Bump ComfyUI version to v0.3.15

Changes release schedule from weekly to fortnightly.

.ci/update_windows/update.py

@@ -28,7 +28,7 @@ def pull(repo, remote_name='origin', branch='master'):
 
                 if repo.index.conflicts is not None:
                     for conflict in repo.index.conflicts:
-                        print('Conflicts found in:', conflict[0].path)
+                        print('Conflicts found in:', conflict[0].path)  # noqa: T201
                     raise AssertionError('Conflicts, ahhhhh!!')
 
                 user = repo.default_signature
@@ -49,18 +49,18 @@ repo_path = str(sys.argv[1])
 repo = pygit2.Repository(repo_path)
 ident = pygit2.Signature('comfyui', 'comfy@ui')
 try:
-    print("stashing current changes")
+    print("stashing current changes")  # noqa: T201
     repo.stash(ident)
 except KeyError:
-    print("nothing to stash")
+    print("nothing to stash")  # noqa: T201
 backup_branch_name = 'backup_branch_{}'.format(datetime.today().strftime('%Y-%m-%d_%H_%M_%S'))
-print("creating backup branch: {}".format(backup_branch_name))
+print("creating backup branch: {}".format(backup_branch_name))  # noqa: T201
 try:
     repo.branches.local.create(backup_branch_name, repo.head.peel())
 except:
     pass
 
-print("checking out master branch")
+print("checking out master branch")  # noqa: T201
 branch = repo.lookup_branch('master')
 if branch is None:
     ref = repo.lookup_reference('refs/remotes/origin/master')
@@ -72,7 +72,7 @@ else:
     ref = repo.lookup_reference(branch.name)
     repo.checkout(ref)
 
-print("pulling latest changes")
+print("pulling latest changes")  # noqa: T201
 pull(repo)
 
 if "--stable" in sys.argv:
@@ -94,7 +94,7 @@ if "--stable" in sys.argv:
     if latest_tag is not None:
         repo.checkout(latest_tag)
 
-print("Done!")
+print("Done!")  # noqa: T201
 
 self_update = True
 if len(sys.argv) > 2:

.github/workflows/stable-release.yml

@@ -12,7 +12,7 @@ on:
         description: 'CUDA version'
         required: true
         type: string
-        default: "124"
+        default: "126"
       python_minor:
         description: 'Python minor version'
         required: true
@@ -22,7 +22,7 @@ on:
         description: 'Python patch version'
         required: true
         type: string
-        default: "7"
+        default: "8"
 
 
 jobs:

.github/workflows/test-build.yml

@@ -18,7 +18,7 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
     steps:
       - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
@@ -28,4 +28,4 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install -r requirements.txt
+          pip install -r requirements.txt

.github/workflows/test-launch.yml

@@ -17,7 +17,7 @@ jobs:
         path: "ComfyUI"
     - uses: actions/setup-python@v4
       with:
-        python-version: '3.8'
+        python-version: '3.9'
     - name: Install requirements
       run: |
         python -m pip install --upgrade pip

.github/workflows/test-unit.yml

@@ -18,7 +18,7 @@ jobs:
     - name: Set up Python      
       uses: actions/setup-python@v4
       with:
-        python-version: '3.10'
+        python-version: '3.12'
     - name: Install requirements
       run: |
         python -m pip install --upgrade pip

.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_dependencies.yml

@@ -17,7 +17,7 @@ on:
         description: 'cuda version'
         required: true
         type: string
-        default: "124"
+        default: "126"
 
       python_minor:
         description: 'python minor version'
@@ -29,7 +29,7 @@ on:
         description: 'python patch version'
         required: true
         type: string
-        default: "7"
+        default: "8"
 #  push:
 #    branches:
 #      - master

.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

.github/workflows/windows_release_package.yml

@@ -7,7 +7,7 @@ on:
         description: 'cuda version'
         required: true
         type: string
-        default: "124"
+        default: "126"
 
       python_minor:
         description: 'python minor version'
@@ -19,7 +19,7 @@ on:
         description: 'python patch version'
         required: true
         type: string
-        default: "7"
+        default: "8"
 #  push:
 #    branches:
 #      - master

CODEOWNERS

@@ -15,9 +15,10 @@
 # Python web server
 /api_server/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata
 /app/ @yoland68 @robinjhuang @huchenlei @webfiltered @pythongosssss @ltdrdata
+/utils/ @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

README.md

@@ -38,10 +38,23 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
 
 ## Features
 - Nodes/graph/flowchart interface to experiment and create complex Stable Diffusion workflows without needing to code anything.
-- Fully supports SD1.x, SD2.x, [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/), [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/), [SD3](https://comfyanonymous.github.io/ComfyUI_examples/sd3/) and [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
-- [LTX-Video](https://comfyanonymous.github.io/ComfyUI_examples/ltxv/)
-- [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/)
-- [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/)
+- Image Models
+   - SD1.x, SD2.x,
+   - [SDXL](https://comfyanonymous.github.io/ComfyUI_examples/sdxl/), [SDXL Turbo](https://comfyanonymous.github.io/ComfyUI_examples/sdturbo/)
+   - [Stable Cascade](https://comfyanonymous.github.io/ComfyUI_examples/stable_cascade/)
+   - [SD3 and SD3.5](https://comfyanonymous.github.io/ComfyUI_examples/sd3/)
+   - Pixart Alpha and Sigma
+   - [AuraFlow](https://comfyanonymous.github.io/ComfyUI_examples/aura_flow/)
+   - [HunyuanDiT](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_dit/)
+   - [Flux](https://comfyanonymous.github.io/ComfyUI_examples/flux/)
+   - [Lumina Image 2.0](https://comfyanonymous.github.io/ComfyUI_examples/lumina2/)
+- Video Models
+   - [Stable Video Diffusion](https://comfyanonymous.github.io/ComfyUI_examples/video/)
+   - [Mochi](https://comfyanonymous.github.io/ComfyUI_examples/mochi/)
+   - [LTX-Video](https://comfyanonymous.github.io/ComfyUI_examples/ltxv/)
+   - [Hunyuan Video](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_video/)
+   - [Nvidia Cosmos](https://comfyanonymous.github.io/ComfyUI_examples/cosmos/)
+- [Stable Audio](https://comfyanonymous.github.io/ComfyUI_examples/audio/)
 - Asynchronous Queue system
 - Many optimizations: Only re-executes the parts of the workflow that changes between executions.
 - Smart memory management: can automatically run models on GPUs with as low as 1GB vram.
@@ -61,9 +74,6 @@ This ui will let you design and execute advanced stable diffusion pipelines usin
 - [GLIGEN](https://comfyanonymous.github.io/ComfyUI_examples/gligen/)
 - [Model Merging](https://comfyanonymous.github.io/ComfyUI_examples/model_merging/)
 - [LCM models and Loras](https://comfyanonymous.github.io/ComfyUI_examples/lcm/)
-- [SDXL Turbo](https://comfyanonymous.github.io/ComfyUI_examples/sdturbo/)
-- [AuraFlow](https://comfyanonymous.github.io/ComfyUI_examples/aura_flow/)
-- [HunyuanDiT](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_dit/)
 - Latent previews with [TAESD](#how-to-show-high-quality-previews)
 - Starts up very fast.
 - Works fully offline: will never download anything.
@@ -121,6 +131,8 @@ Simply download, extract with [7-Zip](https://7-zip.org) and run. Make sure you
 
 If you have trouble extracting it, right click the file -> properties -> unblock
 
+If you have a 50 series Blackwell card like a 5090 or 5080 see [this discussion thread](https://github.com/comfyanonymous/ComfyUI/discussions/6643)
+
 #### How do I share models between another UI and ComfyUI?
 
 See the [Config file](extra_model_paths.yaml.example) to set the search paths for models. In the standalone windows build you can find this file in the ComfyUI directory. Rename this file to extra_model_paths.yaml and edit it with your favorite text editor.
@@ -131,7 +143,7 @@ To run it on services like paperspace, kaggle or colab you can use my [Jupyter N
 
 ## Manual Install (Windows, Linux)
 
-Note that some dependencies do not yet support python 3.13 so using 3.12 is recommended.
+python 3.13 is supported but using 3.12 is recommended because some custom nodes and their dependencies might not support it yet.
 
 Git clone this repo.
 
@@ -143,21 +155,45 @@ Put your VAE in: models/vae
 ### AMD GPUs (Linux only)
 AMD users can install rocm and pytorch with pip if you don't have it already installed, this is the command to install the stable version:
 
-```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2```
+```pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.2.4```
 
-This is the command to install the nightly with ROCm 6.2 which might have some performance improvements:
+This is the command to install the nightly with ROCm 6.3 which might have some performance improvements:
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.2.4```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.3```
+
+### Intel GPUs (Windows and Linux)
+
+(Option 1) Intel Arc GPU users can install native PyTorch with torch.xpu support using pip (currently available in PyTorch nightly builds). More information can be found [here](https://pytorch.org/docs/main/notes/get_start_xpu.html)
+  
+1. To install PyTorch nightly, use the following command:
+
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/xpu```
+
+2. Launch ComfyUI by running `python main.py`
+
+
+(Option 2) Alternatively, Intel GPUs supported by Intel Extension for PyTorch (IPEX) can leverage IPEX for improved performance.
+
+1. For Intel® Arc™ A-Series Graphics utilizing IPEX, create a conda environment and use the commands below:
+
+```
+conda install libuv
+pip install torch==2.3.1.post0+cxx11.abi torchvision==0.18.1.post0+cxx11.abi torchaudio==2.3.1.post0+cxx11.abi intel-extension-for-pytorch==2.3.110.post0+xpu --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/us/ --extra-index-url https://pytorch-extension.intel.com/release-whl/stable/xpu/cn/
+```
+
+For other supported Intel GPUs with IPEX, visit [Installation](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu) for more information.
+
+Additional discussion and help can be found [here](https://github.com/comfyanonymous/ComfyUI/discussions/476).
 
 ### NVIDIA
 
 Nvidia users should install stable pytorch using this command:
 
-```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu124```
+```pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu126```
 
 This is the command to install pytorch nightly instead which might have performance improvements:
 
-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu124```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu126```
 
 #### Troubleshooting
 
@@ -177,17 +213,6 @@ After this you should have everything installed and can proceed to running Comfy
 
 ### Others:
 
-#### Intel GPUs
-
-Intel GPU support is available for all Intel GPUs supported by Intel's Extension for Pytorch (IPEX) with the support requirements listed in the [Installation](https://intel.github.io/intel-extension-for-pytorch/index.html#installation?platform=gpu) page. Choose your platform and method of install and follow the instructions. The steps are as follows:
-
-1. Start by installing the drivers or kernel listed or newer in the Installation page of IPEX linked above for Windows and Linux if needed.
-1. Follow the instructions to install [Intel's oneAPI Basekit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit-download.html) for your platform.
-1. Install the packages for IPEX using the instructions provided in the Installation page for your platform.
-1. Follow the [ComfyUI manual installation](#manual-install-windows-linux) instructions for Windows and Linux and run ComfyUI normally as described above after everything is installed.
-
-Additional discussion and help can be found [here](https://github.com/comfyanonymous/ComfyUI/discussions/476).
-
 #### Apple Mac silicon
 
 You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS version.
@@ -203,6 +228,16 @@ You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS ve
 
 ```pip install torch-directml``` Then you can launch ComfyUI with: ```python main.py --directml```
 
+#### Ascend NPUs
+
+For models compatible with Ascend Extension for PyTorch (torch_npu). To get started, ensure your environment meets the prerequisites outlined on the [installation](https://ascend.github.io/docs/sources/ascend/quick_install.html) page. Here's a step-by-step guide tailored to your platform and installation method:
+
+1. Begin by installing the recommended or newer kernel version for Linux as specified in the Installation page of torch-npu, if necessary.
+2. Proceed with the installation of Ascend Basekit, which includes the driver, firmware, and CANN, following the instructions provided for your specific platform.
+3. Next, install the necessary packages for torch-npu by adhering to the platform-specific instructions on the [Installation](https://ascend.github.io/docs/sources/pytorch/install.html#pytorch) page.
+4. Finally, adhere to the [ComfyUI manual installation](#manual-install-windows-linux) guide for Linux. Once all components are installed, you can run ComfyUI as described earlier.
+
+
 # Running
 
 ```python main.py```
@@ -258,6 +293,8 @@ Use `--tls-keyfile key.pem --tls-certfile cert.pem` to enable TLS/SSL, the app w
 
 ## Support and dev channel
 
+[Discord](https://comfy.org/discord): Try the #help or #feedback channels.
+
 [Matrix space: #comfyui_space:matrix.org](https://app.element.io/#/room/%23comfyui_space%3Amatrix.org) (it's like discord but open source).
 
 See also: [https://www.comfy.org/](https://www.comfy.org/)
@@ -274,7 +311,7 @@ For any bugs, issues, or feature requests related to the frontend, please use th
 
 The new frontend is now the default for ComfyUI. However, please note:
 
-1. The frontend in the main ComfyUI repository is updated weekly.
+1. The frontend in the main ComfyUI repository is updated fortnightly.
 2. Daily releases are available in the separate frontend repository.
 
 To use the most up-to-date frontend version:
@@ -291,7 +328,7 @@ To use the most up-to-date frontend version:
    --front-end-version Comfy-Org/ComfyUI_frontend@1.2.2
    ```
 
-This approach allows you to easily switch between the stable weekly release and the cutting-edge daily updates, or even specific versions for testing purposes.
+This approach allows you to easily switch between the stable fortnightly release and the cutting-edge daily updates, or even specific versions for testing purposes.
 
 ### Accessing the Legacy Frontend
 
@@ -308,4 +345,3 @@ This will use a snapshot of the legacy frontend preserved in the [ComfyUI Legacy
 ### Which GPU should I buy for this?
 
 [See this page for some recommendations](https://github.com/comfyanonymous/ComfyUI/wiki/Which-GPU-should-I-buy-for-ComfyUI)
-

api_server/routes/internal/internal_routes.py

@@ -1,9 +1,9 @@
 from aiohttp import web
 from typing import Optional
-from folder_paths import models_dir, user_directory, output_directory, folder_names_and_paths
-from api_server.services.file_service import FileService
+from folder_paths import folder_names_and_paths, get_directory_by_type
 from api_server.services.terminal_service import TerminalService
 import app.logger
+import os
 
 class InternalRoutes:
     '''
@@ -15,26 +15,10 @@ class InternalRoutes:
     def __init__(self, prompt_server):
         self.routes: web.RouteTableDef = web.RouteTableDef()
         self._app: Optional[web.Application] = None
-        self.file_service = FileService({
-            "models": models_dir,
-            "user": user_directory,
-            "output": output_directory
-        })
         self.prompt_server = prompt_server
         self.terminal_service = TerminalService(prompt_server)
 
     def setup_routes(self):
-        @self.routes.get('/files')
-        async def list_files(request):
-            directory_key = request.query.get('directory', '')
-            try:
-                file_list = self.file_service.list_files(directory_key)
-                return web.json_response({"files": file_list})
-            except ValueError as e:
-                return web.json_response({"error": str(e)}, status=400)
-            except Exception as e:
-                return web.json_response({"error": str(e)}, status=500)
-
         @self.routes.get('/logs')
         async def get_logs(request):
             return web.json_response("".join([(l["t"] + " - " + l["m"]) for l in app.logger.get_logs()]))
@@ -67,6 +51,20 @@ class InternalRoutes:
                 response[key] = folder_names_and_paths[key][0]
             return web.json_response(response)
 
+        @self.routes.get('/files/{directory_type}')
+        async def get_files(request: web.Request) -> web.Response:
+            directory_type = request.match_info['directory_type']
+            if directory_type not in ("output", "input", "temp"):
+                return web.json_response({"error": "Invalid directory type"}, status=400)
+
+            directory = get_directory_by_type(directory_type)
+            sorted_files = sorted(
+                (entry for entry in os.scandir(directory) if entry.is_file()),
+                key=lambda entry: -entry.stat().st_mtime
+            )
+            return web.json_response([entry.name for entry in sorted_files], status=200)
+
+
     def get_app(self):
         if self._app is None:
             self._app = web.Application()

api_server/services/file_service.py

@@ -1,13 +0,0 @@
-from typing import Dict, List, Optional
-from api_server.utils.file_operations import FileSystemOperations, FileSystemItem
-
-class FileService:
-    def __init__(self, allowed_directories: Dict[str, str], file_system_ops: Optional[FileSystemOperations] = None):
-        self.allowed_directories: Dict[str, str] = allowed_directories
-        self.file_system_ops: FileSystemOperations = file_system_ops or FileSystemOperations()
-
-    def list_files(self, directory_key: str) -> List[FileSystemItem]:
-        if directory_key not in self.allowed_directories:
-            raise ValueError("Invalid directory key")
-        directory_path: str = self.allowed_directories[directory_key]
-        return self.file_system_ops.walk_directory(directory_path)

api_server/services/terminal_service.py

@@ -25,10 +25,10 @@ class TerminalService:
     def update_size(self):
         columns, lines = self.get_terminal_size()
         changed = False
-        
+
         if columns != self.cols:
             self.cols = columns
-            changed = True 
+            changed = True
 
         if lines != self.rows:
             self.rows = lines
@@ -48,9 +48,9 @@ class TerminalService:
     def send_messages(self, entries):
         if not len(entries) or not len(self.subscriptions):
             return
-        
+
         new_size = self.update_size()
-        
+
         for client_id in self.subscriptions.copy(): # prevent: Set changed size during iteration
             if client_id not in self.server.sockets:
                 # Automatically unsub if the socket has disconnected

api_server/utils/file_operations.py

@@ -39,4 +39,4 @@ class FileSystemOperations:
                     "path": relative_path,
                     "type": "directory"
                 })
-        return file_list
+        return file_list

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 {}
 
@@ -51,4 +56,4 @@ class AppSettings():
             settings = self.get_settings(request)
             settings[setting_id] = await request.json()
             self.save_settings(request, settings)
-            return web.Response(status=200)
+            return web.Response(status=200)

app/custom_node_manager.py

@@ -0,0 +1,134 @@
+from __future__ import annotations
+
+import os
+import folder_paths
+import glob
+from aiohttp import web
+import json
+import logging
+from functools import lru_cache
+
+from utils.json_util import merge_json_recursive
+
+
+# Extra locale files to load into main.json
+EXTRA_LOCALE_FILES = [
+    "nodeDefs.json",
+    "commands.json",
+    "settings.json",
+]
+
+
+def safe_load_json_file(file_path: str) -> dict:
+    if not os.path.exists(file_path):
+        return {}
+
+    try:
+        with open(file_path, "r", encoding="utf-8") as f:
+            return json.load(f)
+    except json.JSONDecodeError:
+        logging.error(f"Error loading {file_path}")
+        return {}
+
+
+class CustomNodeManager:
+    @lru_cache(maxsize=1)
+    def build_translations(self):
+        """Load all custom nodes translations during initialization. Translations are
+        expected to be loaded from `locales/` folder.
+
+        The folder structure is expected to be the following:
+        - custom_nodes/
+            - custom_node_1/
+                - locales/
+                    - en/
+                        - main.json
+                        - commands.json
+                        - settings.json
+
+        returned translations are expected to be in the following format:
+        {
+            "en": {
+                "nodeDefs": {...},
+                "commands": {...},
+                "settings": {...},
+                ...{other main.json keys}
+            }
+        }
+        """
+
+        translations = {}
+
+        for folder in folder_paths.get_folder_paths("custom_nodes"):
+            # Sort glob results for deterministic ordering
+            for custom_node_dir in sorted(glob.glob(os.path.join(folder, "*/"))):
+                locales_dir = os.path.join(custom_node_dir, "locales")
+                if not os.path.exists(locales_dir):
+                    continue
+
+                for lang_dir in glob.glob(os.path.join(locales_dir, "*/")):
+                    lang_code = os.path.basename(os.path.dirname(lang_dir))
+
+                    if lang_code not in translations:
+                        translations[lang_code] = {}
+
+                    # Load main.json
+                    main_file = os.path.join(lang_dir, "main.json")
+                    node_translations = safe_load_json_file(main_file)
+
+                    # Load extra locale files
+                    for extra_file in EXTRA_LOCALE_FILES:
+                        extra_file_path = os.path.join(lang_dir, extra_file)
+                        key = extra_file.split(".")[0]
+                        json_data = safe_load_json_file(extra_file_path)
+                        if json_data:
+                            node_translations[key] = json_data
+
+                    if node_translations:
+                        translations[lang_code] = merge_json_recursive(
+                            translations[lang_code], node_translations
+                        )
+
+        return translations
+
+    def add_routes(self, routes, webapp, loadedModules):
+
+        @routes.get("/workflow_templates")
+        async def get_workflow_templates(request):
+            """Returns a web response that contains the map of custom_nodes names and their associated workflow templates. The ones without templates are omitted."""
+            files = [
+                file
+                for folder in folder_paths.get_folder_paths("custom_nodes")
+                for file in glob.glob(
+                    os.path.join(folder, "*/example_workflows/*.json")
+                )
+            ]
+            workflow_templates_dict = (
+                {}
+            )  # custom_nodes folder name -> example workflow names
+            for file in files:
+                custom_nodes_name = os.path.basename(
+                    os.path.dirname(os.path.dirname(file))
+                )
+                workflow_name = os.path.splitext(os.path.basename(file))[0]
+                workflow_templates_dict.setdefault(custom_nodes_name, []).append(
+                    workflow_name
+                )
+            return web.json_response(workflow_templates_dict)
+
+        # Serve workflow templates from custom nodes.
+        for module_name, module_dir in loadedModules:
+            workflows_dir = os.path.join(module_dir, "example_workflows")
+            if os.path.exists(workflows_dir):
+                webapp.add_routes(
+                    [
+                        web.static(
+                            "/api/workflow_templates/" + module_name, workflows_dir
+                        )
+                    ]
+                )
+
+        @routes.get("/i18n")
+        async def get_i18n(request):
+            """Returns translations from all custom nodes' locales folders."""
+            return web.json_response(self.build_translations())

app/logger.py

@@ -51,7 +51,7 @@ def on_flush(callback):
     if stderr_interceptor is not None:
         stderr_interceptor.on_flush(callback)
 
-def setup_logger(log_level: str = 'INFO', capacity: int = 300):
+def setup_logger(log_level: str = 'INFO', capacity: int = 300, use_stdout: bool = False):
     global logs
     if logs:
         return
@@ -70,4 +70,15 @@ def setup_logger(log_level: str = 'INFO', capacity: int = 300):
 
     stream_handler = logging.StreamHandler()
     stream_handler.setFormatter(logging.Formatter("%(message)s"))
+
+    if use_stdout:
+        # Only errors and critical to stderr
+        stream_handler.addFilter(lambda record: not record.levelno < logging.ERROR)
+
+        # Lesser to stdout
+        stdout_handler = logging.StreamHandler(sys.stdout)
+        stdout_handler.setFormatter(logging.Formatter("%(message)s"))
+        stdout_handler.addFilter(lambda record: record.levelno < logging.ERROR)
+        logger.addHandler(stdout_handler)
+
     logger.addHandler(stream_handler)

app/model_manager.py

@@ -1,10 +1,13 @@
 from __future__ import annotations
 
 import os
+import base64
+import json
 import time
 import logging
 import folder_paths
 import glob
+import comfy.utils
 from aiohttp import web
 from PIL import Image
 from io import BytesIO
@@ -59,13 +62,13 @@ class ModelFileManager:
             folder = folders[0][path_index]
             full_filename = os.path.join(folder, filename)
 
-            preview_files = self.get_model_previews(full_filename)
-            default_preview_file = preview_files[0] if len(preview_files) > 0 else None
-            if default_preview_file is None or not os.path.isfile(default_preview_file):
+            previews = self.get_model_previews(full_filename)
+            default_preview = previews[0] if len(previews) > 0 else None
+            if default_preview is None or (isinstance(default_preview, str) and not os.path.isfile(default_preview)):
                 return web.Response(status=404)
 
             try:
-                with Image.open(default_preview_file) as img:
+                with Image.open(default_preview) as img:
                     img_bytes = BytesIO()
                     img.save(img_bytes, format="WEBP")
                     img_bytes.seek(0)
@@ -143,7 +146,7 @@ class ModelFileManager:
 
         return [{"name": f, "pathIndex": pathIndex} for f in result], dirs, time.perf_counter()
 
-    def get_model_previews(self, filepath: str) -> list[str]:
+    def get_model_previews(self, filepath: str) -> list[str | BytesIO]:
         dirname = os.path.dirname(filepath)
 
         if not os.path.exists(dirname):
@@ -152,8 +155,10 @@ class ModelFileManager:
         basename = os.path.splitext(filepath)[0]
         match_files = glob.glob(f"{basename}.*", recursive=False)
         image_files = filter_files_content_types(match_files, "image")
+        safetensors_file = next(filter(lambda x: x.endswith(".safetensors"), match_files), None)
+        safetensors_metadata = {}
 
-        result: list[str] = []
+        result: list[str | BytesIO] = []
 
         for filename in image_files:
             _basename = os.path.splitext(filename)[0]
@@ -161,6 +166,18 @@ class ModelFileManager:
                 result.append(filename)
             if _basename == f"{basename}.preview":
                 result.append(filename)
+
+        if safetensors_file:
+            safetensors_filepath = os.path.join(dirname, safetensors_file)
+            header = comfy.utils.safetensors_header(safetensors_filepath, max_size=8*1024*1024)
+            if header:
+                safetensors_metadata = json.loads(header)
+        safetensors_images = safetensors_metadata.get("__metadata__", {}).get("ssmd_cover_images", None)
+        if safetensors_images:
+            safetensors_images = json.loads(safetensors_images)
+            for image in safetensors_images:
+                result.append(BytesIO(base64.b64decode(image)))
+
         return result
 
     def __exit__(self, exc_type, exc_value, traceback):

app/user_manager.py

@@ -38,8 +38,8 @@ class UserManager():
         if not os.path.exists(user_directory):
             os.makedirs(user_directory, exist_ok=True)
             if not args.multi_user:
-                print("****** User settings have been changed to be stored on the server instead of browser storage. ******")
-                print("****** For multi-user setups add the --multi-user CLI argument to enable multiple user profiles. ******")
+                logging.warning("****** User settings have been changed to be stored on the server instead of browser storage. ******")
+                logging.warning("****** For multi-user setups add the --multi-user CLI argument to enable multiple user profiles. ******")
 
         if args.multi_user:
             if os.path.isfile(self.get_users_file()):

comfy/cldm/cldm.py

@@ -160,7 +160,6 @@ class ControlNet(nn.Module):
             if isinstance(self.num_classes, int):
                 self.label_emb = nn.Embedding(num_classes, time_embed_dim)
             elif self.num_classes == "continuous":
-                print("setting up linear c_adm embedding layer")
                 self.label_emb = nn.Linear(1, time_embed_dim)
             elif self.num_classes == "sequential":
                 assert adm_in_channels is not None

comfy/cli_args.py

@@ -43,10 +43,11 @@ parser.add_argument("--tls-certfile", type=str, help="Path to TLS (SSL) certific
 parser.add_argument("--enable-cors-header", type=str, default=None, metavar="ORIGIN", nargs="?", const="*", help="Enable CORS (Cross-Origin Resource Sharing) with optional origin or allow all with default '*'.")
 parser.add_argument("--max-upload-size", type=float, default=100, help="Set the maximum upload size in MB.")
 
+parser.add_argument("--base-directory", type=str, default=None, help="Set the ComfyUI base directory for models, custom_nodes, input, output, temp, and user directories.")
 parser.add_argument("--extra-model-paths-config", type=str, default=None, metavar="PATH", nargs='+', action='append', help="Load one or more extra_model_paths.yaml files.")
-parser.add_argument("--output-directory", type=str, default=None, help="Set the ComfyUI output directory.")
-parser.add_argument("--temp-directory", type=str, default=None, help="Set the ComfyUI temp directory (default is in the ComfyUI directory).")
-parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input directory.")
+parser.add_argument("--output-directory", type=str, default=None, help="Set the ComfyUI output directory. Overrides --base-directory.")
+parser.add_argument("--temp-directory", type=str, default=None, help="Set the ComfyUI temp directory (default is in the ComfyUI directory). Overrides --base-directory.")
+parser.add_argument("--input-directory", type=str, default=None, help="Set the ComfyUI input directory. Overrides --base-directory.")
 parser.add_argument("--auto-launch", action="store_true", help="Automatically launch ComfyUI in the default browser.")
 parser.add_argument("--disable-auto-launch", action="store_true", help="Disable auto launching the browser.")
 parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.")
@@ -84,7 +85,8 @@ parser.add_argument("--force-channels-last", action="store_true", help="Force ch
 
 parser.add_argument("--directml", type=int, nargs="?", metavar="DIRECTML_DEVICE", const=-1, help="Use torch-directml.")
 
-parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize when loading models with Intel GPUs.")
+parser.add_argument("--oneapi-device-selector", type=str, default=None, metavar="SELECTOR_STRING", help="Sets the oneAPI device(s) this instance will use.")
+parser.add_argument("--disable-ipex-optimize", action="store_true", help="Disables ipex.optimize default when loading models with Intel's Extension for Pytorch.")
 
 class LatentPreviewMethod(enum.Enum):
     NoPreviews = "none"
@@ -121,7 +123,7 @@ vram_group.add_argument("--lowvram", action="store_true", help="Split the unet i
 vram_group.add_argument("--novram", action="store_true", help="When lowvram isn't enough.")
 vram_group.add_argument("--cpu", action="store_true", help="To use the CPU for everything (slow).")
 
-parser.add_argument("--reserve-vram", type=float, default=None, help="Set the amount of vram in GB you want to reserve for use by your OS/other software. By default some amount is reverved depending on your OS.")
+parser.add_argument("--reserve-vram", type=float, default=None, help="Set the amount of vram in GB you want to reserve for use by your OS/other software. By default some amount is reserved depending on your OS.")
 
 
 parser.add_argument("--default-hashing-function", type=str, choices=['md5', 'sha1', 'sha256', 'sha512'], default='sha256', help="Allows you to choose the hash function to use for duplicate filename / contents comparison. Default is sha256.")
@@ -140,6 +142,7 @@ parser.add_argument("--disable-all-custom-nodes", action="store_true", help="Dis
 parser.add_argument("--multi-user", action="store_true", help="Enables per-user storage.")
 
 parser.add_argument("--verbose", default='INFO', const='DEBUG', nargs="?", choices=['DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'], help='Set the logging level')
+parser.add_argument("--log-stdout", action="store_true", help="Send normal process output to stdout instead of stderr (default).")
 
 # The default built-in provider hosted under web/
 DEFAULT_VERSION_STRING = "comfyanonymous/ComfyUI@latest"
@@ -174,7 +177,9 @@ parser.add_argument(
     help="The local filesystem path to the directory where the frontend is located. Overrides --front-end-version.",
 )
 
-parser.add_argument("--user-directory", type=is_valid_directory, default=None, help="Set the ComfyUI user directory with an absolute path.")
+parser.add_argument("--user-directory", type=is_valid_directory, default=None, help="Set the ComfyUI user directory with an absolute path. Overrides --base-directory.")
+
+parser.add_argument("--enable-compress-response-body", action="store_true", help="Enable compressing response body.")
 
 if comfy.options.args_parsing:
     args = parser.parse_args()
@@ -186,3 +191,6 @@ if args.windows_standalone_build:
 
 if args.disable_auto_launch:
     args.auto_launch = False
+
+if args.force_fp16:
+    args.fp16_unet = True

comfy/clip_model.py

@@ -102,9 +102,10 @@ class CLIPTextModel_(torch.nn.Module):
         mask = None
         if attention_mask is not None:
             mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
-            mask = mask.masked_fill(mask.to(torch.bool), float("-inf"))
+            mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)
+
+        causal_mask = torch.full((x.shape[1], x.shape[1]), -torch.finfo(x.dtype).max, dtype=x.dtype, device=x.device).triu_(1)
 
