Merge branch 'refs/heads/master' into ruff/C408

# Conflicts:
#	ruff.toml

.github/workflows/update-frontend.yml

@@ -0,0 +1,58 @@
+name: Update Frontend Release
+
+on:
+  workflow_dispatch:
+    inputs:
+      version:
+        description: "Frontend version to update to (e.g., 1.0.0)"
+        required: true
+        type: string
+
+jobs:
+  update-frontend:
+    runs-on: ubuntu-latest
+    permissions:
+      contents: write
+      pull-requests: write
+
+    steps:
+      - name: Checkout ComfyUI
+        uses: actions/checkout@v4
+      - 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
+      # Frontend asset will be downloaded to ComfyUI/web_custom_versions/Comfy-Org_ComfyUI_frontend/{version}
+      - name: Start ComfyUI server
+        run: |
+          python main.py --cpu --front-end-version Comfy-Org/ComfyUI_frontend@${{ github.event.inputs.version }} 2>&1 | tee console_output.log &
+          wait-for-it --service 127.0.0.1:8188 -t 30
+      - name: Configure Git
+        run: |
+          git config --global user.name "GitHub Action"
+          git config --global user.email "action@github.com"
+      # Replace existing frontend content with the new version and remove .js.map files
+      # See https://github.com/Comfy-Org/ComfyUI_frontend/issues/2145 for why we remove .js.map files
+      - name: Update frontend content
+        run: |
+          rm -rf web/
+          cp -r web_custom_versions/Comfy-Org_ComfyUI_frontend/${{ github.event.inputs.version }} web/
+          rm web/**/*.js.map
+      - name: Create Pull Request
+        uses: peter-evans/create-pull-request@v7
+        with:
+          token: ${{ secrets.PR_BOT_PAT }}
+          commit-message: "Update frontend to v${{ github.event.inputs.version }}"
+          title: "Frontend Update: v${{ github.event.inputs.version }}"
+          body: |
+            Automated PR to update frontend content to version ${{ github.event.inputs.version }}
+
+            This PR was created automatically by the frontend update workflow.
+          branch: release-${{ github.event.inputs.version }}
+          base: master
+          labels: Frontend,dependencies

.github/workflows/update-version.yml

@@ -0,0 +1,58 @@
+name: Update Version File
+
+on:
+  pull_request:
+    paths:
+      - "pyproject.toml"
+    branches:
+      - master
+
+jobs:
+  update-version:
+    runs-on: ubuntu-latest
+    # Don't run on fork PRs
+    if: github.event.pull_request.head.repo.full_name == github.repository
+    permissions:
+      pull-requests: write
+      contents: write
+
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.11"
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+
+      - name: Update comfyui_version.py
+        run: |
+          # Read version from pyproject.toml and update comfyui_version.py
+          python -c '
+          import tomllib
+
+          # Read version from pyproject.toml
+          with open("pyproject.toml", "rb") as f:
+              config = tomllib.load(f)
+              version = config["project"]["version"]
+
+          # Write version to comfyui_version.py
+          with open("comfyui_version.py", "w") as f:
+              f.write("# This file is automatically generated by the build process when version is\n")
+              f.write("# updated in pyproject.toml.\n")
+              f.write(f"__version__ = \"{version}\"\n")
+          '
+
+      - name: Commit changes
+        run: |
+          git config --local user.name "github-actions"
+          git config --local user.email "github-actions@github.com"
+          git fetch origin ${{ github.head_ref }}
+          git checkout -B ${{ github.head_ref }} origin/${{ github.head_ref }}
+          git add comfyui_version.py
+          git diff --quiet && git diff --staged --quiet || git commit -m "chore: Update comfyui_version.py to match pyproject.toml"
+          git push origin HEAD:${{ github.head_ref }}

.github/workflows/windows_release_nightly_pytorch.yml

@@ -7,19 +7,19 @@ on:
         description: 'cuda version'
         required: true
         type: string
-        default: "124"
+        default: "126"
 
       python_minor:
         description: 'python minor version'
         required: true
         type: string
-        default: "12"
+        default: "13"
 
       python_patch:
         description: 'python patch version'
         required: true
         type: string
-        default: "4"
+        default: "1"
 #  push:
 #    branches:
 #      - master

CODEOWNERS

@@ -17,7 +17,7 @@
 /app/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata
 
 # Frontend assets
-/web/ @huchenlei @webfiltered @pythongosssss
+/web/ @huchenlei @webfiltered @pythongosssss @yoland68 @robinjhuang
 
 # Extra nodes
 /comfy_extras/ @yoland68 @robinjhuang @huchenlei @pythongosssss @ltdrdata @Kosinkadink

app/app_settings.py

@@ -1,6 +1,7 @@
 import os
 import json
 from aiohttp import web
+import logging
 
 
 class AppSettings():
@@ -11,8 +12,12 @@ class AppSettings():
         file = self.user_manager.get_request_user_filepath(
             request, "comfy.settings.json")
         if os.path.isfile(file):
-            with open(file) as f:
-                return json.load(f)
+            try:
+                with open(file) as f:
+                    return json.load(f)
+            except:
+                logging.error(f"The user settings file is corrupted: {file}")
+                return {}
         else:
             return {}
 

comfy/hooks.py

@@ -16,91 +16,132 @@ import comfy.model_management
 import comfy.patcher_extension
 from node_helpers import conditioning_set_values
 
+# #######################################################################################################
+# Hooks explanation
+# -------------------
+# The purpose of hooks is to allow conds to influence sampling without the need for ComfyUI core code to
+# make explicit special cases like it does for ControlNet and GLIGEN.
+#
+# This is necessary for nodes/features that are intended for use with masked or scheduled conds, or those
+# that should run special code when a 'marked' cond is used in sampling.
+# #######################################################################################################
+
 class EnumHookMode(enum.Enum):
+    '''
+    Priority of hook memory optimization vs. speed, mostly related to WeightHooks.
+
+    MinVram: No caching will occur for any operations related to hooks.
+    MaxSpeed: Excess VRAM (and RAM, once VRAM is sufficiently depleted) will be used to cache hook weights when switching hook groups.
+    '''
     MinVram = "minvram"
     MaxSpeed = "maxspeed"
 
 class EnumHookType(enum.Enum):
+    '''
+    Hook types, each of which has different expected behavior.
+    '''
     Weight = "weight"
-    Patch = "patch"
     ObjectPatch = "object_patch"
-    AddModels = "add_models"
-    Callbacks = "callbacks"
-    Wrappers = "wrappers"
-    SetInjections = "add_injections"
+    AdditionalModels = "add_models"
+    TransformerOptions = "transformer_options"
+    Injections = "add_injections"
 
 class EnumWeightTarget(enum.Enum):
     Model = "model"
     Clip = "clip"
 
+class EnumHookScope(enum.Enum):
+    '''
+    Determines if hook should be limited in its influence over sampling.
+
+    AllConditioning: hook will affect all conds used in sampling.
+    HookedOnly: hook will only affect the conds it was attached to.
+    '''
+    AllConditioning = "all_conditioning"
+    HookedOnly = "hooked_only"
+
+
 class _HookRef:
     pass
 
-# NOTE: this is an example of how the should_register function should look
-def default_should_register(hook: 'Hook', model: 'ModelPatcher', model_options: dict, target: EnumWeightTarget, registered: list[Hook]):
+
+def default_should_register(hook: Hook, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup):
+    '''Example for how custom_should_register function can look like.'''
     return True
 
 
+def create_target_dict(target: EnumWeightTarget=None, **kwargs) -> dict[str]:
+    '''Creates base dictionary for use with Hooks' target param.'''
+    d = {}
+    if target is not None:
+        d['target'] = target
+    d.update(kwargs)
+    return d
+
+
 class Hook:
     def __init__(self, hook_type: EnumHookType=None, hook_ref: _HookRef=None, hook_id: str=None,
-                 hook_keyframe: 'HookKeyframeGroup'=None):
+                 hook_keyframe: HookKeyframeGroup=None, hook_scope=EnumHookScope.AllConditioning):
         self.hook_type = hook_type
+        '''Enum identifying the general class of this hook.'''
         self.hook_ref = hook_ref if hook_ref else _HookRef()
+        '''Reference shared between hook clones that have the same value. Should NOT be modified.'''
         self.hook_id = hook_id
+        '''Optional string ID to identify hook; useful if need to consolidate duplicates at registration time.'''
         self.hook_keyframe = hook_keyframe if hook_keyframe else HookKeyframeGroup()
+        '''Keyframe storage that can be referenced to get strength for current sampling step.'''
+        self.hook_scope = hook_scope
+        '''Scope of where this hook should apply in terms of the conds used in sampling run.'''
         self.custom_should_register = default_should_register
-        self.auto_apply_to_nonpositive = False
+        '''Can be overriden with a compatible function to decide if this hook should be registered without the need to override .should_register'''
 
     @property
     def strength(self):
         return self.hook_keyframe.strength
 
-    def initialize_timesteps(self, model: 'BaseModel'):
+    def initialize_timesteps(self, model: BaseModel):
         self.reset()
         self.hook_keyframe.initialize_timesteps(model)
 
     def reset(self):
         self.hook_keyframe.reset()
 
-    def clone(self, subtype: Callable=None):
-        if subtype is None:
-            subtype = type(self)
-        c: Hook = subtype()
+    def clone(self):
+        c: Hook = self.__class__()
         c.hook_type = self.hook_type
         c.hook_ref = self.hook_ref
         c.hook_id = self.hook_id
         c.hook_keyframe = self.hook_keyframe
+        c.hook_scope = self.hook_scope
         c.custom_should_register = self.custom_should_register
-        # TODO: make this do something
-        c.auto_apply_to_nonpositive = self.auto_apply_to_nonpositive
         return c
 
-    def should_register(self, model: 'ModelPatcher', model_options: dict, target: EnumWeightTarget, registered: list[Hook]):
-        return self.custom_should_register(self, model, model_options, target, registered)
+    def should_register(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup):
+        return self.custom_should_register(self, model, model_options, target_dict, registered)
 
-    def add_hook_patches(self, model: 'ModelPatcher', model_options: dict, target: EnumWeightTarget, registered: list[Hook]):
+    def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup):
         raise NotImplementedError("add_hook_patches should be defined for Hook subclasses")
 
-    def on_apply(self, model: 'ModelPatcher', transformer_options: dict[str]):
-        pass
-
-    def on_unapply(self, model: 'ModelPatcher', transformer_options: dict[str]):
-        pass
-
-    def __eq__(self, other: 'Hook'):
+    def __eq__(self, other: Hook):
         return self.__class__ == other.__class__ and self.hook_ref == other.hook_ref
 
     def __hash__(self):
         return hash(self.hook_ref)
 
 class WeightHook(Hook):
+    '''
+    Hook responsible for tracking weights to be applied to some model/clip.
+
+    Note, value of hook_scope is ignored and is treated as HookedOnly.
+    '''
     def __init__(self, strength_model=1.0, strength_clip=1.0):
-        super().__init__(hook_type=EnumHookType.Weight)
+        super().__init__(hook_type=EnumHookType.Weight, hook_scope=EnumHookScope.HookedOnly)
         self.weights: dict = None
         self.weights_clip: dict = None
         self.need_weight_init = True
         self._strength_model = strength_model
         self._strength_clip = strength_clip
+        self.hook_scope = EnumHookScope.HookedOnly # this value does not matter for WeightHooks, just for docs
 
     @property
     def strength_model(self):
@@ -110,36 +151,36 @@ class WeightHook(Hook):
     def strength_clip(self):
         return self._strength_clip * self.strength
 
-    def add_hook_patches(self, model: 'ModelPatcher', model_options: dict, target: EnumWeightTarget, registered: list[Hook]):
-        if not self.should_register(model, model_options, target, registered):
+    def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup):
+        if not self.should_register(model, model_options, target_dict, registered):
             return False
         weights = None
-        if target == EnumWeightTarget.Model:
-            strength = self._strength_model
-        else:
+
+        target = target_dict.get('target', None)
+        if target == EnumWeightTarget.Clip:
             strength = self._strength_clip
+        else:
+            strength = self._strength_model
 
         if self.need_weight_init:
             key_map = {}
-            if target == EnumWeightTarget.Model:
-                key_map = comfy.lora.model_lora_keys_unet(model.model, key_map)
-            else:
+            if target == EnumWeightTarget.Clip:
                 key_map = comfy.lora.model_lora_keys_clip(model.model, key_map)
+            else:
+                key_map = comfy.lora.model_lora_keys_unet(model.model, key_map)
             weights = comfy.lora.load_lora(self.weights, key_map, log_missing=False)
         else:
-            if target == EnumWeightTarget.Model:
-                weights = self.weights
-            else:
+            if target == EnumWeightTarget.Clip:
                 weights = self.weights_clip
+            else:
+                weights = self.weights
         model.add_hook_patches(hook=self, patches=weights, strength_patch=strength)
-        registered.append(self)
+        registered.add(self)
         return True
         # TODO: add logs about any keys that were not applied
 
-    def clone(self, subtype: Callable=None):
-        if subtype is None:
-            subtype = type(self)
-        c: WeightHook = super().clone(subtype)
+    def clone(self):
+        c: WeightHook = super().clone()
         c.weights = self.weights
         c.weights_clip = self.weights_clip
         c.need_weight_init = self.need_weight_init
@@ -147,127 +188,158 @@ class WeightHook(Hook):
         c._strength_clip = self._strength_clip
         return c
 
-class PatchHook(Hook):
-    def __init__(self):
-        super().__init__(hook_type=EnumHookType.Patch)
-        self.patches: dict = None
-
-    def clone(self, subtype: Callable=None):
-        if subtype is None:
-            subtype = type(self)
-        c: PatchHook = super().clone(subtype)
-        c.patches = self.patches
-        return c
-    # TODO: add functionality
-
 class ObjectPatchHook(Hook):
-    def __init__(self):
+    def __init__(self, object_patches: dict[str]=None,
+                 hook_scope=EnumHookScope.AllConditioning):
         super().__init__(hook_type=EnumHookType.ObjectPatch)
-        self.object_patches: dict = None
+        self.object_patches = object_patches
+        self.hook_scope = hook_scope
 
-    def clone(self, subtype: Callable=None):
-        if subtype is None:
-            subtype = type(self)
-        c: ObjectPatchHook = super().clone(subtype)
+    def clone(self):
+        c: ObjectPatchHook = super().clone()
         c.object_patches = self.object_patches
         return c
-    # TODO: add functionality
 
-class AddModelsHook(Hook):
-    def __init__(self, key: str=None, models: list['ModelPatcher']=None):
-        super().__init__(hook_type=EnumHookType.AddModels)
-        self.key = key
+    def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup):
+        raise NotImplementedError("ObjectPatchHook is not supported yet in ComfyUI.")
+
+class AdditionalModelsHook(Hook):
+    '''
+    Hook responsible for telling model management any additional models that should be loaded.
+
+    Note, value of hook_scope is ignored and is treated as AllConditioning.
+    '''
+    def __init__(self, models: list[ModelPatcher]=None, key: str=None):
+        super().__init__(hook_type=EnumHookType.AdditionalModels)
         self.models = models
-        self.append_when_same = True
-
-    def clone(self, subtype: Callable=None):
-        if subtype is None:
-            subtype = type(self)
-        c: AddModelsHook = super().clone(subtype)
-        c.key = self.key
-        c.models = self.models.copy() if self.models else self.models
-        c.append_when_same = self.append_when_same
-        return c
-    # TODO: add functionality
-
-class CallbackHook(Hook):
-    def __init__(self, key: str=None, callback: Callable=None):
-        super().__init__(hook_type=EnumHookType.Callbacks)
         self.key = key
-        self.callback = callback
 
-    def clone(self, subtype: Callable=None):
-        if subtype is None:
-            subtype = type(self)
-        c: CallbackHook = super().clone(subtype)
+    def clone(self):
+        c: AdditionalModelsHook = super().clone()
+        c.models = self.models.copy() if self.models else self.models
         c.key = self.key
-        c.callback = self.callback
-        return c
-    # TODO: add functionality
-
-class WrapperHook(Hook):
-    def __init__(self, wrappers_dict: dict[str, dict[str, dict[str, list[Callable]]]]=None):
-        super().__init__(hook_type=EnumHookType.Wrappers)
-        self.wrappers_dict = wrappers_dict
-
-    def clone(self, subtype: Callable=None):
-        if subtype is None:
-            subtype = type(self)
-        c: WrapperHook = super().clone(subtype)
-        c.wrappers_dict = self.wrappers_dict
         return c
 
-    def add_hook_patches(self, model: 'ModelPatcher', model_options: dict, target: EnumWeightTarget, registered: list[Hook]):
-        if not self.should_register(model, model_options, target, registered):
+    def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup):
+        if not self.should_register(model, model_options, target_dict, registered):
             return False
-        add_model_options = {"transformer_options": self.wrappers_dict}
-        comfy.patcher_extension.merge_nested_dicts(model_options, add_model_options, copy_dict1=False)
-        registered.append(self)
+        registered.add(self)
         return True
 
-class SetInjectionsHook(Hook):
-    def __init__(self, key: str=None, injections: list['PatcherInjection']=None):
-        super().__init__(hook_type=EnumHookType.SetInjections)
+class TransformerOptionsHook(Hook):
+    '''
+    Hook responsible for adding wrappers, callbacks, patches, or anything else related to transformer_options.
+    '''
+    def __init__(self, transformers_dict: dict[str, dict[str, dict[str, list[Callable]]]]=None,
+                 hook_scope=EnumHookScope.AllConditioning):
+        super().__init__(hook_type=EnumHookType.TransformerOptions)
+        self.transformers_dict = transformers_dict
+        self.hook_scope = hook_scope
+        self._skip_adding = False
+        '''Internal value used to avoid double load of transformer_options when hook_scope is AllConditioning.'''
+
+    def clone(self):
+        c: TransformerOptionsHook = super().clone()
+        c.transformers_dict = self.transformers_dict
+        c._skip_adding = self._skip_adding
+        return c
+
+    def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup):
+        if not self.should_register(model, model_options, target_dict, registered):
+            return False
+        # NOTE: to_load_options will be used to manually load patches/wrappers/callbacks from hooks
+        self._skip_adding = False
+        if self.hook_scope == EnumHookScope.AllConditioning:
+            add_model_options = {"transformer_options": self.transformers_dict,
+                                 "to_load_options": self.transformers_dict}
+            # skip_adding if included in AllConditioning to avoid double loading
+            self._skip_adding = True
+        else:
+            add_model_options = {"to_load_options": self.transformers_dict}
+        registered.add(self)
+        comfy.patcher_extension.merge_nested_dicts(model_options, add_model_options, copy_dict1=False)
+        return True
+
+    def on_apply_hooks(self, model: ModelPatcher, transformer_options: dict[str]):
+        if not self._skip_adding:
+            comfy.patcher_extension.merge_nested_dicts(transformer_options, self.transformers_dict, copy_dict1=False)
+
+WrapperHook = TransformerOptionsHook
+'''Only here for backwards compatibility, WrapperHook is identical to TransformerOptionsHook.'''
+
+class InjectionsHook(Hook):
+    def __init__(self, key: str=None, injections: list[PatcherInjection]=None,
+                 hook_scope=EnumHookScope.AllConditioning):
+        super().__init__(hook_type=EnumHookType.Injections)
         self.key = key
         self.injections = injections
+        self.hook_scope = hook_scope
 
-    def clone(self, subtype: Callable=None):
-        if subtype is None:
-            subtype = type(self)
-        c: SetInjectionsHook = super().clone(subtype)
+    def clone(self):
+        c: InjectionsHook = super().clone()
         c.key = self.key
         c.injections = self.injections.copy() if self.injections else self.injections
         return c
 
-    def add_hook_injections(self, model: 'ModelPatcher'):
-        # TODO: add functionality
-        pass
+    def add_hook_patches(self, model: ModelPatcher, model_options: dict, target_dict: dict[str], registered: HookGroup):
+        raise NotImplementedError("InjectionsHook is not supported yet in ComfyUI.")
 
 class HookGroup:
+    '''
+    Stores groups of hooks, and allows them to be queried by type.
+
+    To prevent breaking their functionality, never modify the underlying self.hooks or self._hook_dict vars directly;
+    always use the provided functions on HookGroup.
+    '''
     def __init__(self):
         self.hooks: list[Hook] = []
+        self._hook_dict: dict[EnumHookType, list[Hook]] = {}
+
+    def __len__(self):
+        return len(self.hooks)
 
     def add(self, hook: Hook):
         if hook not in self.hooks:
             self.hooks.append(hook)
+            self._hook_dict.setdefault(hook.hook_type, []).append(hook)
+
+    def remove(self, hook: Hook):
+        if hook in self.hooks:
+            self.hooks.remove(hook)
+            self._hook_dict[hook.hook_type].remove(hook)
+
+    def get_type(self, hook_type: EnumHookType):
+        return self._hook_dict.get(hook_type, [])
 
     def contains(self, hook: Hook):
         return hook in self.hooks
 
+    def is_subset_of(self, other: HookGroup):
+        self_hooks = set(self.hooks)
+        other_hooks = set(other.hooks)
+        return self_hooks.issubset(other_hooks)
+
+    def new_with_common_hooks(self, other: HookGroup):
+        c = HookGroup()
+        for hook in self.hooks:
+            if other.contains(hook):
+                c.add(hook.clone())
+        return c
+
     def clone(self):
         c = HookGroup()
         for hook in self.hooks:
             c.add(hook.clone())
         return c
 
-    def clone_and_combine(self, other: 'HookGroup'):
+    def clone_and_combine(self, other: HookGroup):
         c = self.clone()
         if other is not None:
             for hook in other.hooks:
                 c.add(hook.clone())
         return c
 
-    def set_keyframes_on_hooks(self, hook_kf: 'HookKeyframeGroup'):
+    def set_keyframes_on_hooks(self, hook_kf: HookKeyframeGroup):
         if hook_kf is None:
             hook_kf = HookKeyframeGroup()
         else:
@@ -275,36 +347,29 @@ class HookGroup:
         for hook in self.hooks:
             hook.hook_keyframe = hook_kf
 
-    def get_dict_repr(self):
-        d: dict[EnumHookType, dict[Hook, None]] = {}
-        for hook in self.hooks:
-            with_type = d.setdefault(hook.hook_type, {})
-            with_type[hook] = None
-        return d
-
     def get_hooks_for_clip_schedule(self):
         scheduled_hooks: dict[WeightHook, list[tuple[tuple[float,float], HookKeyframe]]] = {}
-        for hook in self.hooks:
-            # only care about WeightHooks, for now
-            if hook.hook_type == EnumHookType.Weight:
-                hook_schedule = []
-                # if no hook keyframes, assign default value
-                if len(hook.hook_keyframe.keyframes) == 0:
-                    hook_schedule.append(((0.0, 1.0), None))
-                    scheduled_hooks[hook] = hook_schedule
-                    continue
-                # find ranges of values
-                prev_keyframe = hook.hook_keyframe.keyframes[0]
-                for keyframe in hook.hook_keyframe.keyframes:
-                    if keyframe.start_percent > prev_keyframe.start_percent and not math.isclose(keyframe.strength, prev_keyframe.strength):
-                        hook_schedule.append(((prev_keyframe.start_percent, keyframe.start_percent), prev_keyframe))
-                        prev_keyframe = keyframe
-                    elif keyframe.start_percent == prev_keyframe.start_percent:
-                        prev_keyframe = keyframe
-                # create final range, assuming last start_percent was not 1.0
-                if not math.isclose(prev_keyframe.start_percent, 1.0):
-                    hook_schedule.append(((prev_keyframe.start_percent, 1.0), prev_keyframe))
+        # only care about WeightHooks, for now
+        for hook in self.get_type(EnumHookType.Weight):
+            hook: WeightHook
+            hook_schedule = []
+            # if no hook keyframes, assign default value
+            if len(hook.hook_keyframe.keyframes) == 0:
+                hook_schedule.append(((0.0, 1.0), None))
                 scheduled_hooks[hook] = hook_schedule
+                continue
+            # find ranges of values
+            prev_keyframe = hook.hook_keyframe.keyframes[0]
+            for keyframe in hook.hook_keyframe.keyframes:
+                if keyframe.start_percent > prev_keyframe.start_percent and not math.isclose(keyframe.strength, prev_keyframe.strength):
+                    hook_schedule.append(((prev_keyframe.start_percent, keyframe.start_percent), prev_keyframe))
+                    prev_keyframe = keyframe
+                elif keyframe.start_percent == prev_keyframe.start_percent:
+                    prev_keyframe = keyframe
+            # create final range, assuming last start_percent was not 1.0
+            if not math.isclose(prev_keyframe.start_percent, 1.0):
+                hook_schedule.append(((prev_keyframe.start_percent, 1.0), prev_keyframe))
+            scheduled_hooks[hook] = hook_schedule
         # hooks should not have their schedules in a list of tuples
         all_ranges: list[tuple[float, float]] = []
         for range_kfs in scheduled_hooks.values():
@@ -336,7 +401,7 @@ class HookGroup:
             hook.reset()
 
     @staticmethod
-    def combine_all_hooks(hooks_list: list['HookGroup'], require_count=0) -> 'HookGroup':
+    def combine_all_hooks(hooks_list: list[HookGroup], require_count=0) -> HookGroup:
         actual: list[HookGroup] = []
         for group in hooks_list:
             if group is not None:
@@ -433,7 +498,7 @@ class HookKeyframeGroup:
         c._set_first_as_current()
         return c
 
-    def initialize_timesteps(self, model: 'BaseModel'):
+    def initialize_timesteps(self, model: BaseModel):
         for keyframe in self.keyframes:
             keyframe.start_t = model.model_sampling.percent_to_sigma(keyframe.start_percent)
 
@@ -442,7 +507,7 @@ class HookKeyframeGroup:
             return False
         if curr_t == self._curr_t:
             return False
-        max_sigma = torch.max(transformer_options["sigmas"])
+        max_sigma = torch.max(transformer_options["sample_sigmas"])
         prev_index = self._current_index
         prev_strength = self._current_strength
         # if met guaranteed steps, look for next keyframe in case need to switch
@@ -522,6 +587,17 @@ def get_sorted_list_via_attr(objects: list, attr: str) -> list:
         sorted_list.extend(object_list)
     return sorted_list
 
+def create_transformer_options_from_hooks(model: ModelPatcher, hooks: HookGroup,  transformer_options: dict[str]=None):
+    # if no hooks or is not a ModelPatcher for sampling, return empty dict
+    if hooks is None or model.is_clip:
+        return {}
+    if transformer_options is None:
+        transformer_options = {}
+    for hook in hooks.get_type(EnumHookType.TransformerOptions):
+        hook: TransformerOptionsHook
+        hook.on_apply_hooks(model, transformer_options)
+    return transformer_options
+
 def create_hook_lora(lora: dict[str, torch.Tensor], strength_model: float, strength_clip: float):
     hook_group = HookGroup()
     hook = WeightHook(strength_model=strength_model, strength_clip=strength_clip)
@@ -548,7 +624,7 @@ def create_hook_model_as_lora(weights_model, weights_clip, strength_model: float
     hook.need_weight_init = False
     return hook_group
 
-def get_patch_weights_from_model(model: 'ModelPatcher', discard_model_sampling=True):
+def get_patch_weights_from_model(model: ModelPatcher, discard_model_sampling=True):
     if model is None:
         return None
     patches_model: dict[str, torch.Tensor] = model.model.state_dict()
@@ -560,7 +636,7 @@ def get_patch_weights_from_model(model: 'ModelPatcher', discard_model_sampling=T
     return patches_model
 
 # NOTE: this function shows how to register weight hooks directly on the ModelPatchers
-def load_hook_lora_for_models(model: 'ModelPatcher', clip: 'CLIP', lora: dict[str, torch.Tensor],
+def load_hook_lora_for_models(model: ModelPatcher, clip: CLIP, lora: dict[str, torch.Tensor],
                               strength_model: float, strength_clip: float):
     key_map = {}
     if model is not None:
@@ -612,24 +688,26 @@ def _combine_hooks_from_values(c_dict: dict[str, HookGroup], values: dict[str, H
     else:
         c_dict[hooks_key] = cache[hooks_tuple]
 
-def conditioning_set_values_with_hooks(conditioning, values={}, append_hooks=True):
+def conditioning_set_values_with_hooks(conditioning, values={}, append_hooks=True,
+                                       cache: dict[tuple[HookGroup, HookGroup], HookGroup]=None):
     c = []
-    hooks_combine_cache: dict[tuple[HookGroup, HookGroup], HookGroup] = {}
+    if cache is None:
+        cache = {}
     for t in conditioning:
         n = [t[0], t[1].copy()]
         for k in values:
             if append_hooks and k == 'hooks':
-                _combine_hooks_from_values(n[1], values, hooks_combine_cache)
+                _combine_hooks_from_values(n[1], values, cache)
             else:
                 n[1][k] = values[k]
         c.append(n)
 
     return c
 
-def set_hooks_for_conditioning(cond, hooks: HookGroup, append_hooks=True):
+def set_hooks_for_conditioning(cond, hooks: HookGroup, append_hooks=True, cache: dict[tuple[HookGroup, HookGroup], HookGroup]=None):
     if hooks is None:
         return cond
-    return conditioning_set_values_with_hooks(cond, {'hooks': hooks}, append_hooks=append_hooks)
+    return conditioning_set_values_with_hooks(cond, {'hooks': hooks}, append_hooks=append_hooks, cache=cache)
 
 def set_timesteps_for_conditioning(cond, timestep_range: tuple[float,float]):
     if timestep_range is None:
@@ -664,9 +742,10 @@ def combine_with_new_conds(conds: list, new_conds: list):
 def set_conds_props(conds: list, strength: float, set_cond_area: str,
                    mask: torch.Tensor=None, hooks: HookGroup=None, timesteps_range: tuple[float,float]=None, append_hooks=True):
     final_conds = []
+    cache = {}
     for c in conds:
         # first, apply lora_hook to conditioning, if provided
-        c = set_hooks_for_conditioning(c, hooks, append_hooks=append_hooks)
+        c = set_hooks_for_conditioning(c, hooks, append_hooks=append_hooks, cache=cache)
         # next, apply mask to conditioning
         c = set_mask_for_conditioning(cond=c, mask=mask, strength=strength, set_cond_area=set_cond_area)
         # apply timesteps, if present
@@ -678,9 +757,10 @@ def set_conds_props(conds: list, strength: float, set_cond_area: str,
 def set_conds_props_and_combine(conds: list, new_conds: list, strength: float=1.0, set_cond_area: str="default",
                                mask: torch.Tensor=None, hooks: HookGroup=None, timesteps_range: tuple[float,float]=None, append_hooks=True):
     combined_conds = []
+    cache = {}
     for c, masked_c in zip(conds, new_conds):
         # first, apply lora_hook to new conditioning, if provided
-        masked_c = set_hooks_for_conditioning(masked_c, hooks, append_hooks=append_hooks)
+        masked_c = set_hooks_for_conditioning(masked_c, hooks, append_hooks=append_hooks, cache=cache)
         # next, apply mask to new conditioning, if provided
         masked_c = set_mask_for_conditioning(cond=masked_c, mask=mask, set_cond_area=set_cond_area, strength=strength)
         # apply timesteps, if present
@@ -692,9 +772,10 @@ def set_conds_props_and_combine(conds: list, new_conds: list, strength: float=1.
 def set_default_conds_and_combine(conds: list, new_conds: list,
                                    hooks: HookGroup=None, timesteps_range: tuple[float,float]=None, append_hooks=True):
     combined_conds = []
+    cache = {}
     for c, new_c in zip(conds, new_conds):
         # first, apply lora_hook to new conditioning, if provided
-        new_c = set_hooks_for_conditioning(new_c, hooks, append_hooks=append_hooks)
+        new_c = set_hooks_for_conditioning(new_c, hooks, append_hooks=append_hooks, cache=cache)
         # next, add default_cond key to cond so that during sampling, it can be identified
         new_c = conditioning_set_values(new_c, {'default': True})
         # apply timesteps, if present

comfy/k_diffusion/sampling.py

@@ -70,8 +70,14 @@ def get_ancestral_step(sigma_from, sigma_to, eta=1.):
     return sigma_down, sigma_up
 
 
-def default_noise_sampler(x):
-    return lambda sigma, sigma_next: torch.randn_like(x)
+def default_noise_sampler(x, seed=None):
+    if seed is not None:
+        generator = torch.Generator(device=x.device)
+        generator.manual_seed(seed)
+    else:
+        generator = None
+
+    return lambda sigma, sigma_next: torch.randn(x.size(), dtype=x.dtype, layout=x.layout, device=x.device, generator=generator)
 
 
 class BatchedBrownianTree:
@@ -168,7 +174,8 @@ def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, dis
         return sample_euler_ancestral_RF(model, x, sigmas, extra_args, callback, disable, eta, s_noise, noise_sampler)
     """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
+    seed = extra_args.get("seed", None)
+    noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     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)
@@ -189,7 +196,8 @@ def sample_euler_ancestral(model, x, sigmas, extra_args=None, callback=None, dis
 def sample_euler_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1.0, 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
+    seed = extra_args.get("seed", None)
+    noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     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)
@@ -290,7 +298,8 @@ def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=None, dis
 
     """Ancestral sampling with DPM-Solver second-order 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
+    seed = extra_args.get("seed", None)
+    noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     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)
@@ -318,7 +327,8 @@ def sample_dpm_2_ancestral(model, x, sigmas, extra_args=None, callback=None, dis
 def sample_dpm_2_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
     """Ancestral sampling with DPM-Solver second-order 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
+    seed = extra_args.get("seed", None)
+    noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     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)
@@ -465,7 +475,7 @@ class DPMSolver(nn.Module):
         return x_3, eps_cache
 
     def dpm_solver_fast(self, x, t_start, t_end, nfe, eta=0., s_noise=1., noise_sampler=None):
-        noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+        noise_sampler = default_noise_sampler(x, seed=self.extra_args.get("seed", None)) if noise_sampler is None else noise_sampler
         if not t_end > t_start and eta:
             raise ValueError('eta must be 0 for reverse sampling')
 
@@ -504,7 +514,7 @@ class DPMSolver(nn.Module):
         return x
 
     def dpm_solver_adaptive(self, x, t_start, t_end, order=3, rtol=0.05, atol=0.0078, h_init=0.05, pcoeff=0., icoeff=1., dcoeff=0., accept_safety=0.81, eta=0., s_noise=1., noise_sampler=None):
-        noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+        noise_sampler = default_noise_sampler(x, seed=self.extra_args.get("seed", None)) if noise_sampler is None else noise_sampler
         if order not in {2, 3}:
             raise ValueError('order should be 2 or 3')
         forward = t_end > t_start
@@ -591,7 +601,8 @@ def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None,
 
