_to_image(latent_image)
 
 
 def get_previewer(device, latent_format):
@@ -62,15 +61,13 @@ def get_previewer(device, latent_format):
 
         if method == LatentPreviewMethod.Auto:
             method = LatentPreviewMethod.Latent2RGB
-            if taesd_decoder_path:
-                method = LatentPreviewMethod.TAESD
 
         if method == LatentPreviewMethod.TAESD:
             if taesd_decoder_path:
-                taesd = TAESD(None, taesd_decoder_path).to(device)
+                taesd = TAESD(None, taesd_decoder_path, latent_channels=latent_format.latent_channels).to(device)
                 previewer = TAESDPreviewerImpl(taesd)
             else:
-                print("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(latent_format.taesd_decoder_name))
+                logging.warning("Warning: TAESD previews enabled, but could not find models/vae_approx/{}".format(latent_format.taesd_decoder_name))
 
         if previewer is None:
             if latent_format.latent_rgb_factors is not None:
diff --git a/main.py b/main.py
index 69d9bce6..196351a3 100644
--- a/main.py
+++ b/main.py
@@ -5,6 +5,8 @@ import os
 import importlib.util
 import folder_paths
 import time
+from comfy.cli_args import args
+
 
 def execute_prestartup_script():
     def execute_script(script_path):
@@ -18,6 +20,9 @@ def execute_prestartup_script():
             print(f"Failed to execute startup-script: {script_path} / {e}")
         return False
 
+    if args.disable_all_custom_nodes:
+        return
+
     node_paths = folder_paths.get_folder_paths("custom_nodes")
     for custom_node_path in node_paths:
         possible_modules = os.listdir(custom_node_path)
@@ -53,16 +58,15 @@ import shutil
 import threading
 import gc
 
-from comfy.cli_args import args
+import logging
 
 if os.name == "nt":
-    import logging
     logging.getLogger("xformers").addFilter(lambda record: 'A matching Triton is not available' not in record.getMessage())
 
 if __name__ == "__main__":
     if args.cuda_device is not None:
         os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda_device)
-        print("Set cuda device to:", args.cuda_device)
+        logging.info("Set cuda device to: {}".format(args.cuda_device))
 
     if args.deterministic:
         if 'CUBLAS_WORKSPACE_CONFIG' not in os.environ:
@@ -76,7 +80,7 @@ import yaml
 import execution
 import server
 from server import BinaryEventTypes
-from nodes import init_custom_nodes
+import nodes
 import comfy.model_management
 
 def cuda_malloc_warning():
@@ -88,7 +92,7 @@ def cuda_malloc_warning():
             if b in device_name:
                 cuda_malloc_warning = True
         if cuda_malloc_warning:
-            print("\nWARNING: this card most likely does not support cuda-malloc, if you get \"CUDA error\" please run ComfyUI with: --disable-cuda-malloc\n")
+            logging.warning("\nWARNING: this card most likely does not support cuda-malloc, if you get \"CUDA error\" please run ComfyUI with: --disable-cuda-malloc\n")
 
 def prompt_worker(q, server):
     e = execution.PromptExecutor(server)
@@ -121,7 +125,7 @@ def prompt_worker(q, server):
 
             current_time = time.perf_counter()
             execution_time = current_time - execution_start_time
-            print("Prompt executed in {:.2f} seconds".format(execution_time))
+            logging.info("Prompt executed in {:.2f} seconds".format(execution_time))
 
         flags = q.get_flags()
         free_memory = flags.get("free_memory", False)
@@ -139,6 +143,7 @@ def prompt_worker(q, server):
         if need_gc:
             current_time = time.perf_counter()
             if (current_time - last_gc_collect) > gc_collect_interval:
+                comfy.model_management.cleanup_models()
                 gc.collect()
                 comfy.model_management.soft_empty_cache()
                 last_gc_collect = current_time
@@ -182,17 +187,24 @@ def load_extra_path_config(yaml_path):
                 full_path = y
                 if base_path is not None:
                     full_path = os.path.join(base_path, full_path)
-                print("Adding extra search path", x, full_path)
+                logging.info("Adding extra search path {} {}".format(x, full_path))
                 folder_paths.add_model_folder_path(x, full_path)
 
 
 if __name__ == "__main__":
     if args.temp_directory:
         temp_dir = os.path.join(os.path.abspath(args.temp_directory), "temp")
-        print(f"Setting temp directory to: {temp_dir}")
+        logging.info(f"Setting temp directory to: {temp_dir}")
         folder_paths.set_temp_directory(temp_dir)
     cleanup_temp()
 
+    if args.windows_standalone_build:
+        try:
+            import new_updater
+            new_updater.update_windows_updater()
+        except:
+            pass
+
     loop = asyncio.new_event_loop()
     asyncio.set_event_loop(loop)
     server = server.PromptServer(loop)
@@ -206,7 +218,7 @@ if __name__ == "__main__":
         for config_path in itertools.chain(*args.extra_model_paths_config):
             load_extra_path_config(config_path)
 
-    init_custom_nodes()
+    nodes.init_extra_nodes(init_custom_nodes=not args.disable_all_custom_nodes)
 
     cuda_malloc_warning()
 
@@ -217,7 +229,7 @@ if __name__ == "__main__":
 
     if args.output_directory:
         output_dir = os.path.abspath(args.output_directory)
-        print(f"Setting output directory to: {output_dir}")
+        logging.info(f"Setting output directory to: {output_dir}")
         folder_paths.set_output_directory(output_dir)
 
     #These are the default folders that checkpoints, clip and vae models will be saved to when using CheckpointSave, etc.. nodes
@@ -227,7 +239,7 @@ if __name__ == "__main__":
 
     if args.input_directory:
         input_dir = os.path.abspath(args.input_directory)
-        print(f"Setting input directory to: {input_dir}")
+        logging.info(f"Setting input directory to: {input_dir}")
         folder_paths.set_input_directory(input_dir)
 
     if args.quick_test_for_ci:
@@ -235,16 +247,16 @@ if __name__ == "__main__":
 
     call_on_start = None
     if args.auto_launch:
-        def startup_server(address, port):
+        def startup_server(scheme, address, port):
             import webbrowser
             if os.name == 'nt' and address == '0.0.0.0':
                 address = '127.0.0.1'
-            webbrowser.open(f"http://{address}:{port}")
+            webbrowser.open(f"{scheme}://{address}:{port}")
         call_on_start = startup_server
 
     try:
         loop.run_until_complete(run(server, address=args.listen, port=args.port, verbose=not args.dont_print_server, call_on_start=call_on_start))
     except KeyboardInterrupt:
-        print("\nStopped server")
+        logging.info("\nStopped server")
 
     cleanup_temp()
diff --git a/new_updater.py b/new_updater.py
new file mode 100644
index 00000000..a49e0877
--- /dev/null
+++ b/new_updater.py
@@ -0,0 +1,35 @@
+import os
+import shutil
+
+base_path = os.path.dirname(os.path.realpath(__file__))
+
+
+def update_windows_updater():
+    top_path = os.path.dirname(base_path)
+    updater_path = os.path.join(base_path, ".ci/update_windows/update.py")
+    bat_path = os.path.join(base_path, ".ci/update_windows/update_comfyui.bat")
+
+    dest_updater_path = os.path.join(top_path, "update/update.py")
+    dest_bat_path = os.path.join(top_path, "update/update_comfyui.bat")
+    dest_bat_deps_path = os.path.join(top_path, "update/update_comfyui_and_python_dependencies.bat")
+
+    try:
+        with open(dest_bat_path, 'rb') as f:
+            contents = f.read()
+    except:
+        return
+
+    if not contents.startswith(b"..\\python_embeded\\python.exe .\\update.py"):
+        return
+
+    shutil.copy(updater_path, dest_updater_path)
+    try:
+        with open(dest_bat_deps_path, 'rb') as f:
+            contents = f.read()
+            contents = contents.replace(b'..\\python_embeded\\python.exe .\\update.py ..\\ComfyUI\\', b'call update_comfyui.bat nopause')
+        with open(dest_bat_deps_path, 'wb') as f:
+            f.write(contents)
+    except:
+        pass
+    shutil.copy(bat_path, dest_bat_path)
+    print("Updated the windows standalone package updater.")
diff --git a/node_helpers.py b/node_helpers.py
new file mode 100644
index 00000000..43b9e829
--- /dev/null
+++ b/node_helpers.py
@@ -0,0 +1,24 @@
+from PIL import ImageFile, UnidentifiedImageError
+
+def conditioning_set_values(conditioning, values={}):
+    c = []
+    for t in conditioning:
+        n = [t[0], t[1].copy()]
+        for k in values:
+            n[1][k] = values[k]
+        c.append(n)
+
+    return c
+
+def pillow(fn, arg):
+    prev_value = None
+    try:
+        x = fn(arg)
+    except (OSError, UnidentifiedImageError, ValueError): #PIL issues #4472 and #2445, also fixes ComfyUI issue #3416
+        prev_value = ImageFile.LOAD_TRUNCATED_IMAGES
+        ImageFile.LOAD_TRUNCATED_IMAGES = True
+        x = fn(arg)
+    finally:
+        if prev_value is not None:
+            ImageFile.LOAD_TRUNCATED_IMAGES = prev_value
+        return x
diff --git a/nodes.py b/nodes.py
index fe38be9d..a230f725 100644
--- a/nodes.py
+++ b/nodes.py
@@ -8,15 +8,16 @@ import traceback
 import math
 import time
 import random
+import logging
 
-from PIL import Image, ImageOps, ImageSequence
+from PIL import Image, ImageOps, ImageSequence, ImageFile
 from PIL.PngImagePlugin import PngInfo
+
 import numpy as np
 import safetensors.torch
 
 sys.path.insert(0, os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy"))
 
-
 import comfy.diffusers_load
 import comfy.samplers
 import comfy.sample
@@ -33,6 +34,7 @@ import importlib
 
 import folder_paths
 import latent_preview
+import node_helpers
 
 def before_node_execution():
     comfy.model_management.throw_exception_if_processing_interrupted()
@@ -40,12 +42,12 @@ def before_node_execution():
 def interrupt_processing(value=True):
     comfy.model_management.interrupt_current_processing(value)
 
-MAX_RESOLUTION=8192
+MAX_RESOLUTION=16384
 
 class CLIPTextEncode:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": {"text": ("STRING", {"multiline": True}), "clip": ("CLIP", )}}
+        return {"required": {"text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", )}}
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "encode"
 
@@ -83,7 +85,7 @@ class ConditioningAverage :
         out = []
 
         if len(conditioning_from) > 1:
-            print("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.")
+            logging.warning("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.")
 
         cond_from = conditioning_from[0][0]
         pooled_output_from = conditioning_from[0][1].get("pooled_output", None)
@@ -122,7 +124,7 @@ class ConditioningConcat:
         out = []
 
         if len(conditioning_from) > 1:
-            print("Warning: ConditioningConcat conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.")
+            logging.warning("Warning: ConditioningConcat conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.")
 
         cond_from = conditioning_from[0][0]
 
@@ -150,13 +152,9 @@ class ConditioningSetArea:
     CATEGORY = "conditioning"
 
     def append(self, conditioning, width, height, x, y, strength):
-        c = []
-        for t in conditioning:
-            n = [t[0], t[1].copy()]
-            n[1]['area'] = (height // 8, width // 8, y // 8, x // 8)
-            n[1]['strength'] = strength
-            n[1]['set_area_to_bounds'] = False
-            c.append(n)
+        c = node_helpers.conditioning_set_values(conditioning, {"area": (height // 8, width // 8, y // 8, x // 8),
+                                                                "strength": strength,
+                                                                "set_area_to_bounds": False})
         return (c, )
 
 class ConditioningSetAreaPercentage:
@@ -175,13 +173,9 @@ class ConditioningSetAreaPercentage:
     CATEGORY = "conditioning"
 
     def append(self, conditioning, width, height, x, y, strength):
-        c = []
-        for t in conditioning:
-            n = [t[0], t[1].copy()]
-            n[1]['area'] = ("percentage", height, width, y, x)
-            n[1]['strength'] = strength
-            n[1]['set_area_to_bounds'] = False
-            c.append(n)
+        c = node_helpers.conditioning_set_values(conditioning, {"area": ("percentage", height, width, y, x),
+                                                                "strength": strength,
+                                                                "set_area_to_bounds": False})
         return (c, )
 
 class ConditioningSetAreaStrength:
@@ -196,11 +190,7 @@ class ConditioningSetAreaStrength:
     CATEGORY = "conditioning"
 
     def append(self, conditioning, strength):
-        c = []
-        for t in conditioning:
-            n = [t[0], t[1].copy()]
-            n[1]['strength'] = strength
-            c.append(n)
+        c = node_helpers.conditioning_set_values(conditioning, {"strength": strength})
         return (c, )
 
 
@@ -218,19 +208,15 @@ class ConditioningSetMask:
     CATEGORY = "conditioning"
 
     def append(self, conditioning, mask, set_cond_area, strength):
-        c = []
         set_area_to_bounds = False
         if set_cond_area != "default":
             set_area_to_bounds = True
         if len(mask.shape) < 3:
             mask = mask.unsqueeze(0)
-        for t in conditioning:
-            n = [t[0], t[1].copy()]
-            _, h, w = mask.shape
-            n[1]['mask'] = mask
-            n[1]['set_area_to_bounds'] = set_area_to_bounds
-            n[1]['mask_strength'] = strength
-            c.append(n)
+
+        c = node_helpers.conditioning_set_values(conditioning, {"mask": mask,
+                                                                "set_area_to_bounds": set_area_to_bounds,
+                                                                "mask_strength": strength})
         return (c, )
 
 class ConditioningZeroOut:
@@ -246,8 +232,9 @@ class ConditioningZeroOut:
         c = []
         for t in conditioning:
             d = t[1].copy()
-            if "pooled_output" in d:
-                d["pooled_output"] = torch.zeros_like(d["pooled_output"])
+            pooled_output = d.get("pooled_output", None)
+            if pooled_output is not None:
+                d["pooled_output"] = torch.zeros_like(pooled_output)
             n = [torch.zeros_like(t[0]), d]
             c.append(n)
         return (c, )
@@ -265,13 +252,8 @@ class ConditioningSetTimestepRange:
     CATEGORY = "advanced/conditioning"
 
     def set_range(self, conditioning, start, end):
-        c = []
-        for t in conditioning:
-            d = t[1].copy()
-            d['start_percent'] = start
-            d['end_percent'] = end
-            n = [t[0], d]
-            c.append(n)
+        c = node_helpers.conditioning_set_values(conditioning, {"start_percent": start,
+                                                                "end_percent": end})
         return (c, )
 
 class VAEDecode:
@@ -309,18 +291,7 @@ class VAEEncode:
 
     CATEGORY = "latent"
 
-    @staticmethod
-    def vae_encode_crop_pixels(pixels):
-        x = (pixels.shape[1] // 8) * 8
-        y = (pixels.shape[2] // 8) * 8
-        if pixels.shape[1] != x or pixels.shape[2] != y:
-            x_offset = (pixels.shape[1] % 8) // 2
-            y_offset = (pixels.shape[2] % 8) // 2
-            pixels = pixels[:, x_offset:x + x_offset, y_offset:y + y_offset, :]
-        return pixels
-
     def encode(self, vae, pixels):
-        pixels = self.vae_encode_crop_pixels(pixels)
         t = vae.encode(pixels[:,:,:,:3])
         return ({"samples":t}, )
 
@@ -336,7 +307,6 @@ class VAEEncodeTiled:
     CATEGORY = "_for_testing"
 
     def encode(self, vae, pixels, tile_size):
-        pixels = VAEEncode.vae_encode_crop_pixels(pixels)
         t = vae.encode_tiled(pixels[:,:,:,:3], tile_x=tile_size, tile_y=tile_size, )
         return ({"samples":t}, )
 
@@ -350,14 +320,14 @@ class VAEEncodeForInpaint:
     CATEGORY = "latent/inpaint"
 
     def encode(self, vae, pixels, mask, grow_mask_by=6):
-        x = (pixels.shape[1] // 8) * 8
-        y = (pixels.shape[2] // 8) * 8
+        x = (pixels.shape[1] // vae.downscale_ratio) * vae.downscale_ratio
+        y = (pixels.shape[2] // vae.downscale_ratio) * vae.downscale_ratio
         mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(pixels.shape[1], pixels.shape[2]), mode="bilinear")
 
         pixels = pixels.clone()
         if pixels.shape[1] != x or pixels.shape[2] != y:
-            x_offset = (pixels.shape[1] % 8) // 2
-            y_offset = (pixels.shape[2] % 8) // 2
+            x_offset = (pixels.shape[1] % vae.downscale_ratio) // 2
+            y_offset = (pixels.shape[2] % vae.downscale_ratio) // 2
             pixels = pixels[:,x_offset:x + x_offset, y_offset:y + y_offset,:]
             mask = mask[:,:,x_offset:x + x_offset, y_offset:y + y_offset]
 
@@ -424,13 +394,8 @@ class InpaintModelConditioning:
 
         out = []
         for conditioning in [positive, negative]:
-            c = []
-            for t in conditioning:
-                d = t[1].copy()
-                d["concat_latent_image"] = concat_latent
-                d["concat_mask"] = mask
-                n = [t[0], d]
-                c.append(n)
+            c = node_helpers.conditioning_set_values(conditioning, {"concat_latent_image": concat_latent,
+                                                                    "concat_mask": mask})
             out.append(c)
         return (out[0], out[1], out_latent)
 
@@ -532,7 +497,7 @@ class CheckpointLoader:
 
     CATEGORY = "advanced/loaders"
 
-    def load_checkpoint(self, config_name, ckpt_name, output_vae=True, output_clip=True):
+    def load_checkpoint(self, config_name, ckpt_name):
         config_path = folder_paths.get_full_path("configs", config_name)
         ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
         return comfy.sd.load_checkpoint(config_path, ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
@@ -547,7 +512,7 @@ class CheckpointLoaderSimple:
 
     CATEGORY = "loaders"
 
-    def load_checkpoint(self, ckpt_name, output_vae=True, output_clip=True):
+    def load_checkpoint(self, ckpt_name):
         ckpt_path = folder_paths.get_full_path("checkpoints", ckpt_name)
         out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
         return out[:3]
@@ -619,8 +584,8 @@ class LoraLoader:
         return {"required": { "model": ("MODEL",),
                               "clip": ("CLIP", ),
                               "lora_name": (folder_paths.get_filename_list("loras"), ),
-                              "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
-                              "strength_clip": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
+                              "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}),
+                              "strength_clip": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}),
                               }}
     RETURN_TYPES = ("MODEL", "CLIP")
     FUNCTION = "load_lora"
@@ -653,7 +618,7 @@ class LoraLoaderModelOnly(LoraLoader):
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
                               "lora_name": (folder_paths.get_filename_list("loras"), ),
-                              "strength_model": ("FLOAT", {"default": 1.0, "min": -20.0, "max": 20.0, "step": 0.01}),
+                              "strength_model": ("FLOAT", {"default": 1.0, "min": -100.0, "max": 100.0, "step": 0.01}),
                               }}
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "load_lora_model_only"
@@ -670,6 +635,8 @@ class VAELoader:
         sdxl_taesd_dec = False
         sd1_taesd_enc = False
         sd1_taesd_dec = False
+        sd3_taesd_enc = False
+        sd3_taesd_dec = False
 
         for v in approx_vaes:
             if v.startswith("taesd_decoder."):
@@ -680,10 +647,16 @@ class VAELoader:
                 sdxl_taesd_dec = True
             elif v.startswith("taesdxl_encoder."):
                 sdxl_taesd_enc = True
+            elif v.startswith("taesd3_decoder."):
+                sd3_taesd_dec = True
+            elif v.startswith("taesd3_encoder."):
+                sd3_taesd_enc = True
         if sd1_taesd_dec and sd1_taesd_enc:
             vaes.append("taesd")
         if sdxl_taesd_dec and sdxl_taesd_enc:
             vaes.append("taesdxl")
+        if sd3_taesd_dec and sd3_taesd_enc:
+            vaes.append("taesd3")
         return vaes
 
     @staticmethod
@@ -704,8 +677,13 @@ class VAELoader:
 
         if name == "taesd":
             sd["vae_scale"] = torch.tensor(0.18215)
+            sd["vae_shift"] = torch.tensor(0.0)
         elif name == "taesdxl":
             sd["vae_scale"] = torch.tensor(0.13025)
+            sd["vae_shift"] = torch.tensor(0.0)
+        elif name == "taesd3":
+            sd["vae_scale"] = torch.tensor(1.5305)
+            sd["vae_shift"] = torch.tensor(0.0609)
         return sd
 
     @classmethod
@@ -718,7 +696,7 @@ class VAELoader:
 
     #TODO: scale factor?
     def load_vae(self, vae_name):
-        if vae_name in ["taesd", "taesdxl"]:
+        if vae_name in ["taesd", "taesdxl", "taesd3"]:
             sd = self.load_taesd(vae_name)
         else:
             vae_path = folder_paths.get_full_path("vae", vae_name)
@@ -806,7 +784,7 @@ class ControlNetApplyAdvanced:
 
     CATEGORY = "conditioning"
 
-    def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent):
+    def apply_controlnet(self, positive, negative, control_net, image, strength, start_percent, end_percent, vae=None):
         if strength == 0:
             return (positive, negative)
 
@@ -823,7 +801,7 @@ class ControlNetApplyAdvanced:
                 if prev_cnet in cnets:
                     c_net = cnets[prev_cnet]
                 else:
-                    c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent))
+                    c_net = control_net.copy().set_cond_hint(control_hint, strength, (start_percent, end_percent), vae)
                     c_net.set_previous_controlnet(prev_cnet)
                     cnets[prev_cnet] = c_net
 
@@ -854,31 +832,48 @@ class CLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "clip_name": (folder_paths.get_filename_list("clip"), ),
+                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio"], ),
                              }}
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
 
     CATEGORY = "advanced/loaders"
 
-    def load_clip(self, clip_name):
+    def load_clip(self, clip_name, type="stable_diffusion"):
+        if type == "stable_cascade":
+            clip_type = comfy.sd.CLIPType.STABLE_CASCADE
+        elif type == "sd3":
+            clip_type = comfy.sd.CLIPType.SD3
+        elif type == "stable_audio":
+            clip_type = comfy.sd.CLIPType.STABLE_AUDIO
+        else:
+            clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
+
         clip_path = folder_paths.get_full_path("clip", clip_name)
-        clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type)
         return (clip,)
 
 class DualCLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ), "clip_name2": (folder_paths.get_filename_list("clip"), ),
+        return {"required": { "clip_name1": (folder_paths.get_filename_list("clip"), ),
+                              "clip_name2": (folder_paths.get_filename_list("clip"), ),
+                              "type": (["sdxl", "sd3"], ),
                              }}
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
 
     CATEGORY = "advanced/loaders"
 
-    def load_clip(self, clip_name1, clip_name2):
+    def load_clip(self, clip_name1, clip_name2, type):
         clip_path1 = folder_paths.get_full_path("clip", clip_name1)
         clip_path2 = folder_paths.get_full_path("clip", clip_name2)
-        clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"))
+        if type == "sdxl":
+            clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
+        elif type == "sd3":
+            clip_type = comfy.sd.CLIPType.SD3
+
+        clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type)
         return (clip,)
 
 class CLIPVisionLoader:
@@ -997,7 +992,7 @@ class GLIGENTextBoxApply:
         return {"required": {"conditioning_to": ("CONDITIONING", ),
                               "clip": ("CLIP", ),
                               "gligen_textbox_model": ("GLIGEN", ),
-                              "text": ("STRING", {"multiline": True}),
+                              "text": ("STRING", {"multiline": True, "dynamicPrompts": True}),
                               "width": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}),
                               "height": ("INT", {"default": 64, "min": 8, "max": MAX_RESOLUTION, "step": 8}),
                               "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
@@ -1010,7 +1005,7 @@ class GLIGENTextBoxApply:
 
     def append(self, conditioning_to, clip, gligen_textbox_model, text, width, height, x, y):
         c = []
-        cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled=True)
+        cond, cond_pooled = clip.encode_from_tokens(clip.tokenize(text), return_pooled="unprojected")
         for t in conditioning_to:
             n = [t[0], t[1].copy()]
             position_params = [(cond_pooled, height // 8, width // 8, y // 8, x // 8)]
@@ -1330,6 +1325,8 @@ class SetLatentNoiseMask:
 
 def common_ksampler(model, seed, steps, cfg, sampler_name, scheduler, positive, negative, latent, denoise=1.0, disable_noise=False, start_step=None, last_step=None, force_full_denoise=False):
     latent_image = latent["samples"]
+    latent_image = comfy.sample.fix_empty_latent_channels(model, latent_image)
+
     if disable_noise:
         noise = torch.zeros(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, device="cpu")
     else:
@@ -1434,7 +1431,7 @@ class SaveImage:
         filename_prefix += self.prefix_append
         full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir, images[0].shape[1], images[0].shape[0])
         results = list()
-        for image in images:
+        for (batch_number, image) in enumerate(images):
             i = 255. * image.cpu().numpy()
             img = Image.fromarray(np.clip(i, 0, 255).astype(np.uint8))
             metadata = None
@@ -1446,7 +1443,8 @@ class SaveImage:
                     for x in extra_pnginfo:
                         metadata.add_text(x, json.dumps(extra_pnginfo[x]))
 
-            file = f"{filename}_{counter:05}_.png"
+            filename_with_batch_num = filename.replace("%batch_num%", str(batch_number))
+            file = f"{filename_with_batch_num}_{counter:05}_.png"
             img.save(os.path.join(full_output_folder, file), pnginfo=metadata, compress_level=self.compress_level)
             results.append({
                 "filename": file,
@@ -1486,14 +1484,29 @@ class LoadImage:
     FUNCTION = "load_image"
     def load_image(self, image):
         image_path = folder_paths.get_annotated_filepath(image)
-        img = Image.open(image_path)
+        
+        img = node_helpers.pillow(Image.open, image_path)
+        
         output_images = []
         output_masks = []
+        w, h = None, None
+
+        excluded_formats = ['MPO']
+        
         for i in ImageSequence.Iterator(img):
-            i = ImageOps.exif_transpose(i)
+            i = node_helpers.pillow(ImageOps.exif_transpose, i)
+
             if i.mode == 'I':
                 i = i.point(lambda i: i * (1 / 255))
             image = i.convert("RGB")
+
+            if len(output_images) == 0:
+                w = image.size[0]
+                h = image.size[1]
+            
+            if image.size[0] != w or image.size[1] != h:
+                continue
+            
             image = np.array(image).astype(np.float32) / 255.0
             image = torch.from_numpy(image)[None,]
             if 'A' in i.getbands():
@@ -1504,7 +1517,7 @@ class LoadImage:
             output_images.append(image)
             output_masks.append(mask.unsqueeze(0))
 
-        if len(output_images) > 1:
+        if len(output_images) > 1 and img.format not in excluded_formats:
             output_image = torch.cat(output_images, dim=0)
             output_mask = torch.cat(output_masks, dim=0)
         else:
@@ -1545,8 +1558,8 @@ class LoadImageMask:
     FUNCTION = "load_image"
     def load_image(self, image, channel):
         image_path = folder_paths.get_annotated_filepath(image)
-        i = Image.open(image_path)
-        i = ImageOps.exif_transpose(i)
+        i = node_helpers.pillow(Image.open, image_path)
+        i = node_helpers.pillow(ImageOps.exif_transpose, i)
         if i.getbands() != ("R", "G", "B", "A"):
             if i.mode == 'I':
                 i = i.point(lambda i: i * (1 / 255))
@@ -1875,12 +1888,14 @@ NODE_DISPLAY_NAME_MAPPINGS = {
 
 EXTENSION_WEB_DIRS = {}
 
+
 def load_custom_node(module_path, ignore=set()):
     module_name = os.path.basename(module_path)
     if os.path.isfile(module_path):
         sp = os.path.splitext(module_path)
         module_name = sp[0]
     try:
+        logging.debug("Trying to load custom node {}".format(module_path))
         if os.path.isfile(module_path):
             module_spec = importlib.util.spec_from_file_location(module_name, module_path)
             module_dir = os.path.split(module_path)[0]
@@ -1905,14 +1920,23 @@ def load_custom_node(module_path, ignore=set()):
                 NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS)
             return True
         else:
-            print(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS.")
+            logging.warning(f"Skip {module_path} module for custom nodes due to the lack of NODE_CLASS_MAPPINGS.")
             return False
     except Exception as e:
-        print(traceback.format_exc())
-        print(f"Cannot import {module_path} module for custom nodes:", e)
+        logging.warning(traceback.format_exc())
+        logging.warning(f"Cannot import {module_path} module for custom nodes: {e}")
         return False
 
-def load_custom_nodes():
+def init_external_custom_nodes():
+    """
+    Initializes the external custom nodes.
+
+    This function loads custom nodes from the specified folder paths and imports them into the application.
+    It measures the import times for each custom node and logs the results.
+
+    Returns:
+        None
+    """
     base_node_names = set(NODE_CLASS_MAPPINGS.keys())
     node_paths = folder_paths.get_folder_paths("custom_nodes")
     node_import_times = []
@@ -1930,16 +1954,25 @@ def load_custom_nodes():
             node_import_times.append((time.perf_counter() - time_before, module_path, success))
 
     if len(node_import_times) > 0:
-        print("\nImport times for custom nodes:")
+        logging.info("\nImport times for custom nodes:")
         for n in sorted(node_import_times):
             if n[2]:
                 import_message = ""
             else:
                 import_message = " (IMPORT FAILED)"
-            print("{:6.1f} seconds{}:".format(n[0], import_message), n[1])
-        print()
+            logging.info("{:6.1f} seconds{}: {}".format(n[0], import_message, n[1]))
+        logging.info("")
 
-def init_custom_nodes():
+def init_builtin_extra_nodes():
+    """
+    Initializes the built-in extra nodes in ComfyUI.
+
+    This function loads the extra node files located in the "comfy_extras" directory and imports them into ComfyUI.
+    If any of the extra node files fail to import, a warning message is logged.
+
+    Returns:
+        None
+    """
     extras_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras")
     extras_files = [
         "nodes_latent.py",
@@ -1965,9 +1998,45 @@ def init_custom_nodes():
         "nodes_stable3d.py",
         "nodes_sdupscale.py",
         "nodes_photomaker.py",
+        "nodes_cond.py",
+        "nodes_morphology.py",
+        "nodes_stable_cascade.py",
+        "nodes_differential_diffusion.py",
+        "nodes_ip2p.py",
+        "nodes_model_merging_model_specific.py",
+        "nodes_pag.py",
+        "nodes_align_your_steps.py",
+        "nodes_attention_multiply.py",
+        "nodes_advanced_samplers.py",
+        "nodes_webcam.py",
+        "nodes_audio.py",
+        "nodes_sd3.py",
+        "nodes_gits.py",
     ]
 
+    import_failed = []
     for node_file in extras_files:
-        load_custom_node(os.path.join(extras_dir, node_file))
+        if not load_custom_node(os.path.join(extras_dir, node_file)):
+            import_failed.append(node_file)
 