-        causal_mask = torch.empty(x.shape[1], x.shape[1], dtype=x.dtype, device=x.device).fill_(float("-inf")).triu_(1)
         if mask is not None:
             mask += causal_mask
         else:

comfy/comfy_types/README.md

@@ -5,7 +5,7 @@ This module provides type hinting and concrete convenience types for node develo
 If cloned to the custom_nodes directory of ComfyUI, types can be imported using:
 
 ```python
-from comfy_types import IO, ComfyNodeABC, CheckLazyMixin
+from comfy.comfy_types import IO, ComfyNodeABC, CheckLazyMixin
 
 class ExampleNode(ComfyNodeABC):
     @classmethod

comfy/comfy_types/examples/example_nodes.py

@@ -1,12 +1,12 @@
-from comfy_types import IO, ComfyNodeABC, InputTypeDict
+from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict
 from inspect import cleandoc
 
 
 class ExampleNode(ComfyNodeABC):
     """An example node that just adds 1 to an input integer.
 
-    * Requires an IDE configured with analysis paths etc to be worth looking at.
-    * Not intended for use in ComfyUI.
+    * Requires a modern IDE to provide any benefit (detail: an IDE configured with analysis paths etc).
+    * This node is intended as an example for developers only.
     """
 
     DESCRIPTION = cleandoc(__doc__)

comfy/comfy_types/node_typing.py

@@ -66,13 +66,26 @@ class IO(StrEnum):
         b = frozenset(value.split(","))
         return not (b.issubset(a) or a.issubset(b))
 
+class RemoteInputOptions(TypedDict):
+    route: str
+    """The route to the remote source."""
+    refresh_button: bool
+    """Specifies whether to show a refresh button in the UI below the widget."""
+    control_after_refresh: Literal["first", "last"]
+    """Specifies the control after the refresh button is clicked. If "first", the first item will be automatically selected, and so on."""
+    timeout: int
+    """The maximum amount of time to wait for a response from the remote source in milliseconds."""
+    max_retries: int
+    """The maximum number of retries before aborting the request."""
+    refresh: int
+    """The TTL of the remote input's value in milliseconds. Specifies the interval at which the remote input's value is refreshed."""
 
 class InputTypeOptions(TypedDict):
     """Provides type hinting for the return type of the INPUT_TYPES node function.
 
     Due to IDE limitations with unions, for now all options are available for all types (e.g. `label_on` is hinted even when the type is not `IO.BOOLEAN`).
 
-    Comfy Docs: https://docs.comfy.org/essentials/custom_node_datatypes
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/datatypes
     """
 
     default: bool | str | float | int | list | tuple
@@ -113,6 +126,14 @@ class InputTypeOptions(TypedDict):
     # defaultVal: str
     dynamicPrompts: bool
     """Causes the front-end to evaluate dynamic prompts (``STRING``)"""
+    # class InputTypeCombo(InputTypeOptions):
+    image_upload: bool
+    """Specifies whether the input should have an image upload button and image preview attached to it. Requires that the input's name is `image`."""
+    image_folder: Literal["input", "output", "temp"]
+    """Specifies which folder to get preview images from if the input has the ``image_upload`` flag.
+    """
+    remote: RemoteInputOptions
+    """Specifies the configuration for a remote input."""
 
 
 class HiddenInputTypeDict(TypedDict):
@@ -133,7 +154,7 @@ class HiddenInputTypeDict(TypedDict):
 class InputTypeDict(TypedDict):
     """Provides type hinting for node INPUT_TYPES.
 
-    Comfy Docs: https://docs.comfy.org/essentials/custom_node_more_on_inputs
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/more_on_inputs
     """
 
     required: dict[str, tuple[IO, InputTypeOptions]]
@@ -143,14 +164,14 @@ class InputTypeDict(TypedDict):
     hidden: HiddenInputTypeDict
     """Offers advanced functionality and server-client communication.
 
-    Comfy Docs: https://docs.comfy.org/essentials/custom_node_more_on_inputs#hidden-inputs
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/more_on_inputs#hidden-inputs
     """
 
 
 class ComfyNodeABC(ABC):
     """Abstract base class for Comfy nodes.  Includes the names and expected types of attributes.
 
-    Comfy Docs: https://docs.comfy.org/essentials/custom_node_server_overview
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview
     """
 
     DESCRIPTION: str
@@ -167,7 +188,7 @@ class ComfyNodeABC(ABC):
     CATEGORY: str
     """The category of the node, as per the "Add Node" menu.
 
-    Comfy Docs: https://docs.comfy.org/essentials/custom_node_server_overview#category
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#category
     """
     EXPERIMENTAL: bool
     """Flags a node as experimental, informing users that it may change or not work as expected."""
@@ -181,9 +202,9 @@ class ComfyNodeABC(ABC):
 
         * Must include the ``required`` key, which describes all inputs that must be connected for the node to execute.
         * The ``optional`` key can be added to describe inputs which do not need to be connected.
-        * The ``hidden`` key offers some advanced functionality.  More info at: https://docs.comfy.org/essentials/custom_node_more_on_inputs#hidden-inputs
+        * The ``hidden`` key offers some advanced functionality.  More info at: https://docs.comfy.org/custom-nodes/backend/more_on_inputs#hidden-inputs
 
-        Comfy Docs: https://docs.comfy.org/essentials/custom_node_server_overview#input-types
+        Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#input-types
         """
         return {"required": {}}
 
@@ -198,7 +219,7 @@ class ComfyNodeABC(ABC):
 
     By default, a node is not considered an output. Set ``OUTPUT_NODE = True`` to specify that it is.
 
-    Comfy Docs: https://docs.comfy.org/essentials/custom_node_server_overview#output-node
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#output-node
     """
     INPUT_IS_LIST: bool
     """A flag indicating if this node implements the additional code necessary to deal with OUTPUT_IS_LIST nodes.
@@ -209,7 +230,7 @@ class ComfyNodeABC(ABC):
 
     A node can also override the default input behaviour and receive the whole list in a single call. This is done by setting a class attribute `INPUT_IS_LIST` to ``True``.
 
-    Comfy Docs: https://docs.comfy.org/essentials/custom_node_lists#list-processing
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lists#list-processing
     """
     OUTPUT_IS_LIST: tuple[bool]
     """A tuple indicating which node outputs are lists, but will be connected to nodes that expect individual items.
@@ -227,7 +248,7 @@ class ComfyNodeABC(ABC):
     the node should provide a class attribute `OUTPUT_IS_LIST`, which is a ``tuple[bool]``, of the same length as `RETURN_TYPES`,
     specifying which outputs which should be so treated.
 
-    Comfy Docs: https://docs.comfy.org/essentials/custom_node_lists#list-processing
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lists#list-processing
     """
 
     RETURN_TYPES: tuple[IO]
@@ -237,19 +258,19 @@ class ComfyNodeABC(ABC):
 
         RETURN_TYPES = (IO.INT, "INT", "CUSTOM_TYPE")
 
-    Comfy Docs: https://docs.comfy.org/essentials/custom_node_server_overview#return-types
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#return-types
     """
     RETURN_NAMES: tuple[str]
     """The output slot names for each item in `RETURN_TYPES`, e.g. ``RETURN_NAMES = ("count", "filter_string")``
 
-    Comfy Docs: https://docs.comfy.org/essentials/custom_node_server_overview#return-names
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#return-names
     """
     OUTPUT_TOOLTIPS: tuple[str]
     """A tuple of strings to use as tooltips for node outputs, one for each item in `RETURN_TYPES`."""
     FUNCTION: str
     """The name of the function to execute as a literal string, e.g. `FUNCTION = "execute"`
 
-    Comfy Docs: https://docs.comfy.org/essentials/custom_node_server_overview#function
+    Comfy Docs: https://docs.comfy.org/custom-nodes/backend/server_overview#function
     """
 
 
@@ -267,7 +288,7 @@ class CheckLazyMixin:
         Params should match the nodes execution ``FUNCTION`` (self, and all inputs by name).
         Will be executed repeatedly until it returns an empty list, or all requested items were already evaluated (and sent as params).
 
-        Comfy Docs: https://docs.comfy.org/essentials/custom_node_lazy_evaluation#defining-check-lazy-status
+        Comfy Docs: https://docs.comfy.org/custom-nodes/backend/lazy_evaluation#defining-check-lazy-status
         """
 
         need = [name for name in kwargs if kwargs[name] is None]

comfy/conds.py

@@ -3,9 +3,6 @@ import math
 import comfy.utils
 
 
-def lcm(a, b): #TODO: eventually replace by math.lcm (added in python3.9)
-    return abs(a*b) // math.gcd(a, b)
-
 class CONDRegular:
     def __init__(self, cond):
         self.cond = cond
@@ -46,7 +43,7 @@ class CONDCrossAttn(CONDRegular):
             if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen
                 return False
 
-            mult_min = lcm(s1[1], s2[1])
+            mult_min = math.lcm(s1[1], s2[1])
             diff = mult_min // min(s1[1], s2[1])
             if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much
                 return False
@@ -57,7 +54,7 @@ class CONDCrossAttn(CONDRegular):
         crossattn_max_len = self.cond.shape[1]
         for x in others:
             c = x.cond
-            crossattn_max_len = lcm(crossattn_max_len, c.shape[1])
+            crossattn_max_len = math.lcm(crossattn_max_len, c.shape[1])
             conds.append(c)
 
         out = []

comfy/controlnet.py

@@ -120,7 +120,7 @@ class ControlBase:
         if self.previous_controlnet is not None:
             out += self.previous_controlnet.get_models()
         return out
-    
+
     def get_extra_hooks(self):
         out = []
         if self.extra_hooks is not None:

comfy/diffusers_convert.py

@@ -4,105 +4,6 @@ import logging
 
 # conversion code from https://github.com/huggingface/diffusers/blob/main/scripts/convert_diffusers_to_original_stable_diffusion.py
 
-# =================#
-# UNet Conversion #
-# =================#
-
-unet_conversion_map = [
-    # (stable-diffusion, HF Diffusers)
-    ("time_embed.0.weight", "time_embedding.linear_1.weight"),
-    ("time_embed.0.bias", "time_embedding.linear_1.bias"),
-    ("time_embed.2.weight", "time_embedding.linear_2.weight"),
-    ("time_embed.2.bias", "time_embedding.linear_2.bias"),
-    ("input_blocks.0.0.weight", "conv_in.weight"),
-    ("input_blocks.0.0.bias", "conv_in.bias"),
-    ("out.0.weight", "conv_norm_out.weight"),
-    ("out.0.bias", "conv_norm_out.bias"),
-    ("out.2.weight", "conv_out.weight"),
-    ("out.2.bias", "conv_out.bias"),
-]
-
-unet_conversion_map_resnet = [
-    # (stable-diffusion, HF Diffusers)
-    ("in_layers.0", "norm1"),
-    ("in_layers.2", "conv1"),
-    ("out_layers.0", "norm2"),
-    ("out_layers.3", "conv2"),
-    ("emb_layers.1", "time_emb_proj"),
-    ("skip_connection", "conv_shortcut"),
-]
-
-unet_conversion_map_layer = []
-# hardcoded number of downblocks and resnets/attentions...
-# would need smarter logic for other networks.
-for i in range(4):
-    # loop over downblocks/upblocks
-
-    for j in range(2):
-        # loop over resnets/attentions for downblocks
-        hf_down_res_prefix = f"down_blocks.{i}.resnets.{j}."
-        sd_down_res_prefix = f"input_blocks.{3 * i + j + 1}.0."
-        unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
-
-        if i < 3:
-            # no attention layers in down_blocks.3
-            hf_down_atn_prefix = f"down_blocks.{i}.attentions.{j}."
-            sd_down_atn_prefix = f"input_blocks.{3 * i + j + 1}.1."
-            unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
-
-    for j in range(3):
-        # loop over resnets/attentions for upblocks
-        hf_up_res_prefix = f"up_blocks.{i}.resnets.{j}."
-        sd_up_res_prefix = f"output_blocks.{3 * i + j}.0."
-        unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
-
-        if i > 0:
-            # no attention layers in up_blocks.0
-            hf_up_atn_prefix = f"up_blocks.{i}.attentions.{j}."
-            sd_up_atn_prefix = f"output_blocks.{3 * i + j}.1."
-            unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
-
-    if i < 3:
-        # no downsample in down_blocks.3
-        hf_downsample_prefix = f"down_blocks.{i}.downsamplers.0.conv."
-        sd_downsample_prefix = f"input_blocks.{3 * (i + 1)}.0.op."
-        unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
-
-        # no upsample in up_blocks.3
-        hf_upsample_prefix = f"up_blocks.{i}.upsamplers.0."
-        sd_upsample_prefix = f"output_blocks.{3 * i + 2}.{1 if i == 0 else 2}."
-        unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
-
-hf_mid_atn_prefix = "mid_block.attentions.0."
-sd_mid_atn_prefix = "middle_block.1."
-unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
-
-for j in range(2):
-    hf_mid_res_prefix = f"mid_block.resnets.{j}."
-    sd_mid_res_prefix = f"middle_block.{2 * j}."
-    unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
-
-
-def convert_unet_state_dict(unet_state_dict):
-    # buyer beware: this is a *brittle* function,
-    # and correct output requires that all of these pieces interact in
-    # the exact order in which I have arranged them.
-    mapping = {k: k for k in unet_state_dict.keys()}
-    for sd_name, hf_name in unet_conversion_map:
-        mapping[hf_name] = sd_name
-    for k, v in mapping.items():
-        if "resnets" in k:
-            for sd_part, hf_part in unet_conversion_map_resnet:
-                v = v.replace(hf_part, sd_part)
-            mapping[k] = v
-    for k, v in mapping.items():
-        for sd_part, hf_part in unet_conversion_map_layer:
-            v = v.replace(hf_part, sd_part)
-        mapping[k] = v
-    new_state_dict = {v: unet_state_dict[k] for k, v in mapping.items()}
-    return new_state_dict
-
-
 # ================#
 # VAE Conversion #
 # ================#
@@ -213,6 +114,7 @@ textenc_pattern = re.compile("|".join(protected.keys()))
 # Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
 code2idx = {"q": 0, "k": 1, "v": 2}
 
+
 # This function exists because at the time of writing torch.cat can't do fp8 with cuda
 def cat_tensors(tensors):
     x = 0
@@ -229,6 +131,7 @@ def cat_tensors(tensors):
 
     return out
 
+
 def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""):
     new_state_dict = {}
     capture_qkv_weight = {}
@@ -284,5 +187,3 @@ def convert_text_enc_state_dict_v20(text_enc_dict, prefix=""):
 
 def convert_text_enc_state_dict(text_enc_dict):
     return text_enc_dict
-
-

comfy/extra_samplers/uni_pc.py

@@ -2,6 +2,7 @@
 
 import torch
 import math
+import logging
 
 from tqdm.auto import trange
 
@@ -79,7 +80,7 @@ class NoiseScheduleVP:
                     'linear' or 'cosine' for continuous-time DPMs.
         Returns:
             A wrapper object of the forward SDE (VP type).
-        
+
         ===============================================================
 
         Example:
@@ -207,7 +208,7 @@ def model_wrapper(
                 arXiv preprint arXiv:2202.00512 (2022).
             [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
                 arXiv preprint arXiv:2210.02303 (2022).
-    
+
         4. "score": marginal score function. (Trained by denoising score matching).
             Note that the score function and the noise prediction model follows a simple relationship:
             ```
@@ -225,7 +226,7 @@ def model_wrapper(
             The input `model` has the following format:
             ``
                 model(x, t_input, **model_kwargs) -> noise | x_start | v | score
-            `` 
+            ``
 
             The input `classifier_fn` has the following format:
             ``
@@ -239,12 +240,12 @@ def model_wrapper(
             The input `model` has the following format:
             ``
                 model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
-            `` 
+            ``
             And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
 
             [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
                 arXiv preprint arXiv:2207.12598 (2022).
-        
+
 
     The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
     or continuous-time labels (i.e. epsilon to T).
@@ -253,7 +254,7 @@ def model_wrapper(
     ``
         def model_fn(x, t_continuous) -> noise:
             t_input = get_model_input_time(t_continuous)
-            return noise_pred(model, x, t_input, **model_kwargs)         
+            return noise_pred(model, x, t_input, **model_kwargs)
     ``
     where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
 
@@ -358,7 +359,7 @@ class UniPC:
         max_val=1.,
         variant='bh1',
     ):
-        """Construct a UniPC. 
+        """Construct a UniPC.
 
         We support both data_prediction and noise_prediction.
         """
@@ -371,7 +372,7 @@ class UniPC:
 
     def dynamic_thresholding_fn(self, x0, t=None):
         """
-        The dynamic thresholding method. 
+        The dynamic thresholding method.
         """
         dims = x0.dim()
         p = self.dynamic_thresholding_ratio
@@ -403,7 +404,7 @@ class UniPC:
 
     def model_fn(self, x, t):
         """
-        Convert the model to the noise prediction model or the data prediction model. 
+        Convert the model to the noise prediction model or the data prediction model.
         """
         if self.predict_x0:
             return self.data_prediction_fn(x, t)
@@ -460,7 +461,7 @@ class UniPC:
 
     def denoise_to_zero_fn(self, x, s):
         """
-        Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization. 
+        Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization.
         """
         return self.data_prediction_fn(x, s)
 
@@ -474,7 +475,7 @@ class UniPC:
             return self.multistep_uni_pc_vary_update(x, model_prev_list, t_prev_list, t, order, **kwargs)
 
     def multistep_uni_pc_vary_update(self, x, model_prev_list, t_prev_list, t, order, use_corrector=True):
-        print(f'using unified predictor-corrector with order {order} (solver type: vary coeff)')
+        logging.info(f'using unified predictor-corrector with order {order} (solver type: vary coeff)')
         ns = self.noise_schedule
         assert order <= len(model_prev_list)
 
@@ -509,7 +510,7 @@ class UniPC:
         col = torch.ones_like(rks)
         for k in range(1, K + 1):
             C.append(col)
-            col = col * rks / (k + 1) 
+            col = col * rks / (k + 1)
         C = torch.stack(C, dim=1)
 
         if len(D1s) > 0:
@@ -518,7 +519,6 @@ class UniPC:
             A_p = C_inv_p
 
         if use_corrector:
-            print('using corrector')
             C_inv = torch.linalg.inv(C)
             A_c = C_inv
 
@@ -621,12 +621,12 @@ class UniPC:
             B_h = torch.expm1(hh)
         else:
             raise NotImplementedError()
-            
+
         for i in range(1, order + 1):
             R.append(torch.pow(rks, i - 1))
             b.append(h_phi_k * factorial_i / B_h)
             factorial_i *= (i + 1)
-            h_phi_k = h_phi_k / hh - 1 / factorial_i 
+            h_phi_k = h_phi_k / hh - 1 / factorial_i
 
         R = torch.stack(R)
         b = torch.tensor(b, device=x.device)
@@ -661,7 +661,7 @@ class UniPC:
 
             if x_t is None:
                 if use_predictor:
-                    pred_res = torch.einsum('k,bkchw->bchw', rhos_p, D1s)
+                    pred_res = torch.tensordot(D1s, rhos_p, dims=([1], [0]))  # torch.einsum('k,bkchw->bchw', rhos_p, D1s)
                 else:
                     pred_res = 0
                 x_t = x_t_ - expand_dims(alpha_t * B_h, dims) * pred_res
@@ -669,7 +669,7 @@ class UniPC:
             if use_corrector:
                 model_t = self.model_fn(x_t, t)
                 if D1s is not None:
-                    corr_res = torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s)
+                    corr_res = torch.tensordot(D1s, rhos_c[:-1], dims=([1], [0]))  # torch.einsum('k,bkchw->bchw', rhos_c[:-1], D1s)
                 else:
                     corr_res = 0
                 D1_t = (model_t - model_prev_0)
@@ -870,4 +870,4 @@ def sample_unipc(model, noise, sigmas, extra_args=None, callback=None, disable=F
         return x
 
 def sample_unipc_bh2(model, noise, sigmas, extra_args=None, callback=None, disable=False):
-    return sample_unipc(model, noise, sigmas, extra_args, callback, disable, variant='bh2')
+    return sample_unipc(model, noise, sigmas, extra_args, callback, disable, variant='bh2')

comfy/hooks.py

@@ -5,6 +5,7 @@ import math
 import torch
 import numpy as np
 import itertools
+import logging
 
 if TYPE_CHECKING:
     from comfy.model_patcher import ModelPatcher, PatcherInjection
@@ -15,130 +16,171 @@ 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):
         return self._strength_model * self.strength
-    
+
     @property
     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
@@ -146,127 +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
-    
-    def clone(self, subtype: Callable=None):
-        if subtype is None:
-            subtype = type(self)
-        c: ObjectPatchHook = super().clone(subtype)
+        self.object_patches = object_patches
+        self.hook_scope = hook_scope
+
+    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
-    
-    def clone(self, subtype: Callable=None):
-        if subtype is None:
-            subtype = type(self)
-        c: SetInjectionsHook = super().clone(subtype)
+        self.hook_scope = hook_scope
+
+    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:
@@ -274,36 +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():
@@ -335,7 +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:
@@ -364,10 +430,16 @@ class HookKeyframe:
         self.start_percent = float(start_percent)
         self.start_t = 999999999.9
         self.guarantee_steps = guarantee_steps
-    
+
+    def get_effective_guarantee_steps(self, max_sigma: torch.Tensor):
+        '''If keyframe starts before current sampling range (max_sigma), treat as 0.'''
+        if self.start_t > max_sigma:
+            return 0
+        return self.guarantee_steps
+
     def clone(self):
         c = HookKeyframe(strength=self.strength,
-                                start_percent=self.start_percent, guarantee_steps=self.guarantee_steps)
+                         start_percent=self.start_percent, guarantee_steps=self.guarantee_steps)
         c.start_t = self.start_t
         return c
 
@@ -394,7 +466,7 @@ class HookKeyframeGroup:
         self._current_strength = None
         self.curr_t = -1.
         self._set_first_as_current()
-    
+
     def add(self, keyframe: HookKeyframe):
         # add to end of list, then sort
         self.keyframes.append(keyframe)
@@ -406,33 +478,40 @@ class HookKeyframeGroup:
             self._current_keyframe = self.keyframes[0]
         else:
             self._current_keyframe = None
-    
+
+    def has_guarantee_steps(self):
+        for kf in self.keyframes:
+            if kf.guarantee_steps > 0:
+                return True
+        return False
+
     def has_index(self, index: int):
         return index >= 0 and index < len(self.keyframes)
 
     def is_empty(self):
         return len(self.keyframes) == 0
-    
+
     def clone(self):
         c = HookKeyframeGroup()
         for keyframe in self.keyframes:
             c.keyframes.append(keyframe.clone())
         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)
 
-    def prepare_current_keyframe(self, curr_t: float) -> bool:
+    def prepare_current_keyframe(self, curr_t: float, transformer_options: dict[str, torch.Tensor]) -> bool:
         if self.is_empty():
             return False
         if curr_t == self._curr_t:
             return False
+        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
-        if self._current_used_steps >= self._current_keyframe.guarantee_steps:
+        if self._current_used_steps >= self._current_keyframe.get_effective_guarantee_steps(max_sigma):
             # if has next index, loop through and see if need to switch
             if self.has_index(self._current_index+1):
                 for i in range(self._current_index+1, len(self.keyframes)):
@@ -445,7 +524,7 @@ class HookKeyframeGroup:
                         self._current_keyframe = eval_c
                         self._current_used_steps = 0
                         # if guarantee_steps greater than zero, stop searching for other keyframes
-                        if self._current_keyframe.guarantee_steps > 0:
+                        if self._current_keyframe.get_effective_guarantee_steps(max_sigma) > 0:
                             break
                     # if eval_c is outside the percent range, stop looking further
                     else: break
@@ -508,6 +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)
@@ -534,7 +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()
@@ -546,7 +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:
@@ -564,7 +654,7 @@ def load_hook_lora_for_models(model: 'ModelPatcher', clip: 'CLIP', lora: dict[st
     else:
         k = ()
         new_modelpatcher = None
-    
+
     if clip is not None:
         new_clip = clip.clone()
         k1 = new_clip.patcher.add_hook_patches(hook=hook, patches=loaded, strength_patch=strength_clip)
@@ -575,7 +665,7 @@ def load_hook_lora_for_models(model: 'ModelPatcher', clip: 'CLIP', lora: dict[st
     k1 = set(k1)
     for x in loaded:
         if (x not in k) and (x not in k1):
-            print(f"NOT LOADED {x}")
+            logging.warning(f"NOT LOADED {x}")
     return (new_modelpatcher, new_clip, hook_group)
 
 def _combine_hooks_from_values(c_dict: dict[str, HookGroup], values: dict[str, HookGroup], cache: dict[tuple[HookGroup, HookGroup], HookGroup]):
@@ -598,24 +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:
@@ -650,9 +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
@@ -664,9 +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
@@ -678,9 +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

@@ -40,7 +40,7 @@ def get_sigmas_polyexponential(n, sigma_min, sigma_max, rho=1., device='cpu'):
 def get_sigmas_vp(n, beta_d=19.9, beta_min=0.1, eps_s=1e-3, device='cpu'):
     """Constructs a continuous VP noise schedule."""
     t = torch.linspace(1, eps_s, n, device=device)
-    sigmas = torch.sqrt(torch.exp(beta_d * t ** 2 / 2 + beta_min * t) - 1)
+    sigmas = torch.sqrt(torch.special.expm1(beta_d * t ** 2 / 2 + beta_min * t))
     return append_zero(sigmas)
 
 
@@ -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):
@@ -1230,7 +1246,7 @@ def sample_dpmpp_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=None, dis
         nonlocal uncond_denoised
         uncond_denoised = args["uncond_denoised"]
         return args["denoised"]
-    
+
     model_options = extra_args.get("model_options", {}).copy()
     extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
 
@@ -1249,3 +1265,104 @@ def sample_dpmpp_2m_cfg_pp(model, x, sigmas, extra_args=None, callback=None, dis
         x = denoised + denoised_mix + torch.exp(-h) * x
         old_uncond_denoised = uncond_denoised
     return x
+
+@torch.no_grad()
+def res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None, eta=1., cfg_pp=False):
+    extra_args = {} if extra_args is None else extra_args
+    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()
+    phi1_fn = lambda t: torch.expm1(t) / t
+    phi2_fn = lambda t: (phi1_fn(t) - 1.0) / t
+
+    old_denoised = None
+    uncond_denoised = None
+    def post_cfg_function(args):
+        nonlocal uncond_denoised
+        uncond_denoised = args["uncond_denoised"]
+        return args["denoised"]
+
+    if cfg_pp:
+        model_options = extra_args.get("model_options", {}).copy()
+        extra_args["model_options"] = comfy.model_patcher.set_model_options_post_cfg_function(model_options, post_cfg_function, disable_cfg1_optimization=True)
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        sigma_down, sigma_up = get_ancestral_step(sigmas[i], sigmas[i + 1], eta=eta)
+        if callback is not None:
+            callback({"x": x, "i": i, "sigma": sigmas[i], "sigma_hat": sigmas[i], "denoised": denoised})
+        if sigma_down == 0 or old_denoised is None:
+            # Euler method
+            if cfg_pp:
+                d = to_d(x, sigmas[i], uncond_denoised)
+                x = denoised + d * sigma_down
+            else:
+                d = to_d(x, sigmas[i], denoised)
+                dt = sigma_down - sigmas[i]
+                x = x + d * dt
+        else:
+            # Second order multistep method in https://arxiv.org/pdf/2308.02157
+            t, t_next, t_prev = t_fn(sigmas[i]), t_fn(sigma_down), t_fn(sigmas[i - 1])
+            h = t_next - t
+            c2 = (t_prev - t) / h
+
+            phi1_val, phi2_val = phi1_fn(-h), phi2_fn(-h)
+            b1 = torch.nan_to_num(phi1_val - phi2_val / c2, nan=0.0)
+            b2 = torch.nan_to_num(phi2_val / c2, nan=0.0)
+
+            if cfg_pp:
+                x = x + (denoised - uncond_denoised)
+                x = sigma_fn(h) * x + h * (b1 * uncond_denoised + b2 * old_denoised)
+            else:
+                x = sigma_fn(h) * x + h * (b1 * denoised + b2 * old_denoised)
+
+        # Noise addition
+        if sigmas[i + 1] > 0:
+            x = x + noise_sampler(sigmas[i], sigmas[i + 1]) * s_noise * sigma_up
+
+        if cfg_pp:
+            old_denoised = uncond_denoised
+        else:
+            old_denoised = denoised
+    return x
+
+@torch.no_grad()
+def sample_res_multistep(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None):
+    return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=False)
+
+@torch.no_grad()
+def sample_res_multistep_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, s_noise=1., noise_sampler=None):
+    return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=0., cfg_pp=True)
+
+@torch.no_grad()
+def sample_res_multistep_ancestral(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+    return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=False)
+
+@torch.no_grad()
+def sample_res_multistep_ancestral_cfg_pp(model, x, sigmas, extra_args=None, callback=None, disable=None, eta=1., s_noise=1., noise_sampler=None):
+    return res_multistep(model, x, sigmas, extra_args=extra_args, callback=callback, disable=disable, s_noise=s_noise, noise_sampler=noise_sampler, eta=eta, cfg_pp=True)
+
+@torch.no_grad()
+def sample_gradient_estimation(model, x, sigmas, extra_args=None, callback=None, disable=None, ge_gamma=2.):
+    """Gradient-estimation sampler. Paper: https://openreview.net/pdf?id=o2ND9v0CeK"""
+    extra_args = {} if extra_args is None else extra_args
+    s_in = x.new_ones([x.shape[0]])
+    old_d = None
+
+    for i in trange(len(sigmas) - 1, disable=disable):
+        denoised = model(x, sigmas[i] * s_in, **extra_args)
+        d = to_d(x, sigmas[i], denoised)
+        if callback is not None:
+            callback({'x': x, 'i': i, 'sigma': sigmas[i], 'sigma_hat': sigmas[i], 'denoised': denoised})
+        dt = sigmas[i + 1] - sigmas[i]
+        if i == 0:
+            # Euler method
+            x = x + d * dt
+        else:
+            # Gradient estimation
+            d_bar = ge_gamma * d + (1 - ge_gamma) * old_d
+            x = x + d_bar * dt
+        old_d = d
+    return x

comfy/latent_formats.py

@@ -3,6 +3,7 @@ import torch
 class LatentFormat:
     scale_factor = 1.0
     latent_channels = 4
+    latent_dimensions = 2
     latent_rgb_factors = None
     latent_rgb_factors_bias = None
     taesd_decoder_name = None
@@ -143,6 +144,7 @@ class SD3(LatentFormat):
 
 class StableAudio1(LatentFormat):
     latent_channels = 64
+    latent_dimensions = 1
 
 class Flux(SD3):
     latent_channels = 16
@@ -178,6 +180,7 @@ class Flux(SD3):
 
 class Mochi(LatentFormat):
     latent_channels = 12
+    latent_dimensions = 3
 
     def __init__(self):
         self.scale_factor = 1.0
@@ -219,6 +222,8 @@ class Mochi(LatentFormat):
 
 class LTXV(LatentFormat):
     latent_channels = 128
+    latent_dimensions = 3
+
     def __init__(self):
         self.latent_rgb_factors = [
             [ 1.1202e-02, -6.3815e-04, -1.0021e-02],
@@ -355,6 +360,7 @@ class LTXV(LatentFormat):
 
 class HunyuanVideo(LatentFormat):
     latent_channels = 16
+    latent_dimensions = 3
     scale_factor = 0.476986
     latent_rgb_factors = [
         [-0.0395, -0.0331,  0.0445],
@@ -376,3 +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/aura/mmdit.py

@@ -381,7 +381,6 @@ class MMDiT(nn.Module):
         pe_new = pe_as_2d.squeeze(0).permute(1, 2, 0).flatten(0, 1)
         self.positional_encoding.data = pe_new.unsqueeze(0).contiguous()
         self.h_max, self.w_max = target_dim
-        print("PE extended to", target_dim)
 
     def pe_selection_index_based_on_dim(self, h, w):
         h_p, w_p = h // self.patch_size, w // self.patch_size

comfy/ldm/cascade/stage_b.py

@@ -138,7 +138,7 @@ class StageB(nn.Module):
     #     nn.init.normal_(self.pixels_mapper[2].weight, std=0.02)  # conditionings
     #     torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02)  # inputs
     #     nn.init.constant_(self.clf[1].weight, 0)  # outputs
-    # 
+    #
     #     # blocks
     #     for level_block in self.down_blocks + self.up_blocks:
     #         for block in level_block:
@@ -148,7 +148,7 @@ class StageB(nn.Module):
     #                 for layer in block.modules():
     #                     if isinstance(layer, nn.Linear):
     #                         nn.init.constant_(layer.weight, 0)
-    # 
+    #
     # def _init_weights(self, m):
     #     if isinstance(m, (nn.Conv2d, nn.Linear)):
     #         torch.nn.init.xavier_uniform_(m.weight)

comfy/ldm/cascade/stage_c.py

@@ -142,7 +142,7 @@ class StageC(nn.Module):
     #     nn.init.normal_(self.clip_img_mapper.weight, std=0.02)  # conditionings
     #     torch.nn.init.xavier_uniform_(self.embedding[1].weight, 0.02)  # inputs
     #     nn.init.constant_(self.clf[1].weight, 0)  # outputs
-    # 
+    #
     #     # blocks
     #     for level_block in self.down_blocks + self.up_blocks:
     #         for block in level_block:
@@ -152,7 +152,7 @@ class StageC(nn.Module):
     #                 for layer in block.modules():
     #                     if isinstance(layer, nn.Linear):
     #                         nn.init.constant_(layer.weight, 0)
-    # 
+    #
     # def _init_weights(self, m):
     #     if isinstance(m, (nn.Conv2d, nn.Linear)):
     #         torch.nn.init.xavier_uniform_(m.weight)

comfy/ldm/cosmos/blocks.py

@@ -0,0 +1,808 @@
+# 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!
+            # apply_rotary_pos_emb inlined
+            q_shape = q.shape
+            q = q.reshape(*q.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2)
+            q = rope_emb[..., 0] * q[..., 0] + rope_emb[..., 1] * q[..., 1]
+            q = q.movedim(-1, -2).reshape(*q_shape).to(x.dtype)
+
+            # apply_rotary_pos_emb inlined
+            k_shape = k.shape
+            k = k.reshape(*k.shape[:-1], 2, -1).movedim(-2, -1).unsqueeze(-2)
+            k = rope_emb[..., 0] * k[..., 0] + rope_emb[..., 1] * k[..., 1]
+            k = k.movedim(-1, -2).reshape(*k_shape).to(x.dtype)
+        return q, k, v
+
+    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)
+        out = optimized_attention(q, k, v, self.heads, skip_reshape=True, mask=mask, skip_output_reshape=True)
+        del q, k, v
+        out = rearrange(out, " b n s c -> s b (n c)")
+        return self.to_out(out)
+
+
+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,
+    ) -> torch.Tensor:
+        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 +1,377 @@
+# 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)
+        del x
+
+        # Height height transposed convolutions.
+        xll = F.conv_transpose3d(
+            xlll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+        )
+        del xlll
+
+        xll += F.conv_transpose3d(
+            xllh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+        )
+        del xllh
+
+        xlh = F.conv_transpose3d(
+            xlhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+        )
+        del xlhl
+
+        xlh += F.conv_transpose3d(
+            xlhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+        )
+        del xlhh
+
+        xhl = F.conv_transpose3d(
+            xhll, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+        )
+        del xhll
+
+        xhl += F.conv_transpose3d(
+            xhlh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+        )
+        del xhlh
+
+        xhh = F.conv_transpose3d(
+            xhhl, hl.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+        )
+        del xhhl
+
+        xhh += F.conv_transpose3d(
+            xhhh, hh.unsqueeze(2).unsqueeze(3), groups=g, stride=(1, 1, 2)
+        )
+        del xhhh
+
+        # Handles width transposed convolutions.
+        xl = F.conv_transpose3d(
+            xll, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)
+        )
+        del xll
+
+        xl += F.conv_transpose3d(
+            xlh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)
+        )
+        del xlh
+
+        xh = F.conv_transpose3d(
+            xhl, hl.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)
+        )
+        del xhl
+
+        xh += F.conv_transpose3d(
+            xhh, hh.unsqueeze(2).unsqueeze(4), groups=g, stride=(1, 2, 1)
+        )
+        del xhh
+
+        # Handles time axis transposed convolutions.
+        x = F.conv_transpose3d(
+            xl, hl.unsqueeze(3).unsqueeze(4), groups=g, stride=(2, 1, 1)
+        )
+        del xl
+
+        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 +1,112 @@
+# 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 rearrange
+
+
+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 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 +1,514 @@
+# 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, dtype=dtype)
+        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, dtype=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
+            kwargs["dtype"] = dtype
+            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, dtype=x_B_C_T_H_W.dtype)
+        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)