     """Ancestral sampling with DPM-Solver++(2S) second-order 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
+    seed = extra_args.get("seed", None)
+    noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
     sigma_fn = lambda t: t.neg().exp()
     t_fn = lambda sigma: sigma.log().neg()
@@ -625,7 +636,8 @@ def sample_dpmpp_2s_ancestral(model, x, sigmas, extra_args=None, callback=None,
 def sample_dpmpp_2s_ancestral_RF(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
     """Ancestral sampling with DPM-Solver++(2S) second-order 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
+    seed = extra_args.get("seed", None)
+    noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
     sigma_fn = lambda lbda: (lbda.exp() + 1) ** -1
     lambda_fn = lambda sigma: ((1-sigma)/sigma).log()
@@ -882,7 +894,8 @@ def DDPMSampler_step(x, sigma, sigma_prev, noise, noise_sampler):
 
 def generic_step_sampler(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None, step_function=None):
     extra_args = {} if extra_args is None else extra_args
-    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+    seed = extra_args.get("seed", None)
+    noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     s_in = x.new_ones([x.shape[0]])
 
     for i in trange(len(sigmas) - 1, disable=disable):
@@ -902,7 +915,8 @@ def sample_ddpm(model, x, sigmas, extra_args=None, callback=None, disable=None,
 @torch.no_grad()
 def sample_lcm(model, x, sigmas, extra_args=None, callback=None, disable=None, noise_sampler=None):
     extra_args = {} if extra_args is None else extra_args
-    noise_sampler = default_noise_sampler(x) if noise_sampler is None else noise_sampler
+    seed = extra_args.get("seed", None)
+    noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
     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)
@@ -1153,7 +1167,8 @@ def sample_euler_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disabl
 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
+    seed = extra_args.get("seed", None)
+    noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
 
     temp = [0]
     def post_cfg_function(args):
@@ -1179,7 +1194,8 @@ def sample_euler_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=No
 def sample_dpmpp_2s_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
     """Ancestral sampling with DPM-Solver++(2S) second-order 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
+    seed = extra_args.get("seed", None)
+    noise_sampler = default_noise_sampler(x, seed=seed) if noise_sampler is None else noise_sampler
 
     temp = [0]
     def post_cfg_function(args):

comfy/latent_formats.py

@@ -382,3 +382,28 @@ class HunyuanVideo(LatentFormat):
     ]
 
     latent_rgb_factors_bias = [ 0.0259, -0.0192, -0.0761]
+
+class Cosmos1CV8x8x8(LatentFormat):
+    latent_channels = 16
+    latent_dimensions = 3
+
+    latent_rgb_factors = [
+        [ 0.1817,  0.2284,  0.2423],
+        [-0.0586, -0.0862, -0.3108],
+        [-0.4703, -0.4255, -0.3995],
+        [ 0.0803,  0.1963,  0.1001],
+        [-0.0820, -0.1050,  0.0400],
+        [ 0.2511,  0.3098,  0.2787],
+        [-0.1830, -0.2117, -0.0040],
+        [-0.0621, -0.2187, -0.0939],
+        [ 0.3619,  0.1082,  0.1455],
+        [ 0.3164,  0.3922,  0.2575],
+        [ 0.1152,  0.0231, -0.0462],
+        [-0.1434, -0.3609, -0.3665],
+        [ 0.0635,  0.1471,  0.1680],
+        [-0.3635, -0.1963, -0.3248],
+        [-0.1865,  0.0365,  0.2346],
+        [ 0.0447,  0.0994,  0.0881]
+    ]
+
+    latent_rgb_factors_bias = [-0.1223, -0.1889, -0.1976]

comfy/ldm/cosmos/blocks.py

@@ -0,0 +1,804 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+from typing import Optional
+import logging
+
+import numpy as np
+import torch
+from einops import rearrange, repeat
+from einops.layers.torch import Rearrange
+from torch import nn
+
+from comfy.ldm.modules.diffusionmodules.mmdit import RMSNorm
+from comfy.ldm.modules.attention import optimized_attention
+
+
+def apply_rotary_pos_emb(
+    t: torch.Tensor,
+    freqs: torch.Tensor,
+) -> torch.Tensor:
+    t_ = t.reshape(*t.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2).float()
+    t_out = freqs[..., 0] * t_[..., 0] + freqs[..., 1] * t_[..., 1]
+    t_out = t_out.movedim(-1, -2).reshape(*t.shape).type_as(t)
+    return t_out
+
+
+def get_normalization(name: str, channels: int, weight_args={}):
+    if name == "I":
+        return nn.Identity()
+    elif name == "R":
+        return RMSNorm(channels, elementwise_affine=True, eps=1e-6, **weight_args)
+    else:
+        raise ValueError(f"Normalization {name} not found")
+
+
+class BaseAttentionOp(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+
+class Attention(nn.Module):
+    """
+    Generalized attention impl.
+
+    Allowing for both self-attention and cross-attention configurations depending on whether a `context_dim` is provided.
+    If `context_dim` is None, self-attention is assumed.
+
+    Parameters:
+        query_dim (int): Dimension of each query vector.
+        context_dim (int, optional): Dimension of each context vector. If None, self-attention is assumed.
+        heads (int, optional): Number of attention heads. Defaults to 8.
+        dim_head (int, optional): Dimension of each head. Defaults to 64.
+        dropout (float, optional): Dropout rate applied to the output of the attention block. Defaults to 0.0.
+        attn_op (BaseAttentionOp, optional): Custom attention operation to be used instead of the default.
+        qkv_bias (bool, optional): If True, adds a learnable bias to query, key, and value projections. Defaults to False.
+        out_bias (bool, optional): If True, adds a learnable bias to the output projection. Defaults to False.
+        qkv_norm (str, optional): A string representing normalization strategies for query, key, and value projections.
+                                  Defaults to "SSI".
+        qkv_norm_mode (str, optional): A string representing normalization mode for query, key, and value projections.
+                                        Defaults to 'per_head'. Only support 'per_head'.
+
+    Examples:
+        >>> attn = Attention(query_dim=128, context_dim=256, heads=4, dim_head=32, dropout=0.1)
+        >>> query = torch.randn(10, 128)  # Batch size of 10
+        >>> context = torch.randn(10, 256)  # Batch size of 10
+        >>> output = attn(query, context)  # Perform the attention operation
+
+    Note:
+        https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
+    """
+
+    def __init__(
+        self,
+        query_dim: int,
+        context_dim=None,
+        heads=8,
+        dim_head=64,
+        dropout=0.0,
+        attn_op: Optional[BaseAttentionOp] = None,
+        qkv_bias: bool = False,
+        out_bias: bool = False,
+        qkv_norm: str = "SSI",
+        qkv_norm_mode: str = "per_head",
+        backend: str = "transformer_engine",
+        qkv_format: str = "bshd",
+        weight_args={},
+        operations=None,
+    ) -> None:
+        super().__init__()
+
+        self.is_selfattn = context_dim is None  # self attention
+
+        inner_dim = dim_head * heads
+        context_dim = query_dim if context_dim is None else context_dim
+
+        self.heads = heads
+        self.dim_head = dim_head
+        self.qkv_norm_mode = qkv_norm_mode
+        self.qkv_format = qkv_format
+
+        if self.qkv_norm_mode == "per_head":
+            norm_dim = dim_head
+        else:
+            raise ValueError(f"Normalization mode {self.qkv_norm_mode} not found, only support 'per_head'")
+
+        self.backend = backend
+
+        self.to_q = nn.Sequential(
+            operations.Linear(query_dim, inner_dim, bias=qkv_bias, **weight_args),
+            get_normalization(qkv_norm[0], norm_dim),
+        )
+        self.to_k = nn.Sequential(
+            operations.Linear(context_dim, inner_dim, bias=qkv_bias, **weight_args),
+            get_normalization(qkv_norm[1], norm_dim),
+        )
+        self.to_v = nn.Sequential(
+            operations.Linear(context_dim, inner_dim, bias=qkv_bias, **weight_args),
+            get_normalization(qkv_norm[2], norm_dim),
+        )
+
+        self.to_out = nn.Sequential(
+            operations.Linear(inner_dim, query_dim, bias=out_bias, **weight_args),
+            nn.Dropout(dropout),
+        )
+
+    def cal_qkv(
+        self, x, context=None, mask=None, rope_emb=None, **kwargs
+    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        del kwargs
+
+
+        """
+        self.to_q, self.to_k, self.to_v are nn.Sequential with projection + normalization layers.
+        Before 07/24/2024, these modules normalize across all heads.
+        After 07/24/2024, to support tensor parallelism and follow the common practice in the community,
+        we support to normalize per head.
+        To keep the checkpoint copatibility with the previous code,
+        we keep the nn.Sequential but call the projection and the normalization layers separately.
+        We use a flag `self.qkv_norm_mode` to control the normalization behavior.
+        The default value of `self.qkv_norm_mode` is "per_head", which means we normalize per head.
+        """
+        if self.qkv_norm_mode == "per_head":
+            q = self.to_q[0](x)
+            context = x if context is None else context
+            k = self.to_k[0](context)
+            v = self.to_v[0](context)
+            q, k, v = map(
+                lambda t: rearrange(t, "s b (n c) -> b n s c", n=self.heads, c=self.dim_head),
+                (q, k, v),
+            )
+        else:
+            raise ValueError(f"Normalization mode {self.qkv_norm_mode} not found, only support 'per_head'")
+
+        q = self.to_q[1](q)
+        k = self.to_k[1](k)
+        v = self.to_v[1](v)
+        if self.is_selfattn and rope_emb is not None:  # only apply to self-attention!
+            q = apply_rotary_pos_emb(q, rope_emb)
+            k = apply_rotary_pos_emb(k, rope_emb)
+        return q, k, v
+
+    def cal_attn(self, q, k, v, mask=None):
+        out = optimized_attention(q, k, v, self.heads, skip_reshape=True, mask=mask, skip_output_reshape=True)
+        out = rearrange(out, " b n s c -> s b (n c)")
+        return self.to_out(out)
+
+    def forward(
+        self,
+        x,
+        context=None,
+        mask=None,
+        rope_emb=None,
+        **kwargs,
+    ):
+        """
+        Args:
+            x (Tensor): The query tensor of shape [B, Mq, K]
+            context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None
+        """
+        q, k, v = self.cal_qkv(x, context, mask, rope_emb=rope_emb, **kwargs)
+        return self.cal_attn(q, k, v, mask)
+
+
+class FeedForward(nn.Module):
+    """
+    Transformer FFN with optional gating
+
+    Parameters:
+        d_model (int): Dimensionality of input features.
+        d_ff (int): Dimensionality of the hidden layer.
+        dropout (float, optional): Dropout rate applied after the activation function. Defaults to 0.1.
+        activation (callable, optional): The activation function applied after the first linear layer.
+                                         Defaults to nn.ReLU().
+        is_gated (bool, optional): If set to True, incorporates gating mechanism to the feed-forward layer.
+                                   Defaults to False.
+        bias (bool, optional): If set to True, adds a bias to the linear layers. Defaults to True.
+
+    Example:
+        >>> ff = FeedForward(d_model=512, d_ff=2048)
+        >>> x = torch.randn(64, 10, 512)  # Example input tensor
+        >>> output = ff(x)
+        >>> print(output.shape)  # Expected shape: (64, 10, 512)
+    """
+
+    def __init__(
+        self,
+        d_model: int,
+        d_ff: int,
+        dropout: float = 0.1,
+        activation=nn.ReLU(),
+        is_gated: bool = False,
+        bias: bool = False,
+        weight_args={},
+        operations=None,
+    ) -> None:
+        super().__init__()
+
+        self.layer1 = operations.Linear(d_model, d_ff, bias=bias, **weight_args)
+        self.layer2 = operations.Linear(d_ff, d_model, bias=bias, **weight_args)
+
+        self.dropout = nn.Dropout(dropout)
+        self.activation = activation
+        self.is_gated = is_gated
+        if is_gated:
+            self.linear_gate = operations.Linear(d_model, d_ff, bias=False, **weight_args)
+
+    def forward(self, x: torch.Tensor):
+        g = self.activation(self.layer1(x))
+        if self.is_gated:
+            x = g * self.linear_gate(x)
+        else:
+            x = g
+        assert self.dropout.p == 0.0, "we skip dropout"
+        return self.layer2(x)
+
+
+class GPT2FeedForward(FeedForward):
+    def __init__(self, d_model: int, d_ff: int, dropout: float = 0.1, bias: bool = False, weight_args={}, operations=None):
+        super().__init__(
+            d_model=d_model,
+            d_ff=d_ff,
+            dropout=dropout,
+            activation=nn.GELU(),
+            is_gated=False,
+            bias=bias,
+            weight_args=weight_args,
+            operations=operations,
+        )
+
+    def forward(self, x: torch.Tensor):
+        assert self.dropout.p == 0.0, "we skip dropout"
+
+        x = self.layer1(x)
+        x = self.activation(x)
+        x = self.layer2(x)
+
+        return x
+
+
+def modulate(x, shift, scale):
+    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+
+class Timesteps(nn.Module):
+    def __init__(self, num_channels):
+        super().__init__()
+        self.num_channels = num_channels
+
+    def forward(self, timesteps):
+        half_dim = self.num_channels // 2
+        exponent = -math.log(10000) * torch.arange(half_dim, dtype=torch.float32, device=timesteps.device)
+        exponent = exponent / (half_dim - 0.0)
+
+        emb = torch.exp(exponent)
+        emb = timesteps[:, None].float() * emb[None, :]
+
+        sin_emb = torch.sin(emb)
+        cos_emb = torch.cos(emb)
+        emb = torch.cat([cos_emb, sin_emb], dim=-1)
+
+        return emb
+
+
+class TimestepEmbedding(nn.Module):
+    def __init__(self, in_features: int, out_features: int, use_adaln_lora: bool = False, weight_args={}, operations=None):
+        super().__init__()
+        logging.debug(
+            f"Using AdaLN LoRA Flag:  {use_adaln_lora}. We enable bias if no AdaLN LoRA for backward compatibility."
+        )
+        self.linear_1 = operations.Linear(in_features, out_features, bias=not use_adaln_lora, **weight_args)
+        self.activation = nn.SiLU()
+        self.use_adaln_lora = use_adaln_lora
+        if use_adaln_lora:
+            self.linear_2 = operations.Linear(out_features, 3 * out_features, bias=False, **weight_args)
+        else:
+            self.linear_2 = operations.Linear(out_features, out_features, bias=True, **weight_args)
+
+    def forward(self, sample: torch.Tensor) -> torch.Tensor:
+        emb = self.linear_1(sample)
+        emb = self.activation(emb)
+        emb = self.linear_2(emb)
+
+        if self.use_adaln_lora:
+            adaln_lora_B_3D = emb
+            emb_B_D = sample
+        else:
+            emb_B_D = emb
+            adaln_lora_B_3D = None
+
+        return emb_B_D, adaln_lora_B_3D
+
+
+class FourierFeatures(nn.Module):
+    """
+    Implements a layer that generates Fourier features from input tensors, based on randomly sampled
+    frequencies and phases. This can help in learning high-frequency functions in low-dimensional problems.
+
+    [B] -> [B, D]
+
+    Parameters:
+        num_channels (int): The number of Fourier features to generate.
+        bandwidth (float, optional): The scaling factor for the frequency of the Fourier features. Defaults to 1.
+        normalize (bool, optional): If set to True, the outputs are scaled by sqrt(2), usually to normalize
+                                    the variance of the features. Defaults to False.
+
+    Example:
+        >>> layer = FourierFeatures(num_channels=256, bandwidth=0.5, normalize=True)
+        >>> x = torch.randn(10, 256)  # Example input tensor
+        >>> output = layer(x)
+        >>> print(output.shape)  # Expected shape: (10, 256)
+    """
+
+    def __init__(self, num_channels, bandwidth=1, normalize=False):
+        super().__init__()
+        self.register_buffer("freqs", 2 * np.pi * bandwidth * torch.randn(num_channels), persistent=True)
+        self.register_buffer("phases", 2 * np.pi * torch.rand(num_channels), persistent=True)
+        self.gain = np.sqrt(2) if normalize else 1
+
+    def forward(self, x, gain: float = 1.0):
+        """
+        Apply the Fourier feature transformation to the input tensor.
+
+        Args:
+            x (torch.Tensor): The input tensor.
+            gain (float, optional): An additional gain factor applied during the forward pass. Defaults to 1.
+
+        Returns:
+            torch.Tensor: The transformed tensor, with Fourier features applied.
+        """
+        in_dtype = x.dtype
+        x = x.to(torch.float32).ger(self.freqs.to(torch.float32)).add(self.phases.to(torch.float32))
+        x = x.cos().mul(self.gain * gain).to(in_dtype)
+        return x
+
+
+class PatchEmbed(nn.Module):
+    """
+    PatchEmbed is a module for embedding patches from an input tensor by applying either 3D or 2D convolutional layers,
+    depending on the . This module can process inputs with temporal (video) and spatial (image) dimensions,
+    making it suitable for video and image processing tasks. It supports dividing the input into patches
+    and embedding each patch into a vector of size `out_channels`.
+
+    Parameters:
+    - spatial_patch_size (int): The size of each spatial patch.
+    - temporal_patch_size (int): The size of each temporal patch.
+    - in_channels (int): Number of input channels. Default: 3.
+    - out_channels (int): The dimension of the embedding vector for each patch. Default: 768.
+    - bias (bool): If True, adds a learnable bias to the output of the convolutional layers. Default: True.
+    """
+
+    def __init__(
+        self,
+        spatial_patch_size,
+        temporal_patch_size,
+        in_channels=3,
+        out_channels=768,
+        bias=True,
+        weight_args={},
+        operations=None,
+    ):
+        super().__init__()
+        self.spatial_patch_size = spatial_patch_size
+        self.temporal_patch_size = temporal_patch_size
+
+        self.proj = nn.Sequential(
+            Rearrange(
+                "b c (t r) (h m) (w n) -> b t h w (c r m n)",
+                r=temporal_patch_size,
+                m=spatial_patch_size,
+                n=spatial_patch_size,
+            ),
+            operations.Linear(
+                in_channels * spatial_patch_size * spatial_patch_size * temporal_patch_size, out_channels, bias=bias, **weight_args
+            ),
+        )
+        self.out = nn.Identity()
+
+    def forward(self, x):
+        """
+        Forward pass of the PatchEmbed module.
+
+        Parameters:
+        - x (torch.Tensor): The input tensor of shape (B, C, T, H, W) where
+            B is the batch size,
+            C is the number of channels,
+            T is the temporal dimension,
+            H is the height, and
+            W is the width of the input.
+
+        Returns:
+        - torch.Tensor: The embedded patches as a tensor, with shape b t h w c.
+        """
+        assert x.dim() == 5
+        _, _, T, H, W = x.shape
+        assert H % self.spatial_patch_size == 0 and W % self.spatial_patch_size == 0
+        assert T % self.temporal_patch_size == 0
+        x = self.proj(x)
+        return self.out(x)
+
+
+class FinalLayer(nn.Module):
+    """
+    The final layer of video DiT.
+    """
+
+    def __init__(
+        self,
+        hidden_size,
+        spatial_patch_size,
+        temporal_patch_size,
+        out_channels,
+        use_adaln_lora: bool = False,
+        adaln_lora_dim: int = 256,
+        weight_args={},
+        operations=None,
+    ):
+        super().__init__()
+        self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **weight_args)
+        self.linear = operations.Linear(
+            hidden_size, spatial_patch_size * spatial_patch_size * temporal_patch_size * out_channels, bias=False, **weight_args
+        )
+        self.hidden_size = hidden_size
+        self.n_adaln_chunks = 2
+        self.use_adaln_lora = use_adaln_lora
+        if use_adaln_lora:
+            self.adaLN_modulation = nn.Sequential(
+                nn.SiLU(),
+                operations.Linear(hidden_size, adaln_lora_dim, bias=False, **weight_args),
+                operations.Linear(adaln_lora_dim, self.n_adaln_chunks * hidden_size, bias=False, **weight_args),
+            )
+        else:
+            self.adaLN_modulation = nn.Sequential(
+                nn.SiLU(), operations.Linear(hidden_size, self.n_adaln_chunks * hidden_size, bias=False, **weight_args)
+            )
+
+    def forward(
+        self,
+        x_BT_HW_D,
+        emb_B_D,
+        adaln_lora_B_3D: Optional[torch.Tensor] = None,
+    ):
+        if self.use_adaln_lora:
+            assert adaln_lora_B_3D is not None
+            shift_B_D, scale_B_D = (self.adaLN_modulation(emb_B_D) + adaln_lora_B_3D[:, : 2 * self.hidden_size]).chunk(
+                2, dim=1
+            )
+        else:
+            shift_B_D, scale_B_D = self.adaLN_modulation(emb_B_D).chunk(2, dim=1)
+
+        B = emb_B_D.shape[0]
+        T = x_BT_HW_D.shape[0] // B
+        shift_BT_D, scale_BT_D = repeat(shift_B_D, "b d -> (b t) d", t=T), repeat(scale_B_D, "b d -> (b t) d", t=T)
+        x_BT_HW_D = modulate(self.norm_final(x_BT_HW_D), shift_BT_D, scale_BT_D)
+
+        x_BT_HW_D = self.linear(x_BT_HW_D)
+        return x_BT_HW_D
+
+
+class VideoAttn(nn.Module):
+    """
+    Implements video attention with optional cross-attention capabilities.
+
+    This module processes video features while maintaining their spatio-temporal structure. It can perform
+    self-attention within the video features or cross-attention with external context features.
+
+    Parameters:
+        x_dim (int): Dimension of input feature vectors
+        context_dim (Optional[int]): Dimension of context features for cross-attention. None for self-attention
+        num_heads (int): Number of attention heads
+        bias (bool): Whether to include bias in attention projections. Default: False
+        qkv_norm_mode (str): Normalization mode for query/key/value projections. Must be "per_head". Default: "per_head"
+        x_format (str): Format of input tensor. Must be "BTHWD". Default: "BTHWD"
+
+    Input shape:
+        - x: (T, H, W, B, D) video features
+        - context (optional): (M, B, D) context features for cross-attention
+        where:
+            T: temporal dimension
+            H: height
+            W: width
+            B: batch size
+            D: feature dimension
+            M: context sequence length
+    """
+
+    def __init__(
+        self,
+        x_dim: int,
+        context_dim: Optional[int],
+        num_heads: int,
+        bias: bool = False,
+        qkv_norm_mode: str = "per_head",
+        x_format: str = "BTHWD",
+        weight_args={},
+        operations=None,
+    ) -> None:
+        super().__init__()
+        self.x_format = x_format
+
+        self.attn = Attention(
+            x_dim,
+            context_dim,
+            num_heads,
+            x_dim // num_heads,
+            qkv_bias=bias,
+            qkv_norm="RRI",
+            out_bias=bias,
+            qkv_norm_mode=qkv_norm_mode,
+            qkv_format="sbhd",
+            weight_args=weight_args,
+            operations=operations,
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        context: Optional[torch.Tensor] = None,
+        crossattn_mask: Optional[torch.Tensor] = None,
+        rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """
+        Forward pass for video attention.
+
+        Args:
+            x (Tensor): Input tensor of shape (B, T, H, W, D) or (T, H, W, B, D) representing batches of video data.
+            context (Tensor): Context tensor of shape (B, M, D) or (M, B, D),
+            where M is the sequence length of the context.
+            crossattn_mask (Optional[Tensor]): An optional mask for cross-attention mechanisms.
+            rope_emb_L_1_1_D (Optional[Tensor]):
+            Rotary positional embedding tensor of shape (L, 1, 1, D). L == THW for current video training.
+
+        Returns:
+            Tensor: The output tensor with applied attention, maintaining the input shape.
+        """
+
+        x_T_H_W_B_D = x
+        context_M_B_D = context
+        T, H, W, B, D = x_T_H_W_B_D.shape
+        x_THW_B_D = rearrange(x_T_H_W_B_D, "t h w b d -> (t h w) b d")
+        x_THW_B_D = self.attn(
+            x_THW_B_D,
+            context_M_B_D,
+            crossattn_mask,
+            rope_emb=rope_emb_L_1_1_D,
+        )
+        x_T_H_W_B_D = rearrange(x_THW_B_D, "(t h w) b d -> t h w b d", h=H, w=W)
+        return x_T_H_W_B_D
+
+
+def adaln_norm_state(norm_state, x, scale, shift):
+    normalized = norm_state(x)
+    return normalized * (1 + scale) + shift
+
+
+class DITBuildingBlock(nn.Module):
+    """
+    A building block for the DiT (Diffusion Transformer) architecture that supports different types of
+    attention and MLP operations with adaptive layer normalization.
+
+    Parameters:
+        block_type (str): Type of block - one of:
+            - "cross_attn"/"ca": Cross-attention
+            - "full_attn"/"fa": Full self-attention
+            - "mlp"/"ff": MLP/feedforward block
+        x_dim (int): Dimension of input features
+        context_dim (Optional[int]): Dimension of context features for cross-attention
+        num_heads (int): Number of attention heads
+        mlp_ratio (float): MLP hidden dimension multiplier. Default: 4.0
+        bias (bool): Whether to use bias in layers. Default: False
+        mlp_dropout (float): Dropout rate for MLP. Default: 0.0
+        qkv_norm_mode (str): QKV normalization mode. Default: "per_head"
+        x_format (str): Input tensor format. Default: "BTHWD"
+        use_adaln_lora (bool): Whether to use AdaLN-LoRA. Default: False
+        adaln_lora_dim (int): Dimension for AdaLN-LoRA. Default: 256
+    """
+
+    def __init__(
+        self,
+        block_type: str,
+        x_dim: int,
+        context_dim: Optional[int],
+        num_heads: int,
+        mlp_ratio: float = 4.0,
+        bias: bool = False,
+        mlp_dropout: float = 0.0,
+        qkv_norm_mode: str = "per_head",
+        x_format: str = "BTHWD",
+        use_adaln_lora: bool = False,
+        adaln_lora_dim: int = 256,
+        weight_args={},
+        operations=None
+    ) -> None:
+        block_type = block_type.lower()
+
+        super().__init__()
+        self.x_format = x_format
+        if block_type in ["cross_attn", "ca"]:
+            self.block = VideoAttn(
+                x_dim,
+                context_dim,
+                num_heads,
+                bias=bias,
+                qkv_norm_mode=qkv_norm_mode,
+                x_format=self.x_format,
+                weight_args=weight_args,
+                operations=operations,
+            )
+        elif block_type in ["full_attn", "fa"]:
+            self.block = VideoAttn(
+                x_dim, None, num_heads, bias=bias, qkv_norm_mode=qkv_norm_mode, x_format=self.x_format, weight_args=weight_args, operations=operations
+            )
+        elif block_type in ["mlp", "ff"]:
+            self.block = GPT2FeedForward(x_dim, int(x_dim * mlp_ratio), dropout=mlp_dropout, bias=bias, weight_args=weight_args, operations=operations)
+        else:
+            raise ValueError(f"Unknown block type: {block_type}")
+
+        self.block_type = block_type
+        self.use_adaln_lora = use_adaln_lora
+
+        self.norm_state = nn.LayerNorm(x_dim, elementwise_affine=False, eps=1e-6)
+        self.n_adaln_chunks = 3
+        if use_adaln_lora:
+            self.adaLN_modulation = nn.Sequential(
+                nn.SiLU(),
+                operations.Linear(x_dim, adaln_lora_dim, bias=False, **weight_args),
+                operations.Linear(adaln_lora_dim, self.n_adaln_chunks * x_dim, bias=False, **weight_args),
+            )
+        else:
+            self.adaLN_modulation = nn.Sequential(nn.SiLU(), operations.Linear(x_dim, self.n_adaln_chunks * x_dim, bias=False, **weight_args))
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        emb_B_D: torch.Tensor,
+        crossattn_emb: torch.Tensor,
+        crossattn_mask: Optional[torch.Tensor] = None,
+        rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
+        adaln_lora_B_3D: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        """
+        Forward pass for dynamically configured blocks with adaptive normalization.
+
+        Args:
+            x (Tensor): Input tensor of shape (B, T, H, W, D) or (T, H, W, B, D).
+            emb_B_D (Tensor): Embedding tensor for adaptive layer normalization modulation.
+            crossattn_emb (Tensor): Tensor for cross-attention blocks.
+            crossattn_mask (Optional[Tensor]): Optional mask for cross-attention.
+            rope_emb_L_1_1_D (Optional[Tensor]):
+            Rotary positional embedding tensor of shape (L, 1, 1, D). L == THW for current video training.
+
+        Returns:
+            Tensor: The output tensor after processing through the configured block and adaptive normalization.
+        """
+        if self.use_adaln_lora:
+            shift_B_D, scale_B_D, gate_B_D = (self.adaLN_modulation(emb_B_D) + adaln_lora_B_3D).chunk(
+                self.n_adaln_chunks, dim=1
+            )
+        else:
+            shift_B_D, scale_B_D, gate_B_D = self.adaLN_modulation(emb_B_D).chunk(self.n_adaln_chunks, dim=1)
+
+        shift_1_1_1_B_D, scale_1_1_1_B_D, gate_1_1_1_B_D = (
+            shift_B_D.unsqueeze(0).unsqueeze(0).unsqueeze(0),
+            scale_B_D.unsqueeze(0).unsqueeze(0).unsqueeze(0),
+            gate_B_D.unsqueeze(0).unsqueeze(0).unsqueeze(0),
+        )
+
+        if self.block_type in ["mlp", "ff"]:
+            x = x + gate_1_1_1_B_D * self.block(
+                adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D),
+            )
+        elif self.block_type in ["full_attn", "fa"]:
+            x = x + gate_1_1_1_B_D * self.block(
+                adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D),
+                context=None,
+                rope_emb_L_1_1_D=rope_emb_L_1_1_D,
+            )
+        elif self.block_type in ["cross_attn", "ca"]:
+            x = x + gate_1_1_1_B_D * self.block(
+                adaln_norm_state(self.norm_state, x, scale_1_1_1_B_D, shift_1_1_1_B_D),
+                context=crossattn_emb,
+                crossattn_mask=crossattn_mask,
+                rope_emb_L_1_1_D=rope_emb_L_1_1_D,
+            )
+        else:
+            raise ValueError(f"Unknown block type: {self.block_type}")
+
+        return x
+
+
+class GeneralDITTransformerBlock(nn.Module):
+    """
+    A wrapper module that manages a sequence of DITBuildingBlocks to form a complete transformer layer.
+    Each block in the sequence is specified by a block configuration string.
+
+    Parameters:
+        x_dim (int): Dimension of input features
+        context_dim (int): Dimension of context features for cross-attention blocks
+        num_heads (int): Number of attention heads
+        block_config (str): String specifying block sequence (e.g. "ca-fa-mlp" for cross-attention,
+                          full-attention, then MLP)
+        mlp_ratio (float): MLP hidden dimension multiplier. Default: 4.0
+        x_format (str): Input tensor format. Default: "BTHWD"
+        use_adaln_lora (bool): Whether to use AdaLN-LoRA. Default: False
+        adaln_lora_dim (int): Dimension for AdaLN-LoRA. Default: 256
+
+    The block_config string uses "-" to separate block types:
+        - "ca"/"cross_attn": Cross-attention block
+        - "fa"/"full_attn": Full self-attention block
+        - "mlp"/"ff": MLP/feedforward block
+
+    Example:
+        block_config = "ca-fa-mlp" creates a sequence of:
+        1. Cross-attention block
+        2. Full self-attention block
+        3. MLP block
+    """
+
+    def __init__(
+        self,
+        x_dim: int,
+        context_dim: int,
+        num_heads: int,
+        block_config: str,
+        mlp_ratio: float = 4.0,
+        x_format: str = "BTHWD",
+        use_adaln_lora: bool = False,
+        adaln_lora_dim: int = 256,
+        weight_args={},
+        operations=None
+    ):
+        super().__init__()
+        self.blocks = nn.ModuleList()
+        self.x_format = x_format
+        for block_type in block_config.split("-"):
+            self.blocks.append(
+                DITBuildingBlock(
+                    block_type,
+                    x_dim,
+                    context_dim,
+                    num_heads,
+                    mlp_ratio,
+                    x_format=self.x_format,
+                    use_adaln_lora=use_adaln_lora,
+                    adaln_lora_dim=adaln_lora_dim,
+                    weight_args=weight_args,
+                    operations=operations,
+                )
+            )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        emb_B_D: torch.Tensor,
+        crossattn_emb: torch.Tensor,
+        crossattn_mask: Optional[torch.Tensor] = None,
+        rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
+        adaln_lora_B_3D: Optional[torch.Tensor] = None,
+        extra_per_block_pos_emb: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        if extra_per_block_pos_emb is not None:
+            x = x + extra_per_block_pos_emb
+        for block in self.blocks:
+            x = block(
+                x,
+                emb_B_D,
+                crossattn_emb,
+                crossattn_mask,
+                rope_emb_L_1_1_D=rope_emb_L_1_1_D,
+                adaln_lora_B_3D=adaln_lora_B_3D,
+            )
+        return x

comfy/ldm/cosmos/cosmos_tokenizer/patching.py

@@ -0,0 +1,355 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""The patcher and unpatcher implementation for 2D and 3D data.
+
+The idea of Haar wavelet is to compute LL, LH, HL, HH component as two 1D convolutions.
+One on the rows and one on the columns.
+For example, in 1D signal, we have [a, b], then the low-freq compoenent is [a + b] / 2 and high-freq is [a - b] / 2.
+We can use a 1D convolution with kernel [1, 1] and stride 2 to represent the L component.
+For H component, we can use a 1D convolution with kernel [1, -1] and stride 2.
+Although in principle, we typically only do additional Haar wavelet over the LL component. But here we do it for all
+   as we need to support downsampling for more than 2x.
+For example, 4x downsampling can be done by 2x Haar and additional 2x Haar, and the shape would be.
+   [3, 256, 256] -> [12, 128, 128] -> [48, 64, 64]
+"""
+
+import torch
+import torch.nn.functional as F
+from einops import rearrange
+
+_WAVELETS = {
+    "haar": torch.tensor([0.7071067811865476, 0.7071067811865476]),
+    "rearrange": torch.tensor([1.0, 1.0]),
+}
+_PERSISTENT = False
+
+
+class Patcher(torch.nn.Module):
+    """A module to convert image tensors into patches using torch operations.
+
+    The main difference from `class Patching` is that this module implements
+    all operations using torch, rather than python or numpy, for efficiency purpose.
+
+    It's bit-wise identical to the Patching module outputs, with the added
+    benefit of being torch.jit scriptable.
+    """
+
+    def __init__(self, patch_size=1, patch_method="haar"):
+        super().__init__()
+        self.patch_size = patch_size
+        self.patch_method = patch_method
+        self.register_buffer(
+            "wavelets", _WAVELETS[patch_method], persistent=_PERSISTENT
+        )
+        self.range = range(int(torch.log2(torch.tensor(self.patch_size)).item()))
+        self.register_buffer(
+            "_arange",
+            torch.arange(_WAVELETS[patch_method].shape[0]),
+            persistent=_PERSISTENT,
+        )
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, x):
+        if self.patch_method == "haar":
+            return self._haar(x)
+        elif self.patch_method == "rearrange":
+            return self._arrange(x)
+        else:
+            raise ValueError("Unknown patch method: " + self.patch_method)
+
+    def _dwt(self, x, mode="reflect", rescale=False):
+        dtype = x.dtype
+        h = self.wavelets.to(device=x.device)
+
+        n = h.shape[0]
+        g = x.shape[1]
+        hl = h.flip(0).reshape(1, 1, -1).repeat(g, 1, 1)
+        hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1)
+        hh = hh.to(dtype=dtype)
+        hl = hl.to(dtype=dtype)
+
+        x = F.pad(x, pad=(n - 2, n - 1, n - 2, n - 1), mode=mode).to(dtype)
+        xl = F.conv2d(x, hl.unsqueeze(2), groups=g, stride=(1, 2))
+        xh = F.conv2d(x, hh.unsqueeze(2), groups=g, stride=(1, 2))
+        xll = F.conv2d(xl, hl.unsqueeze(3), groups=g, stride=(2, 1))
+        xlh = F.conv2d(xl, hh.unsqueeze(3), groups=g, stride=(2, 1))
+        xhl = F.conv2d(xh, hl.unsqueeze(3), groups=g, stride=(2, 1))
+        xhh = F.conv2d(xh, hh.unsqueeze(3), groups=g, stride=(2, 1))
+
+        out = torch.cat([xll, xlh, xhl, xhh], dim=1)
+        if rescale:
+            out = out / 2
+        return out
+
+    def _haar(self, x):
+        for _ in self.range:
+            x = self._dwt(x, rescale=True)
+        return x
+
+    def _arrange(self, x):
+        x = rearrange(
+            x,
+            "b c (h p1) (w p2) -> b (c p1 p2) h w",
+            p1=self.patch_size,
+            p2=self.patch_size,
+        ).contiguous()
+        return x
+
+
+class Patcher3D(Patcher):
+    """A 3D discrete wavelet transform for video data, expects 5D tensor, i.e. a batch of videos."""
+
+    def __init__(self, patch_size=1, patch_method="haar"):
+        super().__init__(patch_method=patch_method, patch_size=patch_size)
+        self.register_buffer(
+            "patch_size_buffer",
+            patch_size * torch.ones([1], dtype=torch.int32),
+            persistent=_PERSISTENT,
+        )
+
+    def _dwt(self, x, wavelet, mode="reflect", rescale=False):
+        dtype = x.dtype
+        h = self.wavelets.to(device=x.device)
+
+        n = h.shape[0]
+        g = x.shape[1]
+        hl = h.flip(0).reshape(1, 1, -1).repeat(g, 1, 1)
+        hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1)
+        hh = hh.to(dtype=dtype)
+        hl = hl.to(dtype=dtype)
+
+        # Handles temporal axis.
+        x = F.pad(
+            x, pad=(max(0, n - 2), n - 1, n - 2, n - 1, n - 2, n - 1), mode=mode
+        ).to(dtype)
+        xl = F.conv3d(x, hl.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1))
+        xh = F.conv3d(x, hh.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1))
+
+        # Handles spatial axes.
+        xll = F.conv3d(xl, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1))
+        xlh = F.conv3d(xl, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1))
+        xhl = F.conv3d(xh, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1))
+        xhh = F.conv3d(xh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1))
+
+        xlll = F.conv3d(xll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))
+        xllh = F.conv3d(xll, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))
+        xlhl = F.conv3d(xlh, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))
+        xlhh = F.conv3d(xlh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))
+        xhll = F.conv3d(xhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))
+        xhlh = F.conv3d(xhl, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))
+        xhhl = F.conv3d(xhh, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))
+        xhhh = F.conv3d(xhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2))
+
+        out = torch.cat([xlll, xllh, xlhl, xlhh, xhll, xhlh, xhhl, xhhh], dim=1)
+        if rescale:
+            out = out / (2 * torch.sqrt(torch.tensor(2.0)))
+        return out
+
+    def _haar(self, x):
+        xi, xv = torch.split(x, [1, x.shape[2] - 1], dim=2)
+        x = torch.cat([xi.repeat_interleave(self.patch_size, dim=2), xv], dim=2)
+        for _ in self.range:
+            x = self._dwt(x, "haar", rescale=True)
+        return x
+
+    def _arrange(self, x):
+        xi, xv = torch.split(x, [1, x.shape[2] - 1], dim=2)
+        x = torch.cat([xi.repeat_interleave(self.patch_size, dim=2), xv], dim=2)
+        x = rearrange(
+            x,
+            "b c (t p1) (h p2) (w p3) -> b (c p1 p2 p3) t h w",
+            p1=self.patch_size,
+            p2=self.patch_size,
+            p3=self.patch_size,
+        ).contiguous()
+        return x
+
+
+class UnPatcher(torch.nn.Module):
+    """A module to convert patches into image tensorsusing torch operations.
+
+    The main difference from `class Unpatching` is that this module implements
+    all operations using torch, rather than python or numpy, for efficiency purpose.
+
+    It's bit-wise identical to the Unpatching module outputs, with the added
+    benefit of being torch.jit scriptable.
+    """
+
+    def __init__(self, patch_size=1, patch_method="haar"):
+        super().__init__()
+        self.patch_size = patch_size
+        self.patch_method = patch_method
+        self.register_buffer(
+            "wavelets", _WAVELETS[patch_method], persistent=_PERSISTENT
+        )
+        self.range = range(int(torch.log2(torch.tensor(self.patch_size)).item()))
+        self.register_buffer(
+            "_arange",
+            torch.arange(_WAVELETS[patch_method].shape[0]),
+            persistent=_PERSISTENT,
+        )
+        for param in self.parameters():
+            param.requires_grad = False
+
+    def forward(self, x):
+        if self.patch_method == "haar":
+            return self._ihaar(x)
+        elif self.patch_method == "rearrange":
+            return self._iarrange(x)
+        else:
+            raise ValueError("Unknown patch method: " + self.patch_method)
+
+    def _idwt(self, x, wavelet="haar", mode="reflect", rescale=False):
+        dtype = x.dtype
+        h = self.wavelets.to(device=x.device)
+        n = h.shape[0]
+
+        g = x.shape[1] // 4
+        hl = h.flip([0]).reshape(1, 1, -1).repeat([g, 1, 1])
+        hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1)
+        hh = hh.to(dtype=dtype)
+        hl = hl.to(dtype=dtype)
+
+        xll, xlh, xhl, xhh = torch.chunk(x.to(dtype), 4, dim=1)
+
+        # Inverse transform.
+        yl = torch.nn.functional.conv_transpose2d(
+            xll, hl.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0)
+        )
+        yl += torch.nn.functional.conv_transpose2d(
+            xlh, hh.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0)
+        )
+        yh = torch.nn.functional.conv_transpose2d(
+            xhl, hl.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0)
+        )
+        yh += torch.nn.functional.conv_transpose2d(
+            xhh, hh.unsqueeze(3), groups=g, stride=(2, 1), padding=(n - 2, 0)
+        )
+        y = torch.nn.functional.conv_transpose2d(
+            yl, hl.unsqueeze(2), groups=g, stride=(1, 2), padding=(0, n - 2)
+        )
+        y += torch.nn.functional.conv_transpose2d(
+            yh, hh.unsqueeze(2), groups=g, stride=(1, 2), padding=(0, n - 2)
+        )
+
+        if rescale:
+            y = y * 2
+        return y
+
+    def _ihaar(self, x):
+        for _ in self.range:
+            x = self._idwt(x, "haar", rescale=True)
+        return x
+
+    def _iarrange(self, x):
+        x = rearrange(
+            x,
+            "b (c p1 p2) h w -> b c (h p1) (w p2)",
+            p1=self.patch_size,
+            p2=self.patch_size,
+        )
+        return x
+
+
+class UnPatcher3D(UnPatcher):
+    """A 3D inverse discrete wavelet transform for video wavelet decompositions."""
+
+    def __init__(self, patch_size=1, patch_method="haar"):
+        super().__init__(patch_method=patch_method, patch_size=patch_size)
+
+    def _idwt(self, x, wavelet="haar", mode="reflect", rescale=False):
+        dtype = x.dtype
+        h = self.wavelets.to(device=x.device)
+
+        g = x.shape[1] // 8  # split into 8 spatio-temporal filtered tesnors.
+        hl = h.flip([0]).reshape(1, 1, -1).repeat([g, 1, 1])
+        hh = (h * ((-1) ** self._arange.to(device=x.device))).reshape(1, 1, -1).repeat(g, 1, 1)
+        hl = hl.to(dtype=dtype)
+        hh = hh.to(dtype=dtype)
+
+        xlll, xllh, xlhl, xlhh, xhll, xhlh, xhhl, xhhh = torch.chunk(x, 8, dim=1)
+
+        # Height height transposed convolutions.
+        xll = F.conv_transpose3d(
+            xlll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+        )
+        xll += F.conv_transpose3d(
+            xllh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+        )
+
+        xlh = F.conv_transpose3d(
+            xlhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+        )
+        xlh += F.conv_transpose3d(
+            xlhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+        )
+
+        xhl = F.conv_transpose3d(
+            xhll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+        )
+        xhl += F.conv_transpose3d(
+            xhlh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+        )
+
+        xhh = F.conv_transpose3d(
+            xhhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+        )
+        xhh += F.conv_transpose3d(
+            xhhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+        )
+
+        # Handles width transposed convolutions.
+        xl = F.conv_transpose3d(
+            xll, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)
+        )
+        xl += F.conv_transpose3d(
+            xlh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)
+        )
+        xh = F.conv_transpose3d(
+            xhl, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)
+        )
+        xh += F.conv_transpose3d(
+            xhh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)
+        )
+
+        # Handles time axis transposed convolutions.
+        x = F.conv_transpose3d(
+            xl, hl.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1)
+        )
+        x += F.conv_transpose3d(
+            xh, hh.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1)
+        )
+
+        if rescale:
+            x = x * (2 * torch.sqrt(torch.tensor(2.0)))
+        return x
+
+    def _ihaar(self, x):
+        for _ in self.range:
+            x = self._idwt(x, "haar", rescale=True)
+        x = x[:, :, self.patch_size - 1 :, ...]
+        return x
+
+    def _iarrange(self, x):
+        x = rearrange(
+            x,
+            "b (c p1 p2 p3) t h w -> b c (t p1) (h p2) (w p3)",
+            p1=self.patch_size,
+            p2=self.patch_size,
+            p3=self.patch_size,
+        )
+        x = x[:, :, self.patch_size - 1 :, ...]
+        return x

