ComfyUI version 0.3.63

Update template to 0.1.93 (#10235 )
* Update template to 0.1.92 * Update template to 0.1.93
2025-10-06 14:49:04 -04:00 · 2025-10-06 10:57:00 -07:00 · 2025-10-05 15:41:19 -04:00 · 2025-10-04 23:34:18 -07:00 · 2025-10-04 22:05:05 -04:00 · 2025-10-04 12:33:48 -07:00
141 changed files with 9104 additions and 4024 deletions
@@ -0,0 +1,27 @@
+As of the time of writing this you need this preview driver for best results:
+https://www.amd.com/en/resources/support-articles/release-notes/RN-AMDGPU-WINDOWS-PYTORCH-PREVIEW.html
+
+HOW TO RUN:
+
+If you have a AMD gpu:
+
+run_amd_gpu.bat
+
+If you have memory issues you can try disabling the smart memory management by running comfyui with:
+
+run_amd_gpu_disable_smart_memory.bat
+
+IF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: ComfyUI\models\checkpoints
+
+You can download the stable diffusion XL one from: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0_0.9vae.safetensors
+
+
+RECOMMENDED WAY TO UPDATE:
+To update the ComfyUI code: update\update_comfyui.bat
+
+
+TO SHARE MODELS BETWEEN COMFYUI AND ANOTHER UI:
+In the ComfyUI directory you will find a file: extra_model_paths.yaml.example
+Rename this file to: extra_model_paths.yaml and edit it with your favorite text editor.
+
+
@@ -0,0 +1,2 @@
+.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --disable-smart-memory
+pause
@@ -0,0 +1,2 @@
+.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build
+pause
@@ -0,0 +1,61 @@
+name: "Release Stable All Portable Versions"
+
+on:
+  workflow_dispatch:
+    inputs:
+      git_tag:
+        description: 'Git tag'
+        required: true
+        type: string
+
+jobs:
+  release_nvidia_default:
+    permissions:
+      contents: "write"
+      packages: "write"
+      pull-requests: "read"
+    name: "Release NVIDIA Default (cu129)"
+    uses: ./.github/workflows/stable-release.yml
+    with:
+      git_tag: ${{ inputs.git_tag }}
+      cache_tag: "cu129"
+      python_minor: "13"
+      python_patch: "6"
+      rel_name: "nvidia"
+      rel_extra_name: ""
+      test_release: true
+    secrets: inherit
+
+  release_nvidia_cu128:
+    permissions:
+      contents: "write"
+      packages: "write"
+      pull-requests: "read"
+    name: "Release NVIDIA cu128"
+    uses: ./.github/workflows/stable-release.yml
+    with:
+      git_tag: ${{ inputs.git_tag }}
+      cache_tag: "cu128"
+      python_minor: "12"
+      python_patch: "10"
+      rel_name: "nvidia"
+      rel_extra_name: "_cu128"
+      test_release: true
+    secrets: inherit
+
+  release_amd_rocm:
+    permissions:
+      contents: "write"
+      packages: "write"
+      pull-requests: "read"
+    name: "Release AMD ROCm 6.4.4"
+    uses: ./.github/workflows/stable-release.yml
+    with:
+      git_tag: ${{ inputs.git_tag }}
+      cache_tag: "rocm644"
+      python_minor: "12"
+      python_patch: "10"
+      rel_name: "amd"
+      rel_extra_name: ""
+      test_release: false
+    secrets: inherit
@@ -21,3 +21,28 @@ jobs:

    - name: Run Ruff
      run: ruff check .
+
+  pylint:
+    name: Run Pylint
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: '3.12'
+
+    - 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
+
+    - name: Install Pylint
+      run: pip install pylint
+
+    - name: Run Pylint
+      run: pylint comfy_api_nodes
@@ -2,17 +2,17 @@
 name: "Release Stable Version"

 on:
-  workflow_dispatch:
+  workflow_call:
    inputs:
      git_tag:
        description: 'Git tag'
        required: true
        type: string
-      cu:
-        description: 'CUDA version'
+      cache_tag:
+        description: 'Cached dependencies tag'
        required: true
        type: string
-        default: "129"
+        default: "cu129"
      python_minor:
        description: 'Python minor version'
        required: true
@@ -23,7 +23,57 @@ on:
        required: true
        type: string
        default: "6"
-
+      rel_name:
+        description: 'Release name'
+        required: true
+        type: string
+        default: "nvidia"
+      rel_extra_name:
+        description: 'Release extra name'
+        required: false
+        type: string
+        default: ""
+      test_release:
+        description: 'Test Release'
+        required: true
+        type: boolean
+        default: true
+  workflow_dispatch:
+    inputs:
+      git_tag:
+        description: 'Git tag'
+        required: true
+        type: string
+      cache_tag:
+        description: 'Cached dependencies tag'
+        required: true
+        type: string
+        default: "cu129"
+      python_minor:
+        description: 'Python minor version'
+        required: true
+        type: string
+        default: "13"
+      python_patch:
+        description: 'Python patch version'
+        required: true
+        type: string
+        default: "6"
+      rel_name:
+        description: 'Release name'
+        required: true
+        type: string
+        default: "nvidia"
+      rel_extra_name:
+        description: 'Release extra name'
+        required: false
+        type: string
+        default: ""
+      test_release:
+        description: 'Test Release'
+        required: true
+        type: boolean
+        default: true

 jobs:
  package_comfy_windows:
@@ -42,15 +92,15 @@ jobs:
        id: cache
        with:
          path: |
-            cu${{ inputs.cu }}_python_deps.tar
+            ${{ inputs.cache_tag }}_python_deps.tar
            update_comfyui_and_python_dependencies.bat
-          key: ${{ runner.os }}-build-cu${{ inputs.cu }}-${{ inputs.python_minor }}
+          key: ${{ runner.os }}-build-${{ inputs.cache_tag }}-${{ inputs.python_minor }}
      - shell: bash
        run: |
-          mv cu${{ inputs.cu }}_python_deps.tar ../
+          mv ${{ inputs.cache_tag }}_python_deps.tar ../
          mv update_comfyui_and_python_dependencies.bat ../
          cd ..
-          tar xf cu${{ inputs.cu }}_python_deps.tar
+          tar xf ${{ inputs.cache_tag }}_python_deps.tar
          pwd
          ls

@@ -65,12 +115,19 @@ jobs:
          echo 'import site' >> ./python3${{ inputs.python_minor }}._pth
          curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
          ./python.exe get-pip.py
-          ./python.exe -s -m pip install ../cu${{ inputs.cu }}_python_deps/*
+          ./python.exe -s -m pip install ../${{ inputs.cache_tag }}_python_deps/*
+
+          grep comfyui ../ComfyUI/requirements.txt > ./requirements_comfyui.txt
+          ./python.exe -s -m pip install -r requirements_comfyui.txt
+          rm requirements_comfyui.txt
+
          sed -i '1i../ComfyUI' ./python3${{ inputs.python_minor }}._pth

-          rm ./Lib/site-packages/torch/lib/dnnl.lib #I don't think this is actually used and I need the space
-          rm ./Lib/site-packages/torch/lib/libprotoc.lib
-          rm ./Lib/site-packages/torch/lib/libprotobuf.lib
+          if test -f ./Lib/site-packages/torch/lib/dnnl.lib; then
+            rm ./Lib/site-packages/torch/lib/dnnl.lib #I don't think this is actually used and I need the space
+            rm ./Lib/site-packages/torch/lib/libprotoc.lib
+            rm ./Lib/site-packages/torch/lib/libprotobuf.lib
+          fi

          cd ..

@@ -85,14 +142,18 @@ jobs:

          mkdir update
          cp -r ComfyUI/.ci/update_windows/* ./update/
-          cp -r ComfyUI/.ci/windows_base_files/* ./
+          cp -r ComfyUI/.ci/windows_${{ inputs.rel_name }}_base_files/* ./
          cp ../update_comfyui_and_python_dependencies.bat ./update/

          cd ..

          "C:\Program Files\7-Zip\7z.exe" a -t7z -m0=lzma2 -mx=9 -mfb=128 -md=768m -ms=on -mf=BCJ2 ComfyUI_windows_portable.7z ComfyUI_windows_portable
-          mv ComfyUI_windows_portable.7z ComfyUI/ComfyUI_windows_portable_nvidia.7z
+          mv ComfyUI_windows_portable.7z ComfyUI/ComfyUI_windows_portable_${{ inputs.rel_name }}${{ inputs.rel_extra_name }}.7z

+      - shell: bash
+        if: ${{ inputs.test_release }}
+        run: |
+          cd ..
          cd ComfyUI_windows_portable
          python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu

@@ -101,10 +162,9 @@ jobs:
          ls

      - name: Upload binaries to release
-        uses: svenstaro/upload-release-action@v2
+        uses: softprops/action-gh-release@v2
        with:
-          repo_token: ${{ secrets.GITHUB_TOKEN }}
-          file: ComfyUI_windows_portable_nvidia.7z
-          tag: ${{ inputs.git_tag }}
-          overwrite: true
+          files: ComfyUI_windows_portable_${{ inputs.rel_name }}${{ inputs.rel_extra_name }}.7z
+          tag_name: ${{ inputs.git_tag }}
          draft: true
+          overwrite_files: true
@@ -10,7 +10,7 @@ jobs:
  test:
    strategy:
      matrix:
-        os: [ubuntu-latest, windows-latest, macos-latest]
+        os: [ubuntu-latest, windows-2022, macos-latest]
    runs-on: ${{ matrix.os }}
    continue-on-error: true
    steps:
@@ -56,7 +56,8 @@ jobs:
            ..\python_embeded\python.exe -s -m pip install --upgrade torch torchvision torchaudio ${{ inputs.xformers }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r ../ComfyUI/requirements.txt pygit2
            pause" > update_comfyui_and_python_dependencies.bat

-            python -m pip wheel --no-cache-dir torch torchvision torchaudio ${{ inputs.xformers }} ${{ inputs.extra_dependencies }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r requirements.txt pygit2 -w ./temp_wheel_dir
+            grep -v comfyui requirements.txt > requirements_nocomfyui.txt
+            python -m pip wheel --no-cache-dir torch torchvision torchaudio ${{ inputs.xformers }} ${{ inputs.extra_dependencies }} --extra-index-url https://download.pytorch.org/whl/cu${{ inputs.cu }} -r requirements_nocomfyui.txt pygit2 -w ./temp_wheel_dir
            python -m pip install --no-cache-dir ./temp_wheel_dir/*
            echo installed basic
            ls -lah temp_wheel_dir
@@ -0,0 +1,64 @@
+name: "Windows Release dependencies Manual"
+
+on:
+  workflow_dispatch:
+    inputs:
+      torch_dependencies:
+        description: 'torch dependencies'
+        required: false
+        type: string
+        default: "torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu128"
+      cache_tag:
+        description: 'Cached dependencies tag'
+        required: true
+        type: string
+        default: "cu128"
+
+      python_minor:
+        description: 'python minor version'
+        required: true
+        type: string
+        default: "12"
+
+      python_patch:
+        description: 'python patch version'
+        required: true
+        type: string
+        default: "10"
+
+jobs:
+  build_dependencies:
+    runs-on: windows-latest
+    steps:
+        - uses: actions/checkout@v4
+        - uses: actions/setup-python@v5
+          with:
+            python-version: 3.${{ inputs.python_minor }}.${{ inputs.python_patch }}
+
+        - shell: bash
+          run: |
+            echo "@echo off
+            call update_comfyui.bat nopause
+            echo -
+            echo This will try to update pytorch and all python dependencies.
+            echo -
+            echo If you just want to update normally, close this and run update_comfyui.bat instead.
+            echo -
+            pause
+            ..\python_embeded\python.exe -s -m pip install --upgrade ${{ inputs.torch_dependencies }} -r ../ComfyUI/requirements.txt pygit2
+            pause" > update_comfyui_and_python_dependencies.bat
+
+            grep -v comfyui requirements.txt > requirements_nocomfyui.txt
+            python -m pip wheel --no-cache-dir ${{ inputs.torch_dependencies }} -r requirements_nocomfyui.txt pygit2 -w ./temp_wheel_dir
+            python -m pip install --no-cache-dir ./temp_wheel_dir/*
+            echo installed basic
+            ls -lah temp_wheel_dir
+            mv temp_wheel_dir ${{ inputs.cache_tag }}_python_deps
+            tar cf ${{ inputs.cache_tag }}_python_deps.tar ${{ inputs.cache_tag }}_python_deps
+
+        - uses: actions/cache/save@v4
+          with:
+            path: |
+              ${{ inputs.cache_tag }}_python_deps.tar
+              update_comfyui_and_python_dependencies.bat
+            key: ${{ runner.os }}-build-${{ inputs.cache_tag }}-${{ inputs.python_minor }}
@@ -68,7 +68,7 @@ jobs:

            mkdir update
            cp -r ComfyUI/.ci/update_windows/* ./update/
-            cp -r ComfyUI/.ci/windows_base_files/* ./
+            cp -r ComfyUI/.ci/windows_nvidia_base_files/* ./
            cp -r ComfyUI/.ci/windows_nightly_base_files/* ./

            echo "call update_comfyui.bat nopause
@@ -81,7 +81,7 @@ jobs:

            mkdir update
            cp -r ComfyUI/.ci/update_windows/* ./update/
-            cp -r ComfyUI/.ci/windows_base_files/* ./
+            cp -r ComfyUI/.ci/windows_nvidia_base_files/* ./
            cp ../update_comfyui_and_python_dependencies.bat ./update/

            cd ..
@@ -1,25 +1,3 @@
 # Admins
 * @comfyanonymous
-
-# Note: Github teams syntax cannot be used here as the repo is not owned by Comfy-Org.
-# Inlined the team members for now.
-
-# Maintainers
-*.md @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill
-/tests/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill
-/tests-unit/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill
-/notebooks/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill
-/script_examples/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill
-/.github/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill
-/requirements.txt @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill
-/pyproject.toml @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @Kosinkadink @christian-byrne @guill
-
-# Python web server
-/api_server/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne @guill
-/app/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne @guill
-/utils/ @yoland68 @robinjhuang @webfiltered @pythongosssss @ltdrdata @christian-byrne @guill
-
-# Node developers
-/comfy_extras/ @yoland68 @robinjhuang @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne @guill
-/comfy/comfy_types/ @yoland68 @robinjhuang @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne @guill
-/comfy_api_nodes/ @yoland68 @robinjhuang @pythongosssss @ltdrdata @Kosinkadink @webfiltered @christian-byrne @guill
+* @kosinkadink
@@ -66,6 +66,7 @@ See what ComfyUI can do with the [example workflows](https://comfyanonymous.gith
   - [Lumina Image 2.0](https://comfyanonymous.github.io/ComfyUI_examples/lumina2/)
   - [HiDream](https://comfyanonymous.github.io/ComfyUI_examples/hidream/)
   - [Qwen Image](https://comfyanonymous.github.io/ComfyUI_examples/qwen_image/)
+   - [Hunyuan Image 2.1](https://comfyanonymous.github.io/ComfyUI_examples/hunyuan_image/)
 - Image Editing Models
   - [Omnigen 2](https://comfyanonymous.github.io/ComfyUI_examples/omnigen/)
   - [Flux Kontext](https://comfyanonymous.github.io/ComfyUI_examples/flux/#flux-kontext-image-editing-model)
@@ -175,6 +176,12 @@ 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

+#### Alternative Downloads:
+
+[Experimental portable for AMD GPUs](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_amd.7z)
+
+[Portable with pytorch cuda 12.8 and python 3.12](https://github.com/comfyanonymous/ComfyUI/releases/latest/download/ComfyUI_windows_portable_nvidia_cu128.7z) (Supports Nvidia 10 series and older GPUs).
+
 #### 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.
@@ -199,14 +206,32 @@ Put your SD checkpoints (the huge ckpt/safetensors files) in: models/checkpoints
 Put your VAE in: models/vae


-### AMD GPUs (Linux only)
+### AMD GPUs (Linux)
+
 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/rocm6.4```

-This is the command to install the nightly with ROCm 6.4 which might have some performance improvements:
+This is the command to install the nightly with ROCm 7.0 which might have some performance improvements:

-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm6.4```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm7.0```
+
+
+### AMD GPUs (Experimental: Windows and Linux), RDNA 3, 3.5 and 4 only.
+
+These have less hardware support than the builds above but they work on windows. You also need to install the pytorch version specific to your hardware.
+
+RDNA 3 (RX 7000 series):
+
+```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx110X-dgpu/```
+
+RDNA 3.5 (Strix halo/Ryzen AI Max+ 365):
+
+```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx1151/```
+
+RDNA 4 (RX 9000 series):
+
+```pip install --pre torch torchvision torchaudio --index-url https://rocm.nightlies.amd.com/v2/gfx120X-all/```

 ### Intel GPUs (Windows and Linux)

@@ -232,7 +257,7 @@ Nvidia users should install stable pytorch using this command:

 This is the command to install pytorch nightly instead which might have performance improvements.

-```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu129```
+```pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cu130```

 #### Troubleshooting

@@ -263,12 +288,6 @@ You can install ComfyUI in Apple Mac silicon (M1 or M2) with any recent macOS ve

 > **Note**: Remember to add your models, VAE, LoRAs etc. to the corresponding Comfy folders, as discussed in [ComfyUI manual installation](#manual-install-windows-linux).

-#### DirectML (AMD Cards on Windows)
-
-This is very badly supported and is not recommended. There are some unofficial builds of pytorch ROCm on windows that exist that will give you a much better experience than this. This readme will be updated once official pytorch ROCm builds for windows come out.
-
-```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:
@@ -42,6 +42,7 @@ def get_installed_frontend_version():
    frontend_version_str = version("comfyui-frontend-package")
    return frontend_version_str

+
 def get_required_frontend_version():
    """Get the required frontend version from requirements.txt."""
    try:
@@ -63,6 +64,7 @@ def get_required_frontend_version():
        logging.error(f"Error reading requirements.txt: {e}")
        return None

+
 def check_frontend_version():
    """Check if the frontend version is up to date."""

@@ -203,6 +205,37 @@ class FrontendManager:
        """Get the required frontend package version."""
        return get_required_frontend_version()

+    @classmethod
+    def get_installed_templates_version(cls) -> str:
+        """Get the currently installed workflow templates package version."""
+        try:
+            templates_version_str = version("comfyui-workflow-templates")
+            return templates_version_str
+        except Exception:
+            return None
+
+    @classmethod
+    def get_required_templates_version(cls) -> str:
+        """Get the required workflow templates version from requirements.txt."""
+        try:
+            with open(requirements_path, "r", encoding="utf-8") as f:
+                for line in f:
+                    line = line.strip()
+                    if line.startswith("comfyui-workflow-templates=="):
+                        version_str = line.split("==")[-1]
+                        if not is_valid_version(version_str):
+                            logging.error(f"Invalid templates version format in requirements.txt: {version_str}")
+                            return None
+                        return version_str
+                logging.error("comfyui-workflow-templates not found in requirements.txt")
+                return None
+        except FileNotFoundError:
+            logging.error("requirements.txt not found. Cannot determine required templates version.")
+            return None
+        except Exception as e:
+            logging.error(f"Error reading requirements.txt: {e}")
+            return None
+
    @classmethod
    def default_frontend_path(cls) -> str:
        try:
@@ -1,4 +1,5 @@
 from .wav2vec2 import Wav2Vec2Model
+from .whisper import WhisperLargeV3
 import comfy.model_management
 import comfy.ops
 import comfy.utils
@@ -11,7 +12,18 @@ class AudioEncoderModel():
        self.load_device = comfy.model_management.text_encoder_device()
        offload_device = comfy.model_management.text_encoder_offload_device()
        self.dtype = comfy.model_management.text_encoder_dtype(self.load_device)
-        self.model = Wav2Vec2Model(dtype=self.dtype, device=offload_device, operations=comfy.ops.manual_cast)
+        model_type = config.pop("model_type")
+        model_config = dict(config)
+        model_config.update({
+            "dtype": self.dtype,
+            "device": offload_device,
+            "operations": comfy.ops.manual_cast
+        })
+
+        if model_type == "wav2vec2":
+            self.model = Wav2Vec2Model(**model_config)
+        elif model_type == "whisper3":
+            self.model = WhisperLargeV3(**model_config)
        self.model.eval()
        self.patcher = comfy.model_patcher.ModelPatcher(self.model, load_device=self.load_device, offload_device=offload_device)
        self.model_sample_rate = 16000
@@ -29,14 +41,51 @@ class AudioEncoderModel():
        outputs = {}
        outputs["encoded_audio"] = out
        outputs["encoded_audio_all_layers"] = all_layers
+        outputs["audio_samples"] = audio.shape[2]
        return outputs


 def load_audio_encoder_from_sd(sd, prefix=""):
-    audio_encoder = AudioEncoderModel(None)
    sd = comfy.utils.state_dict_prefix_replace(sd, {"wav2vec2.": ""})
+    if "encoder.layer_norm.bias" in sd: #wav2vec2
+        embed_dim = sd["encoder.layer_norm.bias"].shape[0]
+        if embed_dim == 1024:# large
+            config = {
+                "model_type": "wav2vec2",
+                "embed_dim": 1024,
+                "num_heads": 16,
+                "num_layers": 24,
+                "conv_norm": True,
+                "conv_bias": True,
+                "do_normalize": True,
+                "do_stable_layer_norm": True
+                }
+        elif embed_dim == 768: # base
+            config = {
+                "model_type": "wav2vec2",
+                "embed_dim": 768,
+                "num_heads": 12,
+                "num_layers": 12,
+                "conv_norm": False,
+                "conv_bias": False,
+                "do_normalize": False, # chinese-wav2vec2-base has this False
+                "do_stable_layer_norm": False
+            }
+        else:
+            raise RuntimeError("ERROR: audio encoder file is invalid or unsupported embed_dim: {}".format(embed_dim))
+    elif "model.encoder.embed_positions.weight" in sd:
+        sd = comfy.utils.state_dict_prefix_replace(sd, {"model.": ""})
+        config = {
+            "model_type": "whisper3",
+        }
+    else:
+        raise RuntimeError("ERROR: audio encoder not supported.")
+
+    audio_encoder = AudioEncoderModel(config)
    m, u = audio_encoder.load_sd(sd)
    if len(m) > 0:
        logging.warning("missing audio encoder: {}".format(m))
+    if len(u) > 0:
+        logging.warning("unexpected audio encoder: {}".format(u))

    return audio_encoder
@@ -13,19 +13,49 @@ class LayerNormConv(nn.Module):
        x = self.conv(x)
        return torch.nn.functional.gelu(self.layer_norm(x.transpose(-2, -1)).transpose(-2, -1))

+class LayerGroupNormConv(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.conv = operations.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias, device=device, dtype=dtype)
+        self.layer_norm = operations.GroupNorm(num_groups=out_channels, num_channels=out_channels, affine=True, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x = self.conv(x)
+        return torch.nn.functional.gelu(self.layer_norm(x))
+
+class ConvNoNorm(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, stride, bias=False, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.conv = operations.Conv1d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, bias=bias, device=device, dtype=dtype)
+
+    def forward(self, x):
+        x = self.conv(x)
+        return torch.nn.functional.gelu(x)
+

 class ConvFeatureEncoder(nn.Module):
-    def __init__(self, conv_dim, dtype=None, device=None, operations=None):
+    def __init__(self, conv_dim, conv_bias=False, conv_norm=True, dtype=None, device=None, operations=None):
        super().__init__()
-        self.conv_layers = nn.ModuleList([
-            LayerNormConv(1, conv_dim, kernel_size=10, stride=5, bias=True, device=device, dtype=dtype, operations=operations),
-            LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=True, device=device, dtype=dtype, operations=operations),
-            LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=True, device=device, dtype=dtype, operations=operations),
-            LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=True, device=device, dtype=dtype, operations=operations),
-            LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=True, device=device, dtype=dtype, operations=operations),
-            LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=True, device=device, dtype=dtype, operations=operations),
-            LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=True, device=device, dtype=dtype, operations=operations),
-        ])
+        if conv_norm:
+            self.conv_layers = nn.ModuleList([
+                LayerNormConv(1, conv_dim, kernel_size=10, stride=5, bias=True, device=device, dtype=dtype, operations=operations),
+                LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                LayerNormConv(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                LayerNormConv(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+            ])
+        else:
+            self.conv_layers = nn.ModuleList([
+                LayerGroupNormConv(1, conv_dim, kernel_size=10, stride=5, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                ConvNoNorm(conv_dim, conv_dim, kernel_size=3, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                ConvNoNorm(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+                ConvNoNorm(conv_dim, conv_dim, kernel_size=2, stride=2, bias=conv_bias, device=device, dtype=dtype, operations=operations),
+            ])

    def forward(self, x):
        x = x.unsqueeze(1)
@@ -76,6 +106,7 @@ class TransformerEncoder(nn.Module):
        num_heads=12,
        num_layers=12,
        mlp_ratio=4.0,
+        do_stable_layer_norm=True,
        dtype=None, device=None, operations=None
    ):
        super().__init__()
@@ -86,20 +117,25 @@ class TransformerEncoder(nn.Module):
                embed_dim=embed_dim,
                num_heads=num_heads,
                mlp_ratio=mlp_ratio,
+                do_stable_layer_norm=do_stable_layer_norm,
                device=device, dtype=dtype, operations=operations
            )
            for _ in range(num_layers)
        ])

        self.layer_norm = operations.LayerNorm(embed_dim, eps=1e-05, device=device, dtype=dtype)
+        self.do_stable_layer_norm = do_stable_layer_norm

    def forward(self, x, mask=None):
        x = x + self.pos_conv_embed(x)
        all_x = ()
+        if not self.do_stable_layer_norm:
+            x = self.layer_norm(x)
        for layer in self.layers:
            all_x += (x,)
            x = layer(x, mask)
-        x = self.layer_norm(x)
+        if self.do_stable_layer_norm:
+            x = self.layer_norm(x)
        all_x += (x,)
        return x, all_x

@@ -145,6 +181,7 @@ class TransformerEncoderLayer(nn.Module):
        embed_dim=768,
        num_heads=12,
        mlp_ratio=4.0,
+        do_stable_layer_norm=True,
        dtype=None, device=None, operations=None
    ):
        super().__init__()
@@ -154,15 +191,19 @@ class TransformerEncoderLayer(nn.Module):
        self.layer_norm = operations.LayerNorm(embed_dim, device=device, dtype=dtype)
        self.feed_forward = FeedForward(embed_dim, mlp_ratio, device=device, dtype=dtype, operations=operations)
        self.final_layer_norm = operations.LayerNorm(embed_dim, device=device, dtype=dtype)
+        self.do_stable_layer_norm = do_stable_layer_norm

    def forward(self, x, mask=None):
        residual = x
-        x = self.layer_norm(x)
+        if self.do_stable_layer_norm:
+            x = self.layer_norm(x)
        x = self.attention(x, mask=mask)
        x = residual + x
-
-        x = x + self.feed_forward(self.final_layer_norm(x))
-        return x
+        if not self.do_stable_layer_norm:
+            x = self.layer_norm(x)
+            return self.final_layer_norm(x + self.feed_forward(x))
+        else:
+            return x + self.feed_forward(self.final_layer_norm(x))


 class Wav2Vec2Model(nn.Module):
@@ -174,34 +215,38 @@ class Wav2Vec2Model(nn.Module):
        final_dim=256,
        num_heads=16,
        num_layers=24,
+        conv_norm=True,
+        conv_bias=True,
+        do_normalize=True,
+        do_stable_layer_norm=True,
        dtype=None, device=None, operations=None
    ):
        super().__init__()

        conv_dim = 512
-        self.feature_extractor = ConvFeatureEncoder(conv_dim, device=device, dtype=dtype, operations=operations)
+        self.feature_extractor = ConvFeatureEncoder(conv_dim, conv_norm=conv_norm, conv_bias=conv_bias, device=device, dtype=dtype, operations=operations)
        self.feature_projection = FeatureProjection(conv_dim, embed_dim, device=device, dtype=dtype, operations=operations)

        self.masked_spec_embed = nn.Parameter(torch.empty(embed_dim, device=device, dtype=dtype))
+        self.do_normalize = do_normalize

        self.encoder = TransformerEncoder(
            embed_dim=embed_dim,
            num_heads=num_heads,
            num_layers=num_layers,
+            do_stable_layer_norm=do_stable_layer_norm,
            device=device, dtype=dtype, operations=operations
        )

    def forward(self, x, mask_time_indices=None, return_dict=False):
-
        x = torch.mean(x, dim=1)

-        x = (x - x.mean()) / torch.sqrt(x.var() + 1e-7)
+        if self.do_normalize:
+            x = (x - x.mean()) / torch.sqrt(x.var() + 1e-7)

        features = self.feature_extractor(x)
        features = self.feature_projection(features)
-
        batch_size, seq_len, _ = features.shape

        x, all_x = self.encoder(features)
-
        return x, all_x
@@ -0,0 +1,186 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchaudio
+from typing import Optional
+from comfy.ldm.modules.attention import optimized_attention_masked
+import comfy.ops
+
+class WhisperFeatureExtractor(nn.Module):
+    def __init__(self, n_mels=128, device=None):
+        super().__init__()
+        self.sample_rate = 16000
+        self.n_fft = 400
+        self.hop_length = 160
+        self.n_mels = n_mels
+        self.chunk_length = 30
+        self.n_samples = 480000
+
+        self.mel_spectrogram = torchaudio.transforms.MelSpectrogram(
+            sample_rate=self.sample_rate,
+            n_fft=self.n_fft,
+            hop_length=self.hop_length,
+            n_mels=self.n_mels,
+            f_min=0,
+            f_max=8000,
+            norm="slaney",
+            mel_scale="slaney",
+        ).to(device)
+
+    def __call__(self, audio):
+        audio = torch.mean(audio, dim=1)
+        batch_size = audio.shape[0]
+        processed_audio = []
+
+        for i in range(batch_size):
+            aud = audio[i]
+            if aud.shape[0] > self.n_samples:
+                aud = aud[:self.n_samples]
+            elif aud.shape[0] < self.n_samples:
+                aud = F.pad(aud, (0, self.n_samples - aud.shape[0]))
+            processed_audio.append(aud)
+
+        audio = torch.stack(processed_audio)
+
+        mel_spec = self.mel_spectrogram(audio.to(self.mel_spectrogram.spectrogram.window.device))[:, :, :-1].to(audio.device)
+
+        log_mel_spec = torch.clamp(mel_spec, min=1e-10).log10()
+        log_mel_spec = torch.maximum(log_mel_spec, log_mel_spec.max() - 8.0)
+        log_mel_spec = (log_mel_spec + 4.0) / 4.0
+
+        return log_mel_spec
+
+
+class MultiHeadAttention(nn.Module):
+    def __init__(self, d_model: int, n_heads: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        assert d_model % n_heads == 0
+
+        self.d_model = d_model
+        self.n_heads = n_heads
+        self.d_k = d_model // n_heads
+
+        self.q_proj = operations.Linear(d_model, d_model, dtype=dtype, device=device)
+        self.k_proj = operations.Linear(d_model, d_model, bias=False, dtype=dtype, device=device)
+        self.v_proj = operations.Linear(d_model, d_model, dtype=dtype, device=device)
+        self.out_proj = operations.Linear(d_model, d_model, dtype=dtype, device=device)
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: torch.Tensor,
+        value: torch.Tensor,
+        mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        batch_size, seq_len, _ = query.shape
+
+        q = self.q_proj(query)
+        k = self.k_proj(key)
+        v = self.v_proj(value)
+
+        attn_output = optimized_attention_masked(q, k, v, self.n_heads, mask)
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output
+
+
+class EncoderLayer(nn.Module):
+    def __init__(self, d_model: int, n_heads: int, d_ff: int, dtype=None, device=None, operations=None):
+        super().__init__()
+
+        self.self_attn = MultiHeadAttention(d_model, n_heads, dtype=dtype, device=device, operations=operations)
+        self.self_attn_layer_norm = operations.LayerNorm(d_model, dtype=dtype, device=device)
+
+        self.fc1 = operations.Linear(d_model, d_ff, dtype=dtype, device=device)
+        self.fc2 = operations.Linear(d_ff, d_model, dtype=dtype, device=device)
+        self.final_layer_norm = operations.LayerNorm(d_model, dtype=dtype, device=device)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None
+    ) -> torch.Tensor:
+        residual = x
+        x = self.self_attn_layer_norm(x)
+        x = self.self_attn(x, x, x, attention_mask)
+        x = residual + x
+
+        residual = x
+        x = self.final_layer_norm(x)
+        x = self.fc1(x)
+        x = F.gelu(x)
+        x = self.fc2(x)
+        x = residual + x
+
+        return x
+
+
+class AudioEncoder(nn.Module):
+    def __init__(
+        self,
+        n_mels: int = 128,
+        n_ctx: int = 1500,
+        n_state: int = 1280,
+        n_head: int = 20,
+        n_layer: int = 32,
+        dtype=None,
+        device=None,
+        operations=None
+    ):
+        super().__init__()
+
+        self.conv1 = operations.Conv1d(n_mels, n_state, kernel_size=3, padding=1, dtype=dtype, device=device)
+        self.conv2 = operations.Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1, dtype=dtype, device=device)
+
+        self.embed_positions = operations.Embedding(n_ctx, n_state, dtype=dtype, device=device)
+
+        self.layers = nn.ModuleList([
+            EncoderLayer(n_state, n_head, n_state * 4, dtype=dtype, device=device, operations=operations)
+            for _ in range(n_layer)
+        ])
+
+        self.layer_norm = operations.LayerNorm(n_state, dtype=dtype, device=device)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = F.gelu(self.conv1(x))
+        x = F.gelu(self.conv2(x))
+
+        x = x.transpose(1, 2)
+
+        x = x + comfy.ops.cast_to_input(self.embed_positions.weight[:, :x.shape[1]], x)
+
+        all_x = ()
+        for layer in self.layers:
+            all_x += (x,)
+            x = layer(x)
+
+        x = self.layer_norm(x)
+        all_x += (x,)
+        return x, all_x
+
+
+class WhisperLargeV3(nn.Module):
+    def __init__(
+        self,
+        n_mels: int = 128,
+        n_audio_ctx: int = 1500,
+        n_audio_state: int = 1280,
+        n_audio_head: int = 20,
+        n_audio_layer: int = 32,
+        dtype=None,
+        device=None,
+        operations=None
+    ):
+        super().__init__()
+
+        self.feature_extractor = WhisperFeatureExtractor(n_mels=n_mels, device=device)
+
+        self.encoder = AudioEncoder(
+            n_mels, n_audio_ctx, n_audio_state, n_audio_head, n_audio_layer,
+            dtype=dtype, device=device, operations=operations
+        )
+
+    def forward(self, audio):
+        mel = self.feature_extractor(audio)
+        x, all_x = self.encoder(mel)
+        return x, all_x
@@ -86,24 +86,24 @@ class BatchedBrownianTree:
    """A wrapper around torchsde.BrownianTree that enables batches of entropy."""

    def __init__(self, x, t0, t1, seed=None, **kwargs):
-        self.cpu_tree = True
-        if "cpu" in kwargs:
-            self.cpu_tree = kwargs.pop("cpu")
+        self.cpu_tree = kwargs.pop("cpu", True)
        t0, t1, self.sign = self.sort(t0, t1)
-        w0 = kwargs.get('w0', torch.zeros_like(x))
+        w0 = kwargs.pop('w0', None)
+        if w0 is None:
+            w0 = torch.zeros_like(x)
+        self.batched = False
        if seed is None:
-            seed = torch.randint(0, 2 ** 63 - 1, []).item()
-        self.batched = True
-        try:
-            assert len(seed) == x.shape[0]
+            seed = (torch.randint(0, 2 ** 63 - 1, ()).item(),)
+        elif isinstance(seed, (tuple, list)):
+            if len(seed) != x.shape[0]:
+                raise ValueError("Passing a list or tuple of seeds to BatchedBrownianTree requires a length matching the batch size.")
+            self.batched = True
            w0 = w0[0]
-        except TypeError:
-            seed = [seed]
-            self.batched = False
-        if self.cpu_tree:
-            self.trees = [torchsde.BrownianTree(t0.cpu(), w0.cpu(), t1.cpu(), entropy=s, **kwargs) for s in seed]
        else:
-            self.trees = [torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed]
+            seed = (seed,)
+        if self.cpu_tree:
+            t0, w0, t1 = t0.detach().cpu(), w0.detach().cpu(), t1.detach().cpu()
+        self.trees = tuple(torchsde.BrownianTree(t0, w0, t1, entropy=s, **kwargs) for s in seed)

    @staticmethod
    def sort(a, b):
@@ -111,11 +111,10 @@ class BatchedBrownianTree:

    def __call__(self, t0, t1):
        t0, t1, sign = self.sort(t0, t1)
+        device, dtype = t0.device, t0.dtype
        if self.cpu_tree:
-            w = torch.stack([tree(t0.cpu().float(), t1.cpu().float()).to(t0.dtype).to(t0.device) for tree in self.trees]) * (self.sign * sign)
-        else:
-            w = torch.stack([tree(t0, t1) for tree in self.trees]) * (self.sign * sign)
-
+            t0, t1 = t0.detach().cpu().float(), t1.detach().cpu().float()
+        w = torch.stack([tree(t0, t1) for tree in self.trees]).to(device=device, dtype=dtype) * (self.sign * sign)
        return w if self.batched else w[0]


@@ -538,6 +538,79 @@ class HunyuanImage21(LatentFormat):
    latent_dimensions = 2
    scale_factor = 0.75289

+    latent_rgb_factors = [
+        [-0.0154, -0.0397, -0.0521],
+        [ 0.0005,  0.0093,  0.0006],
+        [-0.0805, -0.0773, -0.0586],
+        [-0.0494, -0.0487, -0.0498],
+        [-0.0212, -0.0076, -0.0261],
+        [-0.0179, -0.0417, -0.0505],
+        [ 0.0158,  0.0310,  0.0239],
+        [ 0.0409,  0.0516,  0.0201],
+        [ 0.0350,  0.0553,  0.0036],
+        [-0.0447, -0.0327, -0.0479],
+        [-0.0038, -0.0221, -0.0365],
+        [-0.0423, -0.0718, -0.0654],
+        [ 0.0039,  0.0368,  0.0104],
+        [ 0.0655,  0.0217,  0.0122],
+        [ 0.0490,  0.1638,  0.2053],
+        [ 0.0932,  0.0829,  0.0650],
+        [-0.0186, -0.0209, -0.0135],
+        [-0.0080, -0.0076, -0.0148],
+        [-0.0284, -0.0201,  0.0011],
+        [-0.0642, -0.0294, -0.0777],
+        [-0.0035,  0.0076, -0.0140],
+        [ 0.0519,  0.0731,  0.0887],
+        [-0.0102,  0.0095,  0.0704],
+        [ 0.0068,  0.0218, -0.0023],
+        [-0.0726, -0.0486, -0.0519],
+        [ 0.0260,  0.0295,  0.0263],
+        [ 0.0250,  0.0333,  0.0341],
+        [ 0.0168, -0.0120, -0.0174],
+        [ 0.0226,  0.1037,  0.0114],
+        [ 0.2577,  0.1906,  0.1604],
+        [-0.0646, -0.0137, -0.0018],
+        [-0.0112,  0.0309,  0.0358],
+        [-0.0347,  0.0146, -0.0481],
+        [ 0.0234,  0.0179,  0.0201],
+        [ 0.0157,  0.0313,  0.0225],
+        [ 0.0423,  0.0675,  0.0524],
+        [-0.0031,  0.0027, -0.0255],
+        [ 0.0447,  0.0555,  0.0330],
+        [-0.0152,  0.0103,  0.0299],
+        [-0.0755, -0.0489, -0.0635],
+        [ 0.0853,  0.0788,  0.1017],
+        [-0.0272, -0.0294, -0.0471],
+        [ 0.0440,  0.0400, -0.0137],
+        [ 0.0335,  0.0317, -0.0036],
+        [-0.0344, -0.0621, -0.0984],
+        [-0.0127, -0.0630, -0.0620],
+        [-0.0648,  0.0360,  0.0924],
+        [-0.0781, -0.0801, -0.0409],
+        [ 0.0363,  0.0613,  0.0499],
+        [ 0.0238,  0.0034,  0.0041],
+        [-0.0135,  0.0258,  0.0310],
+        [ 0.0614,  0.1086,  0.0589],
+        [ 0.0428,  0.0350,  0.0205],
+        [ 0.0153,  0.0173, -0.0018],
+        [-0.0288, -0.0455, -0.0091],
+        [ 0.0344,  0.0109, -0.0157],
+        [-0.0205, -0.0247, -0.0187],
+        [ 0.0487,  0.0126,  0.0064],
+        [-0.0220, -0.0013,  0.0074],
+        [-0.0203, -0.0094, -0.0048],
+        [-0.0719,  0.0429, -0.0442],
+        [ 0.1042,  0.0497,  0.0356],
+        [-0.0659, -0.0578, -0.0280],
+        [-0.0060, -0.0322, -0.0234]]
+
+    latent_rgb_factors_bias = [0.0007, -0.0256, -0.0206]
+
+class HunyuanImage21Refiner(LatentFormat):
+    latent_channels = 64
+    latent_dimensions = 3
+    scale_factor = 1.03682
+
 class Hunyuan3Dv2(LatentFormat):
    latent_channels = 64
    latent_dimensions = 1
@@ -556,3 +629,20 @@ class Hunyuan3Dv2mini(LatentFormat):
 class ACEAudio(LatentFormat):
    latent_channels = 8
    latent_dimensions = 2
+
+class ChromaRadiance(LatentFormat):
+    latent_channels = 3
+
+    def __init__(self):
+        self.latent_rgb_factors = [
+            # R    G    B
+            [ 1.0, 0.0, 0.0 ],
+            [ 0.0, 1.0, 0.0 ],
+            [ 0.0, 0.0, 1.0 ]
+        ]
+
+    def process_in(self, latent):
+        return latent
+
+    def process_out(self, latent):
+        return latent
@@ -133,6 +133,7 @@ class Attention(nn.Module):
        hidden_states: torch.Tensor,
        encoder_hidden_states: Optional[torch.Tensor] = None,
        attention_mask: Optional[torch.Tensor] = None,
+        transformer_options={},
        **cross_attention_kwargs,
    ) -> torch.Tensor:
        return self.processor(
@@ -140,6 +141,7 @@ class Attention(nn.Module):
            hidden_states,
            encoder_hidden_states=encoder_hidden_states,
            attention_mask=attention_mask,
+            transformer_options=transformer_options,
            **cross_attention_kwargs,
        )

@@ -366,6 +368,7 @@ class CustomerAttnProcessor2_0:
        encoder_attention_mask: Optional[torch.FloatTensor] = None,
        rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None,
        rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None,
+        transformer_options={},
        *args,
        **kwargs,
    ) -> torch.Tensor:
@@ -433,7 +436,7 @@ class CustomerAttnProcessor2_0:

        # the output of sdp = (batch, num_heads, seq_len, head_dim)
        hidden_states = optimized_attention(
-            query, key, value, heads=query.shape[1], mask=attention_mask, skip_reshape=True,
+            query, key, value, heads=query.shape[1], mask=attention_mask, skip_reshape=True, transformer_options=transformer_options,
        ).to(query.dtype)

        # linear proj
@@ -697,6 +700,7 @@ class LinearTransformerBlock(nn.Module):
        rotary_freqs_cis: Union[torch.Tensor, Tuple[torch.Tensor]] = None,
        rotary_freqs_cis_cross: Union[torch.Tensor, Tuple[torch.Tensor]] = None,
        temb: torch.FloatTensor = None,
+        transformer_options={},
    ):

        N = hidden_states.shape[0]
@@ -720,6 +724,7 @@ class LinearTransformerBlock(nn.Module):
                encoder_attention_mask=encoder_attention_mask,
                rotary_freqs_cis=rotary_freqs_cis,
                rotary_freqs_cis_cross=rotary_freqs_cis_cross,
+                transformer_options=transformer_options,
            )
        else:
            attn_output, _ = self.attn(
@@ -729,6 +734,7 @@ class LinearTransformerBlock(nn.Module):
                encoder_attention_mask=None,
                rotary_freqs_cis=rotary_freqs_cis,
                rotary_freqs_cis_cross=None,
+                transformer_options=transformer_options,
            )

        if self.use_adaln_single:
@@ -743,6 +749,7 @@ class LinearTransformerBlock(nn.Module):
                encoder_attention_mask=encoder_attention_mask,
                rotary_freqs_cis=rotary_freqs_cis,
                rotary_freqs_cis_cross=rotary_freqs_cis_cross,
+                transformer_options=transformer_options,
            )
            hidden_states = attn_output + hidden_states

@@ -314,6 +314,7 @@ class ACEStepTransformer2DModel(nn.Module):
        output_length: int = 0,
        block_controlnet_hidden_states: Optional[Union[List[torch.Tensor], torch.Tensor]] = None,
        controlnet_scale: Union[float, torch.Tensor] = 1.0,
+        transformer_options={},
    ):
        embedded_timestep = self.timestep_embedder(self.time_proj(timestep).to(dtype=hidden_states.dtype))
        temb = self.t_block(embedded_timestep)
@@ -339,6 +340,7 @@ class ACEStepTransformer2DModel(nn.Module):
                rotary_freqs_cis=rotary_freqs_cis,
                rotary_freqs_cis_cross=encoder_rotary_freqs_cis,
                temb=temb,
+                transformer_options=transformer_options,
            )

        output = self.final_layer(hidden_states, embedded_timestep, output_length)
@@ -393,6 +395,7 @@ class ACEStepTransformer2DModel(nn.Module):

        output_length = hidden_states.shape[-1]

+        transformer_options = kwargs.get("transformer_options", {})
        output = self.decode(
            hidden_states=hidden_states,
            attention_mask=attention_mask,
@@ -402,6 +405,7 @@ class ACEStepTransformer2DModel(nn.Module):
            output_length=output_length,
            block_controlnet_hidden_states=block_controlnet_hidden_states,
            controlnet_scale=controlnet_scale,
+            transformer_options=transformer_options,
        )

        return output
@@ -23,8 +23,6 @@ class MusicDCAE(torch.nn.Module):
        else:
            self.source_sample_rate = source_sample_rate

-        # self.resampler = torchaudio.transforms.Resample(source_sample_rate, 44100)
-
        self.transform = transforms.Compose([
            transforms.Normalize(0.5, 0.5),
        ])
@@ -37,10 +35,6 @@ class MusicDCAE(torch.nn.Module):
        self.scale_factor = 0.1786
        self.shift_factor = -1.9091

-    def load_audio(self, audio_path):
-        audio, sr = torchaudio.load(audio_path)
-        return audio, sr
-
    def forward_mel(self, audios):
        mels = []
        for i in range(len(audios)):
@@ -73,10 +67,8 @@ class MusicDCAE(torch.nn.Module):
            latent = self.dcae.encoder(mel.unsqueeze(0))
            latents.append(latent)
        latents = torch.cat(latents, dim=0)
-        # latent_lengths = (audio_lengths / sr * 44100 / 512 / self.time_dimention_multiple).long()
        latents = (latents - self.shift_factor) * self.scale_factor
        return latents
-        # return latents, latent_lengths

    @torch.no_grad()
    def decode(self, latents, audio_lengths=None, sr=None):
@@ -91,9 +83,7 @@ class MusicDCAE(torch.nn.Module):
            wav = self.vocoder.decode(mels[0]).squeeze(1)

            if sr is not None:
-                # resampler = torchaudio.transforms.Resample(44100, sr).to(latents.device).to(latents.dtype)
                wav = torchaudio.functional.resample(wav, 44100, sr)
-                # wav = resampler(wav)
            else:
                sr = 44100
            pred_wavs.append(wav)
@@ -101,7 +91,6 @@ class MusicDCAE(torch.nn.Module):
        if audio_lengths is not None:
            pred_wavs = [wav[:, :length].cpu() for wav, length in zip(pred_wavs, audio_lengths)]
        return torch.stack(pred_wavs)
-        # return sr, pred_wavs

    def forward(self, audios, audio_lengths=None, sr=None):
        latents, latent_lengths = self.encode(audios=audios, audio_lengths=audio_lengths, sr=sr)
@@ -298,7 +298,8 @@ class Attention(nn.Module):
        mask = None,
        context_mask = None,
        rotary_pos_emb = None,
-        causal = None
+        causal = None,
+        transformer_options={},
    ):
        h, kv_h, has_context = self.num_heads, self.kv_heads, context is not None

@@ -363,7 +364,7 @@ class Attention(nn.Module):
            heads_per_kv_head = h // kv_h
            k, v = map(lambda t: t.repeat_interleave(heads_per_kv_head, dim = 1), (k, v))