+        del inputs
+
+        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}"
+
+            if extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D is not None:
+                x += extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D
+            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,
+            )
+
+        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 +1,208 @@
+# 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, dtype=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, dtype=dtype)
+
+        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,
+        dtype=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,
+        dtype=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, dtype=dtype))
+        self.pos_emb_w = nn.Parameter(torch.empty(len_w, model_channels, device=device, dtype=dtype))
+        self.pos_emb_t = nn.Parameter(torch.empty(len_t, model_channels, device=device, dtype=dtype))
+
+    def generate_embeddings(self, B_T_H_W_C: torch.Size, fps=Optional[torch.Tensor], device=None, dtype=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, dtype=dtype)
+            emb_w_W = self.pos_emb_w[:W].to(device=device, dtype=dtype)
+            emb_t_T = self.pos_emb_t[:T].to(device=device, dtype=dtype)
+            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 +1,131 @@
+# 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
+import math
+
+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.debug(f"model={self.name}, num_parameters={num_parameters:,}")
+        logging.debug(
+            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]
+        in_dtype = z.dtype
+        mean = self.latent_mean.view(latent_ch, -1)
+        std = self.latent_std.view(latent_ch, -1)
+
+        mean = mean.repeat(1, math.ceil(latent_t / mean.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device)
+        std = std.repeat(1, math.ceil(latent_t / std.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_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)
+        std = self.latent_std.view(latent_ch, -1)
+
+        mean = mean.repeat(1, math.ceil(latent_t / mean.shape[-1]))[:, : latent_t].reshape([1, latent_ch, -1, 1, 1]).to(dtype=in_dtype, device=z.device)
+        std = std.repeat(1, math.ceil(latent_t / std.shape[-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/flux/layers.py

@@ -230,8 +230,7 @@ class SingleStreamBlock(nn.Module):
 
     def forward(self, x: Tensor, vec: Tensor, pe: Tensor, attn_mask=None) -> Tensor:
         mod, _ = self.modulation(vec)
-        x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift
-        qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
+        qkv, mlp = torch.split(self.linear1((1 + mod.scale) * self.pre_norm(x) + mod.shift), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
 
         q, k, v = qkv.view(qkv.shape[0], qkv.shape[1], 3, self.num_heads, -1).permute(2, 0, 3, 1, 4)
         q, k = self.norm(q, k, v)

comfy/ldm/flux/math.py

@@ -5,8 +5,15 @@ from torch import Tensor
 from comfy.ldm.modules.attention import optimized_attention
 import comfy.model_management
 
+
 def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None) -> Tensor:
-    q, k = apply_rope(q, k, pe)
+    q_shape = q.shape
+    k_shape = k.shape
+
+    q = q.float().reshape(*q.shape[:-1], -1, 1, 2)
+    k = k.float().reshape(*k.shape[:-1], -1, 1, 2)
+    q = (pe[..., 0] * q[..., 0] + pe[..., 1] * q[..., 1]).reshape(*q_shape).type_as(v)
+    k = (pe[..., 0] * k[..., 0] + pe[..., 1] * k[..., 1]).reshape(*k_shape).type_as(v)
 
     heads = q.shape[1]
     x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask)
@@ -15,7 +22,7 @@ def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None) -> Tensor:
 
 def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
     assert dim % 2 == 0
-    if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu():
+    if comfy.model_management.is_device_mps(pos.device) or comfy.model_management.is_intel_xpu() or comfy.model_management.is_directml_enabled():
         device = torch.device("cpu")
     else:
         device = pos.device

comfy/ldm/flux/model.py

@@ -109,9 +109,8 @@ class Flux(nn.Module):
         img = self.img_in(img)
         vec = self.time_in(timestep_embedding(timesteps, 256).to(img.dtype))
         if self.params.guidance_embed:
-            if guidance is None:
-                raise ValueError("Didn't get guidance strength for guidance distilled model.")
-            vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
+            if guidance is not None:
+                vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
 
         vec = vec + self.vector_in(y[:,:self.params.vec_in_dim])
         txt = self.txt_in(txt)
@@ -168,7 +167,7 @@ class Flux(nn.Module):
                 out = blocks_replace[("single_block", i)]({"img": img,
                                                            "vec": vec,
                                                            "pe": pe,
-                                                           "attn_mask": attn_mask}, 
+                                                           "attn_mask": attn_mask},
                                                           {"original_block": block_wrap})
                 img = out["img"]
             else:
@@ -186,7 +185,7 @@ class Flux(nn.Module):
         img = self.final_layer(img, vec)  # (N, T, patch_size ** 2 * out_channels)
         return img
 
-    def forward(self, x, timestep, context, y, guidance, control=None, transformer_options={}, **kwargs):
+    def forward(self, x, timestep, context, y, guidance=None, control=None, transformer_options={}, **kwargs):
         bs, c, h, w = x.shape
         patch_size = self.patch_size
         x = comfy.ldm.common_dit.pad_to_patch_size(x, (patch_size, patch_size))

comfy/ldm/hunyuan_video/model.py

@@ -240,9 +240,8 @@ class HunyuanVideo(nn.Module):
         vec = vec + self.vector_in(y[:, :self.params.vec_in_dim])
 
         if self.params.guidance_embed:
-            if guidance is None:
-                raise ValueError("Didn't get guidance strength for guidance distilled model.")
-            vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
+            if guidance is not None:
+                vec = vec + self.guidance_in(timestep_embedding(guidance, 256).to(img.dtype))
 
         if txt_mask is not None and not torch.is_floating_point(txt_mask):
             txt_mask = (txt_mask - 1).to(img.dtype) * torch.finfo(img.dtype).max
@@ -311,10 +310,10 @@ class HunyuanVideo(nn.Module):
             shape[i] = shape[i] // self.patch_size[i]
         img = img.reshape([img.shape[0]] + shape + [self.out_channels] + self.patch_size)
         img = img.permute(0, 4, 1, 5, 2, 6, 3, 7)
-        img = img.reshape(initial_shape)
+        img = img.reshape(initial_shape[0], self.out_channels, initial_shape[2], initial_shape[3], initial_shape[4])
         return img
 
-    def forward(self, x, timestep, context, y, guidance, attention_mask=None, control=None, transformer_options={}, **kwargs):
+    def forward(self, x, timestep, context, y, guidance=None, attention_mask=None, control=None, transformer_options={}, **kwargs):
         bs, c, t, h, w = x.shape
         patch_size = self.patch_size
         t_len = ((t + (patch_size[0] // 2)) // patch_size[0])

comfy/ldm/hydit/attn_layers.py

@@ -159,7 +159,7 @@ class CrossAttention(nn.Module):
 
         q = q.transpose(-2, -3).contiguous()        # q ->  B, L1, H, C - B, H, L1, C
         k = k.transpose(-2, -3).contiguous()      # k ->  B, L2, H, C - B, H, C, L2
-        v = v.transpose(-2, -3).contiguous() 
+        v = v.transpose(-2, -3).contiguous()
 
         context = optimized_attention(q, k, v, self.num_heads, skip_reshape=True, attn_precision=self.attn_precision)
 

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/lightricks/symmetric_patchifier.py

@@ -53,7 +53,7 @@ class Patchifier(ABC):
         grid_h = torch.arange(h, dtype=torch.float32, device=device)
         grid_w = torch.arange(w, dtype=torch.float32, device=device)
         grid_f = torch.arange(f, dtype=torch.float32, device=device)
-        grid = torch.meshgrid(grid_f, grid_h, grid_w)
+        grid = torch.meshgrid(grid_f, grid_h, grid_w, indexing='ij')
         grid = torch.stack(grid, dim=0)
         grid = grid.unsqueeze(0).repeat(batch_size, 1, 1, 1, 1)
 

comfy/ldm/lightricks/vae/causal_video_autoencoder.py

@@ -6,7 +6,9 @@ from einops import rearrange
 from typing import Optional, Tuple, Union
 from .conv_nd_factory import make_conv_nd, make_linear_nd
 from .pixel_norm import PixelNorm
-
+from ..model import PixArtAlphaCombinedTimestepSizeEmbeddings
+import comfy.ops
+ops = comfy.ops.disable_weight_init
 
 class Encoder(nn.Module):
     r"""
@@ -236,6 +238,7 @@ class Decoder(nn.Module):
         patch_size: int = 1,
         norm_layer: str = "group_norm",
         causal: bool = True,
+        timestep_conditioning: bool = False,
     ):
         super().__init__()
         self.patch_size = patch_size
@@ -250,6 +253,8 @@ class Decoder(nn.Module):
             block_params = block_params if isinstance(block_params, dict) else {}
             if block_name == "res_x_y":
                 output_channel = output_channel * block_params.get("multiplier", 2)
+            if block_name == "compress_all":
+                output_channel = output_channel * block_params.get("multiplier", 1)
 
         self.conv_in = make_conv_nd(
             dims,
@@ -276,6 +281,19 @@ class Decoder(nn.Module):
                     resnet_eps=1e-6,
                     resnet_groups=norm_num_groups,
                     norm_layer=norm_layer,
+                    inject_noise=block_params.get("inject_noise", False),
+                    timestep_conditioning=timestep_conditioning,
+                )
+            elif block_name == "attn_res_x":
+                block = UNetMidBlock3D(
+                    dims=dims,
+                    in_channels=input_channel,
+                    num_layers=block_params["num_layers"],
+                    resnet_groups=norm_num_groups,
+                    norm_layer=norm_layer,
+                    inject_noise=block_params.get("inject_noise", False),
+                    timestep_conditioning=timestep_conditioning,
+                    attention_head_dim=block_params["attention_head_dim"],
                 )
             elif block_name == "res_x_y":
                 output_channel = output_channel // block_params.get("multiplier", 2)
@@ -286,6 +304,8 @@ class Decoder(nn.Module):
                     eps=1e-6,
                     groups=norm_num_groups,
                     norm_layer=norm_layer,
+                    inject_noise=block_params.get("inject_noise", False),
+                    timestep_conditioning=False,
                 )
             elif block_name == "compress_time":
                 block = DepthToSpaceUpsample(
@@ -296,11 +316,13 @@ class Decoder(nn.Module):
                     dims=dims, in_channels=input_channel, stride=(1, 2, 2)
                 )
             elif block_name == "compress_all":
+                output_channel = output_channel // block_params.get("multiplier", 1)
                 block = DepthToSpaceUpsample(
                     dims=dims,
                     in_channels=input_channel,
                     stride=(2, 2, 2),
                     residual=block_params.get("residual", False),
+                    out_channels_reduction_factor=block_params.get("multiplier", 1),
                 )
             else:
                 raise ValueError(f"unknown layer: {block_name}")
@@ -323,27 +345,75 @@ class Decoder(nn.Module):
 
         self.gradient_checkpointing = False
 
+        self.timestep_conditioning = timestep_conditioning
+
+        if timestep_conditioning:
+            self.timestep_scale_multiplier = nn.Parameter(
+                torch.tensor(1000.0, dtype=torch.float32)
+            )
+            self.last_time_embedder = PixArtAlphaCombinedTimestepSizeEmbeddings(
+                output_channel * 2, 0, operations=ops,
+            )
+            self.last_scale_shift_table = nn.Parameter(torch.empty(2, output_channel))
+
     # def forward(self, sample: torch.FloatTensor, target_shape) -> torch.FloatTensor:
-    def forward(self, sample: torch.FloatTensor) -> torch.FloatTensor:
+    def forward(
+        self,
+        sample: torch.FloatTensor,
+        timestep: Optional[torch.Tensor] = None,
+    ) -> torch.FloatTensor:
         r"""The forward method of the `Decoder` class."""
-        # assert target_shape is not None, "target_shape must be provided"
+        batch_size = sample.shape[0]
 
         sample = self.conv_in(sample, causal=self.causal)
 
-        upscale_dtype = next(iter(self.up_blocks.parameters())).dtype
-
         checkpoint_fn = (
             partial(torch.utils.checkpoint.checkpoint, use_reentrant=False)
             if self.gradient_checkpointing and self.training
             else lambda x: x
         )
 
-        sample = sample.to(upscale_dtype)
+        scaled_timestep = None
+        if self.timestep_conditioning:
+            assert (
+                timestep is not None
+            ), "should pass timestep with timestep_conditioning=True"
+            scaled_timestep = timestep * self.timestep_scale_multiplier.to(dtype=sample.dtype, device=sample.device)
 
         for up_block in self.up_blocks:
-            sample = checkpoint_fn(up_block)(sample, causal=self.causal)
+            if self.timestep_conditioning and isinstance(up_block, UNetMidBlock3D):
+                sample = checkpoint_fn(up_block)(
+                    sample, causal=self.causal, timestep=scaled_timestep
+                )
+            else:
+                sample = checkpoint_fn(up_block)(sample, causal=self.causal)
 
         sample = self.conv_norm_out(sample)
+
+        if self.timestep_conditioning:
+            embedded_timestep = self.last_time_embedder(
+                timestep=scaled_timestep.flatten(),
+                resolution=None,
+                aspect_ratio=None,
+                batch_size=sample.shape[0],
+                hidden_dtype=sample.dtype,
+            )
+            embedded_timestep = embedded_timestep.view(
+                batch_size, embedded_timestep.shape[-1], 1, 1, 1
+            )
+            ada_values = self.last_scale_shift_table[
+                None, ..., None, None, None
+            ].to(device=sample.device, dtype=sample.dtype) + embedded_timestep.reshape(
+                batch_size,
+                2,
+                -1,
+                embedded_timestep.shape[-3],
+                embedded_timestep.shape[-2],
+                embedded_timestep.shape[-1],
+            )
+            shift, scale = ada_values.unbind(dim=1)
+            sample = sample * (1 + scale) + shift
+
         sample = self.conv_act(sample)
         sample = self.conv_out(sample, causal=self.causal)
 
@@ -379,12 +449,21 @@ class UNetMidBlock3D(nn.Module):
         resnet_eps: float = 1e-6,
         resnet_groups: int = 32,
         norm_layer: str = "group_norm",
+        inject_noise: bool = False,
+        timestep_conditioning: bool = False,
     ):
         super().__init__()
         resnet_groups = (
             resnet_groups if resnet_groups is not None else min(in_channels // 4, 32)
         )
 
+        self.timestep_conditioning = timestep_conditioning
+
+        if timestep_conditioning:
+            self.time_embedder = PixArtAlphaCombinedTimestepSizeEmbeddings(
+                in_channels * 4, 0, operations=ops,
+            )
+
         self.res_blocks = nn.ModuleList(
             [
                 ResnetBlock3D(
@@ -395,25 +474,48 @@ class UNetMidBlock3D(nn.Module):
                     groups=resnet_groups,
                     dropout=dropout,
                     norm_layer=norm_layer,
+                    inject_noise=inject_noise,
+                    timestep_conditioning=timestep_conditioning,
                 )
                 for _ in range(num_layers)
             ]
         )
 
     def forward(
-        self, hidden_states: torch.FloatTensor, causal: bool = True
+        self, hidden_states: torch.FloatTensor, causal: bool = True, timestep: Optional[torch.Tensor] = None
     ) -> torch.FloatTensor:
+        timestep_embed = None
+        if self.timestep_conditioning:
+            assert (
+                timestep is not None
+            ), "should pass timestep with timestep_conditioning=True"
+            batch_size = hidden_states.shape[0]
+            timestep_embed = self.time_embedder(
+                timestep=timestep.flatten(),
+                resolution=None,
+                aspect_ratio=None,
+                batch_size=batch_size,
+                hidden_dtype=hidden_states.dtype,
+            )
+            timestep_embed = timestep_embed.view(
+                batch_size, timestep_embed.shape[-1], 1, 1, 1
+            )
+
         for resnet in self.res_blocks:
-            hidden_states = resnet(hidden_states, causal=causal)
+            hidden_states = resnet(hidden_states, causal=causal, timestep=timestep_embed)
 
         return hidden_states
 
 
 class DepthToSpaceUpsample(nn.Module):
-    def __init__(self, dims, in_channels, stride, residual=False):
+    def __init__(
+        self, dims, in_channels, stride, residual=False, out_channels_reduction_factor=1
+    ):
         super().__init__()
         self.stride = stride
-        self.out_channels = math.prod(stride) * in_channels
+        self.out_channels = (
+            math.prod(stride) * in_channels // out_channels_reduction_factor
+        )
         self.conv = make_conv_nd(
             dims=dims,
             in_channels=in_channels,
@@ -423,8 +525,9 @@ class DepthToSpaceUpsample(nn.Module):
             causal=True,
         )
         self.residual = residual
+        self.out_channels_reduction_factor = out_channels_reduction_factor
 
-    def forward(self, x, causal: bool = True):
+    def forward(self, x, causal: bool = True, timestep: Optional[torch.Tensor] = None):
         if self.residual:
             # Reshape and duplicate the input to match the output shape
             x_in = rearrange(
@@ -434,7 +537,8 @@ class DepthToSpaceUpsample(nn.Module):
                 p2=self.stride[1],
                 p3=self.stride[2],
             )
-            x_in = x_in.repeat(1, math.prod(self.stride), 1, 1, 1)
+            num_repeat = math.prod(self.stride) // self.out_channels_reduction_factor
+            x_in = x_in.repeat(1, num_repeat, 1, 1, 1)
             if self.stride[0] == 2:
                 x_in = x_in[:, :, 1:, :, :]
         x = self.conv(x, causal=causal)
@@ -451,7 +555,6 @@ class DepthToSpaceUpsample(nn.Module):
             x = x + x_in
         return x
 
-
 class LayerNorm(nn.Module):
     def __init__(self, dim, eps, elementwise_affine=True) -> None:
         super().__init__()
@@ -486,11 +589,14 @@ class ResnetBlock3D(nn.Module):
         groups: int = 32,
         eps: float = 1e-6,
         norm_layer: str = "group_norm",
+        inject_noise: bool = False,
+        timestep_conditioning: bool = False,
     ):
         super().__init__()
         self.in_channels = in_channels
         out_channels = in_channels if out_channels is None else out_channels
         self.out_channels = out_channels
+        self.inject_noise = inject_noise
 
         if norm_layer == "group_norm":
             self.norm1 = nn.GroupNorm(
@@ -513,6 +619,9 @@ class ResnetBlock3D(nn.Module):
             causal=True,
         )
 
+        if inject_noise:
+            self.per_channel_scale1 = nn.Parameter(torch.zeros((in_channels, 1, 1)))
+
         if norm_layer == "group_norm":
             self.norm2 = nn.GroupNorm(
                 num_groups=groups, num_channels=out_channels, eps=eps, affine=True
@@ -534,6 +643,9 @@ class ResnetBlock3D(nn.Module):
             causal=True,
         )
 
+        if inject_noise:
+            self.per_channel_scale2 = nn.Parameter(torch.zeros((in_channels, 1, 1)))
+
         self.conv_shortcut = (
             make_linear_nd(
                 dims=dims, in_channels=in_channels, out_channels=out_channels
@@ -548,29 +660,84 @@ class ResnetBlock3D(nn.Module):
             else nn.Identity()
         )
 
+        self.timestep_conditioning = timestep_conditioning
+
+        if timestep_conditioning:
+            self.scale_shift_table = nn.Parameter(
+                torch.randn(4, in_channels) / in_channels**0.5
+            )
+
+    def _feed_spatial_noise(
+        self, hidden_states: torch.FloatTensor, per_channel_scale: torch.FloatTensor
+    ) -> torch.FloatTensor:
+        spatial_shape = hidden_states.shape[-2:]
+        device = hidden_states.device
+        dtype = hidden_states.dtype
+
+        # similar to the "explicit noise inputs" method in style-gan
+        spatial_noise = torch.randn(spatial_shape, device=device, dtype=dtype)[None]
+        scaled_noise = (spatial_noise * per_channel_scale)[None, :, None, ...]
+        hidden_states = hidden_states + scaled_noise
+
+        return hidden_states
+
     def forward(
         self,
         input_tensor: torch.FloatTensor,
         causal: bool = True,
+        timestep: Optional[torch.Tensor] = None,
     ) -> torch.FloatTensor:
         hidden_states = input_tensor
+        batch_size = hidden_states.shape[0]
 
         hidden_states = self.norm1(hidden_states)
+        if self.timestep_conditioning:
+            assert (
+                timestep is not None
+            ), "should pass timestep with timestep_conditioning=True"
+            ada_values = self.scale_shift_table[
+                None, ..., None, None, None
+            ].to(device=hidden_states.device, dtype=hidden_states.dtype) + timestep.reshape(
+                batch_size,
+                4,
+                -1,
+                timestep.shape[-3],
+                timestep.shape[-2],
+                timestep.shape[-1],
+            )
+            shift1, scale1, shift2, scale2 = ada_values.unbind(dim=1)
+
+            hidden_states = hidden_states * (1 + scale1) + shift1
 
         hidden_states = self.non_linearity(hidden_states)
 
         hidden_states = self.conv1(hidden_states, causal=causal)
 
+        if self.inject_noise:
+            hidden_states = self._feed_spatial_noise(
+                hidden_states, self.per_channel_scale1.to(device=hidden_states.device, dtype=hidden_states.dtype)
+            )
+
         hidden_states = self.norm2(hidden_states)
 
+        if self.timestep_conditioning:
+            hidden_states = hidden_states * (1 + scale2) + shift2
+
         hidden_states = self.non_linearity(hidden_states)
 
         hidden_states = self.dropout(hidden_states)
 
         hidden_states = self.conv2(hidden_states, causal=causal)
 
+        if self.inject_noise:
+            hidden_states = self._feed_spatial_noise(
+                hidden_states, self.per_channel_scale2.to(device=hidden_states.device, dtype=hidden_states.dtype)
+            )
+
         input_tensor = self.norm3(input_tensor)
 
+        batch_size = input_tensor.shape[0]
+
         input_tensor = self.conv_shortcut(input_tensor)
 
         output_tensor = input_tensor + hidden_states
@@ -634,33 +801,71 @@ class processor(nn.Module):
         return (x - self.get_buffer("mean-of-means").view(1, -1, 1, 1, 1).to(x)) / self.get_buffer("std-of-means").view(1, -1, 1, 1, 1).to(x)
 
 class VideoVAE(nn.Module):
-    def __init__(self):
+    def __init__(self, version=0):
         super().__init__()
-        config = {
-            "_class_name": "CausalVideoAutoencoder",
-            "dims": 3,
-            "in_channels": 3,
-            "out_channels": 3,
-            "latent_channels": 128,
-            "blocks": [
-                ["res_x", 4],
-                ["compress_all", 1],
-                ["res_x_y", 1],
-                ["res_x", 3],
-                ["compress_all", 1],
-                ["res_x_y", 1],
-                ["res_x", 3],
-                ["compress_all", 1],
-                ["res_x", 3],
-                ["res_x", 4],
-            ],
-            "scaling_factor": 1.0,
-            "norm_layer": "pixel_norm",
-            "patch_size": 4,
-            "latent_log_var": "uniform",
-            "use_quant_conv": False,
-            "causal_decoder": False,
-        }
+
+        if version == 0:
+            config = {
+                "_class_name": "CausalVideoAutoencoder",
+                "dims": 3,
+                "in_channels": 3,
+                "out_channels": 3,
+                "latent_channels": 128,
+                "blocks": [
+                    ["res_x", 4],
+                    ["compress_all", 1],
+                    ["res_x_y", 1],
+                    ["res_x", 3],
+                    ["compress_all", 1],
+                    ["res_x_y", 1],
+                    ["res_x", 3],
+                    ["compress_all", 1],
+                    ["res_x", 3],
+                    ["res_x", 4],
+                ],
+                "scaling_factor": 1.0,
+                "norm_layer": "pixel_norm",
+                "patch_size": 4,
+                "latent_log_var": "uniform",
+                "use_quant_conv": False,
+                "causal_decoder": False,
+            }
+        else:
+            config = {
+                "_class_name": "CausalVideoAutoencoder",
+                "dims": 3,
+                "in_channels": 3,
+                "out_channels": 3,
+                "latent_channels": 128,
+                "decoder_blocks": [
+                    ["res_x", {"num_layers": 5, "inject_noise": True}],
+                    ["compress_all", {"residual": True, "multiplier": 2}],
+                    ["res_x", {"num_layers": 6, "inject_noise": True}],
+                    ["compress_all", {"residual": True, "multiplier": 2}],
+                    ["res_x", {"num_layers": 7, "inject_noise": True}],
+                    ["compress_all", {"residual": True, "multiplier": 2}],
+                    ["res_x", {"num_layers": 8, "inject_noise": False}]
+                ],
+                "encoder_blocks": [
+                    ["res_x", {"num_layers": 4}],
+                    ["compress_all", {}],
+                    ["res_x_y", 1],
+                    ["res_x", {"num_layers": 3}],
+                    ["compress_all", {}],
+                    ["res_x_y", 1],
+                    ["res_x", {"num_layers": 3}],
+                    ["compress_all", {}],
+                    ["res_x", {"num_layers": 3}],
+                    ["res_x", {"num_layers": 4}]
+                ],
+                "scaling_factor": 1.0,
+                "norm_layer": "pixel_norm",
+                "patch_size": 4,
+                "latent_log_var": "uniform",
+                "use_quant_conv": False,
+                "causal_decoder": False,
+                "timestep_conditioning": True,
+            }
 
         double_z = config.get("double_z", True)
         latent_log_var = config.get(
@@ -671,7 +876,7 @@ class VideoVAE(nn.Module):
             dims=config["dims"],
             in_channels=config.get("in_channels", 3),
             out_channels=config["latent_channels"],
-            blocks=config.get("encoder_blocks", config.get("blocks")),
+            blocks=config.get("encoder_blocks", config.get("encoder_blocks", config.get("blocks"))),
             patch_size=config.get("patch_size", 1),
             latent_log_var=latent_log_var,
             norm_layer=config.get("norm_layer", "group_norm"),
@@ -681,18 +886,22 @@ class VideoVAE(nn.Module):
             dims=config["dims"],
             in_channels=config["latent_channels"],
             out_channels=config.get("out_channels", 3),
-            blocks=config.get("decoder_blocks", config.get("blocks")),
+            blocks=config.get("decoder_blocks", config.get("decoder_blocks", config.get("blocks"))),
             patch_size=config.get("patch_size", 1),
             norm_layer=config.get("norm_layer", "group_norm"),
             causal=config.get("causal_decoder", False),
+            timestep_conditioning=config.get("timestep_conditioning", False),
         )
 
+        self.timestep_conditioning = config.get("timestep_conditioning", False)
         self.per_channel_statistics = processor()
 
     def encode(self, x):
         means, logvar = torch.chunk(self.encoder(x), 2, dim=1)
         return self.per_channel_statistics.normalize(means)
 
-    def decode(self, x):
-        return self.decoder(self.per_channel_statistics.un_normalize(x))
+    def decode(self, x, timestep=0.05, noise_scale=0.025):
+        if self.timestep_conditioning: #TODO: seed
+            x = torch.randn_like(x) * noise_scale + (1.0 - noise_scale) * x
+        return self.decoder(self.per_channel_statistics.un_normalize(x), timestep=timestep)
 

comfy/ldm/lumina/model.py

@@ -0,0 +1,622 @@
+# Code from: https://github.com/Alpha-VLLM/Lumina-Image-2.0/blob/main/models/model.py
+from __future__ import annotations
+
+from typing import List, Optional, Tuple
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import comfy.ldm.common_dit
+
+from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, RMSNorm
+from comfy.ldm.modules.attention import optimized_attention_masked
+from comfy.ldm.flux.layers import EmbedND
+
+
+def modulate(x, scale):
+    return x * (1 + scale.unsqueeze(1))
+
+#############################################################################
+#                               Core NextDiT Model                              #
+#############################################################################
+
+
+class JointAttention(nn.Module):
+    """Multi-head attention module."""
+
+    def __init__(
+        self,
+        dim: int,
+        n_heads: int,
+        n_kv_heads: Optional[int],
+        qk_norm: bool,
+        operation_settings={},
+    ):
+        """
+        Initialize the Attention module.
+
+        Args:
+            dim (int): Number of input dimensions.
+            n_heads (int): Number of heads.
+            n_kv_heads (Optional[int]): Number of kv heads, if using GQA.
+
+        """
+        super().__init__()
+        self.n_kv_heads = n_heads if n_kv_heads is None else n_kv_heads
+        self.n_local_heads = n_heads
+        self.n_local_kv_heads = self.n_kv_heads
+        self.n_rep = self.n_local_heads // self.n_local_kv_heads
+        self.head_dim = dim // n_heads
+
+        self.qkv = operation_settings.get("operations").Linear(
+            dim,
+            (n_heads + self.n_kv_heads + self.n_kv_heads) * self.head_dim,
+            bias=False,
+            device=operation_settings.get("device"),
+            dtype=operation_settings.get("dtype"),
+        )
+        self.out = operation_settings.get("operations").Linear(
+            n_heads * self.head_dim,
+            dim,
+            bias=False,
+            device=operation_settings.get("device"),
+            dtype=operation_settings.get("dtype"),
+        )
+
+        if qk_norm:
+            self.q_norm = RMSNorm(self.head_dim, elementwise_affine=True, **operation_settings)
+            self.k_norm = RMSNorm(self.head_dim, elementwise_affine=True, **operation_settings)
+        else:
+            self.q_norm = self.k_norm = nn.Identity()
+
+    @staticmethod
+    def apply_rotary_emb(
+        x_in: torch.Tensor,
+        freqs_cis: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Apply rotary embeddings to input tensors using the given frequency
+        tensor.
+
+        This function applies rotary embeddings to the given query 'xq' and
+        key 'xk' tensors using the provided frequency tensor 'freqs_cis'. The
+        input tensors are reshaped as complex numbers, and the frequency tensor
+        is reshaped for broadcasting compatibility. The resulting tensors
+        contain rotary embeddings and are returned as real tensors.
+
+        Args:
+            x_in (torch.Tensor): Query or Key tensor to apply rotary embeddings.
+            freqs_cis (torch.Tensor): Precomputed frequency tensor for complex
+                exponentials.
+
+        Returns:
+            Tuple[torch.Tensor, torch.Tensor]: Tuple of modified query tensor
+                and key tensor with rotary embeddings.
+        """
+
+        t_ = x_in.reshape(*x_in.shape[:-1], -1, 1, 2)
+        t_out = freqs_cis[..., 0] * t_[..., 0] + freqs_cis[..., 1] * t_[..., 1]
+        return t_out.reshape(*x_in.shape)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_mask: torch.Tensor,
+        freqs_cis: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+
+        Args:
+            x:
+            x_mask:
+            freqs_cis:
+
+        Returns:
+
+        """
+        bsz, seqlen, _ = x.shape
+
+        xq, xk, xv = torch.split(
+            self.qkv(x),
+            [
+                self.n_local_heads * self.head_dim,
+                self.n_local_kv_heads * self.head_dim,
+                self.n_local_kv_heads * self.head_dim,
+            ],
+            dim=-1,
+        )
+        xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+        xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
+        xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
+
+        xq = self.q_norm(xq)
+        xk = self.k_norm(xk)
+
+        xq = JointAttention.apply_rotary_emb(xq, freqs_cis=freqs_cis)
+        xk = JointAttention.apply_rotary_emb(xk, freqs_cis=freqs_cis)
+
+        n_rep = self.n_local_heads // self.n_local_kv_heads
+        if n_rep >= 1:
+            xk = xk.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)
+            xv = xv.unsqueeze(3).repeat(1, 1, 1, n_rep, 1).flatten(2, 3)
+        output = optimized_attention_masked(xq.movedim(1, 2), xk.movedim(1, 2), xv.movedim(1, 2), self.n_local_heads, x_mask, skip_reshape=True)
+
+        return self.out(output)
+
+
+class FeedForward(nn.Module):
+    def __init__(
+        self,
+        dim: int,
+        hidden_dim: int,
+        multiple_of: int,
+        ffn_dim_multiplier: Optional[float],
+        operation_settings={},
+    ):
+        """
+        Initialize the FeedForward module.
+
+        Args:
+            dim (int): Input dimension.
+            hidden_dim (int): Hidden dimension of the feedforward layer.
+            multiple_of (int): Value to ensure hidden dimension is a multiple
+                of this value.
+            ffn_dim_multiplier (float, optional): Custom multiplier for hidden
+                dimension. Defaults to None.
+
+        """
+        super().__init__()
+        # custom dim factor multiplier
+        if ffn_dim_multiplier is not None:
+            hidden_dim = int(ffn_dim_multiplier * hidden_dim)
+        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+
+        self.w1 = operation_settings.get("operations").Linear(
+            dim,
+            hidden_dim,
+            bias=False,
+            device=operation_settings.get("device"),
+            dtype=operation_settings.get("dtype"),
+        )
+        self.w2 = operation_settings.get("operations").Linear(
+            hidden_dim,
+            dim,
+            bias=False,
+            device=operation_settings.get("device"),
+            dtype=operation_settings.get("dtype"),
+        )
+        self.w3 = operation_settings.get("operations").Linear(
+            dim,
+            hidden_dim,
+            bias=False,
+            device=operation_settings.get("device"),
+            dtype=operation_settings.get("dtype"),
+        )
+
+    # @torch.compile
+    def _forward_silu_gating(self, x1, x3):
+        return F.silu(x1) * x3
+
+    def forward(self, x):
+        return self.w2(self._forward_silu_gating(self.w1(x), self.w3(x)))
+
+
+class JointTransformerBlock(nn.Module):
+    def __init__(
+        self,
+        layer_id: int,
+        dim: int,
+        n_heads: int,
+        n_kv_heads: int,
+        multiple_of: int,
+        ffn_dim_multiplier: float,
+        norm_eps: float,
+        qk_norm: bool,
+        modulation=True,
+        operation_settings={},
+    ) -> None:
+        """
+        Initialize a TransformerBlock.
+
+        Args:
+            layer_id (int): Identifier for the layer.
+            dim (int): Embedding dimension of the input features.
+            n_heads (int): Number of attention heads.
+            n_kv_heads (Optional[int]): Number of attention heads in key and
+                value features (if using GQA), or set to None for the same as
+                query.
+            multiple_of (int):
+            ffn_dim_multiplier (float):
+            norm_eps (float):
+
+        """
+        super().__init__()
+        self.dim = dim
+        self.head_dim = dim // n_heads
+        self.attention = JointAttention(dim, n_heads, n_kv_heads, qk_norm, operation_settings=operation_settings)
+        self.feed_forward = FeedForward(
+            dim=dim,
+            hidden_dim=4 * dim,
+            multiple_of=multiple_of,
+            ffn_dim_multiplier=ffn_dim_multiplier,
+            operation_settings=operation_settings,
+        )
+        self.layer_id = layer_id
+        self.attention_norm1 = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings)
+        self.ffn_norm1 = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings)
+
+        self.attention_norm2 = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings)
+        self.ffn_norm2 = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings)
+
+        self.modulation = modulation
+        if modulation:
+            self.adaLN_modulation = nn.Sequential(
+                nn.SiLU(),
+                operation_settings.get("operations").Linear(
+                    min(dim, 1024),
+                    4 * dim,
+                    bias=True,
+                    device=operation_settings.get("device"),
+                    dtype=operation_settings.get("dtype"),
+                ),
+            )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_mask: torch.Tensor,
+        freqs_cis: torch.Tensor,
+        adaln_input: Optional[torch.Tensor]=None,
+    ):
+        """
+        Perform a forward pass through the TransformerBlock.
+
+        Args:
+            x (torch.Tensor): Input tensor.
+            freqs_cis (torch.Tensor): Precomputed cosine and sine frequencies.
+
+        Returns:
+            torch.Tensor: Output tensor after applying attention and
+                feedforward layers.
+
+        """
+        if self.modulation:
+            assert adaln_input is not None