comfy/ldm/cosmos/cosmos_tokenizer/utils.py

@@ -0,0 +1,120 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Shared utilities for the networks module."""
+
+from typing import Any
+
+import torch
+from einops import pack, rearrange, unpack
+
+
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+def time2batch(x: torch.Tensor) -> tuple[torch.Tensor, int]:
+    batch_size = x.shape[0]
+    return rearrange(x, "b c t h w -> (b t) c h w"), batch_size
+
+
+def batch2time(x: torch.Tensor, batch_size: int) -> torch.Tensor:
+    return rearrange(x, "(b t) c h w -> b c t h w", b=batch_size)
+
+
+def space2batch(x: torch.Tensor) -> tuple[torch.Tensor, int]:
+    batch_size, height = x.shape[0], x.shape[-2]
+    return rearrange(x, "b c t h w -> (b h w) c t"), batch_size, height
+
+
+def batch2space(x: torch.Tensor, batch_size: int, height: int) -> torch.Tensor:
+    return rearrange(x, "(b h w) c t -> b c t h w", b=batch_size, h=height)
+
+
+def cast_tuple(t: Any, length: int = 1) -> Any:
+    return t if isinstance(t, tuple) else ((t,) * length)
+
+
+def replication_pad(x):
+    return torch.cat([x[:, :, :1, ...], x], dim=2)
+
+
+def divisible_by(num: int, den: int) -> bool:
+    return (num % den) == 0
+
+
+def is_odd(n: int) -> bool:
+    return not divisible_by(n, 2)
+
+
+def nonlinearity(x):
+    return x * torch.sigmoid(x)
+
+
+def Normalize(in_channels, num_groups=32):
+    return ops.GroupNorm(
+        num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True
+    )
+
+
+class CausalNormalize(torch.nn.Module):
+    def __init__(self, in_channels, num_groups=1):
+        super().__init__()
+        self.norm = ops.GroupNorm(
+            num_groups=num_groups,
+            num_channels=in_channels,
+            eps=1e-6,
+            affine=True,
+        )
+        self.num_groups = num_groups
+
+    def forward(self, x):
+        # if num_groups !=1, we apply a spatio-temporal groupnorm for backward compatibility purpose.
+        # All new models should use num_groups=1, otherwise causality is not guaranteed.
+        if self.num_groups == 1:
+            x, batch_size = time2batch(x)
+            return batch2time(self.norm(x), batch_size)
+        return self.norm(x)
+
+
+def exists(v):
+    return v is not None
+
+
+def default(*args):
+    for arg in args:
+        if exists(arg):
+            return arg
+    return None
+
+
+def pack_one(t, pattern):
+    return pack([t], pattern)
+
+
+def unpack_one(t, ps, pattern):
+    return unpack(t, ps, pattern)[0]
+
+
+def round_ste(z: torch.Tensor) -> torch.Tensor:
+    """Round with straight through gradients."""
+    zhat = z.round()
+    return z + (zhat - z).detach()
+
+
+def log(t, eps=1e-5):
+    return t.clamp(min=eps).log()
+
+
+def entropy(prob):
+    return (-prob * log(prob)).sum(dim=-1)

comfy/ldm/cosmos/model.py

@@ -0,0 +1,510 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+A general implementation of adaln-modulated VIT-like~(DiT) transformer for video processing.
+"""
+
+from typing import Optional, Tuple
+
+import torch
+from einops import rearrange
+from torch import nn
+from torchvision import transforms
+
+from enum import Enum
+import logging
+
+from comfy.ldm.modules.diffusionmodules.mmdit import RMSNorm
+
+from .blocks import (
+    FinalLayer,
+    GeneralDITTransformerBlock,
+    PatchEmbed,
+    TimestepEmbedding,
+    Timesteps,
+)
+
+from .position_embedding import LearnablePosEmbAxis, VideoRopePosition3DEmb
+
+
+class DataType(Enum):
+    IMAGE = "image"
+    VIDEO = "video"
+
+
+class GeneralDIT(nn.Module):
+    """
+    A general implementation of adaln-modulated VIT-like~(DiT) transformer for video processing.
+
+    Args:
+        max_img_h (int): Maximum height of the input images.
+        max_img_w (int): Maximum width of the input images.
+        max_frames (int): Maximum number of frames in the video sequence.
+        in_channels (int): Number of input channels (e.g., RGB channels for color images).
+        out_channels (int): Number of output channels.
+        patch_spatial (tuple): Spatial resolution of patches for input processing.
+        patch_temporal (int): Temporal resolution of patches for input processing.
+        concat_padding_mask (bool): If True, includes a mask channel in the input to handle padding.
+        block_config (str): Configuration of the transformer block. See Notes for supported block types.
+        model_channels (int): Base number of channels used throughout the model.
+        num_blocks (int): Number of transformer blocks.
+        num_heads (int): Number of heads in the multi-head attention layers.
+        mlp_ratio (float): Expansion ratio for MLP blocks.
+        block_x_format (str): Format of input tensor for transformer blocks ('BTHWD' or 'THWBD').
+        crossattn_emb_channels (int): Number of embedding channels for cross-attention.
+        use_cross_attn_mask (bool): Whether to use mask in cross-attention.
+        pos_emb_cls (str): Type of positional embeddings.
+        pos_emb_learnable (bool): Whether positional embeddings are learnable.
+        pos_emb_interpolation (str): Method for interpolating positional embeddings.
+        affline_emb_norm (bool): Whether to normalize affine embeddings.
+        use_adaln_lora (bool): Whether to use AdaLN-LoRA.
+        adaln_lora_dim (int): Dimension for AdaLN-LoRA.
+        rope_h_extrapolation_ratio (float): Height extrapolation ratio for RoPE.
+        rope_w_extrapolation_ratio (float): Width extrapolation ratio for RoPE.
+        rope_t_extrapolation_ratio (float): Temporal extrapolation ratio for RoPE.
+        extra_per_block_abs_pos_emb (bool): Whether to use extra per-block absolute positional embeddings.
+        extra_per_block_abs_pos_emb_type (str): Type of extra per-block positional embeddings.
+        extra_h_extrapolation_ratio (float): Height extrapolation ratio for extra embeddings.
+        extra_w_extrapolation_ratio (float): Width extrapolation ratio for extra embeddings.
+        extra_t_extrapolation_ratio (float): Temporal extrapolation ratio for extra embeddings.
+
+    Notes:
+        Supported block types in block_config:
+        * cross_attn, ca: Cross attention
+        * full_attn: Full attention on all flattened tokens
+        * mlp, ff: Feed forward block
+    """
+
+    def __init__(
+        self,
+        max_img_h: int,
+        max_img_w: int,
+        max_frames: int,
+        in_channels: int,
+        out_channels: int,
+        patch_spatial: tuple,
+        patch_temporal: int,
+        concat_padding_mask: bool = True,
+        # attention settings
+        block_config: str = "FA-CA-MLP",
+        model_channels: int = 768,
+        num_blocks: int = 10,
+        num_heads: int = 16,
+        mlp_ratio: float = 4.0,
+        block_x_format: str = "BTHWD",
+        # cross attention settings
+        crossattn_emb_channels: int = 1024,
+        use_cross_attn_mask: bool = False,
+        # positional embedding settings
+        pos_emb_cls: str = "sincos",
+        pos_emb_learnable: bool = False,
+        pos_emb_interpolation: str = "crop",
+        affline_emb_norm: bool = False,  # whether or not to normalize the affine embedding
+        use_adaln_lora: bool = False,
+        adaln_lora_dim: int = 256,
+        rope_h_extrapolation_ratio: float = 1.0,
+        rope_w_extrapolation_ratio: float = 1.0,
+        rope_t_extrapolation_ratio: float = 1.0,
+        extra_per_block_abs_pos_emb: bool = False,
+        extra_per_block_abs_pos_emb_type: str = "sincos",
+        extra_h_extrapolation_ratio: float = 1.0,
+        extra_w_extrapolation_ratio: float = 1.0,
+        extra_t_extrapolation_ratio: float = 1.0,
+        image_model=None,
+        device=None,
+        dtype=None,
+        operations=None,
+    ) -> None:
+        super().__init__()
+        self.max_img_h = max_img_h
+        self.max_img_w = max_img_w
+        self.max_frames = max_frames
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.patch_spatial = patch_spatial
+        self.patch_temporal = patch_temporal
+        self.num_heads = num_heads
+        self.num_blocks = num_blocks
+        self.model_channels = model_channels
+        self.use_cross_attn_mask = use_cross_attn_mask
+        self.concat_padding_mask = concat_padding_mask
+        # positional embedding settings
+        self.pos_emb_cls = pos_emb_cls
+        self.pos_emb_learnable = pos_emb_learnable
+        self.pos_emb_interpolation = pos_emb_interpolation
+        self.affline_emb_norm = affline_emb_norm
+        self.rope_h_extrapolation_ratio = rope_h_extrapolation_ratio
+        self.rope_w_extrapolation_ratio = rope_w_extrapolation_ratio
+        self.rope_t_extrapolation_ratio = rope_t_extrapolation_ratio
+        self.extra_per_block_abs_pos_emb = extra_per_block_abs_pos_emb
+        self.extra_per_block_abs_pos_emb_type = extra_per_block_abs_pos_emb_type.lower()
+        self.extra_h_extrapolation_ratio = extra_h_extrapolation_ratio
+        self.extra_w_extrapolation_ratio = extra_w_extrapolation_ratio
+        self.extra_t_extrapolation_ratio = extra_t_extrapolation_ratio
+        self.dtype = dtype
+        weight_args = {"device": device, "dtype": dtype}
+
+        in_channels = in_channels + 1 if concat_padding_mask else in_channels
+        self.x_embedder = PatchEmbed(
+            spatial_patch_size=patch_spatial,
+            temporal_patch_size=patch_temporal,
+            in_channels=in_channels,
+            out_channels=model_channels,
+            bias=False,
+            weight_args=weight_args,
+            operations=operations,
+        )
+
+        self.build_pos_embed(device=device)
+        self.block_x_format = block_x_format
+        self.use_adaln_lora = use_adaln_lora
+        self.adaln_lora_dim = adaln_lora_dim
+        self.t_embedder = nn.ModuleList(
+            [Timesteps(model_channels),
+             TimestepEmbedding(model_channels, model_channels, use_adaln_lora=use_adaln_lora, weight_args=weight_args, operations=operations),]
+        )
+
+        self.blocks = nn.ModuleDict()
+
+        for idx in range(num_blocks):
+            self.blocks[f"block{idx}"] = GeneralDITTransformerBlock(
+                x_dim=model_channels,
+                context_dim=crossattn_emb_channels,
+                num_heads=num_heads,
+                block_config=block_config,
+                mlp_ratio=mlp_ratio,
+                x_format=self.block_x_format,
+                use_adaln_lora=use_adaln_lora,
+                adaln_lora_dim=adaln_lora_dim,
+                weight_args=weight_args,
+                operations=operations,
+            )
+
+        if self.affline_emb_norm:
+            logging.debug("Building affine embedding normalization layer")
+            self.affline_norm = RMSNorm(model_channels, elementwise_affine=True, eps=1e-6)
+        else:
+            self.affline_norm = nn.Identity()
+
+        self.final_layer = FinalLayer(
+            hidden_size=self.model_channels,
+            spatial_patch_size=self.patch_spatial,
+            temporal_patch_size=self.patch_temporal,
+            out_channels=self.out_channels,
+            use_adaln_lora=self.use_adaln_lora,
+            adaln_lora_dim=self.adaln_lora_dim,
+            weight_args=weight_args,
+            operations=operations,
+        )
+
+    def build_pos_embed(self, device=None):
+        if self.pos_emb_cls == "rope3d":
+            cls_type = VideoRopePosition3DEmb
+        else:
+            raise ValueError(f"Unknown pos_emb_cls {self.pos_emb_cls}")
+
+        logging.debug(f"Building positional embedding with {self.pos_emb_cls} class, impl {cls_type}")
+        kwargs = dict(
+            model_channels=self.model_channels,
+            len_h=self.max_img_h // self.patch_spatial,
+            len_w=self.max_img_w // self.patch_spatial,
+            len_t=self.max_frames // self.patch_temporal,
+            is_learnable=self.pos_emb_learnable,
+            interpolation=self.pos_emb_interpolation,
+            head_dim=self.model_channels // self.num_heads,
+            h_extrapolation_ratio=self.rope_h_extrapolation_ratio,
+            w_extrapolation_ratio=self.rope_w_extrapolation_ratio,
+            t_extrapolation_ratio=self.rope_t_extrapolation_ratio,
+            device=device,
+        )
+        self.pos_embedder = cls_type(
+            **kwargs,
+        )
+
+        if self.extra_per_block_abs_pos_emb:
+            assert self.extra_per_block_abs_pos_emb_type in [
+                "learnable",
+            ], f"Unknown extra_per_block_abs_pos_emb_type {self.extra_per_block_abs_pos_emb_type}"
+            kwargs["h_extrapolation_ratio"] = self.extra_h_extrapolation_ratio
+            kwargs["w_extrapolation_ratio"] = self.extra_w_extrapolation_ratio
+            kwargs["t_extrapolation_ratio"] = self.extra_t_extrapolation_ratio
+            kwargs["device"] = device
+            self.extra_pos_embedder = LearnablePosEmbAxis(
+                **kwargs,
+            )
+
+    def prepare_embedded_sequence(
+        self,
+        x_B_C_T_H_W: torch.Tensor,
+        fps: Optional[torch.Tensor] = None,
+        padding_mask: Optional[torch.Tensor] = None,
+        latent_condition: Optional[torch.Tensor] = None,
+        latent_condition_sigma: Optional[torch.Tensor] = None,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+        """
+        Prepares an embedded sequence tensor by applying positional embeddings and handling padding masks.
+
+        Args:
+            x_B_C_T_H_W (torch.Tensor): video
+            fps (Optional[torch.Tensor]): Frames per second tensor to be used for positional embedding when required.
+                                    If None, a default value (`self.base_fps`) will be used.
+            padding_mask (Optional[torch.Tensor]): current it is not used
+
+        Returns:
+            Tuple[torch.Tensor, Optional[torch.Tensor]]:
+                - A tensor of shape (B, T, H, W, D) with the embedded sequence.
+                - An optional positional embedding tensor, returned only if the positional embedding class
+                (`self.pos_emb_cls`) includes 'rope'. Otherwise, None.
+
+        Notes:
+            - If `self.concat_padding_mask` is True, a padding mask channel is concatenated to the input tensor.
+            - The method of applying positional embeddings depends on the value of `self.pos_emb_cls`.
+            - If 'rope' is in `self.pos_emb_cls` (case insensitive), the positional embeddings are generated using
+                the `self.pos_embedder` with the shape [T, H, W].
+            - If "fps_aware" is in `self.pos_emb_cls`, the positional embeddings are generated using the
+            `self.pos_embedder` with the fps tensor.
+            - Otherwise, the positional embeddings are generated without considering fps.
+        """
+        if self.concat_padding_mask:
+            if padding_mask is not None:
+                padding_mask = transforms.functional.resize(
+                    padding_mask, list(x_B_C_T_H_W.shape[-2:]), interpolation=transforms.InterpolationMode.NEAREST
+                )
+            else:
+                padding_mask = torch.zeros((x_B_C_T_H_W.shape[0], 1, x_B_C_T_H_W.shape[-2], x_B_C_T_H_W.shape[-1]), dtype=x_B_C_T_H_W.dtype, device=x_B_C_T_H_W.device)
+
+            x_B_C_T_H_W = torch.cat(
+                [x_B_C_T_H_W, padding_mask.unsqueeze(1).repeat(1, 1, x_B_C_T_H_W.shape[2], 1, 1)], dim=1
+            )
+        x_B_T_H_W_D = self.x_embedder(x_B_C_T_H_W)
+
+        if self.extra_per_block_abs_pos_emb:
+            extra_pos_emb = self.extra_pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device)
+        else:
+            extra_pos_emb = None
+
+        if "rope" in self.pos_emb_cls.lower():
+            return x_B_T_H_W_D, self.pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device), extra_pos_emb
+
+        if "fps_aware" in self.pos_emb_cls:
+            x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D, fps=fps, device=x_B_C_T_H_W.device)  # [B, T, H, W, D]
+        else:
+            x_B_T_H_W_D = x_B_T_H_W_D + self.pos_embedder(x_B_T_H_W_D, device=x_B_C_T_H_W.device)  # [B, T, H, W, D]
+
+        return x_B_T_H_W_D, None, extra_pos_emb
+
+    def decoder_head(
+        self,
+        x_B_T_H_W_D: torch.Tensor,
+        emb_B_D: torch.Tensor,
+        crossattn_emb: torch.Tensor,
+        origin_shape: Tuple[int, int, int, int, int],  # [B, C, T, H, W]
+        crossattn_mask: Optional[torch.Tensor] = None,
+        adaln_lora_B_3D: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        del crossattn_emb, crossattn_mask
+        B, C, T_before_patchify, H_before_patchify, W_before_patchify = origin_shape
+        x_BT_HW_D = rearrange(x_B_T_H_W_D, "B T H W D -> (B T) (H W) D")
+        x_BT_HW_D = self.final_layer(x_BT_HW_D, emb_B_D, adaln_lora_B_3D=adaln_lora_B_3D)
+        # This is to ensure x_BT_HW_D has the correct shape because
+        # when we merge T, H, W into one dimension, x_BT_HW_D has shape (B * T * H * W, 1*1, D).
+        x_BT_HW_D = x_BT_HW_D.view(
+            B * T_before_patchify // self.patch_temporal,
+            H_before_patchify // self.patch_spatial * W_before_patchify // self.patch_spatial,
+            -1,
+        )
+        x_B_D_T_H_W = rearrange(
+            x_BT_HW_D,
+            "(B T) (H W) (p1 p2 t C) -> B C (T t) (H p1) (W p2)",
+            p1=self.patch_spatial,
+            p2=self.patch_spatial,
+            H=H_before_patchify // self.patch_spatial,
+            W=W_before_patchify // self.patch_spatial,
+            t=self.patch_temporal,
+            B=B,
+        )
+        return x_B_D_T_H_W
+
+    def forward_before_blocks(
+        self,
+        x: torch.Tensor,
+        timesteps: torch.Tensor,
+        crossattn_emb: torch.Tensor,
+        crossattn_mask: Optional[torch.Tensor] = None,
+        fps: Optional[torch.Tensor] = None,
+        image_size: Optional[torch.Tensor] = None,
+        padding_mask: Optional[torch.Tensor] = None,
+        scalar_feature: Optional[torch.Tensor] = None,
+        data_type: Optional[DataType] = DataType.VIDEO,
+        latent_condition: Optional[torch.Tensor] = None,
+        latent_condition_sigma: Optional[torch.Tensor] = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        """
+        Args:
+            x: (B, C, T, H, W) tensor of spatial-temp inputs
+            timesteps: (B, ) tensor of timesteps
+            crossattn_emb: (B, N, D) tensor of cross-attention embeddings
+            crossattn_mask: (B, N) tensor of cross-attention masks
+        """
+        del kwargs
+        assert isinstance(
+            data_type, DataType
+        ), f"Expected DataType, got {type(data_type)}. We need discuss this flag later."
+        original_shape = x.shape
+        x_B_T_H_W_D, rope_emb_L_1_1_D, extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = self.prepare_embedded_sequence(
+            x,
+            fps=fps,
+            padding_mask=padding_mask,
+            latent_condition=latent_condition,
+            latent_condition_sigma=latent_condition_sigma,
+        )
+        # logging affline scale information
+        affline_scale_log_info = {}
+
+        timesteps_B_D, adaln_lora_B_3D = self.t_embedder[1](self.t_embedder[0](timesteps.flatten()).to(x.dtype))
+        affline_emb_B_D = timesteps_B_D
+        affline_scale_log_info["timesteps_B_D"] = timesteps_B_D.detach()
+
+        if scalar_feature is not None:
+            raise NotImplementedError("Scalar feature is not implemented yet.")
+
+        affline_scale_log_info["affline_emb_B_D"] = affline_emb_B_D.detach()
+        affline_emb_B_D = self.affline_norm(affline_emb_B_D)
+
+        if self.use_cross_attn_mask:
+            if crossattn_mask is not None and not torch.is_floating_point(crossattn_mask):
+                crossattn_mask = (crossattn_mask - 1).to(x.dtype) * torch.finfo(x.dtype).max
+            crossattn_mask = crossattn_mask[:, None, None, :]  # .to(dtype=torch.bool)  # [B, 1, 1, length]
+        else:
+            crossattn_mask = None
+
+        if self.blocks["block0"].x_format == "THWBD":
+            x = rearrange(x_B_T_H_W_D, "B T H W D -> T H W B D")
+            if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None:
+                extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = rearrange(
+                    extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D, "B T H W D -> T H W B D"
+                )
+            crossattn_emb = rearrange(crossattn_emb, "B M D -> M B D")
+
+            if crossattn_mask:
+                crossattn_mask = rearrange(crossattn_mask, "B M -> M B")
+
+        elif self.blocks["block0"].x_format == "BTHWD":
+            x = x_B_T_H_W_D
+        else:
+            raise ValueError(f"Unknown x_format {self.blocks[0].x_format}")
+        output = {
+            "x": x,
+            "affline_emb_B_D": affline_emb_B_D,
+            "crossattn_emb": crossattn_emb,
+            "crossattn_mask": crossattn_mask,
+            "rope_emb_L_1_1_D": rope_emb_L_1_1_D,
+            "adaln_lora_B_3D": adaln_lora_B_3D,
+            "original_shape": original_shape,
+            "extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D,
+        }
+        return output
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        timesteps: torch.Tensor,
+        context: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        # crossattn_emb: torch.Tensor,
+        # crossattn_mask: Optional[torch.Tensor] = None,
+        fps: Optional[torch.Tensor] = None,
+        image_size: Optional[torch.Tensor] = None,
+        padding_mask: Optional[torch.Tensor] = None,
+        scalar_feature: Optional[torch.Tensor] = None,
+        data_type: Optional[DataType] = DataType.VIDEO,
+        latent_condition: Optional[torch.Tensor] = None,
+        latent_condition_sigma: Optional[torch.Tensor] = None,
+        condition_video_augment_sigma: Optional[torch.Tensor] = None,
+        **kwargs,
+    ):
+        """
+        Args:
+            x: (B, C, T, H, W) tensor of spatial-temp inputs
+            timesteps: (B, ) tensor of timesteps
+            crossattn_emb: (B, N, D) tensor of cross-attention embeddings
+            crossattn_mask: (B, N) tensor of cross-attention masks
+            condition_video_augment_sigma: (B,) used in lvg(long video generation), we add noise with this sigma to
+                augment condition input, the lvg model will condition on the condition_video_augment_sigma value;
+                we need forward_before_blocks pass to the forward_before_blocks function.
+        """
+
+        crossattn_emb = context
+        crossattn_mask = attention_mask
+
+        inputs = self.forward_before_blocks(
+            x=x,
+            timesteps=timesteps,
+            crossattn_emb=crossattn_emb,
+            crossattn_mask=crossattn_mask,
+            fps=fps,
+            image_size=image_size,
+            padding_mask=padding_mask,
+            scalar_feature=scalar_feature,
+            data_type=data_type,
+            latent_condition=latent_condition,
+            latent_condition_sigma=latent_condition_sigma,
+            condition_video_augment_sigma=condition_video_augment_sigma,
+            **kwargs,
+        )
+        x, affline_emb_B_D, crossattn_emb, crossattn_mask, rope_emb_L_1_1_D, adaln_lora_B_3D, original_shape = (
+            inputs["x"],
+            inputs["affline_emb_B_D"],
+            inputs["crossattn_emb"],
+            inputs["crossattn_mask"],
+            inputs["rope_emb_L_1_1_D"],
+            inputs["adaln_lora_B_3D"],
+            inputs["original_shape"],
+        )
+        extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D = inputs["extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D"].to(x.dtype)
+        if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None:
+            assert (
+                x.shape == extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape
+            ), f"{x.shape} != {extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D.shape} {original_shape}"
+
+        for _, block in self.blocks.items():
+            assert (
+                self.blocks["block0"].x_format == block.x_format
+            ), f"First block has x_format {self.blocks[0].x_format}, got {block.x_format}"
+
+            x = block(
+                x,
+                affline_emb_B_D,
+                crossattn_emb,
+                crossattn_mask,
+                rope_emb_L_1_1_D=rope_emb_L_1_1_D,
+                adaln_lora_B_3D=adaln_lora_B_3D,
+                extra_per_block_pos_emb=extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D,
+            )
+
+        x_B_T_H_W_D = rearrange(x, "T H W B D -> B T H W D")
+
+        x_B_D_T_H_W = self.decoder_head(
+            x_B_T_H_W_D=x_B_T_H_W_D,
+            emb_B_D=affline_emb_B_D,
+            crossattn_emb=None,
+            origin_shape=original_shape,
+            crossattn_mask=None,
+            adaln_lora_B_3D=adaln_lora_B_3D,
+        )
+
+        return x_B_D_T_H_W

comfy/ldm/cosmos/position_embedding.py

@@ -0,0 +1,207 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from typing import List, Optional
+
+import torch
+from einops import rearrange, repeat
+from torch import nn
+import math
+
+
+def normalize(x: torch.Tensor, dim: Optional[List[int]] = None, eps: float = 0) -> torch.Tensor:
+    """
+    Normalizes the input tensor along specified dimensions such that the average square norm of elements is adjusted.
+
+    Args:
+        x (torch.Tensor): The input tensor to normalize.
+        dim (list, optional): The dimensions over which to normalize. If None, normalizes over all dimensions except the first.
+        eps (float, optional): A small constant to ensure numerical stability during division.
+
+    Returns:
+        torch.Tensor: The normalized tensor.
+    """
+    if dim is None:
+        dim = list(range(1, x.ndim))
+    norm = torch.linalg.vector_norm(x, dim=dim, keepdim=True, dtype=torch.float32)
+    norm = torch.add(eps, norm, alpha=math.sqrt(norm.numel() / x.numel()))
+    return x / norm.to(x.dtype)
+
+
+class VideoPositionEmb(nn.Module):
+    def forward(self, x_B_T_H_W_C: torch.Tensor, fps=Optional[torch.Tensor], device=None) -> torch.Tensor:
+        """
+        It delegates the embedding generation to generate_embeddings function.
+        """
+        B_T_H_W_C = x_B_T_H_W_C.shape
+        embeddings = self.generate_embeddings(B_T_H_W_C, fps=fps, device=device)
+
+        return embeddings
+
+    def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torch.Tensor], device=None):
+        raise NotImplementedError
+
+
+class VideoRopePosition3DEmb(VideoPositionEmb):
+    def __init__(
+        self,
+        *,  # enforce keyword arguments
+        head_dim: int,
+        len_h: int,
+        len_w: int,
+        len_t: int,
+        base_fps: int = 24,
+        h_extrapolation_ratio: float = 1.0,
+        w_extrapolation_ratio: float = 1.0,
+        t_extrapolation_ratio: float = 1.0,
+        device=None,
+        **kwargs,  # used for compatibility with other positional embeddings; unused in this class
+    ):
+        del kwargs
+        super().__init__()
+        self.register_buffer("seq", torch.arange(max(len_h, len_w, len_t), dtype=torch.float, device=device))
+        self.base_fps = base_fps
+        self.max_h = len_h
+        self.max_w = len_w
+
+        dim = head_dim
+        dim_h = dim // 6 * 2
+        dim_w = dim_h
+        dim_t = dim - 2 * dim_h
+        assert dim == dim_h + dim_w + dim_t, f"bad dim: {dim} != {dim_h} + {dim_w} + {dim_t}"
+        self.register_buffer(
+            "dim_spatial_range",
+            torch.arange(0, dim_h, 2, device=device)[: (dim_h // 2)].float() / dim_h,
+            persistent=False,
+        )
+        self.register_buffer(
+            "dim_temporal_range",
+            torch.arange(0, dim_t, 2, device=device)[: (dim_t // 2)].float() / dim_t,
+            persistent=False,
+        )
+
+        self.h_ntk_factor = h_extrapolation_ratio ** (dim_h / (dim_h - 2))
+        self.w_ntk_factor = w_extrapolation_ratio ** (dim_w / (dim_w - 2))
+        self.t_ntk_factor = t_extrapolation_ratio ** (dim_t / (dim_t - 2))
+
+    def generate_embeddings(
+        self,
+        B_T_H_W_C: torch.Size,
+        fps: Optional[torch.Tensor] = None,
+        h_ntk_factor: Optional[float] = None,
+        w_ntk_factor: Optional[float] = None,
+        t_ntk_factor: Optional[float] = None,
+        device=None,
+    ):
+        """
+        Generate embeddings for the given input size.
+
+        Args:
+            B_T_H_W_C (torch.Size): Input tensor size (Batch, Time, Height, Width, Channels).
+            fps (Optional[torch.Tensor], optional): Frames per second. Defaults to None.
+            h_ntk_factor (Optional[float], optional): Height NTK factor. If None, uses self.h_ntk_factor.
+            w_ntk_factor (Optional[float], optional): Width NTK factor. If None, uses self.w_ntk_factor.
+            t_ntk_factor (Optional[float], optional): Time NTK factor. If None, uses self.t_ntk_factor.
+
+        Returns:
+            Not specified in the original code snippet.
+        """
+        h_ntk_factor = h_ntk_factor if h_ntk_factor is not None else self.h_ntk_factor
+        w_ntk_factor = w_ntk_factor if w_ntk_factor is not None else self.w_ntk_factor
+        t_ntk_factor = t_ntk_factor if t_ntk_factor is not None else self.t_ntk_factor
+
+        h_theta = 10000.0 * h_ntk_factor
+        w_theta = 10000.0 * w_ntk_factor
+        t_theta = 10000.0 * t_ntk_factor
+
+        h_spatial_freqs = 1.0 / (h_theta**self.dim_spatial_range.to(device=device))
+        w_spatial_freqs = 1.0 / (w_theta**self.dim_spatial_range.to(device=device))
+        temporal_freqs = 1.0 / (t_theta**self.dim_temporal_range.to(device=device))
+
+        B, T, H, W, _ = B_T_H_W_C
+        uniform_fps = (fps is None) or isinstance(fps, (int, float)) or (fps.min() == fps.max())
+        assert (
+            uniform_fps or B == 1 or T == 1
+        ), "For video batch, batch size should be 1 for non-uniform fps. For image batch, T should be 1"
+        assert (
+            H <= self.max_h and W <= self.max_w
+        ), f"Input dimensions (H={H}, W={W}) exceed the maximum dimensions (max_h={self.max_h}, max_w={self.max_w})"
+        half_emb_h = torch.outer(self.seq[:H].to(device=device), h_spatial_freqs)
+        half_emb_w = torch.outer(self.seq[:W].to(device=device), w_spatial_freqs)
+
+        # apply sequence scaling in temporal dimension
+        if fps is None:  # image case
+            half_emb_t = torch.outer(self.seq[:T].to(device=device), temporal_freqs)
+        else:
+            half_emb_t = torch.outer(self.seq[:T].to(device=device) / fps * self.base_fps, temporal_freqs)
+
+        half_emb_h = torch.stack([torch.cos(half_emb_h), -torch.sin(half_emb_h), torch.sin(half_emb_h), torch.cos(half_emb_h)], dim=-1)
+        half_emb_w = torch.stack([torch.cos(half_emb_w), -torch.sin(half_emb_w), torch.sin(half_emb_w), torch.cos(half_emb_w)], dim=-1)
+        half_emb_t = torch.stack([torch.cos(half_emb_t), -torch.sin(half_emb_t), torch.sin(half_emb_t), torch.cos(half_emb_t)], dim=-1)
+
+        em_T_H_W_D = torch.cat(
+            [
+                repeat(half_emb_t, "t d x -> t h w d x", h=H, w=W),
+                repeat(half_emb_h, "h d x -> t h w d x", t=T, w=W),
+                repeat(half_emb_w, "w d x -> t h w d x", t=T, h=H),
+            ]
+            , dim=-2,
+        )
+
+        return rearrange(em_T_H_W_D, "t h w d (i j) -> (t h w) d i j", i=2, j=2).float()
+
+
+class LearnablePosEmbAxis(VideoPositionEmb):
+    def __init__(
+        self,
+        *,  # enforce keyword arguments
+        interpolation: str,
+        model_channels: int,
+        len_h: int,
+        len_w: int,
+        len_t: int,
+        device=None,
+        **kwargs,
+    ):
+        """
+        Args:
+            interpolation (str): we curretly only support "crop", ideally when we need extrapolation capacity, we should adjust frequency or other more advanced methods. they are not implemented yet.
+        """
+        del kwargs  # unused
+        super().__init__()
+        self.interpolation = interpolation
+        assert self.interpolation in ["crop"], f"Unknown interpolation method {self.interpolation}"
+
+        self.pos_emb_h = nn.Parameter(torch.empty(len_h, model_channels, device=device))
+        self.pos_emb_w = nn.Parameter(torch.empty(len_w, model_channels, device=device))
+        self.pos_emb_t = nn.Parameter(torch.empty(len_t, model_channels, device=device))
+
+
+    def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torch.Tensor], device=None) -> torch.Tensor:
+        B, T, H, W, _ = B_T_H_W_C
+        if self.interpolation == "crop":
+            emb_h_H = self.pos_emb_h[:H].to(device=device)
+            emb_w_W = self.pos_emb_w[:W].to(device=device)
+            emb_t_T = self.pos_emb_t[:T].to(device=device)
+            emb = (
+                repeat(emb_t_T, "t d-> b t h w d", b=B, h=H, w=W)
+                + repeat(emb_h_H, "h d-> b t h w d", b=B, t=T, w=W)
+                + repeat(emb_w_W, "w d-> b t h w d", b=B, t=T, h=H)
+            )
+            assert list(emb.shape)[:4] == [B, T, H, W], f"bad shape: {list(emb.shape)[:4]} != {B, T, H, W}"
+        else:
+            raise ValueError(f"Unknown interpolation method {self.interpolation}")
+
+        return normalize(emb, dim=-1, eps=1e-6)

comfy/ldm/cosmos/vae.py

@@ -0,0 +1,124 @@
+# SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""The causal continuous video tokenizer with VAE or AE formulation for 3D data.."""
+
+import logging
+import torch
+from torch import nn
+from enum import Enum
+
+from .cosmos_tokenizer.layers3d import (
+    EncoderFactorized,
+    DecoderFactorized,
+    CausalConv3d,
+)
+
+
+class IdentityDistribution(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, parameters):
+        return parameters, (torch.tensor([0.0]), torch.tensor([0.0]))
+
+
+class GaussianDistribution(torch.nn.Module):
+    def __init__(self, min_logvar: float = -30.0, max_logvar: float = 20.0):
+        super().__init__()
+        self.min_logvar = min_logvar
+        self.max_logvar = max_logvar
+
+    def sample(self, mean, logvar):
+        std = torch.exp(0.5 * logvar)
+        return mean + std * torch.randn_like(mean)
+
+    def forward(self, parameters):
+        mean, logvar = torch.chunk(parameters, 2, dim=1)
+        logvar = torch.clamp(logvar, self.min_logvar, self.max_logvar)
+        return self.sample(mean, logvar), (mean, logvar)
+
+
+class ContinuousFormulation(Enum):
+    VAE = GaussianDistribution
+    AE = IdentityDistribution
+
+
+class CausalContinuousVideoTokenizer(nn.Module):
+    def __init__(
+        self, z_channels: int, z_factor: int, latent_channels: int, **kwargs
+    ) -> None:
+        super().__init__()
+        self.name = kwargs.get("name", "CausalContinuousVideoTokenizer")
+        self.latent_channels = latent_channels
+        self.sigma_data = 0.5
+
+        # encoder_name = kwargs.get("encoder", Encoder3DType.BASE.name)
+        self.encoder = EncoderFactorized(
+            z_channels=z_factor * z_channels, **kwargs
+        )
+        if kwargs.get("temporal_compression", 4) == 4:
+            kwargs["channels_mult"] = [2, 4]
+        # decoder_name = kwargs.get("decoder", Decoder3DType.BASE.name)
+        self.decoder = DecoderFactorized(
+            z_channels=z_channels, **kwargs
+        )
+
+        self.quant_conv = CausalConv3d(
+            z_factor * z_channels,
+            z_factor * latent_channels,
+            kernel_size=1,
+            padding=0,
+        )
+        self.post_quant_conv = CausalConv3d(
+            latent_channels, z_channels, kernel_size=1, padding=0
+        )
+
+        # formulation_name = kwargs.get("formulation", ContinuousFormulation.AE.name)
+        self.distribution = IdentityDistribution()  # ContinuousFormulation[formulation_name].value()
+
+        num_parameters = sum(param.numel() for param in self.parameters())
+        logging.info(f"model={self.name}, num_parameters={num_parameters:,}")
+        logging.info(
+            f"z_channels={z_channels}, latent_channels={self.latent_channels}."
+        )
+
+        latent_temporal_chunk = 16
+        self.latent_mean = nn.Parameter(torch.zeros([self.latent_channels * latent_temporal_chunk], dtype=torch.float32))
+        self.latent_std = nn.Parameter(torch.ones([self.latent_channels * latent_temporal_chunk], dtype=torch.float32))
+
+
+    def encode(self, x):
+        h = self.encoder(x)
+        moments = self.quant_conv(h)
+        z, posteriors = self.distribution(moments)
+        latent_ch = z.shape[1]
+        latent_t = z.shape[2]
+        dtype = z.dtype
+        mean = self.latent_mean.view(latent_ch, -1)[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=dtype, device=z.device)
+        std = self.latent_std.view(latent_ch, -1)[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=dtype, device=z.device)
+        return ((z - mean) / std) * self.sigma_data
+
+    def decode(self, z):
+        in_dtype = z.dtype
+        latent_ch = z.shape[1]
+        latent_t = z.shape[2]
+        mean = self.latent_mean.view(latent_ch, -1)[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device)
+        std = self.latent_std.view(latent_ch, -1)[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device)
+
+        z = z / self.sigma_data
+        z = z * std + mean
+        z = self.post_quant_conv(z)
+        return self.decoder(z)
+

comfy/ldm/lightricks/model.py

@@ -456,9 +456,8 @@ class LTXVModel(torch.nn.Module):
         x = self.patchify_proj(x)
         timestep = timestep * 1000.0
 