-        out = optimized_attention(q, k, v, h, skip_reshape=True)
+        out = optimized_attention(q, k, v, h, skip_reshape=True, transformer_options=transformer_options)
        out = self.to_out(out)

        if mask is not None:
@@ -488,7 +489,8 @@ class TransformerBlock(nn.Module):
        global_cond=None,
        mask = None,
        context_mask = None,
-        rotary_pos_emb = None
+        rotary_pos_emb = None,
+        transformer_options={}
    ):
        if self.global_cond_dim is not None and self.global_cond_dim > 0 and global_cond is not None:

@@ -498,12 +500,12 @@ class TransformerBlock(nn.Module):
            residual = x
            x = self.pre_norm(x)
            x = x * (1 + scale_self) + shift_self
-            x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb)
+            x = self.self_attn(x, mask = mask, rotary_pos_emb = rotary_pos_emb, transformer_options=transformer_options)
            x = x * torch.sigmoid(1 - gate_self)
            x = x + residual

            if context is not None:
-                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask)
+                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask, transformer_options=transformer_options)

            if self.conformer is not None:
                x = x + self.conformer(x)
@@ -517,10 +519,10 @@ class TransformerBlock(nn.Module):
            x = x + residual

        else:
-            x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb)
+            x = x + self.self_attn(self.pre_norm(x), mask = mask, rotary_pos_emb = rotary_pos_emb, transformer_options=transformer_options)

            if context is not None:
-                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask)
+                x = x + self.cross_attn(self.cross_attend_norm(x), context = context, context_mask = context_mask, transformer_options=transformer_options)

            if self.conformer is not None:
                x = x + self.conformer(x)
@@ -606,7 +608,8 @@ class ContinuousTransformer(nn.Module):
        return_info = False,
        **kwargs
    ):
-        patches_replace = kwargs.get("transformer_options", {}).get("patches_replace", {})
+        transformer_options = kwargs.get("transformer_options", {})
+        patches_replace = transformer_options.get("patches_replace", {})
        batch, seq, device = *x.shape[:2], x.device
        context = kwargs["context"]

@@ -645,13 +648,13 @@ class ContinuousTransformer(nn.Module):
            if ("double_block", i) in blocks_replace:
                def block_wrap(args):
                    out = {}
-                    out["img"] = layer(args["img"], rotary_pos_emb=args["pe"], global_cond=args["vec"], context=args["txt"])
+                    out["img"] = layer(args["img"], rotary_pos_emb=args["pe"], global_cond=args["vec"], context=args["txt"], transformer_options=args["transformer_options"])
                    return out

-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": global_cond, "pe": rotary_pos_emb}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": global_cond, "pe": rotary_pos_emb, "transformer_options": transformer_options}, {"original_block": block_wrap})
                x = out["img"]
            else:
-                x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, context=context)
+                x = layer(x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, context=context, transformer_options=transformer_options)
            # x = checkpoint(layer, x, rotary_pos_emb = rotary_pos_emb, global_cond=global_cond, **kwargs)

            if return_info:
@@ -85,7 +85,7 @@ class SingleAttention(nn.Module):
        )

    #@torch.compile()
-    def forward(self, c):
+    def forward(self, c, transformer_options={}):

        bsz, seqlen1, _ = c.shape

@@ -95,7 +95,7 @@ class SingleAttention(nn.Module):
        v = v.view(bsz, seqlen1, self.n_heads, self.head_dim)
        q, k = self.q_norm1(q), self.k_norm1(k)

-        output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True)
+        output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True, transformer_options=transformer_options)
        c = self.w1o(output)
        return c

@@ -144,7 +144,7 @@ class DoubleAttention(nn.Module):


    #@torch.compile()
-    def forward(self, c, x):
+    def forward(self, c, x, transformer_options={}):

        bsz, seqlen1, _ = c.shape
        bsz, seqlen2, _ = x.shape
@@ -168,7 +168,7 @@ class DoubleAttention(nn.Module):
            torch.cat([cv, xv], dim=1),
        )

-        output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True)
+        output = optimized_attention(q.permute(0, 2, 1, 3), k.permute(0, 2, 1, 3), v.permute(0, 2, 1, 3), self.n_heads, skip_reshape=True, transformer_options=transformer_options)

        c, x = output.split([seqlen1, seqlen2], dim=1)
        c = self.w1o(c)
@@ -207,7 +207,7 @@ class MMDiTBlock(nn.Module):
        self.is_last = is_last

    #@torch.compile()
-    def forward(self, c, x, global_cond, **kwargs):
+    def forward(self, c, x, global_cond, transformer_options={}, **kwargs):

        cres, xres = c, x

@@ -225,7 +225,7 @@ class MMDiTBlock(nn.Module):
        x = modulate(self.normX1(x), xshift_msa, xscale_msa)

        # attention
-        c, x = self.attn(c, x)
+        c, x = self.attn(c, x, transformer_options=transformer_options)


        c = self.normC2(cres + cgate_msa.unsqueeze(1) * c)
@@ -255,13 +255,13 @@ class DiTBlock(nn.Module):
        self.mlp = MLP(dim, hidden_dim=dim * 4, dtype=dtype, device=device, operations=operations)

    #@torch.compile()
-    def forward(self, cx, global_cond, **kwargs):
+    def forward(self, cx, global_cond, transformer_options={}, **kwargs):
        cxres = cx
        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.modCX(
            global_cond
        ).chunk(6, dim=1)
        cx = modulate(self.norm1(cx), shift_msa, scale_msa)
-        cx = self.attn(cx)
+        cx = self.attn(cx, transformer_options=transformer_options)
        cx = self.norm2(cxres + gate_msa.unsqueeze(1) * cx)
        mlpout = self.mlp(modulate(cx, shift_mlp, scale_mlp))
        cx = gate_mlp.unsqueeze(1) * mlpout
@@ -473,13 +473,14 @@ class MMDiT(nn.Module):
                        out = {}
                        out["txt"], out["img"] = layer(args["txt"],
                                                       args["img"],
-                                                       args["vec"])
+                                                       args["vec"],
+                                                       transformer_options=args["transformer_options"])
                        return out
-                    out = blocks_replace[("double_block", i)]({"img": x, "txt": c, "vec": global_cond}, {"original_block": block_wrap})
+                    out = blocks_replace[("double_block", i)]({"img": x, "txt": c, "vec": global_cond, "transformer_options": transformer_options}, {"original_block": block_wrap})
                    c = out["txt"]
                    x = out["img"]
                else:
-                    c, x = layer(c, x, global_cond, **kwargs)
+                    c, x = layer(c, x, global_cond, transformer_options=transformer_options, **kwargs)

        if len(self.single_layers) > 0:
            c_len = c.size(1)
@@ -488,13 +489,13 @@ class MMDiT(nn.Module):
                if ("single_block", i) in blocks_replace:
                    def block_wrap(args):
                        out = {}
-                        out["img"] = layer(args["img"], args["vec"])
+                        out["img"] = layer(args["img"], args["vec"], transformer_options=args["transformer_options"])
                        return out

-                    out = blocks_replace[("single_block", i)]({"img": cx, "vec": global_cond}, {"original_block": block_wrap})
+                    out = blocks_replace[("single_block", i)]({"img": cx, "vec": global_cond, "transformer_options": transformer_options}, {"original_block": block_wrap})
                    cx = out["img"]
                else:
-                    cx = layer(cx, global_cond, **kwargs)
+                    cx = layer(cx, global_cond, transformer_options=transformer_options, **kwargs)

            x = cx[:, c_len:]

@@ -32,12 +32,12 @@ class OptimizedAttention(nn.Module):

        self.out_proj = operations.Linear(c, c, bias=True, dtype=dtype, device=device)

-    def forward(self, q, k, v):
+    def forward(self, q, k, v, transformer_options={}):
        q = self.to_q(q)
        k = self.to_k(k)
        v = self.to_v(v)

-        out = optimized_attention(q, k, v, self.heads)
+        out = optimized_attention(q, k, v, self.heads, transformer_options=transformer_options)

        return self.out_proj(out)

@@ -47,13 +47,13 @@ class Attention2D(nn.Module):
        self.attn = OptimizedAttention(c, nhead, dtype=dtype, device=device, operations=operations)
        # self.attn = nn.MultiheadAttention(c, nhead, dropout=dropout, bias=True, batch_first=True, dtype=dtype, device=device)

-    def forward(self, x, kv, self_attn=False):
+    def forward(self, x, kv, self_attn=False, transformer_options={}):
        orig_shape = x.shape
        x = x.view(x.size(0), x.size(1), -1).permute(0, 2, 1)  # Bx4xHxW -> Bx(HxW)x4
        if self_attn:
            kv = torch.cat([x, kv], dim=1)
        # x = self.attn(x, kv, kv, need_weights=False)[0]
-        x = self.attn(x, kv, kv)
+        x = self.attn(x, kv, kv, transformer_options=transformer_options)
        x = x.permute(0, 2, 1).view(*orig_shape)
        return x

@@ -114,9 +114,9 @@ class AttnBlock(nn.Module):
            operations.Linear(c_cond, c, dtype=dtype, device=device)
        )

-    def forward(self, x, kv):
+    def forward(self, x, kv, transformer_options={}):
        kv = self.kv_mapper(kv)
-        x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn)
+        x = x + self.attention(self.norm(x), kv, self_attn=self.self_attn, transformer_options=transformer_options)
        return x


@@ -173,7 +173,7 @@ class StageB(nn.Module):
        clip = self.clip_norm(clip)
        return clip

-    def _down_encode(self, x, r_embed, clip):
+    def _down_encode(self, x, r_embed, clip, transformer_options={}):
        level_outputs = []
        block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers)
        for down_block, downscaler, repmap in block_group:
@@ -187,7 +187,7 @@ class StageB(nn.Module):
                    elif isinstance(block, AttnBlock) or (
                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                  AttnBlock)):
-                        x = block(x, clip)
+                        x = block(x, clip, transformer_options=transformer_options)
                    elif isinstance(block, TimestepBlock) or (
                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                  TimestepBlock)):
@@ -199,7 +199,7 @@ class StageB(nn.Module):
            level_outputs.insert(0, x)
        return level_outputs

-    def _up_decode(self, level_outputs, r_embed, clip):
+    def _up_decode(self, level_outputs, r_embed, clip, transformer_options={}):
        x = level_outputs[0]
        block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers)
        for i, (up_block, upscaler, repmap) in enumerate(block_group):
@@ -216,7 +216,7 @@ class StageB(nn.Module):
                    elif isinstance(block, AttnBlock) or (
                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                  AttnBlock)):
-                        x = block(x, clip)
+                        x = block(x, clip, transformer_options=transformer_options)
                    elif isinstance(block, TimestepBlock) or (
                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                  TimestepBlock)):
@@ -228,7 +228,7 @@ class StageB(nn.Module):
            x = upscaler(x)
        return x

-    def forward(self, x, r, effnet, clip, pixels=None, **kwargs):
+    def forward(self, x, r, effnet, clip, pixels=None, transformer_options={}, **kwargs):
        if pixels is None:
            pixels = x.new_zeros(x.size(0), 3, 8, 8)

@@ -245,8 +245,8 @@ class StageB(nn.Module):
            nn.functional.interpolate(effnet, size=x.shape[-2:], mode='bilinear', align_corners=True))
        x = x + nn.functional.interpolate(self.pixels_mapper(pixels), size=x.shape[-2:], mode='bilinear',
                                          align_corners=True)
-        level_outputs = self._down_encode(x, r_embed, clip)
-        x = self._up_decode(level_outputs, r_embed, clip)
+        level_outputs = self._down_encode(x, r_embed, clip, transformer_options=transformer_options)
+        x = self._up_decode(level_outputs, r_embed, clip, transformer_options=transformer_options)
        return self.clf(x)

    def update_weights_ema(self, src_model, beta=0.999):
@@ -182,7 +182,7 @@ class StageC(nn.Module):
        clip = self.clip_norm(clip)
        return clip

-    def _down_encode(self, x, r_embed, clip, cnet=None):
+    def _down_encode(self, x, r_embed, clip, cnet=None, transformer_options={}):
        level_outputs = []
        block_group = zip(self.down_blocks, self.down_downscalers, self.down_repeat_mappers)
        for down_block, downscaler, repmap in block_group:
@@ -201,7 +201,7 @@ class StageC(nn.Module):
                    elif isinstance(block, AttnBlock) or (
                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                  AttnBlock)):
-                        x = block(x, clip)
+                        x = block(x, clip, transformer_options=transformer_options)
                    elif isinstance(block, TimestepBlock) or (
                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                  TimestepBlock)):
@@ -213,7 +213,7 @@ class StageC(nn.Module):
            level_outputs.insert(0, x)
        return level_outputs

-    def _up_decode(self, level_outputs, r_embed, clip, cnet=None):
+    def _up_decode(self, level_outputs, r_embed, clip, cnet=None, transformer_options={}):
        x = level_outputs[0]
        block_group = zip(self.up_blocks, self.up_upscalers, self.up_repeat_mappers)
        for i, (up_block, upscaler, repmap) in enumerate(block_group):
@@ -235,7 +235,7 @@ class StageC(nn.Module):
                    elif isinstance(block, AttnBlock) or (
                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                  AttnBlock)):
-                        x = block(x, clip)
+                        x = block(x, clip, transformer_options=transformer_options)
                    elif isinstance(block, TimestepBlock) or (
                            hasattr(block, '_fsdp_wrapped_module') and isinstance(block._fsdp_wrapped_module,
                                                                                  TimestepBlock)):
@@ -247,7 +247,7 @@ class StageC(nn.Module):
            x = upscaler(x)
        return x

-    def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control=None, **kwargs):
+    def forward(self, x, r, clip_text, clip_text_pooled, clip_img, control=None, transformer_options={}, **kwargs):
        # Process the conditioning embeddings
        r_embed = self.gen_r_embedding(r).to(dtype=x.dtype)
        for c in self.t_conds:
@@ -262,8 +262,8 @@ class StageC(nn.Module):

        # Model Blocks
        x = self.embedding(x)
-        level_outputs = self._down_encode(x, r_embed, clip, cnet)
-        x = self._up_decode(level_outputs, r_embed, clip, cnet)
+        level_outputs = self._down_encode(x, r_embed, clip, cnet, transformer_options=transformer_options)
+        x = self._up_decode(level_outputs, r_embed, clip, cnet, transformer_options=transformer_options)
        return self.clf(x)

    def update_weights_ema(self, src_model, beta=0.999):
@@ -76,7 +76,7 @@ class DoubleStreamBlock(nn.Module):
        )
        self.flipped_img_txt = flipped_img_txt

-    def forward(self, img: Tensor, txt: Tensor, pe: Tensor, vec: Tensor, attn_mask=None):
+    def forward(self, img: Tensor, txt: Tensor, pe: Tensor, vec: Tensor, attn_mask=None, transformer_options={}):
        (img_mod1, img_mod2), (txt_mod1, txt_mod2) = vec

        # prepare image for attention
@@ -95,7 +95,7 @@ class DoubleStreamBlock(nn.Module):
        attn = attention(torch.cat((txt_q, img_q), dim=2),
                         torch.cat((txt_k, img_k), dim=2),
                         torch.cat((txt_v, img_v), dim=2),
-                         pe=pe, mask=attn_mask)
+                         pe=pe, mask=attn_mask, transformer_options=transformer_options)

        txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1] :]

@@ -148,7 +148,7 @@ class SingleStreamBlock(nn.Module):

        self.mlp_act = nn.GELU(approximate="tanh")

-    def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None) -> Tensor:
+    def forward(self, x: Tensor, pe: Tensor, vec: Tensor, attn_mask=None, transformer_options={}) -> Tensor:
        mod = vec
        x_mod = torch.addcmul(mod.shift, 1 + mod.scale, self.pre_norm(x))
        qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)
@@ -157,7 +157,7 @@ class SingleStreamBlock(nn.Module):
        q, k = self.norm(q, k, v)

        # compute attention
-        attn = attention(q, k, v, pe=pe, mask=attn_mask)
+        attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options)
        # compute activation in mlp stream, cat again and run second linear layer
        output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
        x.addcmul_(mod.gate, output)
@@ -151,8 +151,6 @@ class Chroma(nn.Module):
        attn_mask: Tensor = None,
    ) -> Tensor:
        patches_replace = transformer_options.get("patches_replace", {})
-        if img.ndim != 3 or txt.ndim != 3:
-            raise ValueError("Input img and txt tensors must have 3 dimensions.")

        # running on sequences img
        img = self.img_in(img)
@@ -193,14 +191,16 @@ class Chroma(nn.Module):
                                                       txt=args["txt"],
                                                       vec=args["vec"],
                                                       pe=args["pe"],
-                                                       attn_mask=args.get("attn_mask"))
+                                                       attn_mask=args.get("attn_mask"),
+                                                       transformer_options=args.get("transformer_options"))
                        return out

                    out = blocks_replace[("double_block", i)]({"img": img,
                                                               "txt": txt,
                                                               "vec": double_mod,
                                                               "pe": pe,
-                                                               "attn_mask": attn_mask},
+                                                               "attn_mask": attn_mask,
+                                                               "transformer_options": transformer_options},
                                                              {"original_block": block_wrap})
                    txt = out["txt"]
                    img = out["img"]
@@ -209,7 +209,8 @@ class Chroma(nn.Module):
                                     txt=txt,
                                     vec=double_mod,
                                     pe=pe,
-                                     attn_mask=attn_mask)
+                                     attn_mask=attn_mask,
+                                     transformer_options=transformer_options)

                if control is not None: # Controlnet
                    control_i = control.get("input")
@@ -229,17 +230,19 @@ class Chroma(nn.Module):
                        out["img"] = block(args["img"],
                                           vec=args["vec"],
                                           pe=args["pe"],
-                                           attn_mask=args.get("attn_mask"))
+                                           attn_mask=args.get("attn_mask"),
+                                           transformer_options=args.get("transformer_options"))
                        return out

                    out = blocks_replace[("single_block", i)]({"img": img,
                                                               "vec": single_mod,
                                                               "pe": pe,
-                                                               "attn_mask": attn_mask},
+                                                               "attn_mask": attn_mask,
+                                                               "transformer_options": transformer_options},
                                                              {"original_block": block_wrap})
                    img = out["img"]
                else:
-                    img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask)
+                    img = block(img, vec=single_mod, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options)

                if control is not None: # Controlnet
                    control_o = control.get("output")
@@ -249,8 +252,9 @@ class Chroma(nn.Module):
                            img[:, txt.shape[1] :, ...] += add

        img = img[:, txt.shape[1] :, ...]
-        final_mod = self.get_modulations(mod_vectors, "final")
-        img = self.final_layer(img, vec=final_mod)  # (N, T, patch_size ** 2 * out_channels)
+        if hasattr(self, "final_layer"):
+            final_mod = self.get_modulations(mod_vectors, "final")
+            img = self.final_layer(img, vec=final_mod)  # (N, T, patch_size ** 2 * out_channels)
        return img

    def forward(self, x, timestep, context, guidance, control=None, transformer_options={}, **kwargs):
@@ -266,6 +270,9 @@ class Chroma(nn.Module):

        img = rearrange(x, "b c (h ph) (w pw) -> b (h w) (c ph pw)", ph=self.patch_size, pw=self.patch_size)

+        if img.ndim != 3 or context.ndim != 3:
+            raise ValueError("Input img and txt tensors must have 3 dimensions.")
+
        h_len = ((h + (self.patch_size // 2)) // self.patch_size)
        w_len = ((w + (self.patch_size // 2)) // self.patch_size)
        img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
@@ -0,0 +1,206 @@
+# Adapted from https://github.com/lodestone-rock/flow
+from functools import lru_cache
+
+import torch
+from torch import nn
+
+from comfy.ldm.flux.layers import RMSNorm
+
+
+class NerfEmbedder(nn.Module):
+    """
+    An embedder module that combines input features with a 2D positional
+    encoding that mimics the Discrete Cosine Transform (DCT).
+
+    This module takes an input tensor of shape (B, P^2, C), where P is the
+    patch size, and enriches it with positional information before projecting
+    it to a new hidden size.
+    """
+    def __init__(
+        self,
+        in_channels: int,
+        hidden_size_input: int,
+        max_freqs: int,
+        dtype=None,
+        device=None,
+        operations=None,
+    ):
+        """
+        Initializes the NerfEmbedder.
+
+        Args:
+            in_channels (int): The number of channels in the input tensor.
+            hidden_size_input (int): The desired dimension of the output embedding.
+            max_freqs (int): The number of frequency components to use for both
+                             the x and y dimensions of the positional encoding.
+                             The total number of positional features will be max_freqs^2.
+        """
+        super().__init__()
+        self.dtype = dtype
+        self.max_freqs = max_freqs
+        self.hidden_size_input = hidden_size_input
+
+        # A linear layer to project the concatenated input features and
+        # positional encodings to the final output dimension.
+        self.embedder = nn.Sequential(
+            operations.Linear(in_channels + max_freqs**2, hidden_size_input, dtype=dtype, device=device)
+        )
+
+    @lru_cache(maxsize=4)
+    def fetch_pos(self, patch_size: int, device: torch.device, dtype: torch.dtype) -> torch.Tensor:
+        """
+        Generates and caches 2D DCT-like positional embeddings for a given patch size.
+
+        The LRU cache is a performance optimization that avoids recomputing the
+        same positional grid on every forward pass.
+
+        Args:
+            patch_size (int): The side length of the square input patch.
+            device: The torch device to create the tensors on.
+            dtype: The torch dtype for the tensors.
+
+        Returns:
+            A tensor of shape (1, patch_size^2, max_freqs^2) containing the
+            positional embeddings.
+        """
+        # Create normalized 1D coordinate grids from 0 to 1.
+        pos_x = torch.linspace(0, 1, patch_size, device=device, dtype=dtype)
+        pos_y = torch.linspace(0, 1, patch_size, device=device, dtype=dtype)
+
+        # Create a 2D meshgrid of coordinates.
+        pos_y, pos_x = torch.meshgrid(pos_y, pos_x, indexing="ij")
+
+        # Reshape positions to be broadcastable with frequencies.
+        # Shape becomes (patch_size^2, 1, 1).
+        pos_x = pos_x.reshape(-1, 1, 1)
+        pos_y = pos_y.reshape(-1, 1, 1)
+
+        # Create a 1D tensor of frequency values from 0 to max_freqs-1.
+        freqs = torch.linspace(0, self.max_freqs - 1, self.max_freqs, dtype=dtype, device=device)
+
+        # Reshape frequencies to be broadcastable for creating 2D basis functions.
+        # freqs_x shape: (1, max_freqs, 1)
+        # freqs_y shape: (1, 1, max_freqs)
+        freqs_x = freqs[None, :, None]
+        freqs_y = freqs[None, None, :]
+
+        # A custom weighting coefficient, not part of standard DCT.
+        # This seems to down-weight the contribution of higher-frequency interactions.
+        coeffs = (1 + freqs_x * freqs_y) ** -1
+
+        # Calculate the 1D cosine basis functions for x and y coordinates.
+        # This is the core of the DCT formulation.
+        dct_x = torch.cos(pos_x * freqs_x * torch.pi)
+        dct_y = torch.cos(pos_y * freqs_y * torch.pi)
+
+        # Combine the 1D basis functions to create 2D basis functions by element-wise
+        # multiplication, and apply the custom coefficients. Broadcasting handles the
+        # combination of all (pos_x, freqs_x) with all (pos_y, freqs_y).
+        # The result is flattened into a feature vector for each position.
+        dct = (dct_x * dct_y * coeffs).view(1, -1, self.max_freqs ** 2)
+
+        return dct
+
+    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass for the embedder.
+
+        Args:
+            inputs (Tensor): The input tensor of shape (B, P^2, C).
+
+        Returns:
+            Tensor: The output tensor of shape (B, P^2, hidden_size_input).
+        """
+        # Get the batch size, number of pixels, and number of channels.
+        B, P2, C = inputs.shape
+
+        # Infer the patch side length from the number of pixels (P^2).
+        patch_size = int(P2 ** 0.5)
+
+        input_dtype = inputs.dtype
+        inputs = inputs.to(dtype=self.dtype)
+
+        # Fetch the pre-computed or cached positional embeddings.
+        dct = self.fetch_pos(patch_size, inputs.device, self.dtype)
+
+        # Repeat the positional embeddings for each item in the batch.
+        dct = dct.repeat(B, 1, 1)
+
+        # Concatenate the original input features with the positional embeddings
+        # along the feature dimension.
+        inputs = torch.cat((inputs, dct), dim=-1)
+
+        # Project the combined tensor to the target hidden size.
+        return self.embedder(inputs).to(dtype=input_dtype)
+
+
+class NerfGLUBlock(nn.Module):
+    """
+    A NerfBlock using a Gated Linear Unit (GLU) like MLP.
+    """
+    def __init__(self, hidden_size_s: int, hidden_size_x: int, mlp_ratio, dtype=None, device=None, operations=None):
+        super().__init__()
+        # The total number of parameters for the MLP is increased to accommodate
+        # the gate, value, and output projection matrices.
+        # We now need to generate parameters for 3 matrices.
+        total_params = 3 * hidden_size_x**2 * mlp_ratio
+        self.param_generator = operations.Linear(hidden_size_s, total_params, dtype=dtype, device=device)
+        self.norm = RMSNorm(hidden_size_x, dtype=dtype, device=device, operations=operations)
+        self.mlp_ratio = mlp_ratio
+
+
+    def forward(self, x: torch.Tensor, s: torch.Tensor) -> torch.Tensor:
+        batch_size, num_x, hidden_size_x = x.shape
+        mlp_params = self.param_generator(s)
+
+        # Split the generated parameters into three parts for the gate, value, and output projection.
+        fc1_gate_params, fc1_value_params, fc2_params = mlp_params.chunk(3, dim=-1)
+
+        # Reshape the parameters into matrices for batch matrix multiplication.
+        fc1_gate = fc1_gate_params.view(batch_size, hidden_size_x, hidden_size_x * self.mlp_ratio)
+        fc1_value = fc1_value_params.view(batch_size, hidden_size_x, hidden_size_x * self.mlp_ratio)
+        fc2 = fc2_params.view(batch_size, hidden_size_x * self.mlp_ratio, hidden_size_x)
+
+        # Normalize the generated weight matrices as in the original implementation.
+        fc1_gate = torch.nn.functional.normalize(fc1_gate, dim=-2)
+        fc1_value = torch.nn.functional.normalize(fc1_value, dim=-2)
+        fc2 = torch.nn.functional.normalize(fc2, dim=-2)
+
+        res_x = x
+        x = self.norm(x)
+
+        # Apply the final output projection.
+        x = torch.bmm(torch.nn.functional.silu(torch.bmm(x, fc1_gate)) * torch.bmm(x, fc1_value), fc2)
+
+        return x + res_x
+
+
+class NerfFinalLayer(nn.Module):
+    def __init__(self, hidden_size, out_channels, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.norm = RMSNorm(hidden_size, dtype=dtype, device=device, operations=operations)
+        self.linear = operations.Linear(hidden_size, out_channels, dtype=dtype, device=device)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # RMSNorm normalizes over the last dimension, but our channel dim (C) is at dim=1.
+        # So we temporarily move the channel dimension to the end for the norm operation.
+        return self.linear(self.norm(x.movedim(1, -1))).movedim(-1, 1)
+
+
+class NerfFinalLayerConv(nn.Module):
+    def __init__(self, hidden_size: int, out_channels: int, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.norm = RMSNorm(hidden_size, dtype=dtype, device=device, operations=operations)
+        self.conv = operations.Conv2d(
+            in_channels=hidden_size,
+            out_channels=out_channels,
+            kernel_size=3,
+            padding=1,
+            dtype=dtype,
+            device=device,
+        )
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # RMSNorm normalizes over the last dimension, but our channel dim (C) is at dim=1.
+        # So we temporarily move the channel dimension to the end for the norm operation.
+        return self.conv(self.norm(x.movedim(1, -1)).movedim(-1, 1))
@@ -0,0 +1,329 @@
+# Credits:
+# Original Flux code can be found on: https://github.com/black-forest-labs/flux
+# Chroma Radiance adaption referenced from https://github.com/lodestone-rock/flow
+
+from dataclasses import dataclass
+from typing import Optional
+
+import torch
+from torch import Tensor, nn
+from einops import repeat
+import comfy.ldm.common_dit
+
+from comfy.ldm.flux.layers import EmbedND
+
+from comfy.ldm.chroma.model import Chroma, ChromaParams
+from comfy.ldm.chroma.layers import (
+    DoubleStreamBlock,
+    SingleStreamBlock,
+    Approximator,
+)
+from .layers import (
+    NerfEmbedder,
+    NerfGLUBlock,
+    NerfFinalLayer,
+    NerfFinalLayerConv,
+)
+
+
+@dataclass
+class ChromaRadianceParams(ChromaParams):
+    patch_size: int
+    nerf_hidden_size: int
+    nerf_mlp_ratio: int
+    nerf_depth: int
+    nerf_max_freqs: int
+    # Setting nerf_tile_size to 0 disables tiling.
+    nerf_tile_size: int
+    # Currently one of linear (legacy) or conv.
+    nerf_final_head_type: str
+    # None means use the same dtype as the model.
+    nerf_embedder_dtype: Optional[torch.dtype]
+
+
+class ChromaRadiance(Chroma):
+    """
+    Transformer model for flow matching on sequences.
+    """
+
+    def __init__(self, image_model=None, final_layer=True, dtype=None, device=None, operations=None, **kwargs):
+        if operations is None:
+            raise RuntimeError("Attempt to create ChromaRadiance object without setting operations")
+        nn.Module.__init__(self)
+        self.dtype = dtype
+        params = ChromaRadianceParams(**kwargs)
+        self.params = params
+        self.patch_size = params.patch_size
+        self.in_channels = params.in_channels
+        self.out_channels = params.out_channels
+        if params.hidden_size % params.num_heads != 0:
+            raise ValueError(
+                f"Hidden size {params.hidden_size} must be divisible by num_heads {params.num_heads}"
+            )
+        pe_dim = params.hidden_size // params.num_heads
+        if sum(params.axes_dim) != pe_dim:
+            raise ValueError(f"Got {params.axes_dim} but expected positional dim {pe_dim}")
+        self.hidden_size = params.hidden_size
+        self.num_heads = params.num_heads
+        self.in_dim = params.in_dim
+        self.out_dim = params.out_dim
+        self.hidden_dim = params.hidden_dim
+        self.n_layers = params.n_layers
+        self.pe_embedder = EmbedND(dim=pe_dim, theta=params.theta, axes_dim=params.axes_dim)
+        self.img_in_patch = operations.Conv2d(
+            params.in_channels,
+            params.hidden_size,
+            kernel_size=params.patch_size,
+            stride=params.patch_size,
+            bias=True,
+            dtype=dtype,
+            device=device,
+        )
+        self.txt_in = operations.Linear(params.context_in_dim, self.hidden_size, dtype=dtype, device=device)
+        # set as nn identity for now, will overwrite it later.
+        self.distilled_guidance_layer = Approximator(
+                    in_dim=self.in_dim,
+                    hidden_dim=self.hidden_dim,
+                    out_dim=self.out_dim,
+                    n_layers=self.n_layers,
+                    dtype=dtype, device=device, operations=operations
+                )
+
+
+        self.double_blocks = nn.ModuleList(
+            [
+                DoubleStreamBlock(
+                    self.hidden_size,
+                    self.num_heads,
+                    mlp_ratio=params.mlp_ratio,
+                    qkv_bias=params.qkv_bias,
+                    dtype=dtype, device=device, operations=operations
+                )
+                for _ in range(params.depth)
+            ]
+        )
+
+        self.single_blocks = nn.ModuleList(
+            [
+                SingleStreamBlock(
+                    self.hidden_size,
+                    self.num_heads,
+                    mlp_ratio=params.mlp_ratio,
+                    dtype=dtype, device=device, operations=operations,
+                )
+                for _ in range(params.depth_single_blocks)
+            ]
+        )
+
+        # pixel channel concat with DCT
+        self.nerf_image_embedder = NerfEmbedder(
+            in_channels=params.in_channels,
+            hidden_size_input=params.nerf_hidden_size,
+            max_freqs=params.nerf_max_freqs,
+            dtype=params.nerf_embedder_dtype or dtype,
+            device=device,
+            operations=operations,
+        )
+
+        self.nerf_blocks = nn.ModuleList([
+            NerfGLUBlock(
+                hidden_size_s=params.hidden_size,
+                hidden_size_x=params.nerf_hidden_size,
+                mlp_ratio=params.nerf_mlp_ratio,
+                dtype=dtype,
+                device=device,
+                operations=operations,
+            ) for _ in range(params.nerf_depth)
+        ])
+
+        if params.nerf_final_head_type == "linear":
+            self.nerf_final_layer = NerfFinalLayer(
+                params.nerf_hidden_size,
+                out_channels=params.in_channels,
+                dtype=dtype,
+                device=device,
+                operations=operations,
+            )
+        elif params.nerf_final_head_type == "conv":
+            self.nerf_final_layer_conv = NerfFinalLayerConv(
+                params.nerf_hidden_size,
+                out_channels=params.in_channels,
+                dtype=dtype,
+                device=device,
+                operations=operations,
+            )
+        else:
+            errstr = f"Unsupported nerf_final_head_type {params.nerf_final_head_type}"
+            raise ValueError(errstr)
+
+        self.skip_mmdit = []
+        self.skip_dit = []
+        self.lite = False
+
+    @property
+    def _nerf_final_layer(self) -> nn.Module:
+        if self.params.nerf_final_head_type == "linear":
+            return self.nerf_final_layer
+        if self.params.nerf_final_head_type == "conv":
+            return self.nerf_final_layer_conv
+        # Impossible to get here as we raise an error on unexpected types on initialization.
+        raise NotImplementedError
+
+    def img_in(self, img: Tensor) -> Tensor:
+        img = self.img_in_patch(img) # -> [B, Hidden, H/P, W/P]
+        # flatten into a sequence for the transformer.
+        return img.flatten(2).transpose(1, 2) # -> [B, NumPatches, Hidden]
+
+    def forward_nerf(
+        self,
+        img_orig: Tensor,
+        img_out: Tensor,
+        params: ChromaRadianceParams,
+    ) -> Tensor:
+        B, C, H, W = img_orig.shape
+        num_patches = img_out.shape[1]
+        patch_size = params.patch_size
+
+        # Store the raw pixel values of each patch for the NeRF head later.
+        # unfold creates patches: [B, C * P * P, NumPatches]
+        nerf_pixels = nn.functional.unfold(img_orig, kernel_size=patch_size, stride=patch_size)
+        nerf_pixels = nerf_pixels.transpose(1, 2) # -> [B, NumPatches, C * P * P]
+
+        if params.nerf_tile_size > 0 and num_patches > params.nerf_tile_size:
+            # Enable tiling if nerf_tile_size isn't 0 and we actually have more patches than
+            # the tile size.
+            img_dct = self.forward_tiled_nerf(img_out, nerf_pixels, B, C, num_patches, patch_size, params)
+        else:
+            # Reshape for per-patch processing
+            nerf_hidden = img_out.reshape(B * num_patches, params.hidden_size)
+            nerf_pixels = nerf_pixels.reshape(B * num_patches, C, patch_size**2).transpose(1, 2)
+
+            # Get DCT-encoded pixel embeddings [pixel-dct]
+            img_dct = self.nerf_image_embedder(nerf_pixels)
+
+            # Pass through the dynamic MLP blocks (the NeRF)
+            for block in self.nerf_blocks:
+                img_dct = block(img_dct, nerf_hidden)
+
+        # Reassemble the patches into the final image.
+        img_dct = img_dct.transpose(1, 2) # -> [B*NumPatches, C, P*P]
+        # Reshape to combine with batch dimension for fold
+        img_dct = img_dct.reshape(B, num_patches, -1) # -> [B, NumPatches, C*P*P]
+        img_dct = img_dct.transpose(1, 2) # -> [B, C*P*P, NumPatches]
+        img_dct = nn.functional.fold(
+            img_dct,
+            output_size=(H, W),
+            kernel_size=patch_size,
+            stride=patch_size,
+        )
+        return self._nerf_final_layer(img_dct)
+
+    def forward_tiled_nerf(
+        self,
+        nerf_hidden: Tensor,
+        nerf_pixels: Tensor,
+        batch: int,
+        channels: int,
+        num_patches: int,
+        patch_size: int,
+        params: ChromaRadianceParams,
+    ) -> Tensor:
+        """
+        Processes the NeRF head in tiles to save memory.
+        nerf_hidden has shape [B, L, D]
+        nerf_pixels has shape [B, L, C * P * P]
+        """
+        tile_size = params.nerf_tile_size
+        output_tiles = []
+        # Iterate over the patches in tiles. The dimension L (num_patches) is at index 1.
+        for i in range(0, num_patches, tile_size):
+            end = min(i + tile_size, num_patches)
+
+            # Slice the current tile from the input tensors
+            nerf_hidden_tile = nerf_hidden[:, i:end, :]
+            nerf_pixels_tile = nerf_pixels[:, i:end, :]
+
+            # Get the actual number of patches in this tile (can be smaller for the last tile)
+            num_patches_tile = nerf_hidden_tile.shape[1]
+
+            # Reshape the tile for per-patch processing
+            # [B, NumPatches_tile, D] -> [B * NumPatches_tile, D]
+            nerf_hidden_tile = nerf_hidden_tile.reshape(batch * num_patches_tile, params.hidden_size)
+            # [B, NumPatches_tile, C*P*P] -> [B*NumPatches_tile, C, P*P] -> [B*NumPatches_tile, P*P, C]
+            nerf_pixels_tile = nerf_pixels_tile.reshape(batch * num_patches_tile, channels, patch_size**2).transpose(1, 2)
+
+            # get DCT-encoded pixel embeddings [pixel-dct]
+            img_dct_tile = self.nerf_image_embedder(nerf_pixels_tile)
+
+            # pass through the dynamic MLP blocks (the NeRF)
+            for block in self.nerf_blocks:
+                img_dct_tile = block(img_dct_tile, nerf_hidden_tile)
+
+            output_tiles.append(img_dct_tile)
+
+        # Concatenate the processed tiles along the patch dimension
+        return torch.cat(output_tiles, dim=0)
+
+    def radiance_get_override_params(self, overrides: dict) -> ChromaRadianceParams:
+        params = self.params
+        if not overrides:
+            return params
+        params_dict = {k: getattr(params, k) for k in params.__dataclass_fields__}
+        nullable_keys = frozenset(("nerf_embedder_dtype",))
+        bad_keys = tuple(k for k in overrides if k not in params_dict)
+        if bad_keys:
+            e = f"Unknown key(s) in transformer_options chroma_radiance_options: {', '.join(bad_keys)}"
+            raise ValueError(e)
+        bad_keys = tuple(
+            k
+            for k, v in overrides.items()
+            if type(v) != type(getattr(params, k)) and (v is not None or k not in nullable_keys)
+        )
+        if bad_keys:
+            e = f"Invalid value(s) in transformer_options chroma_radiance_options: {', '.join(bad_keys)}"
+            raise ValueError(e)
+        # At this point it's all valid keys and values so we can merge with the existing params.
+        params_dict |= overrides
+        return params.__class__(**params_dict)
+
+    def _forward(
+        self,
+        x: Tensor,
+        timestep: Tensor,
+        context: Tensor,
+        guidance: Optional[Tensor],
+        control: Optional[dict]=None,
+        transformer_options: dict={},
+        **kwargs: dict,
+    ) -> Tensor:
+        bs, c, h, w = x.shape
+        img = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
+
+        if img.ndim != 4:
+            raise ValueError("Input img tensor must be in [B, C, H, W] format.")
+        if context.ndim != 3:
+            raise ValueError("Input txt tensors must have 3 dimensions.")
+
+        params = self.radiance_get_override_params(transformer_options.get("chroma_radiance_options", {}))
+
+        h_len = (img.shape[-2] // self.patch_size)
+        w_len = (img.shape[-1] // self.patch_size)
+
+        img_ids = torch.zeros((h_len, w_len, 3), device=x.device, dtype=x.dtype)
+        img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, h_len - 1, steps=h_len, device=x.device, dtype=x.dtype).unsqueeze(1)
+        img_ids[:, :, 2] = img_ids[:, :, 2] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
+        img_ids = repeat(img_ids, "h w c -> b (h w) c", b=bs)
+        txt_ids = torch.zeros((bs, context.shape[1], 3), device=x.device, dtype=x.dtype)
+
+        img_out = self.forward_orig(
+            img,
+            img_ids,
+            context,
+            txt_ids,
+            timestep,
+            guidance,
+            control,
+            transformer_options,
+            attn_mask=kwargs.get("attention_mask", None),
+        )
+        return self.forward_nerf(img, img_out, params)[:, :, :h, :w]
@@ -176,6 +176,7 @@ class Attention(nn.Module):
        context=None,
        mask=None,
        rope_emb=None,
+        transformer_options={},
        **kwargs,
    ):
        """
@@ -184,7 +185,7 @@ class Attention(nn.Module):
            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)
+        out = optimized_attention(q, k, v, self.heads, skip_reshape=True, mask=mask, skip_output_reshape=True, transformer_options=transformer_options)
        del q, k, v
        out = rearrange(out, " b n s c -> s b (n c)")
        return self.to_out(out)
@@ -546,6 +547,7 @@ class VideoAttn(nn.Module):
        context: Optional[torch.Tensor] = None,
        crossattn_mask: Optional[torch.Tensor] = None,
        rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
+        transformer_options: Optional[dict] = {},
    ) -> torch.Tensor:
        """
        Forward pass for video attention.
@@ -571,6 +573,7 @@ class VideoAttn(nn.Module):
            context_M_B_D,
            crossattn_mask,
            rope_emb=rope_emb_L_1_1_D,
+            transformer_options=transformer_options,
        )
        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
@@ -665,6 +668,7 @@ class DITBuildingBlock(nn.Module):
        crossattn_mask: Optional[torch.Tensor] = None,
        rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
        adaln_lora_B_3D: Optional[torch.Tensor] = None,
+        transformer_options: Optional[dict] = {},
    ) -> torch.Tensor:
        """
        Forward pass for dynamically configured blocks with adaptive normalization.
@@ -702,6 +706,7 @@ class DITBuildingBlock(nn.Module):
                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,
+                transformer_options=transformer_options,
            )
        elif self.block_type in ["cross_attn", "ca"]:
            x = x + gate_1_1_1_B_D * self.block(
@@ -709,6 +714,7 @@ class DITBuildingBlock(nn.Module):
                context=crossattn_emb,
                crossattn_mask=crossattn_mask,
                rope_emb_L_1_1_D=rope_emb_L_1_1_D,
+                transformer_options=transformer_options,
            )
        else:
            raise ValueError(f"Unknown block type: {self.block_type}")
@@ -784,6 +790,7 @@ class GeneralDITTransformerBlock(nn.Module):
        crossattn_mask: Optional[torch.Tensor] = None,
        rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
        adaln_lora_B_3D: Optional[torch.Tensor] = None,
+        transformer_options: Optional[dict] = {},
    ) -> torch.Tensor:
        for block in self.blocks:
            x = block(
@@ -793,5 +800,6 @@ class GeneralDITTransformerBlock(nn.Module):
                crossattn_mask,
                rope_emb_L_1_1_D=rope_emb_L_1_1_D,
                adaln_lora_B_3D=adaln_lora_B_3D,
+                transformer_options=transformer_options,
            )
        return x
@@ -520,6 +520,7 @@ class GeneralDIT(nn.Module):
                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}"

+        transformer_options = kwargs.get("transformer_options", {})
        for _, block in self.blocks.items():
            assert (
                self.blocks["block0"].x_format == block.x_format
@@ -534,6 +535,7 @@ class GeneralDIT(nn.Module):
                crossattn_mask,
                rope_emb_L_1_1_D=rope_emb_L_1_1_D,
                adaln_lora_B_3D=adaln_lora_B_3D,
+                transformer_options=transformer_options,
            )

        x_B_T_H_W_D = rearrange(x, "T H W B D -> B T H W D")
@@ -44,7 +44,7 @@ class GPT2FeedForward(nn.Module):
        return x


-def torch_attention_op(q_B_S_H_D: torch.Tensor, k_B_S_H_D: torch.Tensor, v_B_S_H_D: torch.Tensor) -> torch.Tensor:
+def torch_attention_op(q_B_S_H_D: torch.Tensor, k_B_S_H_D: torch.Tensor, v_B_S_H_D: torch.Tensor, transformer_options: Optional[dict] = {}) -> torch.Tensor:
    """Computes multi-head attention using PyTorch's native implementation.

