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Merge branch 'master' into feat/correct_mem_in_docker

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Xuexue 2024-07-10 01:04:13 +08:00 committed by GitHub
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219 changed files with 145900 additions and 19925 deletions
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49
.ci/update_windows/update.py
View File

@ -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

8
.ci/update_windows/update_comfyui.bat
View File

@ -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

3
.ci/update_windows/update_comfyui_and_python_dependencies.bat
View File

@ -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

11
.ci/update_windows_cu118/update_comfyui_and_python_dependencies.bat
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@ -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

45
.github/ISSUE_TEMPLATE/bug-report.yml vendored Normal file
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@ -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

8
.github/ISSUE_TEMPLATE/config.yml vendored Normal file
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@ -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.

32
.github/ISSUE_TEMPLATE/feature-request.yml vendored Normal file
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@ -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

32
.github/ISSUE_TEMPLATE/user-support.yml vendored Normal file
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@ -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

63
.github/workflows/test-browser.yml vendored Normal file
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@ -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

71
.github/workflows/windows_release_cu118_dependencies.yml vendored
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@ -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

37
.github/workflows/windows_release_cu118_dependencies_2.yml vendored
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@ -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

79
.github/workflows/windows_release_cu118_package.yml vendored
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@ -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

13
.github/workflows/windows_release_dependencies.yml vendored
View File

@ -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

14
.github/workflows/windows_release_nightly_pytorch.yml vendored
View File

@ -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

8
.github/workflows/windows_release_package.yml vendored
View File

@ -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

4
.gitignore vendored
View File

@ -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/
/user/
*.log

41
CONTRIBUTING.md Normal file
View File

@ -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.

99
README.md
View File

@ -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.

91
app/user_manager.py
View File

@ -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)

126
comfy/cldm/cldm.py
View File

@ -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}

77
comfy/cldm/mmdit.py Normal file
View File

@ -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}

20
comfy/cli_args.py
View File

@ -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)

10
comfy/clip_model.py
View File

@ -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):

3
comfy/clip_vision.py
View File

@ -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:

7
comfy/conds.py
View File

@ -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))

194
comfy/controlnet.py
View File

@ -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)

30
comfy/diffusers_convert.py
View File

@ -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

37
comfy/extra_samplers/uni_pc.py
View File

@ -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')

52
comfy/gligen.py
View File

@ -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):

121
comfy/k_diffusion/deis.py Normal file
View File

@ -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

254
comfy/k_diffusion/sampling.py
View File

@ -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

104
comfy/latent_formats.py
View File

@ -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

282
comfy/ldm/audio/autoencoder.py Normal file
View File

@ -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))

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# 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
)

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# 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)]

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"""
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

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"""
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]}

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comfy/ldm/cascade/stage_a.py Normal file
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"""
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

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"""
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)

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"""
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)

95
comfy/ldm/cascade/stage_c_coder.py Normal file
View File

@ -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)

2
comfy/ldm/models/autoencoder.py
View File

@ -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

260
comfy/ldm/modules/attention.py
View File

@ -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)

978
comfy/ldm/modules/diffusionmodules/mmdit.py Normal file
View File

@ -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)

14
comfy/ldm/modules/diffusionmodules/model.py
View File

@ -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

62
comfy/ldm/modules/diffusionmodules/openaimodel.py
View File

@ -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')

22
comfy/ldm/modules/diffusionmodules/util.py
View File

@ -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

3
comfy/ldm/modules/sub_quadratic_attention.py
View File

@ -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)

65
comfy/lora.py
View File

@ -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

277
comfy/model_base.py
View File

@ -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

185
comfy/model_detection.py
View File

@ -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

393
comfy/model_management.py
View File

@ -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

338
comfy/model_patcher.py
View File

@ -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()

147
comfy/model_sampling.py
View File

@ -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))

114
comfy/ops.py
View File

@ -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

22
comfy/sa_t5.py Normal file
View File

@ -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)

94
comfy/sample.py
View File

@ -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

76
comfy/sampler_helpers.py Normal file
View File

@ -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))

409
comfy/samplers.py
View File

@ -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:

384
comfy/sd.py
View File

@ -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)

123
comfy/sd1_clip.py
View File

@ -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]