-        attention_mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1]))
-        attention_mask = attention_mask.masked_fill(attention_mask.to(torch.bool), float("-inf"))  # not sure about this
-        # attention_mask = (context != 0).any(dim=2).to(dtype=x.dtype)
+        if attention_mask is not None and not torch.is_floating_point(attention_mask):
+            attention_mask = (attention_mask - 1).to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])) * torch.finfo(x.dtype).max
 
         pe = precompute_freqs_cis(indices_grid, dim=self.inner_dim, out_dtype=x.dtype)
 

comfy/ldm/modules/attention.py

@@ -89,7 +89,7 @@ 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, attn_precision=None, skip_reshape=False):
+def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
     attn_precision = get_attn_precision(attn_precision)
 
     if skip_reshape:
@@ -142,16 +142,23 @@ def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
     sim = sim.softmax(dim=-1)
 
     out = einsum('b i j, b j d -> b i d', sim.to(v.dtype), v)
-    out = (
-        out.unsqueeze(0)
-        .reshape(b, heads, -1, dim_head)
-        .permute(0, 2, 1, 3)
-        .reshape(b, -1, heads * dim_head)
-    )
+
+    if skip_output_reshape:
+        out = (
+            out.unsqueeze(0)
+            .reshape(b, heads, -1, dim_head)
+        )
+    else:
+        out = (
+            out.unsqueeze(0)
+            .reshape(b, heads, -1, dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b, -1, heads * dim_head)
+        )
     return out
 
 
-def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False):
+def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
     attn_precision = get_attn_precision(attn_precision)
 
     if skip_reshape:
@@ -215,11 +222,13 @@ def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None,
     )
 
     hidden_states = hidden_states.to(dtype)
-
-    hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2)
+    if skip_output_reshape:
+        hidden_states = hidden_states.unflatten(0, (-1, heads))
+    else:
+        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, attn_precision=None, skip_reshape=False):
+def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
     attn_precision = get_attn_precision(attn_precision)
 
     if skip_reshape:
@@ -326,12 +335,18 @@ def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
 
     del q, k, v
 
-    r1 = (
-        r1.unsqueeze(0)
-        .reshape(b, heads, -1, dim_head)
-        .permute(0, 2, 1, 3)
-        .reshape(b, -1, heads * dim_head)
-    )
+    if skip_output_reshape:
+        r1 = (
+            r1.unsqueeze(0)
+            .reshape(b, heads, -1, dim_head)
+        )
+    else:
+        r1 = (
+            r1.unsqueeze(0)
+            .reshape(b, heads, -1, dim_head)
+            .permute(0, 2, 1, 3)
+            .reshape(b, -1, heads * dim_head)
+        )
     return r1
 
 BROKEN_XFORMERS = False
@@ -342,7 +357,7 @@ try:
 except:
     pass
 
-def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
+def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
     b = q.shape[0]
     dim_head = q.shape[-1]
     # check to make sure xformers isn't broken
@@ -395,9 +410,12 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_resh
 
     out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask)
 
-    out = (
-        out.reshape(b, -1, heads * dim_head)
-    )
+    if skip_output_reshape:
+        out = out.permute(0, 2, 1, 3)
+    else:
+        out = (
+            out.reshape(b, -1, heads * dim_head)
+        )
 
     return out
 
@@ -408,7 +426,7 @@ else:
     SDP_BATCH_LIMIT = 2**31
 
 
-def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
+def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
     if skip_reshape:
         b, _, _, dim_head = q.shape
     else:
@@ -429,9 +447,10 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
 
     if SDP_BATCH_LIMIT >= b:
         out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)
-        out = (
-            out.transpose(1, 2).reshape(b, -1, heads * dim_head)
-        )
+        if not skip_output_reshape:
+            out = (
+                out.transpose(1, 2).reshape(b, -1, heads * dim_head)
+            )
     else:
         out = torch.empty((b, q.shape[2], heads * dim_head), dtype=q.dtype, layout=q.layout, device=q.device)
         for i in range(0, b, SDP_BATCH_LIMIT):
@@ -450,7 +469,7 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
     return out
 
 
-def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False):
+def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
     if skip_reshape:
         b, _, _, dim_head = q.shape
         tensor_layout="HND"
@@ -473,11 +492,15 @@ def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=
 
     out = sageattn(q, k, v, attn_mask=mask, is_causal=False, tensor_layout=tensor_layout)
     if tensor_layout == "HND":
-        out = (
-            out.transpose(1, 2).reshape(b, -1, heads * dim_head)
-        )
+        if not skip_output_reshape:
+            out = (
+                out.transpose(1, 2).reshape(b, -1, heads * dim_head)
+            )
     else:
-        out = out.reshape(b, -1, heads * dim_head)
+        if skip_output_reshape:
+            out = out.transpose(1, 2)
+        else:
+            out = out.reshape(b, -1, heads * dim_head)
     return out
 
 

comfy/model_base.py

@@ -33,6 +33,7 @@ import comfy.ldm.audio.embedders
 import comfy.ldm.flux.model
 import comfy.ldm.lightricks.model
 import comfy.ldm.hunyuan_video.model
+import comfy.ldm.cosmos.model
 
 import comfy.model_management
 import comfy.patcher_extension
@@ -856,3 +857,19 @@ class HunyuanVideo(BaseModel):
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
         out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 6.0)]))
         return out
+
+class CosmosVideo(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.EDM, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.cosmos.model.GeneralDIT)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        attention_mask = kwargs.get("attention_mask", None)
+        if attention_mask is not None:
+            out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+
+        out['fps'] = comfy.conds.CONDConstant(kwargs.get("frame_rate", None))
+        return out

comfy/model_detection.py

@@ -239,6 +239,50 @@ def detect_unet_config(state_dict, key_prefix):
             dit_config["micro_condition"] = False
         return dit_config
 
+    if '{}blocks.block0.blocks.0.block.attn.to_q.0.weight'.format(key_prefix) in state_dict_keys:
+        dit_config = {}
+        dit_config["image_model"] = "cosmos"
+        dit_config["max_img_h"] = 240
+        dit_config["max_img_w"] = 240
+        dit_config["max_frames"] = 128
+        dit_config["in_channels"] = 16
+        dit_config["out_channels"] = 16
+        dit_config["patch_spatial"] = 2
+        dit_config["patch_temporal"] = 1
+        dit_config["model_channels"] = state_dict['{}blocks.block0.blocks.0.block.attn.to_q.0.weight'.format(key_prefix)].shape[0]
+        dit_config["block_config"] = "FA-CA-MLP"
+        dit_config["concat_padding_mask"] = True
+        dit_config["pos_emb_cls"] = "rope3d"
+        dit_config["pos_emb_learnable"] = False
+        dit_config["pos_emb_interpolation"] = "crop"
+        dit_config["block_x_format"] = "THWBD"
+        dit_config["affline_emb_norm"] = True
+        dit_config["use_adaln_lora"] = True
+        dit_config["adaln_lora_dim"] = 256
+
+        if dit_config["model_channels"] == 4096:
+            # 7B
+            dit_config["num_blocks"] = 28
+            dit_config["num_heads"] = 32
+            dit_config["extra_per_block_abs_pos_emb"] = True
+            dit_config["rope_h_extrapolation_ratio"] = 1.0
+            dit_config["rope_w_extrapolation_ratio"] = 1.0
+            dit_config["rope_t_extrapolation_ratio"] = 2.0
+            dit_config["extra_per_block_abs_pos_emb_type"] = "learnable"
+        else:  # 5120
+            # 14B
+            dit_config["num_blocks"] = 36
+            dit_config["num_heads"] = 40
+            dit_config["extra_per_block_abs_pos_emb"] = True
+            dit_config["rope_h_extrapolation_ratio"] = 2.0
+            dit_config["rope_w_extrapolation_ratio"] = 2.0
+            dit_config["rope_t_extrapolation_ratio"] = 2.0
+            dit_config["extra_h_extrapolation_ratio"] = 2.0
+            dit_config["extra_w_extrapolation_ratio"] = 2.0
+            dit_config["extra_t_extrapolation_ratio"] = 2.0
+            dit_config["extra_per_block_abs_pos_emb_type"] = "learnable"
+        return dit_config
+
     if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
         return None
 
@@ -393,6 +437,7 @@ def model_config_from_unet(state_dict, unet_key_prefix, use_base_if_no_match=Fal
 def unet_prefix_from_state_dict(state_dict):
     candidates = ["model.diffusion_model.", #ldm/sgm models
                   "model.model.", #audio models
+                  "net.", #cosmos
                   ]
     counts = {k: 0 for k in candidates}
     for k in state_dict:

comfy/model_management.py

@@ -1121,18 +1121,13 @@ def soft_empty_cache(force=False):
     elif is_ascend_npu():
         torch.npu.empty_cache()
     elif torch.cuda.is_available():
-        if force or is_nvidia(): #This seems to make things worse on ROCm so I only do it for cuda
-            torch.cuda.empty_cache()
-            torch.cuda.ipc_collect()
+        torch.cuda.empty_cache()
+        torch.cuda.ipc_collect()
 
 def unload_all_models():
     free_memory(1e30, get_torch_device())
 
 
-def resolve_lowvram_weight(weight, model, key): #TODO: remove
-    logging.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
 import threading
 

comfy/model_patcher.py

@@ -210,7 +210,7 @@ class ModelPatcher:
         self.injections: dict[str, list[PatcherInjection]] = {}
 
         self.hook_patches: dict[comfy.hooks._HookRef] = {}
-        self.hook_patches_backup: dict[comfy.hooks._HookRef] = {}
+        self.hook_patches_backup: dict[comfy.hooks._HookRef] = None
         self.hook_backup: dict[str, tuple[torch.Tensor, torch.device]] = {}
         self.cached_hook_patches: dict[comfy.hooks.HookGroup, dict[str, torch.Tensor]] = {}
         self.current_hooks: Optional[comfy.hooks.HookGroup] = None
@@ -282,7 +282,7 @@ class ModelPatcher:
             n.injections[k] = i.copy()
         # hooks
         n.hook_patches = create_hook_patches_clone(self.hook_patches)
-        n.hook_patches_backup = create_hook_patches_clone(self.hook_patches_backup)
+        n.hook_patches_backup = create_hook_patches_clone(self.hook_patches_backup) if self.hook_patches_backup else self.hook_patches_backup
         for group in self.cached_hook_patches:
             n.cached_hook_patches[group] = {}
             for k in self.cached_hook_patches[group]:
@@ -402,7 +402,20 @@ class ModelPatcher:
     def add_object_patch(self, name, obj):
         self.object_patches[name] = obj
 
-    def get_model_object(self, name):
+    def get_model_object(self, name: str) -> torch.nn.Module:
+        """Retrieves a nested attribute from an object using dot notation considering
+        object patches.
+
+        Args:
+            name (str): The attribute path using dot notation (e.g. "model.layer.weight")
+
+        Returns:
+            The value of the requested attribute
+
+        Example:
+            patcher = ModelPatcher()
+            weight = patcher.get_model_object("layer1.conv.weight")
+        """
         if name in self.object_patches:
             return self.object_patches[name]
         else:
@@ -842,6 +855,9 @@ class ModelPatcher:
         if key in self.injections:
             self.injections.pop(key)
 
+    def get_injections(self, key: str):
+        return self.injections.get(key, None)
+
     def set_additional_models(self, key: str, models: list['ModelPatcher']):
         self.additional_models[key] = models
 
@@ -912,9 +928,9 @@ class ModelPatcher:
             callback(self, timestep)
 
     def restore_hook_patches(self):
-        if len(self.hook_patches_backup) > 0:
+        if self.hook_patches_backup is not None:
             self.hook_patches = self.hook_patches_backup
-            self.hook_patches_backup = {}
+            self.hook_patches_backup = None
 
     def set_hook_mode(self, hook_mode: comfy.hooks.EnumHookMode):
         self.hook_mode = hook_mode
@@ -940,25 +956,26 @@ class ModelPatcher:
         if reset_current_hooks:
             self.patch_hooks(None)
 
-    def register_all_hook_patches(self, hooks_dict: dict[comfy.hooks.EnumHookType, dict[comfy.hooks.Hook, None]], target: comfy.hooks.EnumWeightTarget, model_options: dict=None):
+    def register_all_hook_patches(self, hooks: comfy.hooks.HookGroup, target_dict: dict[str], model_options: dict=None,
+                                  registered: comfy.hooks.HookGroup = None):
         self.restore_hook_patches()
-        registered_hooks: list[comfy.hooks.Hook] = []
-        # handle WrapperHooks, if model_options provided
-        if model_options is not None:
-            for hook in hooks_dict.get(comfy.hooks.EnumHookType.Wrappers, {}):
-                hook.add_hook_patches(self, model_options, target, registered_hooks)
+        if registered is None:
+            registered = comfy.hooks.HookGroup()
         # handle WeightHooks
         weight_hooks_to_register: list[comfy.hooks.WeightHook] = []
-        for hook in hooks_dict.get(comfy.hooks.EnumHookType.Weight, {}):
+        for hook in hooks.get_type(comfy.hooks.EnumHookType.Weight):
             if hook.hook_ref not in self.hook_patches:
                 weight_hooks_to_register.append(hook)
+            else:
+                registered.add(hook)
         if len(weight_hooks_to_register) > 0:
             # clone hook_patches to become backup so that any non-dynamic hooks will return to their original state
             self.hook_patches_backup = create_hook_patches_clone(self.hook_patches)
             for hook in weight_hooks_to_register:
-                hook.add_hook_patches(self, model_options, target, registered_hooks)
+                hook.add_hook_patches(self, model_options, target_dict, registered)
         for callback in self.get_all_callbacks(CallbacksMP.ON_REGISTER_ALL_HOOK_PATCHES):
-            callback(self, hooks_dict, target)
+            callback(self, hooks, target_dict, model_options, registered)
+        return registered
 
     def add_hook_patches(self, hook: comfy.hooks.WeightHook, patches, strength_patch=1.0, strength_model=1.0):
         with self.use_ejected():
@@ -1009,11 +1026,11 @@ class ModelPatcher:
     def apply_hooks(self, hooks: comfy.hooks.HookGroup, transformer_options: dict=None, force_apply=False):
         # TODO: return transformer_options dict with any additions from hooks
         if self.current_hooks == hooks and (not force_apply or (not self.is_clip and hooks is None)):
-            return {}
+            return comfy.hooks.create_transformer_options_from_hooks(self, hooks, transformer_options)
         self.patch_hooks(hooks=hooks)
         for callback in self.get_all_callbacks(CallbacksMP.ON_APPLY_HOOKS):
             callback(self, hooks)
-        return {}
+        return comfy.hooks.create_transformer_options_from_hooks(self, hooks, transformer_options)
 
     def patch_hooks(self, hooks: comfy.hooks.HookGroup):
         with self.use_ejected():

comfy/sampler_helpers.py

@@ -24,15 +24,13 @@ def get_models_from_cond(cond, model_type):
                 models += [c[model_type]]
     return models
 
-def get_hooks_from_cond(cond, hooks_dict: dict[comfy.hooks.EnumHookType, dict[comfy.hooks.Hook, None]]):
+def get_hooks_from_cond(cond, full_hooks: comfy.hooks.HookGroup):
     # get hooks from conds, and collect cnets so they can be checked for extra_hooks
     cnets: list[ControlBase] = []
     for c in cond:
         if 'hooks' in c:
             for hook in c['hooks'].hooks:
-                hook: comfy.hooks.Hook
-                with_type = hooks_dict.setdefault(hook.hook_type, {})
-                with_type[hook] = None
+                full_hooks.add(hook)
         if 'control' in c:
             cnets.append(c['control'])
 
@@ -50,10 +48,9 @@ def get_hooks_from_cond(cond, hooks_dict: dict[comfy.hooks.EnumHookType, dict[co
     extra_hooks = comfy.hooks.HookGroup.combine_all_hooks(hooks_list)
     if extra_hooks is not None:
         for hook in extra_hooks.hooks:
-            with_type = hooks_dict.setdefault(hook.hook_type, {})
-            with_type[hook] = None
+            full_hooks.add(hook)
 
-    return hooks_dict
+    return full_hooks
 
 def convert_cond(cond):
     out = []
@@ -73,13 +70,11 @@ def get_additional_models(conds, dtype):
     cnets: list[ControlBase] = []
     gligen = []
     add_models = []
-    hooks: dict[comfy.hooks.EnumHookType, dict[comfy.hooks.Hook, None]] = {}
 
     for k in conds:
         cnets += get_models_from_cond(conds[k], "control")
         gligen += get_models_from_cond(conds[k], "gligen")
         add_models += get_models_from_cond(conds[k], "additional_models")
-        get_hooks_from_cond(conds[k], hooks)
 
     control_nets = set(cnets)
 
@@ -90,11 +85,20 @@ def get_additional_models(conds, dtype):
         inference_memory += m.inference_memory_requirements(dtype)
 
     gligen = [x[1] for x in gligen]
-    hook_models = [x.model for x in hooks.get(comfy.hooks.EnumHookType.AddModels, {}).keys()]
-    models = control_models + gligen + add_models + hook_models
+    models = control_models + gligen + add_models
 
     return models, inference_memory
 
+def get_additional_models_from_model_options(model_options: dict[str]=None):
+    """loads additional models from registered AddModels hooks"""
+    models = []
+    if model_options is not None and "registered_hooks" in model_options:
+        registered: comfy.hooks.HookGroup = model_options["registered_hooks"]
+        for hook in registered.get_type(comfy.hooks.EnumHookType.AdditionalModels):
+            hook: comfy.hooks.AdditionalModelsHook
+            models.extend(hook.models)
+    return models
+
 def cleanup_additional_models(models):
     """cleanup additional models that were loaded"""
     for m in models:
@@ -102,9 +106,10 @@ def cleanup_additional_models(models):
             m.cleanup()
 
 
-def prepare_sampling(model: 'ModelPatcher', noise_shape, conds):
-    real_model: 'BaseModel' = None
+def prepare_sampling(model: ModelPatcher, noise_shape, conds, model_options=None):
+    real_model: BaseModel = None
     models, inference_memory = get_additional_models(conds, model.model_dtype())
+    models += get_additional_models_from_model_options(model_options)
     models += model.get_nested_additional_models()  # TODO: does this require inference_memory update?
     memory_required = model.memory_required([noise_shape[0] * 2] + list(noise_shape[1:])) + inference_memory
     minimum_memory_required = model.memory_required([noise_shape[0]] + list(noise_shape[1:])) + inference_memory
@@ -123,12 +128,35 @@ def cleanup_models(conds, models):
     cleanup_additional_models(set(control_cleanup))
 
 def prepare_model_patcher(model: 'ModelPatcher', conds, model_options: dict):
+    '''
+    Registers hooks from conds.
+    '''
     # check for hooks in conds - if not registered, see if can be applied
-    hooks = {}
+    hooks = comfy.hooks.HookGroup()
     for k in conds:
         get_hooks_from_cond(conds[k], hooks)
     # add wrappers and callbacks from ModelPatcher to transformer_options
     model_options["transformer_options"]["wrappers"] = comfy.patcher_extension.copy_nested_dicts(model.wrappers)
     model_options["transformer_options"]["callbacks"] = comfy.patcher_extension.copy_nested_dicts(model.callbacks)
-    # register hooks on model/model_options
-    model.register_all_hook_patches(hooks, comfy.hooks.EnumWeightTarget.Model, model_options)
+    # begin registering hooks
+    registered = comfy.hooks.HookGroup()
+    target_dict = comfy.hooks.create_target_dict(comfy.hooks.EnumWeightTarget.Model)
+    # handle all TransformerOptionsHooks
+    for hook in hooks.get_type(comfy.hooks.EnumHookType.TransformerOptions):
+        hook: comfy.hooks.TransformerOptionsHook
+        hook.add_hook_patches(model, model_options, target_dict, registered)
+    # handle all AddModelsHooks
+    for hook in hooks.get_type(comfy.hooks.EnumHookType.AdditionalModels):
+        hook: comfy.hooks.AdditionalModelsHook
+        hook.add_hook_patches(model, model_options, target_dict, registered)
+    # handle all WeightHooks by registering on ModelPatcher
+    model.register_all_hook_patches(hooks, target_dict, model_options, registered)
+    # add registered_hooks onto model_options for further reference
+    if len(registered) > 0:
+        model_options["registered_hooks"] = registered
+    # merge original wrappers and callbacks with hooked wrappers and callbacks
+    to_load_options: dict[str] = model_options.setdefault("to_load_options", {})
+    for wc_name in ["wrappers", "callbacks"]:
+        comfy.patcher_extension.merge_nested_dicts(to_load_options.setdefault(wc_name, {}), model_options["transformer_options"][wc_name],
+                                                    copy_dict1=False)
+    return to_load_options

comfy/samplers.py

@@ -810,6 +810,33 @@ def preprocess_conds_hooks(conds: dict[str, list[dict[str]]]):
             for cond in conds_to_modify:
                 cond['hooks'] = hooks
 
+def filter_registered_hooks_on_conds(conds: dict[str, list[dict[str]]], model_options: dict[str]):
+    '''Modify 'hooks' on conds so that only hooks that were registered remain. Properly accounts for
+    HookGroups that have the same reference.'''
+    registered: comfy.hooks.HookGroup = model_options.get('registered_hooks', None)
+    # if None were registered, make sure all hooks are cleaned from conds
+    if registered is None:
+        for k in conds:
+            for kk in conds[k]:
+                kk.pop('hooks', None)
+        return
+    # find conds that contain hooks to be replaced - group by common HookGroup refs
+    hook_replacement: dict[comfy.hooks.HookGroup, list[dict]] = {}
+    for k in conds:
+        for kk in conds[k]:
+            hooks: comfy.hooks.HookGroup = kk.get('hooks', None)
+            if hooks is not None:
+                if not hooks.is_subset_of(registered):
+                    to_replace = hook_replacement.setdefault(hooks, [])
+                    to_replace.append(kk)
+    # for each hook to replace, create a new proper HookGroup and assign to all common conds
+    for hooks, conds_to_modify in hook_replacement.items():
+        new_hooks = hooks.new_with_common_hooks(registered)
+        if len(new_hooks) == 0:
+            new_hooks = None
+        for kk in conds_to_modify:
+            kk['hooks'] = new_hooks
+
 
 def get_total_hook_groups_in_conds(conds: dict[str, list[dict[str]]]):
     hooks_set = set()
@@ -819,9 +846,58 @@ def get_total_hook_groups_in_conds(conds: dict[str, list[dict[str]]]):
     return len(hooks_set)
 
 
+def cast_to_load_options(model_options: dict[str], device=None, dtype=None):
+    '''
+    If any patches from hooks, wrappers, or callbacks have .to to be called, call it.
+    '''
+    if model_options is None:
+        return
+    to_load_options = model_options.get("to_load_options", None)
+    if to_load_options is None:
+        return
+
+    casts = []
+    if device is not None:
+        casts.append(device)
+    if dtype is not None:
+        casts.append(dtype)
+    # if nothing to apply, do nothing
+    if len(casts) == 0:
+        return
+
+    # try to call .to on patches
+    if "patches" in to_load_options:
+        patches = to_load_options["patches"]
+        for name in patches:
+            patch_list = patches[name]
+            for i in range(len(patch_list)):
+                if hasattr(patch_list[i], "to"):
+                    for cast in casts:
+                        patch_list[i] = patch_list[i].to(cast)
+    if "patches_replace" in to_load_options:
+        patches = to_load_options["patches_replace"]
+        for name in patches:
+            patch_list = patches[name]
+            for k in patch_list:
+                if hasattr(patch_list[k], "to"):
+                    for cast in casts:
+                        patch_list[k] = patch_list[k].to(cast)
+    # try to call .to on any wrappers/callbacks
+    wrappers_and_callbacks = ["wrappers", "callbacks"]
+    for wc_name in wrappers_and_callbacks:
+        if wc_name in to_load_options:
+            wc: dict[str, list] = to_load_options[wc_name]
+            for wc_dict in wc.values():
+                for wc_list in wc_dict.values():
+                    for i in range(len(wc_list)):
+                        if hasattr(wc_list[i], "to"):
+                            for cast in casts:
+                                wc_list[i] = wc_list[i].to(cast)
+
+
 class CFGGuider:
-    def __init__(self, model_patcher):
-        self.model_patcher: 'ModelPatcher' = model_patcher
+    def __init__(self, model_patcher: ModelPatcher):
+        self.model_patcher = model_patcher
         self.model_options = model_patcher.model_options
         self.original_conds = {}
         self.cfg = 1.0
@@ -849,7 +925,7 @@ class CFGGuider:
         self.conds = process_conds(self.inner_model, noise, self.conds, device, latent_image, denoise_mask, seed)
 
         extra_model_options = comfy.model_patcher.create_model_options_clone(self.model_options)
-        extra_model_options.setdefault("transformer_options", {})["sigmas"] = sigmas
+        extra_model_options.setdefault("transformer_options", {})["sample_sigmas"] = sigmas
         extra_args = {"model_options": extra_model_options, "seed": seed}
 
         executor = comfy.patcher_extension.WrapperExecutor.new_class_executor(
@@ -861,7 +937,7 @@ class CFGGuider:
         return self.inner_model.process_latent_out(samples.to(torch.float32))
 
     def outer_sample(self, noise, latent_image, sampler, sigmas, denoise_mask=None, callback=None, disable_pbar=False, seed=None):
-        self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds)
+        self.inner_model, self.conds, self.loaded_models = comfy.sampler_helpers.prepare_sampling(self.model_patcher, noise.shape, self.conds, self.model_options)
         device = self.model_patcher.load_device
 
         if denoise_mask is not None:
@@ -870,6 +946,7 @@ class CFGGuider:
         noise = noise.to(device)
         latent_image = latent_image.to(device)
         sigmas = sigmas.to(device)
+        cast_to_load_options(self.model_options, device=device, dtype=self.model_patcher.model_dtype())
 
         try:
             self.model_patcher.pre_run()
@@ -899,6 +976,7 @@ class CFGGuider:
             if get_total_hook_groups_in_conds(self.conds) <= 1:
                 self.model_patcher.hook_mode = comfy.hooks.EnumHookMode.MinVram
             comfy.sampler_helpers.prepare_model_patcher(self.model_patcher, self.conds, self.model_options)
+            filter_registered_hooks_on_conds(self.conds, self.model_options)
             executor = comfy.patcher_extension.WrapperExecutor.new_class_executor(
                 self.outer_sample,
                 self,
@@ -906,6 +984,7 @@ class CFGGuider:
             )
             output = executor.execute(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
         finally:
+            cast_to_load_options(self.model_options, device=self.model_patcher.offload_device)
             self.model_options = orig_model_options
             self.model_patcher.hook_mode = orig_hook_mode
             self.model_patcher.restore_hook_patches()

comfy/sd.py

@@ -11,6 +11,7 @@ from .ldm.cascade.stage_c_coder import StageC_coder
 from .ldm.audio.autoencoder import AudioOobleckVAE
 import comfy.ldm.genmo.vae.model
 import comfy.ldm.lightricks.vae.causal_video_autoencoder
+import comfy.ldm.cosmos.vae
 import yaml
 import math
 
@@ -34,6 +35,7 @@ import comfy.text_encoders.long_clipl
 import comfy.text_encoders.genmo
 import comfy.text_encoders.lt
 import comfy.text_encoders.hunyuan_video
+import comfy.text_encoders.cosmos
 
 import comfy.model_patcher
 import comfy.lora
@@ -111,7 +113,7 @@ class CLIP:
             model_management.load_models_gpu([self.patcher], force_full_load=True)
         self.layer_idx = None
         self.use_clip_schedule = False
-        logging.info("CLIP model load device: {}, offload device: {}, current: {}, dtype: {}".format(load_device, offload_device, params['device'], dtype))
+        logging.info("CLIP/text encoder model load device: {}, offload device: {}, current: {}, dtype: {}".format(load_device, offload_device, params['device'], dtype))
 
     def clone(self):
         n = CLIP(no_init=True)
@@ -376,6 +378,19 @@ class VAE:
                 self.memory_used_decode = lambda shape, dtype: (1500 * shape[2] * shape[3] * shape[4] * (4 * 8 * 8)) * model_management.dtype_size(dtype)
                 self.memory_used_encode = lambda shape, dtype: (900 * max(shape[2], 2) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
                 self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
+            elif "decoder.unpatcher3d.wavelets" in sd:
+                self.upscale_ratio = (lambda a: max(0, a * 8 - 7), 8, 8)
+                self.upscale_index_formula = (8, 8, 8)
+                self.downscale_ratio = (lambda a: max(0, math.floor((a + 7) / 8)), 8, 8)
+                self.downscale_index_formula = (8, 8, 8)
+                self.latent_dim = 3
+                self.latent_channels = 16
+                ddconfig = {'z_channels': 16, 'latent_channels': self.latent_channels, 'z_factor': 1, 'resolution': 1024, 'in_channels': 3, 'out_channels': 3, 'channels': 128, 'channels_mult': [2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [32], 'dropout': 0.0, 'patch_size': 4, 'num_groups': 1, 'temporal_compression': 8, 'spacial_compression': 8}
+                self.first_stage_model = comfy.ldm.cosmos.vae.CausalContinuousVideoTokenizer(**ddconfig)
+                #TODO: these values are a bit off because this is not a standard VAE
+                self.memory_used_decode = lambda shape, dtype: (220 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype)
+                self.memory_used_encode = lambda shape, dtype: (500 * max(shape[2], 2) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
+                self.working_dtypes = [torch.bfloat16, torch.float32]
             else:
                 logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
                 self.first_stage_model = None
@@ -641,6 +656,7 @@ class CLIPType(Enum):
     LTXV = 8
     HUNYUAN_VIDEO = 9
     PIXART = 10
+    COSMOS = 11
 
 
 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
@@ -658,6 +674,7 @@ class TEModel(Enum):
     T5_XL = 5
     T5_BASE = 6
     LLAMA3_8 = 7
+    T5_XXL_OLD = 8
 
 def detect_te_model(sd):
     if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
@@ -672,6 +689,8 @@ def detect_te_model(sd):
             return TEModel.T5_XXL
         elif weight.shape[-1] == 2048:
             return TEModel.T5_XL
+    if 'encoder.block.23.layer.1.DenseReluDense.wi.weight' in sd:
+        return TEModel.T5_XXL_OLD
     if "encoder.block.0.layer.0.SelfAttention.k.weight" in sd:
         return TEModel.T5_BASE
     if "model.layers.0.post_attention_layernorm.weight" in sd:
@@ -681,9 +700,10 @@ def detect_te_model(sd):
 
 def t5xxl_detect(clip_data):
     weight_name = "encoder.block.23.layer.1.DenseReluDense.wi_1.weight"
+    weight_name_old = "encoder.block.23.layer.1.DenseReluDense.wi.weight"
 
     for sd in clip_data:
-        if weight_name in sd:
+        if weight_name in sd or weight_name_old in sd:
             return comfy.text_encoders.sd3_clip.t5_xxl_detect(sd)
 
     return {}
@@ -740,6 +760,9 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             else: #CLIPType.MOCHI
                 clip_target.clip = comfy.text_encoders.genmo.mochi_te(**t5xxl_detect(clip_data))
                 clip_target.tokenizer = comfy.text_encoders.genmo.MochiT5Tokenizer
+        elif te_model == TEModel.T5_XXL_OLD:
+            clip_target.clip = comfy.text_encoders.cosmos.te(**t5xxl_detect(clip_data))
+            clip_target.tokenizer = comfy.text_encoders.cosmos.CosmosT5Tokenizer
         elif te_model == TEModel.T5_XL:
             clip_target.clip = comfy.text_encoders.aura_t5.AuraT5Model
             clip_target.tokenizer = comfy.text_encoders.aura_t5.AuraT5Tokenizer
@@ -898,7 +921,7 @@ def load_state_dict_guess_config(sd, output_vae=True, output_clip=True, output_c
     if output_model:
         model_patcher = comfy.model_patcher.ModelPatcher(model, load_device=load_device, offload_device=model_management.unet_offload_device())
         if inital_load_device != torch.device("cpu"):
-            logging.info("loaded straight to GPU")
+            logging.info("loaded diffusion model directly to GPU")
             model_management.load_models_gpu([model_patcher], force_full_load=True)
 
     return (model_patcher, clip, vae, clipvision)

comfy/supported_models.py

@@ -14,6 +14,7 @@ import comfy.text_encoders.flux
 import comfy.text_encoders.genmo
 import comfy.text_encoders.lt
 import comfy.text_encoders.hunyuan_video
+import comfy.text_encoders.cosmos
 
 from . import supported_models_base
 from . import latent_formats
@@ -823,6 +824,37 @@ class HunyuanVideo(supported_models_base.BASE):
         hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}llama.transformer.".format(pref))
         return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_video.HunyuanVideoTokenizer, comfy.text_encoders.hunyuan_video.hunyuan_video_clip(**hunyuan_detect))
 
-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, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideo]
+class Cosmos(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "cosmos",
+    }
+
+    sampling_settings = {
+        "sigma_data": 0.5,
+        "sigma_max": 80.0,
+        "sigma_min": 0.002,
+    }
+
+    unet_extra_config = {}
+    latent_format = latent_formats.Cosmos1CV8x8x8
+
+    memory_usage_factor = 2.4 #TODO
+
+    supported_inference_dtypes = [torch.bfloat16, torch.float16, torch.float32] #TODO
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.CosmosVideo(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        pref = self.text_encoder_key_prefix[0]
+        t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
+        return supported_models_base.ClipTarget(comfy.text_encoders.cosmos.CosmosT5Tokenizer, comfy.text_encoders.cosmos.te(**t5_detect))
+
+
+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, AuraFlow, PixArtAlpha, PixArtSigma, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideo, Cosmos]
 
 models += [SVD_img2vid]

comfy/text_encoders/cosmos.py

@@ -0,0 +1,42 @@
+from comfy import sd1_clip
+import comfy.text_encoders.t5
+import os
+from transformers import T5TokenizerFast
+
+
+class T5XXLModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="last", layer_idx=None, dtype=None, attention_mask=True, model_options={}):
+        textmodel_json_config = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_old_config_xxl.json")
+        t5xxl_scaled_fp8 = model_options.get("t5xxl_scaled_fp8", None)
+        if t5xxl_scaled_fp8 is not None:
+            model_options = model_options.copy()
+            model_options["scaled_fp8"] = t5xxl_scaled_fp8
+
+        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.text_encoders.t5.T5, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, zero_out_masked=attention_mask, model_options=model_options)
+
+class CosmosT5XXL(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, name="t5xxl", clip_model=T5XXLModel, model_options=model_options)
+
+
+class T5XXLTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "t5_tokenizer")
+        super().__init__(tokenizer_path, embedding_directory=embedding_directory, pad_with_end=False, embedding_size=1024, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=512)
+
+
+class CosmosT5Tokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, clip_name="t5xxl", tokenizer=T5XXLTokenizer)
+
+
+def te(dtype_t5=None, t5xxl_scaled_fp8=None):
+    class CosmosTEModel_(CosmosT5XXL):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if t5xxl_scaled_fp8 is not None and "t5xxl_scaled_fp8" not in model_options:
+                model_options = model_options.copy()
+                model_options["t5xxl_scaled_fp8"] = t5xxl_scaled_fp8
+            if dtype is None:
+                dtype = dtype_t5
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return CosmosTEModel_

comfy/text_encoders/t5.py

@@ -227,8 +227,9 @@ class T5(torch.nn.Module):
         super().__init__()
         self.num_layers = config_dict["num_layers"]
         model_dim = config_dict["d_model"]
+        inner_dim = config_dict["d_kv"] * config_dict["num_heads"]
 
-        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"] != "umt5", dtype, device, operations)
+        self.encoder = T5Stack(self.num_layers, model_dim, inner_dim, config_dict["d_ff"], config_dict["dense_act_fn"], config_dict["is_gated_act"], config_dict["num_heads"], config_dict["model_type"] != "umt5", dtype, device, operations)
         self.dtype = dtype
         self.shared = operations.Embedding(config_dict["vocab_size"], model_dim, device=device, dtype=dtype)
 

comfy/text_encoders/t5_old_config_xxl.json

@@ -0,0 +1,22 @@
+{
+  "d_ff": 65536,
+  "d_kv": 128,
+  "d_model": 1024,
+  "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": 24,
+  "num_heads": 128,
+  "num_layers": 24,
+  "output_past": true,
+  "pad_token_id": 0,
+  "relative_attention_num_buckets": 32,
+  "tie_word_embeddings": false,
+  "vocab_size": 32128
+}

comfy/utils.py

@@ -693,7 +693,25 @@ def copy_to_param(obj, attr, value):
     prev = getattr(obj, attrs[-1])
     prev.data.copy_(value)
 
-def get_attr(obj, attr):
+def get_attr(obj, attr: str):
+    """Retrieves a nested attribute from an object using dot notation.
+
+    Args:
+        obj: The object to get the attribute from
+        attr (str): The attribute path using dot notation (e.g. "model.layer.weight")
+
+    Returns:
+        The value of the requested attribute
+
+    Example:
+        model = MyModel()
+        weight = get_attr(model, "layer1.conv.weight")
+        # Equivalent to: model.layer1.conv.weight
+
+    Important:
+        Always prefer `comfy.model_patcher.ModelPatcher.get_model_object` when
+        accessing nested model objects under `ModelPatcher.model`.
+    """
     attrs = attr.split(".")
     for name in attrs:
         obj = getattr(obj, name)

comfy_extras/nodes_cosmos.py

@@ -0,0 +1,23 @@
+import nodes
+import torch
+import comfy.model_management
+
+class EmptyCosmosLatentVideo:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "width": ("INT", {"default": 1280, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+                              "height": ("INT", {"default": 704, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
+                              "length": ("INT", {"default": 121, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 8}),
+                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "generate"
+
+    CATEGORY = "latent/video"
+
+    def generate(self, width, height, length, batch_size=1):
+        latent = torch.zeros([batch_size, 16, ((length - 1) // 8) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+        return ({"samples":latent}, )
+
+NODE_CLASS_MAPPINGS = {
+    "EmptyCosmosLatentVideo": EmptyCosmosLatentVideo,
+}

comfy_extras/nodes_hooks.py

@@ -246,7 +246,7 @@ class SetClipHooks:
     CATEGORY = "advanced/hooks/clip"
     FUNCTION = "apply_hooks"
 