+            scale_msa, gate_msa, scale_mlp, gate_mlp = self.adaLN_modulation(adaln_input).chunk(4, dim=1)
+
+            x = x + gate_msa.unsqueeze(1).tanh() * self.attention_norm2(
+                self.attention(
+                    modulate(self.attention_norm1(x), scale_msa),
+                    x_mask,
+                    freqs_cis,
+                )
+            )
+            x = x + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(
+                self.feed_forward(
+                    modulate(self.ffn_norm1(x), scale_mlp),
+                )
+            )
+        else:
+            assert adaln_input is None
+            x = x + self.attention_norm2(
+                self.attention(
+                    self.attention_norm1(x),
+                    x_mask,
+                    freqs_cis,
+                )
+            )
+            x = x + self.ffn_norm2(
+                self.feed_forward(
+                    self.ffn_norm1(x),
+                )
+            )
+        return x
+
+
+class FinalLayer(nn.Module):
+    """
+    The final layer of NextDiT.
+    """
+
+    def __init__(self, hidden_size, patch_size, out_channels, operation_settings={}):
+        super().__init__()
+        self.norm_final = operation_settings.get("operations").LayerNorm(
+            hidden_size,
+            elementwise_affine=False,
+            eps=1e-6,
+            device=operation_settings.get("device"),
+            dtype=operation_settings.get("dtype"),
+        )
+        self.linear = operation_settings.get("operations").Linear(
+            hidden_size,
+            patch_size * patch_size * out_channels,
+            bias=True,
+            device=operation_settings.get("device"),
+            dtype=operation_settings.get("dtype"),
+        )
+
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            operation_settings.get("operations").Linear(
+                min(hidden_size, 1024),
+                hidden_size,
+                bias=True,
+                device=operation_settings.get("device"),
+                dtype=operation_settings.get("dtype"),
+            ),
+        )
+
+    def forward(self, x, c):
+        scale = self.adaLN_modulation(c)
+        x = modulate(self.norm_final(x), scale)
+        x = self.linear(x)
+        return x
+
+
+class NextDiT(nn.Module):
+    """
+    Diffusion model with a Transformer backbone.
+    """
+
+    def __init__(
+        self,
+        patch_size: int = 2,
+        in_channels: int = 4,
+        dim: int = 4096,
+        n_layers: int = 32,
+        n_refiner_layers: int = 2,
+        n_heads: int = 32,
+        n_kv_heads: Optional[int] = None,
+        multiple_of: int = 256,
+        ffn_dim_multiplier: Optional[float] = None,
+        norm_eps: float = 1e-5,
+        qk_norm: bool = False,
+        cap_feat_dim: int = 5120,
+        axes_dims: List[int] = (16, 56, 56),
+        axes_lens: List[int] = (1, 512, 512),
+        image_model=None,
+        device=None,
+        dtype=None,
+        operations=None,
+    ) -> None:
+        super().__init__()
+        self.dtype = dtype
+        operation_settings = {"operations": operations, "device": device, "dtype": dtype}
+        self.in_channels = in_channels
+        self.out_channels = in_channels
+        self.patch_size = patch_size
+
+        self.x_embedder = operation_settings.get("operations").Linear(
+            in_features=patch_size * patch_size * in_channels,
+            out_features=dim,
+            bias=True,
+            device=operation_settings.get("device"),
+            dtype=operation_settings.get("dtype"),
+        )
+
+        self.noise_refiner = nn.ModuleList(
+            [
+                JointTransformerBlock(
+                    layer_id,
+                    dim,
+                    n_heads,
+                    n_kv_heads,
+                    multiple_of,
+                    ffn_dim_multiplier,
+                    norm_eps,
+                    qk_norm,
+                    modulation=True,
+                    operation_settings=operation_settings,
+                )
+                for layer_id in range(n_refiner_layers)
+            ]
+        )
+        self.context_refiner = nn.ModuleList(
+            [
+                JointTransformerBlock(
+                    layer_id,
+                    dim,
+                    n_heads,
+                    n_kv_heads,
+                    multiple_of,
+                    ffn_dim_multiplier,
+                    norm_eps,
+                    qk_norm,
+                    modulation=False,
+                    operation_settings=operation_settings,
+                )
+                for layer_id in range(n_refiner_layers)
+            ]
+        )
+
+        self.t_embedder = TimestepEmbedder(min(dim, 1024), **operation_settings)
+        self.cap_embedder = nn.Sequential(
+            RMSNorm(cap_feat_dim, eps=norm_eps, elementwise_affine=True, **operation_settings),
+            operation_settings.get("operations").Linear(
+                cap_feat_dim,
+                dim,
+                bias=True,
+                device=operation_settings.get("device"),
+                dtype=operation_settings.get("dtype"),
+            ),
+        )
+
+        self.layers = nn.ModuleList(
+            [
+                JointTransformerBlock(
+                    layer_id,
+                    dim,
+                    n_heads,
+                    n_kv_heads,
+                    multiple_of,
+                    ffn_dim_multiplier,
+                    norm_eps,
+                    qk_norm,
+                    operation_settings=operation_settings,
+                )
+                for layer_id in range(n_layers)
+            ]
+        )
+        self.norm_final = RMSNorm(dim, eps=norm_eps, elementwise_affine=True, **operation_settings)
+        self.final_layer = FinalLayer(dim, patch_size, self.out_channels, operation_settings=operation_settings)
+
+        assert (dim // n_heads) == sum(axes_dims)
+        self.axes_dims = axes_dims
+        self.axes_lens = axes_lens
+        self.rope_embedder = EmbedND(dim=dim // n_heads, theta=10000.0, axes_dim=axes_dims)
+        self.dim = dim
+        self.n_heads = n_heads
+
+    def unpatchify(
+        self, x: torch.Tensor, img_size: List[Tuple[int, int]], cap_size: List[int], return_tensor=False
+    ) -> List[torch.Tensor]:
+        """
+        x: (N, T, patch_size**2 * C)
+        imgs: (N, H, W, C)
+        """
+        pH = pW = self.patch_size
+        imgs = []
+        for i in range(x.size(0)):
+            H, W = img_size[i]
+            begin = cap_size[i]
+            end = begin + (H // pH) * (W // pW)
+            imgs.append(
+                x[i][begin:end]
+                .view(H // pH, W // pW, pH, pW, self.out_channels)
+                .permute(4, 0, 2, 1, 3)
+                .flatten(3, 4)
+                .flatten(1, 2)
+            )
+
+        if return_tensor:
+            imgs = torch.stack(imgs, dim=0)
+        return imgs
+
+    def patchify_and_embed(
+        self, x: List[torch.Tensor] | torch.Tensor, cap_feats: torch.Tensor, cap_mask: torch.Tensor, t: torch.Tensor, num_tokens
+    ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], List[int], torch.Tensor]:
+        bsz = len(x)
+        pH = pW = self.patch_size
+        device = x[0].device
+        dtype = x[0].dtype
+
+        if cap_mask is not None:
+            l_effective_cap_len = cap_mask.sum(dim=1).tolist()
+        else:
+            l_effective_cap_len = [num_tokens] * bsz
+
+        if cap_mask is not None and not torch.is_floating_point(cap_mask):
+            cap_mask = (cap_mask - 1).to(dtype) * torch.finfo(dtype).max
+
+        img_sizes = [(img.size(1), img.size(2)) for img in x]
+        l_effective_img_len = [(H // pH) * (W // pW) for (H, W) in img_sizes]
+
+        max_seq_len = max(
+            (cap_len+img_len for cap_len, img_len in zip(l_effective_cap_len, l_effective_img_len))
+        )
+        max_cap_len = max(l_effective_cap_len)
+        max_img_len = max(l_effective_img_len)
+
+        position_ids = torch.zeros(bsz, max_seq_len, 3, dtype=torch.int32, device=device)
+
+        for i in range(bsz):
+            cap_len = l_effective_cap_len[i]
+            img_len = l_effective_img_len[i]
+            H, W = img_sizes[i]
+            H_tokens, W_tokens = H // pH, W // pW
+            assert H_tokens * W_tokens == img_len
+
+            position_ids[i, :cap_len, 0] = torch.arange(cap_len, dtype=torch.int32, device=device)
+            position_ids[i, cap_len:cap_len+img_len, 0] = cap_len
+            row_ids = torch.arange(H_tokens, dtype=torch.int32, device=device).view(-1, 1).repeat(1, W_tokens).flatten()
+            col_ids = torch.arange(W_tokens, dtype=torch.int32, device=device).view(1, -1).repeat(H_tokens, 1).flatten()
+            position_ids[i, cap_len:cap_len+img_len, 1] = row_ids
+            position_ids[i, cap_len:cap_len+img_len, 2] = col_ids
+
+        freqs_cis = self.rope_embedder(position_ids).movedim(1, 2).to(dtype)
+
+        # build freqs_cis for cap and image individually
+        cap_freqs_cis_shape = list(freqs_cis.shape)
+        # cap_freqs_cis_shape[1] = max_cap_len
+        cap_freqs_cis_shape[1] = cap_feats.shape[1]
+        cap_freqs_cis = torch.zeros(*cap_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
+
+        img_freqs_cis_shape = list(freqs_cis.shape)
+        img_freqs_cis_shape[1] = max_img_len
+        img_freqs_cis = torch.zeros(*img_freqs_cis_shape, device=device, dtype=freqs_cis.dtype)
+
+        for i in range(bsz):
+            cap_len = l_effective_cap_len[i]
+            img_len = l_effective_img_len[i]
+            cap_freqs_cis[i, :cap_len] = freqs_cis[i, :cap_len]
+            img_freqs_cis[i, :img_len] = freqs_cis[i, cap_len:cap_len+img_len]
+
+        # refine context
+        for layer in self.context_refiner:
+            cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis)
+
+        # refine image
+        flat_x = []
+        for i in range(bsz):
+            img = x[i]
+            C, H, W = img.size()
+            img = img.view(C, H // pH, pH, W // pW, pW).permute(1, 3, 2, 4, 0).flatten(2).flatten(0, 1)
+            flat_x.append(img)
+        x = flat_x
+        padded_img_embed = torch.zeros(bsz, max_img_len, x[0].shape[-1], device=device, dtype=x[0].dtype)
+        padded_img_mask = torch.zeros(bsz, max_img_len, dtype=dtype, device=device)
+        for i in range(bsz):
+            padded_img_embed[i, :l_effective_img_len[i]] = x[i]
+            padded_img_mask[i, l_effective_img_len[i]:] = -torch.finfo(dtype).max
+
+        padded_img_embed = self.x_embedder(padded_img_embed)
+        padded_img_mask = padded_img_mask.unsqueeze(1)
+        for layer in self.noise_refiner:
+            padded_img_embed = layer(padded_img_embed, padded_img_mask, img_freqs_cis, t)
+
+        if cap_mask is not None:
+            mask = torch.zeros(bsz, max_seq_len, dtype=dtype, device=device)
+            mask[:, :max_cap_len] = cap_mask[:, :max_cap_len]
+        else:
+            mask = None
+
+        padded_full_embed = torch.zeros(bsz, max_seq_len, self.dim, device=device, dtype=x[0].dtype)
+        for i in range(bsz):
+            cap_len = l_effective_cap_len[i]
+            img_len = l_effective_img_len[i]
+
+            padded_full_embed[i, :cap_len] = cap_feats[i, :cap_len]
+            padded_full_embed[i, cap_len:cap_len+img_len] = padded_img_embed[i, :img_len]
+
+        return padded_full_embed, mask, img_sizes, l_effective_cap_len, freqs_cis
+
+    # def forward(self, x, t, cap_feats, cap_mask):
+    def forward(self, x, timesteps, context, num_tokens, attention_mask=None, **kwargs):
+        t = 1.0 - timesteps
+        cap_feats = context
+        cap_mask = attention_mask
+        bs, c, h, w = x.shape
+        x = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
+        """
+        Forward pass of NextDiT.
+        t: (N,) tensor of diffusion timesteps
+        y: (N,) tensor of text tokens/features
+        """
+
+        t = self.t_embedder(t, dtype=x.dtype)  # (N, D)
+        adaln_input = t
+
+        cap_feats = self.cap_embedder(cap_feats)  # (N, L, D)  # todo check if able to batchify w.o. redundant compute
+
+        x_is_tensor = isinstance(x, torch.Tensor)
+        x, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens)
+        freqs_cis = freqs_cis.to(x.device)
+
+        for layer in self.layers:
+            x = layer(x, mask, freqs_cis, adaln_input)
+
+        x = self.final_layer(x, adaln_input)
+        x = self.unpatchify(x, img_size, cap_size, return_tensor=x_is_tensor)[:,:,:h,:w]
+
+        return -x
+

comfy/ldm/modules/attention.py

@@ -1,4 +1,6 @@
 import math
+import sys
+
 import torch
 import torch.nn.functional as F
 from torch import nn, einsum
@@ -16,7 +18,11 @@ if model_management.xformers_enabled():
     import xformers.ops
 
 if model_management.sage_attention_enabled():
-    from sageattention import sageattn
+    try:
+        from sageattention import sageattn
+    except ModuleNotFoundError:
+        logging.error(f"\n\nTo use the `--use-sage-attention` feature, the `sageattention` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install sageattention")
+        exit(-1)
 
 from comfy.cli_args import args
 import comfy.ops
@@ -89,7 +95,7 @@ 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 +148,23 @@ 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 +228,13 @@ 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 +341,18 @@ 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 +363,7 @@ 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 +416,12 @@ 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 +432,7 @@ 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 +453,10 @@ 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 +475,7 @@ 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 +498,15 @@ 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/ldm/modules/diffusionmodules/mmdit.py

@@ -321,7 +321,7 @@ class SelfAttention(nn.Module):
 
 class RMSNorm(torch.nn.Module):
     def __init__(
-        self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None
+        self, dim: int, elementwise_affine: bool = False, eps: float = 1e-6, device=None, dtype=None, **kwargs
     ):
         """
         Initialize the RMSNorm normalization layer.

comfy/ldm/modules/diffusionmodules/model.py

@@ -293,6 +293,17 @@ def pytorch_attention(q, k, v):
     return out
 
 
+def vae_attention():
+    if model_management.xformers_enabled_vae():
+        logging.info("Using xformers attention in VAE")
+        return xformers_attention
+    elif model_management.pytorch_attention_enabled_vae():
+        logging.info("Using pytorch attention in VAE")
+        return pytorch_attention
+    else:
+        logging.info("Using split attention in VAE")
+        return normal_attention
+
 class AttnBlock(nn.Module):
     def __init__(self, in_channels, conv_op=ops.Conv2d):
         super().__init__()
@@ -320,15 +331,7 @@ class AttnBlock(nn.Module):
                                         stride=1,
                                         padding=0)
 
-        if model_management.xformers_enabled_vae():
-            logging.info("Using xformers attention in VAE")
-            self.optimized_attention = xformers_attention
-        elif model_management.pytorch_attention_enabled():
-            logging.info("Using pytorch attention in VAE")
-            self.optimized_attention = pytorch_attention
-        else:
-            logging.info("Using split attention in VAE")
-            self.optimized_attention = normal_attention
+        self.optimized_attention = vae_attention()
 
     def forward(self, x):
         h_ = x
@@ -699,9 +702,6 @@ class Decoder(nn.Module):
                                         padding=1)
 
     def forward(self, z, **kwargs):
-        #assert z.shape[1:] == self.z_shape[1:]
-        self.last_z_shape = z.shape
-
         # timestep embedding
         temb = None
 

comfy/ldm/modules/diffusionmodules/util.py

@@ -9,6 +9,7 @@
 
 
 import math
+import logging
 import torch
 import torch.nn as nn
 import numpy as np
@@ -130,7 +131,7 @@ def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timestep
     # add one to get the final alpha values right (the ones from first scale to data during sampling)
     steps_out = ddim_timesteps + 1
     if verbose:
-        print(f'Selected timesteps for ddim sampler: {steps_out}')
+        logging.info(f'Selected timesteps for ddim sampler: {steps_out}')
     return steps_out
 
 
@@ -142,8 +143,8 @@ def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
     # according the the formula provided in https://arxiv.org/abs/2010.02502
     sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
     if verbose:
-        print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
-        print(f'For the chosen value of eta, which is {eta}, '
+        logging.info(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
+        logging.info(f'For the chosen value of eta, which is {eta}, '
               f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
     return sigmas, alphas, alphas_prev
 

comfy/ldm/modules/sub_quadratic_attention.py

@@ -17,10 +17,10 @@ import math
 import logging
 
 try:
-	from typing import Optional, NamedTuple, List, Protocol
+    from typing import Optional, NamedTuple, List, Protocol
 except ImportError:
-	from typing import Optional, NamedTuple, List
-	from typing_extensions import Protocol
+    from typing import Optional, NamedTuple, List
+    from typing_extensions import Protocol
 
 from typing import List
 
@@ -261,7 +261,7 @@ def efficient_dot_product_attention(
             value=value,
             mask=mask,
         )
-    
+
     # TODO: maybe we should use torch.empty_like(query) to allocate storage in-advance,
     # and pass slices to be mutated, instead of torch.cat()ing the returned slices
     res = torch.cat([

comfy/ldm/pixart/blocks.py

@@ -0,0 +1,380 @@
+# Based on:
+# https://github.com/PixArt-alpha/PixArt-alpha [Apache 2.0 license]
+# https://github.com/PixArt-alpha/PixArt-sigma [Apache 2.0 license]
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from einops import rearrange
+
+from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, Mlp, timestep_embedding
+from comfy.ldm.modules.attention import optimized_attention
+
+# if model_management.xformers_enabled():
+#     import xformers.ops
+#     if int((xformers.__version__).split(".")[2].split("+")[0]) >= 28:
+#         block_diagonal_mask_from_seqlens = xformers.ops.fmha.attn_bias.BlockDiagonalMask.from_seqlens
+#     else:
+#         block_diagonal_mask_from_seqlens = xformers.ops.fmha.BlockDiagonalMask.from_seqlens
+
+def modulate(x, shift, scale):
+    return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1)
+
+def t2i_modulate(x, shift, scale):
+    return x * (1 + scale) + shift
+
+class MultiHeadCrossAttention(nn.Module):
+    def __init__(self, d_model, num_heads, attn_drop=0., proj_drop=0., dtype=None, device=None, operations=None, **kwargs):
+        super(MultiHeadCrossAttention, self).__init__()
+        assert d_model % num_heads == 0, "d_model must be divisible by num_heads"
+
+        self.d_model = d_model
+        self.num_heads = num_heads
+        self.head_dim = d_model // num_heads
+
+        self.q_linear = operations.Linear(d_model, d_model, dtype=dtype, device=device)
+        self.kv_linear = operations.Linear(d_model, d_model*2, dtype=dtype, device=device)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = operations.Linear(d_model, d_model, dtype=dtype, device=device)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+    def forward(self, x, cond, mask=None):
+        # query/value: img tokens; key: condition; mask: if padding tokens
+        B, N, C = x.shape
+
+        q = self.q_linear(x).view(1, -1, self.num_heads, self.head_dim)
+        kv = self.kv_linear(cond).view(1, -1, 2, self.num_heads, self.head_dim)
+        k, v = kv.unbind(2)
+
+        assert mask is None # TODO?
+        # # TODO: xformers needs separate mask logic here
+        # if model_management.xformers_enabled():
+        #     attn_bias = None
+        #     if mask is not None:
+        #         attn_bias = block_diagonal_mask_from_seqlens([N] * B, mask)
+        #     x = xformers.ops.memory_efficient_attention(q, k, v, p=0, attn_bias=attn_bias)
+        # else:
+        #     q, k, v = map(lambda t: t.transpose(1, 2), (q, k, v),)
+        #     attn_mask = None
+        #     mask = torch.ones(())
+        #     if mask is not None and len(mask) > 1:
+        #         # Create equivalent of xformer diagonal block mask, still only correct for square masks
+        #         # But depth doesn't matter as tensors can expand in that dimension
+        #         attn_mask_template = torch.ones(
+        #             [q.shape[2] // B, mask[0]],
+        #             dtype=torch.bool,
+        #             device=q.device
+        #         )
+        #         attn_mask = torch.block_diag(attn_mask_template)
+        #
+        #         # create a mask on the diagonal for each mask in the batch
+        #         for _ in range(B - 1):
+        #             attn_mask = torch.block_diag(attn_mask, attn_mask_template)
+        #     x = optimized_attention(q, k, v, self.num_heads, mask=attn_mask, skip_reshape=True)
+
+        x = optimized_attention(q.view(B, -1, C), k.view(B, -1, C), v.view(B, -1, C), self.num_heads, mask=None)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class AttentionKVCompress(nn.Module):
+    """Multi-head Attention block with KV token compression and qk norm."""
+    def __init__(self, dim, num_heads=8, qkv_bias=True, sampling='conv', sr_ratio=1, qk_norm=False, dtype=None, device=None, operations=None, **kwargs):
+        """
+        Args:
+            dim (int): Number of input channels.
+            num_heads (int): Number of attention heads.
+            qkv_bias (bool:  If True, add a learnable bias to query, key, value.
+        """
+        super().__init__()
+        assert dim % num_heads == 0, 'dim should be divisible by num_heads'
+        self.num_heads = num_heads
+        self.head_dim = dim // num_heads
+        self.scale = self.head_dim ** -0.5
+
+        self.qkv = operations.Linear(dim, dim * 3, bias=qkv_bias, dtype=dtype, device=device)
+        self.proj = operations.Linear(dim, dim, dtype=dtype, device=device)
+
+        self.sampling=sampling    # ['conv', 'ave', 'uniform', 'uniform_every']
+        self.sr_ratio = sr_ratio
+        if sr_ratio > 1 and sampling == 'conv':
+            # Avg Conv Init.
+            self.sr = operations.Conv2d(dim, dim, groups=dim, kernel_size=sr_ratio, stride=sr_ratio, dtype=dtype, device=device)
+            # self.sr.weight.data.fill_(1/sr_ratio**2)
+            # self.sr.bias.data.zero_()
+            self.norm = operations.LayerNorm(dim, dtype=dtype, device=device)
+        if qk_norm:
+            self.q_norm = operations.LayerNorm(dim, dtype=dtype, device=device)
+            self.k_norm = operations.LayerNorm(dim, dtype=dtype, device=device)
+        else:
+            self.q_norm = nn.Identity()
+            self.k_norm = nn.Identity()
+
+    def downsample_2d(self, tensor, H, W, scale_factor, sampling=None):
+        if sampling is None or scale_factor == 1:
+            return tensor
+        B, N, C = tensor.shape
+
+        if sampling == 'uniform_every':
+            return tensor[:, ::scale_factor], int(N // scale_factor)
+
+        tensor = tensor.reshape(B, H, W, C).permute(0, 3, 1, 2)
+        new_H, new_W = int(H / scale_factor), int(W / scale_factor)
+        new_N = new_H * new_W
+
+        if sampling == 'ave':
+            tensor = F.interpolate(
+                tensor, scale_factor=1 / scale_factor, mode='nearest'
+            ).permute(0, 2, 3, 1)
+        elif sampling == 'uniform':
+            tensor = tensor[:, :, ::scale_factor, ::scale_factor].permute(0, 2, 3, 1)
+        elif sampling == 'conv':
+            tensor = self.sr(tensor).reshape(B, C, -1).permute(0, 2, 1)
+            tensor = self.norm(tensor)
+        else:
+            raise ValueError
+
+        return tensor.reshape(B, new_N, C).contiguous(), new_N
+
+    def forward(self, x, mask=None, HW=None, block_id=None):
+        B, N, C = x.shape # 2 4096 1152
+        new_N = N
+        if HW is None:
+            H = W = int(N ** 0.5)
+        else:
+            H, W = HW
+        qkv = self.qkv(x).reshape(B, N, 3, C)
+
+        q, k, v = qkv.unbind(2)
+        q = self.q_norm(q)
+        k = self.k_norm(k)
+
+        # KV compression
+        if self.sr_ratio > 1:
+            k, new_N = self.downsample_2d(k, H, W, self.sr_ratio, sampling=self.sampling)
+            v, new_N = self.downsample_2d(v, H, W, self.sr_ratio, sampling=self.sampling)
+
+        q = q.reshape(B, N, self.num_heads, C // self.num_heads)
+        k = k.reshape(B, new_N, self.num_heads, C // self.num_heads)
+        v = v.reshape(B, new_N, self.num_heads, C // self.num_heads)
+
+        if mask is not None:
+            raise NotImplementedError("Attn mask logic not added for self attention")
+
+        # This is never called at the moment
+        # attn_bias = None
+        # if mask is not None:
+        #     attn_bias = torch.zeros([B * self.num_heads, q.shape[1], k.shape[1]], dtype=q.dtype, device=q.device)
+        #     attn_bias.masked_fill_(mask.squeeze(1).repeat(self.num_heads, 1, 1) == 0, float('-inf'))
+
+        # attention 2
+        q, k, v = map(lambda t: t.transpose(1, 2), (q, k, v),)
+        x = optimized_attention(q, k, v, self.num_heads, mask=None, skip_reshape=True)
+
+        x = x.view(B, N, C)
+        x = self.proj(x)
+        return x
+
+
+class FinalLayer(nn.Module):
+    """
+    The final layer of PixArt.
+    """
+    def __init__(self, hidden_size, patch_size, out_channels, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device)
+        )
+
+    def forward(self, x, c):
+        shift, scale = self.adaLN_modulation(c).chunk(2, dim=1)
+        x = modulate(self.norm_final(x), shift, scale)
+        x = self.linear(x)
+        return x
+
+class T2IFinalLayer(nn.Module):
+    """
+    The final layer of PixArt.
+    """
+    def __init__(self, hidden_size, patch_size, out_channels, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.norm_final = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.linear = operations.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
+        self.scale_shift_table = nn.Parameter(torch.randn(2, hidden_size) / hidden_size ** 0.5)
+        self.out_channels = out_channels
+
+    def forward(self, x, t):
+        shift, scale = (self.scale_shift_table[None].to(dtype=x.dtype, device=x.device) + t[:, None]).chunk(2, dim=1)
+        x = t2i_modulate(self.norm_final(x), shift, scale)
+        x = self.linear(x)
+        return x
+
+
+class MaskFinalLayer(nn.Module):
+    """
+    The final layer of PixArt.
+    """
+    def __init__(self, final_hidden_size, c_emb_size, patch_size, out_channels, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.norm_final = operations.LayerNorm(final_hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.linear = operations.Linear(final_hidden_size, patch_size * patch_size * out_channels, bias=True, dtype=dtype, device=device)
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(c_emb_size, 2 * final_hidden_size, bias=True, dtype=dtype, device=device)
+        )
+    def forward(self, x, t):
+        shift, scale = self.adaLN_modulation(t).chunk(2, dim=1)
+        x = modulate(self.norm_final(x), shift, scale)
+        x = self.linear(x)
+        return x
+
+
+class DecoderLayer(nn.Module):
+    """
+    The final layer of PixArt.
+    """
+    def __init__(self, hidden_size, decoder_hidden_size, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.norm_decoder = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.linear = operations.Linear(hidden_size, decoder_hidden_size, bias=True, dtype=dtype, device=device)
+        self.adaLN_modulation = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(hidden_size, 2 * hidden_size, bias=True, dtype=dtype, device=device)
+        )
+    def forward(self, x, t):
+        shift, scale = self.adaLN_modulation(t).chunk(2, dim=1)
+        x = modulate(self.norm_decoder(x), shift, scale)
+        x = self.linear(x)
+        return x
+
+
+class SizeEmbedder(TimestepEmbedder):
+    """
+    Embeds scalar timesteps into vector representations.
+    """
+    def __init__(self, hidden_size, frequency_embedding_size=256, dtype=None, device=None, operations=None):
+        super().__init__(hidden_size=hidden_size, frequency_embedding_size=frequency_embedding_size, operations=operations)
+        self.mlp = nn.Sequential(
+            operations.Linear(frequency_embedding_size, hidden_size, bias=True, dtype=dtype, device=device),
+            nn.SiLU(),
+            operations.Linear(hidden_size, hidden_size, bias=True, dtype=dtype, device=device),
+        )
+        self.frequency_embedding_size = frequency_embedding_size
+        self.outdim = hidden_size
+
+    def forward(self, s, bs):
+        if s.ndim == 1:
+            s = s[:, None]
+        assert s.ndim == 2
+        if s.shape[0] != bs:
+            s = s.repeat(bs//s.shape[0], 1)
+            assert s.shape[0] == bs
+        b, dims = s.shape[0], s.shape[1]
+        s = rearrange(s, "b d -> (b d)")
+        s_freq = timestep_embedding(s, self.frequency_embedding_size)
+        s_emb = self.mlp(s_freq.to(s.dtype))
+        s_emb = rearrange(s_emb, "(b d) d2 -> b (d d2)", b=b, d=dims, d2=self.outdim)
+        return s_emb
+
+
+class LabelEmbedder(nn.Module):
+    """
+    Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
+    """
+    def __init__(self, num_classes, hidden_size, dropout_prob, dtype=None, device=None, operations=None):
+        super().__init__()
+        use_cfg_embedding = dropout_prob > 0
+        self.embedding_table = operations.Embedding(num_classes + use_cfg_embedding, hidden_size, dtype=dtype, device=device),
+        self.num_classes = num_classes
+        self.dropout_prob = dropout_prob
+
+    def token_drop(self, labels, force_drop_ids=None):
+        """
+        Drops labels to enable classifier-free guidance.
+        """
+        if force_drop_ids is None:
+            drop_ids = torch.rand(labels.shape[0]).cuda() < self.dropout_prob
+        else:
+            drop_ids = force_drop_ids == 1
+        labels = torch.where(drop_ids, self.num_classes, labels)
+        return labels
+
+    def forward(self, labels, train, force_drop_ids=None):
+        use_dropout = self.dropout_prob > 0
+        if (train and use_dropout) or (force_drop_ids is not None):
+            labels = self.token_drop(labels, force_drop_ids)
+        embeddings = self.embedding_table(labels)
+        return embeddings
+
+
+class CaptionEmbedder(nn.Module):
+    """
+    Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
+    """
+    def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.y_proj = Mlp(
+            in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer,
+            dtype=dtype, device=device, operations=operations,
+        )
+        self.register_buffer("y_embedding", nn.Parameter(torch.randn(token_num, in_channels) / in_channels ** 0.5))
+        self.uncond_prob = uncond_prob
+
+    def token_drop(self, caption, force_drop_ids=None):
+        """
+        Drops labels to enable classifier-free guidance.
+        """
+        if force_drop_ids is None:
+            drop_ids = torch.rand(caption.shape[0]).cuda() < self.uncond_prob
+        else:
+            drop_ids = force_drop_ids == 1
+        caption = torch.where(drop_ids[:, None, None, None], self.y_embedding, caption)
+        return caption
+
+    def forward(self, caption, train, force_drop_ids=None):
+        if train:
+            assert caption.shape[2:] == self.y_embedding.shape
+        use_dropout = self.uncond_prob > 0
+        if (train and use_dropout) or (force_drop_ids is not None):
+            caption = self.token_drop(caption, force_drop_ids)
+        caption = self.y_proj(caption)
+        return caption
+
+
+class CaptionEmbedderDoubleBr(nn.Module):
+    """
+    Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.
+    """
+    def __init__(self, in_channels, hidden_size, uncond_prob, act_layer=nn.GELU(approximate='tanh'), token_num=120, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.proj = Mlp(
+            in_features=in_channels, hidden_features=hidden_size, out_features=hidden_size, act_layer=act_layer,
+            dtype=dtype, device=device, operations=operations,
+        )
+        self.embedding = nn.Parameter(torch.randn(1, in_channels) / 10 ** 0.5)
+        self.y_embedding = nn.Parameter(torch.randn(token_num, in_channels) / 10 ** 0.5)
+        self.uncond_prob = uncond_prob
+
+    def token_drop(self, global_caption, caption, force_drop_ids=None):
+        """
+        Drops labels to enable classifier-free guidance.
+        """
+        if force_drop_ids is None:
+            drop_ids = torch.rand(global_caption.shape[0]).cuda() < self.uncond_prob
+        else:
+            drop_ids = force_drop_ids == 1
+        global_caption = torch.where(drop_ids[:, None], self.embedding, global_caption)
+        caption = torch.where(drop_ids[:, None, None, None], self.y_embedding, caption)
+        return global_caption, caption
+
+    def forward(self, caption, train, force_drop_ids=None):
+        assert caption.shape[2: ] == self.y_embedding.shape
+        global_caption = caption.mean(dim=2).squeeze()
+        use_dropout = self.uncond_prob > 0
+        if (train and use_dropout) or (force_drop_ids is not None):
+            global_caption, caption = self.token_drop(global_caption, caption, force_drop_ids)
+        y_embed = self.proj(global_caption)
+        return y_embed, caption

comfy/ldm/pixart/pixartms.py

@@ -0,0 +1,256 @@
+# Based on:
+# https://github.com/PixArt-alpha/PixArt-alpha [Apache 2.0 license]
+# https://github.com/PixArt-alpha/PixArt-sigma [Apache 2.0 license]
+import torch
+import torch.nn as nn
+
+from .blocks import (
+    t2i_modulate,
+    CaptionEmbedder,
+    AttentionKVCompress,
+    MultiHeadCrossAttention,
+    T2IFinalLayer,
+    SizeEmbedder,
+)
+from comfy.ldm.modules.diffusionmodules.mmdit import TimestepEmbedder, PatchEmbed, Mlp, get_1d_sincos_pos_embed_from_grid_torch
+
+
+def get_2d_sincos_pos_embed_torch(embed_dim, w, h, pe_interpolation=1.0, base_size=16, device=None, dtype=torch.float32):
+    grid_h, grid_w = torch.meshgrid(
+        torch.arange(h, device=device, dtype=dtype) / (h/base_size) / pe_interpolation,
+        torch.arange(w, device=device, dtype=dtype) / (w/base_size) / pe_interpolation,
+        indexing='ij'
+    )
+    emb_h = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_h, device=device, dtype=dtype)
+    emb_w = get_1d_sincos_pos_embed_from_grid_torch(embed_dim // 2, grid_w, device=device, dtype=dtype)
+    emb = torch.cat([emb_w, emb_h], dim=1)  # (H*W, D)
+    return emb
+
+class PixArtMSBlock(nn.Module):
+    """
+    A PixArt block with adaptive layer norm zero (adaLN-Zero) conditioning.
+    """
+    def __init__(self, hidden_size, num_heads, mlp_ratio=4.0, drop_path=0., input_size=None,
+                 sampling=None, sr_ratio=1, qk_norm=False, dtype=None, device=None, operations=None, **block_kwargs):
+        super().__init__()
+        self.hidden_size = hidden_size
+        self.norm1 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        self.attn = AttentionKVCompress(
+            hidden_size, num_heads=num_heads, qkv_bias=True, sampling=sampling, sr_ratio=sr_ratio,
+            qk_norm=qk_norm, dtype=dtype, device=device, operations=operations, **block_kwargs
+        )
+        self.cross_attn = MultiHeadCrossAttention(
+            hidden_size, num_heads, dtype=dtype, device=device, operations=operations, **block_kwargs
+        )
+        self.norm2 = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, dtype=dtype, device=device)
+        # to be compatible with lower version pytorch
+        approx_gelu = lambda: nn.GELU(approximate="tanh")
+        self.mlp = Mlp(
+            in_features=hidden_size, hidden_features=int(hidden_size * mlp_ratio), act_layer=approx_gelu,
+            dtype=dtype, device=device, operations=operations
+        )
+        self.scale_shift_table = nn.Parameter(torch.randn(6, hidden_size) / hidden_size ** 0.5)
+
+    def forward(self, x, y, t, mask=None, HW=None, **kwargs):
+        B, N, C = x.shape
+
+        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None].to(dtype=x.dtype, device=x.device) + t.reshape(B, 6, -1)).chunk(6, dim=1)
+        x = x + (gate_msa * self.attn(t2i_modulate(self.norm1(x), shift_msa, scale_msa), HW=HW))
+        x = x + self.cross_attn(x, y, mask)
+        x = x + (gate_mlp * self.mlp(t2i_modulate(self.norm2(x), shift_mlp, scale_mlp)))
+
+        return x
+
+
+### Core PixArt Model ###
+class PixArtMS(nn.Module):
+    """
+    Diffusion model with a Transformer backbone.
+    """
+    def __init__(
+            self,
+            input_size=32,
+            patch_size=2,
+            in_channels=4,
+            hidden_size=1152,
+            depth=28,
+            num_heads=16,
+            mlp_ratio=4.0,
+            class_dropout_prob=0.1,
+            learn_sigma=True,
+            pred_sigma=True,
+            drop_path: float = 0.,
+            caption_channels=4096,
+            pe_interpolation=None,
+            pe_precision=None,
+            config=None,
+            model_max_length=120,
+            micro_condition=True,
+            qk_norm=False,
+            kv_compress_config=None,
+            dtype=None,
+            device=None,
+            operations=None,
+            **kwargs,
+    ):
+        nn.Module.__init__(self)
+        self.dtype = dtype
+        self.pred_sigma = pred_sigma
+        self.in_channels = in_channels
+        self.out_channels = in_channels * 2 if pred_sigma else in_channels
+        self.patch_size = patch_size
+        self.num_heads = num_heads
+        self.pe_interpolation = pe_interpolation
+        self.pe_precision = pe_precision
+        self.hidden_size = hidden_size
+        self.depth = depth
+
+        approx_gelu = lambda: nn.GELU(approximate="tanh")
+        self.t_block = nn.Sequential(
+            nn.SiLU(),
+            operations.Linear(hidden_size, 6 * hidden_size, bias=True, dtype=dtype, device=device)