-    load_custom_nodes()
+    return import_failed
+
+
+def init_extra_nodes(init_custom_nodes=True):
+    import_failed = init_builtin_extra_nodes()
+
+    if init_custom_nodes:
+        init_external_custom_nodes()
+    else:
+        logging.info("Skipping loading of custom nodes")
+
+    if len(import_failed) > 0:
+        logging.warning("WARNING: some comfy_extras/ nodes did not import correctly. This may be because they are missing some dependencies.\n")
+        for node in import_failed:
+            logging.warning("IMPORT FAILED: {}".format(node))
+        logging.warning("\nThis issue might be caused by new missing dependencies added the last time you updated ComfyUI.")
+        if args.windows_standalone_build:
+            logging.warning("Please run the update script: update/update_comfyui.bat")
+        else:
+            logging.warning("Please do a: pip install -r requirements.txt")
+        logging.warning("")
diff --git a/requirements.txt b/requirements.txt
index e804618e..108958d2 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,12 +1,18 @@
 torch
 torchsde
 torchvision
+torchaudio
 einops
 transformers>=4.25.1
-safetensors>=0.3.0
+safetensors>=0.4.2
 aiohttp
 pyyaml
 Pillow
 scipy
 tqdm
 psutil
+
+#non essential dependencies:
+kornia>=0.7.1
+spandrel
+soundfile
diff --git a/script_examples/websockets_api_example_ws_images.py b/script_examples/websockets_api_example_ws_images.py
new file mode 100644
index 00000000..73748862
--- /dev/null
+++ b/script_examples/websockets_api_example_ws_images.py
@@ -0,0 +1,159 @@
+#This is an example that uses the websockets api and the SaveImageWebsocket node to get images directly without
+#them being saved to disk
+
+import websocket #NOTE: websocket-client (https://github.com/websocket-client/websocket-client)
+import uuid
+import json
+import urllib.request
+import urllib.parse
+
+server_address = "127.0.0.1:8188"
+client_id = str(uuid.uuid4())
+
+def queue_prompt(prompt):
+    p = {"prompt": prompt, "client_id": client_id}
+    data = json.dumps(p).encode('utf-8')
+    req =  urllib.request.Request("http://{}/prompt".format(server_address), data=data)
+    return json.loads(urllib.request.urlopen(req).read())
+
+def get_image(filename, subfolder, folder_type):
+    data = {"filename": filename, "subfolder": subfolder, "type": folder_type}
+    url_values = urllib.parse.urlencode(data)
+    with urllib.request.urlopen("http://{}/view?{}".format(server_address, url_values)) as response:
+        return response.read()
+
+def get_history(prompt_id):
+    with urllib.request.urlopen("http://{}/history/{}".format(server_address, prompt_id)) as response:
+        return json.loads(response.read())
+
+def get_images(ws, prompt):
+    prompt_id = queue_prompt(prompt)['prompt_id']
+    output_images = {}
+    current_node = ""
+    while True:
+        out = ws.recv()
+        if isinstance(out, str):
+            message = json.loads(out)
+            if message['type'] == 'executing':
+                data = message['data']
+                if data['prompt_id'] == prompt_id:
+                    if data['node'] is None:
+                        break #Execution is done
+                    else:
+                        current_node = data['node']
+        else:
+            if current_node == 'save_image_websocket_node':
+                images_output = output_images.get(current_node, [])
+                images_output.append(out[8:])
+                output_images[current_node] = images_output
+
+    return output_images
+
+prompt_text = """
+{
+    "3": {
+        "class_type": "KSampler",
+        "inputs": {
+            "cfg": 8,
+            "denoise": 1,
+            "latent_image": [
+                "5",
+                0
+            ],
+            "model": [
+                "4",
+                0
+            ],
+            "negative": [
+                "7",
+                0
+            ],
+            "positive": [
+                "6",
+                0
+            ],
+            "sampler_name": "euler",
+            "scheduler": "normal",
+            "seed": 8566257,
+            "steps": 20
+        }
+    },
+    "4": {
+        "class_type": "CheckpointLoaderSimple",
+        "inputs": {
+            "ckpt_name": "v1-5-pruned-emaonly.ckpt"
+        }
+    },
+    "5": {
+        "class_type": "EmptyLatentImage",
+        "inputs": {
+            "batch_size": 1,
+            "height": 512,
+            "width": 512
+        }
+    },
+    "6": {
+        "class_type": "CLIPTextEncode",
+        "inputs": {
+            "clip": [
+                "4",
+                1
+            ],
+            "text": "masterpiece best quality girl"
+        }
+    },
+    "7": {
+        "class_type": "CLIPTextEncode",
+        "inputs": {
+            "clip": [
+                "4",
+                1
+            ],
+            "text": "bad hands"
+        }
+    },
+    "8": {
+        "class_type": "VAEDecode",
+        "inputs": {
+            "samples": [
+                "3",
+                0
+            ],
+            "vae": [
+                "4",
+                2
+            ]
+        }
+    },
+    "save_image_websocket_node": {
+        "class_type": "SaveImageWebsocket",
+        "inputs": {
+            "images": [
+                "8",
+                0
+            ]
+        }
+    }
+}
+"""
+
+prompt = json.loads(prompt_text)
+#set the text prompt for our positive CLIPTextEncode
+prompt["6"]["inputs"]["text"] = "masterpiece best quality man"
+
+#set the seed for our KSampler node
+prompt["3"]["inputs"]["seed"] = 5
+
+ws = websocket.WebSocket()
+ws.connect("ws://{}/ws?clientId={}".format(server_address, client_id))
+images = get_images(ws, prompt)
+
+#Commented out code to display the output images:
+
+# for node_id in images:
+#     for image_data in images[node_id]:
+#         from PIL import Image
+#         import io
+#         image = Image.open(io.BytesIO(image_data))
+#         image.show()
+
diff --git a/server.py b/server.py
index dca06f6f..38b1bab8 100644
--- a/server.py
+++ b/server.py
@@ -11,19 +11,15 @@ import urllib
 import json
 import glob
 import struct
+import ssl
+import hashlib
 from PIL import Image, ImageOps
 from PIL.PngImagePlugin import PngInfo
 from io import BytesIO
 
-try:
-    import aiohttp
-    from aiohttp import web
-except ImportError:
-    print("Module 'aiohttp' not installed. Please install it via:")
-    print("pip install aiohttp")
-    print("or")
-    print("pip install -r requirements.txt")
-    sys.exit()
+import aiohttp
+from aiohttp import web
+import logging
 
 import mimetypes
 from comfy.cli_args import args
@@ -40,7 +36,7 @@ async def send_socket_catch_exception(function, message):
     try:
         await function(message)
     except (aiohttp.ClientError, aiohttp.ClientPayloadError, ConnectionResetError) as err:
-        print("send error:", err)
+        logging.warning("send error: {}".format(err))
 
 @web.middleware
 async def cache_control(request: web.Request, handler):
@@ -115,10 +111,10 @@ class PromptServer():
                 # On reconnect if we are the currently executing client send the current node
                 if self.client_id == sid and self.last_node_id is not None:
                     await self.send("executing", { "node": self.last_node_id }, sid)
-                    
+
                 async for msg in ws:
                     if msg.type == aiohttp.WSMsgType.ERROR:
-                        print('ws connection closed with exception %s' % ws.exception())
+                        logging.warning('ws connection closed with exception %s' % ws.exception())
             finally:
                 self.sockets.pop(sid, None)
             return ws
@@ -136,9 +132,9 @@ class PromptServer():
         async def get_extensions(request):
             files = glob.glob(os.path.join(
                 glob.escape(self.web_root), 'extensions/**/*.js'), recursive=True)
-            
+
             extensions = list(map(lambda f: "/" + os.path.relpath(f, self.web_root).replace("\\", "/"), files))
-            
+
             for name, dir in nodes.EXTENSION_WEB_DIRS.items():
                 files = glob.glob(os.path.join(glob.escape(dir), '**/*.js'), recursive=True)
                 extensions.extend(list(map(lambda f: "/extensions/" + urllib.parse.quote(
@@ -159,9 +155,23 @@ class PromptServer():
 
             return type_dir, dir_type
 
+        def compare_image_hash(filepath, image):
+            # function to compare hashes of two images to see if it already exists, fix to #3465
+            if os.path.exists(filepath):
+                a = hashlib.sha256()
+                b = hashlib.sha256()
+                with open(filepath, "rb") as f:
+                    a.update(f.read())
+                    b.update(image.file.read())
+                    image.file.seek(0)
+                    f.close()
+                return a.hexdigest() == b.hexdigest()
+            return False
+
         def image_upload(post, image_save_function=None):
             image = post.get("image")
             overwrite = post.get("overwrite")
+            image_is_duplicate = False
 
             image_upload_type = post.get("type")
             upload_dir, image_upload_type = get_dir_by_type(image_upload_type)
@@ -188,15 +198,19 @@ class PromptServer():
                 else:
                     i = 1
                     while os.path.exists(filepath):
+                        if compare_image_hash(filepath, image): #compare hash to prevent saving of duplicates with same name, fix for #3465
+                            image_is_duplicate = True
+                            break
                         filename = f"{split[0]} ({i}){split[1]}"
                         filepath = os.path.join(full_output_folder, filename)
                         i += 1
 
-                if image_save_function is not None:
-                    image_save_function(image, post, filepath)
-                else:
-                    with open(filepath, "wb") as f:
-                        f.write(image.file.read())
+                if not image_is_duplicate:
+                    if image_save_function is not None:
+                        image_save_function(image, post, filepath)
+                    else:
+                        with open(filepath, "wb") as f:
+                            f.write(image.file.read())
 
                 return web.json_response({"name" : filename, "subfolder": subfolder, "type": image_upload_type})
             else:
@@ -419,8 +433,8 @@ class PromptServer():
                 try:
                     out[x] = node_info(x)
                 except Exception as e:
-                    print(f"[ERROR] An error occurred while retrieving information for the '{x}' node.", file=sys.stderr)
-                    traceback.print_exc()
+                    logging.error(f"[ERROR] An error occurred while retrieving information for the '{x}' node.")
+                    logging.error(traceback.format_exc())
             return web.json_response(out)
 
         @routes.get("/object_info/{node_class}")
@@ -453,7 +467,7 @@ class PromptServer():
 
         @routes.post("/prompt")
         async def post_prompt(request):
-            print("got prompt")
+            logging.info("got prompt")
             resp_code = 200
             out_string = ""
             json_data =  await request.json()
@@ -485,7 +499,7 @@ class PromptServer():
                     response = {"prompt_id": prompt_id, "number": number, "node_errors": valid[3]}
                     return web.json_response(response)
                 else:
-                    print("invalid prompt:", valid[1])
+                    logging.warning("invalid prompt: {}".format(valid[1]))
                     return web.json_response({"error": valid[1], "node_errors": valid[3]}, status=400)
             else:
                 return web.json_response({"error": "no prompt", "node_errors": []}, status=400)
@@ -532,18 +546,31 @@ class PromptServer():
                     self.prompt_queue.delete_history_item(id_to_delete)
 
             return web.Response(status=200)
-        
+
     def add_routes(self):
         self.user_manager.add_routes(self.routes)
+
+        # Prefix every route with /api for easier matching for delegation.
+        # This is very useful for frontend dev server, which need to forward
+        # everything except serving of static files.
+        # Currently both the old endpoints without prefix and new endpoints with
+        # prefix are supported.
+        api_routes = web.RouteTableDef()
+        for route in self.routes:
+            # Custom nodes might add extra static routes. Only process non-static
+            # routes to add /api prefix.
+            if isinstance(route, web.RouteDef):
+                api_routes.route(route.method, "/api" + route.path)(route.handler, **route.kwargs)
+        self.app.add_routes(api_routes)
         self.app.add_routes(self.routes)
 
         for name, dir in nodes.EXTENSION_WEB_DIRS.items():
             self.app.add_routes([
-                web.static('/extensions/' + urllib.parse.quote(name), dir, follow_symlinks=True),
+                web.static('/extensions/' + urllib.parse.quote(name), dir),
             ])
 
         self.app.add_routes([
-            web.static('/', self.web_root, follow_symlinks=True),
+            web.static('/', self.web_root),
         ])
 
     def get_queue_info(self):
@@ -629,14 +656,22 @@ class PromptServer():
     async def start(self, address, port, verbose=True, call_on_start=None):
         runner = web.AppRunner(self.app, access_log=None)
         await runner.setup()
-        site = web.TCPSite(runner, address, port)
+        ssl_ctx = None
+        scheme = "http"
+        if args.tls_keyfile and args.tls_certfile:
+                ssl_ctx = ssl.SSLContext(protocol=ssl.PROTOCOL_TLS_SERVER, verify_mode=ssl.CERT_NONE)
+                ssl_ctx.load_cert_chain(certfile=args.tls_certfile,
+                                keyfile=args.tls_keyfile)
+                scheme = "https"
+
+        site = web.TCPSite(runner, address, port, ssl_context=ssl_ctx)
         await site.start()
 
         if verbose:
-            print("Starting server\n")
-            print("To see the GUI go to: http://{}:{}".format(address, port))
+            logging.info("Starting server\n")
+            logging.info("To see the GUI go to: {}://{}:{}".format(scheme, address, port))
         if call_on_start is not None:
-            call_on_start(address, port)
+            call_on_start(scheme, address, port)
 
     def add_on_prompt_handler(self, handler):
         self.on_prompt_handlers.append(handler)
@@ -646,7 +681,7 @@ class PromptServer():
             try:
                 json_data = handler(json_data)
             except Exception as e:
-                print(f"[ERROR] An error occurred during the on_prompt_handler processing")
-                traceback.print_exc()
+                logging.warning(f"[ERROR] An error occurred during the on_prompt_handler processing")
+                logging.warning(traceback.format_exc())
 
         return json_data
diff --git a/tests-ui/tests/groupNode.test.js b/tests-ui/tests/groupNode.test.js
index e6ebedd9..53a5828d 100644
--- a/tests-ui/tests/groupNode.test.js
+++ b/tests-ui/tests/groupNode.test.js
@@ -947,7 +947,7 @@ describe("group node", () => {
 		expect(p1.widgets.value.widget.options?.step).toBe(80); // width/height step * 10
 
 		expect(p2.widgets.value.widget.options?.min).toBe(16); // width/height min
-		expect(p2.widgets.value.widget.options?.max).toBe(8192); // width/height max
+		expect(p2.widgets.value.widget.options?.max).toBe(16384); // width/height max
 		expect(p2.widgets.value.widget.options?.step).toBe(80); // width/height step * 10
 
 		expect(p1.widgets.value.value).toBe(128);
diff --git a/tests-ui/utils/ezgraph.js b/tests-ui/utils/ezgraph.js
index 8a55246e..97be7aa7 100644
--- a/tests-ui/utils/ezgraph.js
+++ b/tests-ui/utils/ezgraph.js
@@ -204,13 +204,17 @@ export class EzWidget {
 	convertToWidget() {
 		if (!this.isConvertedToInput)
 			throw new Error(`Widget ${this.widget.name} cannot be converted as it is already a widget.`);
-		this.node.menu[`Convert ${this.widget.name} to widget`].call();
+		var menu = this.node.menu["Convert Input to Widget"].item.submenu.options;
+		var index = menu.findIndex(a => a.content == `Convert ${this.widget.name} to widget`);
+		menu[index].callback.call();
 	}
 
 	convertToInput() {
 		if (this.isConvertedToInput)
 			throw new Error(`Widget ${this.widget.name} cannot be converted as it is already an input.`);
-		this.node.menu[`Convert ${this.widget.name} to input`].call();
+		var menu = this.node.menu["Convert Widget to Input"].item.submenu.options;
+		var index = menu.findIndex(a => a.content == `Convert ${this.widget.name} to input`);
+		menu[index].callback.call();
 	}
 }
 
diff --git a/tests-ui/utils/setup.js b/tests-ui/utils/setup.js
index e4625894..3a6d69b7 100644
--- a/tests-ui/utils/setup.js
+++ b/tests-ui/utils/setup.js
@@ -72,6 +72,7 @@ export function mockApi(config = {}) {
 		storeUserData: jest.fn((file, data) => {
 			userData[file] = data;
 		}),
+		listUserData: jest.fn(() => [])
 	};
 	jest.mock("../../web/scripts/api", () => ({
 		get api() {
diff --git a/web/extensions/core/colorPalette.js b/web/extensions/core/colorPalette.js
index b8d83613..245f3b93 100644
--- a/web/extensions/core/colorPalette.js
+++ b/web/extensions/core/colorPalette.js
@@ -20,6 +20,10 @@ const colorPalettes = {
 				"MODEL": "#B39DDB", // light lavender-purple
 				"STYLE_MODEL": "#C2FFAE", // light green-yellow
 				"VAE": "#FF6E6E", // bright red
+				"NOISE": "#B0B0B0", // gray
+				"GUIDER": "#66FFFF", // cyan
+				"SAMPLER": "#ECB4B4", // very soft red
+				"SIGMAS": "#CDFFCD", // soft lime green
 				"TAESD": "#DCC274", // cheesecake
 			},
 			"litegraph_base": {
@@ -59,6 +63,10 @@ const colorPalettes = {
 				"border-color": "#4e4e4e",
 				"tr-even-bg-color": "#222",
 				"tr-odd-bg-color": "#353535",
+				"content-bg": "#4e4e4e",
+				"content-fg": "#fff",
+				"content-hover-bg": "#222",
+				"content-hover-fg": "#fff"
 			}
 		},
 	},
@@ -116,6 +124,10 @@ const colorPalettes = {
 				"border-color": "#888",
 				"tr-even-bg-color": "#f9f9f9",
 				"tr-odd-bg-color": "#fff",
+				"content-bg": "#e0e0e0",
+				"content-fg": "#222",
+				"content-hover-bg": "#adadad",
+				"content-hover-fg": "#222"
 			}
 		},
 	},
@@ -172,6 +184,10 @@ const colorPalettes = {
 				"border-color": "#657b83", // Base00
 				"tr-even-bg-color": "#002b36",
 				"tr-odd-bg-color": "#073642",
+				"content-bg": "#657b83",
+				"content-fg": "#fdf6e3",
+				"content-hover-bg": "#002b36",
+				"content-hover-fg": "#fdf6e3"
 			}
 		},
 	},
@@ -240,7 +256,11 @@ const colorPalettes = {
 			  "error-text": "#ff4444",
 			  "border-color": "#6e7581",
 			  "tr-even-bg-color": "#2b2f38",
-			  "tr-odd-bg-color": "#242730"
+			  "tr-odd-bg-color": "#242730",
+			  "content-bg": "#6e7581",
+			  "content-fg": "#fff",
+			  "content-hover-bg": "#2b2f38",
+			  "content-hover-fg": "#fff"
 			}
 		},
 	},
@@ -309,7 +329,11 @@ const colorPalettes = {
 			  "error-text": "#ff4444",
 			  "border-color": "#545d70",
 			  "tr-even-bg-color": "#2e3440",
-			  "tr-odd-bg-color": "#161b22"
+			  "tr-odd-bg-color": "#161b22",
+			  "content-bg": "#545d70",
+			  "content-fg": "#e5eaf0",
+			  "content-hover-bg": "#2e3440",
+			  "content-hover-fg": "#e5eaf0"
 			}
 		},
 	},
@@ -378,7 +402,11 @@ const colorPalettes = {
 			  "error-text": "#ff4444",
 			  "border-color": "#30363d",
 			  "tr-even-bg-color": "#161b22",
-			  "tr-odd-bg-color": "#13171d"
+			  "tr-odd-bg-color": "#13171d",
+			  "content-bg": "#30363d",
+			  "content-fg": "#e5eaf0",
+			  "content-hover-bg": "#161b22",
+			  "content-hover-fg": "#e5eaf0"
 			}
 		},
 	}
diff --git a/web/extensions/core/dynamicPrompts.js b/web/extensions/core/dynamicPrompts.js
index 599a9e68..7417361b 100644
--- a/web/extensions/core/dynamicPrompts.js
+++ b/web/extensions/core/dynamicPrompts.js
@@ -17,7 +17,7 @@ app.registerExtension({
 			// Locate dynamic prompt text widgets
 			// Include any widgets with dynamicPrompts set to true, and customtext
 			const widgets = node.widgets.filter(
-				(n) => (n.type === "customtext" && n.dynamicPrompts !== false) || n.dynamicPrompts
+				(n) => n.dynamicPrompts
 			);
 			for (const widget of widgets) {
 				// Override the serialization of the value to resolve dynamic prompts for all widgets supporting it in this node
diff --git a/web/extensions/core/groupNode.js b/web/extensions/core/groupNode.js
index 0b0763d1..9a223890 100644
--- a/web/extensions/core/groupNode.js
+++ b/web/extensions/core/groupNode.js
@@ -1278,4 +1278,4 @@ const ext = {
 	}
 };
 
-app.registerExtension(ext);
+app.registerExtension(ext);
\ No newline at end of file
diff --git a/web/extensions/core/keybinds.js b/web/extensions/core/keybinds.js
index cf698ea5..ac367c11 100644
--- a/web/extensions/core/keybinds.js
+++ b/web/extensions/core/keybinds.js
@@ -21,7 +21,6 @@ app.registerExtension({
 				s: "#comfy-save-button",
 				o: "#comfy-file-input",
 				Backspace: "#comfy-clear-button",
-				Delete: "#comfy-clear-button",
 				d: "#comfy-load-default-button",
 			};
 
diff --git a/web/extensions/core/maskeditor.js b/web/extensions/core/maskeditor.js
index 4f69ac76..36f7496e 100644
--- a/web/extensions/core/maskeditor.js
+++ b/web/extensions/core/maskeditor.js
@@ -164,6 +164,41 @@ class MaskEditorDialog extends ComfyDialog {
 		return divElement;
 	}
 
+	createOpacitySlider(self, name, callback) {
+		const divElement = document.createElement('div');
+		divElement.id = "maskeditor-opacity-slider";
+		divElement.style.cssFloat = "left";
+		divElement.style.fontFamily = "sans-serif";
+		divElement.style.marginRight = "4px";
+		divElement.style.color = "var(--input-text)";
+		divElement.style.backgroundColor = "var(--comfy-input-bg)";
+		divElement.style.borderRadius = "8px";
+		divElement.style.borderColor = "var(--border-color)";
+		divElement.style.borderStyle = "solid";
+		divElement.style.fontSize = "15px";
+		divElement.style.height = "21px";
+		divElement.style.padding = "1px 6px";
+		divElement.style.display = "flex";
+		divElement.style.position = "relative";
+		divElement.style.top = "2px";
+		divElement.style.pointerEvents = "auto";
+		self.opacity_slider_input = document.createElement('input');
+		self.opacity_slider_input.setAttribute('type', 'range');
+		self.opacity_slider_input.setAttribute('min', '0.1');
+		self.opacity_slider_input.setAttribute('max', '1.0');
+		self.opacity_slider_input.setAttribute('step', '0.01')
+		self.opacity_slider_input.setAttribute('value', '0.7');
+		const labelElement = document.createElement("label");
+		labelElement.textContent = name;
+
+		divElement.appendChild(labelElement);
+		divElement.appendChild(self.opacity_slider_input);
+
+		self.opacity_slider_input.addEventListener("input", callback);
+
+		return divElement;
+	}
+
 	setlayout(imgCanvas, maskCanvas) {
 		const self = this;
 
@@ -203,6 +238,13 @@ class MaskEditorDialog extends ComfyDialog {
 			self.updateBrushPreview(self, null, null);
 		});
 
+		this.brush_opacity_slider = this.createOpacitySlider(self, "Opacity", (event) => {
+			self.brush_opacity = event.target.value;
+			if (self.brush_color_mode !== "negative") {
+			    self.maskCanvas.style.opacity = self.brush_opacity;
+			}
+		});
+
 		this.colorButton = this.createLeftButton(this.getColorButtonText(), () => {
 			if (self.brush_color_mode === "black") {
 				self.brush_color_mode = "white";
@@ -237,6 +279,7 @@ class MaskEditorDialog extends ComfyDialog {
 		bottom_panel.appendChild(this.saveButton);
 		bottom_panel.appendChild(cancelButton);
 		bottom_panel.appendChild(this.brush_size_slider);
+		bottom_panel.appendChild(this.brush_opacity_slider);
 		bottom_panel.appendChild(this.colorButton);
 
 		imgCanvas.style.position = "absolute";
@@ -472,7 +515,7 @@ class MaskEditorDialog extends ComfyDialog {
 		else {
 			return {
 				mixBlendMode: "initial",
-				opacity: "0.7",
+				opacity: this.brush_opacity,
 			};
 		}
 	}
@@ -538,6 +581,7 @@ class MaskEditorDialog extends ComfyDialog {
 		this.maskCtx.putImageData(maskData, 0, 0);
 	}
 
+	brush_opacity = 0.7;
 	brush_size = 10;
 	brush_color_mode = "black";
 	drawing_mode = false;
diff --git a/web/extensions/core/snapToGrid.js b/web/extensions/core/snapToGrid.js
index dc534d6e..aac01774 100644
--- a/web/extensions/core/snapToGrid.js
+++ b/web/extensions/core/snapToGrid.js
@@ -2,6 +2,13 @@ import { app } from "../../scripts/app.js";
 
 // Shift + drag/resize to snap to grid
 
+/** Rounds a Vector2 in-place to the current CANVAS_GRID_SIZE. */
+function roundVectorToGrid(vec) {
+	vec[0] = LiteGraph.CANVAS_GRID_SIZE * Math.round(vec[0] / LiteGraph.CANVAS_GRID_SIZE);
+	vec[1] = LiteGraph.CANVAS_GRID_SIZE * Math.round(vec[1] / LiteGraph.CANVAS_GRID_SIZE);
+	return vec;
+}
+
 app.registerExtension({
 	name: "Comfy.SnapToGrid",
 	init() {
@@ -43,10 +50,7 @@ app.registerExtension({
 			const onResize = node.onResize;
 			node.onResize = function () {
 				if (app.shiftDown) {
-					const w = LiteGraph.CANVAS_GRID_SIZE * Math.round(node.size[0] / LiteGraph.CANVAS_GRID_SIZE);
-					const h = LiteGraph.CANVAS_GRID_SIZE * Math.round(node.size[1] / LiteGraph.CANVAS_GRID_SIZE);
-					node.size[0] = w;
-					node.size[1] = h;
+					roundVectorToGrid(node.size);
 				}
 				return onResize?.apply(this, arguments);
 			};
@@ -57,9 +61,7 @@ app.registerExtension({
 		const origDrawNode = LGraphCanvas.prototype.drawNode;
 		LGraphCanvas.prototype.drawNode = function (node, ctx) {
 			if (app.shiftDown && this.node_dragged && node.id in this.selected_nodes) {
-				const x = LiteGraph.CANVAS_GRID_SIZE * Math.round(node.pos[0] / LiteGraph.CANVAS_GRID_SIZE);
-				const y = LiteGraph.CANVAS_GRID_SIZE * Math.round(node.pos[1] / LiteGraph.CANVAS_GRID_SIZE);
-
+				const [x, y] = roundVectorToGrid([...node.pos]);
 				const shiftX = x - node.pos[0];
 				let shiftY = y - node.pos[1];
 