-    def apply_hooks(self, clip: 'CLIP', schedule_clip: bool, apply_to_conds: bool, hooks: comfy.hooks.HookGroup=None):
+    def apply_hooks(self, clip: CLIP, schedule_clip: bool, apply_to_conds: bool, hooks: comfy.hooks.HookGroup=None):
         if hooks is not None:
             clip = clip.clone()
             if apply_to_conds:
@@ -255,7 +255,7 @@ class SetClipHooks:
             clip.use_clip_schedule = schedule_clip
             if not clip.use_clip_schedule:
                 clip.patcher.forced_hooks.set_keyframes_on_hooks(None)
-            clip.patcher.register_all_hook_patches(hooks.get_dict_repr(), comfy.hooks.EnumWeightTarget.Clip)
+            clip.patcher.register_all_hook_patches(hooks, comfy.hooks.create_target_dict(comfy.hooks.EnumWeightTarget.Clip))
         return (clip,)
 
 class ConditioningTimestepsRange:

comfy_extras/nodes_load_3d.py

@@ -26,6 +26,7 @@ class Load3D():
             "bg_color": ("STRING", {"default": "#000000", "multiline": False}),
             "light_intensity": ("INT", {"default": 10, "min": 1, "max": 20, "step": 1}),
             "up_direction": (["original", "-x", "+x", "-y", "+y", "-z", "+z"],),
+            "fov": ("INT", {"default": 75, "min": 10, "max": 150, "step": 1}),
         }}
 
     RETURN_TYPES = ("IMAGE", "MASK", "STRING")
@@ -37,13 +38,22 @@ class Load3D():
     CATEGORY = "3d"
 
     def process(self, model_file, image, **kwargs):
-        imagepath = folder_paths.get_annotated_filepath(image)
+        if isinstance(image, dict):
+            image_path = folder_paths.get_annotated_filepath(image['image'])
+            mask_path = folder_paths.get_annotated_filepath(image['mask'])
 
-        load_image_node = nodes.LoadImage()
+            load_image_node = nodes.LoadImage()
+            output_image, ignore_mask = load_image_node.load_image(image=image_path)
+            ignore_image, output_mask = load_image_node.load_image(image=mask_path)
 
-        output_image, output_mask = load_image_node.load_image(image=imagepath)
-
-        return output_image, output_mask, model_file,
+            return output_image, output_mask, model_file,
+        else:
+            # to avoid the format is not dict which will happen the FE code is not compatibility to core,
+            # we need to this to double-check, it can be removed after merged FE into the core
+            image_path = folder_paths.get_annotated_filepath(image)
+            load_image_node = nodes.LoadImage()
+            output_image, output_mask = load_image_node.load_image(image=image_path)
+            return output_image, output_mask, model_file,
 
 class Load3DAnimation():
     @classmethod
@@ -67,6 +77,7 @@ class Load3DAnimation():
             "light_intensity": ("INT", {"default": 10, "min": 1, "max": 20, "step": 1}),
             "up_direction": (["original", "-x", "+x", "-y", "+y", "-z", "+z"],),
             "animation_speed": (["0.1", "0.5", "1", "1.5", "2"], {"default": "1"}),
+            "fov": ("INT", {"default": 75, "min": 10, "max": 150, "step": 1}),
         }}
 
     RETURN_TYPES = ("IMAGE", "MASK", "STRING")
@@ -78,13 +89,20 @@ class Load3DAnimation():
     CATEGORY = "3d"
 
     def process(self, model_file, image, **kwargs):
-        imagepath = folder_paths.get_annotated_filepath(image)
+        if isinstance(image, dict):
+            image_path = folder_paths.get_annotated_filepath(image['image'])
+            mask_path = folder_paths.get_annotated_filepath(image['mask'])
 
-        load_image_node = nodes.LoadImage()
+            load_image_node = nodes.LoadImage()
+            output_image, ignore_mask = load_image_node.load_image(image=image_path)
+            ignore_image, output_mask = load_image_node.load_image(image=mask_path)
 
-        output_image, output_mask = load_image_node.load_image(image=imagepath)
-
-        return output_image, output_mask, model_file,
+            return output_image, output_mask, model_file,
+        else:
+            image_path = folder_paths.get_annotated_filepath(image)
+            load_image_node = nodes.LoadImage()
+            output_image, output_mask = load_image_node.load_image(image=image_path)
+            return output_image, output_mask, model_file,
 
 class Preview3D():
     @classmethod
@@ -98,6 +116,7 @@ class Preview3D():
             "bg_color": ("STRING", {"default": "#000000", "multiline": False}),
             "light_intensity": ("INT", {"default": 10, "min": 1, "max": 20, "step": 1}),
             "up_direction": (["original", "-x", "+x", "-y", "+y", "-z", "+z"],),
+            "fov": ("INT", {"default": 75, "min": 10, "max": 150, "step": 1}),
         }}
 
     OUTPUT_NODE = True

comfy_extras/nodes_model_advanced.py

@@ -189,7 +189,7 @@ class ModelSamplingContinuousEDM:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "model": ("MODEL",),
-                              "sampling": (["v_prediction", "edm_playground_v2.5", "eps"],),
+                              "sampling": (["v_prediction", "edm", "edm_playground_v2.5", "eps"],),
                               "sigma_max": ("FLOAT", {"default": 120.0, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
                               "sigma_min": ("FLOAT", {"default": 0.002, "min": 0.0, "max": 1000.0, "step":0.001, "round": False}),
                               }}
@@ -206,6 +206,9 @@ class ModelSamplingContinuousEDM:
         sigma_data = 1.0
         if sampling == "eps":
             sampling_type = comfy.model_sampling.EPS
+        elif sampling == "edm":
+            sampling_type = comfy.model_sampling.EDM
+            sigma_data = 0.5
         elif sampling == "v_prediction":
             sampling_type = comfy.model_sampling.V_PREDICTION
         elif sampling == "edm_playground_v2.5":

comfyui_version.py

@@ -0,0 +1,3 @@
+# This file is automatically generated by the build process when version is
+# updated in pyproject.toml.
+__version__ = "0.3.10"

nodes.py

@@ -912,16 +912,19 @@ class CLIPLoader:
     @classmethod
     def INPUT_TYPES(s):
         return {"required": { "clip_name": (folder_paths.get_filename_list("text_encoders"), ),
-                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart"], ),
+                              "type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv", "pixart", "cosmos"], ),
+                              },
+                "optional": {
+                              "device": (["default", "cpu"], {"advanced": True}),
                              }}
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
 
     CATEGORY = "advanced/loaders"
 
-    DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 / clip-g / clip-l\nstable_audio: t5\nmochi: t5"
+    DESCRIPTION = "[Recipes]\n\nstable_diffusion: clip-l\nstable_cascade: clip-g\nsd3: t5 / clip-g / clip-l\nstable_audio: t5\nmochi: t5\ncosmos: old t5 xxl"
 
-    def load_clip(self, clip_name, type="stable_diffusion"):
+    def load_clip(self, clip_name, type="stable_diffusion", device="default"):
         if type == "stable_cascade":
             clip_type = comfy.sd.CLIPType.STABLE_CASCADE
         elif type == "sd3":
@@ -937,8 +940,12 @@ class CLIPLoader:
         else:
             clip_type = comfy.sd.CLIPType.STABLE_DIFFUSION
 
+        model_options = {}
+        if device == "cpu":
+            model_options["load_device"] = model_options["offload_device"] = torch.device("cpu")
+
         clip_path = folder_paths.get_full_path_or_raise("text_encoders", clip_name)
-        clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type)
+        clip = comfy.sd.load_clip(ckpt_paths=[clip_path], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type, model_options=model_options)
         return (clip,)
 
 class DualCLIPLoader:
@@ -947,6 +954,9 @@ class DualCLIPLoader:
         return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ),
                               "clip_name2": (folder_paths.get_filename_list("text_encoders"), ),
                               "type": (["sdxl", "sd3", "flux", "hunyuan_video"], ),
+                              },
+                "optional": {
+                              "device": (["default", "cpu"], {"advanced": True}),
                              }}
     RETURN_TYPES = ("CLIP",)
     FUNCTION = "load_clip"
@@ -955,7 +965,7 @@ class DualCLIPLoader:
 
     DESCRIPTION = "[Recipes]\n\nsdxl: clip-l, clip-g\nsd3: clip-l, clip-g / clip-l, t5 / clip-g, t5\nflux: clip-l, t5"
 
-    def load_clip(self, clip_name1, clip_name2, type):
+    def load_clip(self, clip_name1, clip_name2, type, device="default"):
         clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1)
         clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2)
         if type == "sdxl":
@@ -967,7 +977,11 @@ class DualCLIPLoader:
         elif type == "hunyuan_video":
             clip_type = comfy.sd.CLIPType.HUNYUAN_VIDEO
 
-        clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type)
+        model_options = {}
+        if device == "cpu":
+            model_options["load_device"] = model_options["offload_device"] = torch.device("cpu")
+
+        clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2], embedding_directory=folder_paths.get_folder_paths("embeddings"), clip_type=clip_type, model_options=model_options)
         return (clip,)
 
 class CLIPVisionLoader:
@@ -2211,6 +2225,7 @@ def init_builtin_extra_nodes():
         "nodes_lt.py",
         "nodes_hooks.py",
         "nodes_load_3d.py",
+        "nodes_cosmos.py",
     ]
 
     import_failed = []

pyproject.toml

@@ -0,0 +1,23 @@
+[project]
+name = "ComfyUI"
+version = "0.3.10"
+readme = "README.md"
+license = { file = "LICENSE" }
+requires-python = ">=3.9"
+
+[project.urls]
+homepage = "https://www.comfy.org/"
+repository = "https://github.com/comfyanonymous/ComfyUI"
+documentation = "https://docs.comfy.org/"
+
+[tool.ruff]
+lint.select = [
+  "S307", # suspicious-eval-usage
+  "S102", # exec
+  "T",    # print-usage
+  "W",
+  # The "F" series in Ruff stands for "Pyflakes" rules, which catch various Python syntax errors and undefined names.
+  # See all rules here: https://docs.astral.sh/ruff/rules/#pyflakes-f
+  "F",
+]
+exclude = ["*.ipynb"]

ruff.toml

@@ -1,16 +0,0 @@
-target-version = "py39"
-
-# Disable all rules by default
-lint.ignore = ["ALL"]
-
-# Enable specific rules, see all rules here: https://docs.astral.sh/ruff/rules/
-lint.select = [
-    "S307",  # suspicious-eval-usage
-    "T201",  # print-usage
-    "W",
-    # The "F" series in Ruff stands for "Pyflakes" rules, which catch various Python syntax errors and undefined names.
-    "F",
-    "C408",  # unnecessary dict(), list() or tuple() calls that can be rewritten as empty literals.
-]
-
-exclude = ["*.ipynb"]

server.py

@@ -27,6 +27,7 @@ from comfy.cli_args import args
 import comfy.utils
 import comfy.model_management
 import node_helpers
+from comfyui_version import __version__
 from app.frontend_management import FrontendManager
 from app.user_manager import UserManager
 from app.model_manager import ModelFileManager
@@ -44,21 +45,6 @@ async def send_socket_catch_exception(function, message):
     except (aiohttp.ClientError, aiohttp.ClientPayloadError, ConnectionResetError, BrokenPipeError, ConnectionError) as err:
         logging.warning("send error: {}".format(err))
 
-def get_comfyui_version():
-    comfyui_version = "unknown"
-    repo_path = os.path.dirname(os.path.realpath(__file__))
-    try:
-        import pygit2
-        repo = pygit2.Repository(repo_path)
-        comfyui_version = repo.describe(describe_strategy=pygit2.GIT_DESCRIBE_TAGS)
-    except Exception:
-        try:
-            import subprocess
-            comfyui_version = subprocess.check_output(["git", "describe", "--tags"], cwd=repo_path).decode('utf-8')
-        except Exception as e:
-            logging.warning(f"Failed to get ComfyUI version: {e}")
-    return comfyui_version.strip()
-
 @web.middleware
 async def cache_control(request: web.Request, handler):
     response: web.Response = await handler(request)
@@ -518,7 +504,7 @@ class PromptServer():
                     "os": os.name,
                     "ram_total": ram_total,
                     "ram_free": ram_free,
-                    "comfyui_version": get_comfyui_version(),
+                    "comfyui_version": __version__,
                     "python_version": sys.version,
                     "pytorch_version": comfy.model_management.torch_version,
                     "embedded_python": os.path.split(os.path.split(sys.executable)[0])[1] == "python_embeded",

web/assets/BaseViewTemplate-BNGF4K22.js

@@ -0,0 +1,23 @@
+import { d as defineComponent, o as openBlock, f as createElementBlock, J as renderSlot, T as normalizeClass } from "./index-DjNHn37O.js";
+const _sfc_main = /* @__PURE__ */ defineComponent({
+  __name: "BaseViewTemplate",
+  props: {
+    dark: { type: Boolean, default: false }
+  },
+  setup(__props) {
+    const props = __props;
+    return (_ctx, _cache) => {
+      return openBlock(), createElementBlock("div", {
+        class: normalizeClass(["font-sans w-screen h-screen flex items-center justify-center pointer-events-auto overflow-auto", [
+          props.dark ? "text-neutral-300 bg-neutral-900 dark-theme" : "text-neutral-900 bg-neutral-300"
+        ]])
+      }, [
+        renderSlot(_ctx.$slots, "default")
+      ], 2);
+    };
+  }
+});
+export {
+  _sfc_main as _
+};
+//# sourceMappingURL=BaseViewTemplate-BNGF4K22.js.map

web/assets/DownloadGitView-B3f7KHY3.js.map

@@ -1 +0,0 @@
-{"version":3,"file":"DownloadGitView-B3f7KHY3.js","sources":["../../src/views/DownloadGitView.vue"],"sourcesContent":["<template>\n  <div\n    class=\"font-sans w-screen h-screen mx-0 grid place-items-center justify-center items-center text-neutral-900 bg-neutral-300 pointer-events-auto\"\n  >\n    <div\n      class=\"col-start-1 h-screen row-start-1 place-content-center mx-auto overflow-y-auto\"\n    >\n      <div\n        class=\"max-w-screen-sm flex flex-col gap-8 p-8 bg-[url('/assets/images/Git-Logo-White.svg')] bg-no-repeat bg-right-top bg-origin-padding\"\n      >\n        <!-- Header -->\n        <h1 class=\"mt-24 text-4xl font-bold text-red-500\">\n          {{ $t('downloadGit.title') }}\n        </h1>\n\n        <!-- Message -->\n        <div class=\"space-y-4\">\n          <p class=\"text-xl\">\n            {{ $t('downloadGit.message') }}\n          </p>\n          <p class=\"text-xl\">\n            {{ $t('downloadGit.instructions') }}\n          </p>\n          <p class=\"text-m\">\n            {{ $t('downloadGit.warning') }}\n          </p>\n        </div>\n\n        <!-- Actions -->\n        <div class=\"flex gap-4 flex-row-reverse\">\n          <Button\n            :label=\"$t('downloadGit.gitWebsite')\"\n            icon=\"pi pi-external-link\"\n            icon-pos=\"right\"\n            @click=\"openGitDownloads\"\n            severity=\"primary\"\n          />\n          <Button\n            :label=\"$t('downloadGit.skip')\"\n            icon=\"pi pi-exclamation-triangle\"\n            @click=\"skipGit\"\n            severity=\"secondary\"\n          />\n        </div>\n      </div>\n    </div>\n  </div>\n</template>\n\n<script setup lang=\"ts\">\nimport Button from 'primevue/button'\nimport { useRouter } from 'vue-router'\n\nconst openGitDownloads = () => {\n  window.open('https://git-scm.com/downloads/', '_blank')\n}\n\nconst skipGit = () => {\n  console.warn('pushing')\n  const router = useRouter()\n  router.push('install')\n}\n</script>\n"],"names":[],"mappings":";;;;;;;;;;;;;;;AAqDA,UAAM,mBAAmB,6BAAM;AACtB,aAAA,KAAK,kCAAkC,QAAQ;AAAA,IAAA,GAD/B;AAIzB,UAAM,UAAU,6BAAM;AACpB,cAAQ,KAAK,SAAS;AACtB,YAAM,SAAS;AACf,aAAO,KAAK,SAAS;AAAA,IAAA,GAHP;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;"}

web/assets/DownloadGitView-DeC7MBzG.js

@@ -1,15 +1,14 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { a as defineComponent, f as openBlock, g as createElementBlock, A as createBaseVNode, a8 as toDisplayString, h as createVNode, z as unref, D as script, bU as useRouter } from "./index-DIU5yZe9.js";
-const _hoisted_1 = { class: "font-sans w-screen h-screen mx-0 grid place-items-center justify-center items-center text-neutral-900 bg-neutral-300 pointer-events-auto" };
-const _hoisted_2 = { class: "col-start-1 h-screen row-start-1 place-content-center mx-auto overflow-y-auto" };
-const _hoisted_3 = { class: "max-w-screen-sm flex flex-col gap-8 p-8 bg-[url('/assets/images/Git-Logo-White.svg')] bg-no-repeat bg-right-top bg-origin-padding" };
-const _hoisted_4 = { class: "mt-24 text-4xl font-bold text-red-500" };
-const _hoisted_5 = { class: "space-y-4" };
-const _hoisted_6 = { class: "text-xl" };
-const _hoisted_7 = { class: "text-xl" };
-const _hoisted_8 = { class: "text-m" };
-const _hoisted_9 = { class: "flex gap-4 flex-row-reverse" };
+import { d as defineComponent, o as openBlock, k as createBlock, M as withCtx, H as createBaseVNode, X as toDisplayString, N as createVNode, j as unref, l as script, bW as useRouter } from "./index-DjNHn37O.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-BNGF4K22.js";
+const _hoisted_1 = { class: "max-w-screen-sm flex flex-col gap-8 p-8 bg-[url('/assets/images/Git-Logo-White.svg')] bg-no-repeat bg-right-top bg-origin-padding" };
+const _hoisted_2 = { class: "mt-24 text-4xl font-bold text-red-500" };
+const _hoisted_3 = { class: "space-y-4" };
+const _hoisted_4 = { class: "text-xl" };
+const _hoisted_5 = { class: "text-xl" };
+const _hoisted_6 = { class: "text-m" };
+const _hoisted_7 = { class: "flex gap-4 flex-row-reverse" };
 const _sfc_main = /* @__PURE__ */ defineComponent({
   __name: "DownloadGitView",
   setup(__props) {
@@ -22,16 +21,16 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
       router.push("install");
     }, "skipGit");
     return (_ctx, _cache) => {
-      return openBlock(), createElementBlock("div", _hoisted_1, [
-        createBaseVNode("div", _hoisted_2, [
-          createBaseVNode("div", _hoisted_3, [
-            createBaseVNode("h1", _hoisted_4, toDisplayString(_ctx.$t("downloadGit.title")), 1),
-            createBaseVNode("div", _hoisted_5, [
-              createBaseVNode("p", _hoisted_6, toDisplayString(_ctx.$t("downloadGit.message")), 1),
-              createBaseVNode("p", _hoisted_7, toDisplayString(_ctx.$t("downloadGit.instructions")), 1),
-              createBaseVNode("p", _hoisted_8, toDisplayString(_ctx.$t("downloadGit.warning")), 1)
+      return openBlock(), createBlock(_sfc_main$1, null, {
+        default: withCtx(() => [
+          createBaseVNode("div", _hoisted_1, [
+            createBaseVNode("h1", _hoisted_2, toDisplayString(_ctx.$t("downloadGit.title")), 1),
+            createBaseVNode("div", _hoisted_3, [
+              createBaseVNode("p", _hoisted_4, toDisplayString(_ctx.$t("downloadGit.message")), 1),
+              createBaseVNode("p", _hoisted_5, toDisplayString(_ctx.$t("downloadGit.instructions")), 1),
+              createBaseVNode("p", _hoisted_6, toDisplayString(_ctx.$t("downloadGit.warning")), 1)
             ]),
-            createBaseVNode("div", _hoisted_9, [
+            createBaseVNode("div", _hoisted_7, [
               createVNode(unref(script), {
                 label: _ctx.$t("downloadGit.gitWebsite"),
                 icon: "pi pi-external-link",
@@ -47,12 +46,13 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
               }, null, 8, ["label"])
             ])
           ])
-        ])
-      ]);
+        ]),
+        _: 1
+      });
     };
   }
 });
 export {
   _sfc_main as default
 };
-//# sourceMappingURL=DownloadGitView-B3f7KHY3.js.map
+//# sourceMappingURL=DownloadGitView-DeC7MBzG.js.map

web/assets/ExtensionPanel-ByeZ01RF.js.map

@@ -1 +0,0 @@
-{"version":3,"file":"ExtensionPanel-ByeZ01RF.js","sources":["../../src/components/dialog/content/setting/ExtensionPanel.vue"],"sourcesContent":["<template>\n  <PanelTemplate value=\"Extension\" class=\"extension-panel\">\n    <template #header>\n      <SearchBox\n        v-model=\"filters['global'].value\"\n        :placeholder=\"$t('g.searchExtensions') + '...'\"\n      />\n      <Message v-if=\"hasChanges\" severity=\"info\" pt:text=\"w-full\">\n        <ul>\n          <li v-for=\"ext in changedExtensions\" :key=\"ext.name\">\n            <span>\n              {{ extensionStore.isExtensionEnabled(ext.name) ? '[-]' : '[+]' }}\n            </span>\n            {{ ext.name }}\n          </li>\n        </ul>\n        <div class=\"flex justify-end\">\n          <Button\n            :label=\"$t('g.reloadToApplyChanges')\"\n            @click=\"applyChanges\"\n            outlined\n            severity=\"danger\"\n          />\n        </div>\n      </Message>\n    </template>\n    <DataTable\n      :value=\"extensionStore.extensions\"\n      stripedRows\n      size=\"small\"\n      :filters=\"filters\"\n    >\n      <Column field=\"name\" :header=\"$t('g.extensionName')\" sortable></Column>\n      <Column\n        :pt=\"{\n          bodyCell: 'flex items-center justify-end'\n        }\"\n      >\n        <template #body=\"slotProps\">\n          <ToggleSwitch\n            v-model=\"editingEnabledExtensions[slotProps.data.name]\"\n            @change=\"updateExtensionStatus\"\n          />\n        </template>\n      </Column>\n    </DataTable>\n  </PanelTemplate>\n</template>\n\n<script setup lang=\"ts\">\nimport { ref, computed, onMounted } from 'vue'\nimport { useExtensionStore } from '@/stores/extensionStore'\nimport { useSettingStore } from '@/stores/settingStore'\nimport DataTable from 'primevue/datatable'\nimport Column from 'primevue/column'\nimport ToggleSwitch from 'primevue/toggleswitch'\nimport Button from 'primevue/button'\nimport Message from 'primevue/message'\nimport { FilterMatchMode } from '@primevue/core/api'\nimport PanelTemplate from './PanelTemplate.vue'\nimport SearchBox from '@/components/common/SearchBox.vue'\n\nconst filters = ref({\n  global: { value: '', matchMode: FilterMatchMode.CONTAINS }\n})\n\nconst extensionStore = useExtensionStore()\nconst settingStore = useSettingStore()\n\nconst editingEnabledExtensions = ref<Record<string, boolean>>({})\n\nonMounted(() => {\n  extensionStore.extensions.forEach((ext) => {\n    editingEnabledExtensions.value[ext.name] =\n      extensionStore.isExtensionEnabled(ext.name)\n  })\n})\n\nconst changedExtensions = computed(() => {\n  return extensionStore.extensions.filter(\n    (ext) =>\n      editingEnabledExtensions.value[ext.name] !==\n      extensionStore.isExtensionEnabled(ext.name)\n  )\n})\n\nconst hasChanges = computed(() => {\n  return changedExtensions.value.length > 0\n})\n\nconst updateExtensionStatus = () => {\n  const editingDisabledExtensionNames = Object.entries(\n    editingEnabledExtensions.value\n  )\n    .filter(([_, enabled]) => !enabled)\n    .map(([name]) => name)\n\n  settingStore.set('Comfy.Extension.Disabled', [\n    ...extensionStore.inactiveDisabledExtensionNames,\n    ...editingDisabledExtensionNames\n  ])\n}\n\nconst applyChanges = () => {\n  // Refresh the page to apply changes\n  window.location.reload()\n}\n</script>\n"],"names":[],"mappings":";;;;;;;;;AA8DA,UAAM,UAAU,IAAI;AAAA,MAClB,QAAQ,EAAE,OAAO,IAAI,WAAW,gBAAgB,SAAS;AAAA,IAAA,CAC1D;AAED,UAAM,iBAAiB;AACvB,UAAM,eAAe;AAEf,UAAA,2BAA2B,IAA6B,CAAA,CAAE;AAEhE,cAAU,MAAM;AACC,qBAAA,WAAW,QAAQ,CAAC,QAAQ;AACzC,iCAAyB,MAAM,IAAI,IAAI,IACrC,eAAe,mBAAmB,IAAI,IAAI;AAAA,MAAA,CAC7C;AAAA,IAAA,CACF;AAEK,UAAA,oBAAoB,SAAS,MAAM;AACvC,aAAO,eAAe,WAAW;AAAA,QAC/B,CAAC,QACC,yBAAyB,MAAM,IAAI,IAAI,MACvC,eAAe,mBAAmB,IAAI,IAAI;AAAA,MAAA;AAAA,IAC9C,CACD;AAEK,UAAA,aAAa,SAAS,MAAM;AACzB,aAAA,kBAAkB,MAAM,SAAS;AAAA,IAAA,CACzC;AAED,UAAM,wBAAwB,6BAAM;AAClC,YAAM,gCAAgC,OAAO;AAAA,QAC3C,yBAAyB;AAAA,MAExB,EAAA,OAAO,CAAC,CAAC,GAAG,OAAO,MAAM,CAAC,OAAO,EACjC,IAAI,CAAC,CAAC,IAAI,MAAM,IAAI;AAEvB,mBAAa,IAAI,4BAA4B;AAAA,QAC3C,GAAG,eAAe;AAAA,QAClB,GAAG;AAAA,MAAA,CACJ;AAAA,IAAA,GAV2B;AAa9B,UAAM,eAAe,6BAAM;AAEzB,aAAO,SAAS;IAAO,GAFJ;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;"}

web/assets/ExtensionPanel-D4Phn0Zr.js

@@ -1,8 +1,9 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { a as defineComponent, r as ref, ck as FilterMatchMode, co as useExtensionStore, u as useSettingStore, o as onMounted, q as computed, f as openBlock, x as createBlock, y as withCtx, h as createVNode, cl as SearchBox, z as unref, bW as script, A as createBaseVNode, g as createElementBlock, Q as renderList, a8 as toDisplayString, ay as createTextVNode, P as Fragment, D as script$1, i as createCommentVNode, c5 as script$3, cm as _sfc_main$1 } from "./index-DIU5yZe9.js";
-import { s as script$2, a as script$4 } from "./index-D3u7l7ha.js";
-import "./index-d698Brhb.js";
+import { d as defineComponent, ab as ref, cn as FilterMatchMode, cs as useExtensionStore, a as useSettingStore, m as onMounted, c as computed, o as openBlock, k as createBlock, M as withCtx, N as createVNode, co as SearchBox, j as unref, bZ as script, H as createBaseVNode, f as createElementBlock, E as renderList, X as toDisplayString, aE as createTextVNode, F as Fragment, l as script$1, I as createCommentVNode, aI as script$3, bO as script$4, c4 as script$5, cp as _sfc_main$1 } from "./index-DjNHn37O.js";
+import { s as script$2, a as script$6 } from "./index-B5F0uxTQ.js";
+import "./index-B-aVupP5.js";
+import "./index-5HFeZax4.js";
 const _hoisted_1 = { class: "flex justify-end" };
 const _sfc_main = /* @__PURE__ */ defineComponent({
   __name: "ExtensionPanel",
@@ -35,9 +36,49 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
         ...editingDisabledExtensionNames
       ]);
     }, "updateExtensionStatus");
+    const enableAllExtensions = /* @__PURE__ */ __name(() => {
+      extensionStore.extensions.forEach((ext) => {
+        if (extensionStore.isExtensionReadOnly(ext.name)) return;
+        editingEnabledExtensions.value[ext.name] = true;
+      });
+      updateExtensionStatus();
+    }, "enableAllExtensions");
+    const disableAllExtensions = /* @__PURE__ */ __name(() => {
+      extensionStore.extensions.forEach((ext) => {
+        if (extensionStore.isExtensionReadOnly(ext.name)) return;
+        editingEnabledExtensions.value[ext.name] = false;
+      });
+      updateExtensionStatus();
+    }, "disableAllExtensions");
+    const disableThirdPartyExtensions = /* @__PURE__ */ __name(() => {
+      extensionStore.extensions.forEach((ext) => {
+        if (extensionStore.isCoreExtension(ext.name)) return;
+        editingEnabledExtensions.value[ext.name] = false;
+      });
+      updateExtensionStatus();
+    }, "disableThirdPartyExtensions");
     const applyChanges = /* @__PURE__ */ __name(() => {
       window.location.reload();
     }, "applyChanges");
+    const menu = ref();
+    const contextMenuItems = [
+      {
+        label: "Enable All",
+        icon: "pi pi-check",
+        command: enableAllExtensions
+      },
+      {
+        label: "Disable All",
+        icon: "pi pi-times",
+        command: disableAllExtensions
+      },
+      {
+        label: "Disable 3rd Party",
+        icon: "pi pi-times",
+        command: disableThirdPartyExtensions,
+        disabled: !extensionStore.hasThirdPartyExtensions
+      }
+    ];
     return (_ctx, _cache) => {
       return openBlock(), createBlock(_sfc_main$1, {
         value: "Extension",
@@ -52,7 +93,8 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
           hasChanges.value ? (openBlock(), createBlock(unref(script), {
             key: 0,
             severity: "info",
-            "pt:text": "w-full"
+            "pt:text": "w-full",
+            class: "max-h-96 overflow-y-auto"
           }, {
             default: withCtx(() => [
               createBaseVNode("ul", null, [
@@ -78,7 +120,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
           })) : createCommentVNode("", true)
         ]),
         default: withCtx(() => [
-          createVNode(unref(script$4), {
+          createVNode(unref(script$6), {
             value: unref(extensionStore).extensions,
             stripedRows: "",
             size: "small",
@@ -86,19 +128,43 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
           }, {
             default: withCtx(() => [
               createVNode(unref(script$2), {
-                field: "name",
                 header: _ctx.$t("g.extensionName"),
-                sortable: ""
-              }, null, 8, ["header"]),
+                sortable: "",
+                field: "name"
+              }, {
+                body: withCtx((slotProps) => [
+                  createTextVNode(toDisplayString(slotProps.data.name) + " ", 1),
+                  unref(extensionStore).isCoreExtension(slotProps.data.name) ? (openBlock(), createBlock(unref(script$3), {
+                    key: 0,
+                    value: "Core"
+                  })) : createCommentVNode("", true)
+                ]),
+                _: 1
+              }, 8, ["header"]),
               createVNode(unref(script$2), { pt: {
+                headerCell: "flex items-center justify-end",
                 bodyCell: "flex items-center justify-end"
               } }, {
+                header: withCtx(() => [
+                  createVNode(unref(script$1), {
+                    icon: "pi pi-ellipsis-h",
+                    text: "",
+                    severity: "secondary",
+                    onClick: _cache[1] || (_cache[1] = ($event) => menu.value.show($event))
+                  }),
+                  createVNode(unref(script$4), {
+                    ref_key: "menu",
+                    ref: menu,
+                    model: contextMenuItems
+                  }, null, 512)
+                ]),
                 body: withCtx((slotProps) => [
-                  createVNode(unref(script$3), {
+                  createVNode(unref(script$5), {
+                    disabled: unref(extensionStore).isExtensionReadOnly(slotProps.data.name),
                     modelValue: editingEnabledExtensions.value[slotProps.data.name],
                     "onUpdate:modelValue": /* @__PURE__ */ __name(($event) => editingEnabledExtensions.value[slotProps.data.name] = $event, "onUpdate:modelValue"),
                     onChange: updateExtensionStatus
-                  }, null, 8, ["modelValue", "onUpdate:modelValue"])
+                  }, null, 8, ["disabled", "modelValue", "onUpdate:modelValue"])
                 ]),
                 _: 1
               })
@@ -114,4 +180,4 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
 export {
   _sfc_main as default
 };
-//# sourceMappingURL=ExtensionPanel-ByeZ01RF.js.map
+//# sourceMappingURL=ExtensionPanel-D4Phn0Zr.js.map

web/assets/GraphView-CIRWBKTm.css

@@ -1,90 +1,31 @@
 
-.group-title-editor.node-title-editor[data-v-8a100d5a] {
+.comfy-menu-hamburger[data-v-5661bed0] {
+  pointer-events: auto;
+  position: fixed;
   z-index: 9999;
-  padding: 0.25rem;
-}
-[data-v-8a100d5a] .editable-text {
-  width: 100%;
-  height: 100%;
-}
-[data-v-8a100d5a] .editable-text input {
-  width: 100%;
-  height: 100%;
-  /* Override the default font size */
-  font-size: inherit;
 }
 
-.side-bar-button-icon {
-  font-size: var(--sidebar-icon-size) !important;
-}
-.side-bar-button-selected .side-bar-button-icon {
-  font-size: var(--sidebar-icon-size) !important;
-  font-weight: bold;
-}
-
-.side-bar-button[data-v-caa3ee9c] {
-  width: var(--sidebar-width);
-  height: var(--sidebar-width);
-  border-radius: 0;
-}
-.comfyui-body-left .side-bar-button.side-bar-button-selected[data-v-caa3ee9c],
-.comfyui-body-left .side-bar-button.side-bar-button-selected[data-v-caa3ee9c]:hover {
-  border-left: 4px solid var(--p-button-text-primary-color);
-}
-.comfyui-body-right .side-bar-button.side-bar-button-selected[data-v-caa3ee9c],
-.comfyui-body-right .side-bar-button.side-bar-button-selected[data-v-caa3ee9c]:hover {
-  border-right: 4px solid var(--p-button-text-primary-color);
-}
-
-:root {
-  --sidebar-width: 64px;
-  --sidebar-icon-size: 1.5rem;
-}
-:root .small-sidebar {
-  --sidebar-width: 40px;
-  --sidebar-icon-size: 1rem;
-}
-
-.side-tool-bar-container[data-v-7851c166] {
-  display: flex;
-  flex-direction: column;
-  align-items: center;
-
-  pointer-events: auto;
-
-  width: var(--sidebar-width);
-  height: 100%;
-
-  background-color: var(--comfy-menu-secondary-bg);
-  color: var(--fg-color);
-  box-shadow: var(--bar-shadow);
-}
-.side-tool-bar-end[data-v-7851c166] {
-  align-self: flex-end;
-  margin-top: auto;
-}
-
-[data-v-7c3279c1] .p-splitter-gutter {
+[data-v-e50caa15] .p-splitter-gutter {
   pointer-events: auto;
 }
-[data-v-7c3279c1] .p-splitter-gutter:hover,[data-v-7c3279c1] .p-splitter-gutter[data-p-gutter-resizing='true'] {
+[data-v-e50caa15] .p-splitter-gutter:hover,[data-v-e50caa15] .p-splitter-gutter[data-p-gutter-resizing='true'] {
   transition: background-color 0.2s ease 300ms;
   background-color: var(--p-primary-color);
 }
-.side-bar-panel[data-v-7c3279c1] {
+.side-bar-panel[data-v-e50caa15] {
   background-color: var(--bg-color);
   pointer-events: auto;
 }
-.bottom-panel[data-v-7c3279c1] {
+.bottom-panel[data-v-e50caa15] {
   background-color: var(--bg-color);
   pointer-events: auto;
 }
-.splitter-overlay[data-v-7c3279c1] {
+.splitter-overlay[data-v-e50caa15] {
   pointer-events: none;
   border-style: none;
   background-color: transparent;
 }
-.splitter-overlay-root[data-v-7c3279c1] {
+.splitter-overlay-root[data-v-e50caa15] {
   position: absolute;
   top: 0px;
   left: 0px;
@@ -98,7 +39,50 @@
   z-index: 999;
 }
 
-[data-v-d7cc0bce] .highlight {
+.p-buttongroup-vertical[data-v-cf40dd39] {
+  display: flex;
+  flex-direction: column;
+  border-radius: var(--p-button-border-radius);
+  overflow: hidden;
+  border: 1px solid var(--p-panel-border-color);
+}
+.p-buttongroup-vertical .p-button[data-v-cf40dd39] {
+  margin: 0;
+  border-radius: 0;
+}
+
+.node-tooltip[data-v-46859edf] {
+  background: var(--comfy-input-bg);
+  border-radius: 5px;
+  box-shadow: 0 0 5px rgba(0, 0, 0, 0.4);
+  color: var(--input-text);
+  font-family: sans-serif;
+  left: 0;
+  max-width: 30vw;
+  padding: 4px 8px;
+  position: absolute;
+  top: 0;
+  transform: translate(5px, calc(-100% - 5px));
+  white-space: pre-wrap;
+  z-index: 99999;
+}
+
+.group-title-editor.node-title-editor[data-v-12d3fd12] {
+  z-index: 9999;
+  padding: 0.25rem;
+}
+[data-v-12d3fd12] .editable-text {
+  width: 100%;
+  height: 100%;
+}
+[data-v-12d3fd12] .editable-text input {
+  width: 100%;
+  height: 100%;
+  /* Override the default font size */
+  font-size: inherit;
+}
+
+[data-v-5741c9ae] .highlight {
   background-color: var(--p-primary-color);
   color: var(--p-primary-contrast-color);
   font-weight: bold;
@@ -125,58 +109,107 @@
   align-items: flex-start !important;
 }
 
-.node-tooltip[data-v-9ecc8adc] {
-  background: var(--comfy-input-bg);
-  border-radius: 5px;
-  box-shadow: 0 0 5px rgba(0, 0, 0, 0.4);
-  color: var(--input-text);
-  font-family: sans-serif;
-  left: 0;
-  max-width: 30vw;
-  padding: 4px 8px;
-  position: absolute;
-  top: 0;
-  transform: translate(5px, calc(-100% - 5px));
-  white-space: pre-wrap;
-  z-index: 99999;
+.side-bar-button-icon {
+  font-size: var(--sidebar-icon-size) !important;
+}
+.side-bar-button-selected .side-bar-button-icon {
+  font-size: var(--sidebar-icon-size) !important;
+  font-weight: bold;
 }
 
-.p-buttongroup-vertical[data-v-94481f39] {
-  display: flex;
-  flex-direction: column;
-  border-radius: var(--p-button-border-radius);
-  overflow: hidden;
-  border: 1px solid var(--p-panel-border-color);
-}
-.p-buttongroup-vertical .p-button[data-v-94481f39] {
-  margin: 0;
+.side-bar-button[data-v-6ab4daa6] {
+  width: var(--sidebar-width);
+  height: var(--sidebar-width);
   border-radius: 0;
 }
+.comfyui-body-left .side-bar-button.side-bar-button-selected[data-v-6ab4daa6],
+.comfyui-body-left .side-bar-button.side-bar-button-selected[data-v-6ab4daa6]:hover {
+  border-left: 4px solid var(--p-button-text-primary-color);
+}
+.comfyui-body-right .side-bar-button.side-bar-button-selected[data-v-6ab4daa6],
+.comfyui-body-right .side-bar-button.side-bar-button-selected[data-v-6ab4daa6]:hover {
+  border-right: 4px solid var(--p-button-text-primary-color);
+}
+
+:root {
+  --sidebar-width: 64px;
+  --sidebar-icon-size: 1.5rem;
+}
+:root .small-sidebar {
+  --sidebar-width: 40px;
+  --sidebar-icon-size: 1rem;
+}
+
+.side-tool-bar-container[data-v-37d8d7b4] {
+  display: flex;
+  flex-direction: column;
+  align-items: center;
 