1
comfy/sd2_clip.py
View File

@ -1,5 +1,4 @@
from comfy import sd1_clip
import torch
import os
class SD2ClipHModel(sd1_clip.SDClipModel):

150
comfy/sd3_clip.py Normal file
View File

@ -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_

35
comfy/sdxl_clip.py
View File

@ -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)

308
comfy/supported_models.py
View File

@ -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]

23
comfy/supported_models_base.py
View File

@ -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

10
comfy/t2i_adapter/adapter.py
View File

@ -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]}

238
comfy/t5.py Normal file
View File

@ -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)

22
comfy/t5_config_base.json Normal file
View File

@ -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
}

22
comfy/t5_config_xxl.json Normal file
View File

@ -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
}

125
comfy/t5_tokenizer/special_tokens_map.json Normal file
View File

@ -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
}
}

129428
comfy/t5_tokenizer/tokenizer.json Normal file
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File diff suppressed because one or more lines are too long

939
comfy/t5_tokenizer/tokenizer_config.json Normal file
View File

@ -0,0 +1,939 @@
{
"added_tokens_decoder": {
"0": {
"content": "<pad>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"1": {
"content": "</s>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"2": {
"content": "<unk>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"32000": {
"content": "<extra_id_99>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"32001": {
"content": "<extra_id_98>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"32002": {
"content": "<extra_id_97>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"32003": {
"content": "<extra_id_96>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"32004": {
"content": "<extra_id_95>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"32005": {
"content": "<extra_id_94>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"32006": {
"content": "<extra_id_93>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"32007": {
"content": "<extra_id_92>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"32008": {
"content": "<extra_id_91>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"32009": {
"content": "<extra_id_90>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
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}

20
comfy/taesd/taesd.py
View File

@ -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

32
comfy/types.py Normal file
View File

@ -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]

181
comfy/utils.py
View File

@ -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

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@ -1,201 +0,0 @@
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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
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Copyright 2022 Kai Zhang (cskaizhang@gmail.com, https://cszn.github.io/). All rights reserved.
Licensed under the Apache License, Version 2.0 (the "License");
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(c) You must retain, in the Source form of any Derivative Works
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(d) If the Work includes a "NOTICE" text file as part of its
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and charge a fee for, acceptance of support, warranty, indemnity,
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Apache License
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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
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"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
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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
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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"
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Licensor for the purpose of discussing and improving the Work, but
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"Contributor" shall mean Licensor and any individual or Legal Entity
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2. Grant of Copyright License. Subject to the terms and conditions of
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(except as stated in this section) patent license to make, have made,
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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:
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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
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attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
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(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
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as part of the Derivative Works; within the Source form or
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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,
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5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
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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.
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8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
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and charge a fee for, acceptance of support, warranty, indemnity,
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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 "[]"
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Licensed under the Apache License, Version 2.0 (the "License");
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Apache License
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TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
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"You" (or "Your") shall mean an individual or Legal Entity
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"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
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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
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201
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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 "[]"
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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
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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
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See the License for the specific language governing permissions and
limitations under the License.

694
comfy_extras/chainner_models/architecture/LaMa.py
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@ -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

110
comfy_extras/chainner_models/architecture/OmniSR/ChannelAttention.py
View File

@ -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)

201
comfy_extras/chainner_models/architecture/OmniSR/LICENSE
View File

@ -1,201 +0,0 @@
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Version 2.0, January 2004
http://www.apache.org/licenses/
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577
comfy_extras/chainner_models/architecture/OmniSR/OSA.py
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@ -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

60
comfy_extras/chainner_models/architecture/OmniSR/OSAG.py
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@ -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)

143
comfy_extras/chainner_models/architecture/OmniSR/OmniSR.py
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@ -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

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comfy_extras/chainner_models/architecture/OmniSR/esa.py
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#!/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

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comfy_extras/chainner_models/architecture/OmniSR/layernorm.py
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#!/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

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comfy_extras/chainner_models/architecture/OmniSR/pixelshuffle.py
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#!/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])

296
comfy_extras/chainner_models/architecture/RRDB.py
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#!/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)

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comfy_extras/chainner_models/architecture/SCUNet.py
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# 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)

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