    This function provides a PyTorch backend alternative to Transformer Engine's attention operation.
@@ -71,7 +71,7 @@ def torch_attention_op(q_B_S_H_D: torch.Tensor, k_B_S_H_D: torch.Tensor, v_B_S_H
    q_B_H_S_D = rearrange(q_B_S_H_D, "b ... h k -> b h ... k").view(in_q_shape[0], in_q_shape[-2], -1, in_q_shape[-1])
    k_B_H_S_D = rearrange(k_B_S_H_D, "b ... h v -> b h ... v").view(in_k_shape[0], in_k_shape[-2], -1, in_k_shape[-1])
    v_B_H_S_D = rearrange(v_B_S_H_D, "b ... h v -> b h ... v").view(in_k_shape[0], in_k_shape[-2], -1, in_k_shape[-1])
-    return optimized_attention(q_B_H_S_D, k_B_H_S_D, v_B_H_S_D, in_q_shape[-2], skip_reshape=True)
+    return optimized_attention(q_B_H_S_D, k_B_H_S_D, v_B_H_S_D, in_q_shape[-2], skip_reshape=True, transformer_options=transformer_options)


 class Attention(nn.Module):
@@ -180,8 +180,8 @@ class Attention(nn.Module):

        return q, k, v

-    def compute_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor:
-        result = self.attn_op(q, k, v)  # [B, S, H, D]
+    def compute_attention(self, q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, transformer_options: Optional[dict] = {}) -> torch.Tensor:
+        result = self.attn_op(q, k, v, transformer_options=transformer_options)  # [B, S, H, D]
        return self.output_dropout(self.output_proj(result))

    def forward(
@@ -189,6 +189,7 @@ class Attention(nn.Module):
        x: torch.Tensor,
        context: Optional[torch.Tensor] = None,
        rope_emb: Optional[torch.Tensor] = None,
+        transformer_options: Optional[dict] = {},
    ) -> torch.Tensor:
        """
        Args:
@@ -196,7 +197,7 @@ class Attention(nn.Module):
            context (Optional[Tensor]): The key tensor of shape [B, Mk, K] or use x as context [self attention] if None
        """
        q, k, v = self.compute_qkv(x, context, rope_emb=rope_emb)
-        return self.compute_attention(q, k, v)
+        return self.compute_attention(q, k, v, transformer_options=transformer_options)


 class Timesteps(nn.Module):
@@ -459,6 +460,7 @@ class Block(nn.Module):
        rope_emb_L_1_1_D: Optional[torch.Tensor] = None,
        adaln_lora_B_T_3D: Optional[torch.Tensor] = None,
        extra_per_block_pos_emb: Optional[torch.Tensor] = None,
+        transformer_options: Optional[dict] = {},
    ) -> torch.Tensor:
        if extra_per_block_pos_emb is not None:
            x_B_T_H_W_D = x_B_T_H_W_D + extra_per_block_pos_emb
@@ -512,6 +514,7 @@ class Block(nn.Module):
                rearrange(normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"),
                None,
                rope_emb=rope_emb_L_1_1_D,
+                transformer_options=transformer_options,
            ),
            "b (t h w) d -> b t h w d",
            t=T,
@@ -525,6 +528,7 @@ class Block(nn.Module):
            layer_norm_cross_attn: Callable,
            _scale_cross_attn_B_T_1_1_D: torch.Tensor,
            _shift_cross_attn_B_T_1_1_D: torch.Tensor,
+            transformer_options: Optional[dict] = {},
        ) -> torch.Tensor:
            _normalized_x_B_T_H_W_D = _fn(
                _x_B_T_H_W_D, layer_norm_cross_attn, _scale_cross_attn_B_T_1_1_D, _shift_cross_attn_B_T_1_1_D
@@ -534,6 +538,7 @@ class Block(nn.Module):
                    rearrange(_normalized_x_B_T_H_W_D, "b t h w d -> b (t h w) d"),
                    crossattn_emb,
                    rope_emb=rope_emb_L_1_1_D,
+                    transformer_options=transformer_options,
                ),
                "b (t h w) d -> b t h w d",
                t=T,
@@ -547,6 +552,7 @@ class Block(nn.Module):
            self.layer_norm_cross_attn,
            scale_cross_attn_B_T_1_1_D,
            shift_cross_attn_B_T_1_1_D,
+            transformer_options=transformer_options,
        )
        x_B_T_H_W_D = result_B_T_H_W_D * gate_cross_attn_B_T_1_1_D + x_B_T_H_W_D

@@ -865,6 +871,7 @@ class MiniTrainDIT(nn.Module):
            "rope_emb_L_1_1_D": rope_emb_L_1_1_D.unsqueeze(1).unsqueeze(0),
            "adaln_lora_B_T_3D": adaln_lora_B_T_3D,
            "extra_per_block_pos_emb": extra_pos_emb_B_T_H_W_D_or_T_H_W_B_D,
+            "transformer_options": kwargs.get("transformer_options", {}),
        }
        for block in self.blocks:
            x_B_T_H_W_D = block(
@@ -159,7 +159,7 @@ class DoubleStreamBlock(nn.Module):
        )
        self.flipped_img_txt = flipped_img_txt

-    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims_img=None, modulation_dims_txt=None):
+    def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims_img=None, modulation_dims_txt=None, transformer_options={}):
        img_mod1, img_mod2 = self.img_mod(vec)
        txt_mod1, txt_mod2 = self.txt_mod(vec)

@@ -182,7 +182,7 @@ class DoubleStreamBlock(nn.Module):
            attn = attention(torch.cat((img_q, txt_q), dim=2),
                             torch.cat((img_k, txt_k), dim=2),
                             torch.cat((img_v, txt_v), dim=2),
-                             pe=pe, mask=attn_mask)
+                             pe=pe, mask=attn_mask, transformer_options=transformer_options)

            img_attn, txt_attn = attn[:, : img.shape[1]], attn[:, img.shape[1]:]
        else:
@@ -190,7 +190,7 @@ class DoubleStreamBlock(nn.Module):
            attn = attention(torch.cat((txt_q, img_q), dim=2),
                             torch.cat((txt_k, img_k), dim=2),
                             torch.cat((txt_v, img_v), dim=2),
-                             pe=pe, mask=attn_mask)
+                             pe=pe, mask=attn_mask, transformer_options=transformer_options)

            txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1]:]

@@ -244,7 +244,7 @@ class SingleStreamBlock(nn.Module):
        self.mlp_act = nn.GELU(approximate="tanh")
        self.modulation = Modulation(hidden_size, double=False, dtype=dtype, device=device, operations=operations)

-    def forward(self, x: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims=None) -> Tensor:
+    def forward(self, x: Tensor, vec: Tensor, pe: Tensor, attn_mask=None, modulation_dims=None, transformer_options={}) -> Tensor:
        mod, _ = self.modulation(vec)
        qkv, mlp = torch.split(self.linear1(apply_mod(self.pre_norm(x), (1 + mod.scale), mod.shift, modulation_dims)), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1)

@@ -252,7 +252,7 @@ class SingleStreamBlock(nn.Module):
        q, k = self.norm(q, k, v)

        # compute attention
-        attn = attention(q, k, v, pe=pe, mask=attn_mask)
+        attn = attention(q, k, v, pe=pe, mask=attn_mask, transformer_options=transformer_options)
        # compute activation in mlp stream, cat again and run second linear layer
        output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2))
        x += apply_mod(output, mod.gate, None, modulation_dims)
@@ -6,7 +6,7 @@ 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:
+def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None, transformer_options={}) -> Tensor:
    q_shape = q.shape
    k_shape = k.shape

@@ -17,7 +17,7 @@ def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor, mask=None) -> Tensor:
        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)
+    x = optimized_attention(q, k, v, heads, skip_reshape=True, mask=mask, transformer_options=transformer_options)
    return x


@@ -35,11 +35,13 @@ def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
    out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
    return out.to(dtype=torch.float32, device=pos.device)

+def apply_rope1(x: Tensor, freqs_cis: Tensor):
+    x_ = x.to(dtype=freqs_cis.dtype).reshape(*x.shape[:-1], -1, 1, 2)
+
+    x_out = freqs_cis[..., 0] * x_[..., 0]
+    x_out.addcmul_(freqs_cis[..., 1], x_[..., 1])
+
+    return x_out.reshape(*x.shape).type_as(x)

 def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor):
-    xq_ = xq.to(dtype=freqs_cis.dtype).reshape(*xq.shape[:-1], -1, 1, 2)
-    xk_ = xk.to(dtype=freqs_cis.dtype).reshape(*xk.shape[:-1], -1, 1, 2)
-    xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
-    xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
-    return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
-
+    return apply_rope1(xq, freqs_cis), apply_rope1(xk, freqs_cis)
@@ -144,14 +144,16 @@ class Flux(nn.Module):
                                                   txt=args["txt"],
                                                   vec=args["vec"],
                                                   pe=args["pe"],
-                                                   attn_mask=args.get("attn_mask"))
+                                                   attn_mask=args.get("attn_mask"),
+                                                   transformer_options=args.get("transformer_options"))
                    return out

                out = blocks_replace[("double_block", i)]({"img": img,
                                                           "txt": txt,
                                                           "vec": vec,
                                                           "pe": pe,
-                                                           "attn_mask": attn_mask},
+                                                           "attn_mask": attn_mask,
+                                                           "transformer_options": transformer_options},
                                                          {"original_block": block_wrap})
                txt = out["txt"]
                img = out["img"]
@@ -160,7 +162,8 @@ class Flux(nn.Module):
                                 txt=txt,
                                 vec=vec,
                                 pe=pe,
-                                 attn_mask=attn_mask)
+                                 attn_mask=attn_mask,
+                                 transformer_options=transformer_options)

            if control is not None: # Controlnet
                control_i = control.get("input")
@@ -181,17 +184,19 @@ class Flux(nn.Module):
                    out["img"] = block(args["img"],
                                       vec=args["vec"],
                                       pe=args["pe"],
-                                       attn_mask=args.get("attn_mask"))
+                                       attn_mask=args.get("attn_mask"),
+                                       transformer_options=args.get("transformer_options"))
                    return out

                out = blocks_replace[("single_block", i)]({"img": img,
                                                           "vec": vec,
                                                           "pe": pe,
-                                                           "attn_mask": attn_mask},
+                                                           "attn_mask": attn_mask,
+                                                           "transformer_options": transformer_options},
                                                          {"original_block": block_wrap})
                img = out["img"]
            else:
-                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask)
+                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options)

            if control is not None: # Controlnet
                control_o = control.get("output")
@@ -109,6 +109,7 @@ class AsymmetricAttention(nn.Module):
        scale_x: torch.Tensor,  # (B, dim_x), modulation for pre-RMSNorm.
        scale_y: torch.Tensor,  # (B, dim_y), modulation for pre-RMSNorm.
        crop_y,
+        transformer_options={},
        **rope_rotation,
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        rope_cos = rope_rotation.get("rope_cos")
@@ -143,7 +144,7 @@ class AsymmetricAttention(nn.Module):

        xy = optimized_attention(q,
                                 k,
-                                 v, self.num_heads, skip_reshape=True)
+                                 v, self.num_heads, skip_reshape=True, transformer_options=transformer_options)

        x, y = torch.tensor_split(xy, (q_x.shape[1],), dim=1)
        x = self.proj_x(x)
@@ -224,6 +225,7 @@ class AsymmetricJointBlock(nn.Module):
        x: torch.Tensor,
        c: torch.Tensor,
        y: torch.Tensor,
+        transformer_options={},
        **attn_kwargs,
    ):
        """Forward pass of a block.
@@ -256,6 +258,7 @@ class AsymmetricJointBlock(nn.Module):
            y,
            scale_x=scale_msa_x,
            scale_y=scale_msa_y,
+            transformer_options=transformer_options,
            **attn_kwargs,
        )

@@ -524,10 +527,11 @@ class AsymmDiTJoint(nn.Module):
                                                    args["txt"],
                                                    rope_cos=args["rope_cos"],
                                                    rope_sin=args["rope_sin"],
-                                                    crop_y=args["num_tokens"]
+                                                    crop_y=args["num_tokens"],
+                                                    transformer_options=args["transformer_options"]
                                                    )
                    return out
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": y_feat, "vec": c, "rope_cos": rope_cos, "rope_sin": rope_sin, "num_tokens": num_tokens}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": y_feat, "vec": c, "rope_cos": rope_cos, "rope_sin": rope_sin, "num_tokens": num_tokens, "transformer_options": transformer_options}, {"original_block": block_wrap})
                y_feat = out["txt"]
                x = out["img"]
            else:
@@ -538,6 +542,7 @@ class AsymmDiTJoint(nn.Module):
                    rope_cos=rope_cos,
                    rope_sin=rope_sin,
                    crop_y=num_tokens,
+                    transformer_options=transformer_options,
                )  # (B, M, D), (B, L, D)
        del y_feat  # Final layers don't use dense text features.

@@ -72,8 +72,8 @@ class TimestepEmbed(nn.Module):
        return t_emb


-def attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor):
-    return optimized_attention(query.view(query.shape[0], -1, query.shape[-1] * query.shape[-2]), key.view(key.shape[0], -1, key.shape[-1] * key.shape[-2]), value.view(value.shape[0], -1, value.shape[-1] * value.shape[-2]), query.shape[2])
+def attention(query: torch.Tensor, key: torch.Tensor, value: torch.Tensor, transformer_options={}):
+    return optimized_attention(query.view(query.shape[0], -1, query.shape[-1] * query.shape[-2]), key.view(key.shape[0], -1, key.shape[-1] * key.shape[-2]), value.view(value.shape[0], -1, value.shape[-1] * value.shape[-2]), query.shape[2], transformer_options=transformer_options)


 class HiDreamAttnProcessor_flashattn:
@@ -86,6 +86,7 @@ class HiDreamAttnProcessor_flashattn:
        image_tokens_masks: Optional[torch.FloatTensor] = None,
        text_tokens: Optional[torch.FloatTensor] = None,
        rope: torch.FloatTensor = None,
+        transformer_options={},
        *args,
        **kwargs,
    ) -> torch.FloatTensor:
@@ -133,7 +134,7 @@ class HiDreamAttnProcessor_flashattn:
            query = torch.cat([query_1, query_2], dim=-1)
            key = torch.cat([key_1, key_2], dim=-1)

-        hidden_states = attention(query, key, value)
+        hidden_states = attention(query, key, value, transformer_options=transformer_options)

        if not attn.single:
            hidden_states_i, hidden_states_t = torch.split(hidden_states, [num_image_tokens, num_text_tokens], dim=1)
@@ -199,6 +200,7 @@ class HiDreamAttention(nn.Module):
        image_tokens_masks: torch.FloatTensor = None,
        norm_text_tokens: torch.FloatTensor = None,
        rope: torch.FloatTensor = None,
+        transformer_options={},
    ) -> torch.Tensor:
        return self.processor(
            self,
@@ -206,6 +208,7 @@ class HiDreamAttention(nn.Module):
            image_tokens_masks = image_tokens_masks,
            text_tokens = norm_text_tokens,
            rope = rope,
+            transformer_options=transformer_options,
        )


@@ -406,7 +409,7 @@ class HiDreamImageSingleTransformerBlock(nn.Module):
        text_tokens: Optional[torch.FloatTensor] = None,
        adaln_input: Optional[torch.FloatTensor] = None,
        rope: torch.FloatTensor = None,
-
+        transformer_options={},
    ) -> torch.FloatTensor:
        wtype = image_tokens.dtype
        shift_msa_i, scale_msa_i, gate_msa_i, shift_mlp_i, scale_mlp_i, gate_mlp_i = \
@@ -419,6 +422,7 @@ class HiDreamImageSingleTransformerBlock(nn.Module):
            norm_image_tokens,
            image_tokens_masks,
            rope = rope,
+            transformer_options=transformer_options,
        )
        image_tokens = gate_msa_i * attn_output_i + image_tokens

@@ -483,6 +487,7 @@ class HiDreamImageTransformerBlock(nn.Module):
        text_tokens: Optional[torch.FloatTensor] = None,
        adaln_input: Optional[torch.FloatTensor] = None,
        rope: torch.FloatTensor = None,
+        transformer_options={},
    ) -> torch.FloatTensor:
        wtype = image_tokens.dtype
        shift_msa_i, scale_msa_i, gate_msa_i, shift_mlp_i, scale_mlp_i, gate_mlp_i, \
@@ -500,6 +505,7 @@ class HiDreamImageTransformerBlock(nn.Module):
            image_tokens_masks,
            norm_text_tokens,
            rope = rope,
+            transformer_options=transformer_options,
        )

        image_tokens = gate_msa_i * attn_output_i + image_tokens
@@ -550,6 +556,7 @@ class HiDreamImageBlock(nn.Module):
        text_tokens: Optional[torch.FloatTensor] = None,
        adaln_input: torch.FloatTensor = None,
        rope: torch.FloatTensor = None,
+        transformer_options={},
    ) -> torch.FloatTensor:
        return self.block(
            image_tokens,
@@ -557,6 +564,7 @@ class HiDreamImageBlock(nn.Module):
            text_tokens,
            adaln_input,
            rope,
+            transformer_options=transformer_options,
        )


@@ -786,6 +794,7 @@ class HiDreamImageTransformer2DModel(nn.Module):
                text_tokens = cur_encoder_hidden_states,
                adaln_input = adaln_input,
                rope = rope,
+                transformer_options=transformer_options,
            )
            initial_encoder_hidden_states = initial_encoder_hidden_states[:, :initial_encoder_hidden_states_seq_len]
            block_id += 1
@@ -809,6 +818,7 @@ class HiDreamImageTransformer2DModel(nn.Module):
                text_tokens=None,
                adaln_input=adaln_input,
                rope=rope,
+                transformer_options=transformer_options,
            )
            hidden_states = hidden_states[:, :hidden_states_seq_len]
            block_id += 1
@@ -99,14 +99,16 @@ class Hunyuan3Dv2(nn.Module):
                                                   txt=args["txt"],
                                                   vec=args["vec"],
                                                   pe=args["pe"],
-                                                   attn_mask=args.get("attn_mask"))
+                                                   attn_mask=args.get("attn_mask"),
+                                                   transformer_options=args["transformer_options"])
                    return out

                out = blocks_replace[("double_block", i)]({"img": img,
                                                           "txt": txt,
                                                           "vec": vec,
                                                           "pe": pe,
-                                                           "attn_mask": attn_mask},
+                                                           "attn_mask": attn_mask,
+                                                           "transformer_options": transformer_options},
                                                          {"original_block": block_wrap})
                txt = out["txt"]
                img = out["img"]
@@ -115,7 +117,8 @@ class Hunyuan3Dv2(nn.Module):
                                 txt=txt,
                                 vec=vec,
                                 pe=pe,
-                                 attn_mask=attn_mask)
+                                 attn_mask=attn_mask,
+                                 transformer_options=transformer_options)

        img = torch.cat((txt, img), 1)

@@ -126,17 +129,19 @@ class Hunyuan3Dv2(nn.Module):
                    out["img"] = block(args["img"],
                                       vec=args["vec"],
                                       pe=args["pe"],
-                                       attn_mask=args.get("attn_mask"))
+                                       attn_mask=args.get("attn_mask"),
+                                       transformer_options=args["transformer_options"])
                    return out

                out = blocks_replace[("single_block", i)]({"img": img,
                                                           "vec": vec,
                                                           "pe": pe,
-                                                           "attn_mask": attn_mask},
+                                                           "attn_mask": attn_mask,
+                                                           "transformer_options": transformer_options},
                                                          {"original_block": block_wrap})
                img = out["img"]
            else:
-                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask)
+                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, transformer_options=transformer_options)

        img = img[:, txt.shape[1]:, ...]
        img = self.final_layer(img, vec)
@@ -41,6 +41,7 @@ class HunyuanVideoParams:
    qkv_bias: bool
    guidance_embed: bool
    byt5: bool
+    meanflow: bool


 class SelfAttentionRef(nn.Module):
@@ -79,13 +80,13 @@ class TokenRefinerBlock(nn.Module):
            operations.Linear(mlp_hidden_dim, hidden_size, bias=True, dtype=dtype, device=device),
        )

-    def forward(self, x, c, mask):
+    def forward(self, x, c, mask, transformer_options={}):
        mod1, mod2 = self.adaLN_modulation(c).chunk(2, dim=1)

        norm_x = self.norm1(x)
        qkv = self.self_attn.qkv(norm_x)
        q, k, v = qkv.reshape(qkv.shape[0], qkv.shape[1], 3, self.heads, -1).permute(2, 0, 3, 1, 4)
-        attn = optimized_attention(q, k, v, self.heads, mask=mask, skip_reshape=True)
+        attn = optimized_attention(q, k, v, self.heads, mask=mask, skip_reshape=True, transformer_options=transformer_options)

        x = x + self.self_attn.proj(attn) * mod1.unsqueeze(1)
        x = x + self.mlp(self.norm2(x)) * mod2.unsqueeze(1)
@@ -116,14 +117,14 @@ class IndividualTokenRefiner(nn.Module):
            ]
        )

-    def forward(self, x, c, mask):
+    def forward(self, x, c, mask, transformer_options={}):
        m = None
        if mask is not None:
            m = mask.view(mask.shape[0], 1, 1, mask.shape[1]).repeat(1, 1, mask.shape[1], 1)
            m = m + m.transpose(2, 3)

        for block in self.blocks:
-            x = block(x, c, m)
+            x = block(x, c, m, transformer_options=transformer_options)
        return x


@@ -151,6 +152,7 @@ class TokenRefiner(nn.Module):
        x,
        timesteps,
        mask,
+        transformer_options={},
    ):
        t = self.t_embedder(timestep_embedding(timesteps, 256, time_factor=1.0).to(x.dtype))
        # m = mask.float().unsqueeze(-1)
@@ -159,7 +161,7 @@ class TokenRefiner(nn.Module):

        c = t + self.c_embedder(c.to(x.dtype))
        x = self.input_embedder(x)
-        x = self.individual_token_refiner(x, c, mask)
+        x = self.individual_token_refiner(x, c, mask, transformer_options=transformer_options)
        return x


@@ -256,6 +258,11 @@ class HunyuanVideo(nn.Module):
        else:
            self.byt5_in = None

+        if params.meanflow:
+            self.time_r_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size, dtype=dtype, device=device, operations=operations)
+        else:
+            self.time_r_in = None
+
        if final_layer:
            self.final_layer = LastLayer(self.hidden_size, self.patch_size[-1], self.out_channels, dtype=dtype, device=device, operations=operations)

@@ -272,6 +279,7 @@ class HunyuanVideo(nn.Module):
        guidance: Tensor = None,
        guiding_frame_index=None,
        ref_latent=None,
+        disable_time_r=False,
        control=None,
        transformer_options={},
    ) -> Tensor:
@@ -282,6 +290,14 @@ class HunyuanVideo(nn.Module):
        img = self.img_in(img)
        vec = self.time_in(timestep_embedding(timesteps, 256, time_factor=1.0).to(img.dtype))

+        if (self.time_r_in is not None) and (not disable_time_r):
+            w = torch.where(transformer_options['sigmas'][0] == transformer_options['sample_sigmas'])[0]  # This most likely could be improved
+            if len(w) > 0:
+                timesteps_r = transformer_options['sample_sigmas'][w[0] + 1]
+                timesteps_r = timesteps_r.unsqueeze(0).to(device=timesteps.device, dtype=timesteps.dtype)
+                vec_r = self.time_r_in(timestep_embedding(timesteps_r, 256, time_factor=1000.0).to(img.dtype))
+                vec = (vec + vec_r) / 2
+
        if ref_latent is not None:
            ref_latent_ids = self.img_ids(ref_latent)
            ref_latent = self.img_in(ref_latent)
@@ -313,7 +329,7 @@ class HunyuanVideo(nn.Module):
        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

-        txt = self.txt_in(txt, timesteps, txt_mask)
+        txt = self.txt_in(txt, timesteps, txt_mask, transformer_options=transformer_options)

        if self.byt5_in is not None and txt_byt5 is not None:
            txt_byt5 = self.byt5_in(txt_byt5)
@@ -337,14 +353,14 @@ class HunyuanVideo(nn.Module):
            if ("double_block", i) in blocks_replace:
                def block_wrap(args):
                    out = {}
-                    out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims_img=args["modulation_dims_img"], modulation_dims_txt=args["modulation_dims_txt"])
+                    out["img"], out["txt"] = block(img=args["img"], txt=args["txt"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims_img=args["modulation_dims_img"], modulation_dims_txt=args["modulation_dims_txt"], transformer_options=args["transformer_options"])
                    return out

-                out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims_img': modulation_dims, 'modulation_dims_txt': modulation_dims_txt}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": img, "txt": txt, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims_img': modulation_dims, 'modulation_dims_txt': modulation_dims_txt, 'transformer_options': transformer_options}, {"original_block": block_wrap})
                txt = out["txt"]
                img = out["img"]
            else:
-                img, txt = block(img=img, txt=txt, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims_img=modulation_dims, modulation_dims_txt=modulation_dims_txt)
+                img, txt = block(img=img, txt=txt, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims_img=modulation_dims, modulation_dims_txt=modulation_dims_txt, transformer_options=transformer_options)

            if control is not None: # Controlnet
                control_i = control.get("input")
@@ -359,13 +375,13 @@ class HunyuanVideo(nn.Module):
            if ("single_block", i) in blocks_replace:
                def block_wrap(args):
                    out = {}
-                    out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims=args["modulation_dims"])
+                    out["img"] = block(args["img"], vec=args["vec"], pe=args["pe"], attn_mask=args["attention_mask"], modulation_dims=args["modulation_dims"], transformer_options=args["transformer_options"])
                    return out

-                out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims': modulation_dims}, {"original_block": block_wrap})
+                out = blocks_replace[("single_block", i)]({"img": img, "vec": vec, "pe": pe, "attention_mask": attn_mask, 'modulation_dims': modulation_dims, 'transformer_options': transformer_options}, {"original_block": block_wrap})
                img = out["img"]
            else:
-                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims=modulation_dims)
+                img = block(img, vec=vec, pe=pe, attn_mask=attn_mask, modulation_dims=modulation_dims, transformer_options=transformer_options)

            if control is not None: # Controlnet
                control_o = control.get("output")
@@ -414,14 +430,14 @@ class HunyuanVideo(nn.Module):
        img_ids[:, :, 1] = img_ids[:, :, 1] + torch.linspace(0, w_len - 1, steps=w_len, device=x.device, dtype=x.dtype).unsqueeze(0)
        return repeat(img_ids, "h w c -> b (h w) c", b=bs)

-    def forward(self, x, timestep, context, y=None, txt_byt5=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, control=None, transformer_options={}, **kwargs):
+    def forward(self, x, timestep, context, y=None, txt_byt5=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=None, transformer_options={}, **kwargs):
        return comfy.patcher_extension.WrapperExecutor.new_class_executor(
            self._forward,
            self,
            comfy.patcher_extension.get_all_wrappers(comfy.patcher_extension.WrappersMP.DIFFUSION_MODEL, transformer_options)
-        ).execute(x, timestep, context, y, txt_byt5, guidance, attention_mask, guiding_frame_index, ref_latent, control, transformer_options, **kwargs)
+        ).execute(x, timestep, context, y, txt_byt5, guidance, attention_mask, guiding_frame_index, ref_latent, disable_time_r, control, transformer_options, **kwargs)

-    def _forward(self, x, timestep, context, y=None, txt_byt5=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, control=None, transformer_options={}, **kwargs):
+    def _forward(self, x, timestep, context, y=None, txt_byt5=None, guidance=None, attention_mask=None, guiding_frame_index=None, ref_latent=None, disable_time_r=False, control=None, transformer_options={}, **kwargs):
        bs = x.shape[0]
        if len(self.patch_size) == 3:
            img_ids = self.img_ids(x)
@@ -429,5 +445,5 @@ class HunyuanVideo(nn.Module):
        else:
            img_ids = self.img_ids_2d(x)
            txt_ids = torch.zeros((bs, context.shape[1], 2), device=x.device, dtype=x.dtype)
-        out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, txt_byt5, guidance, guiding_frame_index, ref_latent, control=control, transformer_options=transformer_options)
+        out = self.forward_orig(x, img_ids, context, txt_ids, attention_mask, timestep, y, txt_byt5, guidance, guiding_frame_index, ref_latent, disable_time_r=disable_time_r, control=control, transformer_options=transformer_options)
        return out
@@ -0,0 +1,301 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, AttnBlock, VideoConv3d, Normalize
+import comfy.ops
+import comfy.ldm.models.autoencoder
+ops = comfy.ops.disable_weight_init
+
+class RMS_norm(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        shape = (dim, 1, 1, 1)
+        self.scale = dim**0.5
+        self.gamma = nn.Parameter(torch.empty(shape))
+
+    def forward(self, x):
+        return F.normalize(x, dim=1) * self.scale * self.gamma
+
+class DnSmpl(nn.Module):
+    def __init__(self, ic, oc, tds=True, refiner_vae=True, op=VideoConv3d):
+        super().__init__()
+        fct = 2 * 2 * 2 if tds else 1 * 2 * 2
+        assert oc % fct == 0
+        self.conv = op(ic, oc // fct, kernel_size=3, stride=1, padding=1)
+        self.refiner_vae = refiner_vae
+
+        self.tds = tds
+        self.gs = fct * ic // oc
+
+    def forward(self, x):
+        r1 = 2 if self.tds else 1
+        h = self.conv(x)
+
+        if self.tds and self.refiner_vae:
+            hf = h[:, :, :1, :, :]
+            b, c, f, ht, wd = hf.shape
+            hf = hf.reshape(b, c, f, ht // 2, 2, wd // 2, 2)
+            hf = hf.permute(0, 4, 6, 1, 2, 3, 5)
+            hf = hf.reshape(b, 2 * 2 * c, f, ht // 2, wd // 2)
+            hf = torch.cat([hf, hf], dim=1)
+
+            hn = h[:, :, 1:, :, :]
+            b, c, frms, ht, wd = hn.shape
+            nf = frms // r1
+            hn = hn.reshape(b, c, nf, r1, ht // 2, 2, wd // 2, 2)
+            hn = hn.permute(0, 3, 5, 7, 1, 2, 4, 6)
+            hn = hn.reshape(b, r1 * 2 * 2 * c, nf, ht // 2, wd // 2)
+
+            h = torch.cat([hf, hn], dim=2)
+
+            xf = x[:, :, :1, :, :]
+            b, ci, f, ht, wd = xf.shape
+            xf = xf.reshape(b, ci, f, ht // 2, 2, wd // 2, 2)
+            xf = xf.permute(0, 4, 6, 1, 2, 3, 5)
+            xf = xf.reshape(b, 2 * 2 * ci, f, ht // 2, wd // 2)
+            B, C, T, H, W = xf.shape
+            xf = xf.view(B, h.shape[1], self.gs // 2, T, H, W).mean(dim=2)
+
+            xn = x[:, :, 1:, :, :]
+            b, ci, frms, ht, wd = xn.shape
+            nf = frms // r1
+            xn = xn.reshape(b, ci, nf, r1, ht // 2, 2, wd // 2, 2)
+            xn = xn.permute(0, 3, 5, 7, 1, 2, 4, 6)
+            xn = xn.reshape(b, r1 * 2 * 2 * ci, nf, ht // 2, wd // 2)
+            B, C, T, H, W = xn.shape
+            xn = xn.view(B, h.shape[1], self.gs, T, H, W).mean(dim=2)
+            sc = torch.cat([xf, xn], dim=2)
+        else:
+            b, c, frms, ht, wd = h.shape
+
+            nf = frms // r1
+            h = h.reshape(b, c, nf, r1, ht // 2, 2, wd // 2, 2)
+            h = h.permute(0, 3, 5, 7, 1, 2, 4, 6)
+            h = h.reshape(b, r1 * 2 * 2 * c, nf, ht // 2, wd // 2)
+
+            b, ci, frms, ht, wd = x.shape
+            nf = frms // r1
+            sc = x.reshape(b, ci, nf, r1, ht // 2, 2, wd // 2, 2)
+            sc = sc.permute(0, 3, 5, 7, 1, 2, 4, 6)
+            sc = sc.reshape(b, r1 * 2 * 2 * ci, nf, ht // 2, wd // 2)
+            B, C, T, H, W = sc.shape
+            sc = sc.view(B, h.shape[1], self.gs, T, H, W).mean(dim=2)
+
+        return h + sc
+
+
+class UpSmpl(nn.Module):
+    def __init__(self, ic, oc, tus=True, refiner_vae=True, op=VideoConv3d):
+        super().__init__()
+        fct = 2 * 2 * 2 if tus else 1 * 2 * 2
+        self.conv = op(ic, oc * fct, kernel_size=3, stride=1, padding=1)
+        self.refiner_vae = refiner_vae
+
+        self.tus = tus
+        self.rp = fct * oc // ic
+
+    def forward(self, x):
+        r1 = 2 if self.tus else 1
+        h = self.conv(x)
+
+        if self.tus and self.refiner_vae:
+            hf = h[:, :, :1, :, :]
+            b, c, f, ht, wd = hf.shape
+            nc = c // (2 * 2)
+            hf = hf.reshape(b, 2, 2, nc, f, ht, wd)
+            hf = hf.permute(0, 3, 4, 5, 1, 6, 2)
+            hf = hf.reshape(b, nc, f, ht * 2, wd * 2)
+            hf = hf[:, : hf.shape[1] // 2]
+
+            hn = h[:, :, 1:, :, :]
+            b, c, frms, ht, wd = hn.shape
+            nc = c // (r1 * 2 * 2)
+            hn = hn.reshape(b, r1, 2, 2, nc, frms, ht, wd)
+            hn = hn.permute(0, 4, 5, 1, 6, 2, 7, 3)
+            hn = hn.reshape(b, nc, frms * r1, ht * 2, wd * 2)
+
+            h = torch.cat([hf, hn], dim=2)
+
+            xf = x[:, :, :1, :, :]
+            b, ci, f, ht, wd = xf.shape
+            xf = xf.repeat_interleave(repeats=self.rp // 2, dim=1)
+            b, c, f, ht, wd = xf.shape
+            nc = c // (2 * 2)
+            xf = xf.reshape(b, 2, 2, nc, f, ht, wd)
+            xf = xf.permute(0, 3, 4, 5, 1, 6, 2)
+            xf = xf.reshape(b, nc, f, ht * 2, wd * 2)
+
+            xn = x[:, :, 1:, :, :]
+            xn = xn.repeat_interleave(repeats=self.rp, dim=1)
+            b, c, frms, ht, wd = xn.shape
+            nc = c // (r1 * 2 * 2)
+            xn = xn.reshape(b, r1, 2, 2, nc, frms, ht, wd)
+            xn = xn.permute(0, 4, 5, 1, 6, 2, 7, 3)
+            xn = xn.reshape(b, nc, frms * r1, ht * 2, wd * 2)
+            sc = torch.cat([xf, xn], dim=2)
+        else:
+            b, c, frms, ht, wd = h.shape
+            nc = c // (r1 * 2 * 2)
+            h = h.reshape(b, r1, 2, 2, nc, frms, ht, wd)
+            h = h.permute(0, 4, 5, 1, 6, 2, 7, 3)
+            h = h.reshape(b, nc, frms * r1, ht * 2, wd * 2)
+
+            sc = x.repeat_interleave(repeats=self.rp, dim=1)
+            b, c, frms, ht, wd = sc.shape
+            nc = c // (r1 * 2 * 2)
+            sc = sc.reshape(b, r1, 2, 2, nc, frms, ht, wd)
+            sc = sc.permute(0, 4, 5, 1, 6, 2, 7, 3)
+            sc = sc.reshape(b, nc, frms * r1, ht * 2, wd * 2)
+
+        return h + sc
+
+class Encoder(nn.Module):
+    def __init__(self, in_channels, z_channels, block_out_channels, num_res_blocks,
+                 ffactor_spatial, ffactor_temporal, downsample_match_channel=True, refiner_vae=True, **_):
+        super().__init__()
+        self.z_channels = z_channels
+        self.block_out_channels = block_out_channels
+        self.num_res_blocks = num_res_blocks
+        self.ffactor_temporal = ffactor_temporal
+
+        self.refiner_vae = refiner_vae
+        if self.refiner_vae:
+            conv_op = VideoConv3d
+            norm_op = RMS_norm
+        else:
+            conv_op = ops.Conv3d
+            norm_op = Normalize
+
+        self.conv_in = conv_op(in_channels, block_out_channels[0], 3, 1, 1)
+
+        self.down = nn.ModuleList()
+        ch = block_out_channels[0]
+        depth = (ffactor_spatial >> 1).bit_length()
+        depth_temporal = ((ffactor_spatial // self.ffactor_temporal) >> 1).bit_length()
+
+        for i, tgt in enumerate(block_out_channels):
+            stage = nn.Module()
+            stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,
+                                                     out_channels=tgt,
+                                                     temb_channels=0,
+                                                     conv_op=conv_op, norm_op=norm_op)
+                                        for j in range(num_res_blocks)])
+            ch = tgt
+            if i < depth:
+                nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and downsample_match_channel else ch
+                stage.downsample = DnSmpl(ch, nxt, tds=i >= depth_temporal, refiner_vae=self.refiner_vae, op=conv_op)
+                ch = nxt
+            self.down.append(stage)
+
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=conv_op, norm_op=norm_op)
+        self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=norm_op)
+        self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=conv_op, norm_op=norm_op)
+
+        self.norm_out = norm_op(ch)
+        self.conv_out = conv_op(ch, z_channels << 1, 3, 1, 1)
+
+        self.regul = comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer()
+
+    def forward(self, x):
+        if not self.refiner_vae and x.shape[2] == 1:
+            x = x.expand(-1, -1, self.ffactor_temporal, -1, -1)
+
+        x = self.conv_in(x)
+
+        for stage in self.down:
+            for blk in stage.block:
+                x = blk(x)
+            if hasattr(stage, 'downsample'):
+                x = stage.downsample(x)
+
+        x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x)))
+
+        b, c, t, h, w = x.shape
+        grp = c // (self.z_channels << 1)
+        skip = x.view(b, c // grp, grp, t, h, w).mean(2)
+
+        out = self.conv_out(F.silu(self.norm_out(x))) + skip
+
+        if self.refiner_vae:
+            out = self.regul(out)[0]
+
+            out = torch.cat((out[:, :, :1], out), dim=2)
+            out = out.permute(0, 2, 1, 3, 4)
+            b, f_times_2, c, h, w = out.shape
+            out = out.reshape(b, f_times_2 // 2, 2 * c, h, w)
+            out = out.permute(0, 2, 1, 3, 4).contiguous()
+
+        return out
+
+class Decoder(nn.Module):
+    def __init__(self, z_channels, out_channels, block_out_channels, num_res_blocks,
+                 ffactor_spatial, ffactor_temporal, upsample_match_channel=True, refiner_vae=True, **_):
+        super().__init__()
+        block_out_channels = block_out_channels[::-1]
+        self.z_channels = z_channels
+        self.block_out_channels = block_out_channels
+        self.num_res_blocks = num_res_blocks
+
+        self.refiner_vae = refiner_vae
+        if self.refiner_vae:
+            conv_op = VideoConv3d
+            norm_op = RMS_norm
+        else:
+            conv_op = ops.Conv3d
+            norm_op = Normalize
+
+        ch = block_out_channels[0]
+        self.conv_in = conv_op(z_channels, ch, kernel_size=3, stride=1, padding=1)
+
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=conv_op, norm_op=norm_op)
+        self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=norm_op)
+        self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=conv_op, norm_op=norm_op)
+
+        self.up = nn.ModuleList()
+        depth = (ffactor_spatial >> 1).bit_length()
+        depth_temporal = (ffactor_temporal >> 1).bit_length()
+
+        for i, tgt in enumerate(block_out_channels):
+            stage = nn.Module()
+            stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,
+                                                     out_channels=tgt,
+                                                     temb_channels=0,
+                                                     conv_op=conv_op, norm_op=norm_op)
+                                        for j in range(num_res_blocks + 1)])
+            ch = tgt
+            if i < depth:
+                nxt = block_out_channels[i + 1] if i + 1 < len(block_out_channels) and upsample_match_channel else ch
+                stage.upsample = UpSmpl(ch, nxt, tus=i < depth_temporal, refiner_vae=self.refiner_vae, op=conv_op)
+                ch = nxt
+            self.up.append(stage)
+
+        self.norm_out = norm_op(ch)
+        self.conv_out = conv_op(ch, out_channels, 3, stride=1, padding=1)
+
+    def forward(self, z):
+        if self.refiner_vae:
+            z = z.permute(0, 2, 1, 3, 4)
+            b, f, c, h, w = z.shape
+            z = z.reshape(b, f, 2, c // 2, h, w)
+            z = z.permute(0, 1, 2, 3, 4, 5).reshape(b, f * 2, c // 2, h, w)
+            z = z.permute(0, 2, 1, 3, 4)
+            z = z[:, :, 1:]
+
+        x = self.conv_in(z) + z.repeat_interleave(self.block_out_channels[0] // self.z_channels, 1)
+        x = self.mid.block_2(self.mid.attn_1(self.mid.block_1(x)))
+
+        for stage in self.up:
+            for blk in stage.block:
+                x = blk(x)
+            if hasattr(stage, 'upsample'):
+                x = stage.upsample(x)
+
+        out = self.conv_out(F.silu(self.norm_out(x)))
+
+        if not self.refiner_vae:
+            if z.shape[-3] == 1:
+                out = out[:, :, -1:]
+
+        return out
@@ -271,7 +271,7 @@ class CrossAttention(nn.Module):

        self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))

-    def forward(self, x, context=None, mask=None, pe=None):
+    def forward(self, x, context=None, mask=None, pe=None, transformer_options={}):
        q = self.to_q(x)
        context = x if context is None else context
        k = self.to_k(context)
@@ -285,9 +285,9 @@ class CrossAttention(nn.Module):
            k = apply_rotary_emb(k, pe)

        if mask is None:
-            out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision)
+            out = comfy.ldm.modules.attention.optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options)
        else:
-            out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision)
+            out = comfy.ldm.modules.attention.optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options)
        return self.to_out(out)


@@ -303,12 +303,12 @@ class BasicTransformerBlock(nn.Module):

        self.scale_shift_table = nn.Parameter(torch.empty(6, dim, device=device, dtype=dtype))

-    def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None):
+    def forward(self, x, context=None, attention_mask=None, timestep=None, pe=None, transformer_options={}):
        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = (self.scale_shift_table[None, None].to(device=x.device, dtype=x.dtype) + timestep.reshape(x.shape[0], timestep.shape[1], self.scale_shift_table.shape[0], -1)).unbind(dim=2)

-        x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe) * gate_msa
+        x += self.attn1(comfy.ldm.common_dit.rms_norm(x) * (1 + scale_msa) + shift_msa, pe=pe, transformer_options=transformer_options) * gate_msa

-        x += self.attn2(x, context=context, mask=attention_mask)
+        x += self.attn2(x, context=context, mask=attention_mask, transformer_options=transformer_options)

        y = comfy.ldm.common_dit.rms_norm(x) * (1 + scale_mlp) + shift_mlp
        x += self.ff(y) * gate_mlp
@@ -479,10 +479,10 @@ class LTXVModel(torch.nn.Module):
            if ("double_block", i) in blocks_replace:
                def block_wrap(args):
                    out = {}
-                    out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"])
+                    out["img"] = block(args["img"], context=args["txt"], attention_mask=args["attention_mask"], timestep=args["vec"], pe=args["pe"], transformer_options=args["transformer_options"])
                    return out

-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "attention_mask": attention_mask, "vec": timestep, "pe": pe, "transformer_options": transformer_options}, {"original_block": block_wrap})
                x = out["img"]
            else:
                x = block(
@@ -490,7 +490,8 @@ class LTXVModel(torch.nn.Module):
                    context=context,
                    attention_mask=attention_mask,
                    timestep=timestep,
-                    pe=pe
+                    pe=pe,
+                    transformer_options=transformer_options,
                )

        # 3. Output
@@ -104,6 +104,7 @@ class JointAttention(nn.Module):
        x: torch.Tensor,
        x_mask: torch.Tensor,
        freqs_cis: torch.Tensor,
+        transformer_options={},
    ) -> torch.Tensor:
        """

@@ -140,7 +141,7 @@ class JointAttention(nn.Module):
        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)
+        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, transformer_options=transformer_options)

        return self.out(output)

@@ -268,6 +269,7 @@ class JointTransformerBlock(nn.Module):
        x_mask: torch.Tensor,
        freqs_cis: torch.Tensor,
        adaln_input: Optional[torch.Tensor]=None,
+        transformer_options={},
    ):
        """
        Perform a forward pass through the TransformerBlock.
@@ -290,6 +292,7 @@ class JointTransformerBlock(nn.Module):
                    modulate(self.attention_norm1(x), scale_msa),
                    x_mask,
                    freqs_cis,
+                    transformer_options=transformer_options,
                )
            )
            x = x + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(
@@ -304,6 +307,7 @@ class JointTransformerBlock(nn.Module):
                    self.attention_norm1(x),
                    x_mask,
                    freqs_cis,
+                    transformer_options=transformer_options,
                )
            )
            x = x + self.ffn_norm2(
@@ -494,7 +498,7 @@ class NextDiT(nn.Module):
        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
+        self, x: List[torch.Tensor] | torch.Tensor, cap_feats: torch.Tensor, cap_mask: torch.Tensor, t: torch.Tensor, num_tokens, transformer_options={}
    ) -> Tuple[torch.Tensor, torch.Tensor, List[Tuple[int, int]], List[int], torch.Tensor]:
        bsz = len(x)
        pH = pW = self.patch_size
@@ -554,7 +558,7 @@ class NextDiT(nn.Module):

        # refine context
        for layer in self.context_refiner:
-            cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis)
+            cap_feats = layer(cap_feats, cap_mask, cap_freqs_cis, transformer_options=transformer_options)

        # refine image
        flat_x = []
@@ -573,7 +577,7 @@ class NextDiT(nn.Module):
        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)
+            padded_img_embed = layer(padded_img_embed, padded_img_mask, img_freqs_cis, t, transformer_options=transformer_options)

        if cap_mask is not None:
            mask = torch.zeros(bsz, max_seq_len, dtype=dtype, device=device)
@@ -616,12 +620,13 @@ class NextDiT(nn.Module):

        cap_feats = self.cap_embedder(cap_feats)  # (N, L, D)  # todo check if able to batchify w.o. redundant compute

+        transformer_options = kwargs.get("transformer_options", {})
        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)
+        x, mask, img_size, cap_size, freqs_cis = self.patchify_and_embed(x, cap_feats, cap_mask, t, num_tokens, transformer_options=transformer_options)
        freqs_cis = freqs_cis.to(x.device)

        for layer in self.layers:
-            x = layer(x, mask, freqs_cis, adaln_input)
+            x = layer(x, mask, freqs_cis, adaln_input, transformer_options=transformer_options)

        x = self.final_layer(x, adaln_input)
        x = self.unpatchify(x, img_size, cap_size, return_tensor=x_is_tensor)[:,:,:h,:w]
@@ -26,6 +26,12 @@ class DiagonalGaussianRegularizer(torch.nn.Module):
            z = posterior.mode()
        return z, None

+class EmptyRegularizer(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, z: torch.Tensor) -> Tuple[torch.Tensor, dict]:
+        return z, None

 class AbstractAutoencoder(torch.nn.Module):
    """
@@ -5,8 +5,9 @@ import torch
 import torch.nn.functional as F
 from torch import nn, einsum
 from einops import rearrange, repeat
-from typing import Optional
+from typing import Optional, Any, Callable, Union
 import logging
+import functools

 from .diffusionmodules.util import AlphaBlender, timestep_embedding
 from .sub_quadratic_attention import efficient_dot_product_attention
@@ -17,23 +18,45 @@ if model_management.xformers_enabled():
    import xformers
    import xformers.ops

-if model_management.sage_attention_enabled():
-    try:
-        from sageattention import sageattn
-    except ModuleNotFoundError as e:
+SAGE_ATTENTION_IS_AVAILABLE = False
+try:
+    from sageattention import sageattn
+    SAGE_ATTENTION_IS_AVAILABLE = True
+except ImportError as e:
+    if model_management.sage_attention_enabled():
        if e.name == "sageattention":
            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")
        else:
            raise e
        exit(-1)

-if model_management.flash_attention_enabled():
-    try:
-        from flash_attn import flash_attn_func
-    except ModuleNotFoundError:
+FLASH_ATTENTION_IS_AVAILABLE = False
+try:
+    from flash_attn import flash_attn_func
+    FLASH_ATTENTION_IS_AVAILABLE = True
+except ImportError:
+    if model_management.flash_attention_enabled():
        logging.error(f"\n\nTo use the `--use-flash-attention` feature, the `flash-attn` package must be installed first.\ncommand:\n\t{sys.executable} -m pip install flash-attn")
        exit(-1)

+REGISTERED_ATTENTION_FUNCTIONS = {}
+def register_attention_function(name: str, func: Callable):
+    # avoid replacing existing functions
+    if name not in REGISTERED_ATTENTION_FUNCTIONS:
+        REGISTERED_ATTENTION_FUNCTIONS[name] = func
+    else:
+        logging.warning(f"Attention function {name} already registered, skipping registration.")
+
+def get_attention_function(name: str, default: Any=...) -> Union[Callable, None]:
+    if name == "optimized":
+        return optimized_attention
+    elif name not in REGISTERED_ATTENTION_FUNCTIONS:
+        if default is ...:
+            raise KeyError(f"Attention function {name} not found.")
+        else:
+            return default
+    return REGISTERED_ATTENTION_FUNCTIONS[name]
+
 from comfy.cli_args import args
 import comfy.ops
 ops = comfy.ops.disable_weight_init
@@ -91,7 +114,27 @@ 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, skip_output_reshape=False):
+
+def wrap_attn(func):
+    @functools.wraps(func)
+    def wrapper(*args, **kwargs):
+        remove_attn_wrapper_key = False
+        try:
+            if "_inside_attn_wrapper" not in kwargs:
+                transformer_options = kwargs.get("transformer_options", None)
+                remove_attn_wrapper_key = True
+                kwargs["_inside_attn_wrapper"] = True
+                if transformer_options is not None:
+                    if "optimized_attention_override" in transformer_options:
+                        return transformer_options["optimized_attention_override"](func, *args, **kwargs)
+            return func(*args, **kwargs)
+        finally:
+            if remove_attn_wrapper_key:
+                del kwargs["_inside_attn_wrapper"]
+    return wrapper
+
+@wrap_attn
+def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
    attn_precision = get_attn_precision(attn_precision, q.dtype)

    if skip_reshape:
@@ -159,8 +202,8 @@ def attention_basic(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
        )
    return out

-
-def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
+@wrap_attn
+def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
    attn_precision = get_attn_precision(attn_precision, query.dtype)

    if skip_reshape:
@@ -230,7 +273,8 @@ def attention_sub_quad(query, key, value, heads, mask=None, attn_precision=None,
        hidden_states = hidden_states.unflatten(0, (-1, heads)).transpose(1,2).flatten(start_dim=2)
    return hidden_states

-def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
+@wrap_attn
+def attention_split(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
    attn_precision = get_attn_precision(attn_precision, q.dtype)

    if skip_reshape:
@@ -359,7 +403,8 @@ try:
 except:
    pass

-def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
+@wrap_attn
+def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
    b = q.shape[0]
    dim_head = q.shape[-1]
    # check to make sure xformers isn't broken
@@ -374,7 +419,7 @@ def attention_xformers(q, k, v, heads, mask=None, attn_precision=None, skip_resh
            disabled_xformers = True

    if disabled_xformers:
-        return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape)
+        return attention_pytorch(q, k, v, heads, mask, skip_reshape=skip_reshape, **kwargs)

    if skip_reshape:
        # b h k d -> b k h d
@@ -427,8 +472,8 @@ else:
    #TODO: other GPUs ?
    SDP_BATCH_LIMIT = 2**31

-
-def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
+@wrap_attn
+def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
    if skip_reshape:
        b, _, _, dim_head = q.shape
    else:
@@ -470,8 +515,8 @@ def attention_pytorch(q, k, v, heads, mask=None, attn_precision=None, skip_resha
            ).transpose(1, 2).reshape(-1, q.shape[2], heads * dim_head)
    return out

-
-def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
+@wrap_attn
+def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
    if skip_reshape:
        b, _, _, dim_head = q.shape
        tensor_layout = "HND"
@@ -501,7 +546,7 @@ def attention_sage(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=
                lambda t: t.transpose(1, 2),
                (q, k, v),
            )
-        return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape)
+        return attention_pytorch(q, k, v, heads, mask=mask, skip_reshape=True, skip_output_reshape=skip_output_reshape, **kwargs)

    if tensor_layout == "HND":
        if not skip_output_reshape:
@@ -534,8 +579,8 @@ except AttributeError as error:
                    dropout_p: float = 0.0, causal: bool = False) -> torch.Tensor:
        assert False, f"Could not define flash_attn_wrapper: {FLASH_ATTN_ERROR}"