-.comfy-menu-hamburger[data-v-962c4073] {
   pointer-events: auto;
-  position: fixed;
-  z-index: 9999;
+
+  width: var(--sidebar-width);
+  height: 100%;
+
+  background-color: var(--comfy-menu-secondary-bg);
+  color: var(--fg-color);
+  box-shadow: var(--bar-shadow);
+}
+.side-tool-bar-end[data-v-37d8d7b4] {
+  align-self: flex-end;
+  margin-top: auto;
 }
 
-[data-v-4cb762cb] .p-togglebutton::before {
-  display: none
+[data-v-b9328350] .p-inputtext {
+  border-top-left-radius: 0;
+  border-bottom-left-radius: 0;
 }
-[data-v-4cb762cb] .p-togglebutton {
-  position: relative;
-  flex-shrink: 0;
-  border-radius: 0px;
+
+.comfyui-queue-button[data-v-7f4f551b] .p-splitbutton-dropdown {
+  border-top-right-radius: 0;
+  border-bottom-right-radius: 0;
+}
+
+.actionbar[data-v-915e5456] {
+  pointer-events: all;
+  position: fixed;
+  z-index: 1000;
+}
+.actionbar.is-docked[data-v-915e5456] {
+  position: static;
+  border-style: none;
   background-color: transparent;
-  padding: 0px
+  padding: 0px;
 }
-[data-v-4cb762cb] .p-togglebutton.p-togglebutton-checked {
-  border-bottom-width: 2px;
-  border-bottom-color: var(--p-button-text-primary-color)
+.actionbar.is-dragging[data-v-915e5456] {
+  -webkit-user-select: none;
+     -moz-user-select: none;
+          user-select: none;
 }
-[data-v-4cb762cb] .p-togglebutton-checked .close-button,[data-v-4cb762cb] .p-togglebutton:hover .close-button {
-  visibility: visible
+[data-v-915e5456] .p-panel-content {
+  padding: 0.25rem;
 }
-.status-indicator[data-v-4cb762cb] {
+.is-docked[data-v-915e5456] .p-panel-content {
+  padding: 0px;
+}
+[data-v-915e5456] .p-panel-header {
+  display: none;
+}
+
+.top-menubar[data-v-6fecd137] .p-menubar-item-link svg {
+  display: none;
+}
+[data-v-6fecd137] .p-menubar-submenu.dropdown-direction-up {
+  top: auto;
+  bottom: 100%;
+  flex-direction: column-reverse;
+}
+.keybinding-tag[data-v-6fecd137] {
+  background: var(--p-content-hover-background);
+  border-color: var(--p-content-border-color);
+  border-style: solid;
+}
+
+.status-indicator[data-v-8d011a31] {
   position: absolute;
   font-weight: 700;
   font-size: 1.5rem;
@@ -184,61 +217,32 @@
   left: 50%;
   transform: translate(-50%, -50%)
 }
-[data-v-4cb762cb] .p-togglebutton:hover .status-indicator {
+
+[data-v-d485c044] .p-togglebutton::before {
   display: none
 }
-[data-v-4cb762cb] .p-togglebutton .close-button {
+[data-v-d485c044] .p-togglebutton {
+  position: relative;
+  flex-shrink: 0;
+  border-radius: 0px;
+  background-color: transparent;
+  padding: 0px
+}
+[data-v-d485c044] .p-togglebutton.p-togglebutton-checked {
+  border-bottom-width: 2px;
+  border-bottom-color: var(--p-button-text-primary-color)
+}
+[data-v-d485c044] .p-togglebutton-checked .close-button,[data-v-d485c044] .p-togglebutton:hover .close-button {
+  visibility: visible
+}
+[data-v-d485c044] .p-togglebutton:hover .status-indicator {
+  display: none
+}
+[data-v-d485c044] .p-togglebutton .close-button {
   visibility: hidden
 }
 
-.top-menubar[data-v-a2b12676] .p-menubar-item-link svg {
-  display: none;
-}
-[data-v-a2b12676] .p-menubar-submenu.dropdown-direction-up {
-  top: auto;
-  bottom: 100%;
-  flex-direction: column-reverse;
-}
-.keybinding-tag[data-v-a2b12676] {
-  background: var(--p-content-hover-background);
-  border-color: var(--p-content-border-color);
-  border-style: solid;
-}
-
-[data-v-713442be] .p-inputtext {
-  border-top-left-radius: 0;
-  border-bottom-left-radius: 0;
-}
-
-.comfyui-queue-button[data-v-d3897845] .p-splitbutton-dropdown {
-  border-top-right-radius: 0;
-  border-bottom-right-radius: 0;
-}
-
-.actionbar[data-v-542a7001] {
-  pointer-events: all;
-  position: fixed;
-  z-index: 1000;
-}
-.actionbar.is-docked[data-v-542a7001] {
-  position: static;
-  border-style: none;
-  background-color: transparent;
-  padding: 0px;
-}
-.actionbar.is-dragging[data-v-542a7001] {
-  -webkit-user-select: none;
-     -moz-user-select: none;
-          user-select: none;
-}
-[data-v-542a7001] .p-panel-content {
-  padding: 0.25rem;
-}
-[data-v-542a7001] .p-panel-header {
-  display: none;
-}
-
-.comfyui-menu[data-v-d792da31] {
+.comfyui-menu[data-v-878b63b8] {
   width: 100vw;
   background: var(--comfy-menu-bg);
   color: var(--fg-color);
@@ -251,16 +255,16 @@
   grid-column: 1/-1;
   max-height: 90vh;
 }
-.comfyui-menu.dropzone[data-v-d792da31] {
+.comfyui-menu.dropzone[data-v-878b63b8] {
   background: var(--p-highlight-background);
 }
-.comfyui-menu.dropzone-active[data-v-d792da31] {
+.comfyui-menu.dropzone-active[data-v-878b63b8] {
   background: var(--p-highlight-background-focus);
 }
-[data-v-d792da31] .p-menubar-item-label {
+[data-v-878b63b8] .p-menubar-item-label {
   line-height: revert;
 }
-.comfyui-logo[data-v-d792da31] {
+.comfyui-logo[data-v-878b63b8] {
   font-size: 1.2em;
   -webkit-user-select: none;
      -moz-user-select: none;

web/assets/InstallView-8N2LdZUx.css

@@ -1,4 +0,0 @@
-
-[data-v-7ef01cf2] .p-steppanel {
-    background-color: transparent
-}

web/assets/InstallView-CwQdoH-C.css

@@ -0,0 +1,79 @@
+
+:root {
+  --p-tag-gap: 0.5rem;
+}
+.hover-brighten {
+  transition-property: color, background-color, border-color, text-decoration-color, fill, stroke;
+  transition-timing-function: cubic-bezier(0.4, 0, 0.2, 1);
+  transition-duration: 150ms;
+  transition-property: filter, box-shadow;
+&:hover {
+    filter: brightness(107%) contrast(105%);
+    box-shadow: 0 0 0.25rem #ffffff79;
+}
+}
+.p-accordioncontent-content {
+  border-radius: 0.5rem;
+  --tw-bg-opacity: 1;
+  background-color: rgb(23 23 23 / var(--tw-bg-opacity));
+  transition-property: color, background-color, border-color, text-decoration-color, fill, stroke;
+  transition-timing-function: cubic-bezier(0.4, 0, 0.2, 1);
+  transition-duration: 150ms;
+}
+div.selected {
+.gpu-button:not(.selected) {
+    opacity: 0.5;
+}
+.gpu-button:not(.selected):hover {
+    opacity: 1;
+}
+}
+.gpu-button {
+  margin: 0px;
+  display: flex;
+  width: 50%;
+  cursor: pointer;
+  flex-direction: column;
+  align-items: center;
+  justify-content: space-around;
+  border-radius: 0.5rem;
+  background-color: rgb(38 38 38 / var(--tw-bg-opacity));
+  --tw-bg-opacity: 0.5;
+  transition-property: color, background-color, border-color, text-decoration-color, fill, stroke;
+  transition-timing-function: cubic-bezier(0.4, 0, 0.2, 1);
+  transition-duration: 150ms;
+}
+.gpu-button:hover {
+  --tw-bg-opacity: 0.75;
+}
+.gpu-button {
+&.selected {
+    --tw-bg-opacity: 1;
+    background-color: rgb(64 64 64 / var(--tw-bg-opacity));
+}
+&.selected {
+    --tw-bg-opacity: 0.5;
+}
+&.selected {
+    opacity: 1;
+}
+&.selected:hover {
+    --tw-bg-opacity: 0.6;
+}
+}
+.disabled {
+  pointer-events: none;
+  opacity: 0.4;
+}
+.p-card-header {
+  flex-grow: 1;
+  text-align: center;
+}
+.p-card-body {
+  padding-top: 0px;
+  text-align: center;
+}
+
+[data-v-de33872d] .p-steppanel {
+    background-color: transparent
+}

web/assets/KeybindingPanel-C3wT8hYZ.css

@@ -1,8 +0,0 @@
-
-[data-v-c20ad403] .p-datatable-tbody > tr > td {
-  padding: 0.25rem;
-  min-height: 2rem
-}
-[data-v-c20ad403] .p-datatable-row-selected .actions,[data-v-c20ad403] .p-datatable-selectable-row:hover .actions {
-  visibility: visible
-}

web/assets/KeybindingPanel-Dc3C4lG1.js

@@ -1,8 +1,10 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { a as defineComponent, q as computed, f as openBlock, g as createElementBlock, P as Fragment, Q as renderList, h as createVNode, y as withCtx, ay as createTextVNode, a8 as toDisplayString, z as unref, aC as script, i as createCommentVNode, r as ref, ck as FilterMatchMode, O as useKeybindingStore, F as useCommandStore, I as useI18n, aS as normalizeI18nKey, aL as watchEffect, bn as useToast, t as resolveDirective, x as createBlock, cl as SearchBox, A as createBaseVNode, D as script$2, aq as script$4, br as withModifiers, bW as script$5, aI as script$6, v as withDirectives, cm as _sfc_main$2, R as pushScopeId, U as popScopeId, ce as KeyComboImpl, cn as KeybindingImpl, _ as _export_sfc } from "./index-DIU5yZe9.js";
-import { s as script$1, a as script$3 } from "./index-D3u7l7ha.js";
-import "./index-d698Brhb.js";
+import { d as defineComponent, c as computed, o as openBlock, f as createElementBlock, F as Fragment, E as renderList, N as createVNode, M as withCtx, aE as createTextVNode, X as toDisplayString, j as unref, aI as script, I as createCommentVNode, ab as ref, cn as FilterMatchMode, a$ as useKeybindingStore, a2 as useCommandStore, a1 as useI18n, af as normalizeI18nKey, w as watchEffect, bs as useToast, r as resolveDirective, k as createBlock, co as SearchBox, H as createBaseVNode, l as script$2, av as script$4, bM as withModifiers, bZ as script$5, aP as script$6, i as withDirectives, cp as _sfc_main$2, aL as pushScopeId, aM as popScopeId, cq as KeyComboImpl, cr as KeybindingImpl, _ as _export_sfc } from "./index-DjNHn37O.js";
+import { s as script$1, a as script$3 } from "./index-B5F0uxTQ.js";
+import { u as useKeybindingService } from "./keybindingService-Bx7YdkXn.js";
+import "./index-B-aVupP5.js";
+import "./index-5HFeZax4.js";
 const _hoisted_1$1 = {
   key: 0,
   class: "px-2"
@@ -35,7 +37,7 @@ const _sfc_main$1 = /* @__PURE__ */ defineComponent({
     };
   }
 });
-const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-c20ad403"), n = n(), popScopeId(), n), "_withScopeId");
+const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-2554ab36"), n = n(), popScopeId(), n), "_withScopeId");
 const _hoisted_1 = { class: "actions invisible flex flex-row" };
 const _hoisted_2 = ["title"];
 const _hoisted_3 = { key: 1 };
@@ -46,6 +48,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
       global: { value: "", matchMode: FilterMatchMode.CONTAINS }
     });
     const keybindingStore = useKeybindingStore();
+    const keybindingService = useKeybindingService();
     const commandStore = useCommandStore();
     const { t } = useI18n();
     const commandsData = computed(() => {
@@ -90,7 +93,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
     function removeKeybinding(commandData) {
       if (commandData.keybinding) {
         keybindingStore.unsetKeybinding(commandData.keybinding);
-        keybindingStore.persistUserKeybindings();
+        keybindingService.persistUserKeybindings();
       }
     }
     __name(removeKeybinding, "removeKeybinding");
@@ -114,7 +117,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
           })
         );
         if (updated) {
-          keybindingStore.persistUserKeybindings();
+          keybindingService.persistUserKeybindings();
         }
       }
       cancelEdit();
@@ -123,7 +126,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
     const toast = useToast();
     async function resetKeybindings() {
       keybindingStore.resetKeybindings();
-      await keybindingStore.persistUserKeybindings();
+      await keybindingService.persistUserKeybindings();
       toast.add({
         severity: "info",
         summary: "Info",
@@ -182,7 +185,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
               }),
               createVNode(unref(script$1), {
                 field: "id",
-                header: "Command ID",
+                header: _ctx.$t("g.command"),
                 sortable: "",
                 class: "max-w-64 2xl:max-w-full"
               }, {
@@ -193,10 +196,10 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
                   }, toDisplayString(slotProps.data.label), 9, _hoisted_2)
                 ]),
                 _: 1
-              }),
+              }, 8, ["header"]),
               createVNode(unref(script$1), {
                 field: "keybinding",
-                header: "Keybinding"
+                header: _ctx.$t("g.keybinding")
               }, {
                 body: withCtx((slotProps) => [
                   slotProps.data.keybinding ? (openBlock(), createBlock(_sfc_main$1, {
@@ -206,7 +209,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
                   }, null, 8, ["keyCombo", "isModified"])) : (openBlock(), createElementBlock("span", _hoisted_3, "-"))
                 ]),
                 _: 1
-              })
+              }, 8, ["header"])
             ]),
             _: 1
           }, 8, ["value", "selection", "filters"]),
@@ -274,8 +277,8 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
     };
   }
 });
-const KeybindingPanel = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-c20ad403"]]);
+const KeybindingPanel = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-2554ab36"]]);
 export {
   KeybindingPanel as default
 };
-//# sourceMappingURL=KeybindingPanel-DC2AxNNa.js.map
+//# sourceMappingURL=KeybindingPanel-Dc3C4lG1.js.map

web/assets/KeybindingPanel-DvrUYZ4S.css

@@ -0,0 +1,8 @@
+
+[data-v-2554ab36] .p-datatable-tbody > tr > td {
+  padding: 0.25rem;
+  min-height: 2rem
+}
+[data-v-2554ab36] .p-datatable-row-selected .actions,[data-v-2554ab36] .p-datatable-selectable-row:hover .actions {
+  visibility: visible
+}

web/assets/ManualConfigurationView-B6ecEClB.css

@@ -0,0 +1,7 @@
+
+:root {
+  --p-tag-gap: 0.5rem;
+}
+.comfy-installer {
+  margin-top: max(1rem, max(0px, calc((100vh - 42rem) * 0.5)));
+}

web/assets/ManualConfigurationView-Bi_qHE-n.js

@@ -0,0 +1,75 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { d as defineComponent, a1 as useI18n, ab as ref, m as onMounted, o as openBlock, k as createBlock, M as withCtx, H as createBaseVNode, X as toDisplayString, N as createVNode, j as unref, aI as script, l as script$2, c0 as electronAPI } from "./index-DjNHn37O.js";
+import { s as script$1 } from "./index-jXPKy3pP.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-BNGF4K22.js";
+import "./index-5HFeZax4.js";
+const _hoisted_1 = { class: "comfy-installer grow flex flex-col gap-4 text-neutral-300 max-w-110" };
+const _hoisted_2 = { class: "text-2xl font-semibold text-neutral-100" };
+const _hoisted_3 = { class: "m-1 text-neutral-300" };
+const _hoisted_4 = { class: "ml-2" };
+const _hoisted_5 = { class: "m-1 mb-4" };
+const _hoisted_6 = { class: "m-0" };
+const _hoisted_7 = { class: "m-1" };
+const _hoisted_8 = { class: "font-mono" };
+const _hoisted_9 = { class: "m-1" };
+const _sfc_main = /* @__PURE__ */ defineComponent({
+  __name: "ManualConfigurationView",
+  setup(__props) {
+    const { t } = useI18n();
+    const electron = electronAPI();
+    const basePath = ref(null);
+    const sep = ref("/");
+    const restartApp = /* @__PURE__ */ __name((message) => electron.restartApp(message), "restartApp");
+    onMounted(async () => {
+      basePath.value = await electron.getBasePath();
+      if (basePath.value.indexOf("/") === -1) sep.value = "\\";
+    });
+    return (_ctx, _cache) => {
+      return openBlock(), createBlock(_sfc_main$1, { dark: "" }, {
+        default: withCtx(() => [
+          createBaseVNode("div", _hoisted_1, [
+            createBaseVNode("h2", _hoisted_2, toDisplayString(_ctx.$t("install.manualConfiguration.title")), 1),
+            createBaseVNode("p", _hoisted_3, [
+              createVNode(unref(script), {
+                icon: "pi pi-exclamation-triangle",
+                severity: "warn",
+                value: unref(t)("icon.exclamation-triangle")
+              }, null, 8, ["value"]),
+              createBaseVNode("strong", _hoisted_4, toDisplayString(_ctx.$t("install.gpuSelection.customComfyNeedsPython")), 1)
+            ]),
+            createBaseVNode("div", null, [
+              createBaseVNode("p", _hoisted_5, toDisplayString(_ctx.$t("install.manualConfiguration.requirements")) + ": ", 1),
+              createBaseVNode("ul", _hoisted_6, [
+                createBaseVNode("li", null, toDisplayString(_ctx.$t("install.gpuSelection.customManualVenv")), 1),
+                createBaseVNode("li", null, toDisplayString(_ctx.$t("install.gpuSelection.customInstallRequirements")), 1)
+              ])
+            ]),
+            createBaseVNode("p", _hoisted_7, toDisplayString(_ctx.$t("install.manualConfiguration.createVenv")) + ":", 1),
+            createVNode(unref(script$1), {
+              header: unref(t)("install.manualConfiguration.virtualEnvironmentPath")
+            }, {
+              default: withCtx(() => [
+                createBaseVNode("span", _hoisted_8, toDisplayString(`${basePath.value}${sep.value}.venv${sep.value}`), 1)
+              ]),
+              _: 1
+            }, 8, ["header"]),
+            createBaseVNode("p", _hoisted_9, toDisplayString(_ctx.$t("install.manualConfiguration.restartWhenFinished")), 1),
+            createVNode(unref(script$2), {
+              class: "place-self-end",
+              label: unref(t)("menuLabels.Restart"),
+              severity: "warn",
+              icon: "pi pi-refresh",
+              onClick: _cache[0] || (_cache[0] = ($event) => restartApp("Manual configuration complete"))
+            }, null, 8, ["label"])
+          ])
+        ]),
+        _: 1
+      });
+    };
+  }
+});
+export {
+  _sfc_main as default
+};
+//# sourceMappingURL=ManualConfigurationView-Bi_qHE-n.js.map

web/assets/NotSupportedView-C8O1Ed5c.js

@@ -1,82 +0,0 @@
-var __defProp = Object.defineProperty;
-var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { a as defineComponent, bU as useRouter, t as resolveDirective, f as openBlock, g as createElementBlock, A as createBaseVNode, a8 as toDisplayString, h as createVNode, z as unref, D as script, v as withDirectives } from "./index-DIU5yZe9.js";
-const _imports_0 = "" + new URL("images/sad_girl.png", import.meta.url).href;
-const _hoisted_1 = { class: "font-sans w-screen h-screen flex items-center m-0 text-neutral-900 bg-neutral-300 pointer-events-auto" };
-const _hoisted_2 = { class: "flex-grow flex items-center justify-center" };
-const _hoisted_3 = { class: "flex flex-col gap-8 p-8" };
-const _hoisted_4 = { class: "text-4xl font-bold text-red-500" };
-const _hoisted_5 = { class: "space-y-4" };
-const _hoisted_6 = { class: "text-xl" };
-const _hoisted_7 = { class: "list-disc list-inside space-y-1 text-neutral-800" };
-const _hoisted_8 = { class: "flex gap-4" };
-const _hoisted_9 = /* @__PURE__ */ createBaseVNode("div", { class: "h-screen flex-grow-0" }, [
-  /* @__PURE__ */ createBaseVNode("img", {
-    src: _imports_0,
-    alt: "Sad girl illustration",
-    class: "h-full object-cover"
-  })
-], -1);
-const _sfc_main = /* @__PURE__ */ defineComponent({
-  __name: "NotSupportedView",
-  setup(__props) {
-    const openDocs = /* @__PURE__ */ __name(() => {
-      window.open(
-        "https://github.com/Comfy-Org/desktop#currently-supported-platforms",
-        "_blank"
-      );
-    }, "openDocs");
-    const reportIssue = /* @__PURE__ */ __name(() => {
-      window.open("https://forum.comfy.org/c/v1-feedback/", "_blank");
-    }, "reportIssue");
-    const router = useRouter();
-    const continueToInstall = /* @__PURE__ */ __name(() => {
-      router.push("/install");
-    }, "continueToInstall");
-    return (_ctx, _cache) => {
-      const _directive_tooltip = resolveDirective("tooltip");
-      return openBlock(), createElementBlock("div", _hoisted_1, [
-        createBaseVNode("div", _hoisted_2, [
-          createBaseVNode("div", _hoisted_3, [
-            createBaseVNode("h1", _hoisted_4, toDisplayString(_ctx.$t("notSupported.title")), 1),
-            createBaseVNode("div", _hoisted_5, [
-              createBaseVNode("p", _hoisted_6, toDisplayString(_ctx.$t("notSupported.message")), 1),
-              createBaseVNode("ul", _hoisted_7, [
-                createBaseVNode("li", null, toDisplayString(_ctx.$t("notSupported.supportedDevices.macos")), 1),
-                createBaseVNode("li", null, toDisplayString(_ctx.$t("notSupported.supportedDevices.windows")), 1)
-              ])
-            ]),
-            createBaseVNode("div", _hoisted_8, [
-              createVNode(unref(script), {
-                label: _ctx.$t("notSupported.learnMore"),
-                icon: "pi pi-github",
-                onClick: openDocs,
-                severity: "secondary"
-              }, null, 8, ["label"]),
-              createVNode(unref(script), {
-                label: _ctx.$t("notSupported.reportIssue"),
-                icon: "pi pi-flag",
-                onClick: reportIssue,
-                severity: "secondary"
-              }, null, 8, ["label"]),
-              withDirectives(createVNode(unref(script), {
-                label: _ctx.$t("notSupported.continue"),
-                icon: "pi pi-arrow-right",
-                iconPos: "right",
-                onClick: continueToInstall,
-                severity: "danger"
-              }, null, 8, ["label"]), [
-                [_directive_tooltip, _ctx.$t("notSupported.continueTooltip")]
-              ])
-            ])
-          ])
-        ]),
-        _hoisted_9
-      ]);
-    };
-  }
-});
-export {
-  _sfc_main as default
-};
-//# sourceMappingURL=NotSupportedView-C8O1Ed5c.js.map

web/assets/NotSupportedView-C8O1Ed5c.js.map

@@ -1 +0,0 @@
-{"version":3,"file":"NotSupportedView-C8O1Ed5c.js","sources":["../../../../../../../assets/images/sad_girl.png","../../src/views/NotSupportedView.vue"],"sourcesContent":["export default \"__VITE_PUBLIC_ASSET__b82952e7__\"","<template>\n  <div\n    class=\"font-sans w-screen h-screen flex items-center m-0 text-neutral-900 bg-neutral-300 pointer-events-auto\"\n  >\n    <div class=\"flex-grow flex items-center justify-center\">\n      <div class=\"flex flex-col gap-8 p-8\">\n        <!-- Header -->\n        <h1 class=\"text-4xl font-bold text-red-500\">\n          {{ $t('notSupported.title') }}\n        </h1>\n\n        <!-- Message -->\n        <div class=\"space-y-4\">\n          <p class=\"text-xl\">\n            {{ $t('notSupported.message') }}\n          </p>\n          <ul class=\"list-disc list-inside space-y-1 text-neutral-800\">\n            <li>{{ $t('notSupported.supportedDevices.macos') }}</li>\n            <li>{{ $t('notSupported.supportedDevices.windows') }}</li>\n          </ul>\n        </div>\n\n        <!-- Actions -->\n        <div class=\"flex gap-4\">\n          <Button\n            :label=\"$t('notSupported.learnMore')\"\n            icon=\"pi pi-github\"\n            @click=\"openDocs\"\n            severity=\"secondary\"\n          />\n          <Button\n            :label=\"$t('notSupported.reportIssue')\"\n            icon=\"pi pi-flag\"\n            @click=\"reportIssue\"\n            severity=\"secondary\"\n          />\n          <Button\n            :label=\"$t('notSupported.continue')\"\n            icon=\"pi pi-arrow-right\"\n            iconPos=\"right\"\n            @click=\"continueToInstall\"\n            severity=\"danger\"\n            v-tooltip=\"$t('notSupported.continueTooltip')\"\n          />\n        </div>\n      </div>\n    </div>\n\n    <!-- Right side image -->\n    <div class=\"h-screen flex-grow-0\">\n      <img\n        src=\"/assets/images/sad_girl.png\"\n        alt=\"Sad girl illustration\"\n        class=\"h-full object-cover\"\n      />\n    </div>\n  </div>\n</template>\n\n<script setup lang=\"ts\">\nimport Button from 'primevue/button'\nimport { useRouter } from 'vue-router'\n\nconst openDocs = () => {\n  window.open(\n    'https://github.com/Comfy-Org/desktop#currently-supported-platforms',\n    '_blank'\n  )\n}\n\nconst reportIssue = () => {\n  window.open('https://forum.comfy.org/c/v1-feedback/', '_blank')\n}\n\nconst router = useRouter()\nconst continueToInstall = () => {\n  router.push('/install')\n}\n</script>\n"],"names":[],"mappings":";;;AAAA,MAAe,aAAA,KAAA,IAAA,IAAA,uBAAA,YAAA,GAAA,EAAA;;;;;;;;;;;;;;;;;;;AC+Df,UAAM,WAAW,6BAAM;AACd,aAAA;AAAA,QACL;AAAA,QACA;AAAA,MAAA;AAAA,IACF,GAJe;AAOjB,UAAM,cAAc,6BAAM;AACjB,aAAA,KAAK,0CAA0C,QAAQ;AAAA,IAAA,GAD5C;AAIpB,UAAM,SAAS;AACf,UAAM,oBAAoB,6BAAM;AAC9B,aAAO,KAAK,UAAU;AAAA,IAAA,GADE;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;"}

web/assets/NotSupportedView-Drz3x2d-.js

@@ -0,0 +1,86 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { d as defineComponent, bW as useRouter, r as resolveDirective, o as openBlock, k as createBlock, M as withCtx, H as createBaseVNode, X as toDisplayString, N as createVNode, j as unref, l as script, i as withDirectives } from "./index-DjNHn37O.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-BNGF4K22.js";
+const _imports_0 = "" + new URL("images/sad_girl.png", import.meta.url).href;
+const _hoisted_1 = { class: "sad-container" };
+const _hoisted_2 = /* @__PURE__ */ createBaseVNode("img", {
+  class: "sad-girl",
+  src: _imports_0,
+  alt: "Sad girl illustration"
+}, null, -1);
+const _hoisted_3 = { class: "no-drag sad-text flex items-center" };
+const _hoisted_4 = { class: "flex flex-col gap-8 p-8 min-w-110" };
+const _hoisted_5 = { class: "text-4xl font-bold text-red-500" };
+const _hoisted_6 = { class: "space-y-4" };
+const _hoisted_7 = { class: "text-xl" };
+const _hoisted_8 = { class: "list-disc list-inside space-y-1 text-neutral-800" };
+const _hoisted_9 = { class: "flex gap-4" };
+const _sfc_main = /* @__PURE__ */ defineComponent({
+  __name: "NotSupportedView",
+  setup(__props) {
+    const openDocs = /* @__PURE__ */ __name(() => {
+      window.open(
+        "https://github.com/Comfy-Org/desktop#currently-supported-platforms",
+        "_blank"
+      );
+    }, "openDocs");
+    const reportIssue = /* @__PURE__ */ __name(() => {
+      window.open("https://forum.comfy.org/c/v1-feedback/", "_blank");
+    }, "reportIssue");
+    const router = useRouter();
+    const continueToInstall = /* @__PURE__ */ __name(() => {
+      router.push("/install");
+    }, "continueToInstall");
+    return (_ctx, _cache) => {
+      const _directive_tooltip = resolveDirective("tooltip");
+      return openBlock(), createBlock(_sfc_main$1, null, {
+        default: withCtx(() => [
+          createBaseVNode("div", _hoisted_1, [
+            _hoisted_2,
+            createBaseVNode("div", _hoisted_3, [
+              createBaseVNode("div", _hoisted_4, [
+                createBaseVNode("h1", _hoisted_5, toDisplayString(_ctx.$t("notSupported.title")), 1),
+                createBaseVNode("div", _hoisted_6, [
+                  createBaseVNode("p", _hoisted_7, toDisplayString(_ctx.$t("notSupported.message")), 1),
+                  createBaseVNode("ul", _hoisted_8, [
+                    createBaseVNode("li", null, toDisplayString(_ctx.$t("notSupported.supportedDevices.macos")), 1),
+                    createBaseVNode("li", null, toDisplayString(_ctx.$t("notSupported.supportedDevices.windows")), 1)
+                  ])
+                ]),
+                createBaseVNode("div", _hoisted_9, [
+                  createVNode(unref(script), {
+                    label: _ctx.$t("notSupported.learnMore"),
+                    icon: "pi pi-github",
+                    onClick: openDocs,
+                    severity: "secondary"
+                  }, null, 8, ["label"]),
+                  createVNode(unref(script), {
+                    label: _ctx.$t("notSupported.reportIssue"),
+                    icon: "pi pi-flag",
+                    onClick: reportIssue,
+                    severity: "secondary"
+                  }, null, 8, ["label"]),
+                  withDirectives(createVNode(unref(script), {
+                    label: _ctx.$t("notSupported.continue"),
+                    icon: "pi pi-arrow-right",
+                    iconPos: "right",
+                    onClick: continueToInstall,
+                    severity: "danger"
+                  }, null, 8, ["label"]), [
+                    [_directive_tooltip, _ctx.$t("notSupported.continueTooltip")]
+                  ])
+                ])
+              ])
+            ])
+          ])
+        ]),
+        _: 1
+      });
+    };
+  }
+});
+export {
+  _sfc_main as default
+};
+//# sourceMappingURL=NotSupportedView-Drz3x2d-.js.map

web/assets/NotSupportedView-bFzHmqNj.css

@@ -0,0 +1,17 @@
+
+.sad-container {
+  display: grid;
+  align-items: center;
+  justify-content: space-evenly;
+  grid-template-columns: 25rem 1fr;
+& > * {
+    grid-row: 1;
+}
+}
+.sad-text {
+  grid-column: 1/3;
+}
+.sad-girl {
+  grid-column: 2/3;
+  width: min(75vw, 100vh);
+}

web/assets/ServerConfigPanel-Be4StJmv.js

@@ -1,7 +1,7 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { A as createBaseVNode, f as openBlock, g as createElementBlock, aZ as markRaw, a as defineComponent, u as useSettingStore, aK as storeToRefs, w as watch, cL as useCopyToClipboard, I as useI18n, x as createBlock, y as withCtx, z as unref, bW as script, a8 as toDisplayString, Q as renderList, P as Fragment, h as createVNode, D as script$1, i as createCommentVNode, bN as script$2, cM as FormItem, cm as _sfc_main$1, bZ as electronAPI } from "./index-DIU5yZe9.js";
-import { u as useServerConfigStore } from "./serverConfigStore-DYv7_Nld.js";
+import { H as createBaseVNode, o as openBlock, f as createElementBlock, Z as markRaw, d as defineComponent, a as useSettingStore, aS as storeToRefs, a5 as watch, cO as useCopyToClipboard, a1 as useI18n, k as createBlock, M as withCtx, j as unref, bZ as script, X as toDisplayString, E as renderList, F as Fragment, N as createVNode, l as script$1, I as createCommentVNode, bQ as script$2, cP as FormItem, cp as _sfc_main$1, c0 as electronAPI } from "./index-DjNHn37O.js";
+import { u as useServerConfigStore } from "./serverConfigStore-CvyKFVuP.js";
 const _hoisted_1$1 = {
   viewBox: "0 0 24 24",
   width: "1.2em",
@@ -131,7 +131,7 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
               (openBlock(true), createElementBlock(Fragment, null, renderList(items, (item) => {
                 return openBlock(), createElementBlock("div", {
                   key: item.name,
-                  class: "flex items-center mb-4"
+                  class: "mb-4"
                 }, [
                   createVNode(FormItem, {
                     item: translateItem(item),
@@ -155,4 +155,4 @@ const _sfc_main = /* @__PURE__ */ defineComponent({
 export {
   _sfc_main as default
 };
-//# sourceMappingURL=ServerConfigPanel-CvXC1Xmx.js.map
+//# sourceMappingURL=ServerConfigPanel-Be4StJmv.js.map

web/assets/ServerConfigPanel-CvXC1Xmx.js.map

@@ -1 +0,0 @@
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web/assets/ServerStartView-BvuHEhuL.js