@@ -85,5 +87,85 @@ app.registerExtension({
 
 			return origDrawNode.apply(this, arguments);
 		};
+
+		
+		
+		/**
+  		 * The currently moving, selected group only. Set after the `selected_group` has actually started
+		 * moving.
+   		 */
+		let selectedAndMovingGroup = null;
+
+		/**
+  		 * Handles moving a group; tracking when a group has been moved (to show the ghost in `drawGroups`
+     		 * below) as well as handle the last move call from LiteGraph's `processMouseUp`.
+		 */
+		const groupMove = LGraphGroup.prototype.move;
+		LGraphGroup.prototype.move = function(deltax, deltay, ignore_nodes) {
+			const v = groupMove.apply(this, arguments);
+			// When we've started moving, set `selectedAndMovingGroup` as LiteGraph sets `selected_group`
+			// too eagerly and we don't want to behave like we're moving until we get a delta.
+			if (!selectedAndMovingGroup && app.canvas.selected_group === this && (deltax || deltay)) {
+				selectedAndMovingGroup = this;
+			}
+
+			// LiteGraph will call group.move both on mouse-move as well as mouse-up though we only want
+			// to snap on a mouse-up which we can determine by checking if `app.canvas.last_mouse_dragging`
+			// has been set to `false`. Essentially, this check here is the equivilant to calling an
+			// `LGraphGroup.prototype.onNodeMoved` if it had existed.
+			if (app.canvas.last_mouse_dragging === false && app.shiftDown) {
+				// After moving a group (while app.shiftDown), snap all the child nodes and, finally,
+				//  align the group itself.
+				this.recomputeInsideNodes();
+				for (const node of this._nodes) {
+					node.alignToGrid();
+				}
+				LGraphNode.prototype.alignToGrid.apply(this);
+			}
+			return v;
+		};
+
+		/**
+  		 * Handles drawing a group when, snapping the size when one is actively being resized tracking and/or
+     		 * drawing a ghost box when one is actively being moved. This mimics the node snapping behavior for
+		 * both.
+		 */
+		const drawGroups = LGraphCanvas.prototype.drawGroups;
+		LGraphCanvas.prototype.drawGroups = function (canvas, ctx) {
+			if (this.selected_group && app.shiftDown) {
+				if (this.selected_group_resizing) {
+					roundVectorToGrid(this.selected_group.size);
+				} else if (selectedAndMovingGroup) {
+					const [x, y] = roundVectorToGrid([...selectedAndMovingGroup.pos]);
+					const f = ctx.fillStyle;
+					const s = ctx.strokeStyle;
+					ctx.fillStyle = "rgba(100, 100, 100, 0.33)";
+					ctx.strokeStyle = "rgba(100, 100, 100, 0.66)";
+					ctx.rect(x, y, ...selectedAndMovingGroup.size);
+					ctx.fill();
+					ctx.stroke();
+					ctx.fillStyle = f;
+					ctx.strokeStyle = s;
+				}
+			} else if (!this.selected_group) {
+				selectedAndMovingGroup = null;
+			}
+			return drawGroups.apply(this, arguments);
+		};
+
+
+		/** Handles adding a group in a snapping-enabled state. */
+		const onGroupAdd = LGraphCanvas.onGroupAdd;
+		LGraphCanvas.onGroupAdd = function() {
+			const v = onGroupAdd.apply(app.canvas, arguments);
+			if (app.shiftDown) {
+				const lastGroup = app.graph._groups[app.graph._groups.length - 1];
+				if (lastGroup) {
+					roundVectorToGrid(lastGroup.pos);
+					roundVectorToGrid(lastGroup.size);
+				}
+			}
+			return v;
+		};
 	},
 });
diff --git a/web/extensions/core/undoRedo.js b/web/extensions/core/undoRedo.js
deleted file mode 100644
index 900eed2a..00000000
--- a/web/extensions/core/undoRedo.js
+++ /dev/null
@@ -1,177 +0,0 @@
-import { app } from "../../scripts/app.js";
-import { api } from "../../scripts/api.js"
-
-const MAX_HISTORY = 50;
-
-let undo = [];
-let redo = [];
-let activeState = null;
-let isOurLoad = false;
-function checkState() {
-	const currentState = app.graph.serialize();
-	if (!graphEqual(activeState, currentState)) {
-		undo.push(activeState);
-		if (undo.length > MAX_HISTORY) {
-			undo.shift();
-		}
-		activeState = clone(currentState);
-		redo.length = 0;
-		api.dispatchEvent(new CustomEvent("graphChanged", { detail: activeState }));
-	}
-}
-
-const loadGraphData = app.loadGraphData;
-app.loadGraphData = async function () {
-	const v = await loadGraphData.apply(this, arguments);
-	if (isOurLoad) {
-		isOurLoad = false;
-	} else {
-		checkState();
-	}
-	return v;
-};
-
-function clone(obj) {
-	try {
-		if (typeof structuredClone !== "undefined") {
-			return structuredClone(obj);
-		}
-	} catch (error) {
-		// structuredClone is stricter than using JSON.parse/stringify so fallback to that
-	}
-
-	return JSON.parse(JSON.stringify(obj));
-}
-
-function graphEqual(a, b, root = true) {
-	if (a === b) return true;
-
-	if (typeof a == "object" && a && typeof b == "object" && b) {
-		const keys = Object.getOwnPropertyNames(a);
-
-		if (keys.length != Object.getOwnPropertyNames(b).length) {
-			return false;
-		}
-
-		for (const key of keys) {
-			let av = a[key];
-			let bv = b[key];
-			if (root && key === "nodes") {
-				// Nodes need to be sorted as the order changes when selecting nodes
-				av = [...av].sort((a, b) => a.id - b.id);
-				bv = [...bv].sort((a, b) => a.id - b.id);
-			}
-			if (!graphEqual(av, bv, false)) {
-				return false;
-			}
-		}
-
-		return true;
-	}
-
-	return false;
-}
-
-const undoRedo = async (e) => {
-	const updateState = async (source, target) => {
-		const prevState = source.pop();
-		if (prevState) {
-			target.push(activeState);
-			isOurLoad = true;
-			await app.loadGraphData(prevState, false);
-			activeState = prevState;
-		}
-	}
-	if (e.ctrlKey || e.metaKey) {
-		if (e.key === "y") {
-			updateState(redo, undo);
-			return true;
-		} else if (e.key === "z") {
-			updateState(undo, redo);
-			return true;
-		}
-	}
-};
-
-const bindInput = (activeEl) => {
-	if (activeEl && activeEl.tagName !== "CANVAS" && activeEl.tagName !== "BODY") {
-		for (const evt of ["change", "input", "blur"]) {
-			if (`on${evt}` in activeEl) {
-				const listener = () => {
-					checkState();
-					activeEl.removeEventListener(evt, listener);
-				};
-				activeEl.addEventListener(evt, listener);
-				return true;
-			}
-		}
-	}
-};
-
-let keyIgnored = false;
-window.addEventListener(
-	"keydown",
-	(e) => {
-		requestAnimationFrame(async () => {
-			let activeEl;
-			// If we are auto queue in change mode then we do want to trigger on inputs
-			if (!app.ui.autoQueueEnabled || app.ui.autoQueueMode === "instant") {
-				activeEl = document.activeElement;
-				if (activeEl?.tagName === "INPUT" || activeEl?.type === "textarea") {
-					// Ignore events on inputs, they have their native history
-					return;
-				}
-			}
-		
-			keyIgnored = e.key === "Control" || e.key === "Shift" || e.key === "Alt" || e.key === "Meta";
-			if (keyIgnored) return;
-
-			// Check if this is a ctrl+z ctrl+y
-			if (await undoRedo(e)) return;
-
-			// If our active element is some type of input then handle changes after they're done
-			if (bindInput(activeEl)) return;
-			checkState();
-		});
-	},
-	true
-);
-
-window.addEventListener("keyup", (e) => {
-	if (keyIgnored) {
-		keyIgnored = false;
-		checkState();
-	}
-});
-
-// Handle clicking DOM elements (e.g. widgets)
-window.addEventListener("mouseup", () => {
-	checkState();
-});
-
-// Handle prompt queue event for dynamic widget changes
-api.addEventListener("promptQueued", () => {
-	checkState();
-});
-
-// Handle litegraph clicks
-const processMouseUp = LGraphCanvas.prototype.processMouseUp;
-LGraphCanvas.prototype.processMouseUp = function (e) {
-	const v = processMouseUp.apply(this, arguments);
-	checkState();
-	return v;
-};
-const processMouseDown = LGraphCanvas.prototype.processMouseDown;
-LGraphCanvas.prototype.processMouseDown = function (e) {
-	const v = processMouseDown.apply(this, arguments);
-	checkState();
-	return v;
-};
-
-// Handle litegraph context menu for COMBO widgets
-const close = LiteGraph.ContextMenu.prototype.close;
-LiteGraph.ContextMenu.prototype.close = function(e) {
-	const v = close.apply(this, arguments);
-	checkState();
-	return v;
-}
\ No newline at end of file
diff --git a/web/extensions/core/uploadAudio.js b/web/extensions/core/uploadAudio.js
new file mode 100644
index 00000000..0ac9cb80
--- /dev/null
+++ b/web/extensions/core/uploadAudio.js
@@ -0,0 +1,178 @@
+import { app } from "../../scripts/app.js"
+import { api } from "../../scripts/api.js"
+
+function splitFilePath(path) {
+  const folder_separator = path.lastIndexOf("/")
+  if (folder_separator === -1) {
+    return ["", path]
+  }
+  return [
+    path.substring(0, folder_separator),
+    path.substring(folder_separator + 1)
+  ]
+}
+
+function getResourceURL(subfolder, filename, type = "input") {
+  const params = [
+    "filename=" + encodeURIComponent(filename),
+    "type=" + type,
+    "subfolder=" + subfolder,
+    app.getPreviewFormatParam().substring(1),
+    app.getRandParam().substring(1)
+  ].join("&")
+
+  return `/view?${params}`
+}
+
+async function uploadFile(
+  audioWidget,
+  audioUIWidget,
+  file,
+  updateNode,
+  pasted = false
+) {
+  try {
+    // Wrap file in formdata so it includes filename
+    const body = new FormData()
+    body.append("image", file)
+    if (pasted) body.append("subfolder", "pasted")
+    const resp = await api.fetchApi("/upload/image", {
+      method: "POST",
+      body
+    })
+
+    if (resp.status === 200) {
+      const data = await resp.json()
+      // Add the file to the dropdown list and update the widget value
+      let path = data.name
+      if (data.subfolder) path = data.subfolder + "/" + path
+
+      if (!audioWidget.options.values.includes(path)) {
+        audioWidget.options.values.push(path)
+      }
+
+      if (updateNode) {
+        audioUIWidget.element.src = api.apiURL(
+          getResourceURL(...splitFilePath(path))
+        )
+        audioWidget.value = path
+      }
+    } else {
+      alert(resp.status + " - " + resp.statusText)
+    }
+  } catch (error) {
+    alert(error)
+  }
+}
+
+// AudioWidget MUST be registered first, as AUDIOUPLOAD depends on AUDIO_UI to be
+// present.
+app.registerExtension({
+  name: "Comfy.AudioWidget",
+  async beforeRegisterNodeDef(nodeType, nodeData) {
+    if (["LoadAudio", "SaveAudio", "PreviewAudio"].includes(nodeType.comfyClass)) {
+      nodeData.input.required.audioUI = ["AUDIO_UI"]
+    }
+  },
+  getCustomWidgets() {
+    return {
+      AUDIO_UI(node, inputName) {
+        const audio = document.createElement("audio")
+        audio.controls = true
+        audio.classList.add("comfy-audio")
+        audio.setAttribute("name", "media")
+
+        const audioUIWidget = node.addDOMWidget(
+          inputName,
+          /* name=*/ "audioUI",
+          audio
+        )
+        // @ts-ignore
+        // TODO: Sort out the DOMWidget type.
+        audioUIWidget.serialize = false
+
+        const isOutputNode = node.constructor.nodeData.output_node
+        if (isOutputNode) {
+          // Hide the audio widget when there is no audio initially.
+          audioUIWidget.element.classList.add("empty-audio-widget")
+          // Populate the audio widget UI on node execution.
+          const onExecuted = node.onExecuted
+          node.onExecuted = function(message) {
+            onExecuted?.apply(this, arguments)
+            const audios = message.audio
+            if (!audios) return
+            const audio = audios[0]
+            audioUIWidget.element.src = api.apiURL(
+              getResourceURL(audio.subfolder, audio.filename, audio.type)
+            )
+            audioUIWidget.element.classList.remove("empty-audio-widget")
+          }
+        }
+        return { widget: audioUIWidget }
+      }
+    }
+  },
+  onNodeOutputsUpdated(nodeOutputs) {
+    for (const [nodeId, output] of Object.entries(nodeOutputs)) {
+      const node = app.graph.getNodeById(Number.parseInt(nodeId));
+      if ("audio" in output) {
+        const audioUIWidget = node.widgets.find((w) => w.name === "audioUI");
+        const audio = output.audio[0];
+        audioUIWidget.element.src = api.apiURL(getResourceURL(audio.subfolder, audio.filename, audio.type));
+        audioUIWidget.element.classList.remove("empty-audio-widget");
+      }
+    }
+  },
+})
+
+app.registerExtension({
+  name: "Comfy.UploadAudio",
+  async beforeRegisterNodeDef(nodeType, nodeData) {
+    if (nodeData?.input?.required?.audio?.[1]?.audio_upload === true) {
+      nodeData.input.required.upload = ["AUDIOUPLOAD"]
+    }
+  },
+  getCustomWidgets() {
+    return {
+      AUDIOUPLOAD(node, inputName) {
+        // The widget that allows user to select file.
+        const audioWidget = node.widgets.find(w => w.name === "audio")
+        const audioUIWidget = node.widgets.find(w => w.name === "audioUI")
+
+        const onAudioWidgetUpdate = () => {
+          audioUIWidget.element.src = api.apiURL(
+            getResourceURL(...splitFilePath(audioWidget.value))
+          )
+        }
+        // Initially load default audio file to audioUIWidget.
+        if (audioWidget.value) {
+          onAudioWidgetUpdate()
+        }
+        audioWidget.callback = onAudioWidgetUpdate
+
+        const fileInput = document.createElement("input")
+        fileInput.type = "file"
+        fileInput.accept = "audio/*"
+        fileInput.style.display = "none"
+        fileInput.onchange = () => {
+          if (fileInput.files.length) {
+            uploadFile(audioWidget, audioUIWidget, fileInput.files[0], true)
+          }
+        }
+        // The widget to pop up the upload dialog.
+        const uploadWidget = node.addWidget(
+          "button",
+          inputName,
+          /* value=*/ "",
+          () => {
+            fileInput.click()
+          }
+        )
+        uploadWidget.label = "choose file to upload"
+        uploadWidget.serialize = false
+
+        return { widget: uploadWidget }
+      }
+    }
+  }
+})
diff --git a/web/extensions/core/webcamCapture.js b/web/extensions/core/webcamCapture.js
new file mode 100644
index 00000000..dd5725bd
--- /dev/null
+++ b/web/extensions/core/webcamCapture.js
@@ -0,0 +1,126 @@
+import { app } from "../../scripts/app.js";
+import { api } from "../../scripts/api.js";
+
+const WEBCAM_READY = Symbol();
+
+app.registerExtension({
+	name: "Comfy.WebcamCapture",
+	getCustomWidgets(app) {
+		return {
+			WEBCAM(node, inputName) {
+				let res;
+				node[WEBCAM_READY] = new Promise((resolve) => (res = resolve));
+
+				const container = document.createElement("div");
+				container.style.background = "rgba(0,0,0,0.25)";
+				container.style.textAlign = "center";
+
+				const video = document.createElement("video");
+				video.style.height = video.style.width = "100%";
+
+				const loadVideo = async () => {
+					try {
+						const stream = await navigator.mediaDevices.getUserMedia({ video: true, audio: false });
+						container.replaceChildren(video);
+
+						setTimeout(() => res(video), 500); // Fallback as loadedmetadata doesnt fire sometimes?
+						video.addEventListener("loadedmetadata", () => res(video), false);
+						video.srcObject = stream;
+						video.play();
+					} catch (error) {
+						const label = document.createElement("div");
+						label.style.color = "red";
+						label.style.overflow = "auto";
+						label.style.maxHeight = "100%";
+						label.style.whiteSpace = "pre-wrap";
+
+						if (window.isSecureContext) {
+							label.textContent = "Unable to load webcam, please ensure access is granted:\n" + error.message;
+						} else {
+							label.textContent = "Unable to load webcam. A secure context is required, if you are not accessing ComfyUI on localhost (127.0.0.1) you will have to enable TLS (https)\n\n" + error.message;
+						}
+
+						container.replaceChildren(label);
+					}
+				};
+
+				loadVideo();
+
+				return { widget: node.addDOMWidget(inputName, "WEBCAM", container) };
+			},
+		};
+	},
+	nodeCreated(node) {
+		if ((node.type, node.constructor.comfyClass !== "WebcamCapture")) return;
+
+		let video;
+		const camera = node.widgets.find((w) => w.name === "image");
+		const w = node.widgets.find((w) => w.name === "width");
+		const h = node.widgets.find((w) => w.name === "height");
+		const captureOnQueue = node.widgets.find((w) => w.name === "capture_on_queue");
+
+		const canvas = document.createElement("canvas");
+
+		const capture = () => {
+			canvas.width = w.value;
+			canvas.height = h.value;
+			const ctx = canvas.getContext("2d");
+			ctx.drawImage(video, 0, 0, w.value, h.value);
+			const data = canvas.toDataURL("image/png");
+
+			const img = new Image();
+			img.onload = () => {
+				node.imgs = [img];
+				app.graph.setDirtyCanvas(true);
+				requestAnimationFrame(() => {
+					node.setSizeForImage?.();
+				});
+			};
+			img.src = data;
+		};
+
+		const btn = node.addWidget("button", "waiting for camera...", "capture", capture);
+		btn.disabled = true;
+		btn.serializeValue = () => undefined;
+
+		camera.serializeValue = async () => {
+			if (captureOnQueue.value) {
+				capture();
+			} else if (!node.imgs?.length) {
+				const err = `No webcam image captured`;
+				alert(err);
+				throw new Error(err);
+			}
+
+			// Upload image to temp storage
+			const blob = await new Promise((r) => canvas.toBlob(r));
+			const name = `${+new Date()}.png`;
+			const file = new File([blob], name);
+			const body = new FormData();
+			body.append("image", file);
+			body.append("subfolder", "webcam");
+			body.append("type", "temp");
+			const resp = await api.fetchApi("/upload/image", {
+				method: "POST",
+				body,
+			});
+			if (resp.status !== 200) {
+				const err = `Error uploading camera image: ${resp.status} - ${resp.statusText}`;
+				alert(err);
+				throw new Error(err);
+			}
+			return `webcam/${name} [temp]`;
+		};
+
+		node[WEBCAM_READY].then((v) => {
+			video = v;
+			// If width isnt specified then use video output resolution
+			if (!w.value) {
+				w.value = video.videoWidth || 640;
+				h.value = video.videoHeight || 480; 
+			}
+			btn.disabled = false;
+			btn.label = "capture";
+		});
+	},
+});
diff --git a/web/extensions/core/widgetInputs.js b/web/extensions/core/widgetInputs.js
index b12ad968..f1a1d22c 100644
--- a/web/extensions/core/widgetInputs.js
+++ b/web/extensions/core/widgetInputs.js
@@ -22,6 +22,7 @@ function isConvertableWidget(widget, config) {
 }
 
 function hideWidget(node, widget, suffix = "") {
+	if (widget.type?.startsWith(CONVERTED_TYPE)) return;
 	widget.origType = widget.type;
 	widget.origComputeSize = widget.computeSize;
 	widget.origSerializeValue = widget.serializeValue;
@@ -255,8 +256,18 @@ export function mergeIfValid(output, config2, forceUpdate, recreateWidget, confi
 	return { customConfig };
 }
 
+let useConversionSubmenusSetting;
 app.registerExtension({
 	name: "Comfy.WidgetInputs",
+	init() {
+		useConversionSubmenusSetting = app.ui.settings.addSetting({
+			id: "Comfy.NodeInputConversionSubmenus",
+			name: "Node widget/input conversion sub-menus",
+			tooltip: "In the node context menu, place the entries that convert between input/widget in sub-menus.",
+			type: "boolean",
+			defaultValue: true,
+		});
+	},
 	async beforeRegisterNodeDef(nodeType, nodeData, app) {
 		// Add menu options to conver to/from widgets
 		const origGetExtraMenuOptions = nodeType.prototype.getExtraMenuOptions;
@@ -291,12 +302,31 @@ app.registerExtension({
 						}
 					}
 				}
+				
+				//Convert.. main menu
 				if (toInput.length) {
-					options.push(...toInput, null);
+					if (useConversionSubmenusSetting.value) {
+						options.push({
+							content: "Convert Widget to Input",
+							submenu: {
+								options: toInput,
+							},
+						});
+					} else {
+						options.push(...toInput, null);
+					}
 				}
-
 				if (toWidget.length) {
-					options.push(...toWidget, null);
+					if (useConversionSubmenusSetting.value) {
+						options.push({
+							content: "Convert Input to Widget",
+							submenu: {
+								options: toWidget,
+							},
+						});
+					} else {
+						options.push(...toWidget, null);
+					}
 				}
 			}
 
diff --git a/web/fonts/materialdesignicons-webfont.woff2 b/web/fonts/materialdesignicons-webfont.woff2
new file mode 100644
index 00000000..8c69b85f
Binary files /dev/null and b/web/fonts/materialdesignicons-webfont.woff2 differ
diff --git a/web/index.html b/web/index.html
index 094db9d1..5ebf03c0 100644
--- a/web/index.html
+++ b/web/index.html
@@ -5,6 +5,7 @@
 		<title>ComfyUI</title>
 		<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no">
 		<link rel="stylesheet" type="text/css" href="./lib/litegraph.css" />
+		<link rel="stylesheet" type="text/css" href="./lib/materialdesignicons.min.css" />
 		<link rel="stylesheet" type="text/css" href="./style.css" />
 		<link rel="stylesheet" type="text/css" href="./user.css" />
 		<script type="text/javascript" src="./lib/litegraph.core.js"></script>
diff --git a/web/jsconfig.json b/web/jsconfig.json
index b65fa274..cd55a0cc 100644
--- a/web/jsconfig.json
+++ b/web/jsconfig.json
@@ -4,7 +4,9 @@
 		"paths": {
 			"/*": ["./*"]
 		},
-		"lib": ["DOM", "ES2022"]
+		"lib": ["DOM", "ES2022", "DOM.Iterable"],
+		"target": "ES2015",
+		"module": "es2020"
 	},
 	"include": ["."]
 }
diff --git a/web/lib/litegraph.core.js b/web/lib/litegraph.core.js
index 4ff05ae8..427a62b5 100644
--- a/web/lib/litegraph.core.js
+++ b/web/lib/litegraph.core.js
@@ -7247,7 +7247,7 @@ LGraphNode.prototype.executeAction = function(action)
         //create links
         for (var i = 0; i < clipboard_info.links.length; ++i) {
             var link_info = clipboard_info.links[i];
-            var origin_node;
+            var origin_node = undefined;
             var origin_node_relative_id = link_info[0];
             if (origin_node_relative_id != null) {
                 origin_node = nodes[origin_node_relative_id];
diff --git a/web/lib/materialdesignicons.min.css b/web/lib/materialdesignicons.min.css
new file mode 100644
index 00000000..459ce9ea
--- /dev/null
+++ b/web/lib/materialdesignicons.min.css
@@ -0,0 +1,3 @@
+@font-face{font-family:"Material Design Icons";src:url("../fonts/materialdesignicons-webfont.eot?v=7.4.47");src:url("../fonts/materialdesignicons-webfont.eot?#iefix&v=7.4.47") format("embedded-opentype"),url("../fonts/materialdesignicons-webfont.woff2?v=7.4.47") format("woff2"),url("../fonts/materialdesignicons-webfont.woff?v=7.4.47") format("woff"),url("../fonts/materialdesignicons-webfont.ttf?v=7.4.47") format("truetype");font-weight:normal;font-style:normal}.mdi:before,.mdi-set{display:inline-block;font:normal normal normal 24px/1 "Material Design 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::before{content:"\F02E5"}.mdi-pot-mix::before{content:"\F065B"}.mdi-pot-mix-outline::before{content:"\F0677"}.mdi-pot-outline::before{content:"\F02FF"}.mdi-pot-steam::before{content:"\F065A"}.mdi-pot-steam-outline::before{content:"\F0326"}.mdi-pound::before{content:"\F0423"}.mdi-pound-box::before{content:"\F0424"}.mdi-pound-box-outline::before{content:"\F117F"}.mdi-power::before{content:"\F0425"}.mdi-power-cycle::before{content:"\F0901"}.mdi-power-off::before{content:"\F0902"}.mdi-power-on::before{content:"\F0903"}.mdi-power-plug::before{content:"\F06A5"}.mdi-power-plug-battery::before{content:"\F1C3B"}.mdi-power-plug-battery-outline::before{content:"\F1C3C"}.mdi-power-plug-off::before{content:"\F06A6"}.mdi-power-plug-off-outline::before{content:"\F1424"}.mdi-power-plug-outline::before{content:"\F1425"}.mdi-power-settings::before{content:"\F0426"}.mdi-power-sleep::before{content:"\F0904"}.mdi-power-socket::before{content:"\F0427"}.mdi-power-socket-au::before{content:"\F0905"}.mdi-power-socket-ch::before{content:"\F0FB3"}.mdi-power-socket-de::before{content:"\F1107"}.mdi-power-socket-eu::before{content:"\F07E7"}.mdi-power-socket-fr::before{content:"\F1108"}.mdi-power-socket-it::before{content:"\F14FF"}.mdi-power-socket-jp::before{content:"\F1109"}.mdi-power-socket-uk::before{content:"\F07E8"}.mdi-power-socket-us::before{content:"\F07E9"}.mdi-power-standby::before{content:"\F0906"}.mdi-powershell::before{content:"\F0A0A"}.mdi-prescription::before{content:"\F0706"}.mdi-presentation::before{content:"\F0428"}.mdi-presentation-play::before{content:"\F0429"}.mdi-pretzel::before{content:"\F1562"}.mdi-printer::before{content:"\F042A"}.mdi-printer-3d::before{content:"\F042B"}.mdi-printer-3d-nozzle::before{content:"\F0E5B"}.mdi-printer-3d-nozzle-alert::before{content:"\F11C0"}.mdi-printer-3d-nozzle-alert-outline::before{content:"\F11C1"}.mdi-printer-3d-nozzle-heat::before{content:"\F18B8"}.mdi-printer-3d-nozzle-heat-outline::before{content:"\F18B9"}.mdi-printer-3d-nozzle-off::before{content:"\F1B19"}.mdi-printer-3d-nozzle-off-outline::before{content:"\F1B1A"}.mdi-printer-3d-nozzle-outline::before{content:"\F0E5C"}.mdi-printer-3d-off::before{content:"\F1B0E"}.mdi-printer-alert::before{content:"\F042C"}.mdi-printer-check::before{content:"\F1146"}.mdi-printer-eye::before{content:"\F1458"}.mdi-printer-off::before{content:"\F0E5D"}.mdi-printer-off-outline::before{content:"\F1785"}.mdi-printer-outline::before{content:"\F1786"}.mdi-printer-pos::before{content:"\F1057"}.mdi-printer-pos-alert::before{content:"\F1BBC"}.mdi-printer-pos-alert-outline::before{content:"\F1BBD"}.mdi-printer-pos-cancel::before{content:"\F1BBE"}.mdi-printer-pos-cancel-outline::before{content:"\F1BBF"}.mdi-printer-pos-check::before{content:"\F1BC0"}.mdi-printer-pos-check-outline::before{content:"\F1BC1"}.mdi-printer-pos-cog::before{content:"\F1BC2"}.mdi-printer-pos-cog-outline::before{content:"\F1BC3"}.mdi-printer-pos-edit::before{content:"\F1BC4"}.mdi-printer-pos-edit-outline::before{content:"\F1BC5"}.mdi-printer-pos-minus::before{content:"\F1BC6"}.mdi-printer-pos-minus-outline::before{content:"\F1BC7"}.mdi-printer-pos-network::before{content:"\F1BC8"}.mdi-printer-pos-network-outline::before{content:"\F1BC9"}.mdi-printer-pos-off::before{content:"\F1BCA"}.mdi-printer-pos-off-outline::before{content:"\F1BCB"}.mdi-printer-pos-outline::before{content:"\F1BCC"}.mdi-printer-pos-pause::before{content:"\F1BCD"}.mdi-printer-pos-pause-outline::before{content:"\F1BCE"}.mdi-printer-pos-play::before{content:"\F1BCF"}.mdi-printer-pos-play-outline::before{content:"\F1BD0"}.mdi-printer-pos-plus::before{content:"\F1BD1"}.mdi-printer-pos-plus-outline::before{content:"\F1BD2"}.mdi-printer-pos-refresh::before{content:"\F1BD3"}.mdi-printer-pos-refresh-outline::before{content:"\F1BD4"}.mdi-printer-pos-remove::before{content:"\F1BD5"}.mdi-printer-pos-remove-outline::before{content:"\F1BD6"}.mdi-printer-pos-star::before{content:"\F1BD7"}.mdi-printer-pos-star-outline::before{content:"\F1BD8"}.mdi-printer-pos-stop::before{content:"\F1BD9"}.mdi-printer-pos-stop-outline::before{content:"\F1BDA"}.mdi-printer-pos-sync::before{content:"\F1BDB"}.mdi-printer-pos-sync-outline::before{content:"\F1BDC"}.mdi-printer-pos-wrench::before{content:"\F1BDD"}.mdi-printer-pos-wrench-outline::before{content:"\F1BDE"}.mdi-printer-search::before{content:"\F1457"}.mdi-printer-settings::before{content:"\F0707"}.mdi-printer-wireless::before{content:"\F0A0B"}.mdi-priority-high::before{content:"\F0603"}.mdi-priority-low::before{content:"\F0604"}.mdi-professional-hexagon::before{content:"\F042D"}.mdi-progress-alert::before{content:"\F0CBC"}.mdi-progress-check::before{content:"\F0995"}.mdi-progress-clock::before{content:"\F0996"}.mdi-progress-close::before{content:"\F110A"}.mdi-progress-download::before{content:"\F0997"}.mdi-progress-helper::before{content:"\F1BA2"}.mdi-progress-pencil::before{content:"\F1787"}.mdi-progress-question::before{content:"\F1522"}.mdi-progress-star::before{content:"\F1788"}.md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di-wifi-remove::before{content:"\F16C5"}.mdi-wifi-settings::before{content:"\F16C6"}.mdi-wifi-star::before{content:"\F0E0B"}.mdi-wifi-strength-1::before{content:"\F091F"}.mdi-wifi-strength-1-alert::before{content:"\F0920"}.mdi-wifi-strength-1-lock::before{content:"\F0921"}.mdi-wifi-strength-1-lock-open::before{content:"\F16CB"}.mdi-wifi-strength-2::before{content:"\F0922"}.mdi-wifi-strength-2-alert::before{content:"\F0923"}.mdi-wifi-strength-2-lock::before{content:"\F0924"}.mdi-wifi-strength-2-lock-open::before{content:"\F16CC"}.mdi-wifi-strength-3::before{content:"\F0925"}.mdi-wifi-strength-3-alert::before{content:"\F0926"}.mdi-wifi-strength-3-lock::before{content:"\F0927"}.mdi-wifi-strength-3-lock-open::before{content:"\F16CD"}.mdi-wifi-strength-4::before{content:"\F0928"}.mdi-wifi-strength-4-alert::before{content:"\F0929"}.mdi-wifi-strength-4-lock::before{content:"\F092A"}.mdi-wifi-strength-4-lock-open::before{content:"\F16CE"}.mdi-wifi-strength-alert-outline::before{content:"\F092B"}.mdi-wifi-strength-lock-open-outline::before{content:"\F16CF"}.mdi-wifi-strength-lock-outline::before{content:"\F092C"}.mdi-wifi-strength-off::before{content:"\F092D"}.mdi-wifi-strength-off-outline::before{content:"\F092E"}.mdi-wifi-strength-outline::before{content:"\F092F"}.mdi-wifi-sync::before{content:"\F16C7"}.mdi-wikipedia::before{content:"\F05AC"}.mdi-wind-power::before{content:"\F1A88"}.mdi-wind-power-outline::before{content:"\F1A89"}.mdi-wind-turbine::before{content:"\F0DA5"}.mdi-wind-turbine-alert::before{content:"\F19AB"}.mdi-wind-turbine-check::before{content:"\F19AC"}.mdi-window-close::before{content:"\F05AD"}.mdi-window-closed::before{content:"\F05AE"}.mdi-window-closed-variant::before{content:"\F11DB"}.mdi-window-maximize::before{content:"\F05AF"}.mdi-window-minimize::before{content:"\F05B0"}.mdi-window-open::before{content:"\F05B1"}.mdi-window-open-variant::before{content:"\F11DC"}.mdi-window-restore::before{content:"\F05B2"}.mdi-window-shutter::before{content:"\F111C"}.mdi-window-shutter-alert::before{content:"\F111D"}.mdi-window-shutter-auto::before{content:"\F1BA3"}.mdi-window-shutter-cog::before{content:"\F1A8A"}.mdi-window-shutter-open::before{content:"\F111E"}.mdi-window-shutter-settings::before{content:"\F1A8B"}.mdi-windsock::before{content:"\F15FA"}.mdi-wiper::before{content:"\F0AE9"}.mdi-wiper-wash::before{content:"\F0DA6"}.mdi-wiper-wash-alert::before{content:"\F18DF"}.mdi-wizard-hat::before{content:"\F1477"}.mdi-wordpress::before{content:"\F05B4"}.mdi-wrap::before{content:"\F05B6"}.mdi-wrap-disabled::before{content:"\F0BDF"}.mdi-wrench::before{content:"\F05B7"}.mdi-wrench-check::before{content:"\F1B8F"}.mdi-wrench-check-outline::before{content:"\F1B90"}.mdi-wrench-clock::before{content:"\F19A3"}.mdi-wrench-clock-outline::before{content:"\F1B93"}.mdi-wrench-cog::before{content:"\F1B91"}.mdi-wrench-cog-outline::before{content:"\F1B92"}.mdi-wrench-outline::before{content:"\F0BE0"}.mdi-xamarin::before{content:"\F0845"}.mdi-xml::before{content:"\F05C0"}.mdi-xmpp::before{content:"\F07FF"}.mdi-yahoo::before{content:"\F0B4F"}.mdi-yeast::before{content:"\F05C1"}.mdi-yin-yang::before{content:"\F0680"}.mdi-yoga::before{content:"\F117C"}.mdi-youtube::before{content:"\F05C3"}.mdi-youtube-gaming::before{content:"\F0848"}.mdi-youtube-studio::before{content:"\F0847"}.mdi-youtube-subscription::before{content:"\F0D40"}.mdi-youtube-tv::before{content:"\F0448"}.mdi-yurt::before{content:"\F1516"}.mdi-z-wave::before{content:"\F0AEA"}.mdi-zend::before{content:"\F0AEB"}.mdi-zigbee::before{content:"\F0D41"}.mdi-zip-box::before{content:"\F05C4"}.mdi-zip-box-outline::before{content:"\F0FFA"}.mdi-zip-disk::before{content:"\F0A23"}.mdi-zodiac-aquarius::before{content:"\F0A7D"}.mdi-zodiac-aries::before{content:"\F0A7E"}.mdi-zodiac-cancer::before{content:"\F0A7F"}.mdi-zodiac-capricorn::before{content:"\F0A80"}.mdi-zodiac-gemini::before{content:"\F0A81"}.mdi-zodiac-leo::before{content:"\F0A82"}.mdi-zodiac-libra::before{content:"\F0A83"}.mdi-zodiac-pisces::before{content:"\F0A84"}.mdi-zodiac-sagittarius::before{content:"\F0A85"}.mdi-zodiac-scorpio::before{content:"\F0A86"}.mdi-zodiac-taurus::before{content:"\F0A87"}.mdi-zodiac-virgo::before{content:"\F0A88"}.mdi-blank::before{content:"\F68C";visibility:hidden}.mdi-18px.mdi-set,.mdi-18px.mdi:before{font-size:18px}.mdi-24px.mdi-set,.mdi-24px.mdi:before{font-size:24px}.mdi-36px.mdi-set,.mdi-36px.mdi:before{font-size:36px}.mdi-48px.mdi-set,.mdi-48px.mdi:before{font-size:48px}.mdi-dark:before{color:rgba(0,0,0,0.54)}.mdi-dark.mdi-inactive:before{color:rgba(0,0,0,0.26)}.mdi-light:before{color:#fff}.mdi-light.mdi-inactive:before{color:rgba(255,255,255,0.3)}.mdi-rotate-45:before{-webkit-transform:rotate(45deg);-ms-transform:rotate(45deg);transform:rotate(45deg)}.mdi-rotate-90:before{-webkit-transform:rotate(90deg);-ms-transform:rotate(90deg);transform:rotate(90deg)}.mdi-rotate-135:before{-webkit-transform:rotate(135deg);-ms-transform:rotate(135deg);transform:rotate(135deg)}.mdi-rotate-180:before{-webkit-transform:rotate(180deg);-ms-transform:rotate(180deg);transform:rotate(180deg)}.mdi-rotate-225:before{-webkit-transform:rotate(225deg);-ms-transform:rotate(225deg);transform:rotate(225deg)}.mdi-rotate-270:before{-webkit-transform:rotate(270deg);-ms-transform:rotate(270deg);transform:rotate(270deg)}.mdi-rotate-315:before{-webkit-transform:rotate(315deg);-ms-transform:rotate(315deg);transform:rotate(315deg)}.mdi-flip-h:before{-webkit-transform:scaleX(-1);transform:scaleX(-1);filter:FlipH;-ms-filter:"FlipH"}.mdi-flip-v:before{-webkit-transform:scaleY(-1);transform:scaleY(-1);filter:FlipV;-ms-filter:"FlipV"}.mdi-spin:before{-webkit-animation:mdi-spin 2s infinite linear;animation:mdi-spin 2s infinite linear}@-webkit-keyframes mdi-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}100%{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}@keyframes mdi-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}100%{-webkit-transform:rotate(359deg);transform:rotate(359deg)}}
+
+/*# sourceMappingURL=materialdesignicons.css.map */
\ No newline at end of file
diff --git a/web/scripts/api.js b/web/scripts/api.js
index 8c8155be..39f0a9bb 100644
--- a/web/scripts/api.js
+++ b/web/scripts/api.js
@@ -327,7 +327,7 @@ class ComfyApi extends EventTarget {
 
 	/**
 	 * Gets user configuration data and where data should be stored
-	 * @returns { Promise<{ storage: "server" | "browser", users?: Promise<string, unknown>, migrated?: boolean }> } 
+	 * @returns { Promise<{ storage: "server" | "browser", users?: Promise<string, unknown>, migrated?: boolean }> }
 	 */
 	async getUserConfig() {
 		return (await this.fetchApi("/users")).json();
@@ -335,7 +335,7 @@ class ComfyApi extends EventTarget {
 