-
-def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False):
+@wrap_attn
+def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape=False, skip_output_reshape=False, **kwargs):
    if skip_reshape:
        b, _, _, dim_head = q.shape
    else:
@@ -555,7 +600,8 @@ def attention_flash(q, k, v, heads, mask=None, attn_precision=None, skip_reshape
            mask = mask.unsqueeze(1)

    try:
-        assert mask is None
+        if mask is not None:
+            raise RuntimeError("Mask must not be set for Flash attention")
        out = flash_attn_wrapper(
            q.transpose(1, 2),
            k.transpose(1, 2),
@@ -597,6 +643,19 @@ else:

 optimized_attention_masked = optimized_attention

+
+# register core-supported attention functions
+if SAGE_ATTENTION_IS_AVAILABLE:
+    register_attention_function("sage", attention_sage)
+if FLASH_ATTENTION_IS_AVAILABLE:
+    register_attention_function("flash", attention_flash)
+if model_management.xformers_enabled():
+    register_attention_function("xformers", attention_xformers)
+register_attention_function("pytorch", attention_pytorch)
+register_attention_function("sub_quad", attention_sub_quad)
+register_attention_function("split", attention_split)
+
+
 def optimized_attention_for_device(device, mask=False, small_input=False):
    if small_input:
        if model_management.pytorch_attention_enabled():
@@ -629,7 +688,7 @@ class CrossAttention(nn.Module):

        self.to_out = nn.Sequential(operations.Linear(inner_dim, query_dim, dtype=dtype, device=device), nn.Dropout(dropout))

-    def forward(self, x, context=None, value=None, mask=None):
+    def forward(self, x, context=None, value=None, mask=None, transformer_options={}):
        q = self.to_q(x)
        context = default(context, x)
        k = self.to_k(context)
@@ -640,9 +699,9 @@ class CrossAttention(nn.Module):
            v = self.to_v(context)

        if mask is None:
-            out = optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision)
+            out = optimized_attention(q, k, v, self.heads, attn_precision=self.attn_precision, transformer_options=transformer_options)
        else:
-            out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision)
+            out = optimized_attention_masked(q, k, v, self.heads, mask, attn_precision=self.attn_precision, transformer_options=transformer_options)
        return self.to_out(out)


@@ -746,7 +805,7 @@ class BasicTransformerBlock(nn.Module):
            n = attn1_replace_patch[block_attn1](n, context_attn1, value_attn1, extra_options)
            n = self.attn1.to_out(n)
        else:
-            n = self.attn1(n, context=context_attn1, value=value_attn1)
+            n = self.attn1(n, context=context_attn1, value=value_attn1, transformer_options=transformer_options)

        if "attn1_output_patch" in transformer_patches:
            patch = transformer_patches["attn1_output_patch"]
@@ -786,7 +845,7 @@ class BasicTransformerBlock(nn.Module):
                n = attn2_replace_patch[block_attn2](n, context_attn2, value_attn2, extra_options)
                n = self.attn2.to_out(n)
            else:
-                n = self.attn2(n, context=context_attn2, value=value_attn2)
+                n = self.attn2(n, context=context_attn2, value=value_attn2, transformer_options=transformer_options)

        if "attn2_output_patch" in transformer_patches:
            patch = transformer_patches["attn2_output_patch"]
@@ -1017,7 +1076,7 @@ class SpatialVideoTransformer(SpatialTransformer):

            B, S, C = x_mix.shape
            x_mix = rearrange(x_mix, "(b t) s c -> (b s) t c", t=timesteps)
-            x_mix = mix_block(x_mix, context=time_context) #TODO: transformer_options
+            x_mix = mix_block(x_mix, context=time_context, transformer_options=transformer_options)
            x_mix = rearrange(
                x_mix, "(b s) t c -> (b t) s c", s=S, b=B // timesteps, c=C, t=timesteps
            )
@@ -606,7 +606,7 @@ def block_mixing(*args, use_checkpoint=True, **kwargs):
        return _block_mixing(*args, **kwargs)


-def _block_mixing(context, x, context_block, x_block, c):
+def _block_mixing(context, x, context_block, x_block, c, transformer_options={}):
    context_qkv, context_intermediates = context_block.pre_attention(context, c)

    if x_block.x_block_self_attn:
@@ -622,6 +622,7 @@ def _block_mixing(context, x, context_block, x_block, c):
    attn = optimized_attention(
        qkv[0], qkv[1], qkv[2],
        heads=x_block.attn.num_heads,
+        transformer_options=transformer_options,
    )
    context_attn, x_attn = (
        attn[:, : context_qkv[0].shape[1]],
@@ -637,6 +638,7 @@ def _block_mixing(context, x, context_block, x_block, c):
        attn2 = optimized_attention(
                x_qkv2[0], x_qkv2[1], x_qkv2[2],
                heads=x_block.attn2.num_heads,
+                transformer_options=transformer_options,
            )
        x = x_block.post_attention_x(x_attn, attn2, *x_intermediates)
    else:
@@ -958,10 +960,10 @@ class MMDiT(nn.Module):
            if ("double_block", i) in blocks_replace:
                def block_wrap(args):
                    out = {}
-                    out["txt"], out["img"] = self.joint_blocks[i](args["txt"], args["img"], c=args["vec"])
+                    out["txt"], out["img"] = self.joint_blocks[i](args["txt"], args["img"], c=args["vec"], transformer_options=args["transformer_options"])
                    return out

-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": c_mod}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": c_mod, "transformer_options": transformer_options}, {"original_block": block_wrap})
                context = out["txt"]
                x = out["img"]
            else:
@@ -970,6 +972,7 @@ class MMDiT(nn.Module):
                    x,
                    c=c_mod,
                    use_checkpoint=self.use_checkpoint,
+                    transformer_options=transformer_options,
                )
            if control is not None:
                control_o = control.get("output")
@@ -145,7 +145,7 @@ class Downsample(nn.Module):

 class ResnetBlock(nn.Module):
    def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
-                 dropout=0.0, temb_channels=512, conv_op=ops.Conv2d):
+                 dropout=0.0, temb_channels=512, conv_op=ops.Conv2d, norm_op=Normalize):
        super().__init__()
        self.in_channels = in_channels
        out_channels = in_channels if out_channels is None else out_channels
@@ -153,7 +153,7 @@ class ResnetBlock(nn.Module):
        self.use_conv_shortcut = conv_shortcut

        self.swish = torch.nn.SiLU(inplace=True)
-        self.norm1 = Normalize(in_channels)
+        self.norm1 = norm_op(in_channels)
        self.conv1 = conv_op(in_channels,
                                     out_channels,
                                     kernel_size=3,
@@ -162,7 +162,7 @@ class ResnetBlock(nn.Module):
        if temb_channels > 0:
            self.temb_proj = ops.Linear(temb_channels,
                                             out_channels)
-        self.norm2 = Normalize(out_channels)
+        self.norm2 = norm_op(out_channels)
        self.dropout = torch.nn.Dropout(dropout, inplace=True)
        self.conv2 = conv_op(out_channels,
                                     out_channels,
@@ -305,11 +305,11 @@ def vae_attention():
        return normal_attention

 class AttnBlock(nn.Module):
-    def __init__(self, in_channels, conv_op=ops.Conv2d):
+    def __init__(self, in_channels, conv_op=ops.Conv2d, norm_op=Normalize):
        super().__init__()
        self.in_channels = in_channels

-        self.norm = Normalize(in_channels)
+        self.norm = norm_op(in_channels)
        self.q = conv_op(in_channels,
                                 in_channels,
                                 kernel_size=1,
@@ -120,7 +120,7 @@ class Attention(nn.Module):
            nn.Dropout(0.0)
        )

-    def forward(self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, image_rotary_emb: Optional[torch.Tensor] = None) -> torch.Tensor:
+    def forward(self, hidden_states: torch.Tensor, encoder_hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, image_rotary_emb: Optional[torch.Tensor] = None, transformer_options={}) -> torch.Tensor:
        batch_size, sequence_length, _ = hidden_states.shape

        query = self.to_q(hidden_states)
@@ -146,7 +146,7 @@ class Attention(nn.Module):
            key = key.repeat_interleave(self.heads // self.kv_heads, dim=1)
            value = value.repeat_interleave(self.heads // self.kv_heads, dim=1)

-        hidden_states = optimized_attention_masked(query, key, value, self.heads, attention_mask, skip_reshape=True)
+        hidden_states = optimized_attention_masked(query, key, value, self.heads, attention_mask, skip_reshape=True, transformer_options=transformer_options)
        hidden_states = self.to_out[0](hidden_states)
        return hidden_states

@@ -182,16 +182,16 @@ class OmniGen2TransformerBlock(nn.Module):
        self.norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)
        self.ffn_norm2 = operations.RMSNorm(dim, eps=norm_eps, dtype=dtype, device=device)

-    def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, image_rotary_emb: torch.Tensor, temb: Optional[torch.Tensor] = None) -> torch.Tensor:
+    def forward(self, hidden_states: torch.Tensor, attention_mask: torch.Tensor, image_rotary_emb: torch.Tensor, temb: Optional[torch.Tensor] = None, transformer_options={}) -> torch.Tensor:
        if self.modulation:
            norm_hidden_states, gate_msa, scale_mlp, gate_mlp = self.norm1(hidden_states, temb)
-            attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb)
+            attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb, transformer_options=transformer_options)
            hidden_states = hidden_states + gate_msa.unsqueeze(1).tanh() * self.norm2(attn_output)
            mlp_output = self.feed_forward(self.ffn_norm1(hidden_states) * (1 + scale_mlp.unsqueeze(1)))
            hidden_states = hidden_states + gate_mlp.unsqueeze(1).tanh() * self.ffn_norm2(mlp_output)
        else:
            norm_hidden_states = self.norm1(hidden_states)
-            attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb)
+            attn_output = self.attn(norm_hidden_states, norm_hidden_states, attention_mask, image_rotary_emb, transformer_options=transformer_options)
            hidden_states = hidden_states + self.norm2(attn_output)
            mlp_output = self.feed_forward(self.ffn_norm1(hidden_states))
            hidden_states = hidden_states + self.ffn_norm2(mlp_output)
@@ -390,7 +390,7 @@ class OmniGen2Transformer2DModel(nn.Module):
            ref_img_sizes, img_sizes,
        )

-    def img_patch_embed_and_refine(self, hidden_states, ref_image_hidden_states, padded_img_mask, padded_ref_img_mask, noise_rotary_emb, ref_img_rotary_emb, l_effective_ref_img_len, l_effective_img_len, temb):
+    def img_patch_embed_and_refine(self, hidden_states, ref_image_hidden_states, padded_img_mask, padded_ref_img_mask, noise_rotary_emb, ref_img_rotary_emb, l_effective_ref_img_len, l_effective_img_len, temb, transformer_options={}):
        batch_size = len(hidden_states)

        hidden_states = self.x_embedder(hidden_states)
@@ -405,17 +405,17 @@ class OmniGen2Transformer2DModel(nn.Module):
                    shift += ref_img_len

        for layer in self.noise_refiner:
-            hidden_states = layer(hidden_states, padded_img_mask, noise_rotary_emb, temb)
+            hidden_states = layer(hidden_states, padded_img_mask, noise_rotary_emb, temb, transformer_options=transformer_options)

        if ref_image_hidden_states is not None:
            for layer in self.ref_image_refiner:
-                ref_image_hidden_states = layer(ref_image_hidden_states, padded_ref_img_mask, ref_img_rotary_emb, temb)
+                ref_image_hidden_states = layer(ref_image_hidden_states, padded_ref_img_mask, ref_img_rotary_emb, temb, transformer_options=transformer_options)

            hidden_states = torch.cat([ref_image_hidden_states, hidden_states], dim=1)

        return hidden_states

-    def forward(self, x, timesteps, context, num_tokens, ref_latents=None, attention_mask=None, **kwargs):
+    def forward(self, x, timesteps, context, num_tokens, ref_latents=None, attention_mask=None, transformer_options={}, **kwargs):
        B, C, H, W = x.shape
        hidden_states = comfy.ldm.common_dit.pad_to_patch_size(x, (self.patch_size, self.patch_size))
        _, _, H_padded, W_padded = hidden_states.shape
@@ -444,7 +444,7 @@ class OmniGen2Transformer2DModel(nn.Module):
        )

        for layer in self.context_refiner:
-            text_hidden_states = layer(text_hidden_states, text_attention_mask, context_rotary_emb)
+            text_hidden_states = layer(text_hidden_states, text_attention_mask, context_rotary_emb, transformer_options=transformer_options)

        img_len = hidden_states.shape[1]
        combined_img_hidden_states = self.img_patch_embed_and_refine(
@@ -453,13 +453,14 @@ class OmniGen2Transformer2DModel(nn.Module):
            noise_rotary_emb, ref_img_rotary_emb,
            l_effective_ref_img_len, l_effective_img_len,
            temb,
+            transformer_options=transformer_options,
        )

        hidden_states = torch.cat([text_hidden_states, combined_img_hidden_states], dim=1)
        attention_mask = None

        for layer in self.layers:
-            hidden_states = layer(hidden_states, attention_mask, rotary_emb, temb)
+            hidden_states = layer(hidden_states, attention_mask, rotary_emb, temb, transformer_options=transformer_options)

        hidden_states = self.norm_out(hidden_states, temb)

@@ -132,6 +132,7 @@ class Attention(nn.Module):
        encoder_hidden_states_mask: torch.FloatTensor = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        image_rotary_emb: Optional[torch.Tensor] = None,
+        transformer_options={},
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        seq_txt = encoder_hidden_states.shape[1]

@@ -159,7 +160,7 @@ class Attention(nn.Module):
        joint_key = joint_key.flatten(start_dim=2)
        joint_value = joint_value.flatten(start_dim=2)

-        joint_hidden_states = optimized_attention_masked(joint_query, joint_key, joint_value, self.heads, attention_mask)
+        joint_hidden_states = optimized_attention_masked(joint_query, joint_key, joint_value, self.heads, attention_mask, transformer_options=transformer_options)

        txt_attn_output = joint_hidden_states[:, :seq_txt, :]
        img_attn_output = joint_hidden_states[:, seq_txt:, :]
@@ -226,6 +227,7 @@ class QwenImageTransformerBlock(nn.Module):
        encoder_hidden_states_mask: torch.Tensor,
        temb: torch.Tensor,
        image_rotary_emb: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+        transformer_options={},
    ) -> Tuple[torch.Tensor, torch.Tensor]:
        img_mod_params = self.img_mod(temb)
        txt_mod_params = self.txt_mod(temb)
@@ -242,6 +244,7 @@ class QwenImageTransformerBlock(nn.Module):
            encoder_hidden_states=txt_modulated,
            encoder_hidden_states_mask=encoder_hidden_states_mask,
            image_rotary_emb=image_rotary_emb,
+            transformer_options=transformer_options,
        )

        hidden_states = hidden_states + img_gate1 * img_attn_output
@@ -434,9 +437,9 @@ class QwenImageTransformer2DModel(nn.Module):
            if ("double_block", i) in blocks_replace:
                def block_wrap(args):
                    out = {}
-                    out["txt"], out["img"] = block(hidden_states=args["img"], encoder_hidden_states=args["txt"], encoder_hidden_states_mask=encoder_hidden_states_mask, temb=args["vec"], image_rotary_emb=args["pe"])
+                    out["txt"], out["img"] = block(hidden_states=args["img"], encoder_hidden_states=args["txt"], encoder_hidden_states_mask=encoder_hidden_states_mask, temb=args["vec"], image_rotary_emb=args["pe"], transformer_options=args["transformer_options"])
                    return out
-                out = blocks_replace[("double_block", i)]({"img": hidden_states, "txt": encoder_hidden_states, "vec": temb, "pe": image_rotary_emb}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": hidden_states, "txt": encoder_hidden_states, "vec": temb, "pe": image_rotary_emb, "transformer_options": transformer_options}, {"original_block": block_wrap})
                hidden_states = out["img"]
                encoder_hidden_states = out["txt"]
            else:
@@ -446,11 +449,12 @@ class QwenImageTransformer2DModel(nn.Module):
                    encoder_hidden_states_mask=encoder_hidden_states_mask,
                    temb=temb,
                    image_rotary_emb=image_rotary_emb,
+                    transformer_options=transformer_options,
                )

            if "double_block" in patches:
                for p in patches["double_block"]:
-                    out = p({"img": hidden_states, "txt": encoder_hidden_states, "x": x, "block_index": i})
+                    out = p({"img": hidden_states, "txt": encoder_hidden_states, "x": x, "block_index": i, "transformer_options": transformer_options})
                    hidden_states = out["img"]
                    encoder_hidden_states = out["txt"]

@@ -8,7 +8,7 @@ from einops import rearrange

 from comfy.ldm.modules.attention import optimized_attention
 from comfy.ldm.flux.layers import EmbedND
-from comfy.ldm.flux.math import apply_rope
+from comfy.ldm.flux.math import apply_rope1
 import comfy.ldm.common_dit
 import comfy.model_management
 import comfy.patcher_extension
@@ -34,7 +34,9 @@ class WanSelfAttention(nn.Module):
                 num_heads,
                 window_size=(-1, -1),
                 qk_norm=True,
-                 eps=1e-6, operation_settings={}):
+                 eps=1e-6,
+                 kv_dim=None,
+                 operation_settings={}):
        assert dim % num_heads == 0
        super().__init__()
        self.dim = dim
@@ -43,16 +45,18 @@ class WanSelfAttention(nn.Module):
        self.window_size = window_size
        self.qk_norm = qk_norm
        self.eps = eps
+        if kv_dim is None:
+            kv_dim = dim

        # layers
        self.q = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-        self.k = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
-        self.v = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.k = operation_settings.get("operations").Linear(kv_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+        self.v = operation_settings.get("operations").Linear(kv_dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
        self.o = operation_settings.get("operations").Linear(dim, dim, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
        self.norm_q = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()
        self.norm_k = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()

-    def forward(self, x, freqs):
+    def forward(self, x, freqs, transformer_options={}):
        r"""
        Args:
            x(Tensor): Shape [B, L, num_heads, C / num_heads]
@@ -60,21 +64,26 @@ class WanSelfAttention(nn.Module):
        """
        b, s, n, d = *x.shape[:2], self.num_heads, self.head_dim

-        # query, key, value function
-        def qkv_fn(x):
+        def qkv_fn_q(x):
            q = self.norm_q(self.q(x)).view(b, s, n, d)
-            k = self.norm_k(self.k(x)).view(b, s, n, d)
-            v = self.v(x).view(b, s, n * d)
-            return q, k, v
+            return apply_rope1(q, freqs)

-        q, k, v = qkv_fn(x)
-        q, k = apply_rope(q, k, freqs)
+        def qkv_fn_k(x):
+            k = self.norm_k(self.k(x)).view(b, s, n, d)
+            return apply_rope1(k, freqs)
+
+        #These two are VRAM hogs, so we want to do all of q computation and
+        #have pytorch garbage collect the intermediates on the sub function
+        #return before we touch k
+        q = qkv_fn_q(x)
+        k = qkv_fn_k(x)

        x = optimized_attention(
            q.view(b, s, n * d),
            k.view(b, s, n * d),
-            v,
+            self.v(x).view(b, s, n * d),
            heads=self.num_heads,
+            transformer_options=transformer_options,
        )

        x = self.o(x)
@@ -83,7 +92,7 @@ class WanSelfAttention(nn.Module):

 class WanT2VCrossAttention(WanSelfAttention):

-    def forward(self, x, context, **kwargs):
+    def forward(self, x, context, transformer_options={}, **kwargs):
        r"""
        Args:
            x(Tensor): Shape [B, L1, C]
@@ -95,7 +104,7 @@ class WanT2VCrossAttention(WanSelfAttention):
        v = self.v(context)

        # compute attention
-        x = optimized_attention(q, k, v, heads=self.num_heads)
+        x = optimized_attention(q, k, v, heads=self.num_heads, transformer_options=transformer_options)

        x = self.o(x)
        return x
@@ -116,7 +125,7 @@ class WanI2VCrossAttention(WanSelfAttention):
        # self.alpha = nn.Parameter(torch.zeros((1, )))
        self.norm_k_img = operation_settings.get("operations").RMSNorm(dim, eps=eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype")) if qk_norm else nn.Identity()

-    def forward(self, x, context, context_img_len):
+    def forward(self, x, context, context_img_len, transformer_options={}):
        r"""
        Args:
            x(Tensor): Shape [B, L1, C]
@@ -131,9 +140,9 @@ class WanI2VCrossAttention(WanSelfAttention):
        v = self.v(context)
        k_img = self.norm_k_img(self.k_img(context_img))
        v_img = self.v_img(context_img)
-        img_x = optimized_attention(q, k_img, v_img, heads=self.num_heads)
+        img_x = optimized_attention(q, k_img, v_img, heads=self.num_heads, transformer_options=transformer_options)
        # compute attention
-        x = optimized_attention(q, k, v, heads=self.num_heads)
+        x = optimized_attention(q, k, v, heads=self.num_heads, transformer_options=transformer_options)

        # output
        x = x + img_x
@@ -206,6 +215,7 @@ class WanAttentionBlock(nn.Module):
        freqs,
        context,
        context_img_len=257,
+        transformer_options={},
    ):
        r"""
        Args:
@@ -224,12 +234,13 @@ class WanAttentionBlock(nn.Module):
        # self-attention
        y = self.self_attn(
            torch.addcmul(repeat_e(e[0], x), self.norm1(x), 1 + repeat_e(e[1], x)),
-            freqs)
+            freqs, transformer_options=transformer_options)

        x = torch.addcmul(x, y, repeat_e(e[2], x))
+        del y

        # cross-attention & ffn
-        x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len)
+        x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len, transformer_options=transformer_options)
        y = self.ffn(torch.addcmul(repeat_e(e[3], x), self.norm2(x), 1 + repeat_e(e[4], x)))
        x = torch.addcmul(x, y, repeat_e(e[5], x))
        return x
@@ -396,6 +407,7 @@ class WanModel(torch.nn.Module):
                 eps=1e-6,
                 flf_pos_embed_token_number=None,
                 in_dim_ref_conv=None,
+                 wan_attn_block_class=WanAttentionBlock,
                 image_model=None,
                 device=None,
                 dtype=None,
@@ -473,8 +485,8 @@ class WanModel(torch.nn.Module):
        # blocks
        cross_attn_type = 't2v_cross_attn' if model_type == 't2v' else 'i2v_cross_attn'
        self.blocks = nn.ModuleList([
-            WanAttentionBlock(cross_attn_type, dim, ffn_dim, num_heads,
-                              window_size, qk_norm, cross_attn_norm, eps, operation_settings=operation_settings)
+            wan_attn_block_class(cross_attn_type, dim, ffn_dim, num_heads,
+                                 window_size, qk_norm, cross_attn_norm, eps, operation_settings=operation_settings)
            for _ in range(num_layers)
        ])

@@ -559,12 +571,12 @@ class WanModel(torch.nn.Module):
            if ("double_block", i) in blocks_replace:
                def block_wrap(args):
                    out = {}
-                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len)
+                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"])
                    return out
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})
                x = out["img"]
            else:
-                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len)
+                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)

        # head
        x = self.head(x, e)
@@ -742,17 +754,17 @@ class VaceWanModel(WanModel):
            if ("double_block", i) in blocks_replace:
                def block_wrap(args):
                    out = {}
-                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len)
+                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"])
                    return out
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})
                x = out["img"]
            else:
-                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len)
+                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)

            ii = self.vace_layers_mapping.get(i, None)
            if ii is not None:
                for iii in range(len(c)):
-                    c_skip, c[iii] = self.vace_blocks[ii](c[iii], x=x_orig, e=e0, freqs=freqs, context=context, context_img_len=context_img_len)
+                    c_skip, c[iii] = self.vace_blocks[ii](c[iii], x=x_orig, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)
                    x += c_skip * vace_strength[iii]
                del c_skip
        # head
@@ -841,12 +853,12 @@ class CameraWanModel(WanModel):
            if ("double_block", i) in blocks_replace:
                def block_wrap(args):
                    out = {}
-                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len)
+                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"])
                    return out
-                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs}, {"original_block": block_wrap})
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})
                x = out["img"]
            else:
-                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len)
+                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)

        # head
        x = self.head(x, e)
@@ -891,7 +903,7 @@ class MotionEncoder_tc(nn.Module):
    def __init__(self,
                 in_dim: int,
                 hidden_dim: int,
-                 num_heads=int,
+                 num_heads: int,
                 need_global=True,
                 dtype=None,
                 device=None,
@@ -1319,3 +1331,250 @@ class WanModel_S2V(WanModel):
        # unpatchify
        x = self.unpatchify(x, grid_sizes)
        return x
+
+
+class WanT2VCrossAttentionGather(WanSelfAttention):
+
+    def forward(self, x, context, transformer_options={}, **kwargs):
+        r"""
+        Args:
+            x(Tensor): Shape [B, L1, C] - video tokens
+            context(Tensor): Shape [B, L2, C] - audio tokens with shape [B, frames*16, 1536]
+        """
+        b, n, d = x.size(0), self.num_heads, self.head_dim
+
+        q = self.norm_q(self.q(x))
+        k = self.norm_k(self.k(context))
+        v = self.v(context)
+
+        # Handle audio temporal structure (16 tokens per frame)
+        k = k.reshape(-1, 16, n, d).transpose(1, 2)
+        v = v.reshape(-1, 16, n, d).transpose(1, 2)
+
+        # Handle video spatial structure
+        q = q.reshape(k.shape[0], -1, n, d).transpose(1, 2)
+
+        x = optimized_attention(q, k, v, heads=self.num_heads, skip_reshape=True, skip_output_reshape=True, transformer_options=transformer_options)
+
+        x = x.transpose(1, 2).reshape(b, -1, n * d)
+        x = self.o(x)
+        return x
+
+
+class AudioCrossAttentionWrapper(nn.Module):
+    def __init__(self, dim, kv_dim, num_heads, qk_norm=True, eps=1e-6, operation_settings={}):
+        super().__init__()
+
+        self.audio_cross_attn = WanT2VCrossAttentionGather(dim, num_heads, qk_norm=qk_norm, kv_dim=kv_dim, eps=eps, operation_settings=operation_settings)
+        self.norm1_audio = operation_settings.get("operations").LayerNorm(dim, eps, elementwise_affine=True, device=operation_settings.get("device"), dtype=operation_settings.get("dtype"))
+
+    def forward(self, x, audio, transformer_options={}):
+        x = x + self.audio_cross_attn(self.norm1_audio(x), audio, transformer_options=transformer_options)
+        return x
+
+
+class WanAttentionBlockAudio(WanAttentionBlock):
+
+    def __init__(self,
+                 cross_attn_type,
+                 dim,
+                 ffn_dim,
+                 num_heads,
+                 window_size=(-1, -1),
+                 qk_norm=True,
+                 cross_attn_norm=False,
+                 eps=1e-6, operation_settings={}):
+        super().__init__(cross_attn_type, dim, ffn_dim, num_heads, window_size, qk_norm, cross_attn_norm, eps, operation_settings)
+        self.audio_cross_attn_wrapper = AudioCrossAttentionWrapper(dim, 1536, num_heads, qk_norm, eps, operation_settings=operation_settings)
+
+    def forward(
+        self,
+        x,
+        e,
+        freqs,
+        context,
+        context_img_len=257,
+        audio=None,
+        transformer_options={},
+    ):
+        r"""
+        Args:
+            x(Tensor): Shape [B, L, C]
+            e(Tensor): Shape [B, 6, C]
+            freqs(Tensor): Rope freqs, shape [1024, C / num_heads / 2]
+        """
+        # assert e.dtype == torch.float32
+
+        if e.ndim < 4:
+            e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device) + e).chunk(6, dim=1)
+        else:
+            e = (comfy.model_management.cast_to(self.modulation, dtype=x.dtype, device=x.device).unsqueeze(0) + e).unbind(2)
+        # assert e[0].dtype == torch.float32
+
+        # self-attention
+        y = self.self_attn(
+            torch.addcmul(repeat_e(e[0], x), self.norm1(x), 1 + repeat_e(e[1], x)),
+            freqs, transformer_options=transformer_options)
+
+        x = torch.addcmul(x, y, repeat_e(e[2], x))
+
+        # cross-attention & ffn
+        x = x + self.cross_attn(self.norm3(x), context, context_img_len=context_img_len, transformer_options=transformer_options)
+        if audio is not None:
+            x = self.audio_cross_attn_wrapper(x, audio, transformer_options=transformer_options)
+        y = self.ffn(torch.addcmul(repeat_e(e[3], x), self.norm2(x), 1 + repeat_e(e[4], x)))
+        x = torch.addcmul(x, y, repeat_e(e[5], x))
+        return x
+
+class DummyAdapterLayer(nn.Module):
+    def __init__(self, layer):
+        super().__init__()
+        self.layer = layer
+
+    def forward(self, *args, **kwargs):
+        return self.layer(*args, **kwargs)
+
+
+class AudioProjModel(nn.Module):
+    def __init__(
+        self,
+        seq_len=5,
+        blocks=13,  # add a new parameter blocks
+        channels=768,  # add a new parameter channels
+        intermediate_dim=512,
+        output_dim=1536,
+        context_tokens=16,
+        device=None,
+        dtype=None,
+        operations=None,
+    ):
+        super().__init__()
+
+        self.seq_len = seq_len
+        self.blocks = blocks
+        self.channels = channels
+        self.input_dim = seq_len * blocks * channels  # update input_dim to be the product of blocks and channels.
+        self.intermediate_dim = intermediate_dim
+        self.context_tokens = context_tokens
+        self.output_dim = output_dim
+
+        # define multiple linear layers
+        self.audio_proj_glob_1 = DummyAdapterLayer(operations.Linear(self.input_dim, intermediate_dim, dtype=dtype, device=device))
+        self.audio_proj_glob_2 = DummyAdapterLayer(operations.Linear(intermediate_dim, intermediate_dim, dtype=dtype, device=device))
+        self.audio_proj_glob_3 = DummyAdapterLayer(operations.Linear(intermediate_dim, context_tokens * output_dim, dtype=dtype, device=device))
+
+        self.audio_proj_glob_norm = DummyAdapterLayer(operations.LayerNorm(output_dim, dtype=dtype, device=device))
+
+    def forward(self, audio_embeds):
+        video_length = audio_embeds.shape[1]
+        audio_embeds = rearrange(audio_embeds, "bz f w b c -> (bz f) w b c")
+        batch_size, window_size, blocks, channels = audio_embeds.shape
+        audio_embeds = audio_embeds.view(batch_size, window_size * blocks * channels)
+
+        audio_embeds = torch.relu(self.audio_proj_glob_1(audio_embeds))
+        audio_embeds = torch.relu(self.audio_proj_glob_2(audio_embeds))
+
+        context_tokens = self.audio_proj_glob_3(audio_embeds).reshape(batch_size, self.context_tokens, self.output_dim)
+
+        context_tokens = self.audio_proj_glob_norm(context_tokens)
+        context_tokens = rearrange(context_tokens, "(bz f) m c -> bz f m c", f=video_length)
+
+        return context_tokens
+
+
+class HumoWanModel(WanModel):
+    r"""
+    Wan diffusion backbone supporting both text-to-video and image-to-video.
+    """
+
+    def __init__(self,
+                 model_type='humo',
+                 patch_size=(1, 2, 2),
+                 text_len=512,
+                 in_dim=16,
+                 dim=2048,
+                 ffn_dim=8192,
+                 freq_dim=256,
+                 text_dim=4096,
+                 out_dim=16,
+                 num_heads=16,
+                 num_layers=32,
+                 window_size=(-1, -1),
+                 qk_norm=True,
+                 cross_attn_norm=True,
+                 eps=1e-6,
+                 flf_pos_embed_token_number=None,
+                 image_model=None,
+                 audio_token_num=16,
+                 device=None,
+                 dtype=None,
+                 operations=None,
+                 ):
+
+        super().__init__(model_type='t2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, wan_attn_block_class=WanAttentionBlockAudio, image_model=image_model, device=device, dtype=dtype, operations=operations)
+
+        self.audio_proj = AudioProjModel(seq_len=8, blocks=5, channels=1280, intermediate_dim=512, output_dim=1536, context_tokens=audio_token_num, dtype=dtype, device=device, operations=operations)
+
+    def forward_orig(
+        self,
+        x,
+        t,
+        context,
+        freqs=None,
+        audio_embed=None,
+        reference_latent=None,
+        transformer_options={},
+        **kwargs,
+    ):
+        bs, _, time, height, width = x.shape
+
+        # embeddings
+        x = self.patch_embedding(x.float()).to(x.dtype)
+        grid_sizes = x.shape[2:]
+        x = x.flatten(2).transpose(1, 2)
+
+        # time embeddings
+        e = self.time_embedding(
+            sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype))
+        e = e.reshape(t.shape[0], -1, e.shape[-1])
+        e0 = self.time_projection(e).unflatten(2, (6, self.dim))
+
+        if reference_latent is not None:
+            ref = self.patch_embedding(reference_latent.float()).to(x.dtype)
+            ref = ref.flatten(2).transpose(1, 2)
+            freqs_ref = self.rope_encode(reference_latent.shape[-3], reference_latent.shape[-2], reference_latent.shape[-1], t_start=time, device=x.device, dtype=x.dtype)
+            x = torch.cat([x, ref], dim=1)
+            freqs = torch.cat([freqs, freqs_ref], dim=1)
+            del ref, freqs_ref
+
+        # context
+        context = self.text_embedding(context)
+        context_img_len = None
+
+        if audio_embed is not None:
+            if reference_latent is not None:
+                zero_audio_pad = torch.zeros(audio_embed.shape[0], reference_latent.shape[-3], *audio_embed.shape[2:], device=audio_embed.device, dtype=audio_embed.dtype)
+                audio_embed = torch.cat([audio_embed, zero_audio_pad], dim=1)
+            audio = self.audio_proj(audio_embed).permute(0, 3, 1, 2).flatten(2).transpose(1, 2)
+        else:
+            audio = None
+
+        patches_replace = transformer_options.get("patches_replace", {})
+        blocks_replace = patches_replace.get("dit", {})
+        for i, block in enumerate(self.blocks):
+            if ("double_block", i) in blocks_replace:
+                def block_wrap(args):
+                    out = {}
+                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, audio=audio, transformer_options=args["transformer_options"])
+                    return out
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                x = out["img"]
+            else:
+                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, audio=audio, transformer_options=transformer_options)
+
+        # head
+        x = self.head(x, e)
+
+        # unpatchify
+        x = self.unpatchify(x, grid_sizes)
+        return x
@@ -0,0 +1,548 @@
+from torch import nn
+import torch
+from typing import Tuple, Optional
+from einops import rearrange
+import torch.nn.functional as F
+import math
+from .model import WanModel, sinusoidal_embedding_1d
+from comfy.ldm.modules.attention import optimized_attention
+import comfy.model_management
+
+class CausalConv1d(nn.Module):
+
+    def __init__(self, chan_in, chan_out, kernel_size=3, stride=1, dilation=1, pad_mode="replicate", operations=None, **kwargs):
+        super().__init__()
+
+        self.pad_mode = pad_mode
+        padding = (kernel_size - 1, 0)  # T
+        self.time_causal_padding = padding
+
+        self.conv = operations.Conv1d(chan_in, chan_out, kernel_size, stride=stride, dilation=dilation, **kwargs)
+
+    def forward(self, x):
+        x = F.pad(x, self.time_causal_padding, mode=self.pad_mode)
+        return self.conv(x)
+
+
+class FaceEncoder(nn.Module):
+    def __init__(self, in_dim: int, hidden_dim: int, num_heads=int, dtype=None, device=None, operations=None):
+        factory_kwargs = {"dtype": dtype, "device": device}
+        super().__init__()
+
+        self.num_heads = num_heads
+        self.conv1_local = CausalConv1d(in_dim, 1024 * num_heads, 3, stride=1, operations=operations, **factory_kwargs)
+        self.norm1 = operations.LayerNorm(hidden_dim // 8, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+        self.act = nn.SiLU()
+        self.conv2 = CausalConv1d(1024, 1024, 3, stride=2, operations=operations, **factory_kwargs)
+        self.conv3 = CausalConv1d(1024, 1024, 3, stride=2, operations=operations, **factory_kwargs)
+
+        self.out_proj = operations.Linear(1024, hidden_dim, **factory_kwargs)
+        self.norm1 = operations.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+
+        self.norm2 = operations.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+
+        self.norm3 = operations.LayerNorm(1024, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+
+        self.padding_tokens = nn.Parameter(torch.empty(1, 1, 1, hidden_dim, **factory_kwargs))
+
+    def forward(self, x):
+
+        x = rearrange(x, "b t c -> b c t")
+        b, c, t = x.shape
+
+        x = self.conv1_local(x)
+        x = rearrange(x, "b (n c) t -> (b n) t c", n=self.num_heads)
+
+        x = self.norm1(x)
+        x = self.act(x)
+        x = rearrange(x, "b t c -> b c t")
+        x = self.conv2(x)
+        x = rearrange(x, "b c t -> b t c")
+        x = self.norm2(x)
+        x = self.act(x)
+        x = rearrange(x, "b t c -> b c t")
+        x = self.conv3(x)
+        x = rearrange(x, "b c t -> b t c")
+        x = self.norm3(x)
+        x = self.act(x)
+        x = self.out_proj(x)
+        x = rearrange(x, "(b n) t c -> b t n c", b=b)
+        padding = comfy.model_management.cast_to(self.padding_tokens, dtype=x.dtype, device=x.device).repeat(b, x.shape[1], 1, 1)
+        x = torch.cat([x, padding], dim=-2)
+        x_local = x.clone()
+
+        return x_local
+
+
+def get_norm_layer(norm_layer, operations=None):
+    """
+    Get the normalization layer.
+
+    Args:
+        norm_layer (str): The type of normalization layer.
+
+    Returns:
+        norm_layer (nn.Module): The normalization layer.
+    """
+    if norm_layer == "layer":
+        return operations.LayerNorm
+    elif norm_layer == "rms":
+        return operations.RMSNorm
+    else:
+        raise NotImplementedError(f"Norm layer {norm_layer} is not implemented")
+
+
+class FaceAdapter(nn.Module):
+    def __init__(
+        self,
+        hidden_dim: int,
+        heads_num: int,
+        qk_norm: bool = True,
+        qk_norm_type: str = "rms",
+        num_adapter_layers: int = 1,
+        dtype=None, device=None, operations=None
+    ):
+
+        factory_kwargs = {"dtype": dtype, "device": device}
+        super().__init__()
+        self.hidden_size = hidden_dim
+        self.heads_num = heads_num
+        self.fuser_blocks = nn.ModuleList(
+            [
+                FaceBlock(
+                    self.hidden_size,
+                    self.heads_num,
+                    qk_norm=qk_norm,
+                    qk_norm_type=qk_norm_type,
+                    operations=operations,
+                    **factory_kwargs,
+                )
+                for _ in range(num_adapter_layers)
+            ]
+        )
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        motion_embed: torch.Tensor,
+        idx: int,
+        freqs_cis_q: Tuple[torch.Tensor, torch.Tensor] = None,
+        freqs_cis_k: Tuple[torch.Tensor, torch.Tensor] = None,
+    ) -> torch.Tensor:
+
+        return self.fuser_blocks[idx](x, motion_embed, freqs_cis_q, freqs_cis_k)
+
+
+
+class FaceBlock(nn.Module):
+    def __init__(
+        self,
+        hidden_size: int,
+        heads_num: int,
+        qk_norm: bool = True,
+        qk_norm_type: str = "rms",
+        qk_scale: float = None,
+        dtype: Optional[torch.dtype] = None,
+        device: Optional[torch.device] = None,
+        operations=None
+    ):
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super().__init__()
+
+        self.deterministic = False
+        self.hidden_size = hidden_size
+        self.heads_num = heads_num
+        head_dim = hidden_size // heads_num
+        self.scale = qk_scale or head_dim**-0.5
+
+        self.linear1_kv = operations.Linear(hidden_size, hidden_size * 2, **factory_kwargs)
+        self.linear1_q = operations.Linear(hidden_size, hidden_size, **factory_kwargs)
+
+        self.linear2 = operations.Linear(hidden_size, hidden_size, **factory_kwargs)
+
+        qk_norm_layer = get_norm_layer(qk_norm_type, operations=operations)
+        self.q_norm = (
+            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity()
+        )
+        self.k_norm = (
+            qk_norm_layer(head_dim, elementwise_affine=True, eps=1e-6, **factory_kwargs) if qk_norm else nn.Identity()
+        )
+
+        self.pre_norm_feat = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+
+        self.pre_norm_motion = operations.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6, **factory_kwargs)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        motion_vec: torch.Tensor,
+        motion_mask: Optional[torch.Tensor] = None,
+        # use_context_parallel=False,
+    ) -> torch.Tensor:
+
+        B, T, N, C = motion_vec.shape
+        T_comp = T
+
+        x_motion = self.pre_norm_motion(motion_vec)
+        x_feat = self.pre_norm_feat(x)
+
+        kv = self.linear1_kv(x_motion)
+        q = self.linear1_q(x_feat)
+
+        k, v = rearrange(kv, "B L N (K H D) -> K B L N H D", K=2, H=self.heads_num)
+        q = rearrange(q, "B S (H D) -> B S H D", H=self.heads_num)
+
+        # Apply QK-Norm if needed.
+        q = self.q_norm(q).to(v)
+        k = self.k_norm(k).to(v)
+
+        k = rearrange(k, "B L N H D -> (B L) N H D")
+        v = rearrange(v, "B L N H D -> (B L) N H D")
+
+        q = rearrange(q, "B (L S) H D -> (B L) S (H D)", L=T_comp)
+
+        attn = optimized_attention(q, k, v, heads=self.heads_num)
+
+        attn = rearrange(attn, "(B L) S C -> B (L S) C", L=T_comp)
+
+        output = self.linear2(attn)
+
+        if motion_mask is not None:
+            output = output * rearrange(motion_mask, "B T H W -> B (T H W)").unsqueeze(-1)
+
+        return output
+
+# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/ops/upfirdn2d/upfirdn2d.py#L162
+def upfirdn2d_native(input, kernel, up_x, up_y, down_x, down_y, pad_x0, pad_x1, pad_y0, pad_y1):
+    _, minor, in_h, in_w = input.shape
+    kernel_h, kernel_w = kernel.shape
+
+    out = input.view(-1, minor, in_h, 1, in_w, 1)
+    out = F.pad(out, [0, up_x - 1, 0, 0, 0, up_y - 1, 0, 0])
+    out = out.view(-1, minor, in_h * up_y, in_w * up_x)
+
+    out = F.pad(out, [max(pad_x0, 0), max(pad_x1, 0), max(pad_y0, 0), max(pad_y1, 0)])
+    out = out[:, :, max(-pad_y0, 0): out.shape[2] - max(-pad_y1, 0), max(-pad_x0, 0): out.shape[3] - max(-pad_x1, 0)]
+
+    out = out.reshape([-1, 1, in_h * up_y + pad_y0 + pad_y1, in_w * up_x + pad_x0 + pad_x1])
+    w = torch.flip(kernel, [0, 1]).view(1, 1, kernel_h, kernel_w)
+    out = F.conv2d(out, w)
+    out = out.reshape(-1, minor, in_h * up_y + pad_y0 + pad_y1 - kernel_h + 1, in_w * up_x + pad_x0 + pad_x1 - kernel_w + 1)
+    return out[:, :, ::down_y, ::down_x]
+
+def upfirdn2d(input, kernel, up=1, down=1, pad=(0, 0)):
+    return upfirdn2d_native(input, kernel, up, up, down, down, pad[0], pad[1], pad[0], pad[1])
+
+# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/ops/fused_act/fused_act.py#L81
+class FusedLeakyReLU(torch.nn.Module):
+    def __init__(self, channel, negative_slope=0.2, scale=2 ** 0.5, dtype=None, device=None):
+        super().__init__()
+        self.bias = torch.nn.Parameter(torch.empty(1, channel, 1, 1, dtype=dtype, device=device))
+        self.negative_slope = negative_slope
+        self.scale = scale
+
+    def forward(self, input):
+        return fused_leaky_relu(input, comfy.model_management.cast_to(self.bias, device=input.device, dtype=input.dtype), self.negative_slope, self.scale)
+
+def fused_leaky_relu(input, bias, negative_slope=0.2, scale=2 ** 0.5):
+    return F.leaky_relu(input + bias, negative_slope) * scale
+
+class Blur(torch.nn.Module):
+    def __init__(self, kernel, pad, dtype=None, device=None):
+        super().__init__()
+        kernel = torch.tensor(kernel, dtype=dtype, device=device)
+        kernel = kernel[None, :] * kernel[:, None]
+        kernel = kernel / kernel.sum()
+        self.register_buffer('kernel', kernel)
+        self.pad = pad
+
+    def forward(self, input):
+        return upfirdn2d(input, comfy.model_management.cast_to(self.kernel, dtype=input.dtype, device=input.device), pad=self.pad)
+
+#https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L590
+class ScaledLeakyReLU(torch.nn.Module):
+    def __init__(self, negative_slope=0.2):
+        super().__init__()
+        self.negative_slope = negative_slope
+
+    def forward(self, input):
+        return F.leaky_relu(input, negative_slope=self.negative_slope)
+
+# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L605
+class EqualConv2d(torch.nn.Module):
+    def __init__(self, in_channel, out_channel, kernel_size, stride=1, padding=0, bias=True, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.weight = torch.nn.Parameter(torch.empty(out_channel, in_channel, kernel_size, kernel_size, device=device, dtype=dtype))
+        self.scale = 1 / math.sqrt(in_channel * kernel_size ** 2)
+        self.stride = stride
+        self.padding = padding
+        self.bias = torch.nn.Parameter(torch.empty(out_channel, device=device, dtype=dtype)) if bias else None
+
+    def forward(self, input):
+        if self.bias is None:
+            bias = None
+        else:
+            bias = comfy.model_management.cast_to(self.bias, device=input.device, dtype=input.dtype)
+
+        return F.conv2d(input, comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) * self.scale, bias=bias, stride=self.stride, padding=self.padding)
+
+# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L134
+class EqualLinear(torch.nn.Module):
+    def __init__(self, in_dim, out_dim, bias=True, bias_init=0, lr_mul=1, activation=None, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.weight = torch.nn.Parameter(torch.empty(out_dim, in_dim, device=device, dtype=dtype))
+        self.bias = torch.nn.Parameter(torch.empty(out_dim, device=device, dtype=dtype)) if bias else None
+        self.activation = activation
+        self.scale = (1 / math.sqrt(in_dim)) * lr_mul
+        self.lr_mul = lr_mul
+
+    def forward(self, input):
+        if self.bias is None:
+            bias = None
+        else:
+            bias = comfy.model_management.cast_to(self.bias, device=input.device, dtype=input.dtype) * self.lr_mul
+
+        if self.activation:
+            out = F.linear(input, comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) * self.scale)
+            return fused_leaky_relu(out, bias)
+        return F.linear(input, comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) * self.scale, bias=bias)
+
+# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L654
+class ConvLayer(torch.nn.Sequential):
+    def __init__(self, in_channel, out_channel, kernel_size, downsample=False, blur_kernel=[1, 3, 3, 1], bias=True, activate=True, dtype=None, device=None, operations=None):
+        layers = []
+
+        if downsample:
+            factor = 2
+            p = (len(blur_kernel) - factor) + (kernel_size - 1)
+            layers.append(Blur(blur_kernel, pad=((p + 1) // 2, p // 2)))
+            stride, padding = 2, 0
+        else:
+            stride, padding = 1, kernel_size // 2
+
+        layers.append(EqualConv2d(in_channel, out_channel, kernel_size, padding=padding, stride=stride, bias=bias and not activate, dtype=dtype, device=device, operations=operations))
+
+        if activate:
+            layers.append(FusedLeakyReLU(out_channel) if bias else ScaledLeakyReLU(0.2))
+
+        super().__init__(*layers)
+
+# https://github.com/XPixelGroup/BasicSR/blob/8d56e3a045f9fb3e1d8872f92ee4a4f07f886b0a/basicsr/archs/stylegan2_arch.py#L704
+class ResBlock(torch.nn.Module):
+    def __init__(self, in_channel, out_channel, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.conv1 = ConvLayer(in_channel, in_channel, 3, dtype=dtype, device=device, operations=operations)
+        self.conv2 = ConvLayer(in_channel, out_channel, 3, downsample=True, dtype=dtype, device=device, operations=operations)
+        self.skip = ConvLayer(in_channel, out_channel, 1, downsample=True, activate=False, bias=False, dtype=dtype, device=device, operations=operations)
+
+    def forward(self, input):
+        out = self.conv2(self.conv1(input))
+        skip = self.skip(input)
+        return (out + skip) / math.sqrt(2)
+
+
+class EncoderApp(torch.nn.Module):
+    def __init__(self, w_dim=512, dtype=None, device=None, operations=None):
+        super().__init__()
+        kwargs = {"device": device, "dtype": dtype, "operations": operations}
+
+        self.convs = torch.nn.ModuleList([
+            ConvLayer(3, 32, 1, **kwargs), ResBlock(32, 64, **kwargs),
+            ResBlock(64, 128, **kwargs), ResBlock(128, 256, **kwargs),
+            ResBlock(256, 512, **kwargs), ResBlock(512, 512, **kwargs),
+            ResBlock(512, 512, **kwargs), ResBlock(512, 512, **kwargs),
+            EqualConv2d(512, w_dim, 4, padding=0, bias=False, **kwargs)
+        ])
+
+    def forward(self, x):
+        h = x
+        for conv in self.convs:
+            h = conv(h)
+        return h.squeeze(-1).squeeze(-1)
+
+class Encoder(torch.nn.Module):
+    def __init__(self, dim=512, motion_dim=20, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.net_app = EncoderApp(dim, dtype=dtype, device=device, operations=operations)
+        self.fc = torch.nn.Sequential(*[EqualLinear(dim, dim, dtype=dtype, device=device, operations=operations) for _ in range(4)] + [EqualLinear(dim, motion_dim, dtype=dtype, device=device, operations=operations)])
+
+    def encode_motion(self, x):
+        return self.fc(self.net_app(x))
+
+class Direction(torch.nn.Module):
+    def __init__(self, motion_dim, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.weight = torch.nn.Parameter(torch.empty(512, motion_dim, device=device, dtype=dtype))
+        self.motion_dim = motion_dim
+
+    def forward(self, input):
+        stabilized_weight = comfy.model_management.cast_to(self.weight, device=input.device, dtype=input.dtype) + 1e-8 * torch.eye(512, self.motion_dim, device=input.device, dtype=input.dtype)
+        Q, _ = torch.linalg.qr(stabilized_weight.float())
+        if input is None:
+            return Q
+        return torch.sum(input.unsqueeze(-1) * Q.T.to(input.dtype), dim=1)
+
+class Synthesis(torch.nn.Module):
+    def __init__(self, motion_dim, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.direction = Direction(motion_dim, dtype=dtype, device=device, operations=operations)
+
+class Generator(torch.nn.Module):
+    def __init__(self, style_dim=512, motion_dim=20, dtype=None, device=None, operations=None):
+        super().__init__()
+        self.enc = Encoder(style_dim, motion_dim, dtype=dtype, device=device, operations=operations)
+        self.dec = Synthesis(motion_dim, dtype=dtype, device=device, operations=operations)
+
+    def get_motion(self, img):
+        motion_feat = self.enc.encode_motion(img)
+        return self.dec.direction(motion_feat)
+
+class AnimateWanModel(WanModel):
+    r"""
+    Wan diffusion backbone supporting both text-to-video and image-to-video.
+    """
+
+    def __init__(self,
+                 model_type='animate',
+                 patch_size=(1, 2, 2),
+                 text_len=512,
+                 in_dim=16,
+                 dim=2048,
+                 ffn_dim=8192,
+                 freq_dim=256,
+                 text_dim=4096,
+                 out_dim=16,
+                 num_heads=16,
+                 num_layers=32,
+                 window_size=(-1, -1),
+                 qk_norm=True,
+                 cross_attn_norm=True,
+                 eps=1e-6,
+                 flf_pos_embed_token_number=None,
+                 motion_encoder_dim=512,
+                 image_model=None,
+                 device=None,
+                 dtype=None,
+                 operations=None,
+                 ):
+
+        super().__init__(model_type='i2v', patch_size=patch_size, text_len=text_len, in_dim=in_dim, dim=dim, ffn_dim=ffn_dim, freq_dim=freq_dim, text_dim=text_dim, out_dim=out_dim, num_heads=num_heads, num_layers=num_layers, window_size=window_size, qk_norm=qk_norm, cross_attn_norm=cross_attn_norm, eps=eps, flf_pos_embed_token_number=flf_pos_embed_token_number, image_model=image_model, device=device, dtype=dtype, operations=operations)
+
+        self.pose_patch_embedding = operations.Conv3d(
+            16, dim, kernel_size=patch_size, stride=patch_size, device=device, dtype=dtype
+        )
+
+        self.motion_encoder = Generator(style_dim=512, motion_dim=20, device=device, dtype=dtype, operations=operations)
+
+        self.face_adapter = FaceAdapter(
+            heads_num=self.num_heads,
+            hidden_dim=self.dim,
+            num_adapter_layers=self.num_layers // 5,
+            device=device, dtype=dtype, operations=operations
+        )
+
+        self.face_encoder = FaceEncoder(
+            in_dim=motion_encoder_dim,
+            hidden_dim=self.dim,
+            num_heads=4,
+            device=device, dtype=dtype, operations=operations
+        )
+
+    def after_patch_embedding(self, x, pose_latents, face_pixel_values):
+        if pose_latents is not None:
+            pose_latents = self.pose_patch_embedding(pose_latents)
+            x[:, :, 1:pose_latents.shape[2] + 1] += pose_latents[:, :, :x.shape[2] - 1]
+
+        if face_pixel_values is None:
+            return x, None
+
+        b, c, T, h, w = face_pixel_values.shape
+        face_pixel_values = rearrange(face_pixel_values, "b c t h w -> (b t) c h w")
+        encode_bs = 8
+        face_pixel_values_tmp = []
+        for i in range(math.ceil(face_pixel_values.shape[0] / encode_bs)):
+            face_pixel_values_tmp.append(self.motion_encoder.get_motion(face_pixel_values[i * encode_bs: (i + 1) * encode_bs]))
+
+        motion_vec = torch.cat(face_pixel_values_tmp)
+
+        motion_vec = rearrange(motion_vec, "(b t) c -> b t c", t=T)
+        motion_vec = self.face_encoder(motion_vec)
+
+        B, L, H, C = motion_vec.shape
+        pad_face = torch.zeros(B, 1, H, C).type_as(motion_vec)
+        motion_vec = torch.cat([pad_face, motion_vec], dim=1)
+
+        if motion_vec.shape[1] < x.shape[2]:
+            B, L, H, C = motion_vec.shape
+            pad = torch.zeros(B, x.shape[2] - motion_vec.shape[1], H, C).type_as(motion_vec)
+            motion_vec = torch.cat([motion_vec, pad], dim=1)
+        else:
+            motion_vec = motion_vec[:, :x.shape[2]]
+        return x, motion_vec
+
+    def forward_orig(
+        self,
+        x,
+        t,
+        context,
+        clip_fea=None,
+        pose_latents=None,
+        face_pixel_values=None,
+        freqs=None,
+        transformer_options={},
+        **kwargs,
+    ):
+        # embeddings
+        x = self.patch_embedding(x.float()).to(x.dtype)
+        x, motion_vec = self.after_patch_embedding(x, pose_latents, face_pixel_values)
+        grid_sizes = x.shape[2:]
+        x = x.flatten(2).transpose(1, 2)
+
+        # time embeddings
+        e = self.time_embedding(
+            sinusoidal_embedding_1d(self.freq_dim, t.flatten()).to(dtype=x[0].dtype))
+        e = e.reshape(t.shape[0], -1, e.shape[-1])
+        e0 = self.time_projection(e).unflatten(2, (6, self.dim))
+
+        full_ref = None
+        if self.ref_conv is not None:
+            full_ref = kwargs.get("reference_latent", None)
+            if full_ref is not None:
+                full_ref = self.ref_conv(full_ref).flatten(2).transpose(1, 2)
+                x = torch.concat((full_ref, x), dim=1)
+
+        # context
+        context = self.text_embedding(context)
+
+        context_img_len = None
+        if clip_fea is not None:
+            if self.img_emb is not None:
+                context_clip = self.img_emb(clip_fea)  # bs x 257 x dim
+                context = torch.concat([context_clip, context], dim=1)
+            context_img_len = clip_fea.shape[-2]
+
+        patches_replace = transformer_options.get("patches_replace", {})
+        blocks_replace = patches_replace.get("dit", {})
+        for i, block in enumerate(self.blocks):
+            if ("double_block", i) in blocks_replace:
+                def block_wrap(args):
+                    out = {}
+                    out["img"] = block(args["img"], context=args["txt"], e=args["vec"], freqs=args["pe"], context_img_len=context_img_len, transformer_options=args["transformer_options"])
+                    return out
+                out = blocks_replace[("double_block", i)]({"img": x, "txt": context, "vec": e0, "pe": freqs, "transformer_options": transformer_options}, {"original_block": block_wrap})
+                x = out["img"]
+            else:
+                x = block(x, e=e0, freqs=freqs, context=context, context_img_len=context_img_len, transformer_options=transformer_options)
+
+            if i % 5 == 0 and motion_vec is not None:
+                x = x + self.face_adapter.fuser_blocks[i // 5](x, motion_vec)
+
+        # head
+        x = self.head(x, e)
+
+        if full_ref is not None:
+            x = x[:, full_ref.shape[1]:]
+
+        # unpatchify
+        x = self.unpatchify(x, grid_sizes)
+        return x
@@ -468,55 +468,46 @@ class WanVAE(nn.Module):
                                 attn_scales, self.temperal_upsample, dropout)