+        )
+        self.x_embedder = PatchEmbed(
+            patch_size=patch_size,
+            in_chans=in_channels,
+            embed_dim=hidden_size,
+            bias=True,
+            dtype=dtype,
+            device=device,
+            operations=operations
+        )
+        self.t_embedder = TimestepEmbedder(
+            hidden_size, dtype=dtype, device=device, operations=operations,
+        )
+        self.y_embedder = CaptionEmbedder(
+            in_channels=caption_channels, hidden_size=hidden_size, uncond_prob=class_dropout_prob,
+            act_layer=approx_gelu, token_num=model_max_length,
+            dtype=dtype, device=device, operations=operations,
+        )
+
+        self.micro_conditioning = micro_condition
+        if self.micro_conditioning:
+            self.csize_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations)
+            self.ar_embedder = SizeEmbedder(hidden_size//3, dtype=dtype, device=device, operations=operations)
+
+        # For fixed sin-cos embedding:
+        # num_patches = (input_size // patch_size) * (input_size // patch_size)
+        # self.base_size = input_size // self.patch_size
+        # self.register_buffer("pos_embed", torch.zeros(1, num_patches, hidden_size))
+
+        drop_path = [x.item() for x in torch.linspace(0, drop_path, depth)]  # stochastic depth decay rule
+        if kv_compress_config is None:
+            kv_compress_config = {
+                'sampling': None,
+                'scale_factor': 1,
+                'kv_compress_layer': [],
+            }
+        self.blocks = nn.ModuleList([
+            PixArtMSBlock(
+                hidden_size, num_heads, mlp_ratio=mlp_ratio, drop_path=drop_path[i],
+                sampling=kv_compress_config['sampling'],
+                sr_ratio=int(kv_compress_config['scale_factor']) if i in kv_compress_config['kv_compress_layer'] else 1,
+                qk_norm=qk_norm,
+                dtype=dtype,
+                device=device,
+                operations=operations,
+            )
+            for i in range(depth)
+        ])
+        self.final_layer = T2IFinalLayer(
+            hidden_size, patch_size, self.out_channels, dtype=dtype, device=device, operations=operations
+        )
+
+    def forward_orig(self, x, timestep, y, mask=None, c_size=None, c_ar=None, **kwargs):
+        """
+        Original forward pass of PixArt.
+        x: (N, C, H, W) tensor of spatial inputs (images or latent representations of images)
+        t: (N,) tensor of diffusion timesteps
+        y: (N, 1, 120, C) conditioning
+        ar: (N, 1): aspect ratio
+        cs: (N ,2) size conditioning for height/width
+        """
+        B, C, H, W = x.shape
+        c_res = (H + W) // 2
+        pe_interpolation = self.pe_interpolation
+        if pe_interpolation is None or self.pe_precision is not None:
+            # calculate pe_interpolation on-the-fly
+            pe_interpolation = round(c_res / (512/8.0), self.pe_precision or 0)
+
+        pos_embed = get_2d_sincos_pos_embed_torch(
+            self.hidden_size,
+            h=(H // self.patch_size),
+            w=(W // self.patch_size),
+            pe_interpolation=pe_interpolation,
+            base_size=((round(c_res / 64) * 64) // self.patch_size),
+            device=x.device,
+            dtype=x.dtype,
+        ).unsqueeze(0)
+
+        x = self.x_embedder(x) + pos_embed  # (N, T, D), where T = H * W / patch_size ** 2
+        t = self.t_embedder(timestep, x.dtype)  # (N, D)
+
+        if self.micro_conditioning and (c_size is not None and c_ar is not None):
+            bs = x.shape[0]
+            c_size = self.csize_embedder(c_size, bs)  # (N, D)
+            c_ar = self.ar_embedder(c_ar, bs)  # (N, D)
+            t = t + torch.cat([c_size, c_ar], dim=1)
+
+        t0 = self.t_block(t)
+        y = self.y_embedder(y, self.training)  # (N, D)
+
+        if mask is not None:
+            if mask.shape[0] != y.shape[0]:
+                mask = mask.repeat(y.shape[0] // mask.shape[0], 1)
+            mask = mask.squeeze(1).squeeze(1)
+            y = y.squeeze(1).masked_select(mask.unsqueeze(-1) != 0).view(1, -1, x.shape[-1])
+            y_lens = mask.sum(dim=1).tolist()
+        else:
+            y_lens = None
+            y = y.squeeze(1).view(1, -1, x.shape[-1])
+        for block in self.blocks:
+            x = block(x, y, t0, y_lens, (H, W), **kwargs)  # (N, T, D)
+
+        x = self.final_layer(x, t)  # (N, T, patch_size ** 2 * out_channels)
+        x = self.unpatchify(x, H, W)  # (N, out_channels, H, W)
+
+        return x
+
+    def forward(self, x, timesteps, context, c_size=None, c_ar=None, **kwargs):
+        B, C, H, W = x.shape
+
+        # Fallback for missing microconds
+        if self.micro_conditioning:
+            if c_size is None:
+                c_size = torch.tensor([H*8, W*8], dtype=x.dtype, device=x.device).repeat(B, 1)
+
+            if c_ar is None:
+                c_ar = torch.tensor([H/W], dtype=x.dtype, device=x.device).repeat(B, 1)
+
+        ## Still accepts the input w/o that dim but returns garbage
+        if len(context.shape) == 3:
+            context = context.unsqueeze(1)
+
+        ## run original forward pass
+        out = self.forward_orig(x, timesteps, context, c_size=c_size, c_ar=c_ar)
+
+        ## only return EPS
+        if self.pred_sigma:
+            return out[:, :self.in_channels]
+        return out
+
+    def unpatchify(self, x, h, w):
+        """
+        x: (N, T, patch_size**2 * C)
+        imgs: (N, H, W, C)
+        """
+        c = self.out_channels
+        p = self.x_embedder.patch_size[0]
+        h = h // self.patch_size
+        w = w // self.patch_size
+        assert h * w == x.shape[1]
+
+        x = x.reshape(shape=(x.shape[0], h, w, p, p, c))
+        x = torch.einsum('nhwpqc->nchpwq', x)
+        imgs = x.reshape(shape=(x.shape[0], c, h * p, w * p))
+        return imgs

comfy/ldm/util.py

@@ -1,4 +1,5 @@
 import importlib
+import logging
 
 import torch
 from torch import optim
@@ -23,7 +24,7 @@ def log_txt_as_img(wh, xc, size=10):
         try:
             draw.text((0, 0), lines, fill="black", font=font)
         except UnicodeEncodeError:
-            print("Cant encode string for logging. Skipping.")
+            logging.warning("Cant encode string for logging. Skipping.")
 
         txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
         txts.append(txt)
@@ -65,7 +66,7 @@ def mean_flat(tensor):
 def count_params(model, verbose=False):
     total_params = sum(p.numel() for p in model.parameters())
     if verbose:
-        print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
+        logging.info(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
     return total_params
 
 
@@ -193,4 +194,4 @@ class AdamWwithEMAandWings(optim.Optimizer):
             for param, ema_param in zip(params_with_grad, ema_params_with_grad):
                 ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay)
 
-        return loss
+        return loss

comfy/lora.py

@@ -307,7 +307,6 @@ def model_lora_keys_unet(model, key_map={}):
             if k.endswith(".weight"):
                 key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_")
                 key_map["lora_unet_{}".format(key_lora)] = k
-                key_map["lora_prior_unet_{}".format(key_lora)] = k #cascade lora: TODO put lora key prefix in the model config
                 key_map["{}".format(k[:-len(".weight")])] = k #generic lora format without any weird key names
             else:
                 key_map["{}".format(k)] = k #generic lora format for not .weight without any weird key names
@@ -327,6 +326,13 @@ def model_lora_keys_unet(model, key_map={}):
                     diffusers_lora_key = diffusers_lora_key[:-2]
                 key_map[diffusers_lora_key] = unet_key
 
+    if isinstance(model, comfy.model_base.StableCascade_C):
+        for k in sdk:
+            if k.startswith("diffusion_model."):
+                if k.endswith(".weight"):
+                    key_lora = k[len("diffusion_model."):-len(".weight")].replace(".", "_")
+                    key_map["lora_prior_unet_{}".format(key_lora)] = k
+
     if isinstance(model, comfy.model_base.SD3): #Diffusers lora SD3
         diffusers_keys = comfy.utils.mmdit_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
         for k in diffusers_keys:
@@ -344,7 +350,6 @@ def model_lora_keys_unet(model, key_map={}):
                 key_lora = "lycoris_{}".format(k[:-len(".weight")].replace(".", "_")) #simpletuner lycoris format
                 key_map[key_lora] = to
 
-
     if isinstance(model, comfy.model_base.AuraFlow): #Diffusers lora AuraFlow
         diffusers_keys = comfy.utils.auraflow_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
         for k in diffusers_keys:
@@ -353,6 +358,20 @@ def model_lora_keys_unet(model, key_map={}):
                 key_lora = "transformer.{}".format(k[:-len(".weight")]) #simpletrainer and probably regular diffusers lora format
                 key_map[key_lora] = to
 
+    if isinstance(model, comfy.model_base.PixArt):
+        diffusers_keys = comfy.utils.pixart_to_diffusers(model.model_config.unet_config, output_prefix="diffusion_model.")
+        for k in diffusers_keys:
+            if k.endswith(".weight"):
+                to = diffusers_keys[k]
+                key_lora = "transformer.{}".format(k[:-len(".weight")]) #default format
+                key_map[key_lora] = to
+
+                key_lora = "base_model.model.{}".format(k[:-len(".weight")]) #diffusers training script
+                key_map[key_lora] = to
+
+                key_lora = "unet.base_model.model.{}".format(k[:-len(".weight")]) #old reference peft script
+                key_map[key_lora] = to
+
     if isinstance(model, comfy.model_base.HunyuanDiT):
         for k in sdk:
             if k.startswith("diffusion_model.") and k.endswith(".weight"):

comfy/model_base.py

@@ -26,12 +26,15 @@ from comfy.ldm.modules.diffusionmodules.upscaling import ImageConcatWithNoiseAug
 from comfy.ldm.modules.diffusionmodules.mmdit import OpenAISignatureMMDITWrapper
 import comfy.ldm.genmo.joint_model.asymm_models_joint
 import comfy.ldm.aura.mmdit
+import comfy.ldm.pixart.pixartms
 import comfy.ldm.hydit.models
 import comfy.ldm.audio.dit
 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.ldm.lumina.model
 
 import comfy.model_management
 import comfy.patcher_extension
@@ -146,7 +149,9 @@ class BaseModel(torch.nn.Module):
 
         xc = xc.to(dtype)
         t = self.model_sampling.timestep(t).float()
-        context = context.to(dtype)
+        if context is not None:
+            context = context.to(dtype)
+
         extra_conds = {}
         for o in kwargs:
             extra = kwargs[o]
@@ -161,9 +166,6 @@ class BaseModel(torch.nn.Module):
     def get_dtype(self):
         return self.diffusion_model.dtype
 
-    def is_adm(self):
-        return self.adm_channels > 0
-
     def encode_adm(self, **kwargs):
         return None
 
@@ -187,9 +189,10 @@ class BaseModel(torch.nn.Module):
 
             if denoise_mask is not None:
                 if len(denoise_mask.shape) == len(noise.shape):
-                    denoise_mask = denoise_mask[:,:1]
+                    denoise_mask = denoise_mask[:, :1]
 
-                denoise_mask = denoise_mask.reshape((-1, 1, denoise_mask.shape[-2], denoise_mask.shape[-1]))
+                num_dim = noise.ndim - 2
+                denoise_mask = denoise_mask.reshape((-1, 1) + tuple(denoise_mask.shape[-num_dim:]))
                 if denoise_mask.shape[-2:] != noise.shape[-2:]:
                     denoise_mask = utils.common_upscale(denoise_mask, noise.shape[-1], noise.shape[-2], "bilinear", "center")
                 denoise_mask = utils.resize_to_batch_size(denoise_mask.round(), noise.shape[0])
@@ -199,12 +202,16 @@ class BaseModel(torch.nn.Module):
                     if ck == "mask":
                         cond_concat.append(denoise_mask.to(device))
                     elif ck == "masked_image":
-                        cond_concat.append(concat_latent_image.to(device)) #NOTE: the latent_image should be masked by the mask in pixel space
+                        cond_concat.append(concat_latent_image.to(device))  # NOTE: the latent_image should be masked by the mask in pixel space
+                    elif ck == "mask_inverted":
+                        cond_concat.append(1.0 - denoise_mask.to(device))
                 else:
                     if ck == "mask":
-                        cond_concat.append(torch.ones_like(noise)[:,:1])
+                        cond_concat.append(torch.ones_like(noise)[:, :1])
                     elif ck == "masked_image":
                         cond_concat.append(self.blank_inpaint_image_like(noise))
+                    elif ck == "mask_inverted":
+                        cond_concat.append(torch.zeros_like(noise)[:, :1])
             data = torch.cat(cond_concat, dim=1)
             return data
         return None
@@ -292,6 +299,9 @@ class BaseModel(torch.nn.Module):
             return blank_image
         self.blank_inpaint_image_like = blank_inpaint_image_like
 
+    def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
+        return self.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1)), noise, latent_image)
+
     def memory_required(self, input_shape):
         if comfy.model_management.xformers_enabled() or comfy.model_management.pytorch_attention_flash_attention():
             dtype = self.get_dtype()
@@ -539,6 +549,10 @@ class SD_X4Upscaler(BaseModel):
 
         out['c_concat'] = comfy.conds.CONDNoiseShape(image)
         out['y'] = comfy.conds.CONDRegular(noise_level)
+
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDCrossAttn(cross_attn)
         return out
 
 class IP2P:
@@ -718,6 +732,25 @@ class HunyuanDiT(BaseModel):
         out['image_meta_size'] = comfy.conds.CONDRegular(torch.FloatTensor([[height, width, target_height, target_width, 0, 0]]))
         return out
 
+class PixArt(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.EPS, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.pixart.pixartms.PixArtMS)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
+
+        width = kwargs.get("width", None)
+        height = kwargs.get("height", None)
+        if width is not None and height is not None:
+            out["c_size"] = comfy.conds.CONDRegular(torch.FloatTensor([[height, width]]))
+            out["c_ar"] = comfy.conds.CONDRegular(torch.FloatTensor([[kwargs.get("aspect_ratio", height/width)]]))
+
+        return out
+
 class Flux(BaseModel):
     def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.flux.model.Flux)
@@ -754,7 +787,6 @@ class Flux(BaseModel):
             mask = torch.ones_like(noise)[:, :1]
 
         mask = torch.mean(mask, dim=1, keepdim=True)
-        print(mask.shape)
         mask = utils.common_upscale(mask.to(device), noise.shape[-1] * 8, noise.shape[-2] * 8, "bilinear", "center")
         mask = mask.view(mask.shape[0], mask.shape[2] // 8, 8, mask.shape[3] // 8, 8).permute(0, 2, 4, 1, 3).reshape(mask.shape[0], -1, mask.shape[2] // 8, mask.shape[3] // 8)
         mask = utils.resize_to_batch_size(mask, noise.shape[0])
@@ -768,7 +800,7 @@ class Flux(BaseModel):
         cross_attn = kwargs.get("cross_attn", None)
         if cross_attn is not None:
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
-        # upscale the attention mask, since now we 
+        # upscale the attention mask, since now we
         attention_mask = kwargs.get("attention_mask", None)
         if attention_mask is not None:
             shape = kwargs["noise"].shape
@@ -778,7 +810,10 @@ class Flux(BaseModel):
             (h_tok, w_tok) = (math.ceil(shape[2] / self.diffusion_model.patch_size), math.ceil(shape[3] / self.diffusion_model.patch_size))
             attention_mask = utils.upscale_dit_mask(attention_mask, mask_ref_size, (h_tok, w_tok))
             out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
-        out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 3.5)]))
+
+        guidance = kwargs.get("guidance", 3.5)
+        if guidance is not None:
+            out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))
         return out
 
 class GenmoMochi(BaseModel):
@@ -835,5 +870,60 @@ class HunyuanVideo(BaseModel):
         cross_attn = kwargs.get("cross_attn", None)
         if cross_attn is not None:
             out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
-        out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([kwargs.get("guidance", 6.0)]))
+
+        image = kwargs.get("concat_latent_image", None)
+        noise = kwargs.get("noise", None)
+
+        if image is not None:
+            padding_shape = (noise.shape[0], 16, noise.shape[2] - 1, noise.shape[3], noise.shape[4])
+            latent_padding = torch.zeros(padding_shape, device=noise.device, dtype=noise.dtype)
+            image_latents = torch.cat([image.to(noise), latent_padding], dim=2)
+            out['c_concat'] = comfy.conds.CONDNoiseShape(self.process_latent_in(image_latents))
+
+        guidance = kwargs.get("guidance", 6.0)
+        if guidance is not None:
+            out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))
+        return out
+
+class CosmosVideo(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.EDM, image_to_video=False, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.cosmos.model.GeneralDIT)
+        self.image_to_video = image_to_video
+        if self.image_to_video:
+            self.concat_keys = ("mask_inverted",)
+
+    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
+
+    def scale_latent_inpaint(self, sigma, noise, latent_image, **kwargs):
+        sigma = sigma.reshape([sigma.shape[0]] + [1] * (len(noise.shape) - 1))
+        sigma_noise_augmentation = 0 #TODO
+        if sigma_noise_augmentation != 0:
+            latent_image = latent_image + noise
+        latent_image = self.model_sampling.calculate_input(torch.tensor([sigma_noise_augmentation], device=latent_image.device, dtype=latent_image.dtype), latent_image)
+        return latent_image * ((sigma ** 2 + self.model_sampling.sigma_data ** 2) ** 0.5)
+
+class Lumina2(BaseModel):
+    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.lumina.model.NextDiT)
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        attention_mask = kwargs.get("attention_mask", None)
+        if attention_mask is not None:
+            if torch.numel(attention_mask) != attention_mask.sum():
+                out['attention_mask'] = comfy.conds.CONDRegular(attention_mask)
+            out['num_tokens'] = comfy.conds.CONDConstant(max(1, torch.sum(attention_mask).item()))
+        cross_attn = kwargs.get("cross_attn", None)
+        if cross_attn is not None:
+            out['c_crossattn'] = comfy.conds.CONDRegular(cross_attn)
         return out

comfy/model_detection.py

@@ -136,7 +136,7 @@ def detect_unet_config(state_dict, key_prefix):
     if '{}txt_in.individual_token_refiner.blocks.0.norm1.weight'.format(key_prefix) in state_dict_keys: #Hunyuan Video
         dit_config = {}
         dit_config["image_model"] = "hunyuan_video"
-        dit_config["in_channels"] = 16
+        dit_config["in_channels"] = state_dict['{}img_in.proj.weight'.format(key_prefix)].shape[1] #SkyReels img2video has 32 input channels
         dit_config["patch_size"] = [1, 2, 2]
         dit_config["out_channels"] = 16
         dit_config["vec_in_dim"] = 768
@@ -203,11 +203,102 @@ def detect_unet_config(state_dict, key_prefix):
         dit_config["rope_theta"] = 10000.0
         return dit_config
 
+    if '{}adaln_single.emb.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys and '{}pos_embed.proj.bias'.format(key_prefix) in state_dict_keys:
+        # PixArt diffusers
+        return None
+
     if '{}adaln_single.emb.timestep_embedder.linear_1.bias'.format(key_prefix) in state_dict_keys: #Lightricks ltxv
         dit_config = {}
         dit_config["image_model"] = "ltxv"
         return dit_config
 
+    if '{}t_block.1.weight'.format(key_prefix) in state_dict_keys: # PixArt
+        patch_size = 2
+        dit_config = {}
+        dit_config["num_heads"] = 16
+        dit_config["patch_size"] = patch_size
+        dit_config["hidden_size"] = 1152
+        dit_config["in_channels"] = 4
+        dit_config["depth"] = count_blocks(state_dict_keys, '{}blocks.'.format(key_prefix) + '{}.')
+
+        y_key = "{}y_embedder.y_embedding".format(key_prefix)
+        if y_key in state_dict_keys:
+            dit_config["model_max_length"] = state_dict[y_key].shape[0]
+
+        pe_key = "{}pos_embed".format(key_prefix)
+        if pe_key in state_dict_keys:
+            dit_config["input_size"] = int(math.sqrt(state_dict[pe_key].shape[1])) * patch_size
+            dit_config["pe_interpolation"] = dit_config["input_size"] // (512//8) # guess
+
+        ar_key = "{}ar_embedder.mlp.0.weight".format(key_prefix)
+        if ar_key in state_dict_keys:
+            dit_config["image_model"] = "pixart_alpha"
+            dit_config["micro_condition"] = True
+        else:
+            dit_config["image_model"] = "pixart_sigma"
+            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:  # Cosmos
+        dit_config = {}
+        dit_config["image_model"] = "cosmos"
+        dit_config["max_img_h"] = 240
+        dit_config["max_img_w"] = 240
+        dit_config["max_frames"] = 128
+        concat_padding_mask = True
+        dit_config["in_channels"] = (state_dict['{}x_embedder.proj.1.weight'.format(key_prefix)].shape[1] // 4) - int(concat_padding_mask)
+        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"] = concat_padding_mask
+        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 '{}cap_embedder.1.weight'.format(key_prefix) in state_dict_keys:  # Lumina 2
+        dit_config = {}
+        dit_config["image_model"] = "lumina2"
+        dit_config["patch_size"] = 2
+        dit_config["in_channels"] = 16
+        dit_config["dim"] = 2304
+        dit_config["cap_feat_dim"] = 2304
+        dit_config["n_layers"] = 26
+        dit_config["n_heads"] = 24
+        dit_config["n_kv_heads"] = 8
+        dit_config["qk_norm"] = True
+        dit_config["axes_dims"] = [32, 32, 32]
+        dit_config["axes_lens"] = [300, 512, 512]
+        return dit_config
+
     if '{}input_blocks.0.0.weight'.format(key_prefix) not in state_dict_keys:
         return None
 
@@ -362,6 +453,7 @@ 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:
@@ -540,12 +632,12 @@ def unet_config_from_diffusers_unet(state_dict, dtype=None):
             'transformer_depth': [0, 1, 1], 'channel_mult': [1, 2, 4], 'transformer_depth_middle': -2, 'use_linear_in_transformer': False,
             'context_dim': 768, 'num_head_channels': 64, 'transformer_depth_output': [0, 0, 1, 1, 1, 1],
             'use_temporal_attention': False, 'use_temporal_resblock': False}
-    
+
     SD15_diffusers_inpaint = {'use_checkpoint': False, 'image_size': 32, 'out_channels': 4, 'use_spatial_transformer': True, 'legacy': False, 'adm_in_channels': None,
             'dtype': dtype, 'in_channels': 9, 'model_channels': 320, 'num_res_blocks': [2, 2, 2, 2], 'transformer_depth': [1, 1, 1, 1, 1, 1, 0, 0],
             'channel_mult': [1, 2, 4, 4], 'transformer_depth_middle': 1, 'use_linear_in_transformer': False, 'context_dim': 768, 'num_heads': 8,
             'transformer_depth_output': [1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0],
-            'use_temporal_attention': False, 'use_temporal_resblock': False}  
+            'use_temporal_attention': False, 'use_temporal_resblock': False}
 
 
     supported_models = [SDXL, SDXL_refiner, SD21, SD15, SD21_uncliph, SD21_unclipl, SDXL_mid_cnet, SDXL_small_cnet, SDXL_diffusers_inpaint, SSD_1B, Segmind_Vega, KOALA_700M, KOALA_1B, SD09_XS, SD_XS, SDXL_diffusers_ip2p, SD15_diffusers_inpaint]
@@ -573,6 +665,9 @@ def convert_diffusers_mmdit(state_dict, output_prefix=""):
         num_joint = count_blocks(state_dict, 'joint_transformer_blocks.{}.')
         num_single = count_blocks(state_dict, 'single_transformer_blocks.{}.')
         sd_map = comfy.utils.auraflow_to_diffusers({"n_double_layers": num_joint, "n_layers": num_joint + num_single}, output_prefix=output_prefix)
+    elif 'adaln_single.emb.timestep_embedder.linear_1.bias' in state_dict and 'pos_embed.proj.bias' in state_dict: # PixArt
+        num_blocks = count_blocks(state_dict, 'transformer_blocks.{}.')
+        sd_map = comfy.utils.pixart_to_diffusers({"depth": num_blocks}, output_prefix=output_prefix)
     elif 'x_embedder.weight' in state_dict: #Flux
         depth = count_blocks(state_dict, 'transformer_blocks.{}.')
         depth_single_blocks = count_blocks(state_dict, 'single_transformer_blocks.{}.')

comfy/model_management.py

@@ -50,7 +50,9 @@ xpu_available = False
 torch_version = ""
 try:
     torch_version = torch.version.__version__
-    xpu_available = (int(torch_version[0]) < 2 or (int(torch_version[0]) == 2 and int(torch_version[2]) <= 4)) and torch.xpu.is_available()
+    temp = torch_version.split(".")
+    torch_version_numeric = (int(temp[0]), int(temp[1]))
+    xpu_available = (torch_version_numeric[0] < 2 or (torch_version_numeric[0] == 2 and torch_version_numeric[1] <= 4)) and torch.xpu.is_available()
 except:
     pass
 
@@ -75,7 +77,7 @@ if args.directml is not None:
 try:
     import intel_extension_for_pytorch as ipex
     _ = torch.xpu.device_count()
-    xpu_available = torch.xpu.is_available()
+    xpu_available = xpu_available or torch.xpu.is_available()
 except:
     xpu_available = xpu_available or (hasattr(torch, "xpu") and torch.xpu.is_available())
 
@@ -86,6 +88,13 @@ try:
 except:
     pass
 
+try:
+    import torch_npu  # noqa: F401
+    _ = torch.npu.device_count()
+    npu_available = torch.npu.is_available()
+except:
+    npu_available = False
+
 if args.cpu:
     cpu_state = CPUState.CPU
 
@@ -97,6 +106,12 @@ def is_intel_xpu():
             return True
     return False
 
+def is_ascend_npu():
+    global npu_available
+    if npu_available:
+        return True
+    return False
+
 def get_torch_device():
     global directml_enabled
     global cpu_state
@@ -110,6 +125,8 @@ def get_torch_device():
     else:
         if is_intel_xpu():
             return torch.device("xpu", torch.xpu.current_device())
+        elif is_ascend_npu():
+            return torch.device("npu", torch.npu.current_device())
         else:
             return torch.device(torch.cuda.current_device())
 
@@ -130,6 +147,12 @@ def get_total_memory(dev=None, torch_total_too=False):
             mem_reserved = stats['reserved_bytes.all.current']
             mem_total_torch = mem_reserved
             mem_total = torch.xpu.get_device_properties(dev).total_memory
+        elif is_ascend_npu():
+            stats = torch.npu.memory_stats(dev)
+            mem_reserved = stats['reserved_bytes.all.current']
+            _, mem_total_npu = torch.npu.mem_get_info(dev)
+            mem_total_torch = mem_reserved
+            mem_total = mem_total_npu
         else:
             stats = torch.cuda.memory_stats(dev)
             mem_reserved = stats['reserved_bytes.all.current']
@@ -188,31 +211,44 @@ def is_nvidia():
             return True
     return False
 
+def is_amd():
+    global cpu_state
+    if cpu_state == CPUState.GPU:
+        if torch.version.hip:
+            return True
+    return False
+
+MIN_WEIGHT_MEMORY_RATIO = 0.4
+if is_nvidia():
+    MIN_WEIGHT_MEMORY_RATIO = 0.0
+
 ENABLE_PYTORCH_ATTENTION = False
 if args.use_pytorch_cross_attention:
     ENABLE_PYTORCH_ATTENTION = True
     XFORMERS_IS_AVAILABLE = False
 
-VAE_DTYPES = [torch.float32]
-
 try:
     if is_nvidia():
-        if int(torch_version[0]) >= 2:
+        if torch_version_numeric[0] >= 2:
             if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
                 ENABLE_PYTORCH_ATTENTION = True
-            if torch.cuda.is_bf16_supported() and torch.cuda.get_device_properties(torch.cuda.current_device()).major >= 8:
-                VAE_DTYPES = [torch.bfloat16] + VAE_DTYPES
-    if is_intel_xpu():
+    if is_intel_xpu() or is_ascend_npu():
         if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
             ENABLE_PYTORCH_ATTENTION = True
 except:
     pass
 
-if is_intel_xpu():
-    VAE_DTYPES = [torch.bfloat16] + VAE_DTYPES
 
-if args.cpu_vae:
-    VAE_DTYPES = [torch.float32]
+try:
+    if is_amd():
+        arch = torch.cuda.get_device_properties(get_torch_device()).gcnArchName
+        logging.info("AMD arch: {}".format(arch))
+        if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
+            if torch_version_numeric[0] >= 2 and torch_version_numeric[1] >= 7:  # works on 2.6 but doesn't actually seem to improve much
+                if any((a in arch) for a in ["gfx1100", "gfx1101"]):  # TODO: more arches
+                    ENABLE_PYTORCH_ATTENTION = True
+except:
+    pass
 
 
 if ENABLE_PYTORCH_ATTENTION:
@@ -220,6 +256,18 @@ if ENABLE_PYTORCH_ATTENTION:
     torch.backends.cuda.enable_flash_sdp(True)
     torch.backends.cuda.enable_mem_efficient_sdp(True)
 
+try:
+    if is_nvidia() and args.fast:
+        torch.backends.cuda.matmul.allow_fp16_accumulation = True
+except:
+    pass
+
+try:
+    if torch_version_numeric[0] == 2 and torch_version_numeric[1] >= 5:
+        torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp(True)
+except:
+    logging.warning("Warning, could not set allow_fp16_bf16_reduction_math_sdp")
+
 if args.lowvram:
     set_vram_to = VRAMState.LOW_VRAM
     lowvram_available = True
@@ -229,15 +277,10 @@ elif args.highvram or args.gpu_only:
     vram_state = VRAMState.HIGH_VRAM
 
 FORCE_FP32 = False
-FORCE_FP16 = False
 if args.force_fp32:
     logging.info("Forcing FP32, if this improves things please report it.")
     FORCE_FP32 = True
 
-if args.force_fp16:
-    logging.info("Forcing FP16.")
-    FORCE_FP16 = True
-
 if lowvram_available:
     if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM):
         vram_state = set_vram_to
@@ -268,6 +311,8 @@ def get_torch_device_name(device):
             return "{}".format(device.type)
     elif is_intel_xpu():
         return "{} {}".format(device, torch.xpu.get_device_name(device))
+    elif is_ascend_npu():
+        return "{} {}".format(device, torch.npu.get_device_name(device))
     else:
         return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device))
 
@@ -314,6 +359,9 @@ class LoadedModel:
     def model_memory(self):
         return self.model.model_size()
 
+    def model_loaded_memory(self):
+        return self.model.loaded_size()
+
     def model_offloaded_memory(self):
         return self.model.model_size() - self.model.loaded_size()
 
@@ -503,14 +551,14 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
             vram_set_state = vram_state
         lowvram_model_memory = 0
         if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM) and not force_full_load:
-            model_size = loaded_model.model_memory_required(torch_dev)
-            current_free_mem = get_free_memory(torch_dev)
-            lowvram_model_memory = max(64 * (1024 * 1024), (current_free_mem - minimum_memory_required), min(current_free_mem * 0.4, current_free_mem - minimum_inference_memory()))
-            if model_size <= lowvram_model_memory: #only switch to lowvram if really necessary
-                lowvram_model_memory = 0
+            loaded_memory = loaded_model.model_loaded_memory()
+            current_free_mem = get_free_memory(torch_dev) + loaded_memory
+
+            lowvram_model_memory = max(64 * 1024 * 1024, (current_free_mem - minimum_memory_required), min(current_free_mem * MIN_WEIGHT_MEMORY_RATIO, current_free_mem - minimum_inference_memory()))
+            lowvram_model_memory = max(0.1, lowvram_model_memory - loaded_memory)
 
         if vram_set_state == VRAMState.NO_VRAM:
-            lowvram_model_memory = 64 * 1024 * 1024
+            lowvram_model_memory = 0.1
 
         loaded_model.model_load(lowvram_model_memory, force_patch_weights=force_patch_weights)
         current_loaded_models.insert(0, loaded_model)
@@ -738,7 +786,6 @@ def vae_offload_device():
         return torch.device("cpu")
 
 def vae_dtype(device=None, allowed_dtypes=[]):
-    global VAE_DTYPES
     if args.fp16_vae:
         return torch.float16
     elif args.bf16_vae:
@@ -747,12 +794,14 @@ def vae_dtype(device=None, allowed_dtypes=[]):
         return torch.float32
 
     for d in allowed_dtypes:
-        if d == torch.float16 and should_use_fp16(device, prioritize_performance=False):
-            return d
-        if d in VAE_DTYPES:
+        if d == torch.float16 and should_use_fp16(device):
             return d
 
-    return VAE_DTYPES[0]
+        # NOTE: bfloat16 seems to work on AMD for the VAE but is extremely slow in some cases compared to fp32
+        if d == torch.bfloat16 and (not is_amd()) and should_use_bf16(device):
+            return d
+
+    return torch.float32
 
 def get_autocast_device(dev):
     if hasattr(dev, 'type'):
@@ -847,6 +896,8 @@ def xformers_enabled():
         return False
     if is_intel_xpu():
         return False
+    if is_ascend_npu():
+        return False
     if directml_enabled:
         return False
     return XFORMERS_IS_AVAILABLE
@@ -863,6 +914,11 @@ def pytorch_attention_enabled():
     global ENABLE_PYTORCH_ATTENTION
     return ENABLE_PYTORCH_ATTENTION
 
+def pytorch_attention_enabled_vae():
+    if is_amd():
+        return False  # enabling pytorch attention on AMD currently causes crash when doing high res
+    return pytorch_attention_enabled()
+
 def pytorch_attention_flash_attention():
     global ENABLE_PYTORCH_ATTENTION
     if ENABLE_PYTORCH_ATTENTION:
@@ -871,16 +927,25 @@ def pytorch_attention_flash_attention():
             return True
         if is_intel_xpu():
             return True
+        if is_ascend_npu():
+            return True
+        if is_amd():
+            return True #if you have pytorch attention enabled on AMD it probably supports at least mem efficient attention
     return False
 
+def mac_version():
+    try:
+        return tuple(int(n) for n in platform.mac_ver()[0].split("."))
+    except:
+        return None
+
 def force_upcast_attention_dtype():
     upcast = args.force_upcast_attention
-    try:
-        macos_version = tuple(int(n) for n in platform.mac_ver()[0].split("."))
-        if (14, 5) <= macos_version <= (15, 2):  # black image bug on recent versions of macOS
-            upcast = True
-    except:
-        pass
+
+    macos_version = mac_version()
+    if macos_version is not None and ((14, 5) <= macos_version < (16,)):  # black image bug on recent versions of macOS
+        upcast = True
+
     if upcast:
         return torch.float32
     else:
@@ -905,6 +970,13 @@ def get_free_memory(dev=None, torch_free_too=False):
             mem_free_torch = mem_reserved - mem_active
             mem_free_xpu = torch.xpu.get_device_properties(dev).total_memory - mem_reserved
             mem_free_total = mem_free_xpu + mem_free_torch
+        elif is_ascend_npu():
+            stats = torch.npu.memory_stats(dev)
+            mem_active = stats['active_bytes.all.current']
+            mem_reserved = stats['reserved_bytes.all.current']
+            mem_free_npu, _ = torch.npu.mem_get_info(dev)
+            mem_free_torch = mem_reserved - mem_active
+            mem_free_total = mem_free_npu + mem_free_torch
         else:
             stats = torch.cuda.memory_stats(dev)
             mem_active = stats['active_bytes.all.current']
@@ -941,27 +1013,28 @@ def is_device_mps(device):
 def is_device_cuda(device):
     return is_device_type(device, 'cuda')
 
-def should_use_fp16(device=None, model_params=0, prioritize_performance=True, manual_cast=False):
+def is_directml_enabled():
     global directml_enabled
+    if directml_enabled:
+        return True
 
+    return False
+
+def should_use_fp16(device=None, model_params=0, prioritize_performance=True, manual_cast=False):
     if device is not None:
         if is_device_cpu(device):
             return False
 
-    if FORCE_FP16:
+    if args.force_fp16:
         return True
 
-    if device is not None:
-        if is_device_mps(device):
-            return True
-
     if FORCE_FP32:
         return False
 
-    if directml_enabled:
-        return False
+    if is_directml_enabled():
+        return True
 
-    if mps_mode():
+    if (device is not None and is_device_mps(device)) or mps_mode():
         return True
 
     if cpu_mode():
@@ -970,6 +1043,9 @@ def should_use_fp16(device=None, model_params=0, prioritize_performance=True, ma
     if is_intel_xpu():
         return True
 
+    if is_ascend_npu():
+        return True
+
     if torch.version.hip:
         return True
 
@@ -1010,17 +1086,15 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
         if is_device_cpu(device): #TODO ? bf16 works on CPU but is extremely slow
             return False
 
-    if device is not None:
-        if is_device_mps(device):
-            return True
-
     if FORCE_FP32:
         return False
 
     if directml_enabled:
         return False
 
-    if mps_mode():
+    if (device is not None and is_device_mps(device)) or mps_mode():
+        if mac_version() < (14,):
+            return False
         return True
 
     if cpu_mode():
@@ -1029,13 +1103,23 @@ def should_use_bf16(device=None, model_params=0, prioritize_performance=True, ma
     if is_intel_xpu():
         return True
 
+    if is_ascend_npu():
+        return True
+
+    if is_amd():
+        arch = torch.cuda.get_device_properties(device).gcnArchName
+        if any((a in arch) for a in ["gfx1030", "gfx1031", "gfx1010", "gfx1011", "gfx1012", "gfx906", "gfx900", "gfx803"]):  # RDNA2 and older don't support bf16
+            if manual_cast:
+                return True
+            return False
+
     props = torch.cuda.get_device_properties(device)
     if props.major >= 8:
         return True
 
     bf16_works = torch.cuda.is_bf16_supported()
 
-    if bf16_works or manual_cast:
+    if bf16_works and manual_cast:
         free_model_memory = maximum_vram_for_weights(device)
         if (not prioritize_performance) or model_params * 4 > free_model_memory:
             return True
@@ -1054,11 +1138,11 @@ def supports_fp8_compute(device=None):
     if props.minor < 9:
         return False
 
-    if int(torch_version[0]) < 2 or (int(torch_version[0]) == 2 and int(torch_version[2]) < 3):