 	/**
 	 * Creates a new user
-	 * @param { string } username 
+	 * @param { string } username
 	 * @returns The fetch response
 	 */
 	createUser(username) {
@@ -394,7 +394,7 @@ class ComfyApi extends EventTarget {
 	 * Gets a user data file for the current user
 	 * @param { string } file The name of the userdata file to load
 	 * @param { RequestInit } [options]
-	 * @returns { Promise<unknown> } The fetch response object
+	 * @returns { Promise<Response> } The fetch response object
 	 */
 	async getUserData(file, options) {
 		return this.fetchApi(`/userdata/${encodeURIComponent(file)}`, options);
@@ -404,18 +404,75 @@ class ComfyApi extends EventTarget {
 	 * Stores a user data file for the current user
 	 * @param { string } file The name of the userdata file to save
 	 * @param { unknown } data The data to save to the file
-	 * @param { RequestInit & { stringify?: boolean, throwOnError?: boolean } } [options]
-	 * @returns { Promise<void> }
+	 * @param { RequestInit & { overwrite?: boolean, stringify?: boolean, throwOnError?: boolean } } [options]
+	 * @returns { Promise<Response> }
 	 */
-	async storeUserData(file, data, options = { stringify: true, throwOnError: true }) {
-		const resp = await this.fetchApi(`/userdata/${encodeURIComponent(file)}`, {
+	async storeUserData(file, data, options = { overwrite: true, stringify: true, throwOnError: true }) {
+		const resp = await this.fetchApi(`/userdata/${encodeURIComponent(file)}?overwrite=${options?.overwrite}`, {
 			method: "POST",
 			body: options?.stringify ? JSON.stringify(data) : data,
 			...options,
-		});	
-		if (resp.status !== 200) {
+		});
+		if (resp.status !== 200 && options?.throwOnError !== false) {
 			throw new Error(`Error storing user data file '${file}': ${resp.status} ${(await resp).statusText}`);
 		}
+		return resp;
+	}
+
+	/**
+	 * Deletes a user data file for the current user
+	 * @param { string } file The name of the userdata file to delete
+	 */
+	async deleteUserData(file) {
+		const resp = await this.fetchApi(`/userdata/${encodeURIComponent(file)}`, {
+			method: "DELETE",
+		});
+		if (resp.status !== 204) {
+			throw new Error(`Error removing user data file '${file}': ${resp.status} ${(resp).statusText}`);
+		}
+	}
+
+	/**
+	 * Move a user data file for the current user
+	 * @param { string } source The userdata file to move
+	 * @param { string } dest The destination for the file
+	 */
+	async moveUserData(source, dest, options = { overwrite: false }) {
+		const resp = await this.fetchApi(`/userdata/${encodeURIComponent(source)}/move/${encodeURIComponent(dest)}?overwrite=${options?.overwrite}`, {
+			method: "POST",
+		});
+		return resp;
+	}
+
+	/**
+	 * @overload
+	 * Lists user data files for the current user
+	 * @param { string } dir The directory in which to list files
+	 * @param { boolean } [recurse] If the listing should be recursive
+	 * @param { true } [split] If the paths should be split based on the os path separator
+	 * @returns { Promise<string[][]>> } The list of split file paths in the format [fullPath, ...splitPath]
+	 */
+	/**
+	 * @overload
+	 * Lists user data files for the current user
+	 * @param { string } dir The directory in which to list files
+	 * @param { boolean } [recurse] If the listing should be recursive
+	 * @param { false | undefined } [split] If the paths should be split based on the os path separator
+	 * @returns { Promise<string[]>> } The list of files
+	 */
+	async listUserData(dir, recurse, split) {
+		const resp = await this.fetchApi(
+			`/userdata?${new URLSearchParams({
+				recurse,
+				dir,
+				split,
+			})}`
+		);
+		if (resp.status === 404) return [];
+		if (resp.status !== 200) {
+			throw new Error(`Error getting user data list '${dir}': ${resp.status} ${resp.statusText}`);
+		}
+		return resp.json();
 	}
 }
 
diff --git a/web/scripts/app.js b/web/scripts/app.js
index 77f29b8e..8b4478a3 100644
--- a/web/scripts/app.js
+++ b/web/scripts/app.js
@@ -3,11 +3,13 @@ import { ComfyWidgets, initWidgets } from "./widgets.js";
 import { ComfyUI, $el } from "./ui.js";
 import { api } from "./api.js";
 import { defaultGraph } from "./defaultGraph.js";
-import { getPngMetadata, getWebpMetadata, importA1111, getLatentMetadata } from "./pnginfo.js";
+import { getPngMetadata, getWebpMetadata, getFlacMetadata, importA1111, getLatentMetadata } from "./pnginfo.js";
 import { addDomClippingSetting } from "./domWidget.js";
-import { createImageHost, calculateImageGrid } from "./ui/imagePreview.js"
-
-export const ANIM_PREVIEW_WIDGET = "$$comfy_animation_preview"
+import { createImageHost, calculateImageGrid } from "./ui/imagePreview.js";
+import { ComfyAppMenu } from "./ui/menu/index.js";
+import { getStorageValue, setStorageValue } from "./utils.js";
+import { ComfyWorkflowManager } from "./workflows.js";
+export const ANIM_PREVIEW_WIDGET = "$$comfy_animation_preview";
 
 function sanitizeNodeName(string) {
 	let entityMap = {
@@ -52,6 +54,12 @@ export class ComfyApp {
 	constructor() {
 		this.ui = new ComfyUI(this);
 		this.logging = new ComfyLogging(this);
+		this.workflowManager = new ComfyWorkflowManager(this);
+		this.bodyTop = $el("div.comfyui-body-top", { parent: document.body });
+		this.bodyLeft = $el("div.comfyui-body-left", { parent: document.body });
+		this.bodyRight = $el("div.comfyui-body-right", { parent: document.body });
+		this.bodyBottom = $el("div.comfyui-body-bottom", { parent: document.body });		  
+		this.menu = new ComfyAppMenu(this);
 
 		/**
 		 * List of extensions that are registered with the app
@@ -63,7 +71,7 @@ export class ComfyApp {
 		 * Stores the execution output data for each node
 		 * @type {Record<string, any>}
 		 */
-		this.nodeOutputs = {};
+		this._nodeOutputs = {};
 
 		/**
 		 * Stores the preview image data for each node
@@ -78,6 +86,15 @@ export class ComfyApp {
 		this.shiftDown = false;
 	}
 
+	get nodeOutputs() {
+		return this._nodeOutputs;
+	}
+
+	set nodeOutputs(value) {
+		this._nodeOutputs = value;
+		this.#invokeExtensions("onNodeOutputsUpdated", value);
+	}
+
 	getPreviewFormatParam() {
 		let preview_format = this.ui.settings.getSettingValue("Comfy.PreviewFormat");
 		if(preview_format)
@@ -262,6 +279,36 @@ export class ComfyApp {
 			})
 		);
 	}
+	
+	#addRestoreWorkflowView() {
+		const serialize = LGraph.prototype.serialize;
+		const self = this;
+		LGraph.prototype.serialize = function() {
+			const workflow = serialize.apply(this, arguments);
+
+			// Store the drag & scale info in the serialized workflow if the setting is enabled
+			if (self.enableWorkflowViewRestore.value) {
+				if (!workflow.extra) {
+					workflow.extra = {};
+				}
+				workflow.extra.ds = {
+					scale: self.canvas.ds.scale,
+					offset: self.canvas.ds.offset,
+				};
+			} else if (workflow.extra?.ds) {
+				// Clear any old view data
+				delete workflow.extra.ds;
+			}
+
+			return workflow;
+		}
+		this.enableWorkflowViewRestore = this.ui.settings.addSetting({
+			id: "Comfy.EnableWorkflowViewRestore",
+			name: "Save and restore canvas position and zoom level in workflows",
+			type: "boolean",
+			defaultValue: true
+		});
+	}
 
 	/**
 	 * Adds special context menu handling for nodes
@@ -953,6 +1000,12 @@ export class ComfyApp {
 
 		const origProcessMouseDown = LGraphCanvas.prototype.processMouseDown;
 		LGraphCanvas.prototype.processMouseDown = function(e) {
+			// prepare for ctrl+shift drag: zoom start
+			if(e.ctrlKey && e.shiftKey && e.buttons) {
+				self.zoom_drag_start = [e.x, e.y, this.ds.scale];
+				return;
+			}
+
 			const res = origProcessMouseDown.apply(this, arguments);
 
 			this.selected_group_moving = false;
@@ -973,6 +1026,26 @@ export class ComfyApp {
 
 		const origProcessMouseMove = LGraphCanvas.prototype.processMouseMove;
 		LGraphCanvas.prototype.processMouseMove = function(e) {
+			// handle ctrl+shift drag
+			if(e.ctrlKey && e.shiftKey && self.zoom_drag_start) {
+				// stop canvas zoom action
+				if(!e.buttons) {
+					self.zoom_drag_start = null;
+					return;
+				}
+
+				// calculate delta
+				let deltaY = e.y - self.zoom_drag_start[1];
+				let startScale = self.zoom_drag_start[2];
+
+				let scale = startScale - deltaY/100;
+
+				this.ds.changeScale(scale, [this.ds.element.width/2, this.ds.element.height/2]);
+				this.graph.change();
+
+				return;
+			}
+
 			const orig_selected_group = this.selected_group;
 
 			if (this.selected_group && !this.selected_group_resizing && !this.selected_group_moving) {
@@ -1011,7 +1084,7 @@ export class ComfyApp {
 			if (e.type == "keydown" && !e.repeat) {
 
 				// Ctrl + M mute/unmute
-				if (e.key === 'm' && e.ctrlKey) {
+				if (e.key === 'm' && (e.metaKey || e.ctrlKey)) {
 					if (this.selected_nodes) {
 						for (var i in this.selected_nodes) {
 							if (this.selected_nodes[i].mode === 2) { // never
@@ -1025,7 +1098,7 @@ export class ComfyApp {
 				}
 
 				// Ctrl + B bypass
-				if (e.key === 'b' && e.ctrlKey) {
+				if (e.key === 'b' && (e.metaKey || e.ctrlKey)) {
 					if (this.selected_nodes) {
 						for (var i in this.selected_nodes) {
 							if (this.selected_nodes[i].mode === 4) { // never
@@ -1059,6 +1132,20 @@ export class ComfyApp {
 					// Trigger onPaste
 					return true;
 				}
+
+				if((e.key === '+') && e.altKey) {
+					block_default = true;
+					let scale = this.ds.scale * 1.1;
+					this.ds.changeScale(scale, [this.ds.element.width/2, this.ds.element.height/2]);
+					this.graph.change();
+				}
+
+				if((e.key === '-') && e.altKey) {
+					block_default = true;
+					let scale = this.ds.scale * 1 / 1.1;
+					this.ds.changeScale(scale, [this.ds.element.width/2, this.ds.element.height/2]);
+					this.graph.change();
+				}
 			}
 
 			this.graph.change();
@@ -1243,11 +1330,15 @@ export class ComfyApp {
 		});
 
 		api.addEventListener("progress", ({ detail }) => {
+			if (this.workflowManager.activePrompt?.workflow 
+				&& this.workflowManager.activePrompt.workflow !== this.workflowManager.activeWorkflow) return;
 			this.progress = detail;
 			this.graph.setDirtyCanvas(true, false);
 		});
 
 		api.addEventListener("executing", ({ detail }) => {
+			if (this.workflowManager.activePrompt ?.workflow
+				&& this.workflowManager.activePrompt.workflow !== this.workflowManager.activeWorkflow) return;
 			this.progress = null;
 			this.runningNodeId = detail;
 			this.graph.setDirtyCanvas(true, false);
@@ -1255,6 +1346,8 @@ export class ComfyApp {
 		});
 
 		api.addEventListener("executed", ({ detail }) => {
+			if (this.workflowManager.activePrompt ?.workflow
+				&& this.workflowManager.activePrompt.workflow !== this.workflowManager.activeWorkflow) return;
 			const output = this.nodeOutputs[detail.node];
 			if (detail.merge && output) {
 				for (const k in detail.output ?? {}) {
@@ -1363,6 +1456,11 @@ export class ComfyApp {
 	    });
 	
 	    await Promise.all(extensionPromises);
+		try {
+			this.menu.workflows.registerExtension(this);
+		} catch (error) {
+			console.error(error);
+		}
 	}
 
 	async #migrateSettings() {
@@ -1450,27 +1548,30 @@ export class ComfyApp {
 	 */
 	async setup() {
 		await this.#setUser();
-		await this.ui.settings.load();
-		await this.#loadExtensions();
 
 		// Create and mount the LiteGraph in the DOM
 		const mainCanvas = document.createElement("canvas")
 		mainCanvas.style.touchAction = "none"
 		const canvasEl = (this.canvasEl = Object.assign(mainCanvas, { id: "graph-canvas" }));
 		canvasEl.tabIndex = "1";
-		document.body.prepend(canvasEl);
+		document.body.append(canvasEl);
+		this.resizeCanvas();
+
+		await Promise.all([this.workflowManager.loadWorkflows(), this.ui.settings.load()]);
+		await this.#loadExtensions();
 
 		addDomClippingSetting();
 		this.#addProcessMouseHandler();
 		this.#addProcessKeyHandler();
 		this.#addConfigureHandler();
 		this.#addApiUpdateHandlers();
+		this.#addRestoreWorkflowView();
 
 		this.graph = new LGraph();
 
 		this.#addAfterConfigureHandler();
 
-		const canvas = (this.canvas = new LGraphCanvas(canvasEl, this.graph));
+		this.canvas = new LGraphCanvas(canvasEl, this.graph);
 		this.ctx = canvasEl.getContext("2d");
 
 		LiteGraph.release_link_on_empty_shows_menu = true;
@@ -1478,19 +1579,14 @@ export class ComfyApp {
 
 		this.graph.start();
 
-		function resizeCanvas() {
-			// Limit minimal scale to 1, see https://github.com/comfyanonymous/ComfyUI/pull/845
-			const scale = Math.max(window.devicePixelRatio, 1);
-			const { width, height } = canvasEl.getBoundingClientRect();
-			canvasEl.width = Math.round(width * scale);
-			canvasEl.height = Math.round(height * scale);
-			canvasEl.getContext("2d").scale(scale, scale);
-			canvas.draw(true, true);
-		}
-
 		// Ensure the canvas fills the window
-		resizeCanvas();
-		window.addEventListener("resize", resizeCanvas);
+		this.resizeCanvas();
+		window.addEventListener("resize", () => this.resizeCanvas());
+		const ro = new ResizeObserver(() => this.resizeCanvas());
+		ro.observe(this.bodyTop);
+		ro.observe(this.bodyLeft);
+		ro.observe(this.bodyRight);
+		ro.observe(this.bodyBottom);
 
 		await this.#invokeExtensionsAsync("init");
 		await this.registerNodes();
@@ -1502,7 +1598,8 @@ export class ComfyApp {
 			const loadWorkflow = async (json) => {
 				if (json) {
 					const workflow = JSON.parse(json);
-					await this.loadGraphData(workflow);
+					const workflowName = getStorageValue("Comfy.PreviousWorkflow");
+					await this.loadGraphData(workflow, true, true, workflowName);
 					return true;
 				}
 			};
@@ -1538,6 +1635,19 @@ export class ComfyApp {
 		await this.#invokeExtensionsAsync("setup");
 	}
 
+	resizeCanvas() {
+		// Limit minimal scale to 1, see https://github.com/comfyanonymous/ComfyUI/pull/845
+		const scale = Math.max(window.devicePixelRatio, 1);
+		
+		// Clear fixed width and height while calculating rect so it uses 100% instead
+		this.canvasEl.height = this.canvasEl.width = "";
+		const { width, height } = this.canvasEl.getBoundingClientRect();
+		this.canvasEl.width = Math.round(width * scale);
+		this.canvasEl.height = Math.round(height * scale);
+		this.canvasEl.getContext("2d").scale(scale, scale);
+		this.canvas?.draw(true, true);
+	}
+
 	/**
 	 * Registers nodes with the graph
 	 */
@@ -1724,12 +1834,29 @@ export class ComfyApp {
 		});
 	}
 
+	async changeWorkflow(callback, workflow = null) {
+		try {
+			this.workflowManager.activeWorkflow?.changeTracker?.store()
+		} catch (error) {
+			console.error(error);
+		}
+		await callback();
+		try {
+			this.workflowManager.setWorkflow(workflow);
+			this.workflowManager.activeWorkflow?.track()
+		} catch (error) {
+			console.error(error);
+		}
+	}
+
 	/**
 	 * Populates the graph with the specified workflow data
 	 * @param {*} graphData A serialized graph object
 	 * @param { boolean } clean If the graph state, e.g. images, should be cleared
+	 * @param { boolean } restore_view If the graph position should be restored
+	 * @param { import("./workflows.js").ComfyWorkflowInstance | null } workflow The workflow
 	 */
-	async loadGraphData(graphData, clean = true) {
+	async loadGraphData(graphData, clean = true, restore_view = true, workflow = null) {
 		if (clean !== false) {
 			this.clean();
 		}
@@ -1747,6 +1874,12 @@ export class ComfyApp {
 		{
 			graphData = structuredClone(graphData);
 		}
+	
+		try {
+			this.workflowManager.setWorkflow(workflow);
+		} catch (error) {
+			console.error(error);
+		}
 
 		const missingNodeTypes = [];
 		await this.#invokeExtensionsAsync("beforeConfigureGraph", graphData, missingNodeTypes);
@@ -1765,6 +1898,15 @@ export class ComfyApp {
 
 		try {
 			this.graph.configure(graphData);
+			if (restore_view && this.enableWorkflowViewRestore.value && graphData.extra?.ds) {
+				this.canvas.ds.offset = graphData.extra.ds.offset;
+				this.canvas.ds.scale = graphData.extra.ds.scale;
+			}
+			
+			try {
+				this.workflowManager.activeWorkflow?.track()
+			} catch (error) {
+			}
 		} catch (error) {
 			let errorHint = [];
 			// Try extracting filename to see if it was caused by an extension script
@@ -1824,6 +1966,14 @@ export class ComfyApp {
 							if (widget.value.startsWith("sample_")) {
 								widget.value = widget.value.slice(7);
 							}
+							if (widget.value === "euler_pp" || widget.value === "euler_ancestral_pp") {
+								widget.value = widget.value.slice(0, -3);
+								for (let w of node.widgets) {
+									if (w.name == "cfg") {
+										w.value *= 2.0;
+									}
+								}
+							}
 						}
 					}
 					if (node.type == "KSampler" || node.type == "KSamplerAdvanced" || node.type == "PrimitiveNode") {
@@ -1852,14 +2002,17 @@ export class ComfyApp {
 			this.showMissingNodesError(missingNodeTypes);
 		}
 		await this.#invokeExtensionsAsync("afterConfigureGraph", missingNodeTypes);
+		requestAnimationFrame(() => {
+			this.graph.setDirtyCanvas(true, true);
+		});
 	}
 
 	/**
 	 * Converts the current graph workflow for sending to the API
 	 * @returns The workflow and node links
 	 */
-	async graphToPrompt() {
-		for (const outerNode of this.graph.computeExecutionOrder(false)) {
+	async graphToPrompt(graph = this.graph, clean = true) {
+		for (const outerNode of graph.computeExecutionOrder(false)) {
 			if (outerNode.widgets) {
 				for (const widget of outerNode.widgets) {
 					// Allow widgets to run callbacks before a prompt has been queued
@@ -1879,10 +2032,10 @@ export class ComfyApp {
 			}
 		}
 
-		const workflow = this.graph.serialize();
+		const workflow = graph.serialize();
 		const output = {};
 		// Process nodes in order of execution
-		for (const outerNode of this.graph.computeExecutionOrder(false)) {
+		for (const outerNode of graph.computeExecutionOrder(false)) {
 			const skipNode = outerNode.mode === 2 || outerNode.mode === 4;
 			const innerNodes = (!skipNode && outerNode.getInnerNodes) ? outerNode.getInnerNodes() : [outerNode];
 			for (const node of innerNodes) {
@@ -1974,13 +2127,14 @@ export class ComfyApp {
 		}
 
 		// Remove inputs connected to removed nodes
-
-		for (const o in output) {
-			for (const i in output[o].inputs) {
-				if (Array.isArray(output[o].inputs[i])
-					&& output[o].inputs[i].length === 2
-					&& !output[output[o].inputs[i][0]]) {
-					delete output[o].inputs[i];
+		if(clean) {
+			for (const o in output) {
+				for (const i in output[o].inputs) {
+					if (Array.isArray(output[o].inputs[i])
+						&& output[o].inputs[i].length === 2
+						&& !output[output[o].inputs[i][0]]) {
+						delete output[o].inputs[i];
+					}
 				}
 			}
 		}
@@ -2048,6 +2202,14 @@ export class ComfyApp {
 						this.lastNodeErrors = res.node_errors;
 						if (this.lastNodeErrors.length > 0) {
 							this.canvas.draw(true, true);
+						} else {
+							try {
+								this.workflowManager.storePrompt({
+									id: res.prompt_id,
+									nodes: Object.keys(p.output)
+								});
+							} catch (error) {
+							}
 						}
 					} catch (error) {
 						const formattedError = this.#formatPromptError(error)
@@ -2080,6 +2242,15 @@ export class ComfyApp {
 			this.#processingQueue = false;
 		}
 		api.dispatchEvent(new CustomEvent("promptQueued", { detail: { number, batchCount } }));
+		return !this.lastNodeErrors;
+	}
+
+	showErrorOnFileLoad(file) {
+		this.ui.dialog.show(
+			$el("div", [
+				$el("p", {textContent: `Unable to find workflow in ${file.name}`})
+			]).outerHTML
+		);
 	}
 
 	/**
@@ -2087,29 +2258,52 @@ export class ComfyApp {
 	 * @param {File} file
 	 */
 	async handleFile(file) {
+		const removeExt = f => {
+			if(!f) return f;
+			const p = f.lastIndexOf(".");
+			if(p === -1) return f;
+			return f.substring(0, p);
+		};
+
+		const fileName = removeExt(file.name);
 		if (file.type === "image/png") {
 			const pngInfo = await getPngMetadata(file);
-			if (pngInfo) {
-				if (pngInfo.workflow) {
-					await this.loadGraphData(JSON.parse(pngInfo.workflow));
-				} else if (pngInfo.prompt) {
-					this.loadApiJson(JSON.parse(pngInfo.prompt));
-				} else if (pngInfo.parameters) {
+			if (pngInfo?.workflow) {
+				await this.loadGraphData(JSON.parse(pngInfo.workflow), true, true, fileName);
+			} else if (pngInfo?.prompt) {
+				this.loadApiJson(JSON.parse(pngInfo.prompt), fileName);
+			} else if (pngInfo?.parameters) {
+				this.changeWorkflow(() => {
 					importA1111(this.graph, pngInfo.parameters);
-				}
+				}, fileName)
+			} else {
+				this.showErrorOnFileLoad(file);
 			}
 		} else if (file.type === "image/webp") {
 			const pngInfo = await getWebpMetadata(file);
-			if (pngInfo) {
-				if (pngInfo.workflow) {
-					this.loadGraphData(JSON.parse(pngInfo.workflow));
-				} else if (pngInfo.Workflow) {
-					this.loadGraphData(JSON.parse(pngInfo.Workflow)); // Support loading workflows from that webp custom node.
-				} else if (pngInfo.prompt) {
-					this.loadApiJson(JSON.parse(pngInfo.prompt));
-				} else if (pngInfo.Prompt) {
-					this.loadApiJson(JSON.parse(pngInfo.Prompt)); // Support loading prompts from that webp custom node.
-				}
+			// Support loading workflows from that webp custom node.
+			const workflow = pngInfo?.workflow || pngInfo?.Workflow;
+			const prompt = pngInfo?.prompt || pngInfo?.Prompt;
+
+			if (workflow) {
+				this.loadGraphData(JSON.parse(workflow), true, true, fileName);
+			} else if (prompt) {
+				this.loadApiJson(JSON.parse(prompt), fileName);
+			} else {
+				this.showErrorOnFileLoad(file);
+			}
+		} else if (file.type === "audio/flac" || file.type === "audio/x-flac") {
+			const pngInfo = await getFlacMetadata(file);
+			// Support loading workflows from that webp custom node.
+			const workflow = pngInfo?.workflow;
+			const prompt = pngInfo?.prompt;
+
+			if (workflow) {
+				this.loadGraphData(JSON.parse(workflow), true, true, fileName);
+			} else if (prompt) {
+				this.loadApiJson(JSON.parse(prompt), fileName);
+			} else {
+				this.showErrorOnFileLoad(file);
 			}
 		} else if (file.type === "application/json" || file.name?.endsWith(".json")) {
 			const reader = new FileReader();
@@ -2118,19 +2312,23 @@ export class ComfyApp {
 				if (jsonContent?.templates) {
 					this.loadTemplateData(jsonContent);
 				} else if(this.isApiJson(jsonContent)) {
-					this.loadApiJson(jsonContent);
+					this.loadApiJson(jsonContent, fileName);
 				} else {
-					await this.loadGraphData(jsonContent);
+					await this.loadGraphData(jsonContent, true, true, fileName);
 				}
 			};
 			reader.readAsText(file);
 		} else if (file.name?.endsWith(".latent") || file.name?.endsWith(".safetensors")) {
 			const info = await getLatentMetadata(file);
 			if (info.workflow) {
-				await this.loadGraphData(JSON.parse(info.workflow));
+				await this.loadGraphData(JSON.parse(info.workflow), true, true, fileName);
 			} else if (info.prompt) {
 				this.loadApiJson(JSON.parse(info.prompt));
+			} else {
+				this.showErrorOnFileLoad(file);
 			}
+		} else {
+			this.showErrorOnFileLoad(file);
 		}
 	}
 
@@ -2138,7 +2336,7 @@ export class ComfyApp {
 		return Object.values(data).every((v) => v.class_type);
 	}
 
-	loadApiJson(apiData) {
+	loadApiJson(apiData, fileName) {
 		const missingNodeTypes = Object.values(apiData).filter((n) => !LiteGraph.registered_node_types[n.class_type]);
 		if (missingNodeTypes.length) {
 			this.showMissingNodesError(missingNodeTypes.map(t => t.class_type), false);
@@ -2151,41 +2349,43 @@ export class ComfyApp {
 			const data = apiData[id];
 			const node = LiteGraph.createNode(data.class_type);
 			node.id = isNaN(+id) ? id : +id;
-			graph.add(node);
+			node.title = data._meta?.title ?? node.title
+			app.graph.add(node);
 		}
 
-		for (const id of ids) {
-			const data = apiData[id];
-			const node = app.graph.getNodeById(id);
-			for (const input in data.inputs ?? {}) {
-				const value = data.inputs[input];
-				if (value instanceof Array) {
-					const [fromId, fromSlot] = value;
-					const fromNode = app.graph.getNodeById(fromId);
-					let toSlot = node.inputs?.findIndex((inp) => inp.name === input);
-					if (toSlot == null || toSlot === -1) {
-						try {
-							// Target has no matching input, most likely a converted widget
-							const widget = node.widgets?.find((w) => w.name === input);
-							if (widget && node.convertWidgetToInput?.(widget)) {
-								toSlot = node.inputs?.length - 1;
-							}
-						} catch (error) {}
-					}
-					if (toSlot != null || toSlot !== -1) {
-						fromNode.connect(fromSlot, node, toSlot);
-					}
-				} else {
-					const widget = node.widgets?.find((w) => w.name === input);
-					if (widget) {
-						widget.value = value;
-						widget.callback?.(value);
+		this.changeWorkflow(() => {
+			for (const id of ids) {
+				const data = apiData[id];
+				const node = app.graph.getNodeById(id);
+				for (const input in data.inputs ?? {}) {
+					const value = data.inputs[input];
+					if (value instanceof Array) {
+						const [fromId, fromSlot] = value;
+						const fromNode = app.graph.getNodeById(fromId);
+						let toSlot = node.inputs?.findIndex((inp) => inp.name === input);
+						if (toSlot == null || toSlot === -1) {
+							try {
+								// Target has no matching input, most likely a converted widget
+								const widget = node.widgets?.find((w) => w.name === input);
+								if (widget && node.convertWidgetToInput?.(widget)) {
+									toSlot = node.inputs?.length - 1;
+								}
+							} catch (error) {}
+						}
+						if (toSlot != null || toSlot !== -1) {
+							fromNode.connect(fromSlot, node, toSlot);
+						}
+					} else {
+						const widget = node.widgets?.find((w) => w.name === input);
+						if (widget) {
+							widget.value = value;
+							widget.callback?.(value);
+						}
 					}
 				}
 			}
-		}
-
-		app.graph.arrange();
+			app.graph.arrange();
+		}, fileName);
 	}
 
 	/**
@@ -2238,6 +2438,12 @@ export class ComfyApp {
 		await this.#invokeExtensionsAsync("refreshComboInNodes", defs);
 	}
 
+	resetView() {
+		app.canvas.ds.scale = 1;
+		app.canvas.ds.offset = [0, 0]
+		app.graph.setDirtyCanvas(true, true);
+	}
+
 	/**
 	 * Clean current state
 	 */
diff --git a/web/scripts/changeTracker.js b/web/scripts/changeTracker.js
new file mode 100644
index 00000000..39bc4a81
--- /dev/null
+++ b/web/scripts/changeTracker.js
@@ -0,0 +1,254 @@
+// @ts-check
+
+import { api } from "./api.js";
+import { clone } from "./utils.js";
+
+export class ChangeTracker {
+	static MAX_HISTORY = 50;
+	#app;
+	undo = [];
+	redo = [];
+	activeState = null;
+	isOurLoad = false;
+	/** @type { import("./workflows").ComfyWorkflow | null } */
+	workflow;
+
+	ds;
+	nodeOutputs;
+
+	get app() {
+		return this.#app ?? this.workflow.manager.app;
+	}
+
+	constructor(workflow) {
+		this.workflow = workflow;
+	}
+
+	#setApp(app) {
+		this.#app = app;
+	}
+
+	store() {
+		this.ds = { scale: this.app.canvas.ds.scale, offset: [...this.app.canvas.ds.offset] };
+	}
+
+	restore() {
+		if (this.ds) {
+			this.app.canvas.ds.scale = this.ds.scale;
+			this.app.canvas.ds.offset = this.ds.offset;
+		}
+		if (this.nodeOutputs) {
+			this.app.nodeOutputs = this.nodeOutputs;
+		}
+	}
+
+	checkState() {
+		if (!this.app.graph) return;
+
+		const currentState = this.app.graph.serialize();
+		if (!this.activeState) {
+			this.activeState = clone(currentState);
+			return;
+		}
+		if (!ChangeTracker.graphEqual(this.activeState, currentState)) {
+			this.undo.push(this.activeState);
+			if (this.undo.length > ChangeTracker.MAX_HISTORY) {
+				this.undo.shift();
+			}
+			this.activeState = clone(currentState);
+			this.redo.length = 0;
+			this.workflow.unsaved = true;
+			api.dispatchEvent(new CustomEvent("graphChanged", { detail: this.activeState }));
+		}
+	}
+
+	async updateState(source, target) {
+		const prevState = source.pop();
+		if (prevState) {
+			target.push(this.activeState);
+			this.isOurLoad = true;
+			await this.app.loadGraphData(prevState, false, false, this.workflow);
+			this.activeState = prevState;
+		}
+	}
+
+	async undoRedo(e) {
+		if (e.ctrlKey || e.metaKey) {
+			if (e.key === "y") {
+				this.updateState(this.redo, this.undo);
+				return true;
+			} else if (e.key === "z") {
+				this.updateState(this.undo, this.redo);
+				return true;
+			}
+		}
+	}
+
+	/** @param { import("./app.js").ComfyApp } app */
+	static init(app) {
+		const changeTracker = () => app.workflowManager.activeWorkflow?.changeTracker ?? globalTracker;
+		globalTracker.#setApp(app);
+
+		const loadGraphData = app.loadGraphData;
+		app.loadGraphData = async function () {
+			const v = await loadGraphData.apply(this, arguments);
+			const ct = changeTracker();
+			if (ct.isOurLoad) {
+				ct.isOurLoad = false;
+			} else {
+				ct.checkState();
+			}
+			return v;
+		};
+
+		let keyIgnored = false;
+		window.addEventListener(
+			"keydown",
+			(e) => {
+				requestAnimationFrame(async () => {
+					let activeEl;
+					// If we are auto queue in change mode then we do want to trigger on inputs
+					if (!app.ui.autoQueueEnabled || app.ui.autoQueueMode === "instant") {
+						activeEl = document.activeElement;
+						if (activeEl?.tagName === "INPUT" || activeEl?.["type"] === "textarea") {
+							// Ignore events on inputs, they have their native history
+							return;
+						}
+					}
+
+					keyIgnored = e.key === "Control" || e.key === "Shift" || e.key === "Alt" || e.key === "Meta";
+					if (keyIgnored) return;
+
+					// Check if this is a ctrl+z ctrl+y
+					if (await changeTracker().undoRedo(e)) return;
+
+					// If our active element is some type of input then handle changes after they're done
+					if (ChangeTracker.bindInput(activeEl)) return;
+					changeTracker().checkState();
+				});
+			},
+			true
+		);
+
+		window.addEventListener("keyup", (e) => {
+			if (keyIgnored) {
+				keyIgnored = false;
+				changeTracker().checkState();
+			}
+		});
+
+		// Handle clicking DOM elements (e.g. widgets)
+		window.addEventListener("mouseup", () => {
+			changeTracker().checkState();
+		});
+
+		// Handle prompt queue event for dynamic widget changes
+		api.addEventListener("promptQueued", () => {
+			changeTracker().checkState();
+		});
+
+		// Handle litegraph clicks
+		const processMouseUp = LGraphCanvas.prototype.processMouseUp;
+		LGraphCanvas.prototype.processMouseUp = function (e) {
+			const v = processMouseUp.apply(this, arguments);
+			changeTracker().checkState();
+			return v;
+		};
+		const processMouseDown = LGraphCanvas.prototype.processMouseDown;
+		LGraphCanvas.prototype.processMouseDown = function (e) {
+			const v = processMouseDown.apply(this, arguments);
+			changeTracker().checkState();
+			return v;
+		};
+
+		// Handle litegraph context menu for COMBO widgets
+		const close = LiteGraph.ContextMenu.prototype.close;
+		LiteGraph.ContextMenu.prototype.close = function (e) {
+			const v = close.apply(this, arguments);
+			changeTracker().checkState();
+			return v;
+		};
+
+		// Detects nodes being added via the node search dialog
+		const onNodeAdded = LiteGraph.LGraph.prototype.onNodeAdded;
+		LiteGraph.LGraph.prototype.onNodeAdded = function () {
+			const v = onNodeAdded?.apply(this, arguments);
+			if (!app?.configuringGraph) {
+				const ct = changeTracker();
+				if (!ct.isOurLoad) {
+					ct.checkState();
+				}
+			}
+			return v;
+		};
+
+		// Store node outputs
+		api.addEventListener("executed", ({ detail }) => {
+			const prompt = app.workflowManager.queuedPrompts[detail.prompt_id];
+			if (!prompt?.workflow) return;
+			const nodeOutputs = (prompt.workflow.changeTracker.nodeOutputs ??= {});
+			const output = nodeOutputs[detail.node];
+			if (detail.merge && output) {
+				for (const k in detail.output ?? {}) {
+					const v = output[k];
+					if (v instanceof Array) {
+						output[k] = v.concat(detail.output[k]);
+					} else {
+						output[k] = detail.output[k];
+					}
+				}
+			} else {
+				nodeOutputs[detail.node] = detail.output;
+			}
+		});
+	}
+
+	static bindInput(app, activeEl) {
+		if (activeEl && activeEl.tagName !== "CANVAS" && activeEl.tagName !== "BODY") {
+			for (const evt of ["change", "input", "blur"]) {
+				if (`on${evt}` in activeEl) {
+					const listener = () => {
+						app.workflowManager.activeWorkflow.changeTracker.checkState();
+						activeEl.removeEventListener(evt, listener);
+					};
+					activeEl.addEventListener(evt, listener);
+					return true;
+				}
+			}
+		}
+	}
+
+	static graphEqual(a, b, path = "") {
+		if (a === b) return true;
+
+		if (typeof a == "object" && a && typeof b == "object" && b) {
+			const keys = Object.getOwnPropertyNames(a);
+
+			if (keys.length != Object.getOwnPropertyNames(b).length) {
+				return false;
+			}
+
+			for (const key of keys) {
+				let av = a[key];
+				let bv = b[key];
+				if (!path && key === "nodes") {
+					// Nodes need to be sorted as the order changes when selecting nodes
+					av = [...av].sort((a, b) => a.id - b.id);
+					bv = [...bv].sort((a, b) => a.id - b.id);
+				} else if (path === "extra.ds") {
+					// Ignore view changes
+					continue;
+				}
+				if (!ChangeTracker.graphEqual(av, bv, path + (path ? "." : "") + key)) {
+					return false;
+				}
+			}
+
+			return true;
+		}
+
+		return false;
+	}
+}
+
+const globalTracker = new ChangeTracker({});
\ No newline at end of file
diff --git a/web/scripts/domWidget.js b/web/scripts/domWidget.js
index d5eeebdb..d97122f9 100644
--- a/web/scripts/domWidget.js
+++ b/web/scripts/domWidget.js
@@ -11,9 +11,10 @@ function intersect(a, b) {
 	else return null;
 }
 