    def encode(self, x):
-        self.clear_cache()
+        conv_idx = [0]
+        feat_map = [None] * count_conv3d(self.decoder)
        ## cache
        t = x.shape[2]
        iter_ = 1 + (t - 1) // 4
        ## 对encode输入的x，按时间拆分为1、4、4、4....
        for i in range(iter_):
-            self._enc_conv_idx = [0]
+            conv_idx = [0]
            if i == 0:
                out = self.encoder(
                    x[:, :, :1, :, :],
-                    feat_cache=self._enc_feat_map,
-                    feat_idx=self._enc_conv_idx)
+                    feat_cache=feat_map,
+                    feat_idx=conv_idx)
            else:
                out_ = self.encoder(
                    x[:, :, 1 + 4 * (i - 1):1 + 4 * i, :, :],
-                    feat_cache=self._enc_feat_map,
-                    feat_idx=self._enc_conv_idx)
+                    feat_cache=feat_map,
+                    feat_idx=conv_idx)
                out = torch.cat([out, out_], 2)
        mu, log_var = self.conv1(out).chunk(2, dim=1)
-        self.clear_cache()
        return mu

    def decode(self, z):
-        self.clear_cache()
+        conv_idx = [0]
+        feat_map = [None] * count_conv3d(self.decoder)
        # z: [b,c,t,h,w]

        iter_ = z.shape[2]
        x = self.conv2(z)
        for i in range(iter_):
-            self._conv_idx = [0]
+            conv_idx = [0]
            if i == 0:
                out = self.decoder(
                    x[:, :, i:i + 1, :, :],
-                    feat_cache=self._feat_map,
-                    feat_idx=self._conv_idx)
+                    feat_cache=feat_map,
+                    feat_idx=conv_idx)
            else:
                out_ = self.decoder(
                    x[:, :, i:i + 1, :, :],
-                    feat_cache=self._feat_map,
-                    feat_idx=self._conv_idx)
+                    feat_cache=feat_map,
+                    feat_idx=conv_idx)
                out = torch.cat([out, out_], 2)
-        self.clear_cache()
        return out
-
-    def clear_cache(self):
-        self._conv_num = count_conv3d(self.decoder)
-        self._conv_idx = [0]
-        self._feat_map = [None] * self._conv_num
-        #cache encode
-        self._enc_conv_num = count_conv3d(self.encoder)
-        self._enc_conv_idx = [0]
-        self._enc_feat_map = [None] * self._enc_conv_num
@@ -297,6 +297,12 @@ def model_lora_keys_unet(model, key_map={}):
                key_lora = k[len("diffusion_model."):-len(".weight")]
                key_map["{}".format(key_lora)] = k

+    if isinstance(model, comfy.model_base.Omnigen2):
+        for k in sdk:
+            if k.startswith("diffusion_model.") and k.endswith(".weight"):
+                key_lora = k[len("diffusion_model."):-len(".weight")]
+                key_map["{}".format(key_lora)] = k
+
    if isinstance(model, comfy.model_base.QwenImage):
        for k in sdk:
            if k.startswith("diffusion_model.") and k.endswith(".weight"): #QwenImage lora format
@@ -39,9 +39,11 @@ import comfy.ldm.cosmos.model
 import comfy.ldm.cosmos.predict2
 import comfy.ldm.lumina.model
 import comfy.ldm.wan.model
+import comfy.ldm.wan.model_animate
 import comfy.ldm.hunyuan3d.model
 import comfy.ldm.hidream.model
 import comfy.ldm.chroma.model
+import comfy.ldm.chroma_radiance.model
 import comfy.ldm.ace.model
 import comfy.ldm.omnigen.omnigen2
 import comfy.ldm.qwen_image.model
@@ -1212,6 +1214,63 @@ class WAN21_Camera(WAN21):
            out['camera_conditions'] = comfy.conds.CONDRegular(camera_conditions)
        return out

+class WAN21_HuMo(WAN21):
+    def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
+        super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.HumoWanModel)
+        self.image_to_video = image_to_video
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        noise = kwargs.get("noise", None)
+
+        audio_embed = kwargs.get("audio_embed", None)
+        if audio_embed is not None:
+            out['audio_embed'] = comfy.conds.CONDRegular(audio_embed)
+
+        if "c_concat" not in out:  # 1.7B model
+            reference_latents = kwargs.get("reference_latents", None)
+            if reference_latents is not None:
+                out['reference_latent'] = comfy.conds.CONDRegular(self.process_latent_in(reference_latents[-1]))
+        else:
+            noise_shape = list(noise.shape)
+            noise_shape[1] += 4
+            concat_latent = torch.zeros(noise_shape, device=noise.device, dtype=noise.dtype)
+            zero_vae_values_first = torch.tensor([0.8660, -0.4326, -0.0017, -0.4884, -0.5283, 0.9207, -0.9896, 0.4433, -0.5543, -0.0113, 0.5753, -0.6000, -0.8346, -0.3497, -0.1926, -0.6938]).view(1, 16, 1, 1, 1)
+            zero_vae_values_second = torch.tensor([1.0869, -1.2370, 0.0206, -0.4357, -0.6411, 2.0307, -1.5972, 1.2659, -0.8595, -0.4654, 0.9638, -1.6330, -1.4310, -0.1098, -0.3856, -1.4583]).view(1, 16, 1, 1, 1)
+            zero_vae_values = torch.tensor([0.8642, -1.8583, 0.1577, 0.1350, -0.3641, 2.5863, -1.9670, 1.6065, -1.0475, -0.8678, 1.1734, -1.8138, -1.5933, -0.7721, -0.3289, -1.3745]).view(1, 16, 1, 1, 1)
+            concat_latent[:, 4:] = zero_vae_values
+            concat_latent[:, 4:, :1] = zero_vae_values_first
+            concat_latent[:, 4:, 1:2] = zero_vae_values_second
+            out['c_concat'] = comfy.conds.CONDNoiseShape(concat_latent)
+            reference_latents = kwargs.get("reference_latents", None)
+            if reference_latents is not None:
+                ref_latent = self.process_latent_in(reference_latents[-1])
+                ref_latent_shape = list(ref_latent.shape)
+                ref_latent_shape[1] += 4 + ref_latent_shape[1]
+                ref_latent_full = torch.zeros(ref_latent_shape, device=ref_latent.device, dtype=ref_latent.dtype)
+                ref_latent_full[:, 20:] = ref_latent
+                ref_latent_full[:, 16:20] = 1.0
+                out['reference_latent'] = comfy.conds.CONDRegular(ref_latent_full)
+
+        return out
+
+class WAN22_Animate(WAN21):
+    def __init__(self, model_config, model_type=ModelType.FLOW, image_to_video=False, device=None):
+        super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model_animate.AnimateWanModel)
+        self.image_to_video = image_to_video
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+
+        face_video_pixels = kwargs.get("face_video_pixels", None)
+        if face_video_pixels is not None:
+            out['face_pixel_values'] = comfy.conds.CONDRegular(face_video_pixels)
+
+        pose_latents = kwargs.get("pose_video_latent", None)
+        if pose_latents is not None:
+            out['pose_latents'] = comfy.conds.CONDRegular(self.process_latent_in(pose_latents))
+        return out
+
 class WAN22_S2V(WAN21):
    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
        super(WAN21, self).__init__(model_config, model_type, device=device, unet_model=comfy.ldm.wan.model.WanModel_S2V)
@@ -1320,8 +1379,8 @@ class HiDream(BaseModel):
        return out

 class Chroma(Flux):
-    def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
-        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.chroma.model.Chroma)
+    def __init__(self, model_config, model_type=ModelType.FLUX, device=None, unet_model=comfy.ldm.chroma.model.Chroma):
+        super().__init__(model_config, model_type, device=device, unet_model=unet_model)

    def extra_conds(self, **kwargs):
        out = super().extra_conds(**kwargs)
@@ -1331,6 +1390,10 @@ class Chroma(Flux):
            out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))
        return out

+class ChromaRadiance(Chroma):
+    def __init__(self, model_config, model_type=ModelType.FLUX, device=None):
+        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.chroma_radiance.model.ChromaRadiance)
+
 class ACEStep(BaseModel):
    def __init__(self, model_config, model_type=ModelType.FLOW, device=None):
        super().__init__(model_config, model_type, device=device, unet_model=comfy.ldm.ace.model.ACEStepTransformer2DModel)
@@ -1432,3 +1495,31 @@ class HunyuanImage21(BaseModel):
            out['guidance'] = comfy.conds.CONDRegular(torch.FloatTensor([guidance]))

        return out
+
+class HunyuanImage21Refiner(HunyuanImage21):
+    def concat_cond(self, **kwargs):
+        noise = kwargs.get("noise", None)
+        image = kwargs.get("concat_latent_image", None)
+        noise_augmentation = kwargs.get("noise_augmentation", 0.0)
+        device = kwargs["device"]
+
+        if image is None:
+            shape_image = list(noise.shape)
+            image = torch.zeros(shape_image, dtype=noise.dtype, layout=noise.layout, device=noise.device)
+        else:
+            image = utils.common_upscale(image.to(device), noise.shape[-1], noise.shape[-2], "bilinear", "center")
+            image = self.process_latent_in(image)
+            image = utils.resize_to_batch_size(image, noise.shape[0])
+            if noise_augmentation > 0:
+                generator = torch.Generator(device="cpu")
+                generator.manual_seed(kwargs.get("seed", 0) - 10)
+                noise = torch.randn(image.shape, generator=generator, dtype=image.dtype, device="cpu").to(image.device)
+                image = noise_augmentation * noise + min(1.0 - noise_augmentation, 0.75) * image
+            else:
+                image = 0.75 * image
+        return image
+
+    def extra_conds(self, **kwargs):
+        out = super().extra_conds(**kwargs)
+        out['disable_time_r'] = comfy.conds.CONDConstant(True)
+        return out
@@ -142,12 +142,20 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
        dit_config["in_channels"] = in_w.shape[1] #SkyReels img2video has 32 input channels
        dit_config["patch_size"] = list(in_w.shape[2:])
        dit_config["out_channels"] = out_w.shape[0] // math.prod(dit_config["patch_size"])
-        if '{}vector_in.in_layer.weight'.format(key_prefix) in state_dict:
+        if any(s.startswith('{}vector_in.'.format(key_prefix)) for s in state_dict_keys):
            dit_config["vec_in_dim"] = 768
-            dit_config["axes_dim"] = [16, 56, 56]
        else:
            dit_config["vec_in_dim"] = None
+
+        if len(dit_config["patch_size"]) == 2:
            dit_config["axes_dim"] = [64, 64]
+        else:
+            dit_config["axes_dim"] = [16, 56, 56]
+
+        if any(s.startswith('{}time_r_in.'.format(key_prefix)) for s in state_dict_keys):
+            dit_config["meanflow"] = True
+        else:
+            dit_config["meanflow"] = False

        dit_config["context_in_dim"] = state_dict['{}txt_in.input_embedder.weight'.format(key_prefix)].shape[1]
        dit_config["hidden_size"] = in_w.shape[0]
@@ -166,7 +174,7 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
        dit_config["guidance_embed"] = len(guidance_keys) > 0
        return dit_config

-    if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys and '{}img_in.weight'.format(key_prefix) in state_dict_keys: #Flux
+    if '{}double_blocks.0.img_attn.norm.key_norm.scale'.format(key_prefix) in state_dict_keys and ('{}img_in.weight'.format(key_prefix) in state_dict_keys or f"{key_prefix}distilled_guidance_layer.norms.0.scale" in state_dict_keys): #Flux, Chroma or Chroma Radiance (has no img_in.weight)
        dit_config = {}
        dit_config["image_model"] = "flux"
        dit_config["in_channels"] = 16
@@ -196,6 +204,18 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
            dit_config["out_dim"] = 3072
            dit_config["hidden_dim"] = 5120
            dit_config["n_layers"] = 5
+            if f"{key_prefix}nerf_blocks.0.norm.scale" in state_dict_keys: #Chroma Radiance
+                dit_config["image_model"] = "chroma_radiance"
+                dit_config["in_channels"] = 3
+                dit_config["out_channels"] = 3
+                dit_config["patch_size"] = 16
+                dit_config["nerf_hidden_size"] = 64
+                dit_config["nerf_mlp_ratio"] = 4
+                dit_config["nerf_depth"] = 4
+                dit_config["nerf_max_freqs"] = 8
+                dit_config["nerf_tile_size"] = 32
+                dit_config["nerf_final_head_type"] = "conv" if f"{key_prefix}nerf_final_layer_conv.norm.scale" in state_dict_keys else "linear"
+                dit_config["nerf_embedder_dtype"] = torch.float32
        else:
            dit_config["guidance_embed"] = "{}guidance_in.in_layer.weight".format(key_prefix) in state_dict_keys
        return dit_config
@@ -382,6 +402,10 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
                dit_config["model_type"] = "camera_2.2"
        elif '{}casual_audio_encoder.encoder.final_linear.weight'.format(key_prefix) in state_dict_keys:
            dit_config["model_type"] = "s2v"
+        elif '{}audio_proj.audio_proj_glob_1.layer.bias'.format(key_prefix) in state_dict_keys:
+            dit_config["model_type"] = "humo"
+        elif '{}face_adapter.fuser_blocks.0.k_norm.weight'.format(key_prefix) in state_dict_keys:
+            dit_config["model_type"] = "animate"
        else:
            if '{}img_emb.proj.0.bias'.format(key_prefix) in state_dict_keys:
                dit_config["model_type"] = "i2v"
@@ -348,7 +348,7 @@ try:
 #                    if any((a in arch) for a in ["gfx1201"]):
 #                        ENABLE_PYTORCH_ATTENTION = True
        if torch_version_numeric >= (2, 7) and rocm_version >= (6, 4):
-            if any((a in arch) for a in ["gfx1201", "gfx942", "gfx950"]):  # TODO: more arches
+            if any((a in arch) for a in ["gfx1200", "gfx1201", "gfx942", "gfx950"]):  # TODO: more arches
                SUPPORT_FP8_OPS = True

 except:
@@ -645,7 +645,9 @@ def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimu
            if loaded_model.model.is_clone(current_loaded_models[i].model):
                to_unload = [i] + to_unload
        for i in to_unload:
-            current_loaded_models.pop(i).model.detach(unpatch_all=False)
+            model_to_unload = current_loaded_models.pop(i)
+            model_to_unload.model.detach(unpatch_all=False)
+            model_to_unload.model_finalizer.detach()

    total_memory_required = {}
    for loaded_model in models_to_load:
@@ -365,12 +365,13 @@ class fp8_ops(manual_cast):
            return None

        def forward_comfy_cast_weights(self, input):
-            try:
-                out = fp8_linear(self, input)
-                if out is not None:
-                    return out
-            except Exception as e:
-                logging.info("Exception during fp8 op: {}".format(e))
+            if not self.training:
+                try:
+                    out = fp8_linear(self, input)
+                    if out is not None:
+                        return out
+                except Exception as e:
+                    logging.info("Exception during fp8 op: {}".format(e))

            weight, bias = cast_bias_weight(self, input)
            return torch.nn.functional.linear(input, weight, bias)
@@ -0,0 +1,16 @@
+import torch
+
+
+# "Fake" VAE that converts from IMAGE B, H, W, C and values on the scale of 0..1
+# to LATENT B, C, H, W and values on the scale of -1..1.
+class PixelspaceConversionVAE(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.pixel_space_vae = torch.nn.Parameter(torch.tensor(1.0))
+
+    def encode(self, pixels: torch.Tensor, *_args, **_kwargs) -> torch.Tensor:
+        return pixels
+
+    def decode(self, samples: torch.Tensor, *_args, **_kwargs) -> torch.Tensor:
+        return samples
+
@@ -360,7 +360,7 @@ def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options):
 def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_options={}, cond=None, uncond=None):
    if "sampler_cfg_function" in model_options:
        args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep,
-                "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options}
+                "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options, "input_cond": cond, "input_uncond": uncond}
        cfg_result = x - model_options["sampler_cfg_function"](args)
    else:
        cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale
@@ -390,7 +390,7 @@ def sampling_function(model, x, timestep, uncond, cond, cond_scale, model_option
    for fn in model_options.get("sampler_pre_cfg_function", []):
        args = {"conds":conds, "conds_out": out, "cond_scale": cond_scale, "timestep": timestep,
                "input": x, "sigma": timestep, "model": model, "model_options": model_options}
-        out  = fn(args)
+        out = fn(args)

    return cfg_function(model, out[0], out[1], cond_scale, x, timestep, model_options=model_options, cond=cond, uncond=uncond_)

@@ -18,6 +18,7 @@ import comfy.ldm.wan.vae2_2
 import comfy.ldm.hunyuan3d.vae
 import comfy.ldm.ace.vae.music_dcae_pipeline
 import comfy.ldm.hunyuan_video.vae
+import comfy.pixel_space_convert
 import yaml
 import math
 import os
@@ -285,6 +286,7 @@ 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.disable_offload = False
+        self.not_video = False

        self.downscale_index_formula = None
        self.upscale_index_formula = None
@@ -330,35 +332,51 @@ class VAE:
                self.first_stage_model = StageC_coder()
                self.downscale_ratio = 32
                self.latent_channels = 16
-            elif "decoder.conv_in.weight" in sd and sd['decoder.conv_in.weight'].shape[1] == 64:
-                ddconfig = {"block_out_channels": [128, 256, 512, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 32, "downsample_match_channel": True, "upsample_match_channel": True}
-                self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1]
-                self.downscale_ratio = 32
-                self.upscale_ratio = 32
-                self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
-                self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
-                                                            encoder_config={'target': "comfy.ldm.hunyuan_video.vae.Encoder", 'params': ddconfig},
-                                                            decoder_config={'target': "comfy.ldm.hunyuan_video.vae.Decoder", 'params': ddconfig})
-
-                self.memory_used_encode = lambda shape, dtype: (700 * shape[2] * shape[3]) * model_management.dtype_size(dtype)
-                self.memory_used_decode = lambda shape, dtype: (700 * shape[2] * shape[3] * 32 * 32) * model_management.dtype_size(dtype)
-
            elif "decoder.conv_in.weight" in sd:
-                #default SD1.x/SD2.x VAE parameters
-                ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}
-
-                if 'encoder.down.2.downsample.conv.weight' not in sd and 'decoder.up.3.upsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE
-                    ddconfig['ch_mult'] = [1, 2, 4]
-                    self.downscale_ratio = 4
-                    self.upscale_ratio = 4
-
-                self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1]
-                if 'post_quant_conv.weight' in sd:
-                    self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1])
-                else:
+                if sd['decoder.conv_in.weight'].shape[1] == 64:
+                    ddconfig = {"block_out_channels": [128, 256, 512, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 32, "downsample_match_channel": True, "upsample_match_channel": True}
+                    self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1]
+                    self.downscale_ratio = 32
+                    self.upscale_ratio = 32
+                    self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
                    self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
-                                                                encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig},
-                                                                decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig})
+                                                                encoder_config={'target': "comfy.ldm.hunyuan_video.vae.Encoder", 'params': ddconfig},
+                                                                decoder_config={'target': "comfy.ldm.hunyuan_video.vae.Decoder", 'params': ddconfig})
+
+                    self.memory_used_encode = lambda shape, dtype: (700 * shape[2] * shape[3]) * model_management.dtype_size(dtype)
+                    self.memory_used_decode = lambda shape, dtype: (700 * shape[2] * shape[3] * 32 * 32) * model_management.dtype_size(dtype)
+                elif sd['decoder.conv_in.weight'].shape[1] == 32:
+                    ddconfig = {"block_out_channels": [128, 256, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 16, "ffactor_temporal": 4, "downsample_match_channel": True, "upsample_match_channel": True, "refiner_vae": False}
+                    self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1]
+                    self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+                    self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 16, 16)
+                    self.upscale_index_formula = (4, 16, 16)
+                    self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 16, 16)
+                    self.downscale_index_formula = (4, 16, 16)
+                    self.latent_dim = 3
+                    self.not_video = True
+                    self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
+                                                                encoder_config={'target': "comfy.ldm.hunyuan_video.vae_refiner.Encoder", 'params': ddconfig},
+                                                                decoder_config={'target': "comfy.ldm.hunyuan_video.vae_refiner.Decoder", 'params': ddconfig})
+
+                    self.memory_used_encode = lambda shape, dtype: (2800 * shape[-2] * shape[-1]) * model_management.dtype_size(dtype)
+                    self.memory_used_decode = lambda shape, dtype: (2800 * shape[-3] * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype)
+                else:
+                    #default SD1.x/SD2.x VAE parameters
+                    ddconfig = {'double_z': True, 'z_channels': 4, 'resolution': 256, 'in_channels': 3, 'out_ch': 3, 'ch': 128, 'ch_mult': [1, 2, 4, 4], 'num_res_blocks': 2, 'attn_resolutions': [], 'dropout': 0.0}
+
+                    if 'encoder.down.2.downsample.conv.weight' not in sd and 'decoder.up.3.upsample.conv.weight' not in sd: #Stable diffusion x4 upscaler VAE
+                        ddconfig['ch_mult'] = [1, 2, 4]
+                        self.downscale_ratio = 4
+                        self.upscale_ratio = 4
+
+                    self.latent_channels = ddconfig['z_channels'] = sd["decoder.conv_in.weight"].shape[1]
+                    if 'post_quant_conv.weight' in sd:
+                        self.first_stage_model = AutoencoderKL(ddconfig=ddconfig, embed_dim=sd['post_quant_conv.weight'].shape[1])
+                    else:
+                        self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.DiagonalGaussianRegularizer"},
+                                                                    encoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Encoder", 'params': ddconfig},
+                                                                    decoder_config={'target': "comfy.ldm.modules.diffusionmodules.model.Decoder", 'params': ddconfig})
            elif "decoder.layers.1.layers.0.beta" in sd:
                self.first_stage_model = AudioOobleckVAE()
                self.memory_used_encode = lambda shape, dtype: (1000 * shape[2]) * model_management.dtype_size(dtype)
@@ -409,6 +427,23 @@ class VAE:
                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 and sd['decoder.conv_in.conv.weight'].shape[1] == 32:
+                ddconfig = {"block_out_channels": [128, 256, 512, 1024, 1024], "in_channels": 3, "out_channels": 3, "num_res_blocks": 2, "ffactor_spatial": 16, "ffactor_temporal": 4, "downsample_match_channel": True, "upsample_match_channel": True}
+                ddconfig['z_channels'] = sd["decoder.conv_in.conv.weight"].shape[1]
+                self.latent_channels = 64
+                self.upscale_ratio = (lambda a: max(0, a * 4 - 3), 16, 16)
+                self.upscale_index_formula = (4, 16, 16)
+                self.downscale_ratio = (lambda a: max(0, math.floor((a + 3) / 4)), 16, 16)
+                self.downscale_index_formula = (4, 16, 16)
+                self.latent_dim = 3
+                self.not_video = True
+                self.working_dtypes = [torch.float16, torch.bfloat16, torch.float32]
+                self.first_stage_model = AutoencodingEngine(regularizer_config={'target': "comfy.ldm.models.autoencoder.EmptyRegularizer"},
+                                                            encoder_config={'target': "comfy.ldm.hunyuan_video.vae_refiner.Encoder", 'params': ddconfig},
+                                                            decoder_config={'target': "comfy.ldm.hunyuan_video.vae_refiner.Decoder", 'params': ddconfig})
+
+                self.memory_used_encode = lambda shape, dtype: (1400 * shape[-2] * shape[-1]) * model_management.dtype_size(dtype)
+                self.memory_used_decode = lambda shape, dtype: (1400 * shape[-3] * shape[-2] * shape[-1] * 16 * 16) * model_management.dtype_size(dtype)
            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
@@ -498,6 +533,15 @@ class VAE:
                self.working_dtypes = [torch.bfloat16, torch.float16, torch.float32]
                self.disable_offload = True
                self.extra_1d_channel = 16
+            elif "pixel_space_vae" in sd:
+                self.first_stage_model = comfy.pixel_space_convert.PixelspaceConversionVAE()
+                self.memory_used_encode = lambda shape, dtype: (1 * shape[2] * shape[3]) * model_management.dtype_size(dtype)
+                self.memory_used_decode = lambda shape, dtype: (1 * shape[2] * shape[3]) * model_management.dtype_size(dtype)
+                self.downscale_ratio = 1
+                self.upscale_ratio = 1
+                self.latent_channels = 3
+                self.latent_dim = 2
+                self.output_channels = 3
            else:
                logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
                self.first_stage_model = None
@@ -608,6 +652,7 @@ class VAE:
    def decode(self, samples_in, vae_options={}):
        self.throw_exception_if_invalid()
        pixel_samples = None
+        do_tile = False
        try:
            memory_used = self.memory_used_decode(samples_in.shape, self.vae_dtype)
            model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload)
@@ -623,6 +668,13 @@ class VAE:
                pixel_samples[x:x+batch_number] = out
        except model_management.OOM_EXCEPTION:
            logging.warning("Warning: Ran out of memory when regular VAE decoding, retrying with tiled VAE decoding.")
+            #NOTE: We don't know what tensors were allocated to stack variables at the time of the
+            #exception and the exception itself refs them all until we get out of this except block.
+            #So we just set a flag for tiler fallback so that tensor gc can happen once the
+            #exception is fully off the books.
+            do_tile = True
+
+        if do_tile:
            dims = samples_in.ndim - 2
            if dims == 1 or self.extra_1d_channel is not None:
                pixel_samples = self.decode_tiled_1d(samples_in)
@@ -669,8 +721,12 @@ class VAE:
        self.throw_exception_if_invalid()
        pixel_samples = self.vae_encode_crop_pixels(pixel_samples)
        pixel_samples = pixel_samples.movedim(-1, 1)
+        do_tile = False
        if self.latent_dim == 3 and pixel_samples.ndim < 5:
-            pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
+            if not self.not_video:
+                pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
+            else:
+                pixel_samples = pixel_samples.unsqueeze(2)
        try:
            memory_used = self.memory_used_encode(pixel_samples.shape, self.vae_dtype)
            model_management.load_models_gpu([self.patcher], memory_required=memory_used, force_full_load=self.disable_offload)
@@ -687,6 +743,13 @@ class VAE:

        except model_management.OOM_EXCEPTION:
            logging.warning("Warning: Ran out of memory when regular VAE encoding, retrying with tiled VAE encoding.")
+            #NOTE: We don't know what tensors were allocated to stack variables at the time of the
+            #exception and the exception itself refs them all until we get out of this except block.
+            #So we just set a flag for tiler fallback so that tensor gc can happen once the
+            #exception is fully off the books.
+            do_tile = True
+
+        if do_tile:
            if self.latent_dim == 3:
                tile = 256
                overlap = tile // 4
@@ -704,7 +767,10 @@ class VAE:
        dims = self.latent_dim
        pixel_samples = pixel_samples.movedim(-1, 1)
        if dims == 3:
-            pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
+            if not self.not_video:
+                pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
+            else:
+                pixel_samples = pixel_samples.unsqueeze(2)

        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, force_full_load=self.disable_offload)
@@ -761,6 +827,7 @@ class VAE:
        except:
            return None

+
 class StyleModel:
    def __init__(self, model, device="cpu"):
        self.model = model
@@ -995,7 +995,7 @@ class WAN21_T2V(supported_models_base.BASE):
    unet_extra_config = {}
    latent_format = latent_formats.Wan21

-    memory_usage_factor = 1.0
+    memory_usage_factor = 0.9

    supported_inference_dtypes = [torch.float16, torch.bfloat16, torch.float32]

@@ -1004,7 +1004,7 @@ class WAN21_T2V(supported_models_base.BASE):

    def __init__(self, unet_config):
        super().__init__(unet_config)
-        self.memory_usage_factor = self.unet_config.get("dim", 2000) / 2000
+        self.memory_usage_factor = self.unet_config.get("dim", 2000) / 2222

    def get_model(self, state_dict, prefix="", device=None):
        out = model_base.WAN21(self, device=device)
@@ -1073,6 +1073,16 @@ class WAN21_Vace(WAN21_T2V):
        out = model_base.WAN21_Vace(self, image_to_video=False, device=device)
        return out

+class WAN21_HuMo(WAN21_T2V):
+    unet_config = {
+        "image_model": "wan2.1",
+        "model_type": "humo",
+    }
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.WAN21_HuMo(self, image_to_video=False, device=device)
+        return out
+
 class WAN22_S2V(WAN21_T2V):
    unet_config = {
        "image_model": "wan2.1",
@@ -1086,6 +1096,19 @@ class WAN22_S2V(WAN21_T2V):
        out = model_base.WAN22_S2V(self, device=device)
        return out

+class WAN22_Animate(WAN21_T2V):
+    unet_config = {
+        "image_model": "wan2.1",
+        "model_type": "animate",
+    }
+
+    def __init__(self, unet_config):
+        super().__init__(unet_config)
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.WAN22_Animate(self, device=device)
+        return out
+
 class WAN22_T2V(WAN21_T2V):
    unet_config = {
        "image_model": "wan2.1",
@@ -1205,6 +1228,19 @@ class Chroma(supported_models_base.BASE):
        t5_detect = comfy.text_encoders.sd3_clip.t5_xxl_detect(state_dict, "{}t5xxl.transformer.".format(pref))
        return supported_models_base.ClipTarget(comfy.text_encoders.pixart_t5.PixArtTokenizer, comfy.text_encoders.pixart_t5.pixart_te(**t5_detect))

+class ChromaRadiance(Chroma):
+    unet_config = {
+        "image_model": "chroma_radiance",
+    }
+
+    latent_format = comfy.latent_formats.ChromaRadiance
+
+    # Pixel-space model, no spatial compression for model input.
+    memory_usage_factor = 0.038
+
+    def get_model(self, state_dict, prefix="", device=None):
+        return model_base.ChromaRadiance(self, device=device)
+
 class ACEStep(supported_models_base.BASE):
    unet_config = {
        "audio_model": "ace",
@@ -1321,6 +1357,23 @@ class HunyuanImage21(HunyuanVideo):
        hunyuan_detect = comfy.text_encoders.hunyuan_video.llama_detect(state_dict, "{}qwen25_7b.transformer.".format(pref))
        return supported_models_base.ClipTarget(comfy.text_encoders.hunyuan_image.HunyuanImageTokenizer, comfy.text_encoders.hunyuan_image.te(**hunyuan_detect))

-models = [LotusD, 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, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ACEStep, Omnigen2, QwenImage]
+class HunyuanImage21Refiner(HunyuanVideo):
+    unet_config = {
+        "image_model": "hunyuan_video",
+        "patch_size": [1, 1, 1],
+        "vec_in_dim": None,
+    }
+
+    sampling_settings = {
+        "shift": 4.0,
+    }
+
+    latent_format = latent_formats.HunyuanImage21Refiner
+
+    def get_model(self, state_dict, prefix="", device=None):
+        out = model_base.HunyuanImage21Refiner(self, device=device)
+        return out
+
+models = [LotusD, 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, HunyuanImage21Refiner, HunyuanImage21, HunyuanVideoSkyreelsI2V, HunyuanVideoI2V, HunyuanVideo, CosmosT2V, CosmosI2V, CosmosT2IPredict2, CosmosI2VPredict2, Lumina2, WAN22_T2V, WAN21_T2V, WAN21_I2V, WAN21_FunControl2V, WAN21_Vace, WAN21_Camera, WAN22_Camera, WAN22_S2V, WAN21_HuMo, WAN22_Animate, Hunyuan3Dv2mini, Hunyuan3Dv2, Hunyuan3Dv2_1, HiDream, Chroma, ChromaRadiance, ACEStep, Omnigen2, QwenImage]

 models += [SVD_img2vid]
@@ -22,17 +22,14 @@ class HunyuanImageTokenizer(QwenImageTokenizer):

        # ByT5 processing for HunyuanImage
        text_prompt_texts = []
-        pattern_quote_single = r'\'(.*?)\''
        pattern_quote_double = r'\"(.*?)\"'
        pattern_quote_chinese_single = r'‘(.*?)’'
        pattern_quote_chinese_double = r'“(.*?)”'

-        matches_quote_single = re.findall(pattern_quote_single, text)
        matches_quote_double = re.findall(pattern_quote_double, text)
        matches_quote_chinese_single = re.findall(pattern_quote_chinese_single, text)
        matches_quote_chinese_double = re.findall(pattern_quote_chinese_double, text)

-        text_prompt_texts.extend(matches_quote_single)
        text_prompt_texts.extend(matches_quote_double)
        text_prompt_texts.extend(matches_quote_chinese_single)
        text_prompt_texts.extend(matches_quote_chinese_double)
@@ -66,7 +63,13 @@ class HunyuanImageTEModel(QwenImageTEModel):
            self.byt5_small = None

    def encode_token_weights(self, token_weight_pairs):
-        cond, p, extra = super().encode_token_weights(token_weight_pairs)
+        tok_pairs = token_weight_pairs["qwen25_7b"][0]
+        template_end = -1
+        if tok_pairs[0][0] == 27:
+            if len(tok_pairs) > 36:  # refiner prompt uses a fixed 36 template_end
+                template_end = 36
+
+        cond, p, extra = super().encode_token_weights(token_weight_pairs, template_end=template_end)
        if self.byt5_small is not None and "byt5" in token_weight_pairs:
            out = self.byt5_small.encode_token_weights(token_weight_pairs["byt5"])
            extra["conditioning_byt5small"] = out[0]
@@ -400,21 +400,25 @@ class Qwen25_7BVLI(BaseLlama, torch.nn.Module):

    def forward(self, x, attention_mask=None, embeds=None, num_tokens=None, intermediate_output=None, final_layer_norm_intermediate=True, dtype=None, embeds_info=[]):
        grid = None
+        position_ids = None
+        offset = 0
        for e in embeds_info:
            if e.get("type") == "image":
                grid = e.get("extra", None)
-                position_ids = torch.zeros((3, embeds.shape[1]), device=embeds.device)
                start = e.get("index")
-                position_ids[:, :start] = torch.arange(0, start, device=embeds.device)
+                if position_ids is None:
+                    position_ids = torch.zeros((3, embeds.shape[1]), device=embeds.device)
+                    position_ids[:, :start] = torch.arange(0, start, device=embeds.device)
                end = e.get("size") + start
                len_max = int(grid.max()) // 2
                start_next = len_max + start
-                position_ids[:, end:] = torch.arange(start_next, start_next + (embeds.shape[1] - end), device=embeds.device)
-                position_ids[0, start:end] = start
+                position_ids[:, end:] = torch.arange(start_next + offset, start_next + (embeds.shape[1] - end) + offset, device=embeds.device)
+                position_ids[0, start:end] = start + offset
                max_d = int(grid[0][1]) // 2
-                position_ids[1, start:end] = torch.arange(start, start + max_d, device=embeds.device).unsqueeze(1).repeat(1, math.ceil((end - start) / max_d)).flatten(0)[:end - start]
+                position_ids[1, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(1).repeat(1, math.ceil((end - start) / max_d)).flatten(0)[:end - start]
                max_d = int(grid[0][2]) // 2
-                position_ids[2, start:end] = torch.arange(start, start + max_d, device=embeds.device).unsqueeze(0).repeat(math.ceil((end - start) / max_d), 1).flatten(0)[:end - start]
+                position_ids[2, start:end] = torch.arange(start + offset, start + max_d + offset, device=embeds.device).unsqueeze(0).repeat(math.ceil((end - start) / max_d), 1).flatten(0)[:end - start]
+                offset += len_max - (end - start)

        if grid is None:
            position_ids = None
@@ -18,13 +18,22 @@ class QwenImageTokenizer(sd1_clip.SD1Tokenizer):
        self.llama_template_images = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n<|vision_start|><|image_pad|><|vision_end|>{}<|im_end|>\n<|im_start|>assistant\n"

    def tokenize_with_weights(self, text, return_word_ids=False, llama_template=None, images=[], **kwargs):
-        if llama_template is None:
-            if len(images) > 0:
-                llama_text = self.llama_template_images.format(text)
-            else:
-                llama_text = self.llama_template.format(text)
+        skip_template = False
+        if text.startswith('<|im_start|>'):
+            skip_template = True
+        if text.startswith('<|start_header_id|>'):
+            skip_template = True
+
+        if skip_template:
+            llama_text = text
        else:
-            llama_text = llama_template.format(text)
+            if llama_template is None:
+                if len(images) > 0:
+                    llama_text = self.llama_template_images.format(text)
+                else:
+                    llama_text = self.llama_template.format(text)
+            else:
+                llama_text = llama_template.format(text)
        tokens = super().tokenize_with_weights(llama_text, return_word_ids=return_word_ids, disable_weights=True, **kwargs)
        key_name = next(iter(tokens))
        embed_count = 0
@@ -47,22 +56,23 @@ class QwenImageTEModel(sd1_clip.SD1ClipModel):
    def __init__(self, device="cpu", dtype=None, model_options={}):
        super().__init__(device=device, dtype=dtype, name="qwen25_7b", clip_model=Qwen25_7BVLIModel, model_options=model_options)

-    def encode_token_weights(self, token_weight_pairs):
+    def encode_token_weights(self, token_weight_pairs, template_end=-1):
        out, pooled, extra = super().encode_token_weights(token_weight_pairs)
        tok_pairs = token_weight_pairs["qwen25_7b"][0]
        count_im_start = 0
-        for i, v in enumerate(tok_pairs):
-            elem = v[0]
-            if not torch.is_tensor(elem):
-                if isinstance(elem, numbers.Integral):
-                    if elem == 151644 and count_im_start < 2:
-                        template_end = i
-                        count_im_start += 1
+        if template_end == -1:
+            for i, v in enumerate(tok_pairs):
+                elem = v[0]
+                if not torch.is_tensor(elem):
+                    if isinstance(elem, numbers.Integral):
+                        if elem == 151644 and count_im_start < 2:
+                            template_end = i
+                            count_im_start += 1

-        if out.shape[1] > (template_end + 3):
-            if tok_pairs[template_end + 1][0] == 872:
-                if tok_pairs[template_end + 2][0] == 198:
-                    template_end += 3
+            if out.shape[1] > (template_end + 3):
+                if tok_pairs[template_end + 1][0] == 872:
+                    if tok_pairs[template_end + 2][0] == 198:
+                        template_end += 3

        out = out[:, template_end:]