+    if torch_version_numeric[0] < 2 or (torch_version_numeric[0] == 2 and torch_version_numeric[1] < 3):
         return False
 
     if WINDOWS:
-        if (int(torch_version[0]) == 2 and int(torch_version[2]) < 4):
+        if (torch_version_numeric[0] == 2 and torch_version_numeric[1] < 4):
             return False
 
     return True
@@ -1069,19 +1153,16 @@ def soft_empty_cache(force=False):
         torch.mps.empty_cache()
     elif is_intel_xpu():
         torch.xpu.empty_cache()
+    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
-    print("WARNING: The comfy.model_management.resolve_lowvram_weight function will be removed soon, please stop using it.")
-    return weight
-
 #TODO: might be cleaner to put this somewhere else
 import threading
 

comfy/model_patcher.py

@@ -83,7 +83,7 @@ def set_model_options_pre_cfg_function(model_options, pre_cfg_function, disable_
 
 def create_model_options_clone(orig_model_options: dict):
     return comfy.patcher_extension.copy_nested_dicts(orig_model_options)
-        
+
 def create_hook_patches_clone(orig_hook_patches):
     new_hook_patches = {}
     for hook_ref in orig_hook_patches:
@@ -96,8 +96,28 @@ def wipe_lowvram_weight(m):
     if hasattr(m, "prev_comfy_cast_weights"):
         m.comfy_cast_weights = m.prev_comfy_cast_weights
         del m.prev_comfy_cast_weights
-    m.weight_function = None
-    m.bias_function = None
+
+    if hasattr(m, "weight_function"):
+        m.weight_function = []
+
+    if hasattr(m, "bias_function"):
+        m.bias_function = []
+
+def move_weight_functions(m, device):
+    if device is None:
+        return 0
+
+    memory = 0
+    if hasattr(m, "weight_function"):
+        for f in m.weight_function:
+            if hasattr(f, "move_to"):
+                memory += f.move_to(device=device)
+
+    if hasattr(m, "bias_function"):
+        for f in m.bias_function:
+            if hasattr(f, "move_to"):
+                memory += f.move_to(device=device)
+    return memory
 
 class LowVramPatch:
     def __init__(self, key, patches):
@@ -141,7 +161,7 @@ class AutoPatcherEjector:
         self.was_injected = False
         self.prev_skip_injection = False
         self.skip_and_inject_on_exit_only = skip_and_inject_on_exit_only
-    
+
     def __enter__(self):
         self.was_injected = False
         self.prev_skip_injection = self.model.skip_injection
@@ -164,7 +184,7 @@ class MemoryCounter:
         self.value = initial
         self.minimum = minimum
         # TODO: add a safe limit besides 0
-    
+
     def use(self, weight: torch.Tensor):
         weight_size = weight.nelement() * weight.element_size()
         if self.is_useable(weight_size):
@@ -192,11 +212,13 @@ class ModelPatcher:
         self.backup = {}
         self.object_patches = {}
         self.object_patches_backup = {}
+        self.weight_wrapper_patches = {}
         self.model_options = {"transformer_options":{}}
         self.model_size()
         self.load_device = load_device
         self.offload_device = offload_device
         self.weight_inplace_update = weight_inplace_update
+        self.force_cast_weights = False
         self.patches_uuid = uuid.uuid4()
         self.parent = None
 
@@ -210,7 +232,7 @@ 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
@@ -250,11 +272,14 @@ class ModelPatcher:
         n.patches_uuid = self.patches_uuid
 
         n.object_patches = self.object_patches.copy()
+        n.weight_wrapper_patches = self.weight_wrapper_patches.copy()
         n.model_options = copy.deepcopy(self.model_options)
         n.backup = self.backup
         n.object_patches_backup = self.object_patches_backup
         n.parent = self
 
+        n.force_cast_weights = self.force_cast_weights
+
         # attachments
         n.attachments = {}
         for k in self.attachments:
@@ -282,7 +307,7 @@ 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 +427,30 @@ class ModelPatcher:
     def add_object_patch(self, name, obj):
         self.object_patches[name] = obj
 
-    def get_model_object(self, name):
+    def set_model_compute_dtype(self, dtype):
+        self.add_object_patch("manual_cast_dtype", dtype)
+        if dtype is not None:
+            self.force_cast_weights = True
+        self.patches_uuid = uuid.uuid4() #TODO: optimize by preventing a full model reload for this
+
+    def add_weight_wrapper(self, name, function):
+        self.weight_wrapper_patches[name] = self.weight_wrapper_patches.get(name, []) + [function]
+        self.patches_uuid = uuid.uuid4()
+
+    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:
@@ -553,6 +601,9 @@ class ModelPatcher:
 
                 lowvram_weight = False
 
+                weight_key = "{}.weight".format(n)
+                bias_key = "{}.bias".format(n)
+
                 if not full_load and hasattr(m, "comfy_cast_weights"):
                     if mem_counter + module_mem >= lowvram_model_memory:
                         lowvram_weight = True
@@ -560,34 +611,46 @@ class ModelPatcher:
                         if hasattr(m, "prev_comfy_cast_weights"): #Already lowvramed
                             continue
 
-                weight_key = "{}.weight".format(n)
-                bias_key = "{}.bias".format(n)
-
+                cast_weight = self.force_cast_weights
                 if lowvram_weight:
+                    if hasattr(m, "comfy_cast_weights"):
+                        m.weight_function = []
+                        m.bias_function = []
+
                     if weight_key in self.patches:
                         if force_patch_weights:
                             self.patch_weight_to_device(weight_key)
                         else:
-                            m.weight_function = LowVramPatch(weight_key, self.patches)
+                            m.weight_function = [LowVramPatch(weight_key, self.patches)]
                             patch_counter += 1
                     if bias_key in self.patches:
                         if force_patch_weights:
                             self.patch_weight_to_device(bias_key)
                         else:
-                            m.bias_function = LowVramPatch(bias_key, self.patches)
+                            m.bias_function = [LowVramPatch(bias_key, self.patches)]
                             patch_counter += 1
 
-                    m.prev_comfy_cast_weights = m.comfy_cast_weights
-                    m.comfy_cast_weights = True
+                    cast_weight = True
                 else:
                     if hasattr(m, "comfy_cast_weights"):
-                        if m.comfy_cast_weights:
-                            wipe_lowvram_weight(m)
+                        wipe_lowvram_weight(m)
 
                     if full_load or mem_counter + module_mem < lowvram_model_memory:
                         mem_counter += module_mem
                         load_completely.append((module_mem, n, m, params))
 
+                if cast_weight:
+                    m.prev_comfy_cast_weights = m.comfy_cast_weights
+                    m.comfy_cast_weights = True
+
+                if weight_key in self.weight_wrapper_patches:
+                    m.weight_function.extend(self.weight_wrapper_patches[weight_key])
+
+                if bias_key in self.weight_wrapper_patches:
+                    m.bias_function.extend(self.weight_wrapper_patches[bias_key])
+
+                mem_counter += move_weight_functions(m, device_to)
+
             load_completely.sort(reverse=True)
             for x in load_completely:
                 n = x[1]
@@ -649,6 +712,7 @@ class ModelPatcher:
             self.unpatch_hooks()
             if self.model.model_lowvram:
                 for m in self.model.modules():
+                    move_weight_functions(m, device_to)
                     wipe_lowvram_weight(m)
 
                 self.model.model_lowvram = False
@@ -711,19 +775,23 @@ class ModelPatcher:
                             else:
                                 comfy.utils.set_attr_param(self.model, key, bk.weight)
                             self.backup.pop(key)
-                    
+
                     weight_key = "{}.weight".format(n)
                     bias_key = "{}.bias".format(n)
                     if move_weight:
+                        cast_weight = self.force_cast_weights
                         m.to(device_to)
+                        module_mem += move_weight_functions(m, device_to)
                         if lowvram_possible:
                             if weight_key in self.patches:
-                                m.weight_function = LowVramPatch(weight_key, self.patches)
+                                m.weight_function.append(LowVramPatch(weight_key, self.patches))
                                 patch_counter += 1
                             if bias_key in self.patches:
-                                m.bias_function = LowVramPatch(bias_key, self.patches)
+                                m.bias_function.append(LowVramPatch(bias_key, self.patches))
                                 patch_counter += 1
+                            cast_weight = True
 
+                        if cast_weight:
                             m.prev_comfy_cast_weights = m.comfy_cast_weights
                             m.comfy_cast_weights = True
                         m.comfy_patched_weights = False
@@ -773,7 +841,7 @@ class ModelPatcher:
         return self.model.device
 
     def calculate_weight(self, patches, weight, key, intermediate_dtype=torch.float32):
-        print("WARNING the ModelPatcher.calculate_weight function is deprecated, please use: comfy.lora.calculate_weight instead")
+        logging.warning("The ModelPatcher.calculate_weight function is deprecated, please use: comfy.lora.calculate_weight instead")
         return comfy.lora.calculate_weight(patches, weight, key, intermediate_dtype=intermediate_dtype)
 
     def cleanup(self):
@@ -789,7 +857,7 @@ class ModelPatcher:
     def add_callback_with_key(self, call_type: str, key: str, callback: Callable):
         c = self.callbacks.setdefault(call_type, {}).setdefault(key, [])
         c.append(callback)
-    
+
     def remove_callbacks_with_key(self, call_type: str, key: str):
         c = self.callbacks.get(call_type, {})
         if key in c:
@@ -797,7 +865,7 @@ class ModelPatcher:
 
     def get_callbacks(self, call_type: str, key: str):
         return self.callbacks.get(call_type, {}).get(key, [])
-    
+
     def get_all_callbacks(self, call_type: str):
         c_list = []
         for c in self.callbacks.get(call_type, {}).values():
@@ -810,7 +878,7 @@ class ModelPatcher:
     def add_wrapper_with_key(self, wrapper_type: str, key: str, wrapper: Callable):
         w = self.wrappers.setdefault(wrapper_type, {}).setdefault(key, [])
         w.append(wrapper)
-    
+
     def remove_wrappers_with_key(self, wrapper_type: str, key: str):
         w = self.wrappers.get(wrapper_type, {})
         if key in w:
@@ -831,7 +899,7 @@ class ModelPatcher:
     def remove_attachments(self, key: str):
         if key in self.attachments:
             self.attachments.pop(key)
-    
+
     def get_attachment(self, key: str):
         return self.attachments.get(key, None)
 
@@ -842,6 +910,9 @@ 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
 
@@ -851,7 +922,7 @@ class ModelPatcher:
 
     def get_additional_models_with_key(self, key: str):
         return self.additional_models.get(key, [])
-    
+
     def get_additional_models(self):
         all_models = []
         for models in self.additional_models.values():
@@ -906,24 +977,25 @@ class ModelPatcher:
             self.model.current_patcher = self
         for callback in self.get_all_callbacks(CallbacksMP.ON_PRE_RUN):
             callback(self)
-    
+
     def prepare_state(self, timestep):
         for callback in self.get_all_callbacks(CallbacksMP.ON_PREPARE_STATE):
             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
-    
-    def prepare_hook_patches_current_keyframe(self, t: torch.Tensor, hook_group: comfy.hooks.HookGroup):
+
+    def prepare_hook_patches_current_keyframe(self, t: torch.Tensor, hook_group: comfy.hooks.HookGroup, model_options: dict[str]):
         curr_t = t[0]
         reset_current_hooks = False
+        transformer_options = model_options.get("transformer_options", {})
         for hook in hook_group.hooks:
-            changed = hook.hook_keyframe.prepare_current_keyframe(curr_t=curr_t)
+            changed = hook.hook_keyframe.prepare_current_keyframe(curr_t=curr_t, transformer_options=transformer_options)
             # if keyframe changed, remove any cached HookGroups that contain hook with the same hook_ref;
             # this will cause the weights to be recalculated when sampling
             if changed:
@@ -939,25 +1011,26 @@ 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():
@@ -975,7 +1048,7 @@ class ModelPatcher:
                     key = k[0]
                     if len(k) > 2:
                         function = k[2]
-                
+
                 if key in model_sd:
                     p.add(k)
                     current_patches: list[tuple] = current_hook_patches.get(key, [])
@@ -1008,11 +1081,11 @@ 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():
@@ -1029,7 +1102,7 @@ class ModelPatcher:
                 if cached_weights is not None:
                     for key in cached_weights:
                         if key not in model_sd_keys:
-                            print(f"WARNING cached hook could not patch. key does not exist in model: {key}")
+                            logging.warning(f"Cached hook could not patch. Key does not exist in model: {key}")
                             continue
                         self.patch_cached_hook_weights(cached_weights=cached_weights, key=key, memory_counter=memory_counter)
                 else:
@@ -1039,7 +1112,7 @@ class ModelPatcher:
                         original_weights = self.get_key_patches()
                     for key in relevant_patches:
                         if key not in model_sd_keys:
-                            print(f"WARNING cached hook would not patch. key does not exist in model: {key}")
+                            logging.warning(f"Cached hook would not patch. Key does not exist in model: {key}")
                             continue
                         self.patch_hook_weight_to_device(hooks=hooks, combined_patches=relevant_patches, key=key, original_weights=original_weights,
                                                             memory_counter=memory_counter)
@@ -1063,7 +1136,7 @@ class ModelPatcher:
     def patch_hook_weight_to_device(self, hooks: comfy.hooks.HookGroup, combined_patches: dict, key: str, original_weights: dict, memory_counter: MemoryCounter):
         if key not in combined_patches:
             return
-        
+
         weight, set_func, convert_func = get_key_weight(self.model, key)
         weight: torch.Tensor
         if key not in self.hook_backup:
@@ -1098,7 +1171,7 @@ class ModelPatcher:
         del temp_weight
         del out_weight
         del weight
-    
+
     def unpatch_hooks(self) -> None:
         with self.use_ejected():
             if len(self.hook_backup) == 0:
@@ -1107,7 +1180,7 @@ class ModelPatcher:
             keys = list(self.hook_backup.keys())
             for k in keys:
                 comfy.utils.copy_to_param(self.model, k, self.hook_backup[k][0].to(device=self.hook_backup[k][1]))
-                    
+
             self.hook_backup.clear()
             self.current_hooks = None
 

comfy/model_sampling.py

@@ -31,6 +31,7 @@ class EPS:
         return model_input - model_output * sigma
 
     def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
+        sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
         if max_denoise:
             noise = noise * torch.sqrt(1.0 + sigma ** 2.0)
         else:
@@ -61,9 +62,11 @@ class CONST:
         return model_input - model_output * sigma
 
     def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
+        sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
         return sigma * noise + (1.0 - sigma) * latent_image
 
     def inverse_noise_scaling(self, sigma, latent):
+        sigma = sigma.view(sigma.shape[:1] + (1,) * (latent.ndim - 1))
         return latent / (1.0 - sigma)
 
 class ModelSamplingDiscrete(torch.nn.Module):

comfy/ops.py

@@ -38,21 +38,23 @@ def cast_bias_weight(s, input=None, dtype=None, device=None, bias_dtype=None):
     bias = None
     non_blocking = comfy.model_management.device_supports_non_blocking(device)
     if s.bias is not None:
-        has_function = s.bias_function is not None
+        has_function = len(s.bias_function) > 0
         bias = comfy.model_management.cast_to(s.bias, bias_dtype, device, non_blocking=non_blocking, copy=has_function)
         if has_function:
-            bias = s.bias_function(bias)
+            for f in s.bias_function:
+                bias = f(bias)
 
-    has_function = s.weight_function is not None
+    has_function = len(s.weight_function) > 0
     weight = comfy.model_management.cast_to(s.weight, dtype, device, non_blocking=non_blocking, copy=has_function)
     if has_function:
-        weight = s.weight_function(weight)
+        for f in s.weight_function:
+            weight = f(weight)
     return weight, bias
 
 class CastWeightBiasOp:
     comfy_cast_weights = False
-    weight_function = None
-    bias_function = None
+    weight_function = []
+    bias_function = []
 
 class disable_weight_init:
     class Linear(torch.nn.Linear, CastWeightBiasOp):
@@ -64,7 +66,7 @@ class disable_weight_init:
             return torch.nn.functional.linear(input, weight, bias)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
                 return super().forward(*args, **kwargs)
@@ -78,7 +80,7 @@ class disable_weight_init:
             return self._conv_forward(input, weight, bias)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
                 return super().forward(*args, **kwargs)
@@ -92,7 +94,7 @@ class disable_weight_init:
             return self._conv_forward(input, weight, bias)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
                 return super().forward(*args, **kwargs)
@@ -106,7 +108,7 @@ class disable_weight_init:
             return self._conv_forward(input, weight, bias)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
                 return super().forward(*args, **kwargs)
@@ -120,12 +122,11 @@ class disable_weight_init:
             return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
                 return super().forward(*args, **kwargs)
 
-
     class LayerNorm(torch.nn.LayerNorm, CastWeightBiasOp):
         def reset_parameters(self):
             return None
@@ -139,7 +140,7 @@ class disable_weight_init:
             return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
                 return super().forward(*args, **kwargs)
@@ -160,7 +161,7 @@ class disable_weight_init:
                 output_padding, self.groups, self.dilation)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
                 return super().forward(*args, **kwargs)
@@ -181,7 +182,7 @@ class disable_weight_init:
                 output_padding, self.groups, self.dilation)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
                 return super().forward(*args, **kwargs)
@@ -199,7 +200,7 @@ class disable_weight_init:
             return torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype)
 
         def forward(self, *args, **kwargs):
-            if self.comfy_cast_weights:
+            if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
                 if "out_dtype" in kwargs:
@@ -255,9 +256,10 @@ def fp8_linear(self, input):
         tensor_2d = True
         input = input.unsqueeze(1)
 
-
+    input_shape = input.shape
+    input_dtype = input.dtype
     if len(input.shape) == 3:
-        w, bias = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input.dtype)
+        w, bias = cast_bias_weight(self, input, dtype=dtype, bias_dtype=input_dtype)
         w = w.t()
 
         scale_weight = self.scale_weight
@@ -269,23 +271,24 @@ def fp8_linear(self, input):
 
         if scale_input is None:
             scale_input = torch.ones((), device=input.device, dtype=torch.float32)
-            inn = torch.clamp(input, min=-448, max=448).reshape(-1, input.shape[2]).to(dtype)
+            input = torch.clamp(input, min=-448, max=448, out=input)
+            input = input.reshape(-1, input_shape[2]).to(dtype)
         else:
             scale_input = scale_input.to(input.device)
-            inn = (input * (1.0 / scale_input).to(input.dtype)).reshape(-1, input.shape[2]).to(dtype)
+            input = (input * (1.0 / scale_input).to(input_dtype)).reshape(-1, input_shape[2]).to(dtype)
 
         if bias is not None:
-            o = torch._scaled_mm(inn, w, out_dtype=input.dtype, bias=bias, scale_a=scale_input, scale_b=scale_weight)
+            o = torch._scaled_mm(input, w, out_dtype=input_dtype, bias=bias, scale_a=scale_input, scale_b=scale_weight)
         else:
-            o = torch._scaled_mm(inn, w, out_dtype=input.dtype, scale_a=scale_input, scale_b=scale_weight)
+            o = torch._scaled_mm(input, w, out_dtype=input_dtype, scale_a=scale_input, scale_b=scale_weight)
 
         if isinstance(o, tuple):
             o = o[0]
 
         if tensor_2d:
-            return o.reshape(input.shape[0], -1)
+            return o.reshape(input_shape[0], -1)
 
-        return o.reshape((-1, input.shape[1], self.weight.shape[0]))
+        return o.reshape((-1, input_shape[1], self.weight.shape[0]))
 
     return None
 

comfy/patcher_extension.py

@@ -96,12 +96,12 @@ class WrapperExecutor:
         self.wrappers = wrappers.copy()
         self.idx = idx
         self.is_last = idx == len(wrappers)
-    
+
     def __call__(self, *args, **kwargs):
         """Calls the next wrapper or original function, whichever is appropriate."""
         new_executor = self._create_next_executor()
         return new_executor.execute(*args, **kwargs)
-    
+
     def execute(self, *args, **kwargs):
         """Used to initiate executor internally - DO NOT use this if you received executor in wrapper."""
         args = list(args)
@@ -121,7 +121,7 @@ class WrapperExecutor:
     @classmethod
     def new_executor(cls, original: Callable, wrappers: list[Callable], idx=0):
         return cls(original, class_obj=None, wrappers=wrappers, idx=idx)
-    
+
     @classmethod
     def new_class_executor(cls, original: Callable, class_obj: object, wrappers: list[Callable], idx=0):
         return cls(original, class_obj, wrappers, idx=idx)

comfy/sample.py

@@ -13,7 +13,7 @@ def prepare_noise(latent_image, seed, noise_inds=None):
     generator = torch.manual_seed(seed)
     if noise_inds is None:
         return torch.randn(latent_image.size(), dtype=latent_image.dtype, layout=latent_image.layout, generator=generator, device="cpu")
-    
+
     unique_inds, inverse = np.unique(noise_inds, return_inverse=True)
     noises = []
     for i in range(unique_inds[-1]+1):
@@ -25,9 +25,11 @@ def prepare_noise(latent_image, seed, noise_inds=None):
     return noises
 
 def fix_empty_latent_channels(model, latent_image):
-    latent_channels = model.get_model_object("latent_format").latent_channels #Resize the empty latent image so it has the right number of channels
-    if latent_channels != latent_image.shape[1] and torch.count_nonzero(latent_image) == 0:
-        latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_channels, dim=1)
+    latent_format = model.get_model_object("latent_format") #Resize the empty latent image so it has the right number of channels
+    if latent_format.latent_channels != latent_image.shape[1] and torch.count_nonzero(latent_image) == 0:
+        latent_image = comfy.utils.repeat_to_batch_size(latent_image, latent_format.latent_channels, dim=1)
+    if latent_format.latent_dimensions == 3 and latent_image.ndim == 4:
+        latent_image = latent_image.unsqueeze(2)
     return latent_image
 
 def prepare_sampling(model, noise_shape, positive, negative, noise_mask):

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'])
 
@@ -42,7 +40,7 @@ def get_hooks_from_cond(cond, hooks_dict: dict[comfy.hooks.EnumHookType, dict[co
         if cnet.previous_controlnet is None:
             return _list
         return get_extra_hooks_from_cnet(cnet.previous_controlnet, _list)
-        
+
     hooks_list = []
     cnets = set(cnets)
     for base_cnet in cnets:
@@ -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 = []
@@ -61,7 +58,6 @@ def convert_cond(cond):
         temp = c[1].copy()
         model_conds = temp.get("model_conds", {})
         if c[0] is not None:
-            model_conds["c_crossattn"] = comfy.conds.CONDCrossAttn(c[0]) #TODO: remove
             temp["cross_attn"] = c[0]
         temp["model_conds"] = model_conds
         temp["uuid"] = uuid.uuid4()
@@ -73,13 +69,11 @@ 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 +84,20 @@ 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 +105,10 @@ 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 +127,35 @@ 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

@@ -1,17 +1,17 @@
 from __future__ import annotations
 from .k_diffusion import sampling as k_diffusion_sampling
 from .extra_samplers import uni_pc
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Callable, NamedTuple
 if TYPE_CHECKING:
     from comfy.model_patcher import ModelPatcher
     from comfy.model_base import BaseModel
     from comfy.controlnet import ControlBase
 import torch
+from functools import partial
 import collections
 from comfy import model_management
 import math
 import logging
-import comfy.samplers
 import comfy.sampler_helpers
 import comfy.model_patcher
 import comfy.patcher_extension
@@ -144,7 +144,7 @@ def cond_cat(c_list):
 
     return out
 
-def finalize_default_conds(model: 'BaseModel', hooked_to_run: dict[comfy.hooks.HookGroup,list[tuple[tuple,int]]], default_conds: list[list[dict]], x_in, timestep):
+def finalize_default_conds(model: 'BaseModel', hooked_to_run: dict[comfy.hooks.HookGroup,list[tuple[tuple,int]]], default_conds: list[list[dict]], x_in, timestep, model_options):
     # need to figure out remaining unmasked area for conds
     default_mults = []
     for _ in default_conds:
@@ -177,13 +177,13 @@ def finalize_default_conds(model: 'BaseModel', hooked_to_run: dict[comfy.hooks.H
         cond = default_conds[i]
         for x in cond:
             # do get_area_and_mult to get all the expected values
-            p = comfy.samplers.get_area_and_mult(x, x_in, timestep)
+            p = get_area_and_mult(x, x_in, timestep)
             if p is None:
                 continue
             # replace p's mult with calculated mult
             p = p._replace(mult=mult)
             if p.hooks is not None:
-                model.current_patcher.prepare_hook_patches_current_keyframe(timestep, p.hooks)
+                model.current_patcher.prepare_hook_patches_current_keyframe(timestep, p.hooks, model_options)
             hooked_to_run.setdefault(p.hooks, list())
             hooked_to_run[p.hooks] += [(p, i)]
 
@@ -214,17 +214,17 @@ def _calc_cond_batch(model: 'BaseModel', conds: list[list[dict]], x_in: torch.Te
                     default_c.append(x)
                     has_default_conds = True
                     continue
-                p = comfy.samplers.get_area_and_mult(x, x_in, timestep)
+                p = get_area_and_mult(x, x_in, timestep)
                 if p is None:
                     continue
                 if p.hooks is not None:
-                    model.current_patcher.prepare_hook_patches_current_keyframe(timestep, p.hooks)
+                    model.current_patcher.prepare_hook_patches_current_keyframe(timestep, p.hooks, model_options)
                 hooked_to_run.setdefault(p.hooks, list())
                 hooked_to_run[p.hooks] += [(p, i)]
         default_conds.append(default_c)
 
     if has_default_conds:
-        finalize_default_conds(model, hooked_to_run, default_conds, x_in, timestep)
+        finalize_default_conds(model, hooked_to_run, default_conds, x_in, timestep, model_options)
 
     model.current_patcher.prepare_state(timestep)
 
@@ -375,7 +375,7 @@ class KSamplerX0Inpaint:
             if "denoise_mask_function" in model_options:
                 denoise_mask = model_options["denoise_mask_function"](sigma, denoise_mask, extra_options={"model": self.inner_model, "sigmas": self.sigmas})
             latent_mask = 1. - denoise_mask
-            x = x * denoise_mask + self.inner_model.inner_model.model_sampling.noise_scaling(sigma.reshape([sigma.shape[0]] + [1] * (len(self.noise.shape) - 1)), self.noise, self.latent_image) * latent_mask
+            x = x * denoise_mask + self.inner_model.inner_model.scale_latent_inpaint(x=x, sigma=sigma, noise=self.noise, latent_image=self.latent_image) * latent_mask
         out = self.inner_model(x, sigma, model_options=model_options, seed=seed)
         if denoise_mask is not None:
             out = out * denoise_mask + self.latent_image * latent_mask
@@ -467,6 +467,13 @@ def linear_quadratic_schedule(model_sampling, steps, threshold_noise=0.025, line
         sigma_schedule = [1.0 - x for x in sigma_schedule]
     return torch.FloatTensor(sigma_schedule) * model_sampling.sigma_max.cpu()
 
+# Referenced from https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/15608
+def kl_optimal_scheduler(n: int, sigma_min: float, sigma_max: float) -> torch.Tensor:
+    adj_idxs = torch.arange(n, dtype=torch.float).div_(n - 1)
+    sigmas = adj_idxs.new_zeros(n + 1)
+    sigmas[:-1] = (adj_idxs * math.atan(sigma_min) + (1 - adj_idxs) * math.atan(sigma_max)).tan_()
+    return sigmas
+
 def get_mask_aabb(masks):
     if masks.numel() == 0:
         return torch.zeros((0, 4), device=masks.device, dtype=torch.int)
@@ -679,7 +686,8 @@ class Sampler:
 KSAMPLER_NAMES = ["euler", "euler_cfg_pp", "euler_ancestral", "euler_ancestral_cfg_pp", "heun", "heunpp2","dpm_2", "dpm_2_ancestral",
                   "lms", "dpm_fast", "dpm_adaptive", "dpmpp_2s_ancestral", "dpmpp_2s_ancestral_cfg_pp", "dpmpp_sde", "dpmpp_sde_gpu",
                   "dpmpp_2m", "dpmpp_2m_cfg_pp", "dpmpp_2m_sde", "dpmpp_2m_sde_gpu", "dpmpp_3m_sde", "dpmpp_3m_sde_gpu", "ddpm", "lcm",
-                  "ipndm", "ipndm_v", "deis"]
+                  "ipndm", "ipndm_v", "deis", "res_multistep", "res_multistep_cfg_pp", "res_multistep_ancestral", "res_multistep_ancestral_cfg_pp",
+                  "gradient_estimation"]
 
 class KSAMPLER(Sampler):
     def __init__(self, sampler_function, extra_options={}, inpaint_options={}):
@@ -802,6 +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()
@@ -811,9 +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
@@ -840,7 +924,9 @@ class CFGGuider:
 
         self.conds = process_conds(self.inner_model, noise, self.conds, device, latent_image, denoise_mask, seed)
 
-        extra_args = {"model_options": comfy.model_patcher.create_model_options_clone(self.model_options), "seed": seed}
+        extra_model_options = comfy.model_patcher.create_model_options_clone(self.model_options)
+        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(
             sampler.sample,
@@ -851,7 +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:
@@ -860,6 +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()
@@ -889,6 +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,
@@ -896,6 +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()
@@ -911,29 +1000,37 @@ def sample(model, noise, positive, negative, cfg, device, sampler, sigmas, model
     return cfg_guider.sample(noise, latent_image, sampler, sigmas, denoise_mask, callback, disable_pbar, seed)
 
 
-SCHEDULER_NAMES = ["normal", "karras", "exponential", "sgm_uniform", "simple", "ddim_uniform", "beta", "linear_quadratic"]
 SAMPLER_NAMES = KSAMPLER_NAMES + ["ddim", "uni_pc", "uni_pc_bh2"]
 
-def calculate_sigmas(model_sampling, scheduler_name, steps):
-    if scheduler_name == "karras":
-        sigmas = k_diffusion_sampling.get_sigmas_karras(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max))
-    elif scheduler_name == "exponential":
-        sigmas = k_diffusion_sampling.get_sigmas_exponential(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max))
-    elif scheduler_name == "normal":
-        sigmas = normal_scheduler(model_sampling, steps)
-    elif scheduler_name == "simple":
-        sigmas = simple_scheduler(model_sampling, steps)
-    elif scheduler_name == "ddim_uniform":
-        sigmas = ddim_scheduler(model_sampling, steps)
-    elif scheduler_name == "sgm_uniform":
-        sigmas = normal_scheduler(model_sampling, steps, sgm=True)
-    elif scheduler_name == "beta":
-        sigmas = beta_scheduler(model_sampling, steps)
-    elif scheduler_name == "linear_quadratic":
-        sigmas = linear_quadratic_schedule(model_sampling, steps)
-    else:
-        logging.error("error invalid scheduler {}".format(scheduler_name))
-    return sigmas
+class SchedulerHandler(NamedTuple):
+    handler: Callable[..., torch.Tensor]
+    # Boolean indicates whether to call the handler like:
+    #  scheduler_function(model_sampling, steps) or
+    #  scheduler_function(n, sigma_min: float, sigma_max: float)
+    use_ms: bool = True
+
+SCHEDULER_HANDLERS = {
+    "normal": SchedulerHandler(normal_scheduler),
+    "karras": SchedulerHandler(k_diffusion_sampling.get_sigmas_karras, use_ms=False),
+    "exponential": SchedulerHandler(k_diffusion_sampling.get_sigmas_exponential, use_ms=False),
+    "sgm_uniform": SchedulerHandler(partial(normal_scheduler, sgm=True)),
+    "simple": SchedulerHandler(simple_scheduler),
+    "ddim_uniform": SchedulerHandler(ddim_scheduler),
+    "beta": SchedulerHandler(beta_scheduler),
+    "linear_quadratic": SchedulerHandler(linear_quadratic_schedule),
+    "kl_optimal": SchedulerHandler(kl_optimal_scheduler, use_ms=False),
+}
+SCHEDULER_NAMES = list(SCHEDULER_HANDLERS)
+
+def calculate_sigmas(model_sampling: object, scheduler_name: str, steps: int) -> torch.Tensor:
+    handler = SCHEDULER_HANDLERS.get(scheduler_name)
+    if handler is None:
+        err = f"error invalid scheduler {scheduler_name}"
+        logging.error(err)
+        raise ValueError(err)
+    if handler.use_ms:
+        return handler.handler(model_sampling, steps)
+    return handler.handler(n=steps, sigma_min=float(model_sampling.sigma_min), sigma_max=float(model_sampling.sigma_max))
 
 def sampler_object(name):
     if name == "uni_pc":

comfy/sd.py

@@ -11,7 +11,9 @@ 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
 
 import comfy.utils
 
@@ -26,12 +28,15 @@ import comfy.text_encoders.sd2_clip
 import comfy.text_encoders.sd3_clip
 import comfy.text_encoders.sa_t5
 import comfy.text_encoders.aura_t5
+import comfy.text_encoders.pixart_t5
 import comfy.text_encoders.hydit
 import comfy.text_encoders.flux
 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.text_encoders.lumina2
 
 import comfy.model_patcher
 import comfy.lora
@@ -109,7 +114,7 @@ class CLIP:
             model_management.load_models_gpu([self.patcher], force_full_load=True)
         self.layer_idx = None
         self.use_clip_schedule = False
-        logging.debug("CLIP model load device: {}, offload device: {}, current: {}".format(load_device, offload_device, params['device']))
+        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)
@@ -257,6 +262,9 @@ class VAE:
         self.process_output = lambda image: torch.clamp((image + 1.0) / 2.0, min=0.0, max=1.0)
         self.working_dtypes = [torch.bfloat16, torch.float32]
 
+        self.downscale_index_formula = None
+        self.upscale_index_formula = None
+
         if config is None:
             if "decoder.mid.block_1.mix_factor" in sd:
                 encoder_config = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}
@@ -336,26 +344,54 @@ class VAE:
                 self.memory_used_decode = lambda shape, dtype: (1000 * shape[2] * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype)
                 self.memory_used_encode = lambda shape, dtype: (1.5 * max(shape[2], 7) * shape[3] * shape[4] * (6 * 8 * 8)) * model_management.dtype_size(dtype)
                 self.upscale_ratio = (lambda a: max(0, a * 6 - 5), 8, 8)
+                self.upscale_index_formula = (6, 8, 8)
+                self.downscale_ratio = (lambda a: max(0, math.floor((a + 5) / 6)), 8, 8)
+                self.downscale_index_formula = (6, 8, 8)
                 self.working_dtypes = [torch.float16, torch.float32]
             elif "decoder.up_blocks.0.res_blocks.0.conv1.conv.weight" in sd: #lightricks ltxv
-                self.first_stage_model = comfy.ldm.lightricks.vae.causal_video_autoencoder.VideoVAE()
+                tensor_conv1 = sd["decoder.up_blocks.0.res_blocks.0.conv1.conv.weight"]
+                version = 0
+                if tensor_conv1.shape[0] == 512:
+                    version = 0
+                elif tensor_conv1.shape[0] == 1024:
+                    version = 1
+                self.first_stage_model = comfy.ldm.lightricks.vae.causal_video_autoencoder.VideoVAE(version=version)
                 self.latent_channels = 128
                 self.latent_dim = 3
                 self.memory_used_decode = lambda shape, dtype: (900 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype)
                 self.memory_used_encode = lambda shape, dtype: (70 * max(shape[2], 7) * shape[3] * shape[4]) * model_management.dtype_size(dtype)
                 self.upscale_ratio = (lambda a: max(0, a * 8 - 7), 32, 32)
+                self.upscale_index_formula = (8, 32, 32)
+                self.downscale_ratio = (lambda a: max(0, math.floor((a + 7) / 8)), 32, 32)
+                self.downscale_index_formula = (8, 32, 32)
                 self.working_dtypes = [torch.bfloat16, torch.float32]
             elif "decoder.conv_in.conv.weight" in sd:
                 ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}
                 ddconfig["conv3d"] = True
                 ddconfig["time_compress"] = 4
                 self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 8, 8)
+                self.upscale_index_formula = (4, 8, 8)
+                self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 8, 8)
+                self.downscale_index_formula = (4, 8, 8)
                 self.latent_dim = 3
                 self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.conv.weight"].shape[1]