-function getClipPath(node, element, elRect) {
+function getClipPath(node, element) {
 	const selectedNode = Object.values(app.canvas.selected_nodes)[0];
 	if (selectedNode && selectedNode !== node) {
+		const elRect = element.getBoundingClientRect();
 		const MARGIN = 7;
 		const scale = app.canvas.ds.scale;
 
@@ -33,8 +34,8 @@ function getClipPath(node, element, elRect) {
 		}
 
 		const widgetRect = element.getBoundingClientRect();
-		const clipX = intersection[0] - widgetRect.x / scale + "px";
-		const clipY = intersection[1] - widgetRect.y / scale + "px";
+		const clipX = elRect.left + intersection[0] - widgetRect.x / scale + "px";
+		const clipY = elRect.top + intersection[1] - widgetRect.y / scale + "px";
 		const clipWidth = intersection[2] + "px";
 		const clipHeight = intersection[3] + "px";
 		const path = `polygon(0% 0%, 0% 100%, ${clipX} 100%, ${clipX} ${clipY}, calc(${clipX} + ${clipWidth}) ${clipY}, calc(${clipX} + ${clipWidth}) calc(${clipY} + ${clipHeight}), ${clipX} calc(${clipY} + ${clipHeight}), ${clipX} 100%, 100% 100%, 100% 0%)`;
@@ -209,7 +210,9 @@ LGraphNode.prototype.addDOMWidget = function (name, type, element, options) {
 	if (!element.parentElement) {
 		document.body.append(element);
 	}
-
+	element.hidden = true;
+	element.style.display = "none";
+	
 	let mouseDownHandler;
 	if (element.blur) {
 		mouseDownHandler = (event) => {
@@ -253,15 +256,15 @@ LGraphNode.prototype.addDOMWidget = function (name, type, element, options) {
 			const transform = new DOMMatrix()
 				.scaleSelf(elRect.width / ctx.canvas.width, elRect.height / ctx.canvas.height)
 				.multiplySelf(ctx.getTransform())
-				.translateSelf(margin, margin + y);
+				.translateSelf(margin, margin + y );
 
 			const scale = new DOMMatrix().scaleSelf(transform.a, transform.d);
 
 			Object.assign(element.style, {
 				transformOrigin: "0 0",
 				transform: scale,
-				left: `${transform.a + transform.e}px`,
-				top: `${transform.d + transform.f}px`,
+				left: `${transform.a + transform.e + elRect.left}px`,
+				top: `${transform.d + transform.f + elRect.top}px`,
 				width: `${widgetWidth - margin * 2}px`,
 				height: `${(widget.computedHeight ?? 50) - margin * 2}px`,
 				position: "absolute",
@@ -269,7 +272,7 @@ LGraphNode.prototype.addDOMWidget = function (name, type, element, options) {
 			});
 
 			if (enableDomClipping) {
-				element.style.clipPath = getClipPath(node, element, elRect);
+				element.style.clipPath = getClipPath(node, element);
 				element.style.willChange = "clip-path";
 			}
 
diff --git a/web/scripts/pnginfo.js b/web/scripts/pnginfo.js
index 83a4ebc8..2c03cf74 100644
--- a/web/scripts/pnginfo.js
+++ b/web/scripts/pnginfo.js
@@ -24,7 +24,7 @@ export function getPngMetadata(file) {
 				const length = dataView.getUint32(offset);
 				// Get the chunk type
 				const type = String.fromCharCode(...pngData.slice(offset + 4, offset + 8));
-				if (type === "tEXt" || type == "comf") {
+				if (type === "tEXt" || type == "comf" || type === "iTXt") {
 					// Get the keyword
 					let keyword_end = offset + 8;
 					while (pngData[keyword_end] !== 0) {
@@ -33,7 +33,7 @@ export function getPngMetadata(file) {
 					const keyword = String.fromCharCode(...pngData.slice(offset + 8, keyword_end));
 					// Get the text
 					const contentArraySegment = pngData.slice(keyword_end + 1, offset + 8 + length);
-					const contentJson = Array.from(contentArraySegment).map(s=>String.fromCharCode(s)).join('')
+					const contentJson = new TextDecoder("utf-8").decode(contentArraySegment);
 					txt_chunks[keyword] = contentJson;
 				}
 
@@ -163,6 +163,78 @@ export function getLatentMetadata(file) {
 	});
 }
 
+
+function getString(dataView, offset, length) {
+	let string = '';
+	for (let i = 0; i < length; i++) {
+		string += String.fromCharCode(dataView.getUint8(offset + i));
+	}
+	return string;
+}
+
+// Function to parse the Vorbis Comment block
+function parseVorbisComment(dataView) {
+	let offset = 0;
+	const vendorLength = dataView.getUint32(offset, true);
+	offset += 4;
+	const vendorString = getString(dataView, offset, vendorLength);
+	offset += vendorLength;
+
+	const userCommentListLength = dataView.getUint32(offset, true);
+	offset += 4;
+	const comments = {};
+	for (let i = 0; i < userCommentListLength; i++) {
+		const commentLength = dataView.getUint32(offset, true);
+		offset += 4;
+		const comment = getString(dataView, offset, commentLength);
+		offset += commentLength;
+
+		const [key, value] = comment.split('=');
+
+		comments[key] = value;
+	}
+
+	return comments;
+}
+
+// Function to read a FLAC file and parse Vorbis comments
+export function getFlacMetadata(file) {
+	return new Promise((r) => {
+		const reader = new FileReader();
+		reader.onload = function(event) {
+			const arrayBuffer = event.target.result;
+			const dataView = new DataView(arrayBuffer);
+
+			// Verify the FLAC signature
+			const signature = String.fromCharCode(...new Uint8Array(arrayBuffer, 0, 4));
+			if (signature !== 'fLaC') {
+				console.error('Not a valid FLAC file');
+				return;
+			}
+
+			// Parse metadata blocks
+			let offset = 4;
+			let vorbisComment = null;
+			while (offset < dataView.byteLength) {
+				const isLastBlock = dataView.getUint8(offset) & 0x80;
+				const blockType = dataView.getUint8(offset) & 0x7F;
+				const blockSize = dataView.getUint32(offset, false) & 0xFFFFFF;
+				offset += 4;
+
+				if (blockType === 4) { // Vorbis Comment block type
+					vorbisComment = parseVorbisComment(new DataView(arrayBuffer, offset, blockSize));
+				}
+
+				offset += blockSize;
+				if (isLastBlock) break;
+			}
+
+			r(vorbisComment);
+		};
+		reader.readAsArrayBuffer(file);
+	});
+}
+
 export async function importA1111(graph, parameters) {
 	const p = parameters.lastIndexOf("\nSteps:");
 	if (p > -1) {
@@ -170,9 +242,12 @@ export async function importA1111(graph, parameters) {
 		const opts = parameters
 			.substr(p)
 			.split("\n")[1]
-			.split(",")
+			.match(new RegExp("\\s*([^:]+:\\s*([^\"\\{].*?|\".*?\"|\\{.*?\\}))\\s*(,|$)", "g"))
 			.reduce((p, n) => {
 				const s = n.split(":");
+				if (s[1].endsWith(',')) {
+					s[1] = s[1].substr(0, s[1].length -1);
+				}
 				p[s[0].trim().toLowerCase()] = s[1].trim();
 				return p;
 			}, {});
@@ -191,6 +266,7 @@ export async function importA1111(graph, parameters) {
 			const vaeLoaderNode = LiteGraph.createNode("VAELoader");
 			const saveNode = LiteGraph.createNode("SaveImage");
 			let hrSamplerNode = null;
+			let hrSteps = null;
 
 			const ceil64 = (v) => Math.ceil(v / 64) * 64;
 
@@ -290,6 +366,9 @@ export async function importA1111(graph, parameters) {
 				model(v) {
 					setWidgetValue(ckptNode, "ckpt_name", v, true);
 				},
+				"vae"(v) {
+					setWidgetValue(vaeLoaderNode, "vae_name", v, true);
+				},
 				"cfg scale"(v) {
 					setWidgetValue(samplerNode, "cfg", +v);
 				},
@@ -316,6 +395,7 @@ export async function importA1111(graph, parameters) {
 					const h = ceil64(+wxh[1]);
 					const hrUp = popOpt("hires upscale");
 					const hrSz = popOpt("hires resize");
+					hrSteps = popOpt("hires steps");
 					let hrMethod = popOpt("hires upscaler");
 
 					setWidgetValue(imageNode, "width", w);
@@ -398,7 +478,7 @@ export async function importA1111(graph, parameters) {
 			}
 
 			if (hrSamplerNode) {
-				setWidgetValue(hrSamplerNode, "steps", getWidget(samplerNode, "steps").value);
+				setWidgetValue(hrSamplerNode, "steps", hrSteps? +hrSteps : getWidget(samplerNode, "steps").value);
 				setWidgetValue(hrSamplerNode, "cfg", getWidget(samplerNode, "cfg").value);
 				setWidgetValue(hrSamplerNode, "scheduler", getWidget(samplerNode, "scheduler").value);
 				setWidgetValue(hrSamplerNode, "sampler_name", getWidget(samplerNode, "sampler_name").value);
@@ -415,7 +495,7 @@ export async function importA1111(graph, parameters) {
 
 			graph.arrange();
 
-			for (const opt of ["model hash", "ensd"]) {
+			for (const opt of ["model hash", "ensd", "version", "vae hash", "ti hashes", "lora hashes", "hashes"]) {
 				delete opts[opt];
 			}
 
diff --git a/web/scripts/ui.js b/web/scripts/ui.js
index 5ca6214e..2c47412c 100644
--- a/web/scripts/ui.js
+++ b/web/scripts/ui.js
@@ -6,17 +6,22 @@ import { ComfySettingsDialog } from "./ui/settings.js";
 export const ComfyDialog = _ComfyDialog;
 
 /**
- * 
- * @param { string } tag HTML Element Tag and optional classes e.g. div.class1.class2
- * @param { string | Element | Element[] | {
+ * @template { string | (keyof HTMLElementTagNameMap) } K
+ * @typedef { K extends keyof HTMLElementTagNameMap ? HTMLElementTagNameMap[K] : HTMLElement } ElementType 
+ */
+
+/**
+ * @template { string | (keyof HTMLElementTagNameMap) } K 
+ * @param { K } tag HTML Element Tag and optional classes e.g. div.class1.class2
+ * @param { string | Element | Element[] | ({
  * 	 parent?: Element,
- *   $?: (el: Element) => void, 
+ *   $?: (el: ElementType<K>) => void, 
  *   dataset?: DOMStringMap,
- *   style?: CSSStyleDeclaration,
+ *   style?: Partial<CSSStyleDeclaration>,
  * 	 for?: string
- * } | undefined } propsOrChildren 
- * @param { Element[] | undefined } [children]
- * @returns 
+ * } & Omit<Partial<ElementType<K>>, "style">) | undefined } [propsOrChildren]
+ * @param { string | Element | Element[] | undefined } [children]
+ * @returns { ElementType<K> }
  */
 export function $el(tag, propsOrChildren, children) {
 	const split = tag.split(".");
@@ -54,7 +59,7 @@ export function $el(tag, propsOrChildren, children) {
 
 			Object.assign(element, propsOrChildren);
 			if (children) {
-				element.append(...(children instanceof Array ? children : [children]));
+				element.append(...(children instanceof Array ? children.filter(Boolean) : [children]));
 			}
 
 			if (parent) {
@@ -90,15 +95,20 @@ function dragElement(dragEl, settings) {
 	}).observe(dragEl);
 
 	function ensureInBounds() {
-		if (dragEl.classList.contains("comfy-menu-manual-pos")) {
+		try {
 			newPosX = Math.min(document.body.clientWidth - dragEl.clientWidth, Math.max(0, dragEl.offsetLeft));
 			newPosY = Math.min(document.body.clientHeight - dragEl.clientHeight, Math.max(0, dragEl.offsetTop));
 
 			positionElement();
 		}
+		catch(exception){
+			// robust
+		}
 	}
 
 	function positionElement() {
+		if(dragEl.style.display === "none") return;
+
 		const halfWidth = document.body.clientWidth / 2;
 		const anchorRight = newPosX + dragEl.clientWidth / 2 > halfWidth;
 
@@ -188,6 +198,8 @@ function dragElement(dragEl, settings) {
 		document.onmouseup = null;
 		document.onmousemove = null;
 	}
+
+	return restorePos;
 }
 
 class ComfyList {
@@ -225,7 +237,7 @@ class ComfyList {
 							$el("button", {
 								textContent: "Load",
 								onclick: async () => {
-									await app.loadGraphData(item.prompt[3].extra_pnginfo.workflow);
+									await app.loadGraphData(item.prompt[3].extra_pnginfo.workflow, true, false);
 									if (item.outputs) {
 										app.nodeOutputs = item.outputs;
 									}
@@ -361,7 +373,7 @@ export class ComfyUI {
 		const fileInput = $el("input", {
 			id: "comfy-file-input",
 			type: "file",
-			accept: ".json,image/png,.latent,.safetensors,image/webp",
+			accept: ".json,image/png,.latent,.safetensors,image/webp,audio/flac",
 			style: {display: "none"},
 			parent: document.body,
 			onchange: () => {
@@ -369,6 +381,8 @@ export class ComfyUI {
 			},
 		});
 
+		this.loadFile = () => fileInput.click();
+
 		const autoQueueModeEl = toggleSwitch(
 			"autoQueueMode",
 			[
@@ -594,16 +608,23 @@ export class ComfyUI {
 					if (!confirmClear.value || confirm("Clear workflow?")) {
 						app.clean();
 						app.graph.clear();
+						app.resetView();
 					}
 				}
 			}),
 			$el("button", {
 				id: "comfy-load-default-button", textContent: "Load Default", onclick: async () => {
 					if (!confirmClear.value || confirm("Load default workflow?")) {
+						app.resetView();
 						await app.loadGraphData()
 					}
 				}
 			}),
+			$el("button", {
+				id: "comfy-reset-view-button", textContent: "Reset View", onclick: async () => {
+					app.resetView();
+				}
+			}),
 		]);
 
 		const devMode = this.settings.addSetting({
@@ -611,10 +632,10 @@ export class ComfyUI {
 			name: "Enable Dev mode Options",
 			type: "boolean",
 			defaultValue: false,
-			onChange: function(value) { document.getElementById("comfy-dev-save-api-button").style.display = value ? "block" : "none"},
+			onChange: function(value) { document.getElementById("comfy-dev-save-api-button").style.display = value ? "flex" : "none"},
 		});
 
-		dragElement(this.menuContainer, this.settings);
+		this.restoreMenuPosition = dragElement(this.menuContainer, this.settings);
 
 		this.setStatus({exec_info: {queue_remaining: "X"}});
 	}
diff --git a/web/scripts/ui/components/asyncDialog.js b/web/scripts/ui/components/asyncDialog.js
new file mode 100644
index 00000000..434ce4b3
--- /dev/null
+++ b/web/scripts/ui/components/asyncDialog.js
@@ -0,0 +1,64 @@
+import { ComfyDialog } from "../dialog.js";
+import { $el } from "../../ui.js";
+
+export class ComfyAsyncDialog extends ComfyDialog {
+	#resolve;
+
+	constructor(actions) {
+		super(
+			"dialog.comfy-dialog.comfyui-dialog",
+			actions?.map((opt) => {
+				if (typeof opt === "string") {
+					opt = { text: opt };
+				}
+				return $el("button.comfyui-button", {
+					type: "button",
+					textContent: opt.text,
+					onclick: () => this.close(opt.value ?? opt.text),
+				});
+			})
+		);
+	}
+
+	show(html) {
+		this.element.addEventListener("close", () => {
+			this.close();
+		});
+
+		super.show(html);
+
+		return new Promise((resolve) => {
+			this.#resolve = resolve;
+		});
+	}
+
+	showModal(html) {
+		this.element.addEventListener("close", () => {
+			this.close();
+		});
+
+		super.show(html);
+		this.element.showModal();
+
+		return new Promise((resolve) => {
+			this.#resolve = resolve;
+		});
+	}
+
+	close(result = null) {
+		this.#resolve(result);
+		this.element.close();
+		super.close();
+	}
+
+	static async prompt({ title = null, message, actions }) {
+		const dialog = new ComfyAsyncDialog(actions);
+		const content = [$el("span", message)];
+		if (title) {
+			content.unshift($el("h3", title));
+		}
+		const res = await dialog.showModal(content);
+		dialog.element.remove();
+		return res;
+	}
+}
diff --git a/web/scripts/ui/components/button.js b/web/scripts/ui/components/button.js
new file mode 100644
index 00000000..25e5aeeb
--- /dev/null
+++ b/web/scripts/ui/components/button.js
@@ -0,0 +1,163 @@
+// @ts-check
+
+import { $el } from "../../ui.js";
+import { applyClasses, toggleElement } from "../utils.js";
+import { prop } from "../../utils.js";
+
+/**
+ * @typedef {{
+ *    icon?: string;
+ *    overIcon?: string;
+ *    iconSize?: number;
+ *    content?: string | HTMLElement;
+ *    tooltip?: string;
+ *    enabled?: boolean;
+ *    action?: (e: Event, btn: ComfyButton) => void,
+ *    classList?: import("../utils.js").ClassList,
+ * 	  visibilitySetting?: { id: string, showValue: any },
+ * 	  app?: import("../../app.js").ComfyApp
+ * }} ComfyButtonProps
+ */
+export class ComfyButton {
+	#over = 0;
+	#popupOpen = false;
+	isOver = false;	
+	iconElement = $el("i.mdi");
+	contentElement = $el("span");
+	/**
+	 * @type {import("./popup.js").ComfyPopup}
+	 */
+	popup;
+
+	/**
+	 * @param {ComfyButtonProps} opts
+	 */
+	constructor({
+		icon,
+		overIcon,
+		iconSize,
+		content,
+		tooltip,
+		action,
+		classList = "comfyui-button",
+		visibilitySetting,
+		app,
+		enabled = true,
+	}) {
+		this.element = $el("button", {
+			onmouseenter: () => {
+				this.isOver = true;
+				if(this.overIcon) {
+					this.updateIcon();
+				}
+			},
+			onmouseleave: () => {
+				this.isOver = false;
+				if(this.overIcon) {
+					this.updateIcon();
+				}
+			}
+
+		}, [this.iconElement, this.contentElement]);
+
+		this.icon = prop(this, "icon", icon, toggleElement(this.iconElement, { onShow: this.updateIcon }));
+		this.overIcon = prop(this, "overIcon", overIcon, () => {
+			if(this.isOver) {
+				this.updateIcon();
+			}
+		});
+		this.iconSize = prop(this, "iconSize", iconSize, this.updateIcon);
+		this.content = prop(
+			this,
+			"content",
+			content,
+			toggleElement(this.contentElement, {
+				onShow: (el, v) => {
+					if (typeof v === "string") {
+						el.textContent = v;
+					} else {
+						el.replaceChildren(v);
+					}
+				},
+			})
+		);
+
+		this.tooltip = prop(this, "tooltip", tooltip, (v) => {
+			if (v) {
+				this.element.title = v;
+			} else {
+				this.element.removeAttribute("title");
+			}
+		});
+		this.classList = prop(this, "classList", classList, this.updateClasses);
+		this.hidden = prop(this, "hidden", false, this.updateClasses);
+		this.enabled = prop(this, "enabled", enabled, () => {
+			this.updateClasses();
+			this.element.disabled = !this.enabled;
+		});
+		this.action = prop(this, "action", action);
+		this.element.addEventListener("click", (e) => {
+			if (this.popup) {
+				// we are either a touch device or triggered by click not hover
+				if (!this.#over) {
+					this.popup.toggle();
+				}
+			}
+			this.action?.(e, this);
+		});
+
+		if (visibilitySetting?.id) {
+			const settingUpdated = () => {
+				this.hidden = app.ui.settings.getSettingValue(visibilitySetting.id) !== visibilitySetting.showValue;
+			};
+			app.ui.settings.addEventListener(visibilitySetting.id + ".change", settingUpdated);
+			settingUpdated();
+		}
+	}
+
+	updateIcon = () => (this.iconElement.className = `mdi mdi-${(this.isOver && this.overIcon) || this.icon}${this.iconSize ? " mdi-" + this.iconSize + "px" : ""}`);
+	updateClasses = () => {
+		const internalClasses = [];
+		if (this.hidden) {
+			internalClasses.push("hidden");
+		}
+		if (!this.enabled) {
+			internalClasses.push("disabled");
+		}
+		if (this.popup) {
+			if (this.#popupOpen) {
+				internalClasses.push("popup-open");
+			} else {
+				internalClasses.push("popup-closed");
+			}
+		}
+		applyClasses(this.element, this.classList, ...internalClasses);
+	};
+
+	/**
+	 *
+	 * @param { import("./popup.js").ComfyPopup } popup
+	 * @param { "click" | "hover" } mode
+	 */
+	withPopup(popup, mode = "click") {
+		this.popup = popup;
+
+		if (mode === "hover") {
+			for (const el of [this.element, this.popup.element]) {
+				el.addEventListener("mouseenter", () => {
+					this.popup.open = !!++this.#over;
+				});
+				el.addEventListener("mouseleave", () => {
+					this.popup.open = !!--this.#over;
+				});
+			}
+		}
+
+		popup.addEventListener("change", () => {
+			this.#popupOpen = popup.open;
+			this.updateClasses();
+		});
+
+		return this;
+	}
+}
diff --git a/web/scripts/ui/components/buttonGroup.js b/web/scripts/ui/components/buttonGroup.js
new file mode 100644
index 00000000..573572fd
--- /dev/null
+++ b/web/scripts/ui/components/buttonGroup.js
@@ -0,0 +1,45 @@
+// @ts-check
+
+import { $el } from "../../ui.js";
+import { ComfyButton } from "./button.js";
+import { prop } from "../../utils.js";
+
+export class ComfyButtonGroup {
+	element = $el("div.comfyui-button-group");
+
+	/** @param {Array<ComfyButton | HTMLElement>} buttons */
+	constructor(...buttons) {
+		this.buttons = prop(this, "buttons", buttons, () => this.update());
+	}
+
+	/**
+	 * @param {ComfyButton} button
+	 * @param {number} index
+	 */
+	insert(button, index) {
+		this.buttons.splice(index, 0, button);
+		this.update();
+	}
+
+	/** @param {ComfyButton} button */
+	append(button) {
+		this.buttons.push(button);
+		this.update();
+	}
+
+	/** @param {ComfyButton|number} indexOrButton */
+	remove(indexOrButton) {
+		if (typeof indexOrButton !== "number") {
+			indexOrButton = this.buttons.indexOf(indexOrButton);
+		}
+		if (indexOrButton > -1) {
+			const r = this.buttons.splice(indexOrButton, 1);
+			this.update();
+			return r;
+		}
+	}
+
+	update() {
+		this.element.replaceChildren(...this.buttons.map((b) => b["element"] ?? b));
+	}
+}
diff --git a/web/scripts/ui/components/popup.js b/web/scripts/ui/components/popup.js
new file mode 100644
index 00000000..ee59b35d
--- /dev/null
+++ b/web/scripts/ui/components/popup.js
@@ -0,0 +1,128 @@
+// @ts-check
+
+import { prop } from "../../utils.js";
+import { $el } from "../../ui.js";
+import { applyClasses } from "../utils.js";
+
+export class ComfyPopup extends EventTarget {
+	element = $el("div.comfyui-popup");
+
+	/**
+	 * @param {{
+	 *     target: HTMLElement,
+	 *      container?: HTMLElement,
+	 *      classList?: import("../utils.js").ClassList,
+	 * 		ignoreTarget?: boolean,
+	 * 		closeOnEscape?: boolean,
+	 * 		position?: "absolute" | "relative",
+	 * 		horizontal?: "left" | "right"
+	 * }} param0
+	 * @param  {...HTMLElement} children
+	 */
+	constructor(
+		{
+			target,
+			container = document.body,
+			classList = "",
+			ignoreTarget = true,
+			closeOnEscape = true,
+			position = "absolute",
+			horizontal = "left",
+		},
+		...children
+	) {
+		super();
+		this.target = target;
+		this.ignoreTarget = ignoreTarget;
+		this.container = container;
+		this.position = position;
+		this.closeOnEscape = closeOnEscape;
+		this.horizontal = horizontal;
+
+		container.append(this.element);
+
+		this.children = prop(this, "children", children, () => {
+			this.element.replaceChildren(...this.children);
+			this.update();
+		});
+		this.classList = prop(this, "classList", classList, () => applyClasses(this.element, this.classList, "comfyui-popup", horizontal));
+		this.open = prop(this, "open", false, (v, o) => {
+			if (v === o) return;
+			if (v) {
+				this.#show();
+			} else {
+				this.#hide();
+			}
+		});
+	}
+
+	toggle() {
+		this.open = !this.open;
+	}
+
+	#hide() {
+		this.element.classList.remove("open");
+		window.removeEventListener("resize", this.update);
+		window.removeEventListener("click", this.#clickHandler, { capture: true });
+		window.removeEventListener("keydown", this.#escHandler, { capture: true });
+
+		this.dispatchEvent(new CustomEvent("close"));
+		this.dispatchEvent(new CustomEvent("change"));
+	}
+
+	#show() {
+		this.element.classList.add("open");
+		this.update();
+
+		window.addEventListener("resize", this.update);
+		window.addEventListener("click", this.#clickHandler, { capture: true });
+		if (this.closeOnEscape) {
+			window.addEventListener("keydown", this.#escHandler, { capture: true });
+		}
+
+		this.dispatchEvent(new CustomEvent("open"));
+		this.dispatchEvent(new CustomEvent("change"));
+	}
+
+	#escHandler = (e) => {
+		if (e.key === "Escape") {
+			this.open = false;
+			e.preventDefault();
+			e.stopImmediatePropagation();
+		}
+	};
+
+	#clickHandler = (e) => {
+		/** @type {any} */
+		const target = e.target;
+		if (!this.element.contains(target) && this.ignoreTarget && !this.target.contains(target)) {
+			this.open = false;
+		}
+	};
+
+	update = () => {
+		const rect = this.target.getBoundingClientRect();
+		this.element.style.setProperty("--bottom", "unset");
+		if (this.position === "absolute") {
+			if (this.horizontal === "left") {
+				this.element.style.setProperty("--left", rect.left + "px");
+			} else {
+				this.element.style.setProperty("--left", rect.right - this.element.clientWidth + "px");
+			}
+			this.element.style.setProperty("--top", rect.bottom + "px");
+			this.element.style.setProperty("--limit", rect.bottom + "px");
+		} else {
+			this.element.style.setProperty("--left", 0 + "px");
+			this.element.style.setProperty("--top", rect.height + "px");
+			this.element.style.setProperty("--limit", rect.height + "px");
+		}
+
+		const thisRect = this.element.getBoundingClientRect();
+		if (thisRect.height < 30) {
+			// Move up instead
+			this.element.style.setProperty("--top", "unset");
+			this.element.style.setProperty("--bottom", rect.height + 5 + "px");
+			this.element.style.setProperty("--limit", rect.height + 5 + "px");
+		}
+	};
+}
diff --git a/web/scripts/ui/components/splitButton.js b/web/scripts/ui/components/splitButton.js
new file mode 100644
index 00000000..2b4e6d9f
--- /dev/null
+++ b/web/scripts/ui/components/splitButton.js
@@ -0,0 +1,43 @@
+// @ts-check
+
+import { $el } from "../../ui.js";
+import { ComfyButton } from "./button.js";
+import { prop } from "../../utils.js";
+import { ComfyPopup } from "./popup.js";
+
+export class ComfySplitButton {
+	/**
+	 *  @param {{
+	 * 		primary: ComfyButton,
+	 * 		mode?: "hover" | "click",
+	 * 		horizontal?: "left" | "right",
+	 * 		position?: "relative" | "absolute"
+	 *  }} param0
+	 *  @param {Array<ComfyButton> | Array<HTMLElement>} items
+	 */
+	constructor({ primary, mode, horizontal = "left", position = "relative" }, ...items) {
+		this.arrow = new ComfyButton({
+			icon: "chevron-down",
+		});
+		this.element = $el("div.comfyui-split-button" + (mode === "hover" ? ".hover" : ""), [
+			$el("div.comfyui-split-primary", primary.element),
+			$el("div.comfyui-split-arrow", this.arrow.element),
+		]);
+		this.popup = new ComfyPopup({
+			target: this.element,
+			container: position === "relative" ? this.element : document.body,
+			classList: "comfyui-split-button-popup" + (mode === "hover" ? " hover" : ""),
+			closeOnEscape: mode === "click",
+			position,
+			horizontal,
+		});
+
+		this.arrow.withPopup(this.popup, mode);
+
+		this.items = prop(this, "items", items, () => this.update());
+	}
+
+	update() {
+		this.popup.element.replaceChildren(...this.items.map((b) => b.element ?? b));
+	}
+}
diff --git a/web/scripts/ui/dialog.js b/web/scripts/ui/dialog.js
index aee93b3c..803a97a2 100644
--- a/web/scripts/ui/dialog.js
+++ b/web/scripts/ui/dialog.js
@@ -1,20 +1,26 @@
 import { $el } from "../ui.js";
 