@@ -130,12 +130,12 @@ class LoHaAdapter(WeightAdapterBase):
    def create_train(cls, weight, rank=1, alpha=1.0):
        out_dim = weight.shape[0]
        in_dim = weight.shape[1:].numel()
-        mat1 = torch.empty(out_dim, rank, device=weight.device, dtype=weight.dtype)
-        mat2 = torch.empty(rank, in_dim, device=weight.device, dtype=weight.dtype)
+        mat1 = torch.empty(out_dim, rank, device=weight.device, dtype=torch.float32)
+        mat2 = torch.empty(rank, in_dim, device=weight.device, dtype=torch.float32)
        torch.nn.init.normal_(mat1, 0.1)
        torch.nn.init.constant_(mat2, 0.0)
-        mat3 = torch.empty(out_dim, rank, device=weight.device, dtype=weight.dtype)
-        mat4 = torch.empty(rank, in_dim, device=weight.device, dtype=weight.dtype)
+        mat3 = torch.empty(out_dim, rank, device=weight.device, dtype=torch.float32)
+        mat4 = torch.empty(rank, in_dim, device=weight.device, dtype=torch.float32)
        torch.nn.init.normal_(mat3, 0.1)
        torch.nn.init.normal_(mat4, 0.01)
        return LohaDiff(
@@ -89,8 +89,8 @@ class LoKrAdapter(WeightAdapterBase):
        in_dim = weight.shape[1:].numel()
        out1, out2 = factorization(out_dim, rank)
        in1, in2 = factorization(in_dim, rank)
-        mat1 = torch.empty(out1, in1, device=weight.device, dtype=weight.dtype)
-        mat2 = torch.empty(out2, in2, device=weight.device, dtype=weight.dtype)
+        mat1 = torch.empty(out1, in1, device=weight.device, dtype=torch.float32)
+        mat2 = torch.empty(out2, in2, device=weight.device, dtype=torch.float32)
        torch.nn.init.kaiming_uniform_(mat2, a=5**0.5)
        torch.nn.init.constant_(mat1, 0.0)
        return LokrDiff(
@@ -66,8 +66,8 @@ class LoRAAdapter(WeightAdapterBase):
    def create_train(cls, weight, rank=1, alpha=1.0):
        out_dim = weight.shape[0]
        in_dim = weight.shape[1:].numel()
-        mat1 = torch.empty(out_dim, rank, device=weight.device, dtype=weight.dtype)
-        mat2 = torch.empty(rank, in_dim, device=weight.device, dtype=weight.dtype)
+        mat1 = torch.empty(out_dim, rank, device=weight.device, dtype=torch.float32)
+        mat2 = torch.empty(rank, in_dim, device=weight.device, dtype=torch.float32)
        torch.nn.init.kaiming_uniform_(mat1, a=5**0.5)
        torch.nn.init.constant_(mat2, 0.0)
        return LoraDiff(
@@ -68,7 +68,7 @@ class OFTAdapter(WeightAdapterBase):
    def create_train(cls, weight, rank=1, alpha=1.0):
        out_dim = weight.shape[0]
        block_size, block_num = factorization(out_dim, rank)
-        block = torch.zeros(block_num, block_size, block_size, device=weight.device, dtype=weight.dtype)
+        block = torch.zeros(block_num, block_size, block_size, device=weight.device, dtype=torch.float32)
        return OFTDiff(
            (block, None, alpha, None)
        )
@@ -331,7 +331,7 @@ class String(ComfyTypeIO):
            })

@comfytype(io_type="COMBO")
-class Combo(ComfyTypeI):
+class Combo(ComfyTypeIO):
    Type = str
    class Input(WidgetInput):
        """Combo input (dropdown)."""
@@ -360,6 +360,14 @@ class Combo(ComfyTypeI):
                "remote": self.remote.as_dict() if self.remote else None,
            })

+    class Output(Output):
+        def __init__(self, id: str=None, display_name: str=None, options: list[str]=None, tooltip: str=None, is_output_list=False):
+            super().__init__(id, display_name, tooltip, is_output_list)
+            self.options = options if options is not None else []
+
+        @property
+        def io_type(self):
+            return self.options

@comfytype(io_type="COMBO")
 class MultiCombo(ComfyTypeI):
@@ -1597,6 +1605,7 @@ class _IO:
    Model = Model
    ClipVision = ClipVision
    ClipVisionOutput = ClipVisionOutput
+    AudioEncoder = AudioEncoder
    AudioEncoderOutput = AudioEncoderOutput
    StyleModel = StyleModel
    Gligen = Gligen
@@ -2,6 +2,7 @@
 #   filename:  filtered-openapi.yaml
 #   timestamp: 2025-07-30T08:54:00+00:00

+# pylint: disable
 from __future__ import annotations

 from datetime import date, datetime
@@ -1320,6 +1321,7 @@ class KlingTextToVideoModelName(str, Enum):
    kling_v1 = 'kling-v1'
    kling_v1_6 = 'kling-v1-6'
    kling_v2_1_master = 'kling-v2-1-master'
+    kling_v2_5_turbo = 'kling-v2-5-turbo'


 class KlingVideoGenAspectRatio(str, Enum):
@@ -1354,6 +1356,7 @@ class KlingVideoGenModelName(str, Enum):
    kling_v2_master = 'kling-v2-master'
    kling_v2_1 = 'kling-v2-1'
    kling_v2_1_master = 'kling-v2-1-master'
+    kling_v2_5_turbo = 'kling-v2-5-turbo'


 class KlingVideoResult(BaseModel):
@@ -95,6 +95,7 @@ import aiohttp
 import asyncio
 import logging
 import io
+import os
 import socket
 from aiohttp.client_exceptions import ClientError, ClientResponseError
 from typing import Dict, Type, Optional, Any, TypeVar, Generic, Callable, Tuple
@@ -499,7 +500,9 @@ class ApiClient:
        else:
            raise ValueError("File must be BytesIO or str path")

-        operation_id = f"upload_{upload_url.split('/')[-1]}_{uuid.uuid4().hex[:8]}"
+        parsed = urlparse(upload_url)
+        basename = os.path.basename(parsed.path) or parsed.netloc or "upload"
+        operation_id = f"upload_{basename}_{uuid.uuid4().hex[:8]}"
        request_logger.log_request_response(
            operation_id=operation_id,
            request_method="PUT",
@@ -532,7 +535,7 @@ class ApiClient:
                    request_method="PUT",
                    request_url=upload_url,
                    response_status_code=e.status if hasattr(e, "status") else None,
-                    response_headers=dict(e.headers) if getattr(e, "headers") else None,
+                    response_headers=dict(e.headers) if hasattr(e, "headers") else None,
                    response_content=None,
                    error_message=f"{type(e).__name__}: {str(e)}",
                )
@@ -683,7 +686,7 @@ class SynchronousOperation(Generic[T, R]):
        auth_token: Optional[str] = None,
        comfy_api_key: Optional[str] = None,
        auth_kwargs: Optional[Dict[str, str]] = None,
-        timeout: float = 604800.0,
+        timeout: float = 7200.0,
        verify_ssl: bool = True,
        content_type: str = "application/json",
        multipart_parser: Callable | None = None,
@@ -4,16 +4,18 @@ import os
 import datetime
 import json
 import logging
+import re
+import hashlib
+from typing import Any
+
 import folder_paths

 # Get the logger instance
 logger = logging.getLogger(__name__)

+
 def get_log_directory():
-    """
-    Ensures the API log directory exists within ComfyUI's temp directory
-    and returns its path.
-    """
+    """Ensures the API log directory exists within ComfyUI's temp directory and returns its path."""
    base_temp_dir = folder_paths.get_temp_directory()
    log_dir = os.path.join(base_temp_dir, "api_logs")
    try:
@@ -24,42 +26,77 @@ def get_log_directory():
        return base_temp_dir
    return log_dir

-def _format_data_for_logging(data):
+
+def _sanitize_filename_component(name: str) -> str:
+    if not name:
+        return "log"
+    sanitized = re.sub(r"[^A-Za-z0-9._-]+", "_", name)  # Replace disallowed characters with underscore
+    sanitized = sanitized.strip(" ._")  # Windows: trailing dots or spaces are not allowed
+    if not sanitized:
+        sanitized = "log"
+    return sanitized
+
+
+def _short_hash(*parts: str, length: int = 10) -> str:
+    return hashlib.sha1(("|".join(parts)).encode("utf-8")).hexdigest()[:length]
+
+
+def _build_log_filepath(log_dir: str, operation_id: str, request_url: str) -> str:
+    """Build log filepath. We keep it well under common path length limits aiming for <= 240 characters total."""
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
+    slug = _sanitize_filename_component(operation_id)  # Best-effort human-readable slug from operation_id
+    h = _short_hash(operation_id or "", request_url or "")  # Short hash ties log to the full operation and URL
+
+    # Compute how much room we have for the slug given the directory length
+    # Keep total path length reasonably below ~260 on Windows.
+    max_total_path = 240
+    prefix = f"{timestamp}_"
+    suffix = f"_{h}.log"
+    if not slug:
+        slug = "op"
+    max_filename_len = max(60, max_total_path - len(log_dir) - 1)
+    max_slug_len = max(8, max_filename_len - len(prefix) - len(suffix))
+    if len(slug) > max_slug_len:
+        slug = slug[:max_slug_len].rstrip(" ._-")
+    return os.path.join(log_dir, f"{prefix}{slug}{suffix}")
+
+
+def _format_data_for_logging(data: Any) -> str:
    """Helper to format data (dict, str, bytes) for logging."""
    if isinstance(data, bytes):
        try:
-            return data.decode('utf-8')  # Try to decode as text
+            return data.decode("utf-8")  # Try to decode as text
        except UnicodeDecodeError:
            return f"[Binary data of length {len(data)} bytes]"
    elif isinstance(data, (dict, list)):
        try:
            return json.dumps(data, indent=2, ensure_ascii=False)
        except TypeError:
-            return str(data) # Fallback for non-serializable objects
+            return str(data)  # Fallback for non-serializable objects
    return str(data)

+
 def log_request_response(
    operation_id: str,
    request_method: str,
    request_url: str,
    request_headers: dict | None = None,
    request_params: dict | None = None,
-    request_data: any = None,
+    request_data: Any = None,
    response_status_code: int | None = None,
    response_headers: dict | None = None,
-    response_content: any = None,
-    error_message: str | None = None
+    response_content: Any = None,
+    error_message: str | None = None,
 ):
    """
    Logs API request and response details to a file in the temp/api_logs directory.
+    Filenames are sanitized and length-limited for cross-platform safety.
+    If we still fail to write, we fall back to appending into api.log.
    """
    log_dir = get_log_directory()
-    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
-    filename = f"{timestamp}_{operation_id.replace('/', '_').replace(':', '_')}.log"
-    filepath = os.path.join(log_dir, filename)
-
-    log_content = []
+    filepath = _build_log_filepath(log_dir, operation_id, request_url)

+    log_content: list[str] = []
    log_content.append(f"Timestamp: {datetime.datetime.now().isoformat()}")
    log_content.append(f"Operation ID: {operation_id}")
    log_content.append("-" * 30 + " REQUEST " + "-" * 30)
@@ -69,7 +106,7 @@ def log_request_response(
        log_content.append(f"Headers:\n{_format_data_for_logging(request_headers)}")
    if request_params:
        log_content.append(f"Params:\n{_format_data_for_logging(request_params)}")
-    if request_data:
+    if request_data is not None:
        log_content.append(f"Data/Body:\n{_format_data_for_logging(request_data)}")

    log_content.append("\n" + "-" * 30 + " RESPONSE " + "-" * 30)
@@ -77,7 +114,7 @@ def log_request_response(
        log_content.append(f"Status Code: {response_status_code}")
    if response_headers:
        log_content.append(f"Headers:\n{_format_data_for_logging(response_headers)}")
-    if response_content:
+    if response_content is not None:
        log_content.append(f"Content:\n{_format_data_for_logging(response_content)}")
    if error_message:
        log_content.append(f"Error:\n{error_message}")
@@ -89,6 +126,7 @@ def log_request_response(
    except Exception as e:
        logger.error(f"Error writing API log to {filepath}: {e}")

+
 if __name__ == '__main__':
    # Example usage (for testing the logger directly)
    logger.setLevel(logging.DEBUG)
@@ -9,8 +9,9 @@ class Rodin3DGenerateRequest(BaseModel):
    seed: int = Field(..., description="seed_")
    tier: str = Field(..., description="Tier of generation.")
    material: str = Field(..., description="The material type.")
-    quality: str = Field(..., description="The generation quality of the mesh.")
+    quality_override: int = Field(..., description="The poly count of the mesh.")
    mesh_mode: str = Field(..., description="It controls the type of faces of generated models.")
+    TAPose: Optional[bool] = Field(None, description="")

 class GenerateJobsData(BaseModel):
    uuids: List[str] = Field(..., description="str LIST")
@@ -51,7 +52,3 @@ class RodinResourceItem(BaseModel):

 class Rodin3DDownloadResponse(BaseModel):
    list: List[RodinResourceItem] = Field(..., description="Source List")
-
-
-
-
@@ -567,6 +567,12 @@ class ByteDanceSeedreamNode(comfy_io.ComfyNode):
                    tooltip="Whether to add an \"AI generated\" watermark to the image.",
                    optional=True,
                ),
+                comfy_io.Boolean.Input(
+                    "fail_on_partial",
+                    default=True,
+                    tooltip="If enabled, abort execution if any requested images are missing or return an error.",
+                    optional=True,
+                ),
            ],
            outputs=[
                comfy_io.Image.Output(),
@@ -592,6 +598,7 @@ class ByteDanceSeedreamNode(comfy_io.ComfyNode):
        max_images: int = 1,
        seed: int = 0,
        watermark: bool = True,
+        fail_on_partial: bool = True,
    ) -> comfy_io.NodeOutput:
        validate_string(prompt, strip_whitespace=True, min_length=1)
        w = h = None
@@ -651,9 +658,10 @@ class ByteDanceSeedreamNode(comfy_io.ComfyNode):

        if len(response.data) == 1:
            return comfy_io.NodeOutput(await download_url_to_image_tensor(get_image_url_from_response(response)))
-        return comfy_io.NodeOutput(
-            torch.cat([await download_url_to_image_tensor(str(i["url"])) for i in response.data])
-        )
+        urls = [str(d["url"]) for d in response.data if isinstance(d, dict) and "url" in d]
+        if fail_on_partial and len(urls) < len(response.data):
+            raise RuntimeError(f"Only {len(urls)} of {len(response.data)} images were generated before error.")
+        return comfy_io.NodeOutput(torch.cat([await download_url_to_image_tensor(i) for i in urls]))


 class ByteDanceTextToVideoNode(comfy_io.ComfyNode):
@@ -912,7 +920,7 @@ class ByteDanceFirstLastFrameNode(comfy_io.ComfyNode):
            inputs=[
                comfy_io.Combo.Input(
                    "model",
-                    options=[Image2VideoModelName.seedance_1_lite.value],
+                    options=[model.value for model in Image2VideoModelName],
                    default=Image2VideoModelName.seedance_1_lite.value,
                    tooltip="Model name",
                ),
@@ -1171,7 +1179,7 @@ async def process_video_task(
    payload: Union[Text2VideoTaskCreationRequest, Image2VideoTaskCreationRequest],
    auth_kwargs: dict,
    node_id: str,
-    estimated_duration: int | None,
+    estimated_duration: Optional[int],
 ) -> comfy_io.NodeOutput:
    initial_response = await SynchronousOperation(
        endpoint=ApiEndpoint(
@@ -39,6 +39,7 @@ from comfy_api_nodes.apinode_utils import (
    tensor_to_base64_string,
    bytesio_to_image_tensor,
 )
+from comfy_api.util import VideoContainer, VideoCodec


 GEMINI_BASE_ENDPOINT = "/proxy/vertexai/gemini"
@@ -310,7 +311,7 @@ class GeminiNode(ComfyNodeABC):
        Returns:
            List of GeminiPart objects containing the encoded video.
        """
-        from comfy_api.util import VideoContainer, VideoCodec
+
        base_64_string = video_to_base64_string(
            video_input,
            container_format=VideoContainer.MP4,
@@ -490,7 +491,6 @@ class GeminiInputFiles(ComfyNodeABC):
        # Use base64 string directly, not the data URI
        with open(file_path, "rb") as f:
            file_content = f.read()
-        import base64
        base64_str = base64.b64encode(file_content).decode("utf-8")

        return GeminiPart(
@@ -423,6 +423,8 @@ class KlingTextToVideoNode(KlingNodeBase):
            "standard mode / 10s duration / kling-v2-master": ("std", "10", "kling-v2-master"),
            "pro mode / 5s duration / kling-v2-1-master": ("pro", "5", "kling-v2-1-master"),
            "pro mode / 10s duration / kling-v2-1-master": ("pro", "10", "kling-v2-1-master"),
+            "pro mode / 5s duration / kling-v2-5-turbo": ("pro", "5", "kling-v2-5-turbo"),
+            "pro mode / 10s duration / kling-v2-5-turbo": ("pro", "10", "kling-v2-5-turbo"),
        }

    @classmethod
@@ -710,6 +712,9 @@ class KlingImage2VideoNode(KlingNodeBase):
            # Camera control type for image 2 video is always `simple`
            camera_control.type = KlingCameraControlType.simple

+        if mode == "std" and model_name == KlingVideoGenModelName.kling_v2_5_turbo.value:
+            mode = "pro"  # October 5: currently "std" mode is not supported for this model
+
        initial_operation = SynchronousOperation(
            endpoint=ApiEndpoint(
                path=PATH_IMAGE_TO_VIDEO,
@@ -846,6 +851,8 @@ class KlingStartEndFrameNode(KlingImage2VideoNode):
            "pro mode / 10s duration / kling-v1-5": ("pro", "10", "kling-v1-5"),
            "pro mode / 5s duration / kling-v1-6": ("pro", "5", "kling-v1-6"),
            "pro mode / 10s duration / kling-v1-6": ("pro", "10", "kling-v1-6"),
+            "pro mode / 5s duration / kling-v2-1": ("pro", "5", "kling-v2-1"),
+            "pro mode / 10s duration / kling-v2-1": ("pro", "10", "kling-v2-1"),
        }

    @classmethod
@@ -1,7 +1,8 @@
 from __future__ import annotations
 from inspect import cleandoc
 from typing import Optional
-from comfy.comfy_types.node_typing import IO, ComfyNodeABC
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io as comfy_io
 from comfy_api.input_impl.video_types import VideoFromFile
 from comfy_api_nodes.apis.luma_api import (
    LumaImageModel,
@@ -51,174 +52,186 @@ def image_result_url_extractor(response: LumaGeneration):
 def video_result_url_extractor(response: LumaGeneration):
    return response.assets.video if hasattr(response, "assets") and hasattr(response.assets, "video") else None

-class LumaReferenceNode(ComfyNodeABC):
+class LumaReferenceNode(comfy_io.ComfyNode):
    """
    Holds an image and weight for use with Luma Generate Image node.
    """

-    RETURN_TYPES = (LumaIO.LUMA_REF,)
-    RETURN_NAMES = ("luma_ref",)
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
-    FUNCTION = "create_luma_reference"
-    CATEGORY = "api node/image/Luma"
+    @classmethod
+    def define_schema(cls) -> comfy_io.Schema:
+        return comfy_io.Schema(
+            node_id="LumaReferenceNode",
+            display_name="Luma Reference",
+            category="api node/image/Luma",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.Image.Input(
+                    "image",
+                    tooltip="Image to use as reference.",
+                ),
+                comfy_io.Float.Input(
+                    "weight",
+                    default=1.0,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    tooltip="Weight of image reference.",
+                ),
+                comfy_io.Custom(LumaIO.LUMA_REF).Input(
+                    "luma_ref",
+                    optional=True,
+                ),
+            ],
+            outputs=[comfy_io.Custom(LumaIO.LUMA_REF).Output(display_name="luma_ref")],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+        )

    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": (
-                    IO.IMAGE,
-                    {
-                        "tooltip": "Image to use as reference.",
-                    },
-                ),
-                "weight": (
-                    IO.FLOAT,
-                    {
-                        "default": 1.0,
-                        "min": 0.0,
-                        "max": 1.0,
-                        "step": 0.01,
-                        "tooltip": "Weight of image reference.",
-                    },
-                ),
-            },
-            "optional": {"luma_ref": (LumaIO.LUMA_REF,)},
-        }
-
-    def create_luma_reference(
-        self, image: torch.Tensor, weight: float, luma_ref: LumaReferenceChain = None
-    ):
+    def execute(
+        cls, image: torch.Tensor, weight: float, luma_ref: LumaReferenceChain = None
+    ) -> comfy_io.NodeOutput:
        if luma_ref is not None:
            luma_ref = luma_ref.clone()
        else:
            luma_ref = LumaReferenceChain()
        luma_ref.add(LumaReference(image=image, weight=round(weight, 2)))
-        return (luma_ref,)
+        return comfy_io.NodeOutput(luma_ref)


-class LumaConceptsNode(ComfyNodeABC):
+class LumaConceptsNode(comfy_io.ComfyNode):
    """
    Holds one or more Camera Concepts for use with Luma Text to Video and Luma Image to Video nodes.
    """

-    RETURN_TYPES = (LumaIO.LUMA_CONCEPTS,)
-    RETURN_NAMES = ("luma_concepts",)
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
-    FUNCTION = "create_concepts"
-    CATEGORY = "api node/video/Luma"
+    @classmethod
+    def define_schema(cls) -> comfy_io.Schema:
+        return comfy_io.Schema(
+            node_id="LumaConceptsNode",
+            display_name="Luma Concepts",
+            category="api node/video/Luma",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.Combo.Input(
+                    "concept1",
+                    options=get_luma_concepts(include_none=True),
+                ),
+                comfy_io.Combo.Input(
+                    "concept2",
+                    options=get_luma_concepts(include_none=True),
+                ),
+                comfy_io.Combo.Input(
+                    "concept3",
+                    options=get_luma_concepts(include_none=True),
+                ),
+                comfy_io.Combo.Input(
+                    "concept4",
+                    options=get_luma_concepts(include_none=True),
+                ),
+                comfy_io.Custom(LumaIO.LUMA_CONCEPTS).Input(
+                    "luma_concepts",
+                    tooltip="Optional Camera Concepts to add to the ones chosen here.",
+                    optional=True,
+                ),
+            ],
+            outputs=[comfy_io.Custom(LumaIO.LUMA_CONCEPTS).Output(display_name="luma_concepts")],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+        )

    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "concept1": (get_luma_concepts(include_none=True),),
-                "concept2": (get_luma_concepts(include_none=True),),
-                "concept3": (get_luma_concepts(include_none=True),),
-                "concept4": (get_luma_concepts(include_none=True),),
-            },
-            "optional": {
-                "luma_concepts": (
-                    LumaIO.LUMA_CONCEPTS,
-                    {
-                        "tooltip": "Optional Camera Concepts to add to the ones chosen here."
-                    },
-                ),
-            },
-        }
-
-    def create_concepts(
-        self,
+    def execute(
+        cls,
        concept1: str,
        concept2: str,
        concept3: str,
        concept4: str,
        luma_concepts: LumaConceptChain = None,
-    ):
+    ) -> comfy_io.NodeOutput:
        chain = LumaConceptChain(str_list=[concept1, concept2, concept3, concept4])
        if luma_concepts is not None:
            chain = luma_concepts.clone_and_merge(chain)
-        return (chain,)
+        return comfy_io.NodeOutput(chain)


-class LumaImageGenerationNode(ComfyNodeABC):
+class LumaImageGenerationNode(comfy_io.ComfyNode):
    """
    Generates images synchronously based on prompt and aspect ratio.
    """

-    RETURN_TYPES = (IO.IMAGE,)
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
-    FUNCTION = "api_call"
-    API_NODE = True
-    CATEGORY = "api node/image/Luma"
+    @classmethod
+    def define_schema(cls) -> comfy_io.Schema:
+        return comfy_io.Schema(
+            node_id="LumaImageNode",
+            display_name="Luma Text to Image",
+            category="api node/image/Luma",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the image generation",
+                ),
+                comfy_io.Combo.Input(
+                    "model",
+                    options=[model.value for model in LumaImageModel],
+                ),
+                comfy_io.Combo.Input(
+                    "aspect_ratio",
+                    options=[ratio.value for ratio in LumaAspectRatio],
+                    default=LumaAspectRatio.ratio_16_9,
+                ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
+                ),
+                comfy_io.Float.Input(
+                    "style_image_weight",
+                    default=1.0,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    tooltip="Weight of style image. Ignored if no style_image provided.",
+                ),
+                comfy_io.Custom(LumaIO.LUMA_REF).Input(
+                    "image_luma_ref",
+                    tooltip="Luma Reference node connection to influence generation with input images; up to 4 images can be considered.",
+                    optional=True,
+                ),
+                comfy_io.Image.Input(
+                    "style_image",
+                    tooltip="Style reference image; only 1 image will be used.",
+                    optional=True,
+                ),
+                comfy_io.Image.Input(
+                    "character_image",
+                    tooltip="Character reference images; can be a batch of multiple, up to 4 images can be considered.",
+                    optional=True,
+                ),
+            ],
+            outputs=[comfy_io.Image.Output()],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )

    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "prompt": (
-                    IO.STRING,
-                    {
-                        "multiline": True,
-                        "default": "",
-                        "tooltip": "Prompt for the image generation",
-                    },
-                ),
-                "model": ([model.value for model in LumaImageModel],),
-                "aspect_ratio": (
-                    [ratio.value for ratio in LumaAspectRatio],
-                    {
-                        "default": LumaAspectRatio.ratio_16_9,
-                    },
-                ),
-                "seed": (
-                    IO.INT,
-                    {
-                        "default": 0,
-                        "min": 0,
-                        "max": 0xFFFFFFFFFFFFFFFF,
-                        "control_after_generate": True,
-                        "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
-                    },
-                ),
-                "style_image_weight": (
-                    IO.FLOAT,
-                    {
-                        "default": 1.0,
-                        "min": 0.0,
-                        "max": 1.0,
-                        "step": 0.01,
-                        "tooltip": "Weight of style image. Ignored if no style_image provided.",
-                    },
-                ),
-            },
-            "optional": {
-                "image_luma_ref": (
-                    LumaIO.LUMA_REF,
-                    {
-                        "tooltip": "Luma Reference node connection to influence generation with input images; up to 4 images can be considered."
-                    },
-                ),
-                "style_image": (
-                    IO.IMAGE,
-                    {"tooltip": "Style reference image; only 1 image will be used."},
-                ),
-                "character_image": (
-                    IO.IMAGE,
-                    {
-                        "tooltip": "Character reference images; can be a batch of multiple, up to 4 images can be considered."
-                    },
-                ),
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-                "unique_id": "UNIQUE_ID",
-            },
-        }
-
-    async def api_call(
-        self,
+    async def execute(
+        cls,
        prompt: str,
        model: str,
        aspect_ratio: str,
@@ -227,27 +240,29 @@ class LumaImageGenerationNode(ComfyNodeABC):
        image_luma_ref: LumaReferenceChain = None,
        style_image: torch.Tensor = None,
        character_image: torch.Tensor = None,
-        unique_id: str = None,
-        **kwargs,
-    ):
+    ) -> comfy_io.NodeOutput:
        validate_string(prompt, strip_whitespace=True, min_length=3)
+        auth_kwargs = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
+        }
        # handle image_luma_ref
        api_image_ref = None
        if image_luma_ref is not None:
-            api_image_ref = await self._convert_luma_refs(
-                image_luma_ref, max_refs=4, auth_kwargs=kwargs,
+            api_image_ref = await cls._convert_luma_refs(
+                image_luma_ref, max_refs=4, auth_kwargs=auth_kwargs,
            )
        # handle style_luma_ref
        api_style_ref = None
        if style_image is not None:
-            api_style_ref = await self._convert_style_image(
-                style_image, weight=style_image_weight, auth_kwargs=kwargs,
+            api_style_ref = await cls._convert_style_image(
+                style_image, weight=style_image_weight, auth_kwargs=auth_kwargs,
            )
        # handle character_ref images
        character_ref = None
        if character_image is not None:
            download_urls = await upload_images_to_comfyapi(
-                character_image, max_images=4, auth_kwargs=kwargs,
+                character_image, max_images=4, auth_kwargs=auth_kwargs,
            )
            character_ref = LumaCharacterRef(
                identity0=LumaImageIdentity(images=download_urls)
@@ -268,7 +283,7 @@ class LumaImageGenerationNode(ComfyNodeABC):
                style_ref=api_style_ref,
                character_ref=character_ref,
            ),
-            auth_kwargs=kwargs,
+            auth_kwargs=auth_kwargs,
        )
        response_api: LumaGeneration = await operation.execute()

@@ -283,18 +298,19 @@ class LumaImageGenerationNode(ComfyNodeABC):
            failed_statuses=[LumaState.failed],
            status_extractor=lambda x: x.state,
            result_url_extractor=image_result_url_extractor,
-            node_id=unique_id,
-            auth_kwargs=kwargs,
+            node_id=cls.hidden.unique_id,
+            auth_kwargs=auth_kwargs,
        )
        response_poll = await operation.execute()

        async with aiohttp.ClientSession() as session:
            async with session.get(response_poll.assets.image) as img_response:
                img = process_image_response(await img_response.content.read())
-        return (img,)
+        return comfy_io.NodeOutput(img)

+    @classmethod
    async def _convert_luma_refs(
-        self, luma_ref: LumaReferenceChain, max_refs: int, auth_kwargs: Optional[dict[str,str]] = None
+        cls, luma_ref: LumaReferenceChain, max_refs: int, auth_kwargs: Optional[dict[str,str]] = None
    ):
        luma_urls = []
        ref_count = 0
@@ -308,82 +324,84 @@ class LumaImageGenerationNode(ComfyNodeABC):
                break
        return luma_ref.create_api_model(download_urls=luma_urls, max_refs=max_refs)

+    @classmethod
    async def _convert_style_image(
-        self, style_image: torch.Tensor, weight: float, auth_kwargs: Optional[dict[str,str]] = None
+        cls, style_image: torch.Tensor, weight: float, auth_kwargs: Optional[dict[str,str]] = None
    ):
        chain = LumaReferenceChain(
            first_ref=LumaReference(image=style_image, weight=weight)
        )
-        return await self._convert_luma_refs(chain, max_refs=1, auth_kwargs=auth_kwargs)
+        return await cls._convert_luma_refs(chain, max_refs=1, auth_kwargs=auth_kwargs)


-class LumaImageModifyNode(ComfyNodeABC):
+class LumaImageModifyNode(comfy_io.ComfyNode):
    """
    Modifies images synchronously based on prompt and aspect ratio.
    """

-    RETURN_TYPES = (IO.IMAGE,)
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
-    FUNCTION = "api_call"
-    API_NODE = True
-    CATEGORY = "api node/image/Luma"
+    @classmethod
+    def define_schema(cls) -> comfy_io.Schema:
+        return comfy_io.Schema(
+            node_id="LumaImageModifyNode",
+            display_name="Luma Image to Image",
+            category="api node/image/Luma",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.Image.Input(
+                    "image",
+                ),
+                comfy_io.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the image generation",
+                ),
+                comfy_io.Float.Input(
+                    "image_weight",
+                    default=0.1,
+                    min=0.0,
+                    max=0.98,
+                    step=0.01,
+                    tooltip="Weight of the image; the closer to 1.0, the less the image will be modified.",
+                ),
+                comfy_io.Combo.Input(
+                    "model",
+                    options=[model.value for model in LumaImageModel],
+                ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
+                ),
+            ],
+            outputs=[comfy_io.Image.Output()],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )

    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": (IO.IMAGE,),
-                "prompt": (
-                    IO.STRING,
-                    {
-                        "multiline": True,
-                        "default": "",
-                        "tooltip": "Prompt for the image generation",
-                    },
-                ),
-                "image_weight": (
-                    IO.FLOAT,
-                    {
-                        "default": 0.1,
-                        "min": 0.0,
-                        "max": 0.98,
-                        "step": 0.01,
-                        "tooltip": "Weight of the image; the closer to 1.0, the less the image will be modified.",
-                    },
-                ),
-                "model": ([model.value for model in LumaImageModel],),
-                "seed": (
-                    IO.INT,
-                    {
-                        "default": 0,
-                        "min": 0,
-                        "max": 0xFFFFFFFFFFFFFFFF,
-                        "control_after_generate": True,
-                        "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
-                    },
-                ),
-            },
-            "optional": {},
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-                "unique_id": "UNIQUE_ID",
-            },
-        }
-
-    async def api_call(
-        self,
+    async def execute(
+        cls,
        prompt: str,
        model: str,
        image: torch.Tensor,
        image_weight: float,
        seed,
-        unique_id: str = None,
-        **kwargs,
-    ):
+    ) -> comfy_io.NodeOutput:
+        auth_kwargs = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
+        }
        # first, upload image
        download_urls = await upload_images_to_comfyapi(
-            image, max_images=1, auth_kwargs=kwargs,
+            image, max_images=1, auth_kwargs=auth_kwargs,
        )
        image_url = download_urls[0]
        # next, make Luma call with download url provided
@@ -401,7 +419,7 @@ class LumaImageModifyNode(ComfyNodeABC):
                    url=image_url, weight=round(max(min(1.0-image_weight, 0.98), 0.0), 2)
                ),
            ),
-            auth_kwargs=kwargs,
+            auth_kwargs=auth_kwargs,
        )
        response_api: LumaGeneration = await operation.execute()

@@ -416,88 +434,84 @@ class LumaImageModifyNode(ComfyNodeABC):
            failed_statuses=[LumaState.failed],
            status_extractor=lambda x: x.state,
            result_url_extractor=image_result_url_extractor,
-            node_id=unique_id,
-            auth_kwargs=kwargs,
+            node_id=cls.hidden.unique_id,
+            auth_kwargs=auth_kwargs,
        )
        response_poll = await operation.execute()

        async with aiohttp.ClientSession() as session:
            async with session.get(response_poll.assets.image) as img_response:
                img = process_image_response(await img_response.content.read())
-        return (img,)
+        return comfy_io.NodeOutput(img)


-class LumaTextToVideoGenerationNode(ComfyNodeABC):
+class LumaTextToVideoGenerationNode(comfy_io.ComfyNode):
    """
    Generates videos synchronously based on prompt and output_size.
    """

-    RETURN_TYPES = (IO.VIDEO,)
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
-    FUNCTION = "api_call"
-    API_NODE = True
-    CATEGORY = "api node/video/Luma"
+    @classmethod
+    def define_schema(cls) -> comfy_io.Schema:
+        return comfy_io.Schema(
+            node_id="LumaVideoNode",
+            display_name="Luma Text to Video",
+            category="api node/video/Luma",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the video generation",
+                ),
+                comfy_io.Combo.Input(
+                    "model",
+                    options=[model.value for model in LumaVideoModel],
+                ),
+                comfy_io.Combo.Input(
+                    "aspect_ratio",
+                    options=[ratio.value for ratio in LumaAspectRatio],
+                    default=LumaAspectRatio.ratio_16_9,
+                ),
+                comfy_io.Combo.Input(
+                    "resolution",
+                    options=[resolution.value for resolution in LumaVideoOutputResolution],
+                    default=LumaVideoOutputResolution.res_540p,
+                ),
+                comfy_io.Combo.Input(
+                    "duration",
+                    options=[dur.value for dur in LumaVideoModelOutputDuration],
+                ),
+                comfy_io.Boolean.Input(
+                    "loop",
+                    default=False,
+                ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
+                ),
+                comfy_io.Custom(LumaIO.LUMA_CONCEPTS).Input(
+                    "luma_concepts",
+                    tooltip="Optional Camera Concepts to dictate camera motion via the Luma Concepts node.",
+                    optional=True,
+                )
+            ],
+            outputs=[comfy_io.Video.Output()],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )

    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "prompt": (
-                    IO.STRING,
-                    {
-                        "multiline": True,
-                        "default": "",
-                        "tooltip": "Prompt for the video generation",
-                    },
-                ),
-                "model": ([model.value for model in LumaVideoModel],),
-                "aspect_ratio": (
-                    [ratio.value for ratio in LumaAspectRatio],
-                    {
-                        "default": LumaAspectRatio.ratio_16_9,
-                    },
-                ),
-                "resolution": (
-                    [resolution.value for resolution in LumaVideoOutputResolution],
-                    {
-                        "default": LumaVideoOutputResolution.res_540p,
-                    },
-                ),
-                "duration": ([dur.value for dur in LumaVideoModelOutputDuration],),
-                "loop": (
-                    IO.BOOLEAN,
-                    {
-                        "default": False,
-                    },
-                ),
-                "seed": (
-                    IO.INT,
-                    {
-                        "default": 0,
-                        "min": 0,
-                        "max": 0xFFFFFFFFFFFFFFFF,
-                        "control_after_generate": True,
-                        "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
-                    },
-                ),
-            },
-            "optional": {
-                "luma_concepts": (
-                    LumaIO.LUMA_CONCEPTS,
-                    {
-                        "tooltip": "Optional Camera Concepts to dictate camera motion via the Luma Concepts node."
-                    },
-                ),
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-                "unique_id": "UNIQUE_ID",
-            },
-        }
-
-    async def api_call(
-        self,
+    async def execute(
+        cls,
        prompt: str,
        model: str,
        aspect_ratio: str,
@@ -506,13 +520,15 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC):
        loop: bool,
        seed,
        luma_concepts: LumaConceptChain = None,
-        unique_id: str = None,
-        **kwargs,
-    ):
+    ) -> comfy_io.NodeOutput:
        validate_string(prompt, strip_whitespace=False, min_length=3)
        duration = duration if model != LumaVideoModel.ray_1_6 else None
        resolution = resolution if model != LumaVideoModel.ray_1_6 else None

+        auth_kwargs = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
+        }
        operation = SynchronousOperation(
            endpoint=ApiEndpoint(
                path="/proxy/luma/generations",
@@ -529,12 +545,12 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC):
                loop=loop,
                concepts=luma_concepts.create_api_model() if luma_concepts else None,
            ),
-            auth_kwargs=kwargs,
+            auth_kwargs=auth_kwargs,
        )
        response_api: LumaGeneration = await operation.execute()

-        if unique_id:
-            PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", unique_id)
+        if cls.hidden.unique_id:
+            PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", cls.hidden.unique_id)

        operation = PollingOperation(
            poll_endpoint=ApiEndpoint(
@@ -547,90 +563,94 @@ class LumaTextToVideoGenerationNode(ComfyNodeABC):
            failed_statuses=[LumaState.failed],
            status_extractor=lambda x: x.state,
            result_url_extractor=video_result_url_extractor,
-            node_id=unique_id,
+            node_id=cls.hidden.unique_id,
            estimated_duration=LUMA_T2V_AVERAGE_DURATION,
-            auth_kwargs=kwargs,
+            auth_kwargs=auth_kwargs,
        )
        response_poll = await operation.execute()

        async with aiohttp.ClientSession() as session:
            async with session.get(response_poll.assets.video) as vid_response:
-                return (VideoFromFile(BytesIO(await vid_response.content.read())),)
+                return comfy_io.NodeOutput(VideoFromFile(BytesIO(await vid_response.content.read())))


-class LumaImageToVideoGenerationNode(ComfyNodeABC):
+class LumaImageToVideoGenerationNode(comfy_io.ComfyNode):
    """
    Generates videos synchronously based on prompt, input images, and output_size.
    """

-    RETURN_TYPES = (IO.VIDEO,)
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
-    FUNCTION = "api_call"
-    API_NODE = True
-    CATEGORY = "api node/video/Luma"
+    @classmethod
+    def define_schema(cls) -> comfy_io.Schema:
+        return comfy_io.Schema(
+            node_id="LumaImageToVideoNode",
+            display_name="Luma Image to Video",
+            category="api node/video/Luma",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the video generation",
+                ),
+                comfy_io.Combo.Input(
+                    "model",
+                    options=[model.value for model in LumaVideoModel],
+                ),
+                # comfy_io.Combo.Input(
+                #     "aspect_ratio",
+                #     options=[ratio.value for ratio in LumaAspectRatio],
+                #     default=LumaAspectRatio.ratio_16_9,
+                # ),
+                comfy_io.Combo.Input(
+                    "resolution",
+                    options=[resolution.value for resolution in LumaVideoOutputResolution],
+                    default=LumaVideoOutputResolution.res_540p,
+                ),
+                comfy_io.Combo.Input(
+                    "duration",
+                    options=[dur.value for dur in LumaVideoModelOutputDuration],
+                ),
+                comfy_io.Boolean.Input(
+                    "loop",
+                    default=False,
+                ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
+                ),
+                comfy_io.Image.Input(
+                    "first_image",
+                    tooltip="First frame of generated video.",
+                    optional=True,
+                ),
+                comfy_io.Image.Input(
+                    "last_image",
+                    tooltip="Last frame of generated video.",
+                    optional=True,
+                ),
+                comfy_io.Custom(LumaIO.LUMA_CONCEPTS).Input(
+                    "luma_concepts",
+                    tooltip="Optional Camera Concepts to dictate camera motion via the Luma Concepts node.",
+                    optional=True,
+                )
+            ],
+            outputs=[comfy_io.Video.Output()],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )

    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "prompt": (
-                    IO.STRING,
-                    {
-                        "multiline": True,
-                        "default": "",
-                        "tooltip": "Prompt for the video generation",
-                    },
-                ),
-                "model": ([model.value for model in LumaVideoModel],),
-                # "aspect_ratio": ([ratio.value for ratio in LumaAspectRatio], {
-                #     "default": LumaAspectRatio.ratio_16_9,
-                # }),
-                "resolution": (
-                    [resolution.value for resolution in LumaVideoOutputResolution],
-                    {
-                        "default": LumaVideoOutputResolution.res_540p,
-                    },
-                ),
-                "duration": ([dur.value for dur in LumaVideoModelOutputDuration],),
-                "loop": (
-                    IO.BOOLEAN,
-                    {
-                        "default": False,
-                    },
-                ),
-                "seed": (
-                    IO.INT,
-                    {
-                        "default": 0,
-                        "min": 0,
-                        "max": 0xFFFFFFFFFFFFFFFF,
-                        "control_after_generate": True,
-                        "tooltip": "Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
-                    },
-                ),
-            },
-            "optional": {
-                "first_image": (
-                    IO.IMAGE,
-                    {"tooltip": "First frame of generated video."},
-                ),
-                "last_image": (IO.IMAGE, {"tooltip": "Last frame of generated video."}),
-                "luma_concepts": (
-                    LumaIO.LUMA_CONCEPTS,
-                    {
-                        "tooltip": "Optional Camera Concepts to dictate camera motion via the Luma Concepts node."
-                    },
-                ),
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-                "unique_id": "UNIQUE_ID",
-            },
-        }
-
-    async def api_call(
-        self,
+    async def execute(
+        cls,
        prompt: str,
        model: str,
        resolution: str,
@@ -640,14 +660,16 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC):
        first_image: torch.Tensor = None,
        last_image: torch.Tensor = None,
        luma_concepts: LumaConceptChain = None,
-        unique_id: str = None,
-        **kwargs,
-    ):
+    ) -> comfy_io.NodeOutput:
        if first_image is None and last_image is None:
            raise Exception(
                "At least one of first_image and last_image requires an input."
            )
-        keyframes = await self._convert_to_keyframes(first_image, last_image, auth_kwargs=kwargs)
+        auth_kwargs = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
+        }
+        keyframes = await cls._convert_to_keyframes(first_image, last_image, auth_kwargs=auth_kwargs)
        duration = duration if model != LumaVideoModel.ray_1_6 else None
        resolution = resolution if model != LumaVideoModel.ray_1_6 else None

@@ -668,12 +690,12 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC):
                keyframes=keyframes,
                concepts=luma_concepts.create_api_model() if luma_concepts else None,
            ),
-            auth_kwargs=kwargs,
+            auth_kwargs=auth_kwargs,
        )
        response_api: LumaGeneration = await operation.execute()

-        if unique_id:
-            PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", unique_id)
+        if cls.hidden.unique_id:
+            PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", cls.hidden.unique_id)

        operation = PollingOperation(
            poll_endpoint=ApiEndpoint(
@@ -686,18 +708,19 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC):
            failed_statuses=[LumaState.failed],
            status_extractor=lambda x: x.state,
            result_url_extractor=video_result_url_extractor,
-            node_id=unique_id,
+            node_id=cls.hidden.unique_id,
            estimated_duration=LUMA_I2V_AVERAGE_DURATION,
-            auth_kwargs=kwargs,
+            auth_kwargs=auth_kwargs,
        )
        response_poll = await operation.execute()

        async with aiohttp.ClientSession() as session:
            async with session.get(response_poll.assets.video) as vid_response:
-                return (VideoFromFile(BytesIO(await vid_response.content.read())),)
+                return comfy_io.NodeOutput(VideoFromFile(BytesIO(await vid_response.content.read())))

+    @classmethod
    async def _convert_to_keyframes(
-        self,
+        cls,
        first_image: torch.Tensor = None,
        last_image: torch.Tensor = None,
        auth_kwargs: Optional[dict[str,str]] = None,
@@ -719,23 +742,18 @@ class LumaImageToVideoGenerationNode(ComfyNodeABC):
        return LumaKeyframes(frame0=frame0, frame1=frame1)


-# A dictionary that contains all nodes you want to export with their names
-# NOTE: names should be globally unique
-NODE_CLASS_MAPPINGS = {
-    "LumaImageNode": LumaImageGenerationNode,
-    "LumaImageModifyNode": LumaImageModifyNode,
-    "LumaVideoNode": LumaTextToVideoGenerationNode,
-    "LumaImageToVideoNode": LumaImageToVideoGenerationNode,
-    "LumaReferenceNode": LumaReferenceNode,
-    "LumaConceptsNode": LumaConceptsNode,
-}
+class LumaExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[comfy_io.ComfyNode]]:
+        return [
+            LumaImageGenerationNode,
+            LumaImageModifyNode,
+            LumaTextToVideoGenerationNode,
+            LumaImageToVideoGenerationNode,
+            LumaReferenceNode,
+            LumaConceptsNode,
+        ]

-# A dictionary that contains the friendly/humanly readable titles for the nodes
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "LumaImageNode": "Luma Text to Image",
-    "LumaImageModifyNode": "Luma Image to Image",
-    "LumaVideoNode": "Luma Text to Video",
-    "LumaImageToVideoNode": "Luma Image to Video",
-    "LumaReferenceNode": "Luma Reference",
-    "LumaConceptsNode": "Luma Concepts",
-}
+
+async def comfy_entrypoint() -> LumaExtension:
+    return LumaExtension()
@@ -1,9 +1,10 @@
 from inspect import cleandoc
-from typing import Union
+from typing import Optional
 import logging
 import torch

-from comfy.comfy_types.node_typing import IO
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io as comfy_io
 from comfy_api.input_impl.video_types import VideoFromFile
 from comfy_api_nodes.apis import (
    MinimaxVideoGenerationRequest,
@@ -11,7 +12,7 @@ from comfy_api_nodes.apis import (
    MinimaxFileRetrieveResponse,
    MinimaxTaskResultResponse,
    SubjectReferenceItem,
-    MiniMaxModel
+    MiniMaxModel,
 )
 from comfy_api_nodes.apis.client import (
    ApiEndpoint,
@@ -31,372 +32,398 @@ from server import PromptServer
 I2V_AVERAGE_DURATION = 114
 T2V_AVERAGE_DURATION = 234

-class MinimaxTextToVideoNode:
+
+async def _generate_mm_video(
+    *,
+    auth: dict[str, str],
+    node_id: str,
+    prompt_text: str,
+    seed: int,
+    model: str,
+    image: Optional[torch.Tensor] = None,   # used for ImageToVideo
+    subject: Optional[torch.Tensor] = None, # used for SubjectToVideo
+    average_duration: Optional[int] = None,
+) -> comfy_io.NodeOutput:
+    if image is None:
+        validate_string(prompt_text, field_name="prompt_text")
+    # upload image, if passed in
+    image_url = None
+    if image is not None:
+        image_url = (await upload_images_to_comfyapi(image, max_images=1, auth_kwargs=auth))[0]
+
+    # TODO: figure out how to deal with subject properly, API returns invalid params when using S2V-01 model
+    subject_reference = None
+    if subject is not None:
+        subject_url = (await upload_images_to_comfyapi(subject, max_images=1, auth_kwargs=auth))[0]
+        subject_reference = [SubjectReferenceItem(image=subject_url)]
+
+
+    video_generate_operation = SynchronousOperation(
+        endpoint=ApiEndpoint(
+            path="/proxy/minimax/video_generation",
+            method=HttpMethod.POST,
+            request_model=MinimaxVideoGenerationRequest,
+            response_model=MinimaxVideoGenerationResponse,
+        ),
+        request=MinimaxVideoGenerationRequest(
+            model=MiniMaxModel(model),
+            prompt=prompt_text,
+            callback_url=None,
+            first_frame_image=image_url,
+            subject_reference=subject_reference,
+            prompt_optimizer=None,
+        ),
+        auth_kwargs=auth,
+    )
+    response = await video_generate_operation.execute()
+
+    task_id = response.task_id
+    if not task_id:
+        raise Exception(f"MiniMax generation failed: {response.base_resp}")
+
+    video_generate_operation = PollingOperation(
+        poll_endpoint=ApiEndpoint(
+            path="/proxy/minimax/query/video_generation",
+            method=HttpMethod.GET,
+            request_model=EmptyRequest,
+            response_model=MinimaxTaskResultResponse,
+            query_params={"task_id": task_id},
+        ),
+        completed_statuses=["Success"],
+        failed_statuses=["Fail"],
+        status_extractor=lambda x: x.status.value,
+        estimated_duration=average_duration,
+        node_id=node_id,
+        auth_kwargs=auth,
+    )
+    task_result = await video_generate_operation.execute()
+
+    file_id = task_result.file_id
+    if file_id is None:
+        raise Exception("Request was not successful. Missing file ID.")
+    file_retrieve_operation = SynchronousOperation(
+        endpoint=ApiEndpoint(
+            path="/proxy/minimax/files/retrieve",
+            method=HttpMethod.GET,
+            request_model=EmptyRequest,
+            response_model=MinimaxFileRetrieveResponse,
+            query_params={"file_id": int(file_id)},
+        ),
+        request=EmptyRequest(),
+        auth_kwargs=auth,
+    )
+    file_result = await file_retrieve_operation.execute()
+
+    file_url = file_result.file.download_url
+    if file_url is None:
+        raise Exception(
+            f"No video was found in the response. Full response: {file_result.model_dump()}"
+        )
+    logging.info("Generated video URL: %s", file_url)
+    if node_id:
+        if hasattr(file_result.file, "backup_download_url"):
+            message = f"Result URL: {file_url}\nBackup URL: {file_result.file.backup_download_url}"
+        else:
+            message = f"Result URL: {file_url}"
+        PromptServer.instance.send_progress_text(message, node_id)
+
+    # Download and return as VideoFromFile
+    video_io = await download_url_to_bytesio(file_url)
+    if video_io is None:
+        error_msg = f"Failed to download video from {file_url}"
+        logging.error(error_msg)
+        raise Exception(error_msg)
+    return comfy_io.NodeOutput(VideoFromFile(video_io))
+
+
+class MinimaxTextToVideoNode(comfy_io.ComfyNode):
    """
    Generates videos synchronously based on a prompt, and optional parameters using MiniMax's API.
    """