@@ -1,92 +0,0 @@
-var __defProp = Object.defineProperty;
-var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { a as defineComponent, I as useI18n, r as ref, bX as ProgressStatus, o as onMounted, f as openBlock, g as createElementBlock, A as createBaseVNode, ay as createTextVNode, a8 as toDisplayString, z as unref, i as createCommentVNode, h as createVNode, D as script, x as createBlock, v as withDirectives, ad as vShow, bY as BaseTerminal, R as pushScopeId, U as popScopeId, bZ as electronAPI, _ as _export_sfc } from "./index-DIU5yZe9.js";
-const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-c0d3157e"), n = n(), popScopeId(), n), "_withScopeId");
-const _hoisted_1 = { class: "font-sans flex flex-col justify-center items-center h-screen m-0 text-neutral-300 bg-neutral-900 dark-theme pointer-events-auto" };
-const _hoisted_2 = { class: "text-2xl font-bold" };
-const _hoisted_3 = { key: 0 };
-const _hoisted_4 = {
-  key: 0,
-  class: "flex flex-col items-center gap-4"
-};
-const _hoisted_5 = { class: "flex items-center my-4 gap-2" };
-const _sfc_main = /* @__PURE__ */ defineComponent({
-  __name: "ServerStartView",
-  setup(__props) {
-    const electron = electronAPI();
-    const { t } = useI18n();
-    const status = ref(ProgressStatus.INITIAL_STATE);
-    const electronVersion = ref("");
-    let xterm;
-    const terminalVisible = ref(true);
-    const updateProgress = /* @__PURE__ */ __name(({ status: newStatus }) => {
-      status.value = newStatus;
-      if (newStatus === ProgressStatus.ERROR) terminalVisible.value = false;
-      else xterm?.clear();
-    }, "updateProgress");
-    const terminalCreated = /* @__PURE__ */ __name(({ terminal, useAutoSize }, root) => {
-      xterm = terminal;
-      useAutoSize(root, true, true);
-      electron.onLogMessage((message) => {
-        terminal.write(message);
-      });
-      terminal.options.cursorBlink = false;
-      terminal.options.disableStdin = true;
-      terminal.options.cursorInactiveStyle = "block";
-    }, "terminalCreated");
-    const reinstall = /* @__PURE__ */ __name(() => electron.reinstall(), "reinstall");
-    const reportIssue = /* @__PURE__ */ __name(() => {
-      window.open("https://forum.comfy.org/c/v1-feedback/", "_blank");
-    }, "reportIssue");
-    const openLogs = /* @__PURE__ */ __name(() => electron.openLogsFolder(), "openLogs");
-    onMounted(async () => {
-      electron.sendReady();
-      electron.onProgressUpdate(updateProgress);
-      electronVersion.value = await electron.getElectronVersion();
-    });
-    return (_ctx, _cache) => {
-      return openBlock(), createElementBlock("div", _hoisted_1, [
-        createBaseVNode("h2", _hoisted_2, [
-          createTextVNode(toDisplayString(unref(t)(`serverStart.process.${status.value}`)) + " ", 1),
-          status.value === unref(ProgressStatus).ERROR ? (openBlock(), createElementBlock("span", _hoisted_3, " v" + toDisplayString(electronVersion.value), 1)) : createCommentVNode("", true)
-        ]),
-        status.value === unref(ProgressStatus).ERROR ? (openBlock(), createElementBlock("div", _hoisted_4, [
-          createBaseVNode("div", _hoisted_5, [
-            createVNode(unref(script), {
-              icon: "pi pi-flag",
-              severity: "secondary",
-              label: unref(t)("serverStart.reportIssue"),
-              onClick: reportIssue
-            }, null, 8, ["label"]),
-            createVNode(unref(script), {
-              icon: "pi pi-file",
-              severity: "secondary",
-              label: unref(t)("serverStart.openLogs"),
-              onClick: openLogs
-            }, null, 8, ["label"]),
-            createVNode(unref(script), {
-              icon: "pi pi-refresh",
-              label: unref(t)("serverStart.reinstall"),
-              onClick: reinstall
-            }, null, 8, ["label"])
-          ]),
-          !terminalVisible.value ? (openBlock(), createBlock(unref(script), {
-            key: 0,
-            icon: "pi pi-search",
-            severity: "secondary",
-            label: unref(t)("serverStart.showTerminal"),
-            onClick: _cache[0] || (_cache[0] = ($event) => terminalVisible.value = true)
-          }, null, 8, ["label"])) : createCommentVNode("", true)
-        ])) : createCommentVNode("", true),
-        withDirectives(createVNode(BaseTerminal, { onCreated: terminalCreated }, null, 512), [
-          [vShow, terminalVisible.value]
-        ])
-      ]);
-    };
-  }
-});
-const ServerStartView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-c0d3157e"]]);
-export {
-  ServerStartView as default
-};
-//# sourceMappingURL=ServerStartView-BvuHEhuL.js.map

web/assets/ServerStartView-BvuHEhuL.js.map

@@ -1 +0,0 @@
-{"version":3,"file":"ServerStartView-BvuHEhuL.js","sources":["../../src/views/ServerStartView.vue"],"sourcesContent":["<template>\n  <div\n    class=\"font-sans flex flex-col justify-center items-center h-screen m-0 text-neutral-300 bg-neutral-900 dark-theme pointer-events-auto\"\n  >\n    <h2 class=\"text-2xl font-bold\">\n      {{ t(`serverStart.process.${status}`) }}\n      <span v-if=\"status === ProgressStatus.ERROR\">\n        v{{ electronVersion }}\n      </span>\n    </h2>\n    <div\n      v-if=\"status === ProgressStatus.ERROR\"\n      class=\"flex flex-col items-center gap-4\"\n    >\n      <div class=\"flex items-center my-4 gap-2\">\n        <Button\n          icon=\"pi pi-flag\"\n          severity=\"secondary\"\n          :label=\"t('serverStart.reportIssue')\"\n          @click=\"reportIssue\"\n        />\n        <Button\n          icon=\"pi pi-file\"\n          severity=\"secondary\"\n          :label=\"t('serverStart.openLogs')\"\n          @click=\"openLogs\"\n        />\n        <Button\n          icon=\"pi pi-refresh\"\n          :label=\"t('serverStart.reinstall')\"\n          @click=\"reinstall\"\n        />\n      </div>\n      <Button\n        v-if=\"!terminalVisible\"\n        icon=\"pi pi-search\"\n        severity=\"secondary\"\n        :label=\"t('serverStart.showTerminal')\"\n        @click=\"terminalVisible = true\"\n      />\n    </div>\n    <BaseTerminal v-show=\"terminalVisible\" @created=\"terminalCreated\" />\n  </div>\n</template>\n\n<script setup lang=\"ts\">\nimport Button from 'primevue/button'\nimport { ref, onMounted, Ref } from 'vue'\nimport BaseTerminal from '@/components/bottomPanel/tabs/terminal/BaseTerminal.vue'\nimport { ProgressStatus } from '@comfyorg/comfyui-electron-types'\nimport { electronAPI } from '@/utils/envUtil'\nimport type { useTerminal } from '@/hooks/bottomPanelTabs/useTerminal'\nimport { Terminal } from '@xterm/xterm'\nimport { useI18n } from 'vue-i18n'\n\nconst electron = electronAPI()\nconst { t } = useI18n()\n\nconst status = ref<ProgressStatus>(ProgressStatus.INITIAL_STATE)\nconst electronVersion = ref<string>('')\nlet xterm: Terminal | undefined\n\nconst terminalVisible = ref(true)\n\nconst updateProgress = ({ status: newStatus }: { status: ProgressStatus }) => {\n  status.value = newStatus\n\n  // Make critical error screen more obvious.\n  if (newStatus === ProgressStatus.ERROR) terminalVisible.value = false\n  else xterm?.clear()\n}\n\nconst terminalCreated = (\n  { terminal, useAutoSize }: ReturnType<typeof useTerminal>,\n  root: Ref<HTMLElement>\n) => {\n  xterm = terminal\n\n  useAutoSize(root, true, true)\n  electron.onLogMessage((message: string) => {\n    terminal.write(message)\n  })\n\n  terminal.options.cursorBlink = false\n  terminal.options.disableStdin = true\n  terminal.options.cursorInactiveStyle = 'block'\n}\n\nconst reinstall = () => electron.reinstall()\nconst reportIssue = () => {\n  window.open('https://forum.comfy.org/c/v1-feedback/', '_blank')\n}\nconst openLogs = () => electron.openLogsFolder()\n\nonMounted(async () => {\n  electron.sendReady()\n  electron.onProgressUpdate(updateProgress)\n  electronVersion.value = await electron.getElectronVersion()\n})\n</script>\n\n<style scoped>\n:deep(.xterm-helper-textarea) {\n  /* Hide this as it moves all over when uv is running */\n  display: none;\n}\n</style>\n"],"names":[],"mappings":";;;;;;;;;;;;;;;AAuDA,UAAM,WAAW;AACX,UAAA,EAAE,MAAM;AAER,UAAA,SAAS,IAAoB,eAAe,aAAa;AACzD,UAAA,kBAAkB,IAAY,EAAE;AAClC,QAAA;AAEE,UAAA,kBAAkB,IAAI,IAAI;AAEhC,UAAM,iBAAiB,wBAAC,EAAE,QAAQ,gBAA4C;AAC5E,aAAO,QAAQ;AAGf,UAAI,cAAc,eAAe,MAAO,iBAAgB,QAAQ;AAAA,kBACpD,MAAM;AAAA,IAAA,GALG;AAQvB,UAAM,kBAAkB,wBACtB,EAAE,UAAU,YAAA,GACZ,SACG;AACK,cAAA;AAEI,kBAAA,MAAM,MAAM,IAAI;AACnB,eAAA,aAAa,CAAC,YAAoB;AACzC,iBAAS,MAAM,OAAO;AAAA,MAAA,CACvB;AAED,eAAS,QAAQ,cAAc;AAC/B,eAAS,QAAQ,eAAe;AAChC,eAAS,QAAQ,sBAAsB;AAAA,IAAA,GAbjB;AAgBlB,UAAA,YAAY,6BAAM,SAAS,aAAf;AAClB,UAAM,cAAc,6BAAM;AACjB,aAAA,KAAK,0CAA0C,QAAQ;AAAA,IAAA,GAD5C;AAGd,UAAA,WAAW,6BAAM,SAAS,kBAAf;AAEjB,cAAU,YAAY;AACpB,eAAS,UAAU;AACnB,eAAS,iBAAiB,cAAc;AACxB,sBAAA,QAAQ,MAAM,SAAS,mBAAmB;AAAA,IAAA,CAC3D;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;"}

web/assets/ServerStartView-CIDTUh4x.js

@@ -0,0 +1,98 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { d as defineComponent, a1 as useI18n, ab as ref, b_ as ProgressStatus, m as onMounted, o as openBlock, k as createBlock, M as withCtx, H as createBaseVNode, aE as createTextVNode, X as toDisplayString, j as unref, f as createElementBlock, I as createCommentVNode, N as createVNode, l as script, i as withDirectives, v as vShow, b$ as BaseTerminal, aL as pushScopeId, aM as popScopeId, c0 as electronAPI, _ as _export_sfc } from "./index-DjNHn37O.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-BNGF4K22.js";
+const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-42c1131d"), n = n(), popScopeId(), n), "_withScopeId");
+const _hoisted_1 = { class: "text-2xl font-bold" };
+const _hoisted_2 = { key: 0 };
+const _hoisted_3 = {
+  key: 0,
+  class: "flex flex-col items-center gap-4"
+};
+const _hoisted_4 = { class: "flex items-center my-4 gap-2" };
+const _sfc_main = /* @__PURE__ */ defineComponent({
+  __name: "ServerStartView",
+  setup(__props) {
+    const electron = electronAPI();
+    const { t } = useI18n();
+    const status = ref(ProgressStatus.INITIAL_STATE);
+    const electronVersion = ref("");
+    let xterm;
+    const terminalVisible = ref(true);
+    const updateProgress = /* @__PURE__ */ __name(({ status: newStatus }) => {
+      status.value = newStatus;
+      if (newStatus === ProgressStatus.ERROR) terminalVisible.value = false;
+      else xterm?.clear();
+    }, "updateProgress");
+    const terminalCreated = /* @__PURE__ */ __name(({ terminal, useAutoSize }, root) => {
+      xterm = terminal;
+      useAutoSize(root, true, true);
+      electron.onLogMessage((message) => {
+        terminal.write(message);
+      });
+      terminal.options.cursorBlink = false;
+      terminal.options.disableStdin = true;
+      terminal.options.cursorInactiveStyle = "block";
+    }, "terminalCreated");
+    const reinstall = /* @__PURE__ */ __name(() => electron.reinstall(), "reinstall");
+    const reportIssue = /* @__PURE__ */ __name(() => {
+      window.open("https://forum.comfy.org/c/v1-feedback/", "_blank");
+    }, "reportIssue");
+    const openLogs = /* @__PURE__ */ __name(() => electron.openLogsFolder(), "openLogs");
+    onMounted(async () => {
+      electron.sendReady();
+      electron.onProgressUpdate(updateProgress);
+      electronVersion.value = await electron.getElectronVersion();
+    });
+    return (_ctx, _cache) => {
+      return openBlock(), createBlock(_sfc_main$1, {
+        dark: "",
+        class: "flex-col"
+      }, {
+        default: withCtx(() => [
+          createBaseVNode("h2", _hoisted_1, [
+            createTextVNode(toDisplayString(unref(t)(`serverStart.process.${status.value}`)) + " ", 1),
+            status.value === unref(ProgressStatus).ERROR ? (openBlock(), createElementBlock("span", _hoisted_2, " v" + toDisplayString(electronVersion.value), 1)) : createCommentVNode("", true)
+          ]),
+          status.value === unref(ProgressStatus).ERROR ? (openBlock(), createElementBlock("div", _hoisted_3, [
+            createBaseVNode("div", _hoisted_4, [
+              createVNode(unref(script), {
+                icon: "pi pi-flag",
+                severity: "secondary",
+                label: unref(t)("serverStart.reportIssue"),
+                onClick: reportIssue
+              }, null, 8, ["label"]),
+              createVNode(unref(script), {
+                icon: "pi pi-file",
+                severity: "secondary",
+                label: unref(t)("serverStart.openLogs"),
+                onClick: openLogs
+              }, null, 8, ["label"]),
+              createVNode(unref(script), {
+                icon: "pi pi-refresh",
+                label: unref(t)("serverStart.reinstall"),
+                onClick: reinstall
+              }, null, 8, ["label"])
+            ]),
+            !terminalVisible.value ? (openBlock(), createBlock(unref(script), {
+              key: 0,
+              icon: "pi pi-search",
+              severity: "secondary",
+              label: unref(t)("serverStart.showTerminal"),
+              onClick: _cache[0] || (_cache[0] = ($event) => terminalVisible.value = true)
+            }, null, 8, ["label"])) : createCommentVNode("", true)
+          ])) : createCommentVNode("", true),
+          withDirectives(createVNode(BaseTerminal, { onCreated: terminalCreated }, null, 512), [
+            [vShow, terminalVisible.value]
+          ])
+        ]),
+        _: 1
+      });
+    };
+  }
+});
+const ServerStartView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-42c1131d"]]);
+export {
+  ServerStartView as default
+};
+//# sourceMappingURL=ServerStartView-CIDTUh4x.js.map

web/assets/ServerStartView-CnyN4Ib6.css

@@ -1,5 +1,5 @@
 
-[data-v-c0d3157e] .xterm-helper-textarea {
+[data-v-42c1131d] .xterm-helper-textarea {
   /* Hide this as it moves all over when uv is running */
   display: none;
 }

web/assets/UserSelectView-B3jYchWu.js

@@ -0,0 +1,102 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { d as defineComponent, aX as useUserStore, bW as useRouter, ab as ref, c as computed, m as onMounted, o as openBlock, k as createBlock, M as withCtx, H as createBaseVNode, X as toDisplayString, N as createVNode, bX as withKeys, j as unref, av as script, bQ as script$1, bY as script$2, bZ as script$3, aE as createTextVNode, I as createCommentVNode, l as script$4 } from "./index-DjNHn37O.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-BNGF4K22.js";
+const _hoisted_1 = {
+  id: "comfy-user-selection",
+  class: "min-w-84 relative rounded-lg bg-[var(--comfy-menu-bg)] p-5 px-10 shadow-lg"
+};
+const _hoisted_2 = /* @__PURE__ */ createBaseVNode("h1", { class: "my-2.5 mb-7 font-normal" }, "ComfyUI", -1);
+const _hoisted_3 = { class: "flex w-full flex-col items-center" };
+const _hoisted_4 = { class: "flex w-full flex-col gap-2" };
+const _hoisted_5 = { for: "new-user-input" };
+const _hoisted_6 = { class: "flex w-full flex-col gap-2" };
+const _hoisted_7 = { for: "existing-user-select" };
+const _hoisted_8 = { class: "mt-5" };
+const _sfc_main = /* @__PURE__ */ defineComponent({
+  __name: "UserSelectView",
+  setup(__props) {
+    const userStore = useUserStore();
+    const router = useRouter();
+    const selectedUser = ref(null);
+    const newUsername = ref("");
+    const loginError = ref("");
+    const createNewUser = computed(() => newUsername.value.trim() !== "");
+    const newUserExistsError = computed(() => {
+      return userStore.users.find((user) => user.username === newUsername.value) ? `User "${newUsername.value}" already exists` : "";
+    });
+    const error = computed(() => newUserExistsError.value || loginError.value);
+    const login = /* @__PURE__ */ __name(async () => {
+      try {
+        const user = createNewUser.value ? await userStore.createUser(newUsername.value) : selectedUser.value;
+        if (!user) {
+          throw new Error("No user selected");
+        }
+        userStore.login(user);
+        router.push("/");
+      } catch (err) {
+        loginError.value = err.message ?? JSON.stringify(err);
+      }
+    }, "login");
+    onMounted(async () => {
+      if (!userStore.initialized) {
+        await userStore.initialize();
+      }
+    });
+    return (_ctx, _cache) => {
+      return openBlock(), createBlock(_sfc_main$1, { dark: "" }, {
+        default: withCtx(() => [
+          createBaseVNode("main", _hoisted_1, [
+            _hoisted_2,
+            createBaseVNode("div", _hoisted_3, [
+              createBaseVNode("div", _hoisted_4, [
+                createBaseVNode("label", _hoisted_5, toDisplayString(_ctx.$t("userSelect.newUser")) + ":", 1),
+                createVNode(unref(script), {
+                  id: "new-user-input",
+                  modelValue: newUsername.value,
+                  "onUpdate:modelValue": _cache[0] || (_cache[0] = ($event) => newUsername.value = $event),
+                  placeholder: _ctx.$t("userSelect.enterUsername"),
+                  onKeyup: withKeys(login, ["enter"])
+                }, null, 8, ["modelValue", "placeholder"])
+              ]),
+              createVNode(unref(script$1)),
+              createBaseVNode("div", _hoisted_6, [
+                createBaseVNode("label", _hoisted_7, toDisplayString(_ctx.$t("userSelect.existingUser")) + ":", 1),
+                createVNode(unref(script$2), {
+                  modelValue: selectedUser.value,
+                  "onUpdate:modelValue": _cache[1] || (_cache[1] = ($event) => selectedUser.value = $event),
+                  class: "w-full",
+                  inputId: "existing-user-select",
+                  options: unref(userStore).users,
+                  "option-label": "username",
+                  placeholder: _ctx.$t("userSelect.selectUser"),
+                  disabled: createNewUser.value
+                }, null, 8, ["modelValue", "options", "placeholder", "disabled"]),
+                error.value ? (openBlock(), createBlock(unref(script$3), {
+                  key: 0,
+                  severity: "error"
+                }, {
+                  default: withCtx(() => [
+                    createTextVNode(toDisplayString(error.value), 1)
+                  ]),
+                  _: 1
+                })) : createCommentVNode("", true)
+              ]),
+              createBaseVNode("footer", _hoisted_8, [
+                createVNode(unref(script$4), {
+                  label: _ctx.$t("userSelect.next"),
+                  onClick: login
+                }, null, 8, ["label"])
+              ])
+            ])
+          ])
+        ]),
+        _: 1
+      });
+    };
+  }
+});
+export {
+  _sfc_main as default
+};
+//# sourceMappingURL=UserSelectView-B3jYchWu.js.map

web/assets/UserSelectView-C_4L-Yqf.js

@@ -1,98 +0,0 @@
-var __defProp = Object.defineProperty;
-var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { a as defineComponent, J as useUserStore, bU as useRouter, r as ref, q as computed, o as onMounted, f as openBlock, g as createElementBlock, A as createBaseVNode, a8 as toDisplayString, h as createVNode, z as unref, aq as script, bN as script$1, bV as script$2, x as createBlock, y as withCtx, ay as createTextVNode, bW as script$3, i as createCommentVNode, D as script$4 } from "./index-DIU5yZe9.js";
-const _hoisted_1 = {
-  id: "comfy-user-selection",
-  class: "font-sans flex flex-col items-center h-screen m-0 text-neutral-300 bg-neutral-900 dark-theme pointer-events-auto"
-};
-const _hoisted_2 = { class: "mt-[5vh] 2xl:mt-[20vh] min-w-84 relative rounded-lg bg-[var(--comfy-menu-bg)] p-5 px-10 shadow-lg" };
-const _hoisted_3 = /* @__PURE__ */ createBaseVNode("h1", { class: "my-2.5 mb-7 font-normal" }, "ComfyUI", -1);
-const _hoisted_4 = { class: "flex w-full flex-col items-center" };
-const _hoisted_5 = { class: "flex w-full flex-col gap-2" };
-const _hoisted_6 = { for: "new-user-input" };
-const _hoisted_7 = { class: "flex w-full flex-col gap-2" };
-const _hoisted_8 = { for: "existing-user-select" };
-const _hoisted_9 = { class: "mt-5" };
-const _sfc_main = /* @__PURE__ */ defineComponent({
-  __name: "UserSelectView",
-  setup(__props) {
-    const userStore = useUserStore();
-    const router = useRouter();
-    const selectedUser = ref(null);
-    const newUsername = ref("");
-    const loginError = ref("");
-    const createNewUser = computed(() => newUsername.value.trim() !== "");
-    const newUserExistsError = computed(() => {
-      return userStore.users.find((user) => user.username === newUsername.value) ? `User "${newUsername.value}" already exists` : "";
-    });
-    const error = computed(() => newUserExistsError.value || loginError.value);
-    const login = /* @__PURE__ */ __name(async () => {
-      try {
-        const user = createNewUser.value ? await userStore.createUser(newUsername.value) : selectedUser.value;
-        if (!user) {
-          throw new Error("No user selected");
-        }
-        userStore.login(user);
-        router.push("/");
-      } catch (err) {
-        loginError.value = err.message ?? JSON.stringify(err);
-      }
-    }, "login");
-    onMounted(async () => {
-      if (!userStore.initialized) {
-        await userStore.initialize();
-      }
-    });
-    return (_ctx, _cache) => {
-      return openBlock(), createElementBlock("div", _hoisted_1, [
-        createBaseVNode("main", _hoisted_2, [
-          _hoisted_3,
-          createBaseVNode("form", _hoisted_4, [
-            createBaseVNode("div", _hoisted_5, [
-              createBaseVNode("label", _hoisted_6, toDisplayString(_ctx.$t("userSelect.newUser")) + ":", 1),
-              createVNode(unref(script), {
-                id: "new-user-input",
-                modelValue: newUsername.value,
-                "onUpdate:modelValue": _cache[0] || (_cache[0] = ($event) => newUsername.value = $event),
-                placeholder: _ctx.$t("userSelect.enterUsername")
-              }, null, 8, ["modelValue", "placeholder"])
-            ]),
-            createVNode(unref(script$1)),
-            createBaseVNode("div", _hoisted_7, [
-              createBaseVNode("label", _hoisted_8, toDisplayString(_ctx.$t("userSelect.existingUser")) + ":", 1),
-              createVNode(unref(script$2), {
-                modelValue: selectedUser.value,
-                "onUpdate:modelValue": _cache[1] || (_cache[1] = ($event) => selectedUser.value = $event),
-                class: "w-full",
-                inputId: "existing-user-select",
-                options: unref(userStore).users,
-                "option-label": "username",
-                placeholder: _ctx.$t("userSelect.selectUser"),
-                disabled: createNewUser.value
-              }, null, 8, ["modelValue", "options", "placeholder", "disabled"]),
-              error.value ? (openBlock(), createBlock(unref(script$3), {
-                key: 0,
-                severity: "error"
-              }, {
-                default: withCtx(() => [
-                  createTextVNode(toDisplayString(error.value), 1)
-                ]),
-                _: 1
-              })) : createCommentVNode("", true)
-            ]),
-            createBaseVNode("footer", _hoisted_9, [
-              createVNode(unref(script$4), {
-                label: _ctx.$t("userSelect.next"),
-                onClick: login
-              }, null, 8, ["label"])
-            ])
-          ])
-        ])
-      ]);
-    };
-  }
-});
-export {
-  _sfc_main as default
-};
-//# sourceMappingURL=UserSelectView-C_4L-Yqf.js.map

web/assets/UserSelectView-C_4L-Yqf.js.map

@@ -1 +0,0 @@
-{"version":3,"file":"UserSelectView-C_4L-Yqf.js","sources":["../../src/views/UserSelectView.vue"],"sourcesContent":["<template>\n  <div\n    id=\"comfy-user-selection\"\n    class=\"font-sans flex flex-col items-center h-screen m-0 text-neutral-300 bg-neutral-900 dark-theme pointer-events-auto\"\n  >\n    <main\n      class=\"mt-[5vh] 2xl:mt-[20vh] min-w-84 relative rounded-lg bg-[var(--comfy-menu-bg)] p-5 px-10 shadow-lg\"\n    >\n      <h1 class=\"my-2.5 mb-7 font-normal\">ComfyUI</h1>\n      <form class=\"flex w-full flex-col items-center\">\n        <div class=\"flex w-full flex-col gap-2\">\n          <label for=\"new-user-input\">{{ $t('userSelect.newUser') }}:</label>\n          <InputText\n            id=\"new-user-input\"\n            v-model=\"newUsername\"\n            :placeholder=\"$t('userSelect.enterUsername')\"\n          />\n        </div>\n        <Divider />\n        <div class=\"flex w-full flex-col gap-2\">\n          <label for=\"existing-user-select\"\n            >{{ $t('userSelect.existingUser') }}:</label\n          >\n          <Select\n            v-model=\"selectedUser\"\n            class=\"w-full\"\n            inputId=\"existing-user-select\"\n            :options=\"userStore.users\"\n            option-label=\"username\"\n            :placeholder=\"$t('userSelect.selectUser')\"\n            :disabled=\"createNewUser\"\n          />\n          <Message v-if=\"error\" severity=\"error\">{{ error }}</Message>\n        </div>\n        <footer class=\"mt-5\">\n          <Button :label=\"$t('userSelect.next')\" @click=\"login\" />\n        </footer>\n      </form>\n    </main>\n  </div>\n</template>\n\n<script setup lang=\"ts\">\nimport Button from 'primevue/button'\nimport Divider from 'primevue/divider'\nimport InputText from 'primevue/inputtext'\nimport Select from 'primevue/select'\nimport Message from 'primevue/message'\nimport { User, useUserStore } from '@/stores/userStore'\nimport { useRouter } from 'vue-router'\nimport { computed, onMounted, ref } from 'vue'\n\nconst userStore = useUserStore()\nconst router = useRouter()\n\nconst selectedUser = ref<User | null>(null)\nconst newUsername = ref('')\nconst loginError = ref('')\n\nconst createNewUser = computed(() => newUsername.value.trim() !== '')\nconst newUserExistsError = computed(() => {\n  return userStore.users.find((user) => user.username === newUsername.value)\n    ? `User \"${newUsername.value}\" already exists`\n    : ''\n})\nconst error = computed(() => newUserExistsError.value || loginError.value)\n\nconst login = async () => {\n  try {\n    const user = createNewUser.value\n      ? await userStore.createUser(newUsername.value)\n      : selectedUser.value\n\n    if (!user) {\n      throw new Error('No user selected')\n    }\n\n    userStore.login(user)\n    router.push('/')\n  } catch (err) {\n    loginError.value = err.message ?? JSON.stringify(err)\n  }\n}\n\nonMounted(async () => {\n  if (!userStore.initialized) {\n    await userStore.initialize()\n  }\n})\n</script>\n"],"names":[],"mappings":";;;;;;;;;;;;;;;;;;AAoDA,UAAM,YAAY;AAClB,UAAM,SAAS;AAET,UAAA,eAAe,IAAiB,IAAI;AACpC,UAAA,cAAc,IAAI,EAAE;AACpB,UAAA,aAAa,IAAI,EAAE;AAEzB,UAAM,gBAAgB,SAAS,MAAM,YAAY,MAAM,KAAA,MAAW,EAAE;AAC9D,UAAA,qBAAqB,SAAS,MAAM;AACxC,aAAO,UAAU,MAAM,KAAK,CAAC,SAAS,KAAK,aAAa,YAAY,KAAK,IACrE,SAAS,YAAY,KAAK,qBAC1B;AAAA,IAAA,CACL;AACD,UAAM,QAAQ,SAAS,MAAM,mBAAmB,SAAS,WAAW,KAAK;AAEzE,UAAM,QAAQ,mCAAY;AACpB,UAAA;AACI,cAAA,OAAO,cAAc,QACvB,MAAM,UAAU,WAAW,YAAY,KAAK,IAC5C,aAAa;AAEjB,YAAI,CAAC,MAAM;AACH,gBAAA,IAAI,MAAM,kBAAkB;AAAA,QACpC;AAEA,kBAAU,MAAM,IAAI;AACpB,eAAO,KAAK,GAAG;AAAA,eACR,KAAK;AACZ,mBAAW,QAAQ,IAAI,WAAW,KAAK,UAAU,GAAG;AAAA,MACtD;AAAA,IAAA,GAdY;AAiBd,cAAU,YAAY;AAChB,UAAA,CAAC,UAAU,aAAa;AAC1B,cAAM,UAAU;MAClB;AAAA,IAAA,CACD;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;"}

web/assets/WelcomeView-Brz3-luE.css

@@ -1,5 +1,5 @@
 
-.animated-gradient-text[data-v-c4d014c5] {
+.animated-gradient-text[data-v-7dfaf74c] {
   font-weight: 700;
   font-size: clamp(2rem, 8vw, 4rem);
   background: linear-gradient(to right, #12c2e9, #c471ed, #f64f59, #12c2e9);
@@ -7,12 +7,12 @@
   background-clip: text;
   -webkit-background-clip: text;
   -webkit-text-fill-color: transparent;
-  animation: gradient-c4d014c5 8s linear infinite;
+  animation: gradient-7dfaf74c 8s linear infinite;
 }
-.text-glow[data-v-c4d014c5] {
+.text-glow[data-v-7dfaf74c] {
   filter: drop-shadow(0 0 8px rgba(255, 255, 255, 0.3));
 }
-@keyframes gradient-c4d014c5 {
+@keyframes gradient-7dfaf74c {
 0% {
     background-position: 0% center;
 }
@@ -20,11 +20,11 @@
     background-position: 300% center;
 }
 }
-.fade-in-up[data-v-c4d014c5] {
-  animation: fadeInUp-c4d014c5 1.5s ease-out;
+.fade-in-up[data-v-7dfaf74c] {
+  animation: fadeInUp-7dfaf74c 1.5s ease-out;
   animation-fill-mode: both;
 }
-@keyframes fadeInUp-c4d014c5 {
+@keyframes fadeInUp-7dfaf74c {
 0% {
     opacity: 0;
     transform: translateY(20px);

web/assets/WelcomeView-Db7ZDfZo.js

@@ -1,37 +0,0 @@
-var __defProp = Object.defineProperty;
-var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { a as defineComponent, bU as useRouter, f as openBlock, g as createElementBlock, A as createBaseVNode, a8 as toDisplayString, h as createVNode, z as unref, D as script, R as pushScopeId, U as popScopeId, _ as _export_sfc } from "./index-DIU5yZe9.js";
-const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-c4d014c5"), n = n(), popScopeId(), n), "_withScopeId");
-const _hoisted_1 = { class: "font-sans flex flex-col justify-center items-center h-screen m-0 text-neutral-300 bg-neutral-900 dark-theme pointer-events-auto" };
-const _hoisted_2 = { class: "flex flex-col items-center justify-center gap-8 p-8" };
-const _hoisted_3 = { class: "animated-gradient-text text-glow select-none" };
-const _sfc_main = /* @__PURE__ */ defineComponent({
-  __name: "WelcomeView",
-  setup(__props) {
-    const router = useRouter();
-    const navigateTo = /* @__PURE__ */ __name((path) => {
-      router.push(path);
-    }, "navigateTo");
-    return (_ctx, _cache) => {
-      return openBlock(), createElementBlock("div", _hoisted_1, [
-        createBaseVNode("div", _hoisted_2, [
-          createBaseVNode("h1", _hoisted_3, toDisplayString(_ctx.$t("welcome.title")), 1),
-          createVNode(unref(script), {
-            label: _ctx.$t("welcome.getStarted"),
-            icon: "pi pi-arrow-right",
-            iconPos: "right",
-            size: "large",
-            rounded: "",
-            onClick: _cache[0] || (_cache[0] = ($event) => navigateTo("/install")),
-            class: "p-4 text-lg fade-in-up"
-          }, null, 8, ["label"])
-        ])
-      ]);
-    };
-  }
-});
-const WelcomeView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-c4d014c5"]]);
-export {
-  WelcomeView as default
-};
-//# sourceMappingURL=WelcomeView-Db7ZDfZo.js.map

web/assets/WelcomeView-Db7ZDfZo.js.map

@@ -1 +0,0 @@
-{"version":3,"file":"WelcomeView-Db7ZDfZo.js","sources":["../../src/views/WelcomeView.vue"],"sourcesContent":["<template>\n  <div\n    class=\"font-sans flex flex-col justify-center items-center h-screen m-0 text-neutral-300 bg-neutral-900 dark-theme pointer-events-auto\"\n  >\n    <div class=\"flex flex-col items-center justify-center gap-8 p-8\">\n      <!-- Header -->\n      <h1 class=\"animated-gradient-text text-glow select-none\">\n        {{ $t('welcome.title') }}\n      </h1>\n\n      <!-- Get Started Button -->\n      <Button\n        :label=\"$t('welcome.getStarted')\"\n        icon=\"pi pi-arrow-right\"\n        iconPos=\"right\"\n        size=\"large\"\n        rounded\n        @click=\"navigateTo('/install')\"\n        class=\"p-4 text-lg fade-in-up\"\n      />\n    </div>\n  </div>\n</template>\n\n<script setup lang=\"ts\">\nimport Button from 'primevue/button'\nimport { useRouter } from 'vue-router'\n\nconst router = useRouter()\nconst navigateTo = (path: string) => {\n  router.push(path)\n}\n</script>\n\n<style scoped>\n.animated-gradient-text {\n  @apply font-bold;\n  font-size: clamp(2rem, 8vw, 4rem);\n  background: linear-gradient(to right, #12c2e9, #c471ed, #f64f59, #12c2e9);\n  background-size: 300% auto;\n  background-clip: text;\n  -webkit-background-clip: text;\n  -webkit-text-fill-color: transparent;\n  animation: gradient 8s linear infinite;\n}\n\n.text-glow {\n  filter: drop-shadow(0 0 8px rgba(255, 255, 255, 0.3));\n}\n\n@keyframes gradient {\n  0% {\n    background-position: 0% center;\n  }\n\n  100% {\n    background-position: 300% center;\n  }\n}\n\n.fade-in-up {\n  animation: fadeInUp 1.5s ease-out;\n  animation-fill-mode: both;\n}\n\n@keyframes fadeInUp {\n  0% {\n    opacity: 0;\n    transform: translateY(20px);\n  }\n\n  100% {\n    opacity: 1;\n    transform: translateY(0);\n  }\n}\n</style>\n"],"names":[],"mappings":";;;;;;;;;;AA4BA,UAAM,SAAS;AACT,UAAA,aAAa,wBAAC,SAAiB;AACnC,aAAO,KAAK,IAAI;AAAA,IAAA,GADC;;;;;;;;;;;;;;;;;;;;"}

web/assets/WelcomeView-N0ZXLjdi.js

@@ -0,0 +1,40 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { d as defineComponent, bW as useRouter, o as openBlock, k as createBlock, M as withCtx, H as createBaseVNode, X as toDisplayString, N as createVNode, j as unref, l as script, aL as pushScopeId, aM as popScopeId, _ as _export_sfc } from "./index-DjNHn37O.js";
+import { _ as _sfc_main$1 } from "./BaseViewTemplate-BNGF4K22.js";
+const _withScopeId = /* @__PURE__ */ __name((n) => (pushScopeId("data-v-7dfaf74c"), n = n(), popScopeId(), n), "_withScopeId");
+const _hoisted_1 = { class: "flex flex-col items-center justify-center gap-8 p-8" };
+const _hoisted_2 = { class: "animated-gradient-text text-glow select-none" };
+const _sfc_main = /* @__PURE__ */ defineComponent({
+  __name: "WelcomeView",
+  setup(__props) {
+    const router = useRouter();
+    const navigateTo = /* @__PURE__ */ __name((path) => {
+      router.push(path);
+    }, "navigateTo");
+    return (_ctx, _cache) => {
+      return openBlock(), createBlock(_sfc_main$1, { dark: "" }, {
+        default: withCtx(() => [
+          createBaseVNode("div", _hoisted_1, [
+            createBaseVNode("h1", _hoisted_2, toDisplayString(_ctx.$t("welcome.title")), 1),
+            createVNode(unref(script), {
+              label: _ctx.$t("welcome.getStarted"),
+              icon: "pi pi-arrow-right",
+              iconPos: "right",
+              size: "large",
+              rounded: "",
+              onClick: _cache[0] || (_cache[0] = ($event) => navigateTo("/install")),
+              class: "p-4 text-lg fade-in-up"
+            }, null, 8, ["label"])
+          ])
+        ]),
+        _: 1
+      });
+    };
+  }
+});
+const WelcomeView = /* @__PURE__ */ _export_sfc(_sfc_main, [["__scopeId", "data-v-7dfaf74c"]]);
+export {
+  WelcomeView as default
+};
+//# sourceMappingURL=WelcomeView-N0ZXLjdi.js.map

web/assets/images/manual-configuration.svg

@@ -0,0 +1,5 @@
+<?xml version="1.0" encoding="UTF-8"?>
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web/assets/index-jXPKy3pP.js

@@ -0,0 +1,173 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { B as BaseStyle, q as script$2, ak as UniqueComponentId, c9 as script$4, l as script$5, S as Ripple, aB as resolveComponent, o as openBlock, f as createElementBlock, D as mergeProps, H as createBaseVNode, J as renderSlot, T as normalizeClass, X as toDisplayString, I as createCommentVNode, k as createBlock, M as withCtx, G as resolveDynamicComponent, N as createVNode, aC as Transition, i as withDirectives, v as vShow } from "./index-DjNHn37O.js";
+import { s as script$3 } from "./index-5HFeZax4.js";
+var theme = /* @__PURE__ */ __name(function theme2(_ref) {
+  var dt = _ref.dt;
+  return "\n.p-panel {\n    border: 1px solid ".concat(dt("panel.border.color"), ";\n    border-radius: ").concat(dt("panel.border.radius"), ";\n    background: ").concat(dt("panel.background"), ";\n    color: ").concat(dt("panel.color"), ";\n}\n\n.p-panel-header {\n    display: flex;\n    justify-content: space-between;\n    align-items: center;\n    padding: ").concat(dt("panel.header.padding"), ";\n    background: ").concat(dt("panel.header.background"), ";\n    color: ").concat(dt("panel.header.color"), ";\n    border-style: solid;\n    border-width: ").concat(dt("panel.header.border.width"), ";\n    border-color: ").concat(dt("panel.header.border.color"), ";\n    border-radius: ").concat(dt("panel.header.border.radius"), ";\n}\n\n.p-panel-toggleable .p-panel-header {\n    padding: ").concat(dt("panel.toggleable.header.padding"), ";\n}\n\n.p-panel-title {\n    line-height: 1;\n    font-weight: ").concat(dt("panel.title.font.weight"), ";\n}\n\n.p-panel-content {\n    padding: ").concat(dt("panel.content.padding"), ";\n}\n\n.p-panel-footer {\n    padding: ").concat(dt("panel.footer.padding"), ";\n}\n");
+}, "theme");
+var classes = {
+  root: /* @__PURE__ */ __name(function root(_ref2) {
+    var props = _ref2.props;
+    return ["p-panel p-component", {
+      "p-panel-toggleable": props.toggleable
+    }];
+  }, "root"),
+  header: "p-panel-header",
+  title: "p-panel-title",
+  headerActions: "p-panel-header-actions",
+  pcToggleButton: "p-panel-toggle-button",
+  contentContainer: "p-panel-content-container",
+  content: "p-panel-content",
+  footer: "p-panel-footer"
+};
+var PanelStyle = BaseStyle.extend({
+  name: "panel",
+  theme,
+  classes
+});
+var script$1 = {
+  name: "BasePanel",
+  "extends": script$2,
+  props: {
+    header: String,
+    toggleable: Boolean,
+    collapsed: Boolean,
+    toggleButtonProps: {
+      type: Object,
+      "default": /* @__PURE__ */ __name(function _default() {
+        return {
+          severity: "secondary",
+          text: true,
+          rounded: true
+        };
+      }, "_default")
+    }
+  },
+  style: PanelStyle,
+  provide: /* @__PURE__ */ __name(function provide() {
+    return {
+      $pcPanel: this,
+      $parentInstance: this
+    };
+  }, "provide")
+};
+var script = {
+  name: "Panel",
+  "extends": script$1,
+  inheritAttrs: false,
+  emits: ["update:collapsed", "toggle"],
+  data: /* @__PURE__ */ __name(function data() {
+    return {
+      id: this.$attrs.id,
+      d_collapsed: this.collapsed
+    };
+  }, "data"),
+  watch: {
+    "$attrs.id": /* @__PURE__ */ __name(function $attrsId(newValue) {
+      this.id = newValue || UniqueComponentId();
+    }, "$attrsId"),
+    collapsed: /* @__PURE__ */ __name(function collapsed(newValue) {
+      this.d_collapsed = newValue;
+    }, "collapsed")
+  },
+  mounted: /* @__PURE__ */ __name(function mounted() {
+    this.id = this.id || UniqueComponentId();
+  }, "mounted"),
+  methods: {
+    toggle: /* @__PURE__ */ __name(function toggle(event) {
+      this.d_collapsed = !this.d_collapsed;
+      this.$emit("update:collapsed", this.d_collapsed);
+      this.$emit("toggle", {
+        originalEvent: event,
+        value: this.d_collapsed
+      });
+    }, "toggle"),
+    onKeyDown: /* @__PURE__ */ __name(function onKeyDown(event) {
+      if (event.code === "Enter" || event.code === "NumpadEnter" || event.code === "Space") {
+        this.toggle(event);
+        event.preventDefault();
+      }
+    }, "onKeyDown")
+  },
+  computed: {
+    buttonAriaLabel: /* @__PURE__ */ __name(function buttonAriaLabel() {
+      return this.toggleButtonProps && this.toggleButtonProps.ariaLabel ? this.toggleButtonProps.ariaLabel : this.header;
+    }, "buttonAriaLabel")
+  },
+  components: {
+    PlusIcon: script$3,
+    MinusIcon: script$4,
+    Button: script$5
+  },
+  directives: {
+    ripple: Ripple
+  }
+};
+var _hoisted_1 = ["id"];
+var _hoisted_2 = ["id", "aria-labelledby"];
+function render(_ctx, _cache, $props, $setup, $data, $options) {
+  var _component_Button = resolveComponent("Button");
+  return openBlock(), createElementBlock("div", mergeProps({
+    "class": _ctx.cx("root")
+  }, _ctx.ptmi("root")), [createBaseVNode("div", mergeProps({
+    "class": _ctx.cx("header")
+  }, _ctx.ptm("header")), [renderSlot(_ctx.$slots, "header", {
+    id: $data.id + "_header",
+    "class": normalizeClass(_ctx.cx("title"))
+  }, function() {
+    return [_ctx.header ? (openBlock(), createElementBlock("span", mergeProps({
+      key: 0,
+      id: $data.id + "_header",
+      "class": _ctx.cx("title")
+    }, _ctx.ptm("title")), toDisplayString(_ctx.header), 17, _hoisted_1)) : createCommentVNode("", true)];
+  }), createBaseVNode("div", mergeProps({
+    "class": _ctx.cx("headerActions")
+  }, _ctx.ptm("headerActions")), [renderSlot(_ctx.$slots, "icons"), _ctx.toggleable ? (openBlock(), createBlock(_component_Button, mergeProps({
+    key: 0,
+    id: $data.id + "_header",
+    "class": _ctx.cx("pcToggleButton"),
+    "aria-label": $options.buttonAriaLabel,
+    "aria-controls": $data.id + "_content",
+    "aria-expanded": !$data.d_collapsed,
+    unstyled: _ctx.unstyled,
+    onClick: $options.toggle,
+    onKeydown: $options.onKeyDown
+  }, _ctx.toggleButtonProps, {
+    pt: _ctx.ptm("pcToggleButton")
+  }), {
+    icon: withCtx(function(slotProps) {
+      return [renderSlot(_ctx.$slots, _ctx.$slots.toggleicon ? "toggleicon" : "togglericon", {
+        collapsed: $data.d_collapsed
+      }, function() {
+        return [(openBlock(), createBlock(resolveDynamicComponent($data.d_collapsed ? "PlusIcon" : "MinusIcon"), mergeProps({
+          "class": slotProps["class"]
+        }, _ctx.ptm("pcToggleButton")["icon"]), null, 16, ["class"]))];
+      })];
+    }),
+    _: 3
+  }, 16, ["id", "class", "aria-label", "aria-controls", "aria-expanded", "unstyled", "onClick", "onKeydown", "pt"])) : createCommentVNode("", true)], 16)], 16), createVNode(Transition, mergeProps({
+    name: "p-toggleable-content"
+  }, _ctx.ptm("transition")), {
+    "default": withCtx(function() {
+      return [withDirectives(createBaseVNode("div", mergeProps({
+        id: $data.id + "_content",
+        "class": _ctx.cx("contentContainer"),
+        role: "region",
+        "aria-labelledby": $data.id + "_header"
+      }, _ctx.ptm("contentContainer")), [createBaseVNode("div", mergeProps({
+        "class": _ctx.cx("content")
+      }, _ctx.ptm("content")), [renderSlot(_ctx.$slots, "default")], 16), _ctx.$slots.footer ? (openBlock(), createElementBlock("div", mergeProps({
+        key: 0,
+        "class": _ctx.cx("footer")
+      }, _ctx.ptm("footer")), [renderSlot(_ctx.$slots, "footer")], 16)) : createCommentVNode("", true)], 16, _hoisted_2), [[vShow, !$data.d_collapsed]])];
+    }),
+    _: 3
+  }, 16)], 16);
+}
+__name(render, "render");
+script.render = render;
+export {
+  script as s
+};
+//# sourceMappingURL=index-jXPKy3pP.js.map