-export class ComfyDialog {
-	constructor() {
-		this.element = $el("div.comfy-modal", { parent: document.body }, [
+export class ComfyDialog extends EventTarget {
+	#buttons;
+
+	constructor(type = "div", buttons = null) {
+		super();
+		this.#buttons = buttons;
+		this.element = $el(type + ".comfy-modal", { parent: document.body }, [
 			$el("div.comfy-modal-content", [$el("p", { $: (p) => (this.textElement = p) }), ...this.createButtons()]),
 		]);
 	}
 
 	createButtons() {
-		return [
-			$el("button", {
-				type: "button",
-				textContent: "Close",
-				onclick: () => this.close(),
-			}),
-		];
+		return (
+			this.#buttons ?? [
+				$el("button", {
+					type: "button",
+					textContent: "Close",
+					onclick: () => this.close(),
+				}),
+			]
+		);
 	}
 
 	close() {
@@ -25,7 +31,7 @@ export class ComfyDialog {
 		if (typeof html === "string") {
 			this.textElement.innerHTML = html;
 		} else {
-			this.textElement.replaceChildren(html);
+			this.textElement.replaceChildren(...(html instanceof Array ? html : [html]));
 		}
 		this.element.style.display = "flex";
 	}
diff --git a/web/scripts/ui/menu/index.js b/web/scripts/ui/menu/index.js
new file mode 100644
index 00000000..1e00b3d2
--- /dev/null
+++ b/web/scripts/ui/menu/index.js
@@ -0,0 +1,302 @@
+// @ts-check
+
+import { $el } from "../../ui.js";
+import { downloadBlob } from "../../utils.js";
+import { ComfyButton } from "../components/button.js";
+import { ComfyButtonGroup } from "../components/buttonGroup.js";
+import { ComfySplitButton } from "../components/splitButton.js";
+import { ComfyViewHistoryButton } from "./viewHistory.js";
+import { ComfyQueueButton } from "./queueButton.js";
+import { ComfyWorkflowsMenu } from "./workflows.js";
+import { ComfyViewQueueButton } from "./viewQueue.js";
+import { getInteruptButton } from "./interruptButton.js";
+
+const collapseOnMobile = (t) => {
+	(t.element ?? t).classList.add("comfyui-menu-mobile-collapse");
+	return t;
+};
+const showOnMobile = (t) => {
+	(t.element ?? t).classList.add("lt-lg-show");
+	return t;
+};
+
+export class ComfyAppMenu {
+	#sizeBreak = "lg";
+	#lastSizeBreaks = {
+		lg: null,
+		md: null,
+		sm: null,
+		xs: null,
+	};
+	#sizeBreaks = Object.keys(this.#lastSizeBreaks);
+	#cachedInnerSize = null;
+	#cacheTimeout = null;
+
+	/**
+	 * @param { import("../../app.js").ComfyApp } app
+	 */
+	constructor(app) {
+		this.app = app;
+
+		this.workflows = new ComfyWorkflowsMenu(app);
+		const getSaveButton = (t) =>
+			new ComfyButton({
+				icon: "content-save",
+				tooltip: "Save the current workflow",
+				action: () => app.workflowManager.activeWorkflow.save(),
+				content: t,
+			});
+
+		this.logo = $el("h1.comfyui-logo.nlg-hide", { title: "ComfyUI" }, "ComfyUI");
+		this.saveButton = new ComfySplitButton(
+			{
+				primary: getSaveButton(),
+				mode: "hover",
+				position: "absolute",
+			},
+			getSaveButton("Save"),
+			new ComfyButton({
+				icon: "content-save-edit",
+				content: "Save As",
+				tooltip: "Save the current graph as a new workflow",
+				action: () => app.workflowManager.activeWorkflow.save(true),
+			}),
+			new ComfyButton({
+				icon: "download",
+				content: "Export",
+				tooltip: "Export the current workflow as JSON",
+				action: () => this.exportWorkflow("workflow", "workflow"),
+			}),
+			new ComfyButton({
+				icon: "api",
+				content: "Export (API Format)",
+				tooltip: "Export the current workflow as JSON for use with the ComfyUI API",
+				action: () => this.exportWorkflow("workflow_api", "output"),
+				visibilitySetting: { id: "Comfy.DevMode", showValue: true },
+				app,
+			})
+		);
+		this.actionsGroup = new ComfyButtonGroup(
+			new ComfyButton({
+				icon: "refresh",
+				content: "Refresh",
+				tooltip: "Refresh widgets in nodes to find new models or files",
+				action: () => app.refreshComboInNodes(),
+			}),
+			new ComfyButton({
+				icon: "clipboard-edit-outline",
+				content: "Clipspace",
+				tooltip: "Open Clipspace window",
+				action: () => app["openClipspace"](),
+			}),
+			new ComfyButton({
+				icon: "fit-to-page-outline",
+				content: "Reset View",
+				tooltip: "Reset the canvas view",
+				action: () => app.resetView(),
+			}),
+			new ComfyButton({
+				icon: "cancel",
+				content: "Clear",
+				tooltip: "Clears current workflow",
+				action: () => {
+					if (!app.ui.settings.getSettingValue("Comfy.ConfirmClear", true) || confirm("Clear workflow?")) {
+						app.clean();
+						app.graph.clear();
+					}
+				},
+			})
+		);
+		this.settingsGroup = new ComfyButtonGroup(
+			new ComfyButton({
+				icon: "cog",
+				content: "Settings",
+				tooltip: "Open settings",
+				action: () => {
+					app.ui.settings.show();
+				},
+			})
+		);
+		this.viewGroup = new ComfyButtonGroup(
+			new ComfyViewHistoryButton(app).element,
+			new ComfyViewQueueButton(app).element,
+			getInteruptButton("nlg-hide").element
+		);
+		this.mobileMenuButton = new ComfyButton({
+			icon: "menu",
+			action: (_, btn) => {
+				btn.icon = this.element.classList.toggle("expanded") ? "menu-open" : "menu";
+				window.dispatchEvent(new Event("resize"));
+			},
+			classList: "comfyui-button comfyui-menu-button",
+		});
+
+		this.element = $el("nav.comfyui-menu.lg", { style: { display: "none" } }, [
+			this.logo,
+			this.workflows.element,
+			this.saveButton.element,
+			collapseOnMobile(this.actionsGroup).element,
+			$el("section.comfyui-menu-push"),
+			collapseOnMobile(this.settingsGroup).element,
+			collapseOnMobile(this.viewGroup).element,
+
+			getInteruptButton("lt-lg-show").element,
+			new ComfyQueueButton(app).element,
+			showOnMobile(this.mobileMenuButton).element,
+		]);
+
+		let resizeHandler;
+		this.menuPositionSetting = app.ui.settings.addSetting({
+			id: "Comfy.UseNewMenu",
+			defaultValue: "Disabled",
+			name: "[Beta] Use new menu and workflow management. Note: On small screens the menu will always be at the top.",
+			type: "combo",
+			options: ["Disabled", "Top", "Bottom"],
+			onChange: async (v) => {
+				if (v && v !== "Disabled") {
+					if (!resizeHandler) {
+						resizeHandler = () => {
+							this.calculateSizeBreak();
+						};
+						window.addEventListener("resize", resizeHandler);
+					}
+					this.updatePosition(v);
+				} else {
+					if (resizeHandler) {
+						window.removeEventListener("resize", resizeHandler);
+						resizeHandler = null;
+					}
+					document.body.style.removeProperty("display");
+					app.ui.menuContainer.style.removeProperty("display");
+					this.element.style.display = "none";
+					app.ui.restoreMenuPosition();
+				}
+				window.dispatchEvent(new Event("resize"));
+			},
+		});
+	}
+
+	updatePosition(v) {
+		document.body.style.display = "grid";
+		this.app.ui.menuContainer.style.display = "none";
+		this.element.style.removeProperty("display");
+		this.position = v;
+		if (v === "Bottom") {
+			this.app.bodyBottom.append(this.element);
+		} else {
+			this.app.bodyTop.prepend(this.element);
+		}
+		this.calculateSizeBreak();
+	}
+
+	updateSizeBreak(idx, prevIdx, direction) {
+		const newSize = this.#sizeBreaks[idx];
+		if (newSize === this.#sizeBreak) return;
+		this.#cachedInnerSize = null;
+		clearTimeout(this.#cacheTimeout);
+
+		this.#sizeBreak = this.#sizeBreaks[idx];
+		for (let i = 0; i < this.#sizeBreaks.length; i++) {
+			const sz = this.#sizeBreaks[i];
+			if (sz === this.#sizeBreak) {
+				this.element.classList.add(sz);
+			} else {
+				this.element.classList.remove(sz);
+			}
+			if (i < idx) {
+				this.element.classList.add("lt-" + sz);
+			} else {
+				this.element.classList.remove("lt-" + sz);
+			}
+		}
+
+		if (idx) {
+			// We're on a small screen, force the menu at the top
+			if (this.position !== "Top") {
+				this.updatePosition("Top");
+			}
+		} else if (this.position != this.menuPositionSetting.value) {
+			// Restore user position
+			this.updatePosition(this.menuPositionSetting.value);
+		}
+
+		// Allow multiple updates, but prevent bouncing
+		if (!direction) {
+			direction = prevIdx - idx;
+		} else if (direction != prevIdx - idx) {
+			return;
+		}
+		this.calculateSizeBreak(direction);
+	}
+
+	calculateSizeBreak(direction = 0) {
+		let idx = this.#sizeBreaks.indexOf(this.#sizeBreak);
+		const currIdx = idx;
+		const innerSize = this.calculateInnerSize(idx);
+		if (window.innerWidth >= this.#lastSizeBreaks[this.#sizeBreaks[idx - 1]]) {
+			if (idx > 0) {
+				idx--;
+			}
+		} else if (innerSize > this.element.clientWidth) {
+			this.#lastSizeBreaks[this.#sizeBreak] = Math.max(window.innerWidth, innerSize);
+			// We need to shrink
+			if (idx < this.#sizeBreaks.length - 1) {
+				idx++;
+			}
+		}
+
+		this.updateSizeBreak(idx, currIdx, direction);
+	}
+
+	calculateInnerSize(idx) {
+		// Cache the inner size to prevent too much calculation when resizing the window
+		clearTimeout(this.#cacheTimeout);
+		if (this.#cachedInnerSize) {
+			// Extend cache time
+			this.#cacheTimeout = setTimeout(() => (this.#cachedInnerSize = null), 100);
+		} else {
+			let innerSize = 0;
+			let count = 1;
+			for (const c of this.element.children) {
+				if (c.classList.contains("comfyui-menu-push")) continue; // ignore right push
+				if (idx && c.classList.contains("comfyui-menu-mobile-collapse")) continue; // ignore collapse items
+				innerSize += c.clientWidth;
+				count++;
+			}
+			innerSize += 8 * count;
+			this.#cachedInnerSize = innerSize;
+			this.#cacheTimeout = setTimeout(() => (this.#cachedInnerSize = null), 100);
+		}
+		return this.#cachedInnerSize;
+	}
+
+	/**
+	 * @param {string} defaultName
+	 */
+	getFilename(defaultName) {
+		if (this.app.ui.settings.getSettingValue("Comfy.PromptFilename", true)) {
+			defaultName = prompt("Save workflow as:", defaultName);
+			if (!defaultName) return;
+			if (!defaultName.toLowerCase().endsWith(".json")) {
+				defaultName += ".json";
+			}
+		}
+		return defaultName;
+	}
+
+	/**
+	 * @param {string} [filename]
+	 * @param { "workflow" | "output" } [promptProperty]
+	 */
+	async exportWorkflow(filename, promptProperty) {
+		if (this.app.workflowManager.activeWorkflow?.path) {
+			filename = this.app.workflowManager.activeWorkflow.name;
+		}
+		const p = await this.app.graphToPrompt();
+		const json = JSON.stringify(p[promptProperty], null, 2);
+		const blob = new Blob([json], { type: "application/json" });
+		const file = this.getFilename(filename);
+		if (!file) return;
+		downloadBlob(file, blob);
+	}
+}
diff --git a/web/scripts/ui/menu/interruptButton.js b/web/scripts/ui/menu/interruptButton.js
new file mode 100644
index 00000000..4db3328d
--- /dev/null
+++ b/web/scripts/ui/menu/interruptButton.js
@@ -0,0 +1,23 @@
+// @ts-check
+
+import { api } from "../../api.js";
+import { ComfyButton } from "../components/button.js";
+
+export function getInteruptButton(visibility) {
+	const btn = new ComfyButton({
+		icon: "close",
+		tooltip: "Cancel current generation",
+		enabled: false,
+		action: () => {
+			api.interrupt();
+		},
+		classList: ["comfyui-button", "comfyui-interrupt-button", visibility],
+	});
+
+	api.addEventListener("status", ({ detail }) => {
+		const sz = detail?.exec_info?.queue_remaining;
+		btn.enabled = sz > 0;
+	});
+
+	return btn;
+}
diff --git a/web/scripts/ui/menu/menu.css b/web/scripts/ui/menu/menu.css
new file mode 100644
index 00000000..afaed3fb
--- /dev/null
+++ b/web/scripts/ui/menu/menu.css
@@ -0,0 +1,705 @@
+.relative {
+	position: relative;
+}
+.hidden {
+	display: none !important;
+}
+.mdi.rotate270::before {
+	transform: rotate(270deg);
+}
+
+/* Generic */
+.comfyui-button {
+	display: flex;
+	align-items: center;
+	gap: 0.5em;
+	cursor: pointer;
+	border: none;
+	border-radius: 4px;
+	padding: 4px 8px;
+	box-sizing: border-box;
+	margin: 0;
+	transition: box-shadow 0.1s;
+}
+
+.comfyui-button:active {
+	box-shadow: inset 1px 1px 10px rgba(0, 0, 0, 0.5);
+}
+.comfyui-button:disabled {
+	opacity: 0.5;
+	cursor: not-allowed;
+}
+.primary .comfyui-button,
+.primary.comfyui-button {
+	background-color: var(--primary-bg) !important;
+	color: var(--primary-fg) !important;
+}
+
+.primary .comfyui-button:not(:disabled):hover,
+.primary.comfyui-button:not(:disabled):hover {
+	background-color: var(--primary-hover-bg) !important;
+	color: var(--primary-hover-fg) !important;
+}
+
+/* Popup */
+.comfyui-popup {
+	position: absolute;
+	left: var(--left);
+	right: var(--right);
+	top: var(--top);
+	bottom: var(--bottom);
+	z-index: 2000;
+	max-height: calc(100vh - var(--limit) - 10px);
+	box-shadow: 3px 3px 5px 0px rgba(0, 0, 0, 0.3);
+}
+
+.comfyui-popup:not(.open) {
+	display: none;
+}
+
+.comfyui-popup.right.open {
+	border-top-left-radius: 4px;
+	border-bottom-right-radius: 4px;
+	border-bottom-left-radius: 4px;
+	overflow: hidden;
+}
+/* Split button */
+.comfyui-split-button {
+	position: relative;
+	display: flex;
+}
+
+.comfyui-split-primary {
+	flex: auto;
+}
+
+.comfyui-split-primary .comfyui-button {
+	border-top-right-radius: 0;
+	border-bottom-right-radius: 0;
+	border-right: 1px solid var(--comfy-menu-bg);
+	width: 100%;
+}
+
+.comfyui-split-arrow .comfyui-button {
+	border-top-left-radius: 0;
+	border-bottom-left-radius: 0;
+	padding-left: 2px;
+	padding-right: 2px;
+}
+
+.comfyui-split-button-popup {
+	white-space: nowrap;
+	background-color: var(--content-bg);
+	color: var(--content-fg);
+	display: flex;
+	flex-direction: column;
+	overflow: auto;
+}
+
+.comfyui-split-button-popup.hover {
+	z-index: 2001;
+}
+.comfyui-split-button-popup > .comfyui-button {
+	border: none;
+	background-color: transparent;
+	color: var(--fg-color);
+	padding: 8px 12px 8px 8px;
+}
+
+.comfyui-split-button-popup > .comfyui-button:not(:disabled):hover {
+	background-color: var(--comfy-input-bg);
+}
+
+/* Button group */
+.comfyui-button-group {
+	display: flex;
+	border-radius: 4px;
+	overflow: hidden;
+}
+
+.comfyui-button-group > .comfyui-button,
+.comfyui-button-group > .comfyui-button-wrapper > .comfyui-button {
+	padding: 4px 10px;
+	border-radius: 0;
+}
+
+/* Menu */
+.comfyui-menu {
+	width: 100vw;
+	background: var(--comfy-menu-bg);
+	color: var(--fg-color);
+	font-family: Arial, Helvetica, sans-serif;
+	font-size: 0.8em;
+	display: flex;
+	padding: 4px 8px;
+	align-items: center;
+	gap: 8px;
+	box-sizing: border-box;
+	z-index: 1000;
+	order: 0;
+	grid-column: 1/-1;
+	overflow: auto;
+	max-height: 90vh;
+}
+
+.comfyui-menu>* {
+	flex-shrink: 0;
+}
+.comfyui-menu .mdi::before {
+	font-size: 18px;
+}
+
+.comfyui-menu .comfyui-button {
+	background: var(--comfy-input-bg);
+	color: var(--fg-color);
+	white-space: nowrap;
+}
+
+.comfyui-menu .comfyui-button:not(:disabled):hover {
+	background: var(--border-color);
+	color: var(--content-fg);
+}
+
+.comfyui-menu .comfyui-split-button-popup > .comfyui-button {
+	border-radius: 0;
+	background-color: transparent;
+}
+
+.comfyui-menu .comfyui-split-button-popup > .comfyui-button:not(:disabled):hover {
+	background-color: var(--comfy-input-bg);
+}
+
+.comfyui-menu .comfyui-split-button-popup.left {
+	border-top-right-radius: 4px;
+	border-bottom-left-radius: 4px;
+	border-bottom-right-radius: 4px;
+}
+
+.comfyui-menu .comfyui-button.popup-open {
+	background-color: var(--content-bg);
+	color: var(--content-fg);
+}
+
+.comfyui-menu-push {
+	margin-left: -0.8em;
+	flex: auto;
+}
+
+.comfyui-logo {
+	font-size: 1.2em;
+	margin: 0;
+	user-select: none;
+	cursor: default;
+}
+
+/* Workflows */
+.comfyui-workflows-button {
+	flex-direction: row-reverse;
+	max-width: 200px;
+	position: relative;
+	z-index: 0;
+}
+
+.comfyui-workflows-button.popup-open {
+	border-bottom-left-radius: 0;
+	border-bottom-right-radius: 0;
+}
+.comfyui-workflows-button.unsaved {
+	font-style: italic;
+}
+.comfyui-workflows-button-progress {
+	position: absolute;
+	top: 0;
+	left: 0;
+	background-color: green;
+	height: 100%;
+	border-radius: 4px;
+	z-index: -1;
+}
+
+.comfyui-workflows-button > span {
+	flex: auto;
+	text-align: left;
+	overflow: hidden;
+}
+.comfyui-workflows-button-inner {
+	display: flex;
+	align-items: center;
+	gap: 7px;
+	width: 150px;
+}
+.comfyui-workflows-label {
+	overflow: hidden;
+	text-overflow: ellipsis;
+	direction: rtl;
+	flex: auto;
+	position: relative;
+}
+
+.comfyui-workflows-button.unsaved .comfyui-workflows-label {
+	padding-left: 8px;
+}
+
+.comfyui-workflows-button.unsaved .comfyui-workflows-label:after {
+	content: "*";
+	position: absolute;
+	top: 0;
+	left: 0;
+}
+.comfyui-workflows-button-inner .mdi-graph::before {
+	transform: rotate(-90deg);
+}
+
+.comfyui-workflows-popup {
+	font-family: Arial, Helvetica, sans-serif;
+	font-size: 0.8em;
+	padding: 10px;
+	overflow: auto;
+	background-color: var(--content-bg);
+	color: var(--content-fg);
+	border-top-right-radius: 4px;
+	border-bottom-right-radius: 4px;
+	border-bottom-left-radius: 4px;
+	z-index: 400;
+}
+
+.comfyui-workflows-panel {
+	min-height: 150px;
+}
+
+.comfyui-workflows-panel .lds-ring {
+	transform: translate(-50%);
+	position: absolute;
+	left: 50%;
+	top: 75px;
+}
+
+.comfyui-workflows-panel h3 {
+	margin: 10px 0 10px 0;
+	font-size: 11px;
+	opacity: 0.8;
+}
+
+.comfyui-workflows-panel section header {
+	display: flex;
+	justify-content: space-between;
+	align-items: center;
+}
+.comfy-ui-workflows-search .mdi {
+	position: relative;
+	top: 2px;
+	pointer-events: none;
+}
+.comfy-ui-workflows-search input {
+	background-color: var(--comfy-input-bg);
+	color: var(--input-text);
+	border: none;
+	border-radius: 4px;
+	padding: 4px 10px;
+	margin-left: -24px;
+	text-indent: 18px;
+}
+.comfy-ui-workflows-search input:placeholder-shown {
+	width: 10px;
+}
+.comfy-ui-workflows-search input:placeholder-shown:focus {
+	width: auto;
+}
+.comfyui-workflows-actions {
+	display: flex;
+	gap: 10px;
+	margin-bottom: 10px;
+}
+
+.comfyui-workflows-actions .comfyui-button {
+	background: var(--comfy-input-bg);
+	color: var(--input-text);
+}
+
+.comfyui-workflows-actions .comfyui-button:not(:disabled):hover {
+	background: var(--primary-bg);
+	color: var(--primary-fg);
+}
+
+.comfyui-workflows-favorites,
+.comfyui-workflows-open {
+	border-bottom: 1px solid var(--comfy-input-bg);
+	padding-bottom: 5px;
+	margin-bottom: 5px;
+}
+
+.comfyui-workflows-open .active {
+	font-weight: bold;
+}
+
+.comfyui-workflows-favorites:empty {
+	display: none;
+}
+
+.comfyui-workflows-tree {
+	padding: 0;
+	margin: 0;
+}
+
+.comfyui-workflows-tree:empty::after {
+	content: "No saved workflows";
+	display: block;
+	text-align: center;
+}
+.comfyui-workflows-tree > ul {
+	padding: 0;
+}
+
+.comfyui-workflows-tree > ul ul {
+	margin: 0;
+	padding: 0 0 0 25px;
+}
+
+.comfyui-workflows-tree:not(.filtered) .closed > ul {
+	display: none;
+}
+
+.comfyui-workflows-tree li,
+.comfyui-workflows-tree-file {
+	--item-height: 32px;
+	list-style-type: none;
+	height: var(--item-height);
+	display: flex;
+	align-items: center;
+	gap: 5px;
+	cursor: pointer;
+	user-select: none;
+}
+
+.comfyui-workflows-tree-file.active::before,
+.comfyui-workflows-tree li:hover::before,
+.comfyui-workflows-tree-file:hover::before {
+	content: "";
+	position: absolute;
+	width: 100%;
+	left: 0;
+	height: var(--item-height);
+	background-color: var(--content-hover-bg);
+	color: var(--content-hover-fg);
+	z-index: -1;
+}
+
+.comfyui-workflows-tree-file.active::before {
+	background-color: var(--primary-bg);
+	color: var(--primary-fg);
+}
+
+.comfyui-workflows-tree-file.running:not(:hover)::before {
+	content: "";
+	position: absolute;
+	width: var(--progress, 0);
+	left: 0;
+	height: var(--item-height);
+	background-color: green;
+	z-index: -1;
+}
+
+.comfyui-workflows-tree-file.unsaved span {
+	font-style: italic;
+}
+
+.comfyui-workflows-tree-file span {
+	flex: auto;
+}
+
+.comfyui-workflows-tree-file span + .comfyui-workflows-file-action {
+	margin-left: 10px;
+}
+
+.comfyui-workflows-tree-file .comfyui-workflows-file-action {
+	background-color: transparent;
+	color: var(--fg-color);
+	padding: 2px 4px;
+}
+
+.lg ~ .comfyui-workflows-popup .comfyui-workflows-tree-file:not(:hover) .comfyui-workflows-file-action {
+	opacity: 0;
+}
+
+.comfyui-workflows-tree-file .comfyui-workflows-file-action:hover {
+	background-color: var(--primary-bg);
+	color: var(--primary-fg);
+}
+
+.comfyui-workflows-tree-file .comfyui-workflows-file-action-primary {
+	background-color: transparent;
+	color: var(--fg-color);
+	padding: 2px 4px;
+	margin: 0 -4px;
+}
+
+.comfyui-workflows-file-action-favorite .mdi-star {
+	color: orange;
+}
+
+/* View List */
+.comfyui-view-list-popup {
+	padding: 10px;
+	background-color: var(--content-bg);
+	color: var(--content-fg);
+	min-width: 170px;
+	min-height: 435px;
+	display: flex;
+	flex-direction: column;
+	align-items: center;
+	box-sizing: border-box;
+}
+.comfyui-view-list-popup h3 {
+	margin: 0 0 5px 0;
+}
+.comfyui-view-list-items {
+	width: 100%;
+	background: var(--comfy-menu-bg);
+	border-radius: 5px;
+	display: flex;
+	justify-content: center;
+	flex: auto;
+	align-items: center;
+	flex-direction: column;
+}
+.comfyui-view-list-items section {
+	max-height: 400px;
+	overflow: auto;
+	width: 100%;
+	display: grid;
+	grid-template-columns: auto auto auto;
+	align-items: center;
+	justify-content: center;
+	gap: 5px;
+	padding: 5px 0;
+}
+.comfyui-view-list-items section + section {
+	border-top: 1px solid var(--border-color);
+	margin-top: 10px;
+	padding-top: 5px;
+}
+.comfyui-view-list-items section h5 {
+	grid-column: 1 / 4;
+	text-align: center;
+	margin: 5px;
+}
+.comfyui-view-list-items span {
+	text-align: center;
+	padding: 0 2px;
+}
+.comfyui-view-list-popup header {
+	margin-bottom: 10px;
+	display: flex;
+	gap: 5px;
+}
+.comfyui-view-list-popup header .comfyui-button {
+	border: 1px solid transparent;
+}
+.comfyui-view-list-popup header .comfyui-button:not(:disabled):hover {
+	border: 1px solid var(--comfy-menu-bg);
+}
+/* Queue button */
+.comfyui-queue-button .comfyui-split-primary .comfyui-button {
+	padding-right: 12px;
+}
+.comfyui-queue-count {
+	margin-left: 5px;
+	border-radius: 10px;
+	background-color: rgb(8, 80, 153);
+	padding: 2px 4px;
+	font-size: 10px;
+	min-width: 1em;
+	display: inline-block;
+}
+/* Queue options*/
+.comfyui-queue-options {
+	padding: 10px;
+	font-family: Arial, Helvetica, sans-serif;
+	font-size: 12px;
+	display: flex;
+	gap: 10px;
+}
+
+.comfyui-queue-batch {
+	display: flex;
+	flex-direction: column;
+	border-right: 1px solid var(--comfy-menu-bg);
+	padding-right: 10px;
+	gap: 5px;
+}
+
+.comfyui-queue-batch input {
+	width: 145px;
+}
+
+.comfyui-queue-batch .comfyui-queue-batch-value {
+	width: 70px;
+}
+
+.comfyui-queue-mode {
+	display: flex;
+	flex-direction: column;
+}
+
+.comfyui-queue-mode span {
+	font-weight: bold;
+	margin-bottom: 2px;
+}
+
+.comfyui-queue-mode label {
+	display: flex;
+	flex-direction: row-reverse;
+	justify-content: start;
+	gap: 5px;
+	padding: 2px 0;
+}
+
+.comfyui-queue-mode label input {
+	padding: 0;
+	margin: 0;
+}
+
+/** Send to workflow widget selection dialog */
+.comfy-widget-selection-dialog {
+	border: none;
+}
+
+.comfy-widget-selection-dialog div {
+	color: var(--fg-color);
+	font-family: Arial, Helvetica, sans-serif;
+}
+
+.comfy-widget-selection-dialog h2 {
+	margin-top: 0;
+}
+
+.comfy-widget-selection-dialog section {
+	width: fit-content;
+	display: flex;
+	flex-direction: column;
+}
+
+.comfy-widget-selection-item {
+	display: flex;
+	gap: 10px;
+	align-items: center;
+}
+
+.comfy-widget-selection-item span {
+	margin-right: auto;
+}
+
+.comfy-widget-selection-item span::before {
+	content: '#' attr(data-id);
+	opacity: 0.5;
+	margin-right: 5px;
+}
+
+.comfy-modal .comfy-widget-selection-item button {
+	font-size: 1em;
+}
+
+/***** Responsive *****/
+.lg.comfyui-menu .lt-lg-show {
+	display: none !important;
+}
+.comfyui-menu:not(.lg) .nlg-hide {
+	display: none !important;
+}
+/** Large screen */
+.lg.comfyui-menu>.comfyui-menu-mobile-collapse .comfyui-button span,
+.lg.comfyui-menu>.comfyui-menu-mobile-collapse.comfyui-button span {
+	display: none;
+}
+.lg.comfyui-menu>.comfyui-menu-mobile-collapse .comfyui-popup .comfyui-button span {
+	display: unset;
+}
+
+/** Non large screen */
+.lt-lg.comfyui-menu {
+	flex-wrap: wrap;
+}
+
+.lt-lg.comfyui-menu > *:not(.comfyui-menu-mobile-collapse) {
+	order: 1;
+}
+
+.lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse {
+	order: 9999;
+	width: 100%;
+}
+
+.comfyui-body-bottom .lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse {
+	order: -1;
+}
+
+.comfyui-body-bottom .lt-lg.comfyui-menu > .comfyui-menu-button {
+	top: unset;
+	bottom: 4px;
+}
+
+.lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse.comfyui-button-group {
+	flex-wrap: wrap;
+}
+
+.lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse .comfyui-button,
+.lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse.comfyui-button {
+	padding: 10px;
+}
+.lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse .comfyui-button,
+.lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse .comfyui-button-wrapper {
+	width: 100%;
+}
+
+.lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse .comfyui-popup {
+	position: static;
+	background-color: var(--comfy-input-bg);
+	max-width: unset;
+	max-height: 50vh;
+	overflow: auto;
+}
+
+.lt-lg.comfyui-menu:not(.expanded) > .comfyui-menu-mobile-collapse {
+	display: none;
+}
+
+.lt-lg .comfyui-queue-button {
+	margin-right: 44px;
+}
+
+.lt-lg .comfyui-menu-button {
+	position: absolute;
+	top: 4px;
+	right: 8px;
+}
+
+.lt-lg.comfyui-menu > .comfyui-menu-mobile-collapse .comfyui-view-list-popup {
+	border-radius: 0;
+}
+
+.lt-lg.comfyui-menu .comfyui-workflows-popup {
+	width: 100vw;
+}
+
+/** Small */
+.lt-md .comfyui-workflows-button-inner {
+	width: unset !important;
+}
+.lt-md  .comfyui-workflows-label {
+	display: none;
+}
+
+/** Extra small */
+.lt-sm .comfyui-queue-button {
+	margin-right: 0;
+	width: 100%;
+}
+.lt-sm .comfyui-queue-button .comfyui-button {
+	justify-content: center;
+}
+.lt-sm .comfyui-interrupt-button {
+	margin-right: 45px;
+}
+.comfyui-body-bottom .lt-sm.comfyui-menu > .comfyui-menu-button{
+	bottom: 41px;
+}
\ No newline at end of file
diff --git a/web/scripts/ui/menu/queueButton.js b/web/scripts/ui/menu/queueButton.js
new file mode 100644
index 00000000..608f4cc9
--- /dev/null
+++ b/web/scripts/ui/menu/queueButton.js
@@ -0,0 +1,93 @@
+// @ts-check
+
+import { ComfyButton } from "../components/button.js";
+import { $el } from "../../ui.js";
+import { api } from "../../api.js";
+import { ComfySplitButton } from "../components/splitButton.js";
+import { ComfyQueueOptions } from "./queueOptions.js";
+import { prop } from "../../utils.js";
+
+export class ComfyQueueButton {
+	element = $el("div.comfyui-queue-button");
+	#internalQueueSize = 0;
+
+	queuePrompt = async (e) => {
+		this.#internalQueueSize += this.queueOptions.batchCount;
+		// Hold shift to queue front, event is undefined when auto-queue is enabled
+		await this.app.queuePrompt(e?.shiftKey ? -1 : 0, this.queueOptions.batchCount);
+	};
+
+	constructor(app) {
+		this.app = app;
+		this.queueSizeElement = $el("span.comfyui-queue-count", {
+			textContent: "?",
+		});
+
+		const queue = new ComfyButton({
+			content: $el("div", [
+				$el("span", {
+					textContent: "Queue",
+				}),
+				this.queueSizeElement,
+			]),
+			icon: "play",
+			classList: "comfyui-button",
+			action: this.queuePrompt,
+		});
+
+		this.queueOptions = new ComfyQueueOptions(app);
+
+		const btn = new ComfySplitButton(
+			{
+				primary: queue,
+				mode: "click",
+				position: "absolute",
+				horizontal: "right",
+			},
+			this.queueOptions.element
+		);
+		btn.element.classList.add("primary");
+		this.element.append(btn.element);
+
+		this.autoQueueMode = prop(this, "autoQueueMode", "", () => {
+			switch (this.autoQueueMode) {
+				case "instant":
+					queue.icon = "infinity";
+					break;
+				case "change":
+					queue.icon = "auto-mode";
+					break;
+				default:
+					queue.icon = "play";
+					break;
+			}
+		});
+
+		this.queueOptions.addEventListener("autoQueueMode", (e) => (this.autoQueueMode = e["detail"]));
+
+		api.addEventListener("graphChanged", () => {
+			if (this.autoQueueMode === "change") {
+				if (this.#internalQueueSize) {
+					this.graphHasChanged = true;
+				} else {
+					this.graphHasChanged = false;
+					this.queuePrompt();
+				}
+			}
+		});
+
+		api.addEventListener("status", ({ detail }) => {
+			this.#internalQueueSize = detail?.exec_info?.queue_remaining;
+			if (this.#internalQueueSize != null) {
+				this.queueSizeElement.textContent = this.#internalQueueSize > 99 ? "99+" : this.#internalQueueSize + "";
+				this.queueSizeElement.title = `${this.#internalQueueSize} prompts in queue`;
+				if (!this.#internalQueueSize && !app.lastExecutionError) {
+					if (this.autoQueueMode === "instant" || (this.autoQueueMode === "change" && this.graphHasChanged)) {
+						this.graphHasChanged = false;
+						this.queuePrompt();
+					}
+				}
+			}
+		});
+	}
+}
diff --git a/web/scripts/ui/menu/queueOptions.js b/web/scripts/ui/menu/queueOptions.js
new file mode 100644
index 00000000..f3d34e74
--- /dev/null
+++ b/web/scripts/ui/menu/queueOptions.js
@@ -0,0 +1,77 @@
+// @ts-check
+
+import { $el } from "../../ui.js";
+import { prop } from "../../utils.js";
+
+export class ComfyQueueOptions extends EventTarget {
+	element = $el("div.comfyui-queue-options");
+
+	constructor(app) {
+		super();
+		this.app = app;
+
+		this.batchCountInput = $el("input", {
+			className: "comfyui-queue-batch-value",
+			type: "number",
+			min: "1",
+			value: "1",
+			oninput: () => (this.batchCount = +this.batchCountInput.value),
+		});
+
+		this.batchCountRange = $el("input", {
+			type: "range",
+			min: "1",
+			max: "100",
+			value: "1",
+			oninput: () => (this.batchCount = +this.batchCountRange.value),
+		});
+
+		this.element.append(
+			$el("div.comfyui-queue-batch", [
+				$el(
+					"label",
+					{
+						textContent: "Batch count: ",
+					},
+					this.batchCountInput
+				),
+				this.batchCountRange,
+			])
+		);
+
+		const createOption = (text, value, checked = false) =>
+			$el(
+				"label",
+				{ textContent: text },
+				$el("input", {
+					type: "radio",
+					name: "AutoQueueMode",
+					checked,
+					value,
+					oninput: (e) => (this.autoQueueMode = e.target["value"]),
+				})
+			);
+
+		this.autoQueueEl = $el("div.comfyui-queue-mode", [
+			$el("span", "Auto Queue:"),
+			createOption("Disabled", "", true),
+			createOption("Instant", "instant"),
+			createOption("On Change", "change"),
+		]);
+
+		this.element.append(this.autoQueueEl);
+
+		this.batchCount = prop(this, "batchCount", 1, () => {