-    AVERAGE_DURATION = T2V_AVERAGE_DURATION
+    @classmethod
+    def define_schema(cls) -> comfy_io.Schema:
+        return comfy_io.Schema(
+            node_id="MinimaxTextToVideoNode",
+            display_name="MiniMax Text to Video",
+            category="api node/video/MiniMax",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.String.Input(
+                    "prompt_text",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt to guide the video generation",
+                ),
+                comfy_io.Combo.Input(
+                    "model",
+                    options=["T2V-01", "T2V-01-Director"],
+                    default="T2V-01",
+                    tooltip="Model to use for video generation",
+                ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    step=1,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                    optional=True,
+                ),
+            ],
+            outputs=[comfy_io.Video.Output()],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )

    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "prompt_text": (
-                    "STRING",
-                    {
-                        "multiline": True,
-                        "default": "",
-                        "tooltip": "Text prompt to guide the video generation",
-                    },
-                ),
-                "model": (
-                    [
-                        "T2V-01",
-                        "T2V-01-Director",
-                    ],
-                    {
-                        "default": "T2V-01",
-                        "tooltip": "Model to use for video generation",
-                    },
-                ),
+    async def execute(
+        cls,
+        prompt_text: str,
+        model: str = "T2V-01",
+        seed: int = 0,
+    ) -> comfy_io.NodeOutput:
+        return await _generate_mm_video(
+            auth={
+                "auth_token": cls.hidden.auth_token_comfy_org,
+                "comfy_api_key": cls.hidden.api_key_comfy_org,
            },
-            "optional": {
-                "seed": (
-                    IO.INT,
-                    {
-                        "default": 0,
-                        "min": 0,
-                        "max": 0xFFFFFFFFFFFFFFFF,
-                        "control_after_generate": True,
-                        "tooltip": "The random seed used for creating the noise.",
-                    },
-                ),
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-                "unique_id": "UNIQUE_ID",
-            },
-        }
-
-    RETURN_TYPES = ("VIDEO",)
-    DESCRIPTION = "Generates videos from prompts using MiniMax's API"
-    FUNCTION = "generate_video"
-    CATEGORY = "api node/video/MiniMax"
-    API_NODE = True
-
-    async def generate_video(
-        self,
-        prompt_text,
-        seed=0,
-        model="T2V-01",
-        image: torch.Tensor=None, # used for ImageToVideo
-        subject: torch.Tensor=None, # used for SubjectToVideo
-        unique_id: Union[str, None]=None,
-        **kwargs,
-    ):
-        '''
-        Function used between MiniMax nodes - supports T2V, I2V, and S2V, based on provided arguments.
-        '''
-        if image is None:
-            validate_string(prompt_text, field_name="prompt_text")
-        # upload image, if passed in
-        image_url = None
-        if image is not None:
-            image_url = (await upload_images_to_comfyapi(image, max_images=1, auth_kwargs=kwargs))[0]
-
-        # TODO: figure out how to deal with subject properly, API returns invalid params when using S2V-01 model
-        subject_reference = None
-        if subject is not None:
-            subject_url = (await upload_images_to_comfyapi(subject, max_images=1, auth_kwargs=kwargs))[0]
-            subject_reference = [SubjectReferenceItem(image=subject_url)]
-
-
-        video_generate_operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path="/proxy/minimax/video_generation",
-                method=HttpMethod.POST,
-                request_model=MinimaxVideoGenerationRequest,
-                response_model=MinimaxVideoGenerationResponse,
-            ),
-            request=MinimaxVideoGenerationRequest(
-                model=MiniMaxModel(model),
-                prompt=prompt_text,
-                callback_url=None,
-                first_frame_image=image_url,
-                subject_reference=subject_reference,
-                prompt_optimizer=None,
-            ),
-            auth_kwargs=kwargs,
+            node_id=cls.hidden.unique_id,
+            prompt_text=prompt_text,
+            seed=seed,
+            model=model,
+            image=None,
+            subject=None,
+            average_duration=T2V_AVERAGE_DURATION,
        )
-        response = await video_generate_operation.execute()
-
-        task_id = response.task_id
-        if not task_id:
-            raise Exception(f"MiniMax generation failed: {response.base_resp}")
-
-        video_generate_operation = PollingOperation(
-            poll_endpoint=ApiEndpoint(
-                path="/proxy/minimax/query/video_generation",
-                method=HttpMethod.GET,
-                request_model=EmptyRequest,
-                response_model=MinimaxTaskResultResponse,
-                query_params={"task_id": task_id},
-            ),
-            completed_statuses=["Success"],
-            failed_statuses=["Fail"],
-            status_extractor=lambda x: x.status.value,
-            estimated_duration=self.AVERAGE_DURATION,
-            node_id=unique_id,
-            auth_kwargs=kwargs,
-        )
-        task_result = await video_generate_operation.execute()
-
-        file_id = task_result.file_id
-        if file_id is None:
-            raise Exception("Request was not successful. Missing file ID.")
-        file_retrieve_operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path="/proxy/minimax/files/retrieve",
-                method=HttpMethod.GET,
-                request_model=EmptyRequest,
-                response_model=MinimaxFileRetrieveResponse,
-                query_params={"file_id": int(file_id)},
-            ),
-            request=EmptyRequest(),
-            auth_kwargs=kwargs,
-        )
-        file_result = await file_retrieve_operation.execute()
-
-        file_url = file_result.file.download_url
-        if file_url is None:
-            raise Exception(
-                f"No video was found in the response. Full response: {file_result.model_dump()}"
-            )
-        logging.info(f"Generated video URL: {file_url}")
-        if unique_id:
-            if hasattr(file_result.file, "backup_download_url"):
-                message = f"Result URL: {file_url}\nBackup URL: {file_result.file.backup_download_url}"
-            else:
-                message = f"Result URL: {file_url}"
-            PromptServer.instance.send_progress_text(message, unique_id)
-
-        video_io = await download_url_to_bytesio(file_url)
-        if video_io is None:
-            error_msg = f"Failed to download video from {file_url}"
-            logging.error(error_msg)
-            raise Exception(error_msg)
-        return (VideoFromFile(video_io),)


-class MinimaxImageToVideoNode(MinimaxTextToVideoNode):
+class MinimaxImageToVideoNode(comfy_io.ComfyNode):
    """
    Generates videos synchronously based on an image and prompt, and optional parameters using MiniMax's API.
    """

-    AVERAGE_DURATION = I2V_AVERAGE_DURATION
+    @classmethod
+    def define_schema(cls) -> comfy_io.Schema:
+        return comfy_io.Schema(
+            node_id="MinimaxImageToVideoNode",
+            display_name="MiniMax Image to Video",
+            category="api node/video/MiniMax",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.Image.Input(
+                    "image",
+                    tooltip="Image to use as first frame of video generation",
+                ),
+                comfy_io.String.Input(
+                    "prompt_text",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt to guide the video generation",
+                ),
+                comfy_io.Combo.Input(
+                    "model",
+                    options=["I2V-01-Director", "I2V-01", "I2V-01-live"],
+                    default="I2V-01",
+                    tooltip="Model to use for video generation",
+                ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    step=1,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                    optional=True,
+                ),
+            ],
+            outputs=[comfy_io.Video.Output()],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )

    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": (
-                    IO.IMAGE,
-                    {
-                        "tooltip": "Image to use as first frame of video generation"
-                    },
-                ),
-                "prompt_text": (
-                    "STRING",
-                    {
-                        "multiline": True,
-                        "default": "",
-                        "tooltip": "Text prompt to guide the video generation",
-                    },
-                ),
-                "model": (
-                    [
-                        "I2V-01-Director",
-                        "I2V-01",
-                        "I2V-01-live",
-                    ],
-                    {
-                        "default": "I2V-01",
-                        "tooltip": "Model to use for video generation",
-                    },
-                ),
+    async def execute(
+        cls,
+        image: torch.Tensor,
+        prompt_text: str,
+        model: str = "I2V-01",
+        seed: int = 0,
+    ) -> comfy_io.NodeOutput:
+        return await _generate_mm_video(
+            auth={
+                "auth_token": cls.hidden.auth_token_comfy_org,
+                "comfy_api_key": cls.hidden.api_key_comfy_org,
            },
-            "optional": {
-                "seed": (
-                    IO.INT,
-                    {
-                        "default": 0,
-                        "min": 0,
-                        "max": 0xFFFFFFFFFFFFFFFF,
-                        "control_after_generate": True,
-                        "tooltip": "The random seed used for creating the noise.",
-                    },
-                ),
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-                "unique_id": "UNIQUE_ID",
-            },
-        }
-
-    RETURN_TYPES = ("VIDEO",)
-    DESCRIPTION = "Generates videos from an image and prompts using MiniMax's API"
-    FUNCTION = "generate_video"
-    CATEGORY = "api node/video/MiniMax"
-    API_NODE = True
+            node_id=cls.hidden.unique_id,
+            prompt_text=prompt_text,
+            seed=seed,
+            model=model,
+            image=image,
+            subject=None,
+            average_duration=I2V_AVERAGE_DURATION,
+        )


-class MinimaxSubjectToVideoNode(MinimaxTextToVideoNode):
+class MinimaxSubjectToVideoNode(comfy_io.ComfyNode):
    """
    Generates videos synchronously based on an image and prompt, and optional parameters using MiniMax's API.
    """

-    AVERAGE_DURATION = T2V_AVERAGE_DURATION
+    @classmethod
+    def define_schema(cls) -> comfy_io.Schema:
+        return comfy_io.Schema(
+            node_id="MinimaxSubjectToVideoNode",
+            display_name="MiniMax Subject to Video",
+            category="api node/video/MiniMax",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.Image.Input(
+                    "subject",
+                    tooltip="Image of subject to reference for video generation",
+                ),
+                comfy_io.String.Input(
+                    "prompt_text",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt to guide the video generation",
+                ),
+                comfy_io.Combo.Input(
+                    "model",
+                    options=["S2V-01"],
+                    default="S2V-01",
+                    tooltip="Model to use for video generation",
+                ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    step=1,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                    optional=True,
+                ),
+            ],
+            outputs=[comfy_io.Video.Output()],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )

    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "subject": (
-                    IO.IMAGE,
-                    {
-                        "tooltip": "Image of subject to reference video generation"
-                    },
-                ),
-                "prompt_text": (
-                    "STRING",
-                    {
-                        "multiline": True,
-                        "default": "",
-                        "tooltip": "Text prompt to guide the video generation",
-                    },
-                ),
-                "model": (
-                    [
-                        "S2V-01",
-                    ],
-                    {
-                        "default": "S2V-01",
-                        "tooltip": "Model to use for video generation",
-                    },
-                ),
+    async def execute(
+        cls,
+        subject: torch.Tensor,
+        prompt_text: str,
+        model: str = "S2V-01",
+        seed: int = 0,
+    ) -> comfy_io.NodeOutput:
+        return await _generate_mm_video(
+            auth={
+                "auth_token": cls.hidden.auth_token_comfy_org,
+                "comfy_api_key": cls.hidden.api_key_comfy_org,
            },
-            "optional": {
-                "seed": (
-                    IO.INT,
-                    {
-                        "default": 0,
-                        "min": 0,
-                        "max": 0xFFFFFFFFFFFFFFFF,
-                        "control_after_generate": True,
-                        "tooltip": "The random seed used for creating the noise.",
-                    },
-                ),
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-                "unique_id": "UNIQUE_ID",
-            },
-        }
-
-    RETURN_TYPES = ("VIDEO",)
-    DESCRIPTION = "Generates videos from an image and prompts using MiniMax's API"
-    FUNCTION = "generate_video"
-    CATEGORY = "api node/video/MiniMax"
-    API_NODE = True
+            node_id=cls.hidden.unique_id,
+            prompt_text=prompt_text,
+            seed=seed,
+            model=model,
+            image=None,
+            subject=subject,
+            average_duration=T2V_AVERAGE_DURATION,
+        )


-class MinimaxHailuoVideoNode:
+class MinimaxHailuoVideoNode(comfy_io.ComfyNode):
    """Generates videos from prompt, with optional start frame using the new MiniMax Hailuo-02 model."""

    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "prompt_text": (
-                    "STRING",
-                    {
-                        "multiline": True,
-                        "default": "",
-                        "tooltip": "Text prompt to guide the video generation.",
-                    },
+    def define_schema(cls) -> comfy_io.Schema:
+        return comfy_io.Schema(
+            node_id="MinimaxHailuoVideoNode",
+            display_name="MiniMax Hailuo Video",
+            category="api node/video/MiniMax",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.String.Input(
+                    "prompt_text",
+                    multiline=True,
+                    default="",
+                    tooltip="Text prompt to guide the video generation.",
                ),
-            },
-            "optional": {
-                "seed": (
-                    IO.INT,
-                    {
-                        "default": 0,
-                        "min": 0,
-                        "max": 0xFFFFFFFFFFFFFFFF,
-                        "control_after_generate": True,
-                        "tooltip": "The random seed used for creating the noise.",
-                    },
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    step=1,
+                    control_after_generate=True,
+                    tooltip="The random seed used for creating the noise.",
+                    optional=True,
                ),
-                "first_frame_image": (
-                    IO.IMAGE,
-                    {
-                        "tooltip": "Optional image to use as the first frame to generate a video."
-                    },
+                comfy_io.Image.Input(
+                    "first_frame_image",
+                    tooltip="Optional image to use as the first frame to generate a video.",
+                    optional=True,
                ),
-                "prompt_optimizer": (
-                    IO.BOOLEAN,
-                    {
-                        "tooltip": "Optimize prompt to improve generation quality when needed.",
-                        "default": True,
-                    },
+                comfy_io.Boolean.Input(
+                    "prompt_optimizer",
+                    default=True,
+                    tooltip="Optimize prompt to improve generation quality when needed.",
+                    optional=True,
                ),
-                "duration": (
-                    IO.COMBO,
-                    {
-                        "tooltip": "The length of the output video in seconds.",
-                        "default": 6,
-                        "options": [6, 10],
-                    },
+                comfy_io.Combo.Input(
+                    "duration",
+                    options=[6, 10],
+                    default=6,
+                    tooltip="The length of the output video in seconds.",
+                    optional=True,
                ),
-                "resolution": (
-                    IO.COMBO,
-                    {
-                        "tooltip": "The dimensions of the video display. "
-                                   "1080p corresponds to 1920 x 1080 pixels, 768p corresponds to 1366 x 768 pixels.",
-                        "default": "768P",
-                        "options": ["768P", "1080P"],
-                    },
+                comfy_io.Combo.Input(
+                    "resolution",
+                    options=["768P", "1080P"],
+                    default="768P",
+                    tooltip="The dimensions of the video display. 1080p is 1920x1080, 768p is 1366x768.",
+                    optional=True,
                ),
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-                "unique_id": "UNIQUE_ID",
-            },
+            ],
+            outputs=[comfy_io.Video.Output()],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt_text: str,
+        seed: int = 0,
+        first_frame_image: Optional[torch.Tensor] = None,  # used for ImageToVideo
+        prompt_optimizer: bool = True,
+        duration: int = 6,
+        resolution: str = "768P",
+        model: str = "MiniMax-Hailuo-02",
+    ) -> comfy_io.NodeOutput:
+        auth = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
        }
-
-    RETURN_TYPES = ("VIDEO",)
-    DESCRIPTION = cleandoc(__doc__ or "")
-    FUNCTION = "generate_video"
-    CATEGORY = "api node/video/MiniMax"
-    API_NODE = True
-
-    async def generate_video(
-        self,
-        prompt_text,
-        seed=0,
-        first_frame_image: torch.Tensor=None, # used for ImageToVideo
-        prompt_optimizer=True,
-        duration=6,
-        resolution="768P",
-        model="MiniMax-Hailuo-02",
-        unique_id: Union[str, None]=None,
-        **kwargs,
-    ):
        if first_frame_image is None:
            validate_string(prompt_text, field_name="prompt_text")

@@ -408,7 +435,7 @@ class MinimaxHailuoVideoNode:
        # upload image, if passed in
        image_url = None
        if first_frame_image is not None:
-            image_url = (await upload_images_to_comfyapi(first_frame_image, max_images=1, auth_kwargs=kwargs))[0]
+            image_url = (await upload_images_to_comfyapi(first_frame_image, max_images=1, auth_kwargs=auth))[0]

        video_generate_operation = SynchronousOperation(
            endpoint=ApiEndpoint(
@@ -426,7 +453,7 @@ class MinimaxHailuoVideoNode:
                duration=duration,
                resolution=resolution,
            ),
-            auth_kwargs=kwargs,
+            auth_kwargs=auth,
        )
        response = await video_generate_operation.execute()

@@ -447,8 +474,8 @@ class MinimaxHailuoVideoNode:
            failed_statuses=["Fail"],
            status_extractor=lambda x: x.status.value,
            estimated_duration=average_duration,
-            node_id=unique_id,
-            auth_kwargs=kwargs,
+            node_id=cls.hidden.unique_id,
+            auth_kwargs=auth,
        )
        task_result = await video_generate_operation.execute()

@@ -464,7 +491,7 @@ class MinimaxHailuoVideoNode:
                query_params={"file_id": int(file_id)},
            ),
            request=EmptyRequest(),
-            auth_kwargs=kwargs,
+            auth_kwargs=auth,
        )
        file_result = await file_retrieve_operation.execute()

@@ -474,34 +501,31 @@ class MinimaxHailuoVideoNode:
                f"No video was found in the response. Full response: {file_result.model_dump()}"
            )
        logging.info(f"Generated video URL: {file_url}")
-        if unique_id:
+        if cls.hidden.unique_id:
            if hasattr(file_result.file, "backup_download_url"):
                message = f"Result URL: {file_url}\nBackup URL: {file_result.file.backup_download_url}"
            else:
                message = f"Result URL: {file_url}"
-            PromptServer.instance.send_progress_text(message, unique_id)
+            PromptServer.instance.send_progress_text(message, cls.hidden.unique_id)

        video_io = await download_url_to_bytesio(file_url)
        if video_io is None:
            error_msg = f"Failed to download video from {file_url}"
            logging.error(error_msg)
            raise Exception(error_msg)
-        return (VideoFromFile(video_io),)
+        return comfy_io.NodeOutput(VideoFromFile(video_io))


-# A dictionary that contains all nodes you want to export with their names
-# NOTE: names should be globally unique
-NODE_CLASS_MAPPINGS = {
-    "MinimaxTextToVideoNode": MinimaxTextToVideoNode,
-    "MinimaxImageToVideoNode": MinimaxImageToVideoNode,
-    # "MinimaxSubjectToVideoNode": MinimaxSubjectToVideoNode,
-    "MinimaxHailuoVideoNode": MinimaxHailuoVideoNode,
-}
+class MinimaxExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[comfy_io.ComfyNode]]:
+        return [
+            MinimaxTextToVideoNode,
+            MinimaxImageToVideoNode,
+            # MinimaxSubjectToVideoNode,
+            MinimaxHailuoVideoNode,
+        ]

-# A dictionary that contains the friendly/humanly readable titles for the nodes
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "MinimaxTextToVideoNode": "MiniMax Text to Video",
-    "MinimaxImageToVideoNode": "MiniMax Image to Video",
-    "MinimaxSubjectToVideoNode": "MiniMax Subject to Video",
-    "MinimaxHailuoVideoNode": "MiniMax Hailuo Video",
-}
+
+async def comfy_entrypoint() -> MinimaxExtension:
+    return MinimaxExtension()
@@ -1,11 +1,8 @@
 import logging
 from typing import Any, Callable, Optional, TypeVar
 import torch
-from comfy_api_nodes.util.validation_utils import (
-    get_image_dimensions,
-    validate_image_dimensions,
-)
-
+from typing_extensions import override
+from comfy_api_nodes.util.validation_utils import validate_image_dimensions

 from comfy_api_nodes.apis import (
    MoonvalleyTextToVideoRequest,
@@ -26,11 +23,9 @@ from comfy_api_nodes.apinode_utils import (
    upload_images_to_comfyapi,
    upload_video_to_comfyapi,
 )
-from comfy_api_nodes.mapper_utils import model_field_to_node_input

-from comfy_api.input.video_types import VideoInput
-from comfy.comfy_types.node_typing import IO
-from comfy_api.input_impl import VideoFromFile
+from comfy_api.input import VideoInput
+from comfy_api.latest import ComfyExtension, InputImpl, io as comfy_io
 import av
 import io

@@ -133,47 +128,6 @@ def validate_prompts(
    return True


-def validate_input_media(width, height, with_frame_conditioning, num_frames_in=None):
-    # inference validation
-    # T = num_frames
-    # in all cases, the following must be true: T divisible by 16 and H,W by 8. in addition...
-    # with image conditioning: H*W must be divisible by 8192
-    # without image conditioning: T divisible by 32
-    if num_frames_in and not num_frames_in % 16 == 0:
-        return False, ("The input video total frame count must be divisible by 16!")
-
-    if height % 8 != 0 or width % 8 != 0:
-        return False, (
-            f"Height ({height}) and width ({width}) must be " "divisible by 8"
-        )
-
-    if with_frame_conditioning:
-        if (height * width) % 8192 != 0:
-            return False, (
-                f"Height * width ({height * width}) must be "
-                "divisible by 8192 for frame conditioning"
-            )
-    else:
-        if num_frames_in and not num_frames_in % 32 == 0:
-            return False, ("The input video total frame count must be divisible by 32!")
-
-
-def validate_input_image(
-    image: torch.Tensor, with_frame_conditioning: bool = False
-) -> None:
-    """
-    Validates the input image adheres to the expectations of the API:
-    - The image resolution should not be less than 300*300px
-    - The aspect ratio of the image should be between 1:2.5 ~ 2.5:1
-
-    """
-    height, width = get_image_dimensions(image)
-    validate_input_media(width, height, with_frame_conditioning)
-    validate_image_dimensions(
-        image, min_width=300, min_height=300, max_height=MAX_HEIGHT, max_width=MAX_WIDTH
-    )
-
-
 def validate_video_to_video_input(video: VideoInput) -> VideoInput:
    """
    Validates and processes video input for Moonvalley Video-to-Video generation.
@@ -362,7 +316,7 @@ def trim_video(video: VideoInput, duration_sec: float) -> VideoInput:

        # Return as VideoFromFile using the buffer
        output_buffer.seek(0)
-        return VideoFromFile(output_buffer)
+        return InputImpl.VideoFromFile(output_buffer)

    except Exception as e:
        # Clean up on error
@@ -373,166 +327,150 @@ def trim_video(video: VideoInput, duration_sec: float) -> VideoInput:
        raise RuntimeError(f"Failed to trim video: {str(e)}") from e


-# --- BaseMoonvalleyVideoNode ---
-class BaseMoonvalleyVideoNode:
-    def parseWidthHeightFromRes(self, resolution: str):
-        # Accepts a string like "16:9 (1920 x 1080)" and returns width, height as a dict
-        res_map = {
-            "16:9 (1920 x 1080)": {"width": 1920, "height": 1080},
-            "9:16 (1080 x 1920)": {"width": 1080, "height": 1920},
-            "1:1 (1152 x 1152)": {"width": 1152, "height": 1152},
-            "4:3 (1536 x 1152)": {"width": 1536, "height": 1152},
-            "3:4 (1152 x 1536)": {"width": 1152, "height": 1536},
-            "21:9 (2560 x 1080)": {"width": 2560, "height": 1080},
-        }
-        if resolution in res_map:
-            return res_map[resolution]
-        else:
-            # Default to 1920x1080 if unknown
-            return {"width": 1920, "height": 1080}
+def parse_width_height_from_res(resolution: str):
+    # Accepts a string like "16:9 (1920 x 1080)" and returns width, height as a dict
+    res_map = {
+        "16:9 (1920 x 1080)": {"width": 1920, "height": 1080},
+        "9:16 (1080 x 1920)": {"width": 1080, "height": 1920},
+        "1:1 (1152 x 1152)": {"width": 1152, "height": 1152},
+        "4:3 (1536 x 1152)": {"width": 1536, "height": 1152},
+        "3:4 (1152 x 1536)": {"width": 1152, "height": 1536},
+        "21:9 (2560 x 1080)": {"width": 2560, "height": 1080},
+    }
+    return res_map.get(resolution, {"width": 1920, "height": 1080})

-    def parseControlParameter(self, value):
-        control_map = {
-            "Motion Transfer": "motion_control",
-            "Canny": "canny_control",
-            "Pose Transfer": "pose_control",
-            "Depth": "depth_control",
-        }
-        if value in control_map:
-            return control_map[value]
-        else:
-            return control_map["Motion Transfer"]

-    async def get_response(
-        self, task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
-    ) -> MoonvalleyPromptResponse:
-        return await poll_until_finished(
-            auth_kwargs,
-            ApiEndpoint(
-                path=f"{API_PROMPTS_ENDPOINT}/{task_id}",
-                method=HttpMethod.GET,
-                request_model=EmptyRequest,
-                response_model=MoonvalleyPromptResponse,
-            ),
-            result_url_extractor=get_video_url_from_response,
-            node_id=node_id,
-        )
+def parse_control_parameter(value):
+    control_map = {
+        "Motion Transfer": "motion_control",
+        "Canny": "canny_control",
+        "Pose Transfer": "pose_control",
+        "Depth": "depth_control",
+    }
+    return control_map.get(value, control_map["Motion Transfer"])
+
+
+async def get_response(
+    task_id: str, auth_kwargs: dict[str, str], node_id: Optional[str] = None
+) -> MoonvalleyPromptResponse:
+    return await poll_until_finished(
+        auth_kwargs,
+        ApiEndpoint(
+            path=f"{API_PROMPTS_ENDPOINT}/{task_id}",
+            method=HttpMethod.GET,
+            request_model=EmptyRequest,
+            response_model=MoonvalleyPromptResponse,
+        ),
+        result_url_extractor=get_video_url_from_response,
+        node_id=node_id,
+    )
+
+
+class MoonvalleyImg2VideoNode(comfy_io.ComfyNode):

    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "prompt": model_field_to_node_input(
-                    IO.STRING,
-                    MoonvalleyTextToVideoRequest,
-                    "prompt_text",
+    def define_schema(cls) -> comfy_io.Schema:
+        return comfy_io.Schema(
+            node_id="MoonvalleyImg2VideoNode",
+            display_name="Moonvalley Marey Image to Video",
+            category="api node/video/Moonvalley Marey",
+            description="Moonvalley Marey Image to Video Node",
+            inputs=[
+                comfy_io.Image.Input(
+                    "image",
+                    tooltip="The reference image used to generate the video",
+                ),
+                comfy_io.String.Input(
+                    "prompt",
                    multiline=True,
                ),
-                "negative_prompt": model_field_to_node_input(
-                    IO.STRING,
-                    MoonvalleyTextToVideoInferenceParams,
+                comfy_io.String.Input(
                    "negative_prompt",
                    multiline=True,
-                    default="<synthetic> <scene cut> gopro, bright, contrast, static, overexposed, vignette, artifacts, still, noise, texture, scanlines, videogame, 360 camera, VR, transition, flare, saturation, distorted, warped, wide angle, saturated, vibrant, glowing, cross dissolve, cheesy, ugly hands, mutated hands, mutant, disfigured, extra fingers, blown out, horrible, blurry, worst quality, bad, dissolve, melt, fade in, fade out, wobbly, weird, low quality, plastic, stock footage, video camera, boring",
+                    default="<synthetic> <scene cut> gopro, bright, contrast, static, overexposed, vignette, "
+                            "artifacts, still, noise, texture, scanlines, videogame, 360 camera, VR, transition, "
+                            "flare, saturation, distorted, warped, wide angle, saturated, vibrant, glowing, "
+                            "cross dissolve, cheesy, ugly hands, mutated hands, mutant, disfigured, extra fingers, "
+                            "blown out, horrible, blurry, worst quality, bad, dissolve, melt, fade in, fade out, "
+                            "wobbly, weird, low quality, plastic, stock footage, video camera, boring",
+                    tooltip="Negative prompt text",
                ),
-                "resolution": (
-                    IO.COMBO,
-                    {
-                        "options": [
-                            "16:9 (1920 x 1080)",
-                            "9:16 (1080 x 1920)",
-                            "1:1 (1152 x 1152)",
-                            "4:3 (1440 x 1080)",
-                            "3:4 (1080 x 1440)",
-                            "21:9 (2560 x 1080)",
-                        ],
-                        "default": "16:9 (1920 x 1080)",
-                        "tooltip": "Resolution of the output video",
-                    },
+                comfy_io.Combo.Input(
+                    "resolution",
+                    options=[
+                        "16:9 (1920 x 1080)",
+                        "9:16 (1080 x 1920)",
+                        "1:1 (1152 x 1152)",
+                        "4:3 (1536 x 1152)",
+                        "3:4 (1152 x 1536)",
+                        "21:9 (2560 x 1080)",
+                    ],
+                    default="16:9 (1920 x 1080)",
+                    tooltip="Resolution of the output video",
                ),
-                "prompt_adherence": model_field_to_node_input(
-                    IO.FLOAT,
-                    MoonvalleyTextToVideoInferenceParams,
-                    "guidance_scale",
+                comfy_io.Float.Input(
+                    "prompt_adherence",
                    default=10.0,
-                    step=1,
-                    min=1,
-                    max=20,
+                    min=1.0,
+                    max=20.0,
+                    step=1.0,
+                    tooltip="Guidance scale for generation control",
                ),
-                "seed": model_field_to_node_input(
-                    IO.INT,
-                    MoonvalleyTextToVideoInferenceParams,
+                comfy_io.Int.Input(
                    "seed",
                    default=9,
                    min=0,
                    max=4294967295,
                    step=1,
-                    display="number",
+                    display_mode=comfy_io.NumberDisplay.number,
                    tooltip="Random seed value",
                ),
-                "steps": model_field_to_node_input(
-                    IO.INT,
-                    MoonvalleyTextToVideoInferenceParams,
+                comfy_io.Int.Input(
                    "steps",
                    default=100,
                    min=1,
                    max=100,
+                    step=1,
+                    tooltip="Number of denoising steps",
                ),
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-                "unique_id": "UNIQUE_ID",
-            },
-            "optional": {
-                "image": model_field_to_node_input(
-                    IO.IMAGE,
-                    MoonvalleyTextToVideoRequest,
-                    "image_url",
-                    tooltip="The reference image used to generate the video",
-                ),
-            },
-        }
-
-    RETURN_TYPES = ("STRING",)
-    FUNCTION = "generate"
-    CATEGORY = "api node/video/Moonvalley Marey"
-    API_NODE = True
-
-    def generate(self, **kwargs):
-        return None
-
-
-# --- MoonvalleyImg2VideoNode ---
-class MoonvalleyImg2VideoNode(BaseMoonvalleyVideoNode):
+            ],
+            outputs=[comfy_io.Video.Output()],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )

    @classmethod
-    def INPUT_TYPES(cls):
-        return super().INPUT_TYPES()
-
-    RETURN_TYPES = ("VIDEO",)
-    RETURN_NAMES = ("video",)
-    DESCRIPTION = "Moonvalley Marey Image to Video Node"
-
-    async def generate(
-        self, prompt, negative_prompt, unique_id: Optional[str] = None, **kwargs
-    ):
-        image = kwargs.get("image", None)
-        if image is None:
-            raise MoonvalleyApiError("image is required")
-
-        validate_input_image(image, True)
+    async def execute(
+        cls,
+        image: torch.Tensor,
+        prompt: str,
+        negative_prompt: str,
+        resolution: str,
+        prompt_adherence: float,
+        seed: int,
+        steps: int,
+    ) -> comfy_io.NodeOutput:
+        validate_image_dimensions(image, min_width=300, min_height=300, max_height=MAX_HEIGHT, max_width=MAX_WIDTH)
        validate_prompts(prompt, negative_prompt, MOONVALLEY_MAREY_MAX_PROMPT_LENGTH)
-        width_height = self.parseWidthHeightFromRes(kwargs.get("resolution"))
+        width_height = parse_width_height_from_res(resolution)
+
+        auth = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
+        }

        inference_params = MoonvalleyTextToVideoInferenceParams(
            negative_prompt=negative_prompt,
-            steps=kwargs.get("steps"),
-            seed=kwargs.get("seed"),
-            guidance_scale=kwargs.get("prompt_adherence"),
+            steps=steps,
+            seed=seed,
+            guidance_scale=prompt_adherence,
            num_frames=128,
-            width=width_height.get("width"),
-            height=width_height.get("height"),
+            width=width_height["width"],
+            height=width_height["height"],
            use_negative_prompts=True,
        )
        """Upload image to comfy backend to have a URL available for further processing"""
@@ -541,7 +479,7 @@ class MoonvalleyImg2VideoNode(BaseMoonvalleyVideoNode):

        image_url = (
            await upload_images_to_comfyapi(
-                image, max_images=1, auth_kwargs=kwargs, mime_type=mime_type
+                image, max_images=1, auth_kwargs=auth, mime_type=mime_type
            )
        )[0]

@@ -556,127 +494,102 @@ class MoonvalleyImg2VideoNode(BaseMoonvalleyVideoNode):
                response_model=MoonvalleyPromptResponse,
            ),
            request=request,
-            auth_kwargs=kwargs,
+            auth_kwargs=auth,
        )
        task_creation_response = await initial_operation.execute()
        validate_task_creation_response(task_creation_response)
        task_id = task_creation_response.id

-        final_response = await self.get_response(
-            task_id, auth_kwargs=kwargs, node_id=unique_id
+        final_response = await get_response(
+            task_id, auth_kwargs=auth, node_id=cls.hidden.unique_id
        )
        video = await download_url_to_video_output(final_response.output_url)
-        return (video,)
+        return comfy_io.NodeOutput(video)


-# --- MoonvalleyVid2VidNode ---
-class MoonvalleyVideo2VideoNode(BaseMoonvalleyVideoNode):
-    def __init__(self):
-        super().__init__()
+class MoonvalleyVideo2VideoNode(comfy_io.ComfyNode):

    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "prompt": model_field_to_node_input(
-                    IO.STRING,
-                    MoonvalleyVideoToVideoRequest,
-                    "prompt_text",
+    def define_schema(cls) -> comfy_io.Schema:
+        return comfy_io.Schema(
+            node_id="MoonvalleyVideo2VideoNode",
+            display_name="Moonvalley Marey Video to Video",
+            category="api node/video/Moonvalley Marey",
+            description="",
+            inputs=[
+                comfy_io.String.Input(
+                    "prompt",
                    multiline=True,
+                    tooltip="Describes the video to generate",
                ),
-                "negative_prompt": model_field_to_node_input(
-                    IO.STRING,
-                    MoonvalleyVideoToVideoInferenceParams,
+                comfy_io.String.Input(
                    "negative_prompt",
                    multiline=True,
-                    default="<synthetic> <scene cut> gopro, bright, contrast, static, overexposed, vignette, artifacts, still, noise, texture, scanlines, videogame, 360 camera, VR, transition, flare, saturation, distorted, warped, wide angle, saturated, vibrant, glowing, cross dissolve, cheesy, ugly hands, mutated hands, mutant, disfigured, extra fingers, blown out, horrible, blurry, worst quality, bad, dissolve, melt, fade in, fade out, wobbly, weird, low quality, plastic, stock footage, video camera, boring",
+                    default="<synthetic> <scene cut> gopro, bright, contrast, static, overexposed, vignette, "
+                            "artifacts, still, noise, texture, scanlines, videogame, 360 camera, VR, transition, "
+                            "flare, saturation, distorted, warped, wide angle, saturated, vibrant, glowing, "
+                            "cross dissolve, cheesy, ugly hands, mutated hands, mutant, disfigured, extra fingers, "
+                            "blown out, horrible, blurry, worst quality, bad, dissolve, melt, fade in, fade out, "
+                            "wobbly, weird, low quality, plastic, stock footage, video camera, boring",
+                    tooltip="Negative prompt text",
                ),
-                "seed": model_field_to_node_input(
-                    IO.INT,
-                    MoonvalleyVideoToVideoInferenceParams,
+                comfy_io.Int.Input(
                    "seed",
                    default=9,
                    min=0,
                    max=4294967295,
                    step=1,
-                    display="number",
+                    display_mode=comfy_io.NumberDisplay.number,
                    tooltip="Random seed value",
                    control_after_generate=False,
                ),
-                "prompt_adherence": model_field_to_node_input(
-                    IO.FLOAT,
-                    MoonvalleyVideoToVideoInferenceParams,
-                    "guidance_scale",
-                    default=10.0,
+                comfy_io.Video.Input(
+                    "video",
+                    tooltip="The reference video used to generate the output video. Must be at least 5 seconds long. "
+                            "Videos longer than 5s will be automatically trimmed. Only MP4 format supported.",
+                ),
+                comfy_io.Combo.Input(
+                    "control_type",
+                    options=["Motion Transfer", "Pose Transfer"],
+                    default="Motion Transfer",
+                    optional=True,
+                ),
+                comfy_io.Int.Input(
+                    "motion_intensity",
+                    default=100,
+                    min=0,
+                    max=100,
                    step=1,
-                    min=1,
-                    max=20,
+                    tooltip="Only used if control_type is 'Motion Transfer'",
+                    optional=True,
                ),
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-                "unique_id": "UNIQUE_ID",
-            },
-            "optional": {
-                "video": (
-                    IO.VIDEO,
-                    {
-                        "default": "",
-                        "multiline": False,
-                        "tooltip": "The reference video used to generate the output video. Must be at least 5 seconds long. Videos longer than 5s will be automatically trimmed. Only MP4 format supported.",
-                    },
-                ),
-                "control_type": (
-                    ["Motion Transfer", "Pose Transfer"],
-                    {"default": "Motion Transfer"},
-                ),
-                "motion_intensity": (
-                    "INT",
-                    {
-                        "default": 100,
-                        "step": 1,
-                        "min": 0,
-                        "max": 100,
-                        "tooltip": "Only used if control_type is 'Motion Transfer'",
-                    },
-                ),
-                "image": model_field_to_node_input(
-                    IO.IMAGE,
-                    MoonvalleyTextToVideoRequest,
-                    "image_url",
-                    tooltip="The reference image used to generate the video",
-                ),
-            },
+            ],
+            outputs=[comfy_io.Video.Output()],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        negative_prompt: str,
+        seed: int,
+        video: Optional[VideoInput] = None,
+        control_type: str = "Motion Transfer",
+        motion_intensity: Optional[int] = 100,
+    ) -> comfy_io.NodeOutput:
+        auth = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
        }

-    RETURN_TYPES = ("VIDEO",)
-    RETURN_NAMES = ("video",)
-
-    async def generate(
-        self, prompt, negative_prompt, unique_id: Optional[str] = None, **kwargs
-    ):
-        video = kwargs.get("video")
-        image = kwargs.get("image", None)
-
-        if not video:
-            raise MoonvalleyApiError("video is required")
-
-        video_url = ""
-        if video:
-            validated_video = validate_video_to_video_input(video)
-            video_url = await upload_video_to_comfyapi(
-                validated_video, auth_kwargs=kwargs
-            )
-        mime_type = "image/png"
-
-        if not image is None:
-            validate_input_image(image, with_frame_conditioning=True)
-            image_url = await upload_images_to_comfyapi(
-                image=image, auth_kwargs=kwargs, max_images=1, mime_type=mime_type
-            )
-        control_type = kwargs.get("control_type")
-        motion_intensity = kwargs.get("motion_intensity")
+        validated_video = validate_video_to_video_input(video)
+        video_url = await upload_video_to_comfyapi(validated_video, auth_kwargs=auth)

        """Validate prompts and inference input"""
        validate_prompts(prompt, negative_prompt)
@@ -688,11 +601,11 @@ class MoonvalleyVideo2VideoNode(BaseMoonvalleyVideoNode):

        inference_params = MoonvalleyVideoToVideoInferenceParams(
            negative_prompt=negative_prompt,
-            seed=kwargs.get("seed"),
+            seed=seed,
            control_params=control_params,
        )

-        control = self.parseControlParameter(control_type)
+        control = parse_control_parameter(control_type)

        request = MoonvalleyVideoToVideoRequest(
            control_type=control,
@@ -700,7 +613,6 @@ class MoonvalleyVideo2VideoNode(BaseMoonvalleyVideoNode):
            prompt_text=prompt,
            inference_params=inference_params,
        )
-        request.image_url = image_url if not image is None else None

        initial_operation = SynchronousOperation(
            endpoint=ApiEndpoint(
@@ -710,58 +622,125 @@ class MoonvalleyVideo2VideoNode(BaseMoonvalleyVideoNode):
                response_model=MoonvalleyPromptResponse,
            ),
            request=request,
-            auth_kwargs=kwargs,
+            auth_kwargs=auth,
        )
        task_creation_response = await initial_operation.execute()
        validate_task_creation_response(task_creation_response)
        task_id = task_creation_response.id

-        final_response = await self.get_response(
-            task_id, auth_kwargs=kwargs, node_id=unique_id
+        final_response = await get_response(
+            task_id, auth_kwargs=auth, node_id=cls.hidden.unique_id
        )

        video = await download_url_to_video_output(final_response.output_url)
-
-        return (video,)
+        return comfy_io.NodeOutput(video)


-# --- MoonvalleyTxt2VideoNode ---
-class MoonvalleyTxt2VideoNode(BaseMoonvalleyVideoNode):
-    def __init__(self):
-        super().__init__()
-
-    RETURN_TYPES = ("VIDEO",)
-    RETURN_NAMES = ("video",)
+class MoonvalleyTxt2VideoNode(comfy_io.ComfyNode):

    @classmethod
-    def INPUT_TYPES(cls):
-        input_types = super().INPUT_TYPES()
-        # Remove image-specific parameters
-        for param in ["image"]:
-            if param in input_types["optional"]:
-                del input_types["optional"][param]
-        return input_types
+    def define_schema(cls) -> comfy_io.Schema:
+        return comfy_io.Schema(
+            node_id="MoonvalleyTxt2VideoNode",
+            display_name="Moonvalley Marey Text to Video",
+            category="api node/video/Moonvalley Marey",
+            description="",
+            inputs=[
+                comfy_io.String.Input(
+                    "prompt",
+                    multiline=True,
+                ),
+                comfy_io.String.Input(
+                    "negative_prompt",
+                    multiline=True,
+                    default="<synthetic> <scene cut> gopro, bright, contrast, static, overexposed, vignette, "
+                            "artifacts, still, noise, texture, scanlines, videogame, 360 camera, VR, transition, "
+                            "flare, saturation, distorted, warped, wide angle, saturated, vibrant, glowing, "
+                            "cross dissolve, cheesy, ugly hands, mutated hands, mutant, disfigured, extra fingers, "
+                            "blown out, horrible, blurry, worst quality, bad, dissolve, melt, fade in, fade out, "
+                            "wobbly, weird, low quality, plastic, stock footage, video camera, boring",
+                    tooltip="Negative prompt text",
+                ),
+                comfy_io.Combo.Input(
+                    "resolution",
+                    options=[
+                        "16:9 (1920 x 1080)",
+                        "9:16 (1080 x 1920)",
+                        "1:1 (1152 x 1152)",
+                        "4:3 (1536 x 1152)",
+                        "3:4 (1152 x 1536)",
+                        "21:9 (2560 x 1080)",
+                    ],
+                    default="16:9 (1920 x 1080)",
+                    tooltip="Resolution of the output video",
+                ),
+                comfy_io.Float.Input(
+                    "prompt_adherence",
+                    default=10.0,
+                    min=1.0,
+                    max=20.0,
+                    step=1.0,
+                    tooltip="Guidance scale for generation control",
+                ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=9,
+                    min=0,
+                    max=4294967295,
+                    step=1,
+                    display_mode=comfy_io.NumberDisplay.number,
+                    tooltip="Random seed value",
+                ),
+                comfy_io.Int.Input(
+                    "steps",
+                    default=100,
+                    min=1,
+                    max=100,
+                    step=1,
+                    tooltip="Inference steps",
+                ),
+            ],
+            outputs=[comfy_io.Video.Output()],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )

-    async def generate(
-        self, prompt, negative_prompt, unique_id: Optional[str] = None, **kwargs
-    ):
+    @classmethod
+    async def execute(
+        cls,
+        prompt: str,
+        negative_prompt: str,
+        resolution: str,
+        prompt_adherence: float,
+        seed: int,
+        steps: int,
+    ) -> comfy_io.NodeOutput:
        validate_prompts(prompt, negative_prompt, MOONVALLEY_MAREY_MAX_PROMPT_LENGTH)
-        width_height = self.parseWidthHeightFromRes(kwargs.get("resolution"))
+        width_height = parse_width_height_from_res(resolution)
+
+        auth = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
+        }

        inference_params = MoonvalleyTextToVideoInferenceParams(
            negative_prompt=negative_prompt,
-            steps=kwargs.get("steps"),
-            seed=kwargs.get("seed"),
-            guidance_scale=kwargs.get("prompt_adherence"),
+            steps=steps,
+            seed=seed,
+            guidance_scale=prompt_adherence,
            num_frames=128,
-            width=width_height.get("width"),
-            height=width_height.get("height"),
+            width=width_height["width"],
+            height=width_height["height"],
        )
        request = MoonvalleyTextToVideoRequest(
            prompt_text=prompt, inference_params=inference_params
        )

-        initial_operation = SynchronousOperation(
+        init_op = SynchronousOperation(
            endpoint=ApiEndpoint(
                path=API_TXT2VIDEO_ENDPOINT,
                method=HttpMethod.POST,
@@ -769,29 +748,29 @@ class MoonvalleyTxt2VideoNode(BaseMoonvalleyVideoNode):
                response_model=MoonvalleyPromptResponse,
            ),
            request=request,
-            auth_kwargs=kwargs,
+            auth_kwargs=auth,
        )
-        task_creation_response = await initial_operation.execute()
+        task_creation_response = await init_op.execute()
        validate_task_creation_response(task_creation_response)
        task_id = task_creation_response.id

-        final_response = await self.get_response(
-            task_id, auth_kwargs=kwargs, node_id=unique_id
+        final_response = await get_response(
+            task_id, auth_kwargs=auth, node_id=cls.hidden.unique_id
        )

        video = await download_url_to_video_output(final_response.output_url)
-        return (video,)
+        return comfy_io.NodeOutput(video)


-NODE_CLASS_MAPPINGS = {
-    "MoonvalleyImg2VideoNode": MoonvalleyImg2VideoNode,
-    "MoonvalleyTxt2VideoNode": MoonvalleyTxt2VideoNode,
-    "MoonvalleyVideo2VideoNode": MoonvalleyVideo2VideoNode,
-}
+class MoonvalleyExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[comfy_io.ComfyNode]]:
+        return [
+            MoonvalleyImg2VideoNode,
+            MoonvalleyTxt2VideoNode,
+            MoonvalleyVideo2VideoNode,
+        ]