web/assets/keybindingService-Bx7YdkXn.js

@@ -0,0 +1,250 @@
+var __defProp = Object.defineProperty;
+var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
+import { a$ as useKeybindingStore, a2 as useCommandStore, a as useSettingStore, cq as KeyComboImpl, cr as KeybindingImpl } from "./index-DjNHn37O.js";
+const CORE_KEYBINDINGS = [
+  {
+    combo: {
+      ctrl: true,
+      key: "Enter"
+    },
+    commandId: "Comfy.QueuePrompt"
+  },
+  {
+    combo: {
+      ctrl: true,
+      shift: true,
+      key: "Enter"
+    },
+    commandId: "Comfy.QueuePromptFront"
+  },
+  {
+    combo: {
+      ctrl: true,
+      alt: true,
+      key: "Enter"
+    },
+    commandId: "Comfy.Interrupt"
+  },
+  {
+    combo: {
+      key: "r"
+    },
+    commandId: "Comfy.RefreshNodeDefinitions"
+  },
+  {
+    combo: {
+      key: "q"
+    },
+    commandId: "Workspace.ToggleSidebarTab.queue"
+  },
+  {
+    combo: {
+      key: "w"
+    },
+    commandId: "Workspace.ToggleSidebarTab.workflows"
+  },
+  {
+    combo: {
+      key: "n"
+    },
+    commandId: "Workspace.ToggleSidebarTab.node-library"
+  },
+  {
+    combo: {
+      key: "m"
+    },
+    commandId: "Workspace.ToggleSidebarTab.model-library"
+  },
+  {
+    combo: {
+      key: "s",
+      ctrl: true
+    },
+    commandId: "Comfy.SaveWorkflow"
+  },
+  {
+    combo: {
+      key: "o",
+      ctrl: true
+    },
+    commandId: "Comfy.OpenWorkflow"
+  },
+  {
+    combo: {
+      key: "Backspace"
+    },
+    commandId: "Comfy.ClearWorkflow"
+  },
+  {
+    combo: {
+      key: "g",
+      ctrl: true
+    },
+    commandId: "Comfy.Graph.GroupSelectedNodes"
+  },
+  {
+    combo: {
+      key: ",",
+      ctrl: true
+    },
+    commandId: "Comfy.ShowSettingsDialog"
+  },
+  // For '=' both holding shift and not holding shift
+  {
+    combo: {
+      key: "=",
+      alt: true
+    },
+    commandId: "Comfy.Canvas.ZoomIn",
+    targetSelector: "#graph-canvas"
+  },
+  {
+    combo: {
+      key: "+",
+      alt: true,
+      shift: true
+    },
+    commandId: "Comfy.Canvas.ZoomIn",
+    targetSelector: "#graph-canvas"
+  },
+  // For number pad '+'
+  {
+    combo: {
+      key: "+",
+      alt: true
+    },
+    commandId: "Comfy.Canvas.ZoomIn",
+    targetSelector: "#graph-canvas"
+  },
+  {
+    combo: {
+      key: "-",
+      alt: true
+    },
+    commandId: "Comfy.Canvas.ZoomOut",
+    targetSelector: "#graph-canvas"
+  },
+  {
+    combo: {
+      key: "."
+    },
+    commandId: "Comfy.Canvas.FitView",
+    targetSelector: "#graph-canvas"
+  },
+  {
+    combo: {
+      key: "p"
+    },
+    commandId: "Comfy.Canvas.ToggleSelected.Pin",
+    targetSelector: "#graph-canvas"
+  },
+  {
+    combo: {
+      key: "c",
+      alt: true
+    },
+    commandId: "Comfy.Canvas.ToggleSelectedNodes.Collapse",
+    targetSelector: "#graph-canvas"
+  },
+  {
+    combo: {
+      key: "b",
+      ctrl: true
+    },
+    commandId: "Comfy.Canvas.ToggleSelectedNodes.Bypass",
+    targetSelector: "#graph-canvas"
+  },
+  {
+    combo: {
+      key: "m",
+      ctrl: true
+    },
+    commandId: "Comfy.Canvas.ToggleSelectedNodes.Mute",
+    targetSelector: "#graph-canvas"
+  },
+  {
+    combo: {
+      key: "`",
+      ctrl: true
+    },
+    commandId: "Workspace.ToggleBottomPanelTab.logs-terminal"
+  },
+  {
+    combo: {
+      key: "f"
+    },
+    commandId: "Workspace.ToggleFocusMode"
+  }
+];
+const useKeybindingService = /* @__PURE__ */ __name(() => {
+  const keybindingStore = useKeybindingStore();
+  const commandStore = useCommandStore();
+  const settingStore = useSettingStore();
+  const keybindHandler = /* @__PURE__ */ __name(async function(event) {
+    const keyCombo = KeyComboImpl.fromEvent(event);
+    if (keyCombo.isModifier) {
+      return;
+    }
+    const target = event.composedPath()[0];
+    if (!keyCombo.hasModifier && (target.tagName === "TEXTAREA" || target.tagName === "INPUT" || target.tagName === "SPAN" && target.classList.contains("property_value"))) {
+      return;
+    }
+    const keybinding = keybindingStore.getKeybinding(keyCombo);
+    if (keybinding && keybinding.targetSelector !== "#graph-canvas") {
+      event.preventDefault();
+      await commandStore.execute(keybinding.commandId);
+      return;
+    }
+    if (event.ctrlKey || event.altKey || event.metaKey) {
+      return;
+    }
+    if (event.key === "Escape") {
+      const modals = document.querySelectorAll(".comfy-modal");
+      for (const modal of modals) {
+        const modalDisplay = window.getComputedStyle(modal).getPropertyValue("display");
+        if (modalDisplay !== "none") {
+          modal.style.display = "none";
+          break;
+        }
+      }
+      for (const d of document.querySelectorAll("dialog")) d.close();
+    }
+  }, "keybindHandler");
+  const registerCoreKeybindings = /* @__PURE__ */ __name(() => {
+    for (const keybinding of CORE_KEYBINDINGS) {
+      keybindingStore.addDefaultKeybinding(new KeybindingImpl(keybinding));
+    }
+  }, "registerCoreKeybindings");
+  function registerUserKeybindings() {
+    const unsetBindings = settingStore.get("Comfy.Keybinding.UnsetBindings");
+    for (const keybinding of unsetBindings) {
+      keybindingStore.unsetKeybinding(new KeybindingImpl(keybinding));
+    }
+    const newBindings = settingStore.get("Comfy.Keybinding.NewBindings");
+    for (const keybinding of newBindings) {
+      keybindingStore.addUserKeybinding(new KeybindingImpl(keybinding));
+    }
+  }
+  __name(registerUserKeybindings, "registerUserKeybindings");
+  async function persistUserKeybindings() {
+    await settingStore.set(
+      "Comfy.Keybinding.NewBindings",
+      Object.values(keybindingStore.getUserKeybindings())
+    );
+    await settingStore.set(
+      "Comfy.Keybinding.UnsetBindings",
+      Object.values(keybindingStore.getUserUnsetKeybindings())
+    );
+  }
+  __name(persistUserKeybindings, "persistUserKeybindings");
+  return {
+    keybindHandler,
+    registerCoreKeybindings,
+    registerUserKeybindings,
+    persistUserKeybindings
+  };
+}, "useKeybindingService");
+export {
+  useKeybindingService as u
+};
+//# sourceMappingURL=keybindingService-Bx7YdkXn.js.map

web/assets/serverConfigStore-CvyKFVuP.js

@@ -1,6 +1,6 @@
 var __defProp = Object.defineProperty;
 var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { d as defineStore, r as ref, q as computed } from "./index-DIU5yZe9.js";
+import { $ as defineStore, ab as ref, c as computed } from "./index-DjNHn37O.js";
 const useServerConfigStore = defineStore("serverConfig", () => {
   const serverConfigById = ref({});
   const serverConfigs = computed(() => {
@@ -87,4 +87,4 @@ const useServerConfigStore = defineStore("serverConfig", () => {
 export {
   useServerConfigStore as u
 };
-//# sourceMappingURL=serverConfigStore-DYv7_Nld.js.map
+//# sourceMappingURL=serverConfigStore-CvyKFVuP.js.map

web/assets/widgetInputs-Bvm3AgOa.js

@@ -1,766 +0,0 @@
-var __defProp = Object.defineProperty;
-var __name = (target, value) => __defProp(target, "name", { value, configurable: true });
-import { c as LGraphNode, b as app, cg as applyTextReplacements, cf as ComfyWidgets, cj as addValueControlWidgets, j as LiteGraph } from "./index-DIU5yZe9.js";
-const CONVERTED_TYPE = "converted-widget";
-const VALID_TYPES = [
-  "STRING",
-  "combo",
-  "number",
-  "toggle",
-  "BOOLEAN",
-  "text",
-  "string"
-];
-const CONFIG = Symbol();
-const GET_CONFIG = Symbol();
-const TARGET = Symbol();
-const replacePropertyName = "Run widget replace on values";
-class PrimitiveNode extends LGraphNode {
-  static {
-    __name(this, "PrimitiveNode");
-  }
-  controlValues;
-  lastType;
-  static category;
-  constructor(title) {
-    super(title);
-    this.addOutput("connect to widget input", "*");
-    this.serialize_widgets = true;
-    this.isVirtualNode = true;
-    if (!this.properties || !(replacePropertyName in this.properties)) {
-      this.addProperty(replacePropertyName, false, "boolean");
-    }
-  }
-  applyToGraph(extraLinks = []) {
-    if (!this.outputs[0].links?.length) return;
-    function get_links(node) {
-      let links2 = [];
-      for (const l of node.outputs[0].links) {
-        const linkInfo = app.graph.links[l];
-        const n = node.graph.getNodeById(linkInfo.target_id);
-        if (n.type == "Reroute") {
-          links2 = links2.concat(get_links(n));
-        } else {
-          links2.push(l);
-        }
-      }
-      return links2;
-    }
-    __name(get_links, "get_links");
-    let links = [
-      ...get_links(this).map((l) => app.graph.links[l]),
-      ...extraLinks
-    ];
-    let v = this.widgets?.[0].value;
-    if (v && this.properties[replacePropertyName]) {
-      v = applyTextReplacements(app, v);
-    }
-    for (const linkInfo of links) {
-      const node = this.graph.getNodeById(linkInfo.target_id);
-      const input = node.inputs[linkInfo.target_slot];
-      let widget;
-      if (input.widget[TARGET]) {
-        widget = input.widget[TARGET];
-      } else {
-        const widgetName = input.widget.name;
-        if (widgetName) {
-          widget = node.widgets.find((w) => w.name === widgetName);
-        }
-      }
-      if (widget) {
-        widget.value = v;
-        if (widget.callback) {
-          widget.callback(
-            widget.value,
-            app.canvas,
-            node,
-            app.canvas.graph_mouse,
-            {}
-          );
-        }
-      }
-    }
-  }
-  refreshComboInNode() {
-    const widget = this.widgets?.[0];
-    if (widget?.type === "combo") {
-      widget.options.values = this.outputs[0].widget[GET_CONFIG]()[0];
-      if (!widget.options.values.includes(widget.value)) {
-        widget.value = widget.options.values[0];
-        widget.callback(widget.value);
-      }
-    }
-  }
-  onAfterGraphConfigured() {
-    if (this.outputs[0].links?.length && !this.widgets?.length) {
-      if (!this.#onFirstConnection()) return;
-      if (this.widgets) {
-        for (let i = 0; i < this.widgets_values.length; i++) {
-          const w = this.widgets[i];
-          if (w) {
-            w.value = this.widgets_values[i];
-          }
-        }
-      }
-      this.#mergeWidgetConfig();
-    }
-  }
-  onConnectionsChange(_, index, connected) {
-    if (app.configuringGraph) {
-      return;
-    }
-    const links = this.outputs[0].links;
-    if (connected) {
-      if (links?.length && !this.widgets?.length) {
-        this.#onFirstConnection();
-      }
-    } else {
-      this.#mergeWidgetConfig();
-      if (!links?.length) {
-        this.onLastDisconnect();
-      }
-    }
-  }
-  onConnectOutput(slot, type, input, target_node, target_slot) {
-    if (!input.widget) {
-      if (!(input.type in ComfyWidgets)) return false;
-    }
-    if (this.outputs[slot].links?.length) {
-      const valid = this.#isValidConnection(input);
-      if (valid) {
-        this.applyToGraph([{ target_id: target_node.id, target_slot }]);
-      }
-      return valid;
-    }
-  }
-  #onFirstConnection(recreating) {
-    if (!this.outputs[0].links) {
-      this.onLastDisconnect();
-      return;
-    }
-    const linkId = this.outputs[0].links[0];
-    const link = this.graph.links[linkId];
-    if (!link) return;
-    const theirNode = this.graph.getNodeById(link.target_id);
-    if (!theirNode || !theirNode.inputs) return;
-    const input = theirNode.inputs[link.target_slot];
-    if (!input) return;
-    let widget;
-    if (!input.widget) {
-      if (!(input.type in ComfyWidgets)) return;
-      widget = { name: input.name, [GET_CONFIG]: () => [input.type, {}] };
-    } else {
-      widget = input.widget;
-    }
-    const config = widget[GET_CONFIG]?.();
-    if (!config) return;
-    const { type } = getWidgetType(config);
-    this.outputs[0].type = type;
-    this.outputs[0].name = type;
-    this.outputs[0].widget = widget;
-    this.#createWidget(
-      widget[CONFIG] ?? config,
-      theirNode,
-      widget.name,
-      recreating,
-      widget[TARGET]
-    );
-  }
-  #createWidget(inputData, node, widgetName, recreating, targetWidget) {
-    let type = inputData[0];
-    if (type instanceof Array) {
-      type = "COMBO";
-    }
-    const [oldWidth, oldHeight] = this.size;
-    let widget;
-    if (type in ComfyWidgets) {
-      widget = (ComfyWidgets[type](this, "value", inputData, app) || {}).widget;
-    } else {
-      widget = this.addWidget(type, "value", null, () => {
-      }, {});
-    }
-    if (targetWidget) {
-      widget.value = targetWidget.value;
-    } else if (node?.widgets && widget) {
-      const theirWidget = node.widgets.find((w) => w.name === widgetName);
-      if (theirWidget) {
-        widget.value = theirWidget.value;
-      }
-    }
-    if (!inputData?.[1]?.control_after_generate && (widget.type === "number" || widget.type === "combo")) {
-      let control_value = this.widgets_values?.[1];
-      if (!control_value) {
-        control_value = "fixed";
-      }
-      addValueControlWidgets(
-        this,
-        widget,
-        control_value,
-        void 0,
-        inputData
-      );
-      let filter = this.widgets_values?.[2];
-      if (filter && this.widgets.length === 3) {
-        this.widgets[2].value = filter;
-      }
-    }
-    const controlValues = this.controlValues;
-    if (this.lastType === this.widgets[0].type && controlValues?.length === this.widgets.length - 1) {
-      for (let i = 0; i < controlValues.length; i++) {
-        this.widgets[i + 1].value = controlValues[i];
-      }
-    }
-    const callback = widget.callback;
-    const self = this;
-    widget.callback = function() {
-      const r = callback ? callback.apply(this, arguments) : void 0;
-      self.applyToGraph();
-      return r;
-    };
-    this.size = [
-      Math.max(this.size[0], oldWidth),
-      Math.max(this.size[1], oldHeight)
-    ];
-    if (!recreating) {
-      const sz = this.computeSize();
-      if (this.size[0] < sz[0]) {
-        this.size[0] = sz[0];
-      }
-      if (this.size[1] < sz[1]) {
-        this.size[1] = sz[1];
-      }
-      requestAnimationFrame(() => {
-        if (this.onResize) {
-          this.onResize(this.size);
-        }
-      });
-    }
-  }
-  recreateWidget() {
-    const values = this.widgets?.map((w) => w.value);
-    this.#removeWidgets();
-    this.#onFirstConnection(true);
-    if (values?.length) {
-      for (let i = 0; i < this.widgets?.length; i++)
-        this.widgets[i].value = values[i];
-    }
-    return this.widgets?.[0];
-  }
-  #mergeWidgetConfig() {
-    const output = this.outputs[0];
-    const links = output.links;
-    const hasConfig = !!output.widget[CONFIG];
-    if (hasConfig) {
-      delete output.widget[CONFIG];
-    }
-    if (links?.length < 2 && hasConfig) {
-      if (links.length) {
-        this.recreateWidget();
-      }
-      return;
-    }
-    const config1 = output.widget[GET_CONFIG]();
-    const isNumber = config1[0] === "INT" || config1[0] === "FLOAT";
-    if (!isNumber) return;
-    for (const linkId of links) {
-      const link = app.graph.links[linkId];
-      if (!link) continue;
-      const theirNode = app.graph.getNodeById(link.target_id);
-      const theirInput = theirNode.inputs[link.target_slot];
-      this.#isValidConnection(theirInput, hasConfig);
-    }
-  }
-  isValidWidgetLink(originSlot, targetNode, targetWidget) {
-    const config2 = getConfig.call(targetNode, targetWidget.name) ?? [
-      targetWidget.type,
-      targetWidget.options || {}
-    ];
-    if (!isConvertibleWidget(targetWidget, config2)) return false;
-    const output = this.outputs[originSlot];
-    if (!(output.widget?.[CONFIG] ?? output.widget?.[GET_CONFIG]())) {
-      return true;
-    }
-    return !!mergeIfValid.call(this, output, config2);
-  }
-  #isValidConnection(input, forceUpdate) {
-    const output = this.outputs[0];
-    const config2 = input.widget[GET_CONFIG]();
-    return !!mergeIfValid.call(
-      this,
-      output,
-      config2,
-      forceUpdate,
-      this.recreateWidget
-    );
-  }
-  #removeWidgets() {
-    if (this.widgets) {
-      for (const w of this.widgets) {
-        if (w.onRemove) {
-          w.onRemove();
-        }
-      }
-      this.controlValues = [];
-      this.lastType = this.widgets[0]?.type;
-      for (let i = 1; i < this.widgets.length; i++) {
-        this.controlValues.push(this.widgets[i].value);
-      }
-      setTimeout(() => {
-        delete this.lastType;
-        delete this.controlValues;
-      }, 15);
-      this.widgets.length = 0;
-    }
-  }
-  onLastDisconnect() {
-    this.outputs[0].type = "*";
-    this.outputs[0].name = "connect to widget input";
-    delete this.outputs[0].widget;
-    this.#removeWidgets();
-  }
-}
-function getWidgetConfig(slot) {
-  return slot.widget[CONFIG] ?? slot.widget[GET_CONFIG]?.() ?? ["*", {}];
-}
-__name(getWidgetConfig, "getWidgetConfig");
-function getConfig(widgetName) {
-  const { nodeData } = this.constructor;
-  return nodeData?.input?.required?.[widgetName] ?? nodeData?.input?.optional?.[widgetName];
-}
-__name(getConfig, "getConfig");
-function isConvertibleWidget(widget, config) {
-  return (VALID_TYPES.includes(widget.type) || VALID_TYPES.includes(config[0])) && !widget.options?.forceInput;
-}
-__name(isConvertibleWidget, "isConvertibleWidget");
-function hideWidget(node, widget, suffix = "") {
-  if (widget.type?.startsWith(CONVERTED_TYPE)) return;
-  widget.origType = widget.type;
-  widget.origComputeSize = widget.computeSize;
-  widget.origSerializeValue = widget.serializeValue;
-  widget.computeSize = () => [0, -4];
-  widget.type = CONVERTED_TYPE + suffix;
-  widget.serializeValue = () => {
-    if (!node.inputs) {
-      return void 0;
-    }
-    let node_input = node.inputs.find((i) => i.widget?.name === widget.name);
-    if (!node_input || !node_input.link) {
-      return void 0;
-    }
-    return widget.origSerializeValue ? widget.origSerializeValue() : widget.value;
-  };
-  if (widget.linkedWidgets) {
-    for (const w of widget.linkedWidgets) {
-      hideWidget(node, w, ":" + widget.name);
-    }
-  }
-}
-__name(hideWidget, "hideWidget");
-function showWidget(widget) {
-  widget.type = widget.origType;
-  widget.computeSize = widget.origComputeSize;
-  widget.serializeValue = widget.origSerializeValue;
-  delete widget.origType;
-  delete widget.origComputeSize;
-  delete widget.origSerializeValue;
-  if (widget.linkedWidgets) {
-    for (const w of widget.linkedWidgets) {
-      showWidget(w);
-    }
-  }
-}
-__name(showWidget, "showWidget");
-function convertToInput(node, widget, config) {
-  hideWidget(node, widget);
-  const { type } = getWidgetType(config);
-  const [oldWidth, oldHeight] = node.size;
-  const inputIsOptional = !!widget.options?.inputIsOptional;
-  const input = node.addInput(widget.name, type, {
-    widget: { name: widget.name, [GET_CONFIG]: () => config },
-    ...inputIsOptional ? { shape: LiteGraph.SlotShape.HollowCircle } : {}
-  });
-  for (const widget2 of node.widgets) {
-    widget2.last_y += LiteGraph.NODE_SLOT_HEIGHT;
-  }
-  node.setSize([
-    Math.max(oldWidth, node.size[0]),
-    Math.max(oldHeight, node.size[1])
-  ]);
-  return input;
-}
-__name(convertToInput, "convertToInput");
-function convertToWidget(node, widget) {
-  showWidget(widget);
-  const [oldWidth, oldHeight] = node.size;
-  node.removeInput(node.inputs.findIndex((i) => i.widget?.name === widget.name));
-  for (const widget2 of node.widgets) {
-    widget2.last_y -= LiteGraph.NODE_SLOT_HEIGHT;
-  }
-  node.setSize([
-    Math.max(oldWidth, node.size[0]),
-    Math.max(oldHeight, node.size[1])
-  ]);
-}
-__name(convertToWidget, "convertToWidget");
-function getWidgetType(config) {
-  let type = config[0];
-  if (type instanceof Array) {
-    type = "COMBO";
-  }
-  return { type };
-}
-__name(getWidgetType, "getWidgetType");
-function isValidCombo(combo, obj) {
-  if (!(obj instanceof Array)) {
-    console.log(`connection rejected: tried to connect combo to ${obj}`);
-    return false;
-  }
-  if (combo.length !== obj.length) {
-    console.log(`connection rejected: combo lists dont match`);
-    return false;
-  }
-  if (combo.find((v, i) => obj[i] !== v)) {
-    console.log(`connection rejected: combo lists dont match`);
-    return false;
-  }
-  return true;
-}
-__name(isValidCombo, "isValidCombo");
-function isPrimitiveNode(node) {
-  return node.type === "PrimitiveNode";
-}
-__name(isPrimitiveNode, "isPrimitiveNode");
-function setWidgetConfig(slot, config, target) {
-  if (!slot.widget) return;
-  if (config) {
-    slot.widget[GET_CONFIG] = () => config;
-    slot.widget[TARGET] = target;
-  } else {
-    delete slot.widget;
-  }
-  if (slot.link) {
-    const link = app.graph.links[slot.link];
-    if (link) {
-      const originNode = app.graph.getNodeById(link.origin_id);
-      if (isPrimitiveNode(originNode)) {
-        if (config) {
-          originNode.recreateWidget();
-        } else if (!app.configuringGraph) {
-          originNode.disconnectOutput(0);
-          originNode.onLastDisconnect();
-        }
-      }
-    }
-  }
-}
-__name(setWidgetConfig, "setWidgetConfig");
-function mergeIfValid(output, config2, forceUpdate, recreateWidget, config1) {
-  if (!config1) {
-    config1 = getWidgetConfig(output);
-  }
-  if (config1[0] instanceof Array) {
-    if (!isValidCombo(config1[0], config2[0])) return;
-  } else if (config1[0] !== config2[0]) {
-    console.log(`connection rejected: types dont match`, config1[0], config2[0]);
-    return;
-  }
-  const keys = /* @__PURE__ */ new Set([
-    ...Object.keys(config1[1] ?? {}),
-    ...Object.keys(config2[1] ?? {})
-  ]);
-  let customConfig;
-  const getCustomConfig = /* @__PURE__ */ __name(() => {
-    if (!customConfig) {
-      if (typeof structuredClone === "undefined") {
-        customConfig = JSON.parse(JSON.stringify(config1[1] ?? {}));
-      } else {
-        customConfig = structuredClone(config1[1] ?? {});
-      }
-    }
-    return customConfig;
-  }, "getCustomConfig");
-  const isNumber = config1[0] === "INT" || config1[0] === "FLOAT";
-  for (const k of keys.values()) {
-    if (k !== "default" && k !== "forceInput" && k !== "defaultInput" && k !== "control_after_generate" && k !== "multiline" && k !== "tooltip") {
-      let v1 = config1[1][k];
-      let v2 = config2[1]?.[k];
-      if (v1 === v2 || !v1 && !v2) continue;
-      if (isNumber) {
-        if (k === "min") {
-          const theirMax = config2[1]?.["max"];
-          if (theirMax != null && v1 > theirMax) {
-            console.log("connection rejected: min > max", v1, theirMax);
-            return;
-          }
-          getCustomConfig()[k] = v1 == null ? v2 : v2 == null ? v1 : Math.max(v1, v2);
-          continue;
-        } else if (k === "max") {
-          const theirMin = config2[1]?.["min"];
-          if (theirMin != null && v1 < theirMin) {
-            console.log("connection rejected: max < min", v1, theirMin);
-            return;
-          }
-          getCustomConfig()[k] = v1 == null ? v2 : v2 == null ? v1 : Math.min(v1, v2);
-          continue;
-        } else if (k === "step") {
-          let step;
-          if (v1 == null) {
-            step = v2;
-          } else if (v2 == null) {
-            step = v1;
-          } else {
-            if (v1 < v2) {
-              const a = v2;
-              v2 = v1;
-              v1 = a;
-            }
-            if (v1 % v2) {
-              console.log(
-                "connection rejected: steps not divisible",
-                "current:",
-                v1,
-                "new:",
-                v2
-              );
-              return;
-            }
-            step = v1;
-          }
-          getCustomConfig()[k] = step;
-          continue;
-        }
-      }
-      console.log(`connection rejected: config ${k} values dont match`, v1, v2);
-      return;
-    }
-  }
-  if (customConfig || forceUpdate) {
-    if (customConfig) {
-      output.widget[CONFIG] = [config1[0], customConfig];
-    }
-    const widget = recreateWidget?.call(this);
-    if (widget) {
-      const min = widget.options.min;
-      const max = widget.options.max;
-      if (min != null && widget.value < min) widget.value = min;
-      if (max != null && widget.value > max) widget.value = max;
-      widget.callback(widget.value);
-    }
-  }
-  return { customConfig };
-}
-__name(mergeIfValid, "mergeIfValid");
-let useConversionSubmenusSetting;
-app.registerExtension({
-  name: "Comfy.WidgetInputs",
-  init() {
-    useConversionSubmenusSetting = app.ui.settings.addSetting({
-      id: "Comfy.NodeInputConversionSubmenus",
-      name: "In the node context menu, place the entries that convert between input/widget in sub-menus.",
-      type: "boolean",
-      defaultValue: true
-    });
-  },
-  async beforeRegisterNodeDef(nodeType, nodeData, app2) {
-    const origGetExtraMenuOptions = nodeType.prototype.getExtraMenuOptions;
-    nodeType.prototype.convertWidgetToInput = function(widget) {
-      const config = getConfig.call(this, widget.name) ?? [
-        widget.type,
-        widget.options || {}
-      ];
-      if (!isConvertibleWidget(widget, config)) return false;
-      if (widget.type?.startsWith(CONVERTED_TYPE)) return false;
-      convertToInput(this, widget, config);
-      return true;
-    };
-    nodeType.prototype.getExtraMenuOptions = function(_, options) {
-      const r = origGetExtraMenuOptions ? origGetExtraMenuOptions.apply(this, arguments) : void 0;
-      if (this.widgets) {
-        let toInput = [];
-        let toWidget = [];
-        for (const w of this.widgets) {
-          if (w.options?.forceInput) {
-            continue;
-          }
-          if (w.type === CONVERTED_TYPE) {
-            toWidget.push({
-              // @ts-expect-error never
-              content: `Convert ${w.name} to widget`,
-              callback: /* @__PURE__ */ __name(() => convertToWidget(this, w), "callback")
-            });
-          } else {
-            const config = getConfig.call(this, w.name) ?? [
-              w.type,
-              w.options || {}
-            ];
-            if (isConvertibleWidget(w, config)) {
-              toInput.push({
-                content: `Convert ${w.name} to input`,
-                callback: /* @__PURE__ */ __name(() => convertToInput(this, w, config), "callback")
-              });
-            }
-          }
-        }
-        if (toInput.length) {
-          if (useConversionSubmenusSetting.value) {
-            options.push({
-              content: "Convert Widget to Input",
-              submenu: {
-                options: toInput
-              }
-            });
-          } else {
-            options.push(...toInput, null);
-          }
-        }
-        if (toWidget.length) {
-          if (useConversionSubmenusSetting.value) {
-            options.push({
-              content: "Convert Input to Widget",
-              submenu: {
-                options: toWidget
-              }
-            });
-          } else {
-            options.push(...toWidget, null);
-          }
-        }
-      }
-      return r;
-    };
-    nodeType.prototype.onGraphConfigured = function() {
-      if (!this.inputs) return;
-      this.widgets ??= [];
-      for (const input of this.inputs) {
-        if (input.widget) {
-          if (!input.widget[GET_CONFIG]) {
-            input.widget[GET_CONFIG] = () => getConfig.call(this, input.widget.name);
-          }
-          if (input.widget.config) {
-            if (input.widget.config[0] instanceof Array) {
-              input.type = "COMBO";
-              const link = app2.graph.links[input.link];
-              if (link) {
-                link.type = input.type;
-              }
-            }
-            delete input.widget.config;
-          }
-          const w = this.widgets.find((w2) => w2.name === input.widget.name);
-          if (w) {
-            hideWidget(this, w);
-          } else {
-            convertToWidget(this, input);
-          }
-        }
-      }
-    };
-    const origOnNodeCreated = nodeType.prototype.onNodeCreated;
-    nodeType.prototype.onNodeCreated = function() {
-      const r = origOnNodeCreated ? origOnNodeCreated.apply(this) : void 0;
-      if (!app2.configuringGraph && this.widgets) {
-        for (const w of this.widgets) {
-          if (w?.options?.forceInput || w?.options?.defaultInput) {
-            const config = getConfig.call(this, w.name) ?? [
-              w.type,
-              w.options || {}
-            ];
-            convertToInput(this, w, config);
-          }
-        }
-      }
-      return r;
-    };
-    const origOnConfigure = nodeType.prototype.onConfigure;
-    nodeType.prototype.onConfigure = function() {
-      const r = origOnConfigure ? origOnConfigure.apply(this, arguments) : void 0;
-      if (!app2.configuringGraph && this.inputs) {
-        for (const input of this.inputs) {
-          if (input.widget && !input.widget[GET_CONFIG]) {
-            input.widget[GET_CONFIG] = () => (
-              // @ts-expect-error input.widget has unknown type
-              getConfig.call(this, input.widget.name)
-            );
-            const w = this.widgets.find((w2) => w2.name === input.widget.name);
-            if (w) {
-              hideWidget(this, w);
-            }
-          }
-        }
-      }
-      return r;
-    };
-    function isNodeAtPos(pos) {
-      for (const n of app2.graph.nodes) {
-        if (n.pos[0] === pos[0] && n.pos[1] === pos[1]) {
-          return true;
-        }
-      }
-      return false;
-    }
-    __name(isNodeAtPos, "isNodeAtPos");
-    const origOnInputDblClick = nodeType.prototype.onInputDblClick;
-    const ignoreDblClick = Symbol();
-    nodeType.prototype.onInputDblClick = function(slot) {
-      const r = origOnInputDblClick ? origOnInputDblClick.apply(this, arguments) : void 0;
-      const input = this.inputs[slot];
-      if (!input.widget || !input[ignoreDblClick]) {
-        if (!(input.type in ComfyWidgets) && !(input.widget?.[GET_CONFIG]?.()?.[0] instanceof Array)) {
-          return r;
-        }
-      }
-      const node = LiteGraph.createNode("PrimitiveNode");
-      app2.graph.add(node);
-      const pos = [
-        this.pos[0] - node.size[0] - 30,
-        this.pos[1]
-      ];
-      while (isNodeAtPos(pos)) {
-        pos[1] += LiteGraph.NODE_TITLE_HEIGHT;
-      }
-      node.pos = pos;
-      node.connect(0, this, slot);
-      node.title = input.name;
-      input[ignoreDblClick] = true;
-      setTimeout(() => {
-        delete input[ignoreDblClick];
-      }, 300);
-      return r;
-    };
-    const onConnectInput = nodeType.prototype.onConnectInput;
-    nodeType.prototype.onConnectInput = function(targetSlot, type, output, originNode, originSlot) {
-      const v = onConnectInput?.(this, arguments);
-      if (type !== "COMBO") return v;
-      if (originNode.outputs[originSlot].widget) return v;
-      const targetCombo = this.inputs[targetSlot].widget?.[GET_CONFIG]?.()?.[0];
-      if (!targetCombo || !(targetCombo instanceof Array)) return v;
-      const originConfig = originNode.constructor?.nodeData?.output?.[originSlot];
-      if (!originConfig || !isValidCombo(targetCombo, originConfig)) {
-        return false;
-      }
-      return v;
-    };
-  },
-  registerCustomNodes() {
-    LiteGraph.registerNodeType(
-      "PrimitiveNode",
-      Object.assign(PrimitiveNode, {
-        title: "Primitive"
-      })
-    );
-    PrimitiveNode.category = "utils";
-  }
-});
-window.comfyAPI = window.comfyAPI || {};
-window.comfyAPI.widgetInputs = window.comfyAPI.widgetInputs || {};
-window.comfyAPI.widgetInputs.getWidgetConfig = getWidgetConfig;
-window.comfyAPI.widgetInputs.convertToInput = convertToInput;
-window.comfyAPI.widgetInputs.setWidgetConfig = setWidgetConfig;
-window.comfyAPI.widgetInputs.mergeIfValid = mergeIfValid;
-export {
-  convertToInput,
-  getWidgetConfig,
-  mergeIfValid,
-  setWidgetConfig
-};
-//# sourceMappingURL=widgetInputs-Bvm3AgOa.js.map

web/extensions/core/colorPalette.js

@@ -1,3 +0,0 @@
-// Shim for extensions/core/colorPalette.ts
-export const defaultColorPalette = window.comfyAPI.colorPalette.defaultColorPalette;
-export const getColorPalette = window.comfyAPI.colorPalette.getColorPalette;

web/extensions/core/vintageClipboard.js

@@ -1,3 +0,0 @@
-// Shim for extensions/core/vintageClipboard.ts
-export const serialise = window.comfyAPI.vintageClipboard.serialise;
-export const deserialiseAndCreate = window.comfyAPI.vintageClipboard.deserialiseAndCreate;

web/index.html

@@ -6,8 +6,8 @@
     <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no">
     <link rel="stylesheet" type="text/css" href="user.css" />
     <link rel="stylesheet" type="text/css" href="materialdesignicons.min.css" />
-    <script type="module" crossorigin src="./assets/index-DIU5yZe9.js"></script>
-    <link rel="stylesheet" crossorigin href="./assets/index-1vLlIVor.css">
+    <script type="module" crossorigin src="./assets/index-DjNHn37O.js"></script>
+    <link rel="stylesheet" crossorigin href="./assets/index-t-sFBuUC.css">
   </head>
   <body class="litegraph grid">
     <div id="vue-app"></div>

web/scripts/utils.js

@@ -3,6 +3,7 @@ export const clone = window.comfyAPI.utils.clone;
 export const applyTextReplacements = window.comfyAPI.utils.applyTextReplacements;
 export const addStylesheet = window.comfyAPI.utils.addStylesheet;
 export const downloadBlob = window.comfyAPI.utils.downloadBlob;
+export const uploadFile = window.comfyAPI.utils.uploadFile;
 export const prop = window.comfyAPI.utils.prop;
 export const getStorageValue = window.comfyAPI.utils.getStorageValue;
 export const setStorageValue = window.comfyAPI.utils.setStorageValue;

web/scripts/widgets.js

@@ -1,6 +1,6 @@
 // Shim for scripts/widgets.ts
 export const updateControlWidgetLabel = window.comfyAPI.widgets.updateControlWidgetLabel;
+export const IS_CONTROL_WIDGET = window.comfyAPI.widgets.IS_CONTROL_WIDGET;
 export const addValueControlWidget = window.comfyAPI.widgets.addValueControlWidget;
 export const addValueControlWidgets = window.comfyAPI.widgets.addValueControlWidgets;
-export const initWidgets = window.comfyAPI.widgets.initWidgets;
 export const ComfyWidgets = window.comfyAPI.widgets.ComfyWidgets;