+			this.batchCountInput.value = this.batchCount + "";
+			this.batchCountRange.value = this.batchCount + "";
+		});
+
+		this.autoQueueMode = prop(this, "autoQueueMode", "Disabled", () => {
+			this.dispatchEvent(
+				new CustomEvent("autoQueueMode", {
+					detail: this.autoQueueMode,
+				})
+			);
+		});
+	}
+}
diff --git a/web/scripts/ui/menu/viewHistory.js b/web/scripts/ui/menu/viewHistory.js
new file mode 100644
index 00000000..de6b343d
--- /dev/null
+++ b/web/scripts/ui/menu/viewHistory.js
@@ -0,0 +1,27 @@
+// @ts-check
+
+import { ComfyButton } from "../components/button.js";
+import { ComfyViewList, ComfyViewListButton } from "./viewList.js";
+
+export class ComfyViewHistoryButton extends ComfyViewListButton {
+	constructor(app) {
+		super(app, {
+			button: new ComfyButton({
+				content: "View History",
+				icon: "history",
+				tooltip: "View history",
+				classList: "comfyui-button comfyui-history-button",
+			}),
+			list: ComfyViewHistoryList,
+			mode: "History",
+		});
+	}
+}
+
+export class ComfyViewHistoryList extends ComfyViewList {
+	async loadItems() {
+		const items = await super.loadItems();
+		items["History"].reverse();
+		return items;
+	}
+}
diff --git a/web/scripts/ui/menu/viewList.js b/web/scripts/ui/menu/viewList.js
new file mode 100644
index 00000000..693ca335
--- /dev/null
+++ b/web/scripts/ui/menu/viewList.js
@@ -0,0 +1,203 @@
+// @ts-check
+
+import { ComfyButton } from "../components/button.js";
+import { $el } from "../../ui.js";
+import { api } from "../../api.js";
+import { ComfyPopup } from "../components/popup.js";
+
+export class ComfyViewListButton {
+	get open() {
+		return this.popup.open;
+	}
+
+	set open(open) {
+		this.popup.open = open;
+	}
+
+	constructor(app, { button, list, mode }) {
+		this.app = app;
+		this.button = button;
+		this.element = $el("div.comfyui-button-wrapper", this.button.element);
+		this.popup = new ComfyPopup({
+			target: this.element,
+			container: this.element,
+			horizontal: "right",
+		});
+		this.list = new (list ?? ComfyViewList)(app, mode, this.popup);
+		this.popup.children = [this.list.element];
+		this.popup.addEventListener("open", () => {
+			this.list.update();
+		});
+		this.popup.addEventListener("close", () => {
+			this.list.close();
+		});
+		this.button.withPopup(this.popup);
+
+		api.addEventListener("status", () => {
+			if (this.popup.open) {
+				this.popup.update();
+			}
+		});
+	}
+}
+
+export class ComfyViewList {
+	popup;
+
+	constructor(app, mode, popup) {
+		this.app = app;
+		this.mode = mode;
+		this.popup = popup;
+		this.type = mode.toLowerCase();
+
+		this.items = $el(`div.comfyui-${this.type}-items.comfyui-view-list-items`);
+		this.clear = new ComfyButton({
+			icon: "cancel",
+			content: "Clear",
+			action: async () => {
+				this.showSpinner(false);
+				await api.clearItems(this.type);
+				await this.update();
+			},
+		});
+
+		this.refresh = new ComfyButton({
+			icon: "refresh",
+			content: "Refresh",
+			action: async () => {
+				await this.update(false);
+			},
+		});
+
+		this.element = $el(`div.comfyui-${this.type}-popup.comfyui-view-list-popup`, [
+			$el("h3", mode),
+			$el("header", [this.clear.element, this.refresh.element]),
+			this.items,
+		]);
+
+		api.addEventListener("status", () => {
+			if (this.popup.open) {
+				this.update();
+			}
+		});
+	}
+
+	async close() {
+		this.items.replaceChildren();
+	}
+
+	async update(resize = true) {
+		this.showSpinner(resize);
+		const res = await this.loadItems();
+		let any = false;
+
+		const names = Object.keys(res);
+		const sections = names
+			.map((section) => {
+				const items = res[section];
+				if (items?.length) {
+					any = true;
+				} else {
+					return;
+				}
+
+				const rows = [];
+				if (names.length > 1) {
+					rows.push($el("h5", section));
+				}
+				rows.push(...items.flatMap((item) => this.createRow(item, section)));
+				return $el("section", rows);
+			})
+			.filter(Boolean);
+
+		if (any) {
+			this.items.replaceChildren(...sections);
+		} else {
+			this.items.replaceChildren($el("h5", "None"));
+		}
+
+		this.popup.update();
+		this.clear.enabled = this.refresh.enabled = true;
+		this.element.style.removeProperty("height");
+	}
+
+	showSpinner(resize = true) {
+		// if (!this.spinner) {
+		// 	this.spinner = createSpinner();
+		// }
+		// if (!resize) {
+		// 	this.element.style.height = this.element.clientHeight + "px";
+		// }
+		// this.clear.enabled = this.refresh.enabled = false;
+		// this.items.replaceChildren(
+		// 	$el(
+		// 		"div",
+		// 		{
+		// 			style: {
+		// 				fontSize: "18px",
+		// 			},
+		// 		},
+		// 		this.spinner
+		// 	)
+		// );
+		// this.popup.update();
+	}
+
+	async loadItems() {
+		return await api.getItems(this.type);
+	}
+
+	getRow(item, section) {
+		return {
+			text: item.prompt[0] + "",
+			actions: [
+				{
+					text: "Load",
+					action: async () => {
+						try {
+							await this.app.loadGraphData(item.prompt[3].extra_pnginfo.workflow);
+							if (item.outputs) {
+								this.app.nodeOutputs = item.outputs;
+							}
+						} catch (error) {
+							alert("Error loading workflow: " + error.message);
+							console.error(error);
+						}
+					},
+				},
+				{
+					text: "Delete",
+					action: async () => {
+						try {
+							await api.deleteItem(this.type, item.prompt[1]);
+							this.update();
+						} catch (error) {}
+					},
+				},
+			],
+		};
+	}
+
+	createRow = (item, section) => {
+		const row = this.getRow(item, section);
+		return [
+			$el("span", row.text),
+			...row.actions.map(
+				(a) =>
+					new ComfyButton({
+						content: a.text,
+						action: async (e, btn) => {
+							btn.enabled = false;
+							try {
+								await a.action();
+							} catch (error) {
+								throw error;
+							} finally {
+								btn.enabled = true;
+							}
+						},
+					}).element
+			),
+		];
+	};
+}
diff --git a/web/scripts/ui/menu/viewQueue.js b/web/scripts/ui/menu/viewQueue.js
new file mode 100644
index 00000000..97d01298
--- /dev/null
+++ b/web/scripts/ui/menu/viewQueue.js
@@ -0,0 +1,55 @@
+// @ts-check
+
+import { ComfyButton } from "../components/button.js";
+import { ComfyViewList, ComfyViewListButton } from "./viewList.js";
+import { api } from "../../api.js";
+
+export class ComfyViewQueueButton extends ComfyViewListButton {
+	constructor(app) {
+		super(app, {
+			button: new ComfyButton({
+				content: "View Queue",
+				icon: "format-list-numbered",
+				tooltip: "View queue",
+				classList: "comfyui-button comfyui-queue-button",
+			}),
+			list: ComfyViewQueueList,
+			mode: "Queue",
+		});
+	}
+}
+
+export class ComfyViewQueueList extends ComfyViewList {
+	getRow = (item, section) => {
+		if (section !== "Running") {
+			return super.getRow(item, section);
+		}
+		return {
+			text: item.prompt[0] + "",
+			actions: [
+				{
+					text: "Load",
+					action: async () => {
+						try {
+							await this.app.loadGraphData(item.prompt[3].extra_pnginfo.workflow);
+							if (item.outputs) {
+								this.app.nodeOutputs = item.outputs;
+							}
+						} catch (error) {
+							alert("Error loading workflow: " + error.message);
+							console.error(error);
+						}
+					},
+				},
+				{
+					text: "Cancel",
+					action: async () => {
+						try {
+							await api.interrupt();
+						} catch (error) {}
+					},
+				},
+			],
+		};
+	}
+}
diff --git a/web/scripts/ui/menu/workflows.js b/web/scripts/ui/menu/workflows.js
new file mode 100644
index 00000000..afdff538
--- /dev/null
+++ b/web/scripts/ui/menu/workflows.js
@@ -0,0 +1,764 @@
+// @ts-check
+
+import { ComfyButton } from "../components/button.js";
+import { prop, getStorageValue, setStorageValue } from "../../utils.js";
+import { $el } from "../../ui.js";
+import { api } from "../../api.js";
+import { ComfyPopup } from "../components/popup.js";
+import { createSpinner } from "../spinner.js";
+import { ComfyWorkflow, trimJsonExt } from "../../workflows.js";
+import { ComfyAsyncDialog } from "../components/asyncDialog.js";
+
+export class ComfyWorkflowsMenu {
+	#first = true;
+	element = $el("div.comfyui-workflows");
+
+	get open() {
+		return this.popup.open;
+	}
+
+	set open(open) {
+		this.popup.open = open;
+	}
+
+	/**
+	 * @param {import("../../app.js").ComfyApp} app
+	 */
+	constructor(app) {
+		this.app = app;
+		this.#bindEvents();
+
+		const classList = {
+			"comfyui-workflows-button": true,
+			"comfyui-button": true,
+			unsaved: getStorageValue("Comfy.PreviousWorkflowUnsaved") === "true",
+			running: false,
+		};
+		this.buttonProgress = $el("div.comfyui-workflows-button-progress");
+		this.workflowLabel = $el("span.comfyui-workflows-label", "");
+		this.button = new ComfyButton({
+			content: $el("div.comfyui-workflows-button-inner", [$el("i.mdi.mdi-graph"), this.workflowLabel, this.buttonProgress]),
+			icon: "chevron-down",
+			classList,
+		});
+
+		this.element.append(this.button.element);
+
+		this.popup = new ComfyPopup({ target: this.element, classList: "comfyui-workflows-popup" });
+		this.content = new ComfyWorkflowsContent(app, this.popup);
+		this.popup.children = [this.content.element];
+		this.popup.addEventListener("change", () => {
+			this.button.icon = "chevron-" + (this.popup.open ? "up" : "down");
+		});
+		this.button.withPopup(this.popup);
+
+		this.unsaved = prop(this, "unsaved", classList.unsaved, (v) => {
+			classList.unsaved = v;
+			this.button.classList = classList;
+			setStorageValue("Comfy.PreviousWorkflowUnsaved", v);
+		});
+	}
+
+	#updateProgress = () => {
+		const prompt = this.app.workflowManager.activePrompt;
+		let percent = 0;
+		if (this.app.workflowManager.activeWorkflow === prompt?.workflow) {
+			const total = Object.values(prompt.nodes);
+			const done = total.filter(Boolean);
+			percent = (done.length / total.length) * 100;
+		}
+		this.buttonProgress.style.width = percent + "%";
+	};
+
+	#updateActive = () => {
+		const active = this.app.workflowManager.activeWorkflow;
+		this.button.tooltip = active.path;
+		this.workflowLabel.textContent = active.name;
+		this.unsaved = active.unsaved;
+
+		if (this.#first) {
+			this.#first = false;
+			this.content.load();
+		}
+
+		this.#updateProgress();
+	};
+
+	#bindEvents() {
+		this.app.workflowManager.addEventListener("changeWorkflow", this.#updateActive);
+		this.app.workflowManager.addEventListener("rename", this.#updateActive);
+		this.app.workflowManager.addEventListener("delete", this.#updateActive);
+
+		this.app.workflowManager.addEventListener("save", () => {
+			this.unsaved = this.app.workflowManager.activeWorkflow.unsaved;
+		});
+
+		this.app.workflowManager.addEventListener("execute", (e) => {
+			this.#updateProgress();
+		});
+
+		api.addEventListener("graphChanged", () => {
+			this.unsaved = true;
+		});
+	}
+
+	#getMenuOptions(callback) {
+		const menu = [];
+		const directories = new Map();
+		for (const workflow of this.app.workflowManager.workflows || []) {
+			const path = workflow.pathParts;
+			if (!path) continue;
+			let parent = menu;
+			let currentPath = "";
+			for (let i = 0; i < path.length - 1; i++) {
+				currentPath += "/" + path[i];
+				let newParent = directories.get(currentPath);
+				if (!newParent) {
+					newParent = {
+						title: path[i],
+						has_submenu: true,
+						submenu: {
+							options: [],
+						},
+					};
+					parent.push(newParent);
+					newParent = newParent.submenu.options;
+					directories.set(currentPath, newParent);
+				}
+				parent = newParent;
+			}
+			parent.push({
+				title: trimJsonExt(path[path.length - 1]),
+				callback: () => callback(workflow),
+			});
+		}
+		return menu;
+	}
+
+	#getFavoriteMenuOptions(callback) {
+		const menu = [];
+		for (const workflow of this.app.workflowManager.workflows || []) {
+			if (workflow.isFavorite) {
+				menu.push({
+					title: "⭐ " + workflow.name,
+					callback: () => callback(workflow),
+				});
+			}
+		}
+		return menu;
+	}
+
+	/**
+	 * @param {import("../../app.js").ComfyApp} app
+	 */
+	registerExtension(app) {
+		const self = this;
+		app.registerExtension({
+			name: "Comfy.Workflows",
+			async beforeRegisterNodeDef(nodeType) {
+				function getImageWidget(node) {
+					const inputs = { ...node.constructor?.nodeData?.input?.required, ...node.constructor?.nodeData?.input?.optional };
+					for (const input in inputs) {
+						if (inputs[input][0] === "IMAGEUPLOAD") {
+							const imageWidget = node.widgets.find((w) => w.name === (inputs[input]?.[1]?.widget ?? "image"));
+							if (imageWidget) return imageWidget;
+						}
+					}
+				}
+
+				function setWidgetImage(node, widget, img) {
+					const url = new URL(img.src);
+					const filename = url.searchParams.get("filename");
+					const subfolder = url.searchParams.get("subfolder");
+					const type = url.searchParams.get("type");
+					const imageId = `${subfolder ? subfolder + "/" : ""}${filename} [${type}]`;
+					widget.value = imageId;
+					node.imgs = [img];
+					app.graph.setDirtyCanvas(true, true);
+				}
+
+				/**
+				 * @param {HTMLImageElement} img
+				 * @param {ComfyWorkflow} workflow
+				 */
+				async function sendToWorkflow(img, workflow) {
+					await workflow.load();
+					let options = [];
+					const nodes = app.graph.computeExecutionOrder(false);
+					for (const node of nodes) {
+						const widget = getImageWidget(node);
+						if (widget == null) continue;
+
+						if (node.title?.toLowerCase().includes("input")) {
+							options = [{ widget, node }];
+							break;
+						} else {
+							options.push({ widget, node });
+						}
+					}
+
+					if (!options.length) {
+						alert("No image nodes have been found in this workflow!");
+						return;
+					} else if (options.length > 1) {
+						const dialog = new WidgetSelectionDialog(options);
+						const res = await dialog.show(app);
+						if (!res) return;
+						options = [res];
+					}
+
+					setWidgetImage(options[0].node, options[0].widget, img);
+				}
+
+				const getExtraMenuOptions = nodeType.prototype["getExtraMenuOptions"];
+				nodeType.prototype["getExtraMenuOptions"] = function (_, options) {
+					const r = getExtraMenuOptions?.apply?.(this, arguments);
+
+					if (app.ui.settings.getSettingValue("Comfy.UseNewMenu", false) === true) {
+						const t = /** @type { {imageIndex?: number, overIndex?: number, imgs: string[]} } */ /** @type {any} */ (this);
+						let img;
+						if (t.imageIndex != null) {
+							// An image is selected so select that
+							img = t.imgs?.[t.imageIndex];
+						} else if (t.overIndex != null) {
+							// No image is selected but one is hovered
+							img = t.img?.s[t.overIndex];
+						}
+
+						if (img) {
+							let pos = options.findIndex((o) => o.content === "Save Image");
+							if (pos === -1) {
+								pos = 0;
+							} else {
+								pos++;
+							}
+
+							options.splice(pos, 0, {
+								content: "Send to workflow",
+								has_submenu: true,
+								submenu: {
+									options: [
+										{
+											callback: () => sendToWorkflow(img, app.workflowManager.activeWorkflow),
+											title: "[Current workflow]",
+										},
+										...self.#getFavoriteMenuOptions(sendToWorkflow.bind(null, img)),
+										null,
+										...self.#getMenuOptions(sendToWorkflow.bind(null, img)),
+									],
+								},
+							});
+						}
+					}
+
+					return r;
+				};
+			},
+		});
+	}
+}
+
+export class ComfyWorkflowsContent {
+	element = $el("div.comfyui-workflows-panel");
+	treeState = {};
+	treeFiles = {};
+	/** @type { Map<ComfyWorkflow, WorkflowElement> } */
+	openFiles = new Map();
+	/** @type {WorkflowElement} */
+	activeElement = null;
+
+	/**
+	 * @param {import("../../app.js").ComfyApp} app
+	 * @param {ComfyPopup} popup
+	 */
+	constructor(app, popup) {
+		this.app = app;
+		this.popup = popup;
+		this.actions = $el("div.comfyui-workflows-actions", [
+			new ComfyButton({
+				content: "Default",
+				icon: "file-code",
+				iconSize: 18,
+				classList: "comfyui-button primary",
+				tooltip: "Load default workflow",
+				action: () => {
+					popup.open = false;
+					app.loadGraphData();
+					app.resetView();
+				},
+			}).element,
+			new ComfyButton({
+				content: "Browse",
+				icon: "folder",
+				iconSize: 18,
+				tooltip: "Browse for an image or exported workflow",
+				action: () => {
+					popup.open = false;
+					app.ui.loadFile();
+				},
+			}).element,
+			new ComfyButton({
+				content: "Blank",
+				icon: "plus-thick",
+				iconSize: 18,
+				tooltip: "Create a new blank workflow",
+				action: () => {
+					app.workflowManager.setWorkflow(null);
+					app.clean();
+					app.graph.clear();
+					app.workflowManager.activeWorkflow.track();
+					popup.open = false;
+				},
+			}).element,
+		]);
+
+		this.spinner = createSpinner();
+		this.element.replaceChildren(this.actions, this.spinner);
+
+		this.popup.addEventListener("open", () => this.load());
+		this.popup.addEventListener("close", () => this.element.replaceChildren(this.actions, this.spinner));
+
+		this.app.workflowManager.addEventListener("favorite", (e) => {
+			const workflow = e["detail"];
+			const button = this.treeFiles[workflow.path]?.primary;
+			if (!button) return; // Can happen when a workflow is renamed
+			button.icon = this.#getFavoriteIcon(workflow);
+			button.overIcon = this.#getFavoriteOverIcon(workflow);
+			this.updateFavorites();
+		});
+
+		for (const e of ["save", "open", "close", "changeWorkflow"]) {
+			// TODO: dont be lazy and just update the specific element
+			app.workflowManager.addEventListener(e, () => this.updateOpen());
+		}
+		this.app.workflowManager.addEventListener("rename", () => this.load());
+		this.app.workflowManager.addEventListener("execute", (e) => this.#updateActive());
+	}
+
+	async load() {
+		await this.app.workflowManager.loadWorkflows();
+		this.updateTree();
+		this.updateFavorites();
+		this.updateOpen();
+		this.element.replaceChildren(this.actions, this.openElement, this.favoritesElement, this.treeElement);
+	}
+
+	updateOpen() {
+		const current = this.openElement;
+		this.openFiles.clear();
+
+		this.openElement = $el("div.comfyui-workflows-open", [
+			$el("h3", "Open"),
+			...this.app.workflowManager.openWorkflows.map((w) => {
+				const wrapper = new WorkflowElement(this, w, {
+					primary: { element: $el("i.mdi.mdi-18px.mdi-progress-pencil") },
+					buttons: [
+						this.#getRenameButton(w),
+						new ComfyButton({
+							icon: "close",
+							iconSize: 18,
+							classList: "comfyui-button comfyui-workflows-file-action",
+							tooltip: "Close workflow",
+							action: (e) => {
+								e.stopImmediatePropagation();
+								this.app.workflowManager.closeWorkflow(w);
+							},
+						}),
+					],
+				});
+				if (w.unsaved) {
+					wrapper.element.classList.add("unsaved");
+				}
+				if(w === this.app.workflowManager.activeWorkflow) {
+					wrapper.element.classList.add("active");
+				}
+
+				this.openFiles.set(w, wrapper);
+				return wrapper.element;
+			}),
+		]);
+
+		this.#updateActive();
+		current?.replaceWith(this.openElement);
+	}
+
+	updateFavorites() {
+		const current = this.favoritesElement;
+		const favorites = [...this.app.workflowManager.workflows.filter((w) => w.isFavorite)];
+
+		this.favoritesElement = $el("div.comfyui-workflows-favorites", [
+			$el("h3", "Favorites"),
+			...favorites
+				.map((w) => {
+					return this.#getWorkflowElement(w).element;
+				})
+				.filter(Boolean),
+		]);
+
+		current?.replaceWith(this.favoritesElement);
+	}
+
+	filterTree() {
+		if (!this.filterText) {
+			this.treeRoot.classList.remove("filtered");
+			// Unfilter whole tree
+			for (const item of Object.values(this.treeFiles)) {
+				item.element.parentElement.style.removeProperty("display");
+				this.showTreeParents(item.element.parentElement);
+			}
+			return;
+		}
+		this.treeRoot.classList.add("filtered");
+		const searchTerms = this.filterText.toLocaleLowerCase().split(" ");
+		for (const item of Object.values(this.treeFiles)) {
+			const parts = item.workflow.pathParts;
+			let termIndex = 0;
+			let valid = false;
+			for (const part of parts) {
+				let currentIndex = 0;
+				do {
+					currentIndex = part.indexOf(searchTerms[termIndex], currentIndex);
+					if (currentIndex > -1) currentIndex += searchTerms[termIndex].length;
+				} while (currentIndex !== -1 && ++termIndex < searchTerms.length);
+
+				if (termIndex >= searchTerms.length) {
+					valid = true;
+					break;
+				}
+			}
+			if (valid) {
+				item.element.parentElement.style.removeProperty("display");
+				this.showTreeParents(item.element.parentElement);
+			} else {
+				item.element.parentElement.style.display = "none";
+				this.hideTreeParents(item.element.parentElement);
+			}
+		}
+	}
+
+	hideTreeParents(element) {
+		// Hide all parents if no children are visible
+		if (element.parentElement?.classList.contains("comfyui-workflows-tree") === false) {
+			for (let i = 1; i < element.parentElement.children.length; i++) {
+				const c = element.parentElement.children[i];
+				if (c.style.display !== "none") {
+					return;
+				}
+			}
+			element.parentElement.style.display = "none";
+			this.hideTreeParents(element.parentElement);
+		}
+	}
+
+	showTreeParents(element) {
+		if (element.parentElement?.classList.contains("comfyui-workflows-tree") === false) {
+			element.parentElement.style.removeProperty("display");
+			this.showTreeParents(element.parentElement);
+		}
+	}
+
+	updateTree() {
+		const current = this.treeElement;
+		const nodes = {};
+		let typingTimeout;
+
+		this.treeFiles = {};
+		this.treeRoot = $el("ul.comfyui-workflows-tree");
+		this.treeElement = $el("section", [
+			$el("header", [
+				$el("h3", "Browse"),
+				$el("div.comfy-ui-workflows-search", [
+					$el("i.mdi.mdi-18px.mdi-magnify"),
+					$el("input", {
+						placeholder: "Search",
+						value: this.filterText ?? "",
+						oninput: (e) => {
+							this.filterText = e.target["value"]?.trim();
+							clearTimeout(typingTimeout);
+							typingTimeout = setTimeout(() => this.filterTree(), 250);
+						},
+					}),
+				]),
+			]),
+			this.treeRoot,
+		]);
+
+		for (const workflow of this.app.workflowManager.workflows) {
+			if (!workflow.pathParts) continue;
+
+			let currentPath = "";
+			let currentRoot = this.treeRoot;
+
+			for (let i = 0; i < workflow.pathParts.length; i++) {
+				currentPath += (currentPath ? "\\" : "") + workflow.pathParts[i];
+				const parentNode = nodes[currentPath] ?? this.#createNode(currentPath, workflow, i, currentRoot);
+
+				nodes[currentPath] = parentNode;
+				currentRoot = parentNode;
+			}
+		}
+
+		current?.replaceWith(this.treeElement);
+		this.filterTree();
+	}
+
+	#expandNode(el, workflow, thisPath, i) {
+		const expanded = !el.classList.toggle("closed");
+		if (expanded) {
+			let c = "";
+			for (let j = 0; j <= i; j++) {
+				c += (c ? "\\" : "") + workflow.pathParts[j];
+				this.treeState[c] = true;
+			}
+		} else {
+			let c = thisPath;
+			for (let j = i + 1; j < workflow.pathParts.length; j++) {
+				c += (c ? "\\" : "") + workflow.pathParts[j];
+				delete this.treeState[c];
+			}
+			delete this.treeState[thisPath];
+		}
+	}
+
+	#updateActive() {
+		this.#removeActive();
+
+		const active = this.app.workflowManager.activePrompt;
+		if (!active?.workflow) return;
+
+		const open = this.openFiles.get(active.workflow);
+		if (!open) return;
+
+		this.activeElement = open;
+
+		const total = Object.values(active.nodes);
+		const done = total.filter(Boolean);
+		const percent = done.length / total.length;
+		open.element.classList.add("running");
+		open.element.style.setProperty("--progress", percent * 100 + "%");
+		open.primary.element.classList.remove("mdi-progress-pencil");
+		open.primary.element.classList.add("mdi-play");
+	}
+
+	#removeActive() {
+		if (!this.activeElement) return;
+		this.activeElement.element.classList.remove("running");
+		this.activeElement.element.style.removeProperty("--progress");
+		this.activeElement.primary.element.classList.add("mdi-progress-pencil");
+		this.activeElement.primary.element.classList.remove("mdi-play");
+	}
+
+	/** @param {ComfyWorkflow} workflow */
+	#getFavoriteIcon(workflow) {
+		return workflow.isFavorite ? "star" : "file-outline";
+	}
+
+	/** @param {ComfyWorkflow} workflow */
+	#getFavoriteOverIcon(workflow) {
+		return workflow.isFavorite ? "star-off" : "star-outline";
+	}
+
+	/** @param {ComfyWorkflow} workflow */
+	#getFavoriteTooltip(workflow) {
+		return workflow.isFavorite ? "Remove this workflow from your favorites" : "Add this workflow to your favorites";
+	}
+
+	/** @param {ComfyWorkflow} workflow */
+	#getFavoriteButton(workflow, primary) {
+		return new ComfyButton({
+			icon: this.#getFavoriteIcon(workflow),
+			overIcon: this.#getFavoriteOverIcon(workflow),
+			iconSize: 18,
+			classList: "comfyui-button comfyui-workflows-file-action-favorite" + (primary ? " comfyui-workflows-file-action-primary" : ""),
+			tooltip: this.#getFavoriteTooltip(workflow),
+			action: (e) => {
+				e.stopImmediatePropagation();
+				workflow.favorite(!workflow.isFavorite);
+			},
+		});
+	}
+
+	/** @param {ComfyWorkflow} workflow */
+	#getDeleteButton(workflow) {
+		const deleteButton = new ComfyButton({
+			icon: "delete",
+			tooltip: "Delete this workflow",
+			classList: "comfyui-button comfyui-workflows-file-action",
+			iconSize: 18,
+			action: async (e, btn) => {
+				e.stopImmediatePropagation();
+
+				if (btn.icon === "delete-empty") {
+					btn.enabled = false;
+					await workflow.delete();
+					await this.load();
+				} else {
+					btn.icon = "delete-empty";
+					btn.element.style.background = "red";
+				}
+			},
+		});
+		deleteButton.element.addEventListener("mouseleave", () => {
+			deleteButton.icon = "delete";
+			deleteButton.element.style.removeProperty("background");
+		});
+		return deleteButton;
+	}
+
+	/** @param {ComfyWorkflow} workflow */
+	#getInsertButton(workflow) {
+		return new ComfyButton({
+			icon: "file-move-outline",
+			iconSize: 18,
+			tooltip: "Insert this workflow into the current workflow",
+			classList: "comfyui-button comfyui-workflows-file-action",
+			action: (e) => {
+				if (!this.app.shiftDown) {
+					this.popup.open = false;
+				}
+				e.stopImmediatePropagation();
+				if (!this.app.shiftDown) {
+					this.popup.open = false;
+				}
+				workflow.insert();
+			},
+		});
+	}
+
+	/** @param {ComfyWorkflow} workflow */
+	#getRenameButton(workflow) {
+		return new ComfyButton({
+			icon: "pencil",
+			tooltip: workflow.path ? "Rename this workflow" : "This workflow can't be renamed as it hasn't been saved.",
+			classList: "comfyui-button comfyui-workflows-file-action",
+			iconSize: 18,
+			enabled: !!workflow.path,
+			action: async (e) => {
+				e.stopImmediatePropagation();
+				const newName = prompt("Enter new name", workflow.path);
+				if (newName) {
+					await workflow.rename(newName);
+				}
+			},
+		});
+	}
+
+	/** @param {ComfyWorkflow} workflow */
+	#getWorkflowElement(workflow) {
+		return new WorkflowElement(this, workflow, {
+			primary: this.#getFavoriteButton(workflow, true),
+			buttons: [this.#getInsertButton(workflow), this.#getRenameButton(workflow), this.#getDeleteButton(workflow)],
+		});
+	}
+
+	/** @param {ComfyWorkflow} workflow */
+	#createLeafNode(workflow) {
+		const fileNode = this.#getWorkflowElement(workflow);
+		this.treeFiles[workflow.path] = fileNode;
+		return fileNode;
+	}
+
+	#createNode(currentPath, workflow, i, currentRoot) {
+		const part = workflow.pathParts[i];
+
+		const parentNode = $el("ul" + (this.treeState[currentPath] ? "" : ".closed"), {
+			$: (el) => {
+				el.onclick = (e) => {
+					this.#expandNode(el, workflow, currentPath, i);
+					e.stopImmediatePropagation();
+				};
+			},
+		});
+		currentRoot.append(parentNode);
+
+		// Create a node for the current part and an inner UL for its children if it isnt a leaf node
+		const leaf = i === workflow.pathParts.length - 1;
+		let nodeElement;
+		if (leaf) {
+			nodeElement = this.#createLeafNode(workflow).element;
+		} else {
+			nodeElement = $el("li", [$el("i.mdi.mdi-18px.mdi-folder"), $el("span", part)]);
+		}
+		parentNode.append(nodeElement);
+		return parentNode;
+	}
+}
+
+class WorkflowElement {
+	/**
+	 * @param { ComfyWorkflowsContent } parent
+	 * @param { ComfyWorkflow } workflow
+	 */
+	constructor(parent, workflow, { tagName = "li", primary, buttons }) {
+		this.parent = parent;
+		this.workflow = workflow;
+		this.primary = primary;
+		this.buttons = buttons;
+
+		this.element = $el(
+			tagName + ".comfyui-workflows-tree-file",
+			{
+				onclick: () => {
+					workflow.load();
+					this.parent.popup.open = false;
+				},
+				title: this.workflow.path,
+			},
+			[this.primary?.element, $el("span", workflow.name), ...buttons.map((b) => b.element)]
+		);
+	}
+}
+
+class WidgetSelectionDialog extends ComfyAsyncDialog {
+	#options;
+
+	/**
+	 * @param {Array<{widget: {name: string}, node: {pos: [number, number], title: string, id: string, type: string}}>} options
+	 */
+	constructor(options) {
+		super();
+		this.#options = options;
+	}
+
+	show(app) {
+		this.element.classList.add("comfy-widget-selection-dialog");
+		return super.show(
+			$el("div", [
+				$el("h2", "Select image target"),
+				$el(
+					"p",
+					"This workflow has multiple image loader nodes, you can rename a node to include 'input' in the title for it to be automatically selected, or select one below."
+				),
+				$el(
+					"section",
+					this.#options.map((opt) => {
+						return $el("div.comfy-widget-selection-item", [
+							$el("span", { dataset: { id: opt.node.id } }, `${opt.node.title ?? opt.node.type} ${opt.widget.name}`),
+							$el(
+								"button.comfyui-button",
+								{
+									onclick: () => {
+										app.canvas.ds.offset[0] = -opt.node.pos[0] + 50;
+										app.canvas.ds.offset[1] = -opt.node.pos[1] + 50;
+										app.canvas.selectNode(opt.node);
+										app.graph.setDirtyCanvas(true, true);
+									},
+								},
+								"Show"
+							),
+							$el(
+								"button.comfyui-button.primary",
+								{
+									onclick: () => {
+										this.close(opt);
+									},
+								},
+								"Select"
+							),
+						]);
+					})
+				),
+			])
+		);
+	}
+}
\ No newline at end of file
diff --git a/web/scripts/ui/settings.js b/web/scripts/ui/settings.js
index 9e9d13af..819e4e7d 100644
--- a/web/scripts/ui/settings.js
+++ b/web/scripts/ui/settings.js
@@ -47,6 +47,17 @@ export class ComfySettingsDialog extends ComfyDialog {
 		return Object.values(this.settingsLookup);
 	}
 