                 self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1])
                 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: (50 * shape[2] * shape[3] * shape[4] * (8 * 8 * 8)) * model_management.dtype_size(dtype)
+                self.memory_used_encode = lambda shape, dtype: (50 * (round((shape[2] + 7) / 8) * 8) * 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
@@ -382,13 +418,15 @@ class VAE:
         self.output_device = model_management.intermediate_device()
 
         self.patcher = comfy.model_patcher.ModelPatcher(self.first_stage_model, load_device=self.device, offload_device=offload_device)
-        logging.debug("VAE load device: {}, offload device: {}, dtype: {}".format(self.device, offload_device, self.vae_dtype))
+        logging.info("VAE load device: {}, offload device: {}, dtype: {}".format(self.device, offload_device, self.vae_dtype))
 
     def vae_encode_crop_pixels(self, pixels):
+        downscale_ratio = self.spacial_compression_encode()
+
         dims = pixels.shape[1:-1]
         for d in range(len(dims)):
-            x = (dims[d] // self.downscale_ratio) * self.downscale_ratio
-            x_offset = (dims[d] % self.downscale_ratio) // 2
+            x = (dims[d] // downscale_ratio) * downscale_ratio
+            x_offset = (dims[d] % downscale_ratio) // 2
             if x != dims[d]:
                 pixels = pixels.narrow(d + 1, x_offset, x)
         return pixels
@@ -409,11 +447,11 @@ class VAE:
 
     def decode_tiled_1d(self, samples, tile_x=128, overlap=32):
         decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
-        return comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device)
+        return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device))
 
     def decode_tiled_3d(self, samples, tile_t=999, tile_x=32, tile_y=32, overlap=(1, 8, 8)):
         decode_fn = lambda a: self.first_stage_model.decode(a.to(self.vae_dtype).to(self.device)).float()
-        return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, output_device=self.output_device))
+        return self.process_output(comfy.utils.tiled_scale_multidim(samples, decode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.upscale_ratio, out_channels=self.output_channels, index_formulas=self.upscale_index_formula, output_device=self.output_device))
 
     def encode_tiled_(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
         steps = pixel_samples.shape[0] * comfy.utils.get_tiled_scale_steps(pixel_samples.shape[3], pixel_samples.shape[2], tile_x, tile_y, overlap)
@@ -432,6 +470,10 @@ class VAE:
         encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
         return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_x,), overlap=overlap, upscale_amount=(1/self.downscale_ratio), out_channels=self.latent_channels, output_device=self.output_device)
 
+    def encode_tiled_3d(self, samples, tile_t=9999, tile_x=512, tile_y=512, overlap=(1, 64, 64)):
+        encode_fn = lambda a: self.first_stage_model.encode((self.process_input(a)).to(self.vae_dtype).to(self.device)).float()
+        return comfy.utils.tiled_scale_multidim(samples, encode_fn, tile=(tile_t, tile_x, tile_y), overlap=overlap, upscale_amount=self.downscale_ratio, out_channels=self.latent_channels, downscale=True, index_formulas=self.downscale_index_formula, output_device=self.output_device)
+
     def decode(self, samples_in):
         pixel_samples = None
         try:
@@ -462,7 +504,7 @@ class VAE:
         pixel_samples = pixel_samples.to(self.output_device).movedim(1,-1)
         return pixel_samples
 
-    def decode_tiled(self, samples, tile_x=None, tile_y=None, overlap=None):
+    def decode_tiled(self, samples, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None):
         memory_used = self.memory_used_decode(samples.shape, self.vae_dtype) #TODO: calculate mem required for tile
         model_management.load_models_gpu([self.patcher], memory_required=memory_used)
         dims = samples.ndim - 2
@@ -480,13 +522,20 @@ class VAE:
         elif dims == 2:
             output = self.decode_tiled_(samples, **args)
         elif dims == 3:
+            if overlap_t is None:
+                args["overlap"] = (1, overlap, overlap)
+            else:
+                args["overlap"] = (max(1, overlap_t), overlap, overlap)
+            if tile_t is not None:
+                args["tile_t"] = max(2, tile_t)
+
             output = self.decode_tiled_3d(samples, **args)
         return output.movedim(1, -1)
 
     def encode(self, pixel_samples):
         pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
         pixel_samples = pixel_samples.movedim(-1, 1)
-        if self.latent_dim == 3:
+        if self.latent_dim == 3 and pixel_samples.ndim < 5:
             pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
         try:
             memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype)
@@ -504,18 +553,56 @@ class VAE:
 
         except model_management.OOM_EXCEPTION:
             logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
-            if len(pixel_samples.shape) == 3:
+            if self.latent_dim == 3:
+                tile = 256
+                overlap = tile // 4
+                samples = self.encode_tiled_3d(pixel_samples, tile_x=tile, tile_y=tile, overlap=(1, overlap, overlap))
+            elif self.latent_dim == 1:
                 samples = self.encode_tiled_1d(pixel_samples)
             else:
                 samples = self.encode_tiled_(pixel_samples)
 
         return samples
 
-    def encode_tiled(self, pixel_samples, tile_x=512, tile_y=512, overlap = 64):
+    def encode_tiled(self, pixel_samples, tile_x=None, tile_y=None, overlap=None, tile_t=None, overlap_t=None):
         pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
-        model_management.load_model_gpu(self.patcher)
-        pixel_samples = pixel_samples.movedim(-1,1)
-        samples = self.encode_tiled_(pixel_samples, tile_x=tile_x, tile_y=tile_y, overlap=overlap)
+        dims = self.latent_dim
+        pixel_samples = pixel_samples.movedim(-1, 1)
+        if dims == 3:
+            pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
+
+        memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype)  # TODO: calculate mem required for tile
+        model_management.load_models_gpu([self.patcher], memory_required=memory_used)
+
+        args = {}
+        if tile_x is not None:
+            args["tile_x"] = tile_x
+        if tile_y is not None:
+            args["tile_y"] = tile_y
+        if overlap is not None:
+            args["overlap"] = overlap
+
+        if dims == 1:
+            args.pop("tile_y")
+            samples = self.encode_tiled_1d(pixel_samples, **args)
+        elif dims == 2:
+            samples = self.encode_tiled_(pixel_samples, **args)
+        elif dims == 3:
+            if tile_t is not None:
+                tile_t_latent = max(2, self.downscale_ratio[0](tile_t))
+            else:
+                tile_t_latent = 9999
+            args["tile_t"] = self.upscale_ratio[0](tile_t_latent)
+
+            if overlap_t is None:
+                args["overlap"] = (1, overlap, overlap)
+            else:
+                args["overlap"] = (self.upscale_ratio[0](max(1, min(tile_t_latent // 2, self.downscale_ratio[0](overlap_t)))), overlap, overlap)
+            maximum = pixel_samples.shape[2]
+            maximum = self.upscale_ratio[0](self.downscale_ratio[0](maximum))
+
+            samples = self.encode_tiled_3d(pixel_samples[:,:,:maximum], **args)
+
         return samples
 
     def get_sd(self):
@@ -527,6 +614,18 @@ class VAE:
         except:
             return self.upscale_ratio
 
+    def spacial_compression_encode(self):
+        try:
+            return self.downscale_ratio[-1]
+        except:
+            return self.downscale_ratio
+
+    def temporal_compression_decode(self):
+        try:
+            return round(self.upscale_ratio[0](8192) / 8192)
+        except:
+            return None
+
 class StyleModel:
     def __init__(self, model, device="cpu"):
         self.model = model
@@ -557,6 +656,10 @@ class CLIPType(Enum):
     MOCHI = 7
     LTXV = 8
     HUNYUAN_VIDEO = 9
+    PIXART = 10
+    COSMOS = 11
+    LUMINA2 = 12
+
 
 def load_clip(ckpt_paths, embedding_directory=None, clip_type=CLIPType.STABLE_DIFFUSION, model_options={}):
     clip_data = []
@@ -573,6 +676,8 @@ class TEModel(Enum):
     T5_XL = 5
     T5_BASE = 6
     LLAMA3_8 = 7
+    T5_XXL_OLD = 8
+    GEMMA_2_2B = 9
 
 def detect_te_model(sd):
     if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
@@ -587,8 +692,12 @@ 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_feedforward_layernorm.weight' in sd:
+        return TEModel.GEMMA_2_2B
     if "model.layers.0.post_attention_layernorm.weight" in sd:
         return TEModel.LLAMA3_8
     return None
@@ -596,9 +705,10 @@ 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 {}
@@ -625,6 +735,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             if "text_projection" in clip_data[i]:
                 clip_data[i]["text_projection.weight"] = clip_data[i]["text_projection"].transpose(0, 1) #old models saved with the CLIPSave node
 
+    tokenizer_data = {}
     clip_target = EmptyClass()
     clip_target.params = {}
     if len(clip_data) == 1:
@@ -649,15 +760,25 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             elif clip_type == CLIPType.LTXV:
                 clip_target.clip = comfy.text_encoders.lt.ltxv_te(**t5xxl_detect(clip_data))
                 clip_target.tokenizer = comfy.text_encoders.lt.LTXVT5Tokenizer
+            elif clip_type == CLIPType.PIXART:
+                clip_target.clip = comfy.text_encoders.pixart_t5.pixart_te(**t5xxl_detect(clip_data))
+                clip_target.tokenizer = comfy.text_encoders.pixart_t5.PixArtTokenizer
             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
         elif te_model == TEModel.T5_BASE:
             clip_target.clip = comfy.text_encoders.sa_t5.SAT5Model
             clip_target.tokenizer = comfy.text_encoders.sa_t5.SAT5Tokenizer
+        elif te_model == TEModel.GEMMA_2_2B:
+            clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data))
+            clip_target.tokenizer = comfy.text_encoders.lumina2.LuminaTokenizer
+            tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
         else:
             if clip_type == CLIPType.SD3:
                 clip_target.clip = comfy.text_encoders.sd3_clip.sd3_clip(clip_l=True, clip_g=False, t5=False)
@@ -687,7 +808,6 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
         clip_target.tokenizer = comfy.text_encoders.sd3_clip.SD3Tokenizer
 
     parameters = 0
-    tokenizer_data = {}
     for c in clip_data:
         parameters += comfy.utils.calculate_parameters(c)
         tokenizer_data, model_options = comfy.text_encoders.long_clipl.model_options_long_clip(c, tokenizer_data, model_options)
@@ -810,7 +930,7 @@ 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)
@@ -887,11 +1007,11 @@ def load_diffusion_model(unet_path, model_options={}):
     return model
 
 def load_unet(unet_path, dtype=None):
-    print("WARNING: the load_unet function has been deprecated and will be removed please switch to: load_diffusion_model")
+    logging.warning("The load_unet function has been deprecated and will be removed please switch to: load_diffusion_model")
     return load_diffusion_model(unet_path, model_options={"dtype": dtype})
 
 def load_unet_state_dict(sd, dtype=None):
-    print("WARNING: the load_unet_state_dict function has been deprecated and will be removed please switch to: load_diffusion_model_state_dict")
+    logging.warning("The load_unet_state_dict function has been deprecated and will be removed please switch to: load_diffusion_model_state_dict")
     return load_diffusion_model_state_dict(sd, model_options={"dtype": dtype})
 
 def save_checkpoint(output_path, model, clip=None, vae=None, clip_vision=None, metadata=None, extra_keys={}):

comfy/sd1_clip.py

@@ -37,7 +37,10 @@ class ClipTokenWeightEncoder:
 
         sections = len(to_encode)
         if has_weights or sections == 0:
-            to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len))
+            if hasattr(self, "gen_empty_tokens"):
+                to_encode.append(self.gen_empty_tokens(self.special_tokens, max_token_len))
+            else:
+                to_encode.append(gen_empty_tokens(self.special_tokens, max_token_len))
 
         o = self.encode(to_encode)
         out, pooled = o[:2]
@@ -385,13 +388,10 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No
             import safetensors.torch
             embed = safetensors.torch.load_file(embed_path, device="cpu")
         else:
-            if 'weights_only' in torch.load.__code__.co_varnames:
-                try:
-                    embed = torch.load(embed_path, weights_only=True, map_location="cpu")
-                except:
-                    embed_out = safe_load_embed_zip(embed_path)
-            else:
-                embed = torch.load(embed_path, map_location="cpu")
+            try:
+                embed = torch.load(embed_path, weights_only=True, map_location="cpu")
+            except:
+                embed_out = safe_load_embed_zip(embed_path)
     except Exception:
         logging.warning("{}\n\nerror loading embedding, skipping loading: {}".format(traceback.format_exc(), embedding_name))
         return None
@@ -421,10 +421,10 @@ def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=No
     return embed_out
 
 class SDTokenizer:
-    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, tokenizer_data={}):
+    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l', tokenizer_class=CLIPTokenizer, has_start_token=True, has_end_token=True, pad_to_max_length=True, min_length=None, pad_token=None, end_token=None, tokenizer_data={}, tokenizer_args={}):
         if tokenizer_path is None:
             tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer")
-        self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path)
+        self.tokenizer = tokenizer_class.from_pretrained(tokenizer_path, **tokenizer_args)
         self.max_length = max_length
         self.min_length = min_length
         self.end_token = None
@@ -585,9 +585,14 @@ class SDTokenizer:
         return {}
 
 class SD1Tokenizer:
-    def __init__(self, embedding_directory=None, tokenizer_data={}, clip_name="l", tokenizer=SDTokenizer):
-        self.clip_name = clip_name
-        self.clip = "clip_{}".format(self.clip_name)
+    def __init__(self, embedding_directory=None, tokenizer_data={}, clip_name="l", tokenizer=SDTokenizer, name=None):
+        if name is not None:
+            self.clip_name = name
+            self.clip = "{}".format(self.clip_name)
+        else:
+            self.clip_name = clip_name
+            self.clip = "clip_{}".format(self.clip_name)
+
         tokenizer = tokenizer_data.get("{}_tokenizer_class".format(self.clip), tokenizer)
         setattr(self, self.clip, tokenizer(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data))
 
@@ -600,7 +605,7 @@ class SD1Tokenizer:
         return getattr(self, self.clip).untokenize(token_weight_pair)
 
     def state_dict(self):
-        return {}
+        return getattr(self, self.clip).state_dict()
 
 class SD1CheckpointClipModel(SDClipModel):
     def __init__(self, device="cpu", dtype=None, model_options={}):

comfy/supported_models.py

@@ -8,11 +8,14 @@ import comfy.text_encoders.sd2_clip
 import comfy.text_encoders.sd3_clip
 import comfy.text_encoders.sa_t5
 import comfy.text_encoders.aura_t5
+import comfy.text_encoders.pixart_t5
 import comfy.text_encoders.hydit
 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
+import comfy.text_encoders.lumina2
 
 from . import supported_models_base
 from . import latent_formats
@@ -592,6 +595,39 @@ class AuraFlow(supported_models_base.BASE):
     def clip_target(self, state_dict={}):
         return supported_models_base.ClipTarget(comfy.text_encoders.aura_t5.AuraT5Tokenizer, comfy.text_encoders.aura_t5.AuraT5Model)
 
+class PixArtAlpha(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "pixart_alpha",
+    }
+
+    sampling_settings = {
+        "beta_schedule" : "sqrt_linear",
+        "linear_start"  : 0.0001,
+        "linear_end"    : 0.02,
+        "timesteps"     : 1000,
+    }
+
+    unet_extra_config = {}
+    latent_format = latent_formats.SD15
+
+    memory_usage_factor = 0.5
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.PixArt(self, device=device)
+        return out.eval()
+
+    def clip_target(self, state_dict={}):
+        return supported_models_base.ClipTarget(comfy.text_encoders.pixart_t5.PixArtTokenizer, comfy.text_encoders.pixart_t5.PixArtT5XXL)
+
+class PixArtSigma(PixArtAlpha):
+    unet_config = {
+        "image_model": "pixart_sigma",
+    }
+    latent_format = latent_formats.SDXL
+
 class HunyuanDiT(supported_models_base.BASE):
     unet_config = {
         "image_model": "hydit",
@@ -608,6 +644,8 @@ class HunyuanDiT(supported_models_base.BASE):
 
     latent_format = latent_formats.SDXL
 
+    memory_usage_factor = 1.3
+
     vae_key_prefix = ["vae."]
     text_encoder_key_prefix = ["text_encoders."]
 
@@ -751,7 +789,7 @@ class HunyuanVideo(supported_models_base.BASE):
     unet_extra_config = {}
     latent_format = latent_formats.HunyuanVideo
 
-    memory_usage_factor = 2.0 #TODO
+    memory_usage_factor = 1.8 #TODO
 
     supported_inference_dtypes = [torch.bfloat16, torch.float32]
 
@@ -787,6 +825,76 @@ 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, HunyuanDiT, HunyuanDiT1, FluxInpaint, Flux, FluxSchnell, GenmoMochi, LTXV, HunyuanVideo]
+class CosmosT2V(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "cosmos",
+        "in_channels": 16,
+    }
+
+    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 = 1.6 #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))
+
+class CosmosI2V(CosmosT2V):
+    unet_config = {
+        "image_model": "cosmos",
+        "in_channels": 17,
+    }
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.CosmosVideo(self, image_to_video=True, device=device)
+        return out
+
+class Lumina2(supported_models_base.BASE):
+    unet_config = {
+        "image_model": "lumina2",
+    }
+
+    sampling_settings = {
+        "multiplier": 1.0,
+        "shift": 6.0,
+    }
+
+    memory_usage_factor = 1.2
+
+    unet_extra_config = {}
+    latent_format = latent_formats.Flux
+
+    supported_inference_dtypes = [torch.bfloat16, torch.float32]
+
+    vae_key_prefix = ["vae."]
+    text_encoder_key_prefix = ["text_encoders."]
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.Lumina2(self, device=device)
+        return out
+
+    def clip_target(self, state_dict={}):
+        pref = self.text_encoder_key_prefix[0]
+        hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}gemma2_2b.transformer.".format(pref))
+        return supported_models_base.ClipTarget(comfy.text_encoders.lumina2.LuminaTokenizer, comfy.text_encoders.lumina2.te(**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, CosmosT2V, CosmosI2V, Lumina2]
 
 models += [SVD_img2vid]

comfy/text_encoders/bert.py

@@ -118,7 +118,7 @@ class BertModel_(torch.nn.Module):
         mask = None
         if attention_mask is not None:
             mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
-            mask = mask.masked_fill(mask.to(torch.bool), float("-inf"))
+            mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)
 
         x, i = self.encoder(x, mask, intermediate_output)
         return x, i

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/llama.py

@@ -1,6 +1,5 @@
 import torch
 import torch.nn as nn
-import torch.nn.functional as F
 from dataclasses import dataclass
 from typing import Optional, Any
 
@@ -21,15 +20,41 @@ class Llama2Config:
     max_position_embeddings: int = 8192
     rms_norm_eps: float = 1e-5
     rope_theta: float = 500000.0
+    transformer_type: str = "llama"
+    head_dim = 128
+    rms_norm_add = False
+    mlp_activation = "silu"
+
+@dataclass
+class Gemma2_2B_Config:
+    vocab_size: int = 256000
+    hidden_size: int = 2304
+    intermediate_size: int = 9216
+    num_hidden_layers: int = 26
+    num_attention_heads: int = 8
+    num_key_value_heads: int = 4
+    max_position_embeddings: int = 8192
+    rms_norm_eps: float = 1e-6
+    rope_theta: float = 10000.0
+    transformer_type: str = "gemma2"
+    head_dim = 256
+    rms_norm_add = True
+    mlp_activation = "gelu_pytorch_tanh"
 
 class RMSNorm(nn.Module):
-    def __init__(self, dim: int, eps: float = 1e-5, device=None, dtype=None):
+    def __init__(self, dim: int, eps: float = 1e-5, add=False, device=None, dtype=None):
         super().__init__()
         self.eps = eps
         self.weight = nn.Parameter(torch.empty(dim, device=device, dtype=dtype))
+        self.add = add
 
     def forward(self, x: torch.Tensor):
-        return comfy.ldm.common_dit.rms_norm(x, self.weight, self.eps)
+        w = self.weight
+        if self.add:
+            w = w + 1.0
+
+        return comfy.ldm.common_dit.rms_norm(x, w, self.eps)
+
 
 
 def rotate_half(x):
@@ -68,13 +93,15 @@ class Attention(nn.Module):
         self.num_heads = config.num_attention_heads
         self.num_kv_heads = config.num_key_value_heads
         self.hidden_size = config.hidden_size
-        self.head_dim = self.hidden_size // self.num_heads
+
+        self.head_dim = config.head_dim
+        self.inner_size = self.num_heads * self.head_dim
 
         ops = ops or nn
-        self.q_proj = ops.Linear(config.hidden_size, config.hidden_size, bias=False, device=device, dtype=dtype)
+        self.q_proj = ops.Linear(config.hidden_size, self.inner_size, bias=False, device=device, dtype=dtype)
         self.k_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=False, device=device, dtype=dtype)
         self.v_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=False, device=device, dtype=dtype)
-        self.o_proj = ops.Linear(config.hidden_size, config.hidden_size, bias=False, device=device, dtype=dtype)
+        self.o_proj = ops.Linear(self.inner_size, config.hidden_size, bias=False, device=device, dtype=dtype)
 
     def forward(
         self,
@@ -84,7 +111,6 @@ class Attention(nn.Module):
         optimized_attention=None,
     ):
         batch_size, seq_length, _ = hidden_states.shape
-
         xq = self.q_proj(hidden_states)
         xk = self.k_proj(hidden_states)
         xv = self.v_proj(hidden_states)
@@ -108,9 +134,13 @@ class MLP(nn.Module):
         self.gate_proj = ops.Linear(config.hidden_size, config.intermediate_size, bias=False, device=device, dtype=dtype)
         self.up_proj = ops.Linear(config.hidden_size, config.intermediate_size, bias=False, device=device, dtype=dtype)
         self.down_proj = ops.Linear(config.intermediate_size, config.hidden_size, bias=False, device=device, dtype=dtype)
+        if config.mlp_activation == "silu":
+            self.activation = torch.nn.functional.silu
+        elif config.mlp_activation == "gelu_pytorch_tanh":
+            self.activation = lambda a: torch.nn.functional.gelu(a, approximate="tanh")
 
     def forward(self, x):
-        return self.down_proj(F.silu(self.gate_proj(x)) * self.up_proj(x))
+        return self.down_proj(self.activation(self.gate_proj(x)) * self.up_proj(x))
 
 class TransformerBlock(nn.Module):
     def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None):
@@ -146,6 +176,45 @@ class TransformerBlock(nn.Module):
 
         return x
 
+class TransformerBlockGemma2(nn.Module):
+    def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None):
+        super().__init__()
+        self.self_attn = Attention(config, device=device, dtype=dtype, ops=ops)
+        self.mlp = MLP(config, device=device, dtype=dtype, ops=ops)
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+        self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+        self.pre_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+        self.post_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        freqs_cis: Optional[torch.Tensor] = None,
+        optimized_attention=None,
+    ):
+        # Self Attention
+        residual = x
+        x = self.input_layernorm(x)
+        x = self.self_attn(
+            hidden_states=x,
+            attention_mask=attention_mask,
+            freqs_cis=freqs_cis,
+            optimized_attention=optimized_attention,
+        )
+
+        x = self.post_attention_layernorm(x)
+        x = residual + x
+
+        # MLP
+        residual = x
+        x = self.pre_feedforward_layernorm(x)
+        x = self.mlp(x)
+        x = self.post_feedforward_layernorm(x)
+        x = residual + x
+
+        return x
+
 class Llama2_(nn.Module):
     def __init__(self, config, device=None, dtype=None, ops=None):
         super().__init__()
@@ -158,17 +227,27 @@ class Llama2_(nn.Module):
             device=device,
             dtype=dtype
         )
+        if self.config.transformer_type == "gemma2":
+            transformer = TransformerBlockGemma2
+            self.normalize_in = True
+        else:
+            transformer = TransformerBlock
+            self.normalize_in = False
+
         self.layers = nn.ModuleList([
-            TransformerBlock(config, device=device, dtype=dtype, ops=ops)
+            transformer(config, device=device, dtype=dtype, ops=ops)
             for _ in range(config.num_hidden_layers)
         ])
-        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, device=device, dtype=dtype)
+        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
         # self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype)
 
     def forward(self, x, attention_mask=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None):
         x = self.embed_tokens(x, out_dtype=dtype)
 
-        freqs_cis = precompute_freqs_cis(self.config.hidden_size // self.config.num_attention_heads,
+        if self.normalize_in:
+            x *= self.config.hidden_size ** 0.5
+
+        freqs_cis = precompute_freqs_cis(self.config.head_dim,
                                          x.shape[1],
                                          self.config.rope_theta,
                                          device=x.device)
@@ -206,16 +285,7 @@ class Llama2_(nn.Module):
 
         return x, intermediate
 
-
-class Llama2(torch.nn.Module):
-    def __init__(self, config_dict, dtype, device, operations):
-        super().__init__()
-        config = Llama2Config(**config_dict)
-        self.num_layers = config.num_hidden_layers
-
-        self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
-        self.dtype = dtype
-
+class BaseLlama:
     def get_input_embeddings(self):
         return self.model.embed_tokens
 
@@ -224,3 +294,23 @@ class Llama2(torch.nn.Module):
 
     def forward(self, input_ids, *args, **kwargs):
         return self.model(input_ids, *args, **kwargs)
+
+
+class Llama2(BaseLlama, torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        config = Llama2Config(**config_dict)
+        self.num_layers = config.num_hidden_layers
+
+        self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
+        self.dtype = dtype
+
+
+class Gemma2_2B(BaseLlama, torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        config = Gemma2_2B_Config(**config_dict)
+        self.num_layers = config.num_hidden_layers
+
+        self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
+        self.dtype = dtype

comfy/text_encoders/lumina2.py

@@ -0,0 +1,44 @@
+from comfy import sd1_clip
+from .spiece_tokenizer import SPieceTokenizer
+import comfy.text_encoders.llama
+
+
+class Gemma2BTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer = tokenizer_data.get("spiece_model", None)
+        super().__init__(tokenizer, pad_with_end=False, embedding_size=2304, embedding_key='gemma2_2b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False})
+
+    def state_dict(self):
+        return {"spiece_model": self.tokenizer.serialize_model()}
+
+
+class LuminaTokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="gemma2_2b", tokenizer=Gemma2BTokenizer)
+
+
+class Gemma2_2BModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
+        llama_scaled_fp8 = model_options.get("llama_scaled_fp8", None)
+        if llama_scaled_fp8 is not None:
+            model_options = model_options.copy()
+            model_options["scaled_fp8"] = llama_scaled_fp8
+
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma2_2B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
+
+
+class LuminaModel(sd1_clip.SD1ClipModel):
+    def __init__(self, device="cpu", dtype=None, model_options={}):
+        super().__init__(device=device, dtype=dtype, name="gemma2_2b", clip_model=Gemma2_2BModel, model_options=model_options)
+
+
+def te(dtype_llama=None, llama_scaled_fp8=None):
+    class LuminaTEModel_(LuminaModel):
+        def __init__(self, device="cpu", dtype=None, model_options={}):
+            if llama_scaled_fp8 is not None and "llama_scaled_fp8" not in model_options:
+                model_options = model_options.copy()
+                model_options["llama_scaled_fp8"] = llama_scaled_fp8
+                if dtype_llama is not None:
+                    dtype = dtype_llama
+            super().__init__(device=device, dtype=dtype, model_options=model_options)
+    return LuminaTEModel_

comfy/text_encoders/pixart_t5.py

@@ -0,0 +1,42 @@
+import os
+
+from comfy import sd1_clip
+import comfy.text_encoders.t5
+import comfy.text_encoders.sd3_clip
+from comfy.sd1_clip import gen_empty_tokens
+
+from transformers import T5TokenizerFast
+
+class T5XXLModel(comfy.text_encoders.sd3_clip.T5XXLModel):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    def gen_empty_tokens(self, special_tokens, *args, **kwargs):
+        # PixArt expects the negative to be all pad tokens
+        special_tokens = special_tokens.copy()
+        special_tokens.pop("end")
+        return gen_empty_tokens(special_tokens, *args, **kwargs)
+
+class PixArtT5XXL(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=4096, embedding_key='t5xxl', tokenizer_class=T5TokenizerFast, has_start_token=False, pad_to_max_length=False, max_length=99999999, min_length=1) # no padding
+
+class PixArtTokenizer(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 pixart_te(dtype_t5=None, t5xxl_scaled_fp8=None):
+    class PixArtTEModel_(PixArtT5XXL):
+        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 PixArtTEModel_

comfy/text_encoders/spiece_tokenizer.py

@@ -1,21 +1,21 @@
 import torch
 
 class SPieceTokenizer:
-    add_eos = True
-
     @staticmethod
-    def from_pretrained(path):
-        return SPieceTokenizer(path)
+    def from_pretrained(path, **kwargs):
+        return SPieceTokenizer(path, **kwargs)
 
-    def __init__(self, tokenizer_path):
+    def __init__(self, tokenizer_path, add_bos=False, add_eos=True):
+        self.add_bos = add_bos
+        self.add_eos = add_eos
         import sentencepiece
         if torch.is_tensor(tokenizer_path):
             tokenizer_path = tokenizer_path.numpy().tobytes()
 
         if isinstance(tokenizer_path, bytes):
-            self.tokenizer = sentencepiece.SentencePieceProcessor(model_proto=tokenizer_path, add_eos=self.add_eos)
+            self.tokenizer = sentencepiece.SentencePieceProcessor(model_proto=tokenizer_path, add_bos=self.add_bos, add_eos=self.add_eos)
         else:
-            self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=tokenizer_path, add_eos=self.add_eos)
+            self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=tokenizer_path, add_bos=self.add_bos, add_eos=self.add_eos)
 
     def get_vocab(self):
         out = {}

comfy/text_encoders/t5.py

@@ -203,7 +203,7 @@ class T5Stack(torch.nn.Module):
         mask = None
         if attention_mask is not None:
             mask = 1.0 - attention_mask.to(x.dtype).reshape((attention_mask.shape[0], 1, -1, attention_mask.shape[-1])).expand(attention_mask.shape[0], 1, attention_mask.shape[-1], attention_mask.shape[-1])
-            mask = mask.masked_fill(mask.to(torch.bool), float("-inf"))
+            mask = mask.masked_fill(mask.to(torch.bool), -torch.finfo(x.dtype).max)
 
         intermediate = None
         optimized_attention = optimized_attention_for_device(x.device, mask=attention_mask is not None, small_input=True)
@@ -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

@@ -29,17 +29,39 @@ import itertools
 from torch.nn.functional import interpolate
 from einops import rearrange
 
+ALWAYS_SAFE_LOAD = False
+if hasattr(torch.serialization, "add_safe_globals"):  # TODO: this was added in pytorch 2.4, the unsafe path should be removed once earlier versions are deprecated
+    class ModelCheckpoint:
+        pass
+    ModelCheckpoint.__module__ = "pytorch_lightning.callbacks.model_checkpoint"
+
+    from numpy.core.multiarray import scalar
+    from numpy import dtype
+    from numpy.dtypes import Float64DType
+    from _codecs import encode
+
+    torch.serialization.add_safe_globals([ModelCheckpoint, scalar, dtype, Float64DType, encode])
+    ALWAYS_SAFE_LOAD = True
+    logging.info("Checkpoint files will always be loaded safely.")
+else:
+    logging.info("Warning, you are using an old pytorch version and some ckpt/pt files might be loaded unsafely. Upgrading to 2.4 or above is recommended.")
+
 def load_torch_file(ckpt, safe_load=False, device=None):
     if device is None:
         device = torch.device("cpu")
     if ckpt.lower().endswith(".safetensors") or ckpt.lower().endswith(".sft"):
-        sd = safetensors.torch.load_file(ckpt, device=device.type)
+        try:
+            sd = safetensors.torch.load_file(ckpt, device=device.type)
+        except Exception as e:
+            if len(e.args) > 0:
+                message = e.args[0]
+                if "HeaderTooLarge" in message:
+                    raise ValueError("{}\n\nFile path: {}\n\nThe safetensors file is corrupt or invalid. Make sure this is actually a safetensors file and not a ckpt or pt or other filetype.".format(message, ckpt))
+                if "MetadataIncompleteBuffer" in message:
+                    raise ValueError("{}\n\nFile path: {}\n\nThe safetensors file is corrupt/incomplete. Check the file size and make sure you have copied/downloaded it correctly.".format(message, ckpt))
+            raise e
     else:
-        if safe_load:
-            if not 'weights_only' in torch.load.__code__.co_varnames:
-                logging.warning("Warning torch.load doesn't support weights_only on this pytorch version, loading unsafely.")
-                safe_load = False
-        if safe_load:
+        if safe_load or ALWAYS_SAFE_LOAD:
             pl_sd = torch.load(ckpt, map_location=device, weights_only=True)
         else:
             pl_sd = torch.load(ckpt, map_location=device, pickle_module=comfy.checkpoint_pickle)
@@ -386,6 +408,77 @@ def mmdit_to_diffusers(mmdit_config, output_prefix=""):
 
     return key_map
 
+PIXART_MAP_BASIC = {
+    ("csize_embedder.mlp.0.weight", "adaln_single.emb.resolution_embedder.linear_1.weight"),
+    ("csize_embedder.mlp.0.bias", "adaln_single.emb.resolution_embedder.linear_1.bias"),
+    ("csize_embedder.mlp.2.weight", "adaln_single.emb.resolution_embedder.linear_2.weight"),
+    ("csize_embedder.mlp.2.bias", "adaln_single.emb.resolution_embedder.linear_2.bias"),
+    ("ar_embedder.mlp.0.weight", "adaln_single.emb.aspect_ratio_embedder.linear_1.weight"),
+    ("ar_embedder.mlp.0.bias", "adaln_single.emb.aspect_ratio_embedder.linear_1.bias"),
+    ("ar_embedder.mlp.2.weight", "adaln_single.emb.aspect_ratio_embedder.linear_2.weight"),
+    ("ar_embedder.mlp.2.bias", "adaln_single.emb.aspect_ratio_embedder.linear_2.bias"),
+    ("x_embedder.proj.weight", "pos_embed.proj.weight"),
+    ("x_embedder.proj.bias", "pos_embed.proj.bias"),
+    ("y_embedder.y_embedding", "caption_projection.y_embedding"),
+    ("y_embedder.y_proj.fc1.weight", "caption_projection.linear_1.weight"),
+    ("y_embedder.y_proj.fc1.bias", "caption_projection.linear_1.bias"),
+    ("y_embedder.y_proj.fc2.weight", "caption_projection.linear_2.weight"),
+    ("y_embedder.y_proj.fc2.bias", "caption_projection.linear_2.bias"),
+    ("t_embedder.mlp.0.weight", "adaln_single.emb.timestep_embedder.linear_1.weight"),
+    ("t_embedder.mlp.0.bias", "adaln_single.emb.timestep_embedder.linear_1.bias"),
+    ("t_embedder.mlp.2.weight", "adaln_single.emb.timestep_embedder.linear_2.weight"),
+    ("t_embedder.mlp.2.bias", "adaln_single.emb.timestep_embedder.linear_2.bias"),
+    ("t_block.1.weight", "adaln_single.linear.weight"),
+    ("t_block.1.bias", "adaln_single.linear.bias"),
+    ("final_layer.linear.weight", "proj_out.weight"),
+    ("final_layer.linear.bias", "proj_out.bias"),
+    ("final_layer.scale_shift_table", "scale_shift_table"),
+}
+
+PIXART_MAP_BLOCK = {
+    ("scale_shift_table", "scale_shift_table"),
+    ("attn.proj.weight", "attn1.to_out.0.weight"),
+    ("attn.proj.bias", "attn1.to_out.0.bias"),
+    ("mlp.fc1.weight", "ff.net.0.proj.weight"),
+    ("mlp.fc1.bias", "ff.net.0.proj.bias"),
+    ("mlp.fc2.weight", "ff.net.2.weight"),
+    ("mlp.fc2.bias", "ff.net.2.bias"),
+    ("cross_attn.proj.weight" ,"attn2.to_out.0.weight"),
+    ("cross_attn.proj.bias"   ,"attn2.to_out.0.bias"),
+}
+
+def pixart_to_diffusers(mmdit_config, output_prefix=""):
+    key_map = {}
+
+    depth = mmdit_config.get("depth", 0)
+    offset = mmdit_config.get("hidden_size", 1152)
+
+    for i in range(depth):
+        block_from = "transformer_blocks.{}".format(i)
+        block_to = "{}blocks.{}".format(output_prefix, i)
+
+        for end in ("weight", "bias"):
+            s = "{}.attn1.".format(block_from)
+            qkv = "{}.attn.qkv.{}".format(block_to, end)
+            key_map["{}to_q.{}".format(s, end)] = (qkv, (0, 0, offset))
+            key_map["{}to_k.{}".format(s, end)] = (qkv, (0, offset, offset))
+            key_map["{}to_v.{}".format(s, end)] = (qkv, (0, offset * 2, offset))
+
+            s = "{}.attn2.".format(block_from)
+            q = "{}.cross_attn.q_linear.{}".format(block_to, end)
+            kv = "{}.cross_attn.kv_linear.{}".format(block_to, end)
+
+            key_map["{}to_q.{}".format(s, end)] = q
+            key_map["{}to_k.{}".format(s, end)] = (kv, (0, 0, offset))
+            key_map["{}to_v.{}".format(s, end)] = (kv, (0, offset, offset))
+
+        for k in PIXART_MAP_BLOCK:
+            key_map["{}.{}".format(block_from, k[1])] = "{}.{}".format(block_to, k[0])
+
+    for k in PIXART_MAP_BASIC:
+        key_map[k[1]] = "{}{}".format(output_prefix, k[0])
+
+    return key_map
 
 def auraflow_to_diffusers(mmdit_config, output_prefix=""):
     n_double_layers = mmdit_config.get("n_double_layers", 0)
@@ -622,7 +715,25 @@ 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)
@@ -631,7 +742,7 @@ def get_attr(obj, attr):
 def bislerp(samples, width, height):
     def slerp(b1, b2, r):
         '''slerps batches b1, b2 according to ratio r, batches should be flat e.g. NxC'''
-        
+
         c = b1.shape[-1]
 
         #norms
@@ -656,16 +767,16 @@ def bislerp(samples, width, height):
         res *= (b1_norms * (1.0-r) + b2_norms * r).expand(-1,c)
 
         #edge cases for same or polar opposites
-        res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5] 
+        res[dot > 1 - 1e-5] = b1[dot > 1 - 1e-5]
         res[dot < 1e-5 - 1] = (b1 * (1.0-r) + b2 * r)[dot < 1e-5 - 1]
         return res
-    
+
     def generate_bilinear_data(length_old, length_new, device):
         coords_1 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1))
         coords_1 = torch.nn.functional.interpolate(coords_1, size=(1, length_new), mode="bilinear")
         ratios = coords_1 - coords_1.floor()
         coords_1 = coords_1.to(torch.int64)
-        
+
         coords_2 = torch.arange(length_old, dtype=torch.float32, device=device).reshape((1,1,1,-1)) + 1
         coords_2[:,:,:,-1] -= 1
         coords_2 = torch.nn.functional.interpolate(coords_2, size=(1, length_new), mode="bilinear")
@@ -676,7 +787,7 @@ def bislerp(samples, width, height):
     samples = samples.float()
     n,c,h,w = samples.shape
     h_new, w_new = (height, width)
-    
+
     #linear w
     ratios, coords_1, coords_2 = generate_bilinear_data(w, w_new, samples.device)
     coords_1 = coords_1.expand((n, c, h, -1))
@@ -751,7 +862,7 @@ def get_tiled_scale_steps(width, height, tile_x, tile_y, overlap):
     return rows * cols
 
 @torch.inference_mode()
-def tiled_scale_multidim(samples, function, tile=(64, 64), overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):
+def tiled_scale_multidim(samples, function, tile=(64, 64), overlap=8, upscale_amount=4, out_channels=3, output_device="cpu", downscale=False, index_formulas=None, pbar=None):
     dims = len(tile)
 
     if not (isinstance(upscale_amount, (tuple, list))):
@@ -760,6 +871,12 @@ def tiled_scale_multidim(samples, function, tile=(64, 64), overlap = 8, upscale_
     if not (isinstance(overlap, (tuple, list))):
         overlap = [overlap] * dims
 
+    if index_formulas is None:
+        index_formulas = upscale_amount
+
+    if not (isinstance(index_formulas, (tuple, list))):
+        index_formulas = [index_formulas] * dims
+
     def get_upscale(dim, val):
         up = upscale_amount[dim]
         if callable(up):
@@ -767,10 +884,38 @@ def tiled_scale_multidim(samples, function, tile=(64, 64), overlap = 8, upscale_
         else:
             return up * val
 
+    def get_downscale(dim, val):
+        up = upscale_amount[dim]
+        if callable(up):
+            return up(val)
+        else:
+            return val / up
+
+    def get_upscale_pos(dim, val):
+        up = index_formulas[dim]
+        if callable(up):
+            return up(val)
+        else:
+            return up * val
+
+    def get_downscale_pos(dim, val):
+        up = index_formulas[dim]
+        if callable(up):
+            return up(val)
+        else:
+            return val / up
+
+    if downscale:
+        get_scale = get_downscale
+        get_pos = get_downscale_pos
+    else:
+        get_scale = get_upscale
+        get_pos = get_upscale_pos
+
     def mult_list_upscale(a):
         out = []
         for i in range(len(a)):
-            out.append(round(get_upscale(i, a[i])))
+            out.append(round(get_scale(i, a[i])))
         return out
 
     output = torch.empty([samples.shape[0], out_channels] + mult_list_upscale(samples.shape[2:]), device=output_device)
@@ -788,23 +933,23 @@ def tiled_scale_multidim(samples, function, tile=(64, 64), overlap = 8, upscale_
         out = torch.zeros([s.shape[0], out_channels] + mult_list_upscale(s.shape[2:]), device=output_device)
         out_div = torch.zeros([s.shape[0], out_channels] + mult_list_upscale(s.shape[2:]), device=output_device)
 
-        positions = [range(0, s.shape[d+2], tile[d] - overlap[d]) if s.shape[d+2] > tile[d] else [0] for d in range(dims)]
+        positions = [range(0, s.shape[d+2] - overlap[d], tile[d] - overlap[d]) if s.shape[d+2] > tile[d] else [0] for d in range(dims)]
 
         for it in itertools.product(*positions):
             s_in = s
             upscaled = []
 
             for d in range(dims):
-                pos = max(0, min(s.shape[d + 2] - (overlap[d] + 1), it[d]))
+                pos = max(0, min(s.shape[d + 2] - overlap[d], it[d]))
                 l = min(tile[d], s.shape[d + 2] - pos)
                 s_in = s_in.narrow(d + 2, pos, l)
-                upscaled.append(round(get_upscale(d, pos)))
+                upscaled.append(round(get_pos(d, pos)))
 
             ps = function(s_in).to(output_device)
             mask = torch.ones_like(ps)
 
             for d in range(2, dims + 2):
-                feather = round(get_upscale(d - 2, overlap[d - 2]))
+                feather = round(get_scale(d - 2, overlap[d - 2]))
                 if feather >= mask.shape[d]:
                     continue
                 for t in range(feather):
@@ -828,7 +973,7 @@ def tiled_scale_multidim(samples, function, tile=(64, 64), overlap = 8, upscale_
     return output
 
 def tiled_scale(samples, function, tile_x=64, tile_y=64, overlap = 8, upscale_amount = 4, out_channels = 3, output_device="cpu", pbar = None):
-    return tiled_scale_multidim(samples, function, (tile_y, tile_x), overlap, upscale_amount, out_channels, output_device, pbar)
+    return tiled_scale_multidim(samples, function, (tile_y, tile_x), overlap=overlap, upscale_amount=upscale_amount, out_channels=out_channels, output_device=output_device, pbar=pbar)
 
 PROGRESS_BAR_ENABLED = True
 def set_progress_bar_enabled(enabled):

comfy_execution/graph.py

@@ -54,8 +54,8 @@ class DynamicPrompt:
     def get_original_prompt(self):
         return self.original_prompt
 
-def get_input_info(class_def, input_name):
-    valid_inputs = class_def.INPUT_TYPES()
+def get_input_info(class_def, input_name, valid_inputs=None):
+    valid_inputs = valid_inputs or class_def.INPUT_TYPES()
     input_info = None
     input_category = None
     if "required" in valid_inputs and input_name in valid_inputs["required"]:
@@ -131,7 +131,7 @@ class TopologicalSort:
                     if (include_lazy or not is_lazy) and not self.is_cached(from_node_id):
                         node_ids.append(from_node_id)
                         links.append((from_node_id, from_socket, unique_id))
-                        
+
         for link in links:
             self.add_strong_link(*link)
 

comfy_extras/chainner_models/model_loading.py

@@ -1,5 +1,6 @@
+import logging
 from spandrel import ModelLoader
 
 def load_state_dict(state_dict):
-    print("WARNING: comfy_extras.chainner_models is deprecated and has been replaced by the spandrel library.")
+    logging.warning("comfy_extras.chainner_models is deprecated and has been replaced by the spandrel library.")
     return ModelLoader().load_from_state_dict(state_dict).eval()

comfy_extras/nodes_align_your_steps.py

@@ -24,7 +24,7 @@ class AlignYourStepsScheduler:
     def INPUT_TYPES(s):
         return {"required":
                     {"model_type": (["SD1", "SDXL", "SVD"], ),
-                     "steps": ("INT", {"default": 10, "min": 10, "max": 10000}),
+                     "steps": ("INT", {"default": 10, "min": 1, "max": 10000}),
                      "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                       }
                }

comfy_extras/nodes_cosmos.py

@@ -0,0 +1,82 @@
+import nodes
+import torch
+import comfy.model_management
+import comfy.utils
+
+
+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}, )
+
+
+def vae_encode_with_padding(vae, image, width, height, length, padding=0):
+    pixels = comfy.utils.common_upscale(image[..., :3].movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
+    pixel_len = min(pixels.shape[0], length)
+    padded_length = min(length, (((pixel_len - 1) // 8) + 1 + padding) * 8 - 7)
+    padded_pixels = torch.ones((padded_length, height, width, 3)) * 0.5
+    padded_pixels[:pixel_len] = pixels[:pixel_len]
+    latent_len = ((pixel_len - 1) // 8) + 1
+    latent_temp = vae.encode(padded_pixels)
+    return latent_temp[:, :, :latent_len]
+
+
+class CosmosImageToVideoLatent:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {"vae": ("VAE", ),
+                             "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}),
+                },
+                "optional": {"start_image": ("IMAGE", ),
+                             "end_image": ("IMAGE", ),
+                }}
+
+
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "encode"
+
+    CATEGORY = "conditioning/inpaint"
+
+    def encode(self, vae, width, height, length, batch_size, start_image=None, end_image=None):
+        latent = torch.zeros([1, 16, ((length - 1) // 8) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
+        if start_image is None and end_image is None:
+            out_latent = {}
+            out_latent["samples"] = latent
+            return (out_latent,)
+
+        mask = torch.ones([latent.shape[0], 1, ((length - 1) // 8) + 1, latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device())
+
+        if start_image is not None:
+            latent_temp = vae_encode_with_padding(vae, start_image, width, height, length, padding=1)
+            latent[:, :, :latent_temp.shape[-3]] = latent_temp
+            mask[:, :, :latent_temp.shape[-3]] *= 0.0
+
+        if end_image is not None:
+            latent_temp = vae_encode_with_padding(vae, end_image, width, height, length, padding=0)
+            latent[:, :, -latent_temp.shape[-3]:] = latent_temp
+            mask[:, :, -latent_temp.shape[-3]:] *= 0.0
+
+        out_latent = {}
+        out_latent["samples"] = latent.repeat((batch_size, ) + (1,) * (latent.ndim - 1))
+        out_latent["noise_mask"] = mask.repeat((batch_size, ) + (1,) * (mask.ndim - 1))
+        return (out_latent,)
+
+
+NODE_CLASS_MAPPINGS = {
+    "EmptyCosmosLatentVideo": EmptyCosmosLatentVideo,
+    "CosmosImageToVideoLatent": CosmosImageToVideoLatent,
+}

comfy_extras/nodes_custom_sampler.py

@@ -231,6 +231,24 @@ class FlipSigmas:
             sigmas[0] = 0.0001
         return (sigmas,)
 
+class SetFirstSigma:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"sigmas": ("SIGMAS", ),
+                     "sigma": ("FLOAT", {"default": 136.0, "min": 0.0, "max": 20000.0, "step": 0.001, "round": False}),
+                    }
+               }
+    RETURN_TYPES = ("SIGMAS",)
+    CATEGORY = "sampling/custom_sampling/sigmas"
+
+    FUNCTION = "set_first_sigma"
+
+    def set_first_sigma(self, sigmas, sigma):
+        sigmas = sigmas.clone()
+        sigmas[0] = sigma
+        return (sigmas, )
+
 class KSamplerSelect:
     @classmethod
     def INPUT_TYPES(s):
@@ -710,6 +728,7 @@ NODE_CLASS_MAPPINGS = {
     "SplitSigmas": SplitSigmas,
     "SplitSigmasDenoise": SplitSigmasDenoise,
     "FlipSigmas": FlipSigmas,
+    "SetFirstSigma": SetFirstSigma,
 
     "CFGGuider": CFGGuider,
     "DualCFGGuider": DualCFGGuider,

comfy_extras/nodes_flux.py

@@ -38,7 +38,26 @@ class FluxGuidance:
         return (c, )
 
 
+class FluxDisableGuidance:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "conditioning": ("CONDITIONING", ),
+            }}
+
+    RETURN_TYPES = ("CONDITIONING",)
+    FUNCTION = "append"
+
+    CATEGORY = "advanced/conditioning/flux"
+    DESCRIPTION = "This node completely disables the guidance embed on Flux and Flux like models"
+
+    def append(self, conditioning):
+        c = node_helpers.conditioning_set_values(conditioning, {"guidance": None})
+        return (c, )
+
+
 NODE_CLASS_MAPPINGS = {
     "CLIPTextEncodeFlux": CLIPTextEncodeFlux,
     "FluxGuidance": FluxGuidance,
+    "FluxDisableGuidance": FluxDisableGuidance,
 }

comfy_extras/nodes_gits.py

@@ -162,7 +162,7 @@ NOISE_LEVELS = {
         [14.61464119, 7.49001646, 5.85520077, 4.45427561, 3.46139455, 2.84484982, 2.19988537, 1.72759056, 1.36964464, 1.08895338, 0.86115354, 0.69515091, 0.54755926, 0.43325692, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
         [14.61464119, 11.54541874, 7.49001646, 5.85520077, 4.45427561, 3.46139455, 2.84484982, 2.19988537, 1.72759056, 1.36964464, 1.08895338, 0.86115354, 0.69515091, 0.54755926, 0.43325692, 0.34370604, 0.25053367, 0.17026083, 0.09824532, 0.02916753],
         [14.61464119, 11.54541874, 7.49001646, 5.85520077, 4.45427561, 3.46139455, 2.84484982, 2.19988537, 1.72759056, 1.36964464, 1.08895338, 0.89115214, 0.72133851, 0.59516323, 0.4783645, 0.38853383, 0.29807833, 0.22545385, 0.17026083, 0.09824532, 0.02916753],
-    ], 
+    ],
     1.15: [
         [14.61464119, 0.83188516, 0.02916753],
         [14.61464119, 1.84880662, 0.59516323, 0.02916753],
@@ -246,7 +246,7 @@ NOISE_LEVELS = {
         [14.61464119, 5.85520077, 2.84484982, 1.72759056, 1.162866, 0.83188516, 0.64427125, 0.52423614, 0.43325692, 0.36617002, 0.32104823, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
         [14.61464119, 5.85520077, 2.84484982, 1.78698075, 1.24153244, 0.92192322, 0.72133851, 0.57119018, 0.45573691, 0.38853383, 0.34370604, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
         [14.61464119, 5.85520077, 2.84484982, 1.78698075, 1.24153244, 0.92192322, 0.72133851, 0.57119018, 0.4783645, 0.41087446, 0.36617002, 0.32104823, 0.29807833, 0.27464288, 0.25053367, 0.22545385, 0.19894916, 0.17026083, 0.13792117, 0.09824532, 0.02916753],
-    ], 
+    ],
     1.35: [
         [14.61464119, 0.69515091, 0.02916753],
         [14.61464119, 0.95350921, 0.34370604, 0.02916753],

comfy_extras/nodes_hooks.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 from typing import TYPE_CHECKING, Union
+import logging
 import torch
 from collections.abc import Iterable
 
@@ -32,7 +33,7 @@ class PairConditioningSetProperties:
                 "timesteps": ("TIMESTEPS_RANGE",),
             }
         }
-    
+
     EXPERIMENTAL = True
     RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
     RETURN_NAMES = ("positive", "negative")
@@ -46,7 +47,7 @@ class PairConditioningSetProperties:
                                                                     strength=strength, set_cond_area=set_cond_area,
                                                                     mask=mask, hooks=hooks, timesteps_range=timesteps)
         return (final_positive, final_negative)
-    
+
 class PairConditioningSetPropertiesAndCombine:
     NodeId = 'PairConditioningSetPropertiesAndCombine'
     NodeName = 'Cond Pair Set Props Combine'
@@ -67,7 +68,7 @@ class PairConditioningSetPropertiesAndCombine:
                 "timesteps": ("TIMESTEPS_RANGE",),
             }
         }
-    
+
     EXPERIMENTAL = True
     RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
     RETURN_NAMES = ("positive", "negative")
@@ -158,7 +159,7 @@ class PairConditioningCombine:
                 "negative_B": ("CONDITIONING",),
             },
         }
-    
+
     EXPERIMENTAL = True
     RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
     RETURN_NAMES = ("positive", "negative")
@@ -185,7 +186,7 @@ class PairConditioningSetDefaultAndCombine:
                 "hooks": ("HOOKS",),
             }
         }
-    
+
     EXPERIMENTAL = True
     RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
     RETURN_NAMES = ("positive", "negative")
@@ -197,7 +198,7 @@ class PairConditioningSetDefaultAndCombine:
         final_positive, final_negative = comfy.hooks.set_default_conds_and_combine(conds=[positive, negative], new_conds=[positive_DEFAULT, negative_DEFAULT],
                                                                                    hooks=hooks)
         return (final_positive, final_negative)
-    
+
 class ConditioningSetDefaultAndCombine:
     NodeId = 'ConditioningSetDefaultCombine'
     NodeName = 'Cond Set Default Combine'
@@ -223,7 +224,7 @@ class ConditioningSetDefaultAndCombine:
         (final_conditioning,) = comfy.hooks.set_default_conds_and_combine(conds=[cond], new_conds=[cond_DEFAULT],
                                                                         hooks=hooks)
         return (final_conditioning,)
-    
+
 class SetClipHooks:
     NodeId = 'SetClipHooks'
     NodeName = 'Set CLIP Hooks'
@@ -239,13 +240,13 @@ class SetClipHooks:
                 "hooks": ("HOOKS",)
             }
         }
-    
+
     EXPERIMENTAL = True
     RETURN_TYPES = ("CLIP",)
     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:
@@ -254,7 +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:
@@ -268,7 +269,7 @@ class ConditioningTimestepsRange:
                 "end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001})
             },
         }
-    
+
     EXPERIMENTAL = True
     RETURN_TYPES = ("TIMESTEPS_RANGE", "TIMESTEPS_RANGE", "TIMESTEPS_RANGE")
     RETURN_NAMES = ("TIMESTEPS_RANGE", "BEFORE_RANGE", "AFTER_RANGE")
@@ -289,7 +290,7 @@ class CreateHookLora:
     NodeName = 'Create Hook LoRA'
     def __init__(self):
         self.loaded_lora = None
-    
+
     @classmethod
     def INPUT_TYPES(s):
         return {
@@ -302,7 +303,7 @@ class CreateHookLora:
                 "prev_hooks": ("HOOKS",)
             }
         }
-    
+
     EXPERIMENTAL = True
     RETURN_TYPES = ("HOOKS",)
     CATEGORY = "advanced/hooks/create"
@@ -315,7 +316,7 @@ class CreateHookLora:
 
         if strength_model == 0 and strength_clip == 0:
             return (prev_hooks,)
-        
+
         lora_path = folder_paths.get_full_path("loras", lora_name)
         lora = None
         if self.loaded_lora is not None:
@@ -325,7 +326,7 @@ class CreateHookLora:
                 temp = self.loaded_lora
                 self.loaded_lora = None
                 del temp
-        
+
         if lora is None:
             lora = comfy.utils.load_torch_file(lora_path, safe_load=True)
             self.loaded_lora = (lora_path, lora)
@@ -347,7 +348,7 @@ class CreateHookLoraModelOnly(CreateHookLora):
                 "prev_hooks": ("HOOKS",)
             }
         }
-    
+
     EXPERIMENTAL = True
     RETURN_TYPES = ("HOOKS",)
     CATEGORY = "advanced/hooks/create"
@@ -377,7 +378,7 @@ class CreateHookModelAsLora:
                 "prev_hooks": ("HOOKS",)
             }
         }
-    
+
     EXPERIMENTAL = True
     RETURN_TYPES = ("HOOKS",)
     CATEGORY = "advanced/hooks/create"
@@ -400,7 +401,7 @@ class CreateHookModelAsLora:
                 temp = self.loaded_weights
                 self.loaded_weights = None
                 del temp
-        
+
         if weights_model is None:
             out = comfy.sd.load_checkpoint_guess_config(ckpt_path, output_vae=True, output_clip=True, embedding_directory=folder_paths.get_folder_paths("embeddings"))
             weights_model = comfy.hooks.get_patch_weights_from_model(out[0])
@@ -425,7 +426,7 @@ class CreateHookModelAsLoraModelOnly(CreateHookModelAsLora):
                 "prev_hooks": ("HOOKS",)
             }
         }
-    
+
     EXPERIMENTAL = True
     RETURN_TYPES = ("HOOKS",)
     CATEGORY = "advanced/hooks/create"
@@ -454,7 +455,7 @@ class SetHookKeyframes:
                 "hook_kf": ("HOOK_KEYFRAMES",),
             }
         }
-    
+
     EXPERIMENTAL = True
     RETURN_TYPES = ("HOOKS",)
     CATEGORY = "advanced/hooks/scheduling"
@@ -480,7 +481,7 @@ class CreateHookKeyframe:
                 "prev_hook_kf": ("HOOK_KEYFRAMES",),
             }
         }
-    
+
     EXPERIMENTAL = True
     RETURN_TYPES = ("HOOK_KEYFRAMES",)
     RETURN_NAMES = ("HOOK_KF",)
@@ -514,7 +515,7 @@ class CreateHookKeyframesInterpolated:
                 "prev_hook_kf": ("HOOK_KEYFRAMES",),
             },
         }
-    
+
     EXPERIMENTAL = True
     RETURN_TYPES = ("HOOK_KEYFRAMES",)
     RETURN_NAMES = ("HOOK_KF",)
@@ -539,7 +540,7 @@ class CreateHookKeyframesInterpolated:
                 is_first = False
             prev_hook_kf.add(comfy.hooks.HookKeyframe(strength=strength, start_percent=percent, guarantee_steps=guarantee_steps))
             if print_keyframes:
-                print(f"Hook Keyframe - start_percent:{percent} = {strength}")
+                logging.info(f"Hook Keyframe - start_percent:{percent} = {strength}")
         return (prev_hook_kf,)
 
 class CreateHookKeyframesFromFloats:
@@ -558,7 +559,7 @@ class CreateHookKeyframesFromFloats:
                 "prev_hook_kf": ("HOOK_KEYFRAMES",),
             }
         }
-    
+
     EXPERIMENTAL = True
     RETURN_TYPES = ("HOOK_KEYFRAMES",)
     RETURN_NAMES = ("HOOK_KF",)
@@ -579,7 +580,7 @@ class CreateHookKeyframesFromFloats:
             raise Exception(f"floats_strength must be either an iterable input or a float, but was{type(floats_strength).__repr__}.")
         percents = comfy.hooks.InterpolationMethod.get_weights(num_from=start_percent, num_to=end_percent, length=len(floats_strength),
                                                                method=comfy.hooks.InterpolationMethod.LINEAR)
-        
+
         is_first = True
         for percent, strength in zip(percents, floats_strength):
             guarantee_steps = 0
@@ -588,7 +589,7 @@ class CreateHookKeyframesFromFloats:
                 is_first = False
             prev_hook_kf.add(comfy.hooks.HookKeyframe(strength=strength, start_percent=percent, guarantee_steps=guarantee_steps))
             if print_keyframes:
-                print(f"Hook Keyframe - start_percent:{percent} = {strength}")
+                logging.info(f"Hook Keyframe - start_percent:{percent} = {strength}")
         return (prev_hook_kf,)
 #------------------------------------------
 ###########################################
@@ -603,7 +604,7 @@ class SetModelHooksOnCond:
                 "hooks": ("HOOKS",),
             },
         }
-    
+
     EXPERIMENTAL = True
     RETURN_TYPES = ("CONDITIONING",)
     CATEGORY = "advanced/hooks/manual"
@@ -629,7 +630,7 @@ class CombineHooks:
                 "hooks_B": ("HOOKS",),
             }
         }
-    
+
     EXPERIMENTAL = True
     RETURN_TYPES = ("HOOKS",)
     CATEGORY = "advanced/hooks/combine"
@@ -656,7 +657,7 @@ class CombineHooksFour:
                 "hooks_D": ("HOOKS",),
             }
         }
-    
+
     EXPERIMENTAL = True
     RETURN_TYPES = ("HOOKS",)
     CATEGORY = "advanced/hooks/combine"
@@ -689,7 +690,7 @@ class CombineHooksEight:
                 "hooks_H": ("HOOKS",),
             }
         }
-    
+
     EXPERIMENTAL = True
     RETURN_TYPES = ("HOOKS",)
     CATEGORY = "advanced/hooks/combine"

comfy_extras/nodes_latent.py

@@ -2,10 +2,14 @@ import comfy.utils
 import comfy_extras.nodes_post_processing
 import torch
 
-def reshape_latent_to(target_shape, latent):
+
+def reshape_latent_to(target_shape, latent, repeat_batch=True):
     if latent.shape[1:] != target_shape[1:]:
-        latent = comfy.utils.common_upscale(latent, target_shape[3], target_shape[2], "bilinear", "center")
-    return comfy.utils.repeat_to_batch_size(latent, target_shape[0])
+        latent = comfy.utils.common_upscale(latent, target_shape[-1], target_shape[-2], "bilinear", "center")
+    if repeat_batch:
+        return comfy.utils.repeat_to_batch_size(latent, target_shape[0])
+    else:
+        return latent
 
 
 class LatentAdd:
@@ -116,8 +120,7 @@ class LatentBatch:
         s1 = samples1["samples"]
         s2 = samples2["samples"]
 
-        if s1.shape[1:] != s2.shape[1:]:
-            s2 = comfy.utils.common_upscale(s2, s1.shape[3], s1.shape[2], "bilinear", "center")
+        s2 = reshape_latent_to(s1.shape, s2, repeat_batch=False)
         s = torch.cat((s1, s2), dim=0)
         samples_out["samples"] = s
         samples_out["batch_index"] = samples1.get("batch_index", [x for x in range(0, s1.shape[0])]) + samples2.get("batch_index", [x for x in range(0, s2.shape[0])])

comfy_extras/nodes_load_3d.py

@@ -19,12 +19,7 @@ class Load3D():
             "image": ("LOAD_3D", {}),
             "width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
             "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
-            "show_grid": ([True, False],),
-            "camera_type": (["perspective", "orthographic"],),
-            "view": (["front", "right", "top", "isometric"],),
             "material": (["original", "normal", "wireframe", "depth"],),
-            "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"],),
         }}
 
@@ -37,11 +32,12 @@ class Load3D():
     CATEGORY = "3d"
 
     def process(self, model_file, image, **kwargs):
-        imagepath = folder_paths.get_annotated_filepath(image)
+        image_path = folder_paths.get_annotated_filepath(image['image'])
+        mask_path = folder_paths.get_annotated_filepath(image['mask'])
 
         load_image_node = nodes.LoadImage()
-
-        output_image, output_mask = load_image_node.load_image(image=imagepath)
+        output_image, ignore_mask = load_image_node.load_image(image=image_path)
+        ignore_image, output_mask = load_image_node.load_image(image=mask_path)
 
         return output_image, output_mask, model_file,
 
@@ -59,14 +55,8 @@ class Load3DAnimation():
             "image": ("LOAD_3D_ANIMATION", {}),
             "width": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
             "height": ("INT", {"default": 1024, "min": 1, "max": 4096, "step": 1}),
-            "show_grid": ([True, False],),
-            "camera_type": (["perspective", "orthographic"],),
-            "view": (["front", "right", "top", "isometric"],),
             "material": (["original", "normal", "wireframe", "depth"],),
-            "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"],),
-            "animation_speed": (["0.1", "0.5", "1", "1.5", "2"], {"default": "1"}),
         }}
 
     RETURN_TYPES = ("IMAGE", "MASK", "STRING")
@@ -78,11 +68,12 @@ class Load3DAnimation():
     CATEGORY = "3d"
 
     def process(self, model_file, image, **kwargs):
-        imagepath = folder_paths.get_annotated_filepath(image)
+        image_path = folder_paths.get_annotated_filepath(image['image'])
+        mask_path = folder_paths.get_annotated_filepath(image['mask'])
 
         load_image_node = nodes.LoadImage()
-
-        output_image, output_mask = load_image_node.load_image(image=imagepath)
+        output_image, ignore_mask = load_image_node.load_image(image=image_path)
+        ignore_image, output_mask = load_image_node.load_image(image=mask_path)
 
         return output_image, output_mask, model_file,
 
@@ -91,12 +82,27 @@ class Preview3D():
     def INPUT_TYPES(s):
         return {"required": {
             "model_file": ("STRING", {"default": "", "multiline": False}),
-            "show_grid": ([True, False],),
-            "camera_type": (["perspective", "orthographic"],),
-            "view": (["front", "right", "top", "isometric"],),
             "material": (["original", "normal", "wireframe", "depth"],),
-            "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"],),
+        }}
+
+    OUTPUT_NODE = True
+    RETURN_TYPES = ()
+
+    CATEGORY = "3d"
+
+    FUNCTION = "process"
+    EXPERIMENTAL = True
+
+    def process(self, model_file, **kwargs):
+        return {"ui": {"model_file": [model_file]}, "result": ()}
+
+class Preview3DAnimation():
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "model_file": ("STRING", {"default": "", "multiline": False}),
+            "material": (["original", "normal", "wireframe", "depth"],),
             "up_direction": (["original", "-x", "+x", "-y", "+y", "-z", "+z"],),
         }}
 
@@ -114,11 +120,13 @@ class Preview3D():
 NODE_CLASS_MAPPINGS = {
     "Load3D": Load3D,
     "Load3DAnimation": Load3DAnimation,
-    "Preview3D": Preview3D
+    "Preview3D": Preview3D,
+    "Preview3DAnimation": Preview3DAnimation
 }
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     "Load3D": "Load 3D",
     "Load3DAnimation": "Load 3D - Animation",
-    "Preview3D": "Preview 3D"
-}
+    "Preview3D": "Preview 3D",
+    "Preview3DAnimation": "Preview 3D - Animation"
+}

comfy_extras/nodes_lumina2.py

@@ -0,0 +1,104 @@
+from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict
+import torch
+
+
+class RenormCFG:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "cfg_trunc": ("FLOAT", {"default": 100, "min": 0.0, "max": 100.0, "step": 0.01}),
+                              "renorm_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
+                              }}
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/model"
+
+    def patch(self, model, cfg_trunc, renorm_cfg):
+        def renorm_cfg_func(args):
+            cond_denoised = args["cond_denoised"]
+            uncond_denoised = args["uncond_denoised"]
+            cond_scale = args["cond_scale"]
+            timestep = args["timestep"]
+            x_orig = args["input"]
+            in_channels = model.model.diffusion_model.in_channels
+
+            if timestep[0] < cfg_trunc:
+                cond_eps, uncond_eps = cond_denoised[:, :in_channels], uncond_denoised[:, :in_channels]
+                cond_rest, _ = cond_denoised[:, in_channels:], uncond_denoised[:, in_channels:]
+                half_eps = uncond_eps + cond_scale * (cond_eps - uncond_eps)
+                half_rest = cond_rest
+
+                if float(renorm_cfg) > 0.0:
+                    ori_pos_norm = torch.linalg.vector_norm(cond_eps
+                            , dim=tuple(range(1, len(cond_eps.shape))), keepdim=True
+                    )
+                    max_new_norm = ori_pos_norm * float(renorm_cfg)
+                    new_pos_norm = torch.linalg.vector_norm(
+                            half_eps, dim=tuple(range(1, len(half_eps.shape))), keepdim=True
+                        )
+                    if new_pos_norm >= max_new_norm:
+                        half_eps = half_eps * (max_new_norm / new_pos_norm)
+            else:
+                cond_eps, uncond_eps = cond_denoised[:, :in_channels], uncond_denoised[:, :in_channels]
+                cond_rest, _ = cond_denoised[:, in_channels:], uncond_denoised[:, in_channels:]
+                half_eps = cond_eps
+                half_rest = cond_rest
+
+            cfg_result = torch.cat([half_eps, half_rest], dim=1)
+
+            # cfg_result = uncond_denoised + (cond_denoised - uncond_denoised) * cond_scale
+
+            return x_orig - cfg_result
+
+        m = model.clone()
+        m.set_model_sampler_cfg_function(renorm_cfg_func)
+        return (m, )
+
+
+class CLIPTextEncodeLumina2(ComfyNodeABC):
+    SYSTEM_PROMPT = {
+        "superior": "You are an assistant designed to generate superior images with the superior "\
+            "degree of image-text alignment based on textual prompts or user prompts.",
+        "alignment": "You are an assistant designed to generate high-quality images with the "\
+            "highest degree of image-text alignment based on textual prompts."
+    }
+    SYSTEM_PROMPT_TIP = "Lumina2 provide two types of system prompts:" \
+        "Superior: You are an assistant designed to generate superior images with the superior "\
+        "degree of image-text alignment based on textual prompts or user prompts. "\
+        "Alignment: You are an assistant designed to generate high-quality images with the highest "\
+        "degree of image-text alignment based on textual prompts."
+    @classmethod
+    def INPUT_TYPES(s) -> InputTypeDict:
+        return {
+            "required": {
+                "system_prompt": (list(CLIPTextEncodeLumina2.SYSTEM_PROMPT.keys()), {"tooltip": CLIPTextEncodeLumina2.SYSTEM_PROMPT_TIP}),
+                "user_prompt": (IO.STRING, {"multiline": True, "dynamicPrompts": True, "tooltip": "The text to be encoded."}),
+                "clip": (IO.CLIP, {"tooltip": "The CLIP model used for encoding the text."})
+            }
+        }
+    RETURN_TYPES = (IO.CONDITIONING,)
+    OUTPUT_TOOLTIPS = ("A conditioning containing the embedded text used to guide the diffusion model.",)
+    FUNCTION = "encode"
+
+    CATEGORY = "conditioning"
+    DESCRIPTION = "Encodes a system prompt and a user prompt using a CLIP model into an embedding that can be used to guide the diffusion model towards generating specific images."
+
+    def encode(self, clip, user_prompt, system_prompt):
+        if clip is None:
+            raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.")
+        system_prompt = CLIPTextEncodeLumina2.SYSTEM_PROMPT[system_prompt]
+        prompt = f'{system_prompt} <Prompt Start> {user_prompt}'
+        tokens = clip.tokenize(prompt)
+        return (clip.encode_from_tokens_scheduled(tokens), )
+
+
+NODE_CLASS_MAPPINGS = {
+    "CLIPTextEncodeLumina2": CLIPTextEncodeLumina2,
+    "RenormCFG": RenormCFG
+}
+
+
+NODE_DISPLAY_NAME_MAPPINGS = {
+    "CLIPTextEncodeLumina2": "CLIP Text Encode for Lumina2",
+}

comfy_extras/nodes_mask.py

@@ -305,7 +305,7 @@ class FeatherMask:
             output[:, -y, :] *= feather_rate
 
         return (output,)
-    
+
 class GrowMask:
     @classmethod
     def INPUT_TYPES(cls):
@@ -316,7 +316,7 @@ class GrowMask:
                 "tapered_corners": ("BOOLEAN", {"default": True}),
             },
         }
-    
+
     CATEGORY = "mask"
 
     RETURN_TYPES = ("MASK",)

comfy_extras/nodes_model_advanced.py

@@ -3,6 +3,8 @@ import comfy.model_sampling
 import comfy.latent_formats
 import nodes
 import torch
+import node_helpers
+
 
 class LCM(comfy.model_sampling.EPS):
     def calculate_denoised(self, sigma, model_output, model_input):
@@ -189,7 +191,7 @@ 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 +208,9 @@ 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":
@@ -291,6 +296,24 @@ class RescaleCFG:
         m.set_model_sampler_cfg_function(rescale_cfg)
         return (m, )
 
+class ModelComputeDtype:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": { "model": ("MODEL",),
+                              "dtype": (["default", "fp32", "fp16", "bf16"],),
+                              }}
+
+    RETURN_TYPES = ("MODEL",)
+    FUNCTION = "patch"
+
+    CATEGORY = "advanced/debug/model"
+
+    def patch(self, model, dtype):
+        m = model.clone()
+        m.set_model_compute_dtype(node_helpers.string_to_torch_dtype(dtype))
+        return (m, )
+
+
 NODE_CLASS_MAPPINGS = {
     "ModelSamplingDiscrete": ModelSamplingDiscrete,
     "ModelSamplingContinuousEDM": ModelSamplingContinuousEDM,
@@ -300,4 +323,5 @@ NODE_CLASS_MAPPINGS = {
     "ModelSamplingAuraFlow": ModelSamplingAuraFlow,
     "ModelSamplingFlux": ModelSamplingFlux,
     "RescaleCFG": RescaleCFG,
+    "ModelComputeDtype": ModelComputeDtype,
 }

comfy_extras/nodes_model_merging_model_specific.py

@@ -196,6 +196,54 @@ class ModelMergeLTXV(comfy_extras.nodes_model_merging.ModelMergeBlocks):
 
         return {"required": arg_dict}
 
+class ModelMergeCosmos7B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
+    CATEGORY = "advanced/model_merging/model_specific"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        arg_dict = { "model1": ("MODEL",),
+                              "model2": ("MODEL",)}
+
+        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+
+        arg_dict["pos_embedder."] = argument
+        arg_dict["extra_pos_embedder."] = argument
+        arg_dict["x_embedder."] = argument
+        arg_dict["t_embedder."] = argument
+        arg_dict["affline_norm."] = argument
+
+
+        for i in range(28):
+            arg_dict["blocks.block{}.".format(i)] = argument
+
+        arg_dict["final_layer."] = argument
+
+        return {"required": arg_dict}
+
+class ModelMergeCosmos14B(comfy_extras.nodes_model_merging.ModelMergeBlocks):
+    CATEGORY = "advanced/model_merging/model_specific"
+
+    @classmethod
+    def INPUT_TYPES(s):
+        arg_dict = { "model1": ("MODEL",),
+                              "model2": ("MODEL",)}
+
+        argument = ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01})
+
+        arg_dict["pos_embedder."] = argument
+        arg_dict["extra_pos_embedder."] = argument
+        arg_dict["x_embedder."] = argument
+        arg_dict["t_embedder."] = argument
+        arg_dict["affline_norm."] = argument
+
+
+        for i in range(36):
+            arg_dict["blocks.block{}.".format(i)] = argument
+
+        arg_dict["final_layer."] = argument
+
+        return {"required": arg_dict}
+
 NODE_CLASS_MAPPINGS = {
     "ModelMergeSD1": ModelMergeSD1,
     "ModelMergeSD2": ModelMergeSD1, #SD1 and SD2 have the same blocks
@@ -206,4 +254,6 @@ NODE_CLASS_MAPPINGS = {
     "ModelMergeSD35_Large": ModelMergeSD35_Large,
     "ModelMergeMochiPreview": ModelMergeMochiPreview,
     "ModelMergeLTXV": ModelMergeLTXV,
+    "ModelMergeCosmos7B": ModelMergeCosmos7B,
+    "ModelMergeCosmos14B": ModelMergeCosmos14B,
 }

comfy_extras/nodes_morphology.py

@@ -46,4 +46,4 @@ NODE_CLASS_MAPPINGS = {
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     "Morphology": "ImageMorphology",
-}
+}

comfy_extras/nodes_perpneg.py

@@ -64,7 +64,7 @@ class Guider_PerpNeg(comfy.samplers.CFGGuider):
     def predict_noise(self, x, timestep, model_options={}, seed=None):
         # in CFGGuider.predict_noise, we call sampling_function(), which uses cfg_function() to compute pos & neg
         # but we'd rather do a single batch of sampling pos, neg, and empty, so we call calc_cond_batch([pos,neg,empty]) directly
-        
+
         positive_cond = self.conds.get("positive", None)
         negative_cond = self.conds.get("negative", None)
         empty_cond = self.conds.get("empty_negative_prompt", None)
@@ -73,7 +73,7 @@ class Guider_PerpNeg(comfy.samplers.CFGGuider):
             comfy.samplers.calc_cond_batch(self.inner_model, [positive_cond, negative_cond, empty_cond], x, timestep, model_options)
         cfg_result = perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_empty, self.neg_scale, self.cfg)
 
-        # normally this would be done in cfg_function, but we skipped 
+        # normally this would be done in cfg_function, but we skipped
         # that for efficiency: we can compute the noise predictions in
         # a single call to calc_cond_batch() (rather than two)
         # so we replicate the hook here