+	#dispatchChange(id, value, oldValue) {
+		this.dispatchEvent(
+			new CustomEvent(id + ".change", {
+				detail: {
+					value,
+					oldValue
+				},
+			})
+		);
+	}
+
 	async load() {
 		if (this.app.storageLocation === "browser") {
 			this.settingsValues = localStorage;
@@ -56,7 +67,9 @@ export class ComfySettingsDialog extends ComfyDialog {
 
 		// Trigger onChange for any settings added before load
 		for (const id in this.settingsLookup) {
-			this.settingsLookup[id].onChange?.(this.settingsValues[this.getId(id)]);
+			const value = this.settingsValues[this.getId(id)];
+			this.settingsLookup[id].onChange?.(value);
+			this.#dispatchChange(id, value);
 		}
 	}
 
@@ -90,6 +103,7 @@ export class ComfySettingsDialog extends ComfyDialog {
 		if (id in this.settingsLookup) {
 			this.settingsLookup[id].onChange?.(value, oldValue);
 		}
+		this.#dispatchChange(id, value, oldValue);
 
 		await api.storeSetting(id, value);
 	}
@@ -136,6 +150,8 @@ export class ComfySettingsDialog extends ComfyDialog {
 			onChange,
 			name,
 			render: () => {
+				if (type === "hidden") return;
+
 				const setter = (v) => {
 					if (onChange) {
 						onChange(v, value);
@@ -310,7 +326,7 @@ export class ComfySettingsDialog extends ComfyDialog {
 				},
 				[$el("th"), $el("th", { style: { width: "33%" } })]
 			),
-			...this.settings.sort((a, b) => a.name.localeCompare(b.name)).map((s) => s.render())
+			...this.settings.sort((a, b) => a.name.localeCompare(b.name)).map((s) => s.render()).filter(Boolean)
 		);
 		this.element.showModal();
 	}
diff --git a/web/scripts/ui/utils.js b/web/scripts/ui/utils.js
new file mode 100644
index 00000000..e37d8b41
--- /dev/null
+++ b/web/scripts/ui/utils.js
@@ -0,0 +1,56 @@
+/**
+ * @typedef {  string | string[] | Record<string, boolean> } ClassList
+ */
+
+/**
+ * @param { HTMLElement } element
+ * @param { ClassList } classList
+ * @param { string[] } requiredClasses
+ */
+export function applyClasses(element, classList, ...requiredClasses) {
+	classList ??= "";
+
+	let str;
+	if (typeof classList === "string") {
+		str = classList;
+	} else if (classList instanceof Array) {
+		str = classList.join(" ");
+	} else {
+		str = Object.entries(classList).reduce((p, c) => {
+			if (c[1]) {
+				p += (p.length ? " " : "") + c[0];
+			}
+			return p;
+		}, "");
+	}
+	element.className = str;
+	if (requiredClasses) {
+		element.classList.add(...requiredClasses);
+	}
+}
+
+/**
+ * @param { HTMLElement } element
+ * @param { { onHide?: (el: HTMLElement) => void, onShow?: (el: HTMLElement, value) => void } } [param1]
+ * @returns
+ */
+export function toggleElement(element, { onHide, onShow } = {}) {
+	let placeholder;
+	let hidden;
+	return (value) => {
+		if (value) {
+			if (hidden) {
+				hidden = false;
+				placeholder.replaceWith(element);
+			}
+			onShow?.(element, value);
+		} else {
+			if (!placeholder) {
+				placeholder = document.createComment("");
+			}
+			hidden = true;
+			element.replaceWith(placeholder);
+			onHide?.(element);
+		}
+	};
+}
diff --git a/web/scripts/utils.js b/web/scripts/utils.js
index 01b98846..cda7600f 100644
--- a/web/scripts/utils.js
+++ b/web/scripts/utils.js
@@ -1,4 +1,5 @@
 import { $el } from "./ui.js";
+import { api } from "./api.js";
 
 // Simple date formatter
 const parts = {
@@ -25,6 +26,19 @@ function formatDate(text, date) {
 	});
 }
 
+
+export function clone(obj) {
+	try {
+		if (typeof structuredClone !== "undefined") {
+			return structuredClone(obj);
+		}
+	} catch (error) {
+		// structuredClone is stricter than using JSON.parse/stringify so fallback to that
+	}
+
+	return JSON.parse(JSON.stringify(obj));
+}
+
 export function applyTextReplacements(app, value) {
 	return value.replace(/%([^%]+)%/g, function (match, text) {
 		const split = text.split(".");
@@ -86,3 +100,57 @@ export async function addStylesheet(urlOrFile, relativeTo) {
 		});
 	});
 }
+
+/**
+ * @param { string } filename
+ * @param { Blob } blob
+ */
+export function downloadBlob(filename, blob) {
+	const url = URL.createObjectURL(blob);
+	const a = $el("a", {
+		href: url,
+		download: filename,
+		style: { display: "none" },
+		parent: document.body,
+	});
+	a.click();
+	setTimeout(function () {
+		a.remove();
+		window.URL.revokeObjectURL(url);
+	}, 0);
+}
+
+/**
+ * @template T
+ * @param {string} name
+ * @param {T} [defaultValue]
+ * @param {(currentValue: any, previousValue: any)=>void} [onChanged]
+ * @returns {T}
+ */
+export function prop(target, name, defaultValue, onChanged) {
+	let currentValue;
+	Object.defineProperty(target, name, {
+		get() {
+			return currentValue;
+		},
+		set(newValue) {
+			const prevValue = currentValue;
+			currentValue = newValue;
+			onChanged?.(currentValue, prevValue, target, name);
+		},
+	});
+	return defaultValue;
+}
+
+export function getStorageValue(id) {
+	const clientId = api.clientId ?? api.initialClientId;
+	return (clientId && sessionStorage.getItem(`${id}:${clientId}`)) ?? localStorage.getItem(id);
+}
+
+export function setStorageValue(id, value) {
+	const clientId = api.clientId ?? api.initialClientId;
+	if (clientId) {
+		sessionStorage.setItem(`${id}:${clientId}`, value);
+	}
+	localStorage.setItem(id, value);
+}
\ No newline at end of file
diff --git a/web/scripts/widgets.js b/web/scripts/widgets.js
index 678b1b8e..6a689970 100644
--- a/web/scripts/widgets.js
+++ b/web/scripts/widgets.js
@@ -229,7 +229,11 @@ function createIntWidget(node, inputName, inputData, app, isSeedInput) {
 			val,
 			function (v) {
 				const s = this.options.step / 10;
-				this.value = Math.round(v / s) * s;
+				let sh = this.options.min % s;
+				if (isNaN(sh)) {
+					sh = 0;
+				}
+				this.value = Math.round((v - sh) / s) * s + sh;
 			},
 			config
 		),
@@ -307,7 +311,9 @@ export const ComfyWidgets = {
 		return { widget: node.addWidget(widgetType, inputName, val,
 			function (v) {
 				if (config.round) {
-					this.value = Math.round(v/config.round)*config.round;
+					this.value = Math.round((v + Number.EPSILON)/config.round)*config.round;
+					if (this.value > config.max) this.value = config.max;
+					if (this.value < config.min) this.value = config.min;
 				} else {
 					this.value = v;
 				}
diff --git a/web/scripts/workflows.js b/web/scripts/workflows.js
new file mode 100644
index 00000000..d38b6f5f
--- /dev/null
+++ b/web/scripts/workflows.js
@@ -0,0 +1,450 @@
+// @ts-check
+
+import { api } from "./api.js";
+import { ChangeTracker } from "./changeTracker.js";
+import { ComfyAsyncDialog } from "./ui/components/asyncDialog.js";
+import { getStorageValue, setStorageValue } from "./utils.js";
+
+function appendJsonExt(path) {
+	if (!path.toLowerCase().endsWith(".json")) {
+		path += ".json";
+	}
+	return path;
+}
+
+export function trimJsonExt(path) {
+	return path?.replace(/\.json$/, "");
+}
+
+export class ComfyWorkflowManager extends EventTarget {
+	/** @type {string | null} */
+	#activePromptId = null;
+	#unsavedCount = 0;
+	#activeWorkflow;
+
+	/** @type {Record<string, ComfyWorkflow>} */
+	workflowLookup = {};
+	/** @type {Array<ComfyWorkflow>} */
+	workflows = [];
+	/** @type {Array<ComfyWorkflow>} */
+	openWorkflows = [];
+	/** @type {Record<string, {workflow?: ComfyWorkflow, nodes?: Record<string, boolean>}>} */
+	queuedPrompts = {};
+
+	get activeWorkflow() {
+		return this.#activeWorkflow ?? this.openWorkflows[0];
+	}
+
+	get activePromptId() {
+		return this.#activePromptId;
+	}
+
+	get activePrompt() {
+		return this.queuedPrompts[this.#activePromptId];
+	}
+
+	/**
+	 * @param {import("./app.js").ComfyApp} app
+	 */
+	constructor(app) {
+		super();
+		this.app = app;
+		ChangeTracker.init(app);
+
+		this.#bindExecutionEvents();
+	}
+
+	#bindExecutionEvents() {
+		// TODO: on reload, set active prompt based on the latest ws message
+
+		const emit = () => this.dispatchEvent(new CustomEvent("execute", { detail: this.activePrompt }));
+		let executing = null;
+		api.addEventListener("execution_start", (e) => {
+			this.#activePromptId = e.detail.prompt_id;
+
+			// This event can fire before the event is stored, so put a placeholder
+			this.queuedPrompts[this.#activePromptId] ??= { nodes: {} };
+			emit();
+		});
+		api.addEventListener("execution_cached", (e) => {
+			if (!this.activePrompt) return;
+			for (const n of e.detail.nodes) {
+				this.activePrompt.nodes[n] = true;
+			}
+			emit();
+		});
+		api.addEventListener("executed", (e) => {
+			if (!this.activePrompt) return;
+			this.activePrompt.nodes[e.detail.node] = true;
+			emit();
+		});
+		api.addEventListener("executing", (e) => {
+			if (!this.activePrompt) return;
+
+			if (executing) {
+				// Seems sometimes nodes that are cached fire executing but not executed
+				this.activePrompt.nodes[executing] = true;
+			}
+			executing = e.detail;
+			if (!executing) {
+				delete this.queuedPrompts[this.#activePromptId];
+				this.#activePromptId = null;
+			}
+			emit();
+		});
+	}
+
+	async loadWorkflows() {
+		try {
+			let favorites;
+			const resp = await api.getUserData("workflows/.index.json");
+			let info;
+			if (resp.status === 200) {
+				info = await resp.json();
+				favorites = new Set(info?.favorites ?? []);
+			} else {
+				favorites = new Set();
+			}
+
+			const workflows = (await api.listUserData("workflows", true, true)).map((w) => {
+				let workflow = this.workflowLookup[w[0]];
+				if (!workflow) {
+					workflow = new ComfyWorkflow(this, w[0], w.slice(1), favorites.has(w[0]));
+					this.workflowLookup[workflow.path] = workflow;
+				}
+				return workflow;
+			});
+
+			this.workflows = workflows;
+		} catch (error) {
+			alert("Error loading workflows: " + (error.message ?? error));
+			this.workflows = [];
+		}
+	}
+
+	async saveWorkflowMetadata() {
+		await api.storeUserData("workflows/.index.json", {
+			favorites: [...this.workflows.filter((w) => w.isFavorite).map((w) => w.path)],
+		});
+	}
+
+	/**
+	 * @param {string | ComfyWorkflow | null} workflow
+	 */
+	setWorkflow(workflow) {
+		if (workflow && typeof workflow === "string") {
+			// Selected by path, i.e. on reload of last workflow
+			const found = this.workflows.find((w) => w.path === workflow);
+			if (found) {
+				workflow = found;
+				workflow.unsaved = !workflow || getStorageValue("Comfy.PreviousWorkflowUnsaved") === "true";
+			}
+		}
+
+		if (!(workflow instanceof ComfyWorkflow)) {
+			// Still not found, either reloading a deleted workflow or blank
+			workflow = new ComfyWorkflow(this, workflow || "Unsaved Workflow" + (this.#unsavedCount++ ? ` (${this.#unsavedCount})` : ""));
+		}
+
+		const index = this.openWorkflows.indexOf(workflow);
+		if (index === -1) {
+			// Opening a new workflow
+			this.openWorkflows.push(workflow);
+		}
+
+		this.#activeWorkflow = workflow;
+
+		setStorageValue("Comfy.PreviousWorkflow", this.activeWorkflow.path ?? "");
+		this.dispatchEvent(new CustomEvent("changeWorkflow"));
+	}
+
+	storePrompt({ nodes, id }) {
+		this.queuedPrompts[id] ??= {};
+		this.queuedPrompts[id].nodes = {
+			...nodes.reduce((p, n) => {
+				p[n] = false;
+				return p;
+			}, {}),
+			...this.queuedPrompts[id].nodes,
+		};
+		this.queuedPrompts[id].workflow = this.activeWorkflow;
+	}
+
+	/**
+	 * @param {ComfyWorkflow} workflow
+	 */
+	async closeWorkflow(workflow, warnIfUnsaved = true) {
+		if (!workflow.isOpen) {
+			return true;
+		}
+		if (workflow.unsaved && warnIfUnsaved) {
+			const res = await ComfyAsyncDialog.prompt({
+				title: "Save Changes?",
+				message: `Do you want to save changes to "${workflow.path ?? workflow.name}" before closing?`,
+				actions: ["Yes", "No", "Cancel"],
+			});
+			if (res === "Yes") {
+				const active = this.activeWorkflow;
+				if (active !== workflow) {
+					// We need to switch to the workflow to save it
+					await workflow.load();
+				}
+
+				if (!(await workflow.save())) {
+					// Save was canceled, restore the previous workflow
+					if (active !== workflow) {
+						await active.load();
+					}
+					return;
+				}
+			} else if (res === "Cancel") {
+				return;
+			}
+		}
+		workflow.changeTracker = null;
+		this.openWorkflows.splice(this.openWorkflows.indexOf(workflow), 1);
+		if (this.openWorkflows.length) {
+			this.#activeWorkflow = this.openWorkflows[0];
+			await this.#activeWorkflow.load();
+		} else {
+			// Load default
+			await this.app.loadGraphData();
+		}
+	}
+}
+
+export class ComfyWorkflow {
+	#name;
+	#path;
+	#pathParts;
+	#isFavorite = false;
+	/** @type {ChangeTracker | null} */
+	changeTracker = null;
+	unsaved = false;
+
+	get name() {
+		return this.#name;
+	}
+
+	get path() {
+		return this.#path;
+	}
+
+	get pathParts() {
+		return this.#pathParts;
+	}
+
+	get isFavorite() {
+		return this.#isFavorite;
+	}
+
+	get isOpen() {
+		return !!this.changeTracker;
+	}
+
+	/**
+	 * @overload
+	 * @param {ComfyWorkflowManager} manager
+	 * @param {string} path
+	 */
+	/**
+	 * @overload
+	 * @param {ComfyWorkflowManager} manager
+	 * @param {string} path
+	 * @param {string[]} pathParts
+	 * @param {boolean} isFavorite
+	 */
+	/**
+	 * @param {ComfyWorkflowManager} manager
+	 * @param {string} path
+	 * @param {string[]} [pathParts]
+	 * @param {boolean} [isFavorite]
+	 */
+	constructor(manager, path, pathParts, isFavorite) {
+		this.manager = manager;
+		if (pathParts) {
+			this.#updatePath(path, pathParts);
+			this.#isFavorite = isFavorite;
+		} else {
+			this.#name = path;
+			this.unsaved = true;
+		}
+	}
+
+	/**
+	 * @param {string} path
+	 * @param {string[]} [pathParts]
+	 */
+	#updatePath(path, pathParts) {
+		this.#path = path;
+
+		if (!pathParts) {
+			if (!path.includes("\\")) {
+				pathParts = path.split("/");
+			} else {
+				pathParts = path.split("\\");
+			}
+		}
+
+		this.#pathParts = pathParts;
+		this.#name = trimJsonExt(pathParts[pathParts.length - 1]);
+	}
+
+	async getWorkflowData() {
+		const resp = await api.getUserData("workflows/" + this.path);
+		if (resp.status !== 200) {
+			alert(`Error loading workflow file '${this.path}': ${resp.status} ${resp.statusText}`);
+			return;
+		}
+		return await resp.json();
+	}
+
+	load = async () => {
+		if (this.isOpen) {
+			await this.manager.app.loadGraphData(this.changeTracker.activeState, true, true, this);
+		} else {
+			const data = await this.getWorkflowData();
+			if (!data) return;
+			await this.manager.app.loadGraphData(data, true, true, this);
+		}
+	};
+
+	async save(saveAs = false) {
+		if (!this.path || saveAs) {
+			return !!(await this.#save(null, false));
+		} else {
+			return !!(await this.#save(this.path, true));
+		}
+	}
+
+	/**
+	 * @param {boolean} value
+	 */
+	async favorite(value) {
+		try {
+			if (this.#isFavorite === value) return;
+			this.#isFavorite = value;
+			await this.manager.saveWorkflowMetadata();
+			this.manager.dispatchEvent(new CustomEvent("favorite", { detail: this }));
+		} catch (error) {
+			alert("Error favoriting workflow " + this.path + "\n" + (error.message ?? error));
+		}
+	}
+
+	/**
+	 * @param {string} path
+	 */
+	async rename(path) {
+		path = appendJsonExt(path);
+		let resp = await api.moveUserData("workflows/" + this.path, "workflows/" + path);
+
+		if (resp.status === 409) {
+			if (!confirm(`Workflow '${path}' already exists, do you want to overwrite it?`)) return resp;
+			resp = await api.moveUserData("workflows/" + this.path, "workflows/" + path, { overwrite: true });
+		}
+
+		if (resp.status !== 200) {
+			alert(`Error renaming workflow file '${this.path}': ${resp.status} ${resp.statusText}`);
+			return;
+		}
+
+		const isFav = this.isFavorite;
+		if (isFav) {
+			await this.favorite(false);
+		}
+		path = (await resp.json()).substring("workflows/".length);
+		this.#updatePath(path, null);
+		if (isFav) {
+			await this.favorite(true);
+		}
+		this.manager.dispatchEvent(new CustomEvent("rename", { detail: this }));
+		setStorageValue("Comfy.PreviousWorkflow", this.path ?? "");
+	}
+
+	async insert() {
+		const data = await this.getWorkflowData();
+		if (!data) return;
+
+		const old = localStorage.getItem("litegrapheditor_clipboard");
+		const graph = new LGraph(data);
+		const canvas = new LGraphCanvas(null, graph, { skip_events: true, skip_render: true });
+		canvas.selectNodes();
+		canvas.copyToClipboard();
+		this.manager.app.canvas.pasteFromClipboard();
+		localStorage.setItem("litegrapheditor_clipboard", old);
+	}
+
+	async delete() {
+		// TODO: fix delete of current workflow - should mark workflow as unsaved and when saving use old name by default
+
+		try {
+			if (this.isFavorite) {
+				await this.favorite(false);
+			}
+			await api.deleteUserData("workflows/" + this.path);
+			this.unsaved = true;
+			this.#path = null;
+			this.#pathParts = null;
+			this.manager.workflows.splice(this.manager.workflows.indexOf(this), 1);
+			this.manager.dispatchEvent(new CustomEvent("delete", { detail: this }));
+		} catch (error) {
+			alert(`Error deleting workflow: ${error.message || error}`);
+		}
+	}
+
+	track() {
+		if (this.changeTracker) {
+			this.changeTracker.restore();
+		} else {
+			this.changeTracker = new ChangeTracker(this);
+		}
+	}
+
+	/**
+	 * @param {string|null} path
+	 * @param {boolean} overwrite
+	 */
+	async #save(path, overwrite) {
+		if (!path) {
+			path = prompt("Save workflow as:", trimJsonExt(this.path) ?? this.name ?? "workflow");
+			if (!path) return;
+		}
+
+		path = appendJsonExt(path);
+
+		const p = await this.manager.app.graphToPrompt();
+		const json = JSON.stringify(p.workflow, null, 2);
+		let resp = await api.storeUserData("workflows/" + path, json, { stringify: false, throwOnError: false, overwrite });
+		if (resp.status === 409) {
+			if (!confirm(`Workflow '${path}' already exists, do you want to overwrite it?`)) return;
+			resp = await api.storeUserData("workflows/" + path, json, { stringify: false });
+		}
+
+		if (resp.status !== 200) {
+			alert(`Error saving workflow '${this.path}': ${resp.status} ${resp.statusText}`);
+			return;
+		}
+
+		path = (await resp.json()).substring("workflows/".length);
+
+		if (!this.path) {
+			// Saved new workflow, patch this instance
+			this.#updatePath(path, null);
+			await this.manager.loadWorkflows();
+			this.unsaved = false;
+			this.manager.dispatchEvent(new CustomEvent("rename", { detail: this }));
+			setStorageValue("Comfy.PreviousWorkflow", this.path ?? "");
+		} else if (path !== this.path) {
+			// Saved as, open the new copy
+			await this.manager.loadWorkflows();
+			const workflow = this.manager.workflowLookup[path];
+			await workflow.load();
+		} else {
+			// Normal save
+			this.unsaved = false;
+			this.manager.dispatchEvent(new CustomEvent("save", { detail: this }));
+		}
+
+		return true;
+	}
+}
diff --git a/web/style.css b/web/style.css
index 863840b2..e983b652 100644
--- a/web/style.css
+++ b/web/style.css
@@ -1,3 +1,5 @@
+@import url("scripts/ui/menu/menu.css");
+
 :root {
 	--fg-color: #000;
 	--bg-color: #fff;
@@ -10,12 +12,24 @@
 	--border-color: #4e4e4e;
 	--tr-even-bg-color: #222;
 	--tr-odd-bg-color: #353535;
+	--primary-bg: #236692;
+	--primary-fg: #ffffff;
+	--primary-hover-bg: #3485bb;
+	--primary-hover-fg: #ffffff;
+	--content-bg: #e0e0e0;
+	--content-fg: #000;
+	--content-hover-bg: #adadad;
+	--content-hover-fg: #000;
 }
 
 @media (prefers-color-scheme: dark) {
 	:root {
 		--fg-color: #fff;
 		--bg-color: #202020;
+		--content-bg: #4e4e4e;
+		--content-fg: #fff;
+		--content-hover-bg: #222;
+		--content-hover-fg: #fff;
 	}
 }
 
@@ -26,11 +40,41 @@ body {
 	overflow: hidden;
 	background-color: var(--bg-color);
 	color: var(--fg-color);
+	grid-template-columns: auto 1fr auto;
+	grid-template-rows: auto auto 1fr auto;
+	min-height: -webkit-fill-available;
+	max-height: -webkit-fill-available;
+	min-width: -webkit-fill-available;
+	max-width: -webkit-fill-available;
+}
+
+.comfyui-body-top {
+	order: 0;
+	grid-column: 1/-1;
+	z-index: 10;
+}
+
+.comfyui-body-left {
+	order: 1;
+	z-index: 10;
 }
 
 #graph-canvas {
 	width: 100%;
 	height: 100%;
+	order: 2;
+	grid-column: 1/-1;
+}
+
+.comfyui-body-right {
+	order: 3;
+	z-index: 10;
+}
+
+.comfyui-body-bottom {
+	order: 4;
+	grid-column: 1/-1;
+	z-index: 10;
 }
 
 .comfy-multiline-input {
@@ -197,6 +241,7 @@ button.comfy-close-menu-btn {
 .comfy-modal button:hover,
 .comfy-menu-actions button:hover {
 	filter: brightness(1.2);
+	will-change: transform;
 	cursor: pointer;
 }
 
@@ -363,6 +408,37 @@ dialog::backdrop {
 	background: rgba(0, 0, 0, 0.5);
 }
 
+.comfy-dialog.comfyui-dialog {
+	top: 0;
+}
+
+.comfy-dialog.comfy-modal {
+	font-family: Arial, sans-serif;
+	border-color: var(--bg-color);
+	box-shadow: none;
+  	border: 2px solid var(--border-color);
+}
+
+.comfy-dialog .comfy-modal-content {
+	flex-direction: row;
+	flex-wrap: wrap;
+	gap: 10px;
+	color: var(--fg-color);
+}
+
+.comfy-dialog .comfy-modal-content h3 {
+	margin-top: 0;
+}
+
+.comfy-dialog .comfy-modal-content > p {
+	width: 100%;
+}
+
+.comfy-dialog .comfy-modal-content > .comfyui-button {
+	flex: 1;
+	justify-content: center;
+}
+
 #comfy-settings-dialog {
 	padding: 0;
 	width: 41rem;
@@ -462,11 +538,13 @@ dialog::backdrop {
 	z-index: 9999 !important;
 	background-color: var(--comfy-menu-bg) !important;
 	filter: brightness(95%);
+	will-change: transform;
 }
 
 .litegraph.litecontextmenu .litemenu-entry:hover:not(.disabled):not(.separator) {
 	background-color: var(--comfy-menu-bg) !important;
 	filter: brightness(155%);
+	will-change: transform;
 	color: var(--input-text);
 }
 
@@ -527,12 +605,14 @@ dialog::backdrop {
 	color: var(--input-text);
 	background-color: var(--comfy-input-bg);
 	filter: brightness(80%);
+	will-change: transform;
 	padding-left: 0.2em;
 }
 
 .litegraph.lite-search-item.generic_type {
 	color: var(--input-text);
 	filter: brightness(50%);
+	will-change: transform;
 }
 
 @media only screen and (max-width: 450px) {
@@ -551,4 +631,8 @@ dialog::backdrop {
 		text-align: center;
 		border-top: none;
 	}
-}
\ No newline at end of file
+}
+
+audio.comfy-audio.empty-audio-widget {
+	display: none;
+}
diff --git a/web/types/comfy.d.ts b/web/types/comfy.d.ts
index f7129b55..9a338b34 100644
--- a/web/types/comfy.d.ts
+++ b/web/types/comfy.d.ts
@@ -10,24 +10,24 @@ export interface ComfyExtension {
 	 * Allows any initialisation, e.g. loading resources. Called after the canvas is created but before nodes are added
 	 * @param app The ComfyUI app instance
 	 */
-	init(app: ComfyApp): Promise<void>;
+	init?(app: ComfyApp): Promise<void>;
 	/**
 	 * Allows any additonal setup, called after the application is fully set up and running
 	 * @param app The ComfyUI app instance
 	 */
-	setup(app: ComfyApp): Promise<void>;
+	setup?(app: ComfyApp): Promise<void>;
 	/**
 	 * Called before nodes are registered with the graph
 	 * @param defs The collection of node definitions, add custom ones or edit existing ones
 	 * @param app The ComfyUI app instance
 	 */
-	addCustomNodeDefs(defs: Record<string, ComfyObjectInfo>, app: ComfyApp): Promise<void>;
+	addCustomNodeDefs?(defs: Record<string, ComfyObjectInfo>, app: ComfyApp): Promise<void>;
 	/**
 	 * Allows the extension to add custom widgets
 	 * @param app The ComfyUI app instance
 	 * @returns An array of {[widget name]: widget data}
 	 */
-	getCustomWidgets(
+	getCustomWidgets?(
 		app: ComfyApp
 	): Promise<
 		Record<string, (node, inputName, inputData, app) => { widget?: IWidget; minWidth?: number; minHeight?: number }>
@@ -38,12 +38,12 @@ export interface ComfyExtension {
 	 * @param nodeData The original node object info config object
 	 * @param app The ComfyUI app instance
 	 */
-	beforeRegisterNodeDef(nodeType: typeof LGraphNode, nodeData: ComfyObjectInfo, app: ComfyApp): Promise<void>;
+	beforeRegisterNodeDef?(nodeType: typeof LGraphNode, nodeData: ComfyObjectInfo, app: ComfyApp): Promise<void>;
 	/**
 	 * Allows the extension to register additional nodes with LGraph after standard nodes are added
 	 * @param app The ComfyUI app instance
 	 */
-	registerCustomNodes(app: ComfyApp): Promise<void>;
+	registerCustomNodes?(app: ComfyApp): Promise<void>;
 	/**
 	 * Allows the extension to modify a node that has been reloaded onto the graph.
 	 * If you break something in the backend and want to patch workflows in the frontend
@@ -51,13 +51,13 @@ export interface ComfyExtension {
 	 * @param node The node that has been loaded
 	 * @param app The ComfyUI app instance
 	 */
-	loadedGraphNode(node: LGraphNode, app: ComfyApp);
+	loadedGraphNode?(node: LGraphNode, app: ComfyApp);
 	/**
 	 * Allows the extension to run code after the constructor of the node
 	 * @param node The node that has been created
 	 * @param app The ComfyUI app instance
 	 */
-	nodeCreated(node: LGraphNode, app: ComfyApp);
+	nodeCreated?(node: LGraphNode, app: ComfyApp);
 }
 
 export type ComfyObjectInfo = {