-NODE_DISPLAY_NAME_MAPPINGS = {
-    "MoonvalleyImg2VideoNode": "Moonvalley Marey Image to Video",
-    "MoonvalleyTxt2VideoNode": "Moonvalley Marey Text to Video",
-    "MoonvalleyVideo2VideoNode": "Moonvalley Marey Video to Video",
-}
+async def comfy_entrypoint() -> MoonvalleyExtension:
+    return MoonvalleyExtension()
@@ -1,5 +1,7 @@
 from inspect import cleandoc
 from typing import Optional
+from typing_extensions import override
+from io import BytesIO
 from comfy_api_nodes.apis.pixverse_api import (
    PixverseTextVideoRequest,
    PixverseImageVideoRequest,
@@ -26,12 +28,11 @@ from comfy_api_nodes.apinode_utils import (
    tensor_to_bytesio,
    validate_string,
 )
-from comfy.comfy_types.node_typing import IO, ComfyNodeABC
 from comfy_api.input_impl import VideoFromFile
+from comfy_api.latest import ComfyExtension, io as comfy_io

 import torch
 import aiohttp
-from io import BytesIO


 AVERAGE_DURATION_T2V = 32
@@ -72,100 +73,101 @@ async def upload_image_to_pixverse(image: torch.Tensor, auth_kwargs=None):
    return response_upload.Resp.img_id


-class PixverseTemplateNode:
+class PixverseTemplateNode(comfy_io.ComfyNode):
    """
    Select template for PixVerse Video generation.
    """

-    RETURN_TYPES = (PixverseIO.TEMPLATE,)
-    RETURN_NAMES = ("pixverse_template",)
-    FUNCTION = "create_template"
-    CATEGORY = "api node/video/PixVerse"
+    @classmethod
+    def define_schema(cls) -> comfy_io.Schema:
+        return comfy_io.Schema(
+            node_id="PixverseTemplateNode",
+            display_name="PixVerse Template",
+            category="api node/video/PixVerse",
+            inputs=[
+                comfy_io.Combo.Input("template", options=[list(pixverse_templates.keys())]),
+            ],
+            outputs=[comfy_io.Custom(PixverseIO.TEMPLATE).Output(display_name="pixverse_template")],
+        )

    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "template": (list(pixverse_templates.keys()),),
-            }
-        }
-
-    def create_template(self, template: str):
+    def execute(cls, template: str) -> comfy_io.NodeOutput:
        template_id = pixverse_templates.get(template, None)
        if template_id is None:
            raise Exception(f"Template '{template}' is not recognized.")
        # just return the integer
-        return (template_id,)
+        return comfy_io.NodeOutput(template_id)


-class PixverseTextToVideoNode(ComfyNodeABC):
+class PixverseTextToVideoNode(comfy_io.ComfyNode):
    """
    Generates videos based on prompt and output_size.
    """

-    RETURN_TYPES = (IO.VIDEO,)
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
-    FUNCTION = "api_call"
-    API_NODE = True
-    CATEGORY = "api node/video/PixVerse"
+    @classmethod
+    def define_schema(cls) -> comfy_io.Schema:
+        return comfy_io.Schema(
+            node_id="PixverseTextToVideoNode",
+            display_name="PixVerse Text to Video",
+            category="api node/video/PixVerse",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the video generation",
+                ),
+                comfy_io.Combo.Input(
+                    "aspect_ratio",
+                    options=[ratio.value for ratio in PixverseAspectRatio],
+                ),
+                comfy_io.Combo.Input(
+                    "quality",
+                    options=[resolution.value for resolution in PixverseQuality],
+                    default=PixverseQuality.res_540p,
+                ),
+                comfy_io.Combo.Input(
+                    "duration_seconds",
+                    options=[dur.value for dur in PixverseDuration],
+                ),
+                comfy_io.Combo.Input(
+                    "motion_mode",
+                    options=[mode.value for mode in PixverseMotionMode],
+                ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed for video generation.",
+                ),
+                comfy_io.String.Input(
+                    "negative_prompt",
+                    default="",
+                    force_input=True,
+                    tooltip="An optional text description of undesired elements on an image.",
+                    optional=True,
+                ),
+                comfy_io.Custom(PixverseIO.TEMPLATE).Input(
+                    "pixverse_template",
+                    tooltip="An optional template to influence style of generation, created by the PixVerse Template node.",
+                    optional=True,
+                ),
+            ],
+            outputs=[comfy_io.Video.Output()],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )

    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "prompt": (
-                    IO.STRING,
-                    {
-                        "multiline": True,
-                        "default": "",
-                        "tooltip": "Prompt for the video generation",
-                    },
-                ),
-                "aspect_ratio": ([ratio.value for ratio in PixverseAspectRatio],),
-                "quality": (
-                    [resolution.value for resolution in PixverseQuality],
-                    {
-                        "default": PixverseQuality.res_540p,
-                    },
-                ),
-                "duration_seconds": ([dur.value for dur in PixverseDuration],),
-                "motion_mode": ([mode.value for mode in PixverseMotionMode],),
-                "seed": (
-                    IO.INT,
-                    {
-                        "default": 0,
-                        "min": 0,
-                        "max": 2147483647,
-                        "control_after_generate": True,
-                        "tooltip": "Seed for video generation.",
-                    },
-                ),
-            },
-            "optional": {
-                "negative_prompt": (
-                    IO.STRING,
-                    {
-                        "default": "",
-                        "forceInput": True,
-                        "tooltip": "An optional text description of undesired elements on an image.",
-                    },
-                ),
-                "pixverse_template": (
-                    PixverseIO.TEMPLATE,
-                    {
-                        "tooltip": "An optional template to influence style of generation, created by the PixVerse Template node."
-                    },
-                ),
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-                "unique_id": "UNIQUE_ID",
-            },
-        }
-
-    async def api_call(
-        self,
+    async def execute(
+        cls,
        prompt: str,
        aspect_ratio: str,
        quality: str,
@@ -174,9 +176,7 @@ class PixverseTextToVideoNode(ComfyNodeABC):
        seed,
        negative_prompt: str = None,
        pixverse_template: int = None,
-        unique_id: Optional[str] = None,
-        **kwargs,
-    ):
+    ) -> comfy_io.NodeOutput:
        validate_string(prompt, strip_whitespace=False)
        # 1080p is limited to 5 seconds duration
        # only normal motion_mode supported for 1080p or for non-5 second duration
@@ -186,6 +186,10 @@ class PixverseTextToVideoNode(ComfyNodeABC):
        elif duration_seconds != PixverseDuration.dur_5:
            motion_mode = PixverseMotionMode.normal

+        auth = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
+        }
        operation = SynchronousOperation(
            endpoint=ApiEndpoint(
                path="/proxy/pixverse/video/text/generate",
@@ -203,7 +207,7 @@ class PixverseTextToVideoNode(ComfyNodeABC):
                template_id=pixverse_template,
                seed=seed,
            ),
-            auth_kwargs=kwargs,
+            auth_kwargs=auth,
        )
        response_api = await operation.execute()

@@ -224,8 +228,8 @@ class PixverseTextToVideoNode(ComfyNodeABC):
                PixverseStatus.deleted,
            ],
            status_extractor=lambda x: x.Resp.status,
-            auth_kwargs=kwargs,
-            node_id=unique_id,
+            auth_kwargs=auth,
+            node_id=cls.hidden.unique_id,
            result_url_extractor=get_video_url_from_response,
            estimated_duration=AVERAGE_DURATION_T2V,
        )
@@ -233,77 +237,75 @@ class PixverseTextToVideoNode(ComfyNodeABC):

        async with aiohttp.ClientSession() as session:
            async with session.get(response_poll.Resp.url) as vid_response:
-                return (VideoFromFile(BytesIO(await vid_response.content.read())),)
+                return comfy_io.NodeOutput(VideoFromFile(BytesIO(await vid_response.content.read())))


-class PixverseImageToVideoNode(ComfyNodeABC):
+class PixverseImageToVideoNode(comfy_io.ComfyNode):
    """
    Generates videos based on prompt and output_size.
    """

-    RETURN_TYPES = (IO.VIDEO,)
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
-    FUNCTION = "api_call"
-    API_NODE = True
-    CATEGORY = "api node/video/PixVerse"
+    @classmethod
+    def define_schema(cls) -> comfy_io.Schema:
+        return comfy_io.Schema(
+            node_id="PixverseImageToVideoNode",
+            display_name="PixVerse Image to Video",
+            category="api node/video/PixVerse",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.Image.Input("image"),
+                comfy_io.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the video generation",
+                ),
+                comfy_io.Combo.Input(
+                    "quality",
+                    options=[resolution.value for resolution in PixverseQuality],
+                    default=PixverseQuality.res_540p,
+                ),
+                comfy_io.Combo.Input(
+                    "duration_seconds",
+                    options=[dur.value for dur in PixverseDuration],
+                ),
+                comfy_io.Combo.Input(
+                    "motion_mode",
+                    options=[mode.value for mode in PixverseMotionMode],
+                ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed for video generation.",
+                ),
+                comfy_io.String.Input(
+                    "negative_prompt",
+                    default="",
+                    force_input=True,
+                    tooltip="An optional text description of undesired elements on an image.",
+                    optional=True,
+                ),
+                comfy_io.Custom(PixverseIO.TEMPLATE).Input(
+                    "pixverse_template",
+                    tooltip="An optional template to influence style of generation, created by the PixVerse Template node.",
+                    optional=True,
+                ),
+            ],
+            outputs=[comfy_io.Video.Output()],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )

    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": (IO.IMAGE,),
-                "prompt": (
-                    IO.STRING,
-                    {
-                        "multiline": True,
-                        "default": "",
-                        "tooltip": "Prompt for the video generation",
-                    },
-                ),
-                "quality": (
-                    [resolution.value for resolution in PixverseQuality],
-                    {
-                        "default": PixverseQuality.res_540p,
-                    },
-                ),
-                "duration_seconds": ([dur.value for dur in PixverseDuration],),
-                "motion_mode": ([mode.value for mode in PixverseMotionMode],),
-                "seed": (
-                    IO.INT,
-                    {
-                        "default": 0,
-                        "min": 0,
-                        "max": 2147483647,
-                        "control_after_generate": True,
-                        "tooltip": "Seed for video generation.",
-                    },
-                ),
-            },
-            "optional": {
-                "negative_prompt": (
-                    IO.STRING,
-                    {
-                        "default": "",
-                        "forceInput": True,
-                        "tooltip": "An optional text description of undesired elements on an image.",
-                    },
-                ),
-                "pixverse_template": (
-                    PixverseIO.TEMPLATE,
-                    {
-                        "tooltip": "An optional template to influence style of generation, created by the PixVerse Template node."
-                    },
-                ),
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-                "unique_id": "UNIQUE_ID",
-            },
-        }
-
-    async def api_call(
-        self,
+    async def execute(
+        cls,
        image: torch.Tensor,
        prompt: str,
        quality: str,
@@ -312,11 +314,13 @@ class PixverseImageToVideoNode(ComfyNodeABC):
        seed,
        negative_prompt: str = None,
        pixverse_template: int = None,
-        unique_id: Optional[str] = None,
-        **kwargs,
-    ):
+    ) -> comfy_io.NodeOutput:
        validate_string(prompt, strip_whitespace=False)
-        img_id = await upload_image_to_pixverse(image, auth_kwargs=kwargs)
+        auth = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
+        }
+        img_id = await upload_image_to_pixverse(image, auth_kwargs=auth)

        # 1080p is limited to 5 seconds duration
        # only normal motion_mode supported for 1080p or for non-5 second duration
@@ -343,7 +347,7 @@ class PixverseImageToVideoNode(ComfyNodeABC):
                template_id=pixverse_template,
                seed=seed,
            ),
-            auth_kwargs=kwargs,
+            auth_kwargs=auth,
        )
        response_api = await operation.execute()

@@ -364,8 +368,8 @@ class PixverseImageToVideoNode(ComfyNodeABC):
                PixverseStatus.deleted,
            ],
            status_extractor=lambda x: x.Resp.status,
-            auth_kwargs=kwargs,
-            node_id=unique_id,
+            auth_kwargs=auth,
+            node_id=cls.hidden.unique_id,
            result_url_extractor=get_video_url_from_response,
            estimated_duration=AVERAGE_DURATION_I2V,
        )
@@ -373,72 +377,71 @@ class PixverseImageToVideoNode(ComfyNodeABC):

        async with aiohttp.ClientSession() as session:
            async with session.get(response_poll.Resp.url) as vid_response:
-                return (VideoFromFile(BytesIO(await vid_response.content.read())),)
+                return comfy_io.NodeOutput(VideoFromFile(BytesIO(await vid_response.content.read())))


-class PixverseTransitionVideoNode(ComfyNodeABC):
+class PixverseTransitionVideoNode(comfy_io.ComfyNode):
    """
    Generates videos based on prompt and output_size.
    """

-    RETURN_TYPES = (IO.VIDEO,)
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
-    FUNCTION = "api_call"
-    API_NODE = True
-    CATEGORY = "api node/video/PixVerse"
+    @classmethod
+    def define_schema(cls) -> comfy_io.Schema:
+        return comfy_io.Schema(
+            node_id="PixverseTransitionVideoNode",
+            display_name="PixVerse Transition Video",
+            category="api node/video/PixVerse",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.Image.Input("first_frame"),
+                comfy_io.Image.Input("last_frame"),
+                comfy_io.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the video generation",
+                ),
+                comfy_io.Combo.Input(
+                    "quality",
+                    options=[resolution.value for resolution in PixverseQuality],
+                    default=PixverseQuality.res_540p,
+                ),
+                comfy_io.Combo.Input(
+                    "duration_seconds",
+                    options=[dur.value for dur in PixverseDuration],
+                ),
+                comfy_io.Combo.Input(
+                    "motion_mode",
+                    options=[mode.value for mode in PixverseMotionMode],
+                ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    control_after_generate=True,
+                    tooltip="Seed for video generation.",
+                ),
+                comfy_io.String.Input(
+                    "negative_prompt",
+                    default="",
+                    force_input=True,
+                    tooltip="An optional text description of undesired elements on an image.",
+                    optional=True,
+                ),
+            ],
+            outputs=[comfy_io.Video.Output()],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )

    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "first_frame": (IO.IMAGE,),
-                "last_frame": (IO.IMAGE,),
-                "prompt": (
-                    IO.STRING,
-                    {
-                        "multiline": True,
-                        "default": "",
-                        "tooltip": "Prompt for the video generation",
-                    },
-                ),
-                "quality": (
-                    [resolution.value for resolution in PixverseQuality],
-                    {
-                        "default": PixverseQuality.res_540p,
-                    },
-                ),
-                "duration_seconds": ([dur.value for dur in PixverseDuration],),
-                "motion_mode": ([mode.value for mode in PixverseMotionMode],),
-                "seed": (
-                    IO.INT,
-                    {
-                        "default": 0,
-                        "min": 0,
-                        "max": 2147483647,
-                        "control_after_generate": True,
-                        "tooltip": "Seed for video generation.",
-                    },
-                ),
-            },
-            "optional": {
-                "negative_prompt": (
-                    IO.STRING,
-                    {
-                        "default": "",
-                        "forceInput": True,
-                        "tooltip": "An optional text description of undesired elements on an image.",
-                    },
-                ),
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-                "unique_id": "UNIQUE_ID",
-            },
-        }
-
-    async def api_call(
-        self,
+    async def execute(
+        cls,
        first_frame: torch.Tensor,
        last_frame: torch.Tensor,
        prompt: str,
@@ -447,12 +450,14 @@ class PixverseTransitionVideoNode(ComfyNodeABC):
        motion_mode: str,
        seed,
        negative_prompt: str = None,
-        unique_id: Optional[str] = None,
-        **kwargs,
-    ):
+    ) -> comfy_io.NodeOutput:
        validate_string(prompt, strip_whitespace=False)
-        first_frame_id = await upload_image_to_pixverse(first_frame, auth_kwargs=kwargs)
-        last_frame_id = await upload_image_to_pixverse(last_frame, auth_kwargs=kwargs)
+        auth = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
+        }
+        first_frame_id = await upload_image_to_pixverse(first_frame, auth_kwargs=auth)
+        last_frame_id = await upload_image_to_pixverse(last_frame, auth_kwargs=auth)

        # 1080p is limited to 5 seconds duration
        # only normal motion_mode supported for 1080p or for non-5 second duration
@@ -479,7 +484,7 @@ class PixverseTransitionVideoNode(ComfyNodeABC):
                negative_prompt=negative_prompt if negative_prompt else None,
                seed=seed,
            ),
-            auth_kwargs=kwargs,
+            auth_kwargs=auth,
        )
        response_api = await operation.execute()

@@ -500,8 +505,8 @@ class PixverseTransitionVideoNode(ComfyNodeABC):
                PixverseStatus.deleted,
            ],
            status_extractor=lambda x: x.Resp.status,
-            auth_kwargs=kwargs,
-            node_id=unique_id,
+            auth_kwargs=auth,
+            node_id=cls.hidden.unique_id,
            result_url_extractor=get_video_url_from_response,
            estimated_duration=AVERAGE_DURATION_T2V,
        )
@@ -509,19 +514,19 @@ class PixverseTransitionVideoNode(ComfyNodeABC):

        async with aiohttp.ClientSession() as session:
            async with session.get(response_poll.Resp.url) as vid_response:
-                return (VideoFromFile(BytesIO(await vid_response.content.read())),)
+                return comfy_io.NodeOutput(VideoFromFile(BytesIO(await vid_response.content.read())))


-NODE_CLASS_MAPPINGS = {
-    "PixverseTextToVideoNode": PixverseTextToVideoNode,
-    "PixverseImageToVideoNode": PixverseImageToVideoNode,
-    "PixverseTransitionVideoNode": PixverseTransitionVideoNode,
-    "PixverseTemplateNode": PixverseTemplateNode,
-}
+class PixVerseExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[comfy_io.ComfyNode]]:
+        return [
+            PixverseTextToVideoNode,
+            PixverseImageToVideoNode,
+            PixverseTransitionVideoNode,
+            PixverseTemplateNode,
+        ]

-NODE_DISPLAY_NAME_MAPPINGS = {
-    "PixverseTextToVideoNode": "PixVerse Text to Video",
-    "PixverseImageToVideoNode": "PixVerse Image to Video",
-    "PixverseTransitionVideoNode": "PixVerse Transition Video",
-    "PixverseTemplateNode": "PixVerse Template",
-}
+
+async def comfy_entrypoint() -> PixVerseExtension:
+    return PixVerseExtension()
@@ -38,48 +38,48 @@ from PIL import UnidentifiedImageError


 async def handle_recraft_file_request(
-        image: torch.Tensor,
-        path: str,
-        mask: torch.Tensor=None,
-        total_pixels=4096*4096,
-        timeout=1024,
-        request=None,
-        auth_kwargs: dict[str,str] = None,
-    ) -> list[BytesIO]:
-        """
-        Handle sending common Recraft file-only request to get back file bytes.
-        """
-        if request is None:
-            request = EmptyRequest()
+    image: torch.Tensor,
+    path: str,
+    mask: torch.Tensor=None,
+    total_pixels=4096*4096,
+    timeout=1024,
+    request=None,
+    auth_kwargs: dict[str,str] = None,
+) -> list[BytesIO]:
+    """
+    Handle sending common Recraft file-only request to get back file bytes.
+    """
+    if request is None:
+        request = EmptyRequest()

-        files = {
-            'image': tensor_to_bytesio(image, total_pixels=total_pixels).read()
-        }
-        if mask is not None:
-            files['mask'] = tensor_to_bytesio(mask, total_pixels=total_pixels).read()
+    files = {
+        'image': tensor_to_bytesio(image, total_pixels=total_pixels).read()
+    }
+    if mask is not None:
+        files['mask'] = tensor_to_bytesio(mask, total_pixels=total_pixels).read()

-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path=path,
-                method=HttpMethod.POST,
-                request_model=type(request),
-                response_model=RecraftImageGenerationResponse,
-            ),
-            request=request,
-            files=files,
-            content_type="multipart/form-data",
-            auth_kwargs=auth_kwargs,
-            multipart_parser=recraft_multipart_parser,
-        )
-        response: RecraftImageGenerationResponse = await operation.execute()
-        all_bytesio = []
-        if response.image is not None:
-            all_bytesio.append(await download_url_to_bytesio(response.image.url, timeout=timeout))
-        else:
-            for data in response.data:
-                all_bytesio.append(await download_url_to_bytesio(data.url, timeout=timeout))
+    operation = SynchronousOperation(
+        endpoint=ApiEndpoint(
+            path=path,
+            method=HttpMethod.POST,
+            request_model=type(request),
+            response_model=RecraftImageGenerationResponse,
+        ),
+        request=request,
+        files=files,
+        content_type="multipart/form-data",
+        auth_kwargs=auth_kwargs,
+        multipart_parser=recraft_multipart_parser,
+    )
+    response: RecraftImageGenerationResponse = await operation.execute()
+    all_bytesio = []
+    if response.image is not None:
+        all_bytesio.append(await download_url_to_bytesio(response.image.url, timeout=timeout))
+    else:
+        for data in response.data:
+            all_bytesio.append(await download_url_to_bytesio(data.url, timeout=timeout))

-        return all_bytesio
+    return all_bytesio


 def recraft_multipart_parser(data, parent_key=None, formatter: callable=None, converted_to_check: list[list]=None, is_list=False) -> dict:
@@ -7,15 +7,15 @@ Rodin API docs: https://developer.hyper3d.ai/

 from __future__ import annotations
 from inspect import cleandoc
-from comfy.comfy_types.node_typing import IO
 import folder_paths as comfy_paths
 import aiohttp
 import os
-import datetime
 import asyncio
-import io
 import logging
 import math
+from typing import Optional
+from io import BytesIO
+from typing_extensions import override
 from PIL import Image
 from comfy_api_nodes.apis.rodin_api import (
    Rodin3DGenerateRequest,
@@ -32,444 +32,548 @@ from comfy_api_nodes.apis.client import (
    SynchronousOperation,
    PollingOperation,
 )
+from comfy_api.latest import ComfyExtension, io as comfy_io


-COMMON_PARAMETERS = {
-    "Seed": (
-        IO.INT,
-        {
-            "default":0,
-            "min":0,
-            "max":65535,
-            "display":"number"
-        }
+COMMON_PARAMETERS = [
+    comfy_io.Int.Input(
+        "Seed",
+        default=0,
+        min=0,
+        max=65535,
+        display_mode=comfy_io.NumberDisplay.number,
+        optional=True,
    ),
-    "Material_Type": (
-        IO.COMBO,
-        {
-            "options": ["PBR", "Shaded"],
-            "default": "PBR"
-        }
+    comfy_io.Combo.Input("Material_Type", options=["PBR", "Shaded"], default="PBR", optional=True),
+    comfy_io.Combo.Input(
+        "Polygon_count",
+        options=["4K-Quad", "8K-Quad", "18K-Quad", "50K-Quad", "200K-Triangle"],
+        default="18K-Quad",
+        optional=True,
    ),
-    "Polygon_count": (
-        IO.COMBO,
-        {
-            "options": ["4K-Quad", "8K-Quad", "18K-Quad", "50K-Quad", "200K-Triangle"],
-            "default": "18K-Quad"
-        }
+]
+
+
+def get_quality_mode(poly_count):
+    polycount = poly_count.split("-")
+    poly = polycount[1]
+    count = polycount[0]
+    if poly == "Triangle":
+        mesh_mode = "Raw"
+    elif poly == "Quad":
+        mesh_mode = "Quad"
+    else:
+        mesh_mode = "Quad"
+
+    if count == "4K":
+        quality_override = 4000
+    elif count == "8K":
+        quality_override = 8000
+    elif count == "18K":
+        quality_override = 18000
+    elif count == "50K":
+        quality_override = 50000
+    elif count == "2K":
+        quality_override = 2000
+    elif count == "20K":
+        quality_override = 20000
+    elif count == "150K":
+        quality_override = 150000
+    elif count == "500K":
+        quality_override = 500000
+    else:
+        quality_override = 18000
+
+    return mesh_mode, quality_override
+
+
+def tensor_to_filelike(tensor, max_pixels: int = 2048*2048):
+    """
+    Converts a PyTorch tensor to a file-like object.
+
+    Args:
+    - tensor (torch.Tensor): A tensor representing an image of shape (H, W, C)
+      where C is the number of channels (3 for RGB), H is height, and W is width.
+
+    Returns:
+    - io.BytesIO: A file-like object containing the image data.
+    """
+    array = tensor.cpu().numpy()
+    array = (array * 255).astype('uint8')
+    image = Image.fromarray(array, 'RGB')
+
+    original_width, original_height = image.size
+    original_pixels = original_width * original_height
+    if original_pixels > max_pixels:
+        scale = math.sqrt(max_pixels / original_pixels)
+        new_width = int(original_width * scale)
+        new_height = int(original_height * scale)
+    else:
+        new_width, new_height = original_width, original_height
+
+    if new_width != original_width or new_height != original_height:
+        image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
+
+    img_byte_arr = BytesIO()
+    image.save(img_byte_arr, format='PNG')  # PNG is used for lossless compression
+    img_byte_arr.seek(0)
+    return img_byte_arr
+
+
+async def create_generate_task(
+    images=None,
+    seed=1,
+    material="PBR",
+    quality_override=18000,
+    tier="Regular",
+    mesh_mode="Quad",
+    TAPose = False,
+    auth_kwargs: Optional[dict[str, str]] = None,
+):
+    if images is None:
+        raise Exception("Rodin 3D generate requires at least 1 image.")
+    if len(images) > 5:
+        raise Exception("Rodin 3D generate requires up to 5 image.")
+
+    path = "/proxy/rodin/api/v2/rodin"
+    operation = SynchronousOperation(
+        endpoint=ApiEndpoint(
+            path=path,
+            method=HttpMethod.POST,
+            request_model=Rodin3DGenerateRequest,
+            response_model=Rodin3DGenerateResponse,
+        ),
+        request=Rodin3DGenerateRequest(
+            seed=seed,
+            tier=tier,
+            material=material,
+            quality_override=quality_override,
+            mesh_mode=mesh_mode,
+            TAPose=TAPose,
+        ),
+        files=[
+            (
+                "images",
+                open(image, "rb") if isinstance(image, str) else tensor_to_filelike(image)
+            )
+            for image in images if image is not None
+        ],
+        content_type="multipart/form-data",
+        auth_kwargs=auth_kwargs,
    )
-}

-def create_task_error(response: Rodin3DGenerateResponse):
-    """Check if the response has error"""
-    return hasattr(response, "error")
+    response = await operation.execute()
+
+    if hasattr(response, "error"):
+        error_message = f"Rodin3D Create 3D generate Task Failed. Message: {response.message}, error: {response.error}"
+        logging.error(error_message)
+        raise Exception(error_message)
+
+    logging.info("[ Rodin3D API - Submit Jobs ] Submit Generate Task Success!")
+    subscription_key = response.jobs.subscription_key
+    task_uuid = response.uuid
+    logging.info(f"[ Rodin3D API - Submit Jobs ] UUID: {task_uuid}")
+    return task_uuid, subscription_key


-class Rodin3DAPI:
-    """
-    Generate 3D Assets using Rodin API
-    """
-    RETURN_TYPES = (IO.STRING,)
-    RETURN_NAMES = ("3D Model Path",)
-    CATEGORY = "api node/3d/Rodin"
-    DESCRIPTION = cleandoc(__doc__ or "")
-    FUNCTION = "api_call"
-    API_NODE = True
-
-    def tensor_to_filelike(self, tensor, max_pixels: int = 2048*2048):
-        """
-        Converts a PyTorch tensor to a file-like object.
-
-        Args:
-        - tensor (torch.Tensor): A tensor representing an image of shape (H, W, C)
-          where C is the number of channels (3 for RGB), H is height, and W is width.
-
-        Returns:
-        - io.BytesIO: A file-like object containing the image data.
-        """
-        array = tensor.cpu().numpy()
-        array = (array * 255).astype('uint8')
-        image = Image.fromarray(array, 'RGB')
-
-        original_width, original_height = image.size
-        original_pixels = original_width * original_height
-        if original_pixels > max_pixels:
-            scale = math.sqrt(max_pixels / original_pixels)
-            new_width = int(original_width * scale)
-            new_height = int(original_height * scale)
-        else:
-            new_width, new_height = original_width, original_height
-
-        if new_width != original_width or new_height != original_height:
-            image = image.resize((new_width, new_height), Image.Resampling.LANCZOS)
-
-        img_byte_arr = io.BytesIO()
-        image.save(img_byte_arr, format='PNG')  # PNG is used for lossless compression
-        img_byte_arr.seek(0)
-        return img_byte_arr
-
-    def check_rodin_status(self, response: Rodin3DCheckStatusResponse) -> str:
-        has_failed = any(job.status == JobStatus.Failed for job in response.jobs)
-        all_done = all(job.status == JobStatus.Done for job in response.jobs)
-        status_list = [str(job.status) for job in response.jobs]
-        logging.info(f"[ Rodin3D API - CheckStatus ] Generate Status: {status_list}")
-        if has_failed:
-            logging.error(f"[ Rodin3D API - CheckStatus ] Generate Failed: {status_list}, Please try again.")
-            raise Exception("[ Rodin3D API ] Generate Failed, Please Try again.")
-        elif all_done:
-            return "DONE"
-        else:
-            return "Generating"
-
-    async def create_generate_task(self, images=None, seed=1, material="PBR", quality="medium", tier="Regular", mesh_mode="Quad", **kwargs):
-        if images is None:
-            raise Exception("Rodin 3D generate requires at least 1 image.")
-        if len(images) >= 5:
-            raise Exception("Rodin 3D generate requires up to 5 image.")
-
-        path = "/proxy/rodin/api/v2/rodin"
-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path=path,
-                method=HttpMethod.POST,
-                request_model=Rodin3DGenerateRequest,
-                response_model=Rodin3DGenerateResponse,
-            ),
-            request=Rodin3DGenerateRequest(
-                seed=seed,
-                tier=tier,
-                material=material,
-                quality=quality,
-                mesh_mode=mesh_mode
-            ),
-            files=[
-                (
-                    "images",
-                    open(image, "rb") if isinstance(image, str) else self.tensor_to_filelike(image)
-                )
-                for image in images if image is not None
-            ],
-            content_type = "multipart/form-data",
-            auth_kwargs=kwargs,
-        )
-
-        response = await operation.execute()
-
-        if create_task_error(response):
-            error_message = f"Rodin3D Create 3D generate Task Failed. Message: {response.message}, error: {response.error}"
-            logging.error(error_message)
-            raise Exception(error_message)
-
-        logging.info("[ Rodin3D API - Submit Jobs ] Submit Generate Task Success!")
-        subscription_key = response.jobs.subscription_key
-        task_uuid = response.uuid
-        logging.info(f"[ Rodin3D API - Submit Jobs ] UUID: {task_uuid}")
-        return task_uuid, subscription_key
-
-    async def poll_for_task_status(self, subscription_key, **kwargs) -> Rodin3DCheckStatusResponse:
-
-        path = "/proxy/rodin/api/v2/status"
-
-        poll_operation = PollingOperation(
-            poll_endpoint=ApiEndpoint(
-                path = path,
-                method=HttpMethod.POST,
-                request_model=Rodin3DCheckStatusRequest,
-                response_model=Rodin3DCheckStatusResponse,
-            ),
-            request=Rodin3DCheckStatusRequest(
-                subscription_key = subscription_key
-            ),
-            completed_statuses=["DONE"],
-            failed_statuses=["FAILED"],
-            status_extractor=self.check_rodin_status,
-            poll_interval=3.0,
-            auth_kwargs=kwargs,
-        )
-
-        logging.info("[ Rodin3D API - CheckStatus ] Generate Start!")
-
-        return await poll_operation.execute()
-
-    async def get_rodin_download_list(self, uuid, **kwargs) -> Rodin3DDownloadResponse:
-        logging.info("[ Rodin3D API - Downloading ] Generate Successfully!")
-
-        path = "/proxy/rodin/api/v2/download"
-        operation = SynchronousOperation(
-            endpoint=ApiEndpoint(
-                path=path,
-                method=HttpMethod.POST,
-                request_model=Rodin3DDownloadRequest,
-                response_model=Rodin3DDownloadResponse,
-            ),
-            request=Rodin3DDownloadRequest(
-                task_uuid=uuid
-            ),
-            auth_kwargs=kwargs
-        )
-
-        return await operation.execute()
-
-    def get_quality_mode(self, poly_count):
-        if poly_count == "200K-Triangle":
-            mesh_mode = "Raw"
-            quality = "medium"
-        else:
-            mesh_mode = "Quad"
-            if poly_count == "4K-Quad":
-                quality = "extra-low"
-            elif poly_count == "8K-Quad":
-                quality = "low"
-            elif poly_count == "18K-Quad":
-                quality = "medium"
-            elif poly_count == "50K-Quad":
-                quality = "high"
-            else:
-                quality = "medium"
-
-        return mesh_mode, quality
-
-    async def download_files(self, url_list):
-        save_path = os.path.join(comfy_paths.get_output_directory(), "Rodin3D", datetime.datetime.now().strftime("%Y-%m-%d_%H-%M-%S"))
-        os.makedirs(save_path, exist_ok=True)
-        model_file_path = None
-        async with aiohttp.ClientSession() as session:
-            for i in url_list.list:
-                url = i.url
-                file_name = i.name
-                file_path = os.path.join(save_path, file_name)
-                if file_path.endswith(".glb"):
-                    model_file_path = file_path
-                logging.info(f"[ Rodin3D API - download_files ] Downloading file: {file_path}")
-                max_retries = 5
-                for attempt in range(max_retries):
-                    try:
-                        async with session.get(url) as resp:
-                            resp.raise_for_status()
-                            with open(file_path, "wb") as f:
-                                async for chunk in resp.content.iter_chunked(32 * 1024):
-                                    f.write(chunk)
-                        break
-                    except Exception as e:
-                        logging.info(f"[ Rodin3D API - download_files ] Error downloading {file_path}:{e}")
-                        if attempt < max_retries - 1:
-                            logging.info("Retrying...")
-                            await asyncio.sleep(2)
-                        else:
-                            logging.info(
-                                "[ Rodin3D API - download_files ] Failed to download %s after %s attempts.",
-                                file_path,
-                                max_retries,
-                            )
-
-        return model_file_path
+def check_rodin_status(response: Rodin3DCheckStatusResponse) -> str:
+    all_done = all(job.status == JobStatus.Done for job in response.jobs)
+    status_list = [str(job.status) for job in response.jobs]
+    logging.info(f"[ Rodin3D API - CheckStatus ] Generate Status: {status_list}")
+    if any(job.status == JobStatus.Failed for job in response.jobs):
+        logging.error(f"[ Rodin3D API - CheckStatus ] Generate Failed: {status_list}, Please try again.")
+        raise Exception("[ Rodin3D API ] Generate Failed, Please Try again.")
+    if all_done:
+        return "DONE"
+    return "Generating"


-class Rodin3D_Regular(Rodin3DAPI):
+async def poll_for_task_status(
+    subscription_key, auth_kwargs: Optional[dict[str, str]] = None,
+) -> Rodin3DCheckStatusResponse:
+    poll_operation = PollingOperation(
+        poll_endpoint=ApiEndpoint(
+            path="/proxy/rodin/api/v2/status",
+            method=HttpMethod.POST,
+            request_model=Rodin3DCheckStatusRequest,
+            response_model=Rodin3DCheckStatusResponse,
+        ),
+        request=Rodin3DCheckStatusRequest(subscription_key=subscription_key),
+        completed_statuses=["DONE"],
+        failed_statuses=["FAILED"],
+        status_extractor=check_rodin_status,
+        poll_interval=3.0,
+        auth_kwargs=auth_kwargs,
+    )
+    logging.info("[ Rodin3D API - CheckStatus ] Generate Start!")
+    return await poll_operation.execute()
+
+
+async def get_rodin_download_list(uuid, auth_kwargs: Optional[dict[str, str]] = None) -> Rodin3DDownloadResponse:
+    logging.info("[ Rodin3D API - Downloading ] Generate Successfully!")
+    operation = SynchronousOperation(
+        endpoint=ApiEndpoint(
+            path="/proxy/rodin/api/v2/download",
+            method=HttpMethod.POST,
+            request_model=Rodin3DDownloadRequest,
+            response_model=Rodin3DDownloadResponse,
+        ),
+        request=Rodin3DDownloadRequest(task_uuid=uuid),
+        auth_kwargs=auth_kwargs,
+    )
+    return await operation.execute()
+
+
+async def download_files(url_list, task_uuid):
+    save_path = os.path.join(comfy_paths.get_output_directory(), f"Rodin3D_{task_uuid}")
+    os.makedirs(save_path, exist_ok=True)
+    model_file_path = None
+    async with aiohttp.ClientSession() as session:
+        for i in url_list.list:
+            url = i.url
+            file_name = i.name
+            file_path = os.path.join(save_path, file_name)
+            if file_path.endswith(".glb"):
+                model_file_path = file_path
+            logging.info(f"[ Rodin3D API - download_files ] Downloading file: {file_path}")
+            max_retries = 5
+            for attempt in range(max_retries):
+                try:
+                    async with session.get(url) as resp:
+                        resp.raise_for_status()
+                        with open(file_path, "wb") as f:
+                            async for chunk in resp.content.iter_chunked(32 * 1024):
+                                f.write(chunk)
+                    break
+                except Exception as e:
+                    logging.info(f"[ Rodin3D API - download_files ] Error downloading {file_path}:{e}")
+                    if attempt < max_retries - 1:
+                        logging.info("Retrying...")
+                        await asyncio.sleep(2)
+                    else:
+                        logging.info(
+                            "[ Rodin3D API - download_files ] Failed to download %s after %s attempts.",
+                            file_path,
+                            max_retries,
+                        )
+    return model_file_path
+
+
+class Rodin3D_Regular(comfy_io.ComfyNode):
+    """Generate 3D Assets using Rodin API"""
+
    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "Images":
-                (
-                    IO.IMAGE,
-                    {
-                        "forceInput":True,
-                    }
-                )
-            },
-            "optional": {
-                **COMMON_PARAMETERS
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-            },
-        }
+    def define_schema(cls) -> comfy_io.Schema:
+        return comfy_io.Schema(
+            node_id="Rodin3D_Regular",
+            display_name="Rodin 3D Generate - Regular Generate",
+            category="api node/3d/Rodin",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.Image.Input("Images"),
+                *COMMON_PARAMETERS,
+            ],
+            outputs=[comfy_io.String.Output(display_name="3D Model Path")],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+            ],
+            is_api_node=True,
+        )

-    async def api_call(
-        self,
+    @classmethod
+    async def execute(
+        cls,
        Images,
        Seed,
        Material_Type,
        Polygon_count,
-        **kwargs
-    ):
+    ) -> comfy_io.NodeOutput:
        tier = "Regular"
        num_images = Images.shape[0]
        m_images = []
        for i in range(num_images):
            m_images.append(Images[i])
-        mesh_mode, quality = self.get_quality_mode(Polygon_count)
-        task_uuid, subscription_key = await self.create_generate_task(images=m_images, seed=Seed, material=Material_Type,
-                                                                quality=quality, tier=tier, mesh_mode=mesh_mode,
-                                                                **kwargs)
-        await self.poll_for_task_status(subscription_key, **kwargs)
-        download_list = await self.get_rodin_download_list(task_uuid, **kwargs)
-        model = await self.download_files(download_list)
-
-        return (model,)
-
-
-class Rodin3D_Detail(Rodin3DAPI):
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "Images":
-                (
-                    IO.IMAGE,
-                    {
-                        "forceInput":True,
-                    }
-                )
-            },
-            "optional": {
-                **COMMON_PARAMETERS
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-            },
+        mesh_mode, quality_override = get_quality_mode(Polygon_count)
+        auth = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
        }
+        task_uuid, subscription_key = await create_generate_task(
+            images=m_images,
+            seed=Seed,
+            material=Material_Type,
+            quality_override=quality_override,
+            tier=tier,
+            mesh_mode=mesh_mode,
+            auth_kwargs=auth,
+        )
+        await poll_for_task_status(subscription_key, auth_kwargs=auth)
+        download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth)
+        model = await download_files(download_list, task_uuid)

-    async def api_call(
-        self,
+        return comfy_io.NodeOutput(model)
+
+
+class Rodin3D_Detail(comfy_io.ComfyNode):
+    """Generate 3D Assets using Rodin API"""
+
+    @classmethod
+    def define_schema(cls) -> comfy_io.Schema:
+        return comfy_io.Schema(
+            node_id="Rodin3D_Detail",
+            display_name="Rodin 3D Generate - Detail Generate",
+            category="api node/3d/Rodin",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.Image.Input("Images"),
+                *COMMON_PARAMETERS,
+            ],
+            outputs=[comfy_io.String.Output(display_name="3D Model Path")],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
        Images,
        Seed,
        Material_Type,
        Polygon_count,
-        **kwargs
-    ):
+    ) -> comfy_io.NodeOutput:
        tier = "Detail"
        num_images = Images.shape[0]
        m_images = []
        for i in range(num_images):
            m_images.append(Images[i])
-        mesh_mode, quality = self.get_quality_mode(Polygon_count)
-        task_uuid, subscription_key = await self.create_generate_task(images=m_images, seed=Seed, material=Material_Type,
-                                                                quality=quality, tier=tier, mesh_mode=mesh_mode,
-                                                                **kwargs)
-        await self.poll_for_task_status(subscription_key, **kwargs)
-        download_list = await self.get_rodin_download_list(task_uuid, **kwargs)
-        model = await self.download_files(download_list)
-
-        return (model,)
-
-
-class Rodin3D_Smooth(Rodin3DAPI):
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "Images":
-                (
-                    IO.IMAGE,
-                    {
-                        "forceInput":True,
-                    }
-                )
-            },
-            "optional": {
-                **COMMON_PARAMETERS
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-            },
+        mesh_mode, quality_override = get_quality_mode(Polygon_count)
+        auth = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
        }
+        task_uuid, subscription_key = await create_generate_task(
+            images=m_images,
+            seed=Seed,
+            material=Material_Type,
+            quality_override=quality_override,
+            tier=tier,
+            mesh_mode=mesh_mode,
+            auth_kwargs=auth,
+        )
+        await poll_for_task_status(subscription_key, auth_kwargs=auth)
+        download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth)
+        model = await download_files(download_list, task_uuid)

-    async def api_call(
-        self,
+        return comfy_io.NodeOutput(model)
+
+
+class Rodin3D_Smooth(comfy_io.ComfyNode):
+    """Generate 3D Assets using Rodin API"""
+
+    @classmethod
+    def define_schema(cls) -> comfy_io.Schema:
+        return comfy_io.Schema(
+            node_id="Rodin3D_Smooth",
+            display_name="Rodin 3D Generate - Smooth Generate",
+            category="api node/3d/Rodin",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.Image.Input("Images"),
+                *COMMON_PARAMETERS,
+            ],
+            outputs=[comfy_io.String.Output(display_name="3D Model Path")],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
        Images,
        Seed,
        Material_Type,
        Polygon_count,
-        **kwargs
-    ):
+    ) -> comfy_io.NodeOutput:
        tier = "Smooth"
        num_images = Images.shape[0]
        m_images = []
        for i in range(num_images):
            m_images.append(Images[i])
-        mesh_mode, quality = self.get_quality_mode(Polygon_count)
-        task_uuid, subscription_key = await self.create_generate_task(images=m_images, seed=Seed, material=Material_Type,
-                                                                quality=quality, tier=tier, mesh_mode=mesh_mode,
-                                                                **kwargs)
-        await self.poll_for_task_status(subscription_key, **kwargs)
-        download_list = await self.get_rodin_download_list(task_uuid, **kwargs)
-        model = await self.download_files(download_list)
-
-        return (model,)
-
-
-class Rodin3D_Sketch(Rodin3DAPI):
-    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "Images":
-                (
-                    IO.IMAGE,
-                    {
-                        "forceInput":True,
-                    }
-                )
-            },
-            "optional": {
-                "Seed":
-                (
-                    IO.INT,
-                    {
-                        "default":0,
-                        "min":0,
-                        "max":65535,
-                        "display":"number"
-                    }
-                )
-            },
-            "hidden": {
-                "auth_token": "AUTH_TOKEN_COMFY_ORG",
-                "comfy_api_key": "API_KEY_COMFY_ORG",
-            },
+        mesh_mode, quality_override = get_quality_mode(Polygon_count)
+        auth = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
        }
+        task_uuid, subscription_key = await create_generate_task(
+            images=m_images,
+            seed=Seed,
+            material=Material_Type,
+            quality_override=quality_override,
+            tier=tier,
+            mesh_mode=mesh_mode,
+            auth_kwargs=auth,
+        )
+        await poll_for_task_status(subscription_key, auth_kwargs=auth)
+        download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth)
+        model = await download_files(download_list, task_uuid)

-    async def api_call(
-        self,
+        return comfy_io.NodeOutput(model)
+
+
+class Rodin3D_Sketch(comfy_io.ComfyNode):
+    """Generate 3D Assets using Rodin API"""
+
+    @classmethod
+    def define_schema(cls) -> comfy_io.Schema:
+        return comfy_io.Schema(
+            node_id="Rodin3D_Sketch",
+            display_name="Rodin 3D Generate - Sketch Generate",
+            category="api node/3d/Rodin",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.Image.Input("Images"),
+                comfy_io.Int.Input(
+                    "Seed",
+                    default=0,
+                    min=0,
+                    max=65535,
+                    display_mode=comfy_io.NumberDisplay.number,
+                    optional=True,
+                ),
+            ],
+            outputs=[comfy_io.String.Output(display_name="3D Model Path")],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
        Images,
        Seed,
-        **kwargs
-    ):
+    ) -> comfy_io.NodeOutput:
        tier = "Sketch"
        num_images = Images.shape[0]
        m_images = []
        for i in range(num_images):
            m_images.append(Images[i])
        material_type = "PBR"
-        quality = "medium"
+        quality_override = 18000
        mesh_mode = "Quad"
-        task_uuid, subscription_key = await self.create_generate_task(
-            images=m_images, seed=Seed, material=material_type, quality=quality, tier=tier, mesh_mode=mesh_mode, **kwargs
+        auth = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
+        }
+        task_uuid, subscription_key = await create_generate_task(
+            images=m_images,
+            seed=Seed,
+            material=material_type,
+            quality_override=quality_override,
+            tier=tier,
+            mesh_mode=mesh_mode,
+            auth_kwargs=auth,
        )
-        await self.poll_for_task_status(subscription_key, **kwargs)
-        download_list = await self.get_rodin_download_list(task_uuid, **kwargs)
-        model = await self.download_files(download_list)
+        await poll_for_task_status(subscription_key, auth_kwargs=auth)
+        download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth)
+        model = await download_files(download_list, task_uuid)

-        return (model,)
+        return comfy_io.NodeOutput(model)

-# A dictionary that contains all nodes you want to export with their names
-# NOTE: names should be globally unique
-NODE_CLASS_MAPPINGS = {
-    "Rodin3D_Regular": Rodin3D_Regular,
-    "Rodin3D_Detail": Rodin3D_Detail,
-    "Rodin3D_Smooth": Rodin3D_Smooth,
-    "Rodin3D_Sketch": Rodin3D_Sketch,
-}

-# A dictionary that contains the friendly/humanly readable titles for the nodes
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "Rodin3D_Regular": "Rodin 3D Generate - Regular Generate",
-    "Rodin3D_Detail": "Rodin 3D Generate - Detail Generate",
-    "Rodin3D_Smooth": "Rodin 3D Generate - Smooth Generate",
-    "Rodin3D_Sketch": "Rodin 3D Generate - Sketch Generate",
-}
+class Rodin3D_Gen2(comfy_io.ComfyNode):
+    """Generate 3D Assets using Rodin API"""
+
+    @classmethod
+    def define_schema(cls) -> comfy_io.Schema:
+        return comfy_io.Schema(
+            node_id="Rodin3D_Gen2",
+            display_name="Rodin 3D Generate - Gen-2 Generate",
+            category="api node/3d/Rodin",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.Image.Input("Images"),
+                comfy_io.Int.Input(
+                    "Seed",
+                    default=0,
+                    min=0,
+                    max=65535,
+                    display_mode=comfy_io.NumberDisplay.number,
+                    optional=True,
+                ),
+                comfy_io.Combo.Input("Material_Type", options=["PBR", "Shaded"], default="PBR", optional=True),
+                comfy_io.Combo.Input(
+                    "Polygon_count",
+                    options=["4K-Quad", "8K-Quad", "18K-Quad", "50K-Quad", "2K-Triangle", "20K-Triangle", "150K-Triangle", "500K-Triangle"],
+                    default="500K-Triangle",
+                    optional=True,
+                ),
+                comfy_io.Boolean.Input("TAPose", default=False),
+            ],
+            outputs=[comfy_io.String.Output(display_name="3D Model Path")],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        Images,
+        Seed,
+        Material_Type,
+        Polygon_count,
+        TAPose,
+    ) -> comfy_io.NodeOutput:
+        tier = "Gen-2"
+        num_images = Images.shape[0]
+        m_images = []
+        for i in range(num_images):
+            m_images.append(Images[i])
+        mesh_mode, quality_override = get_quality_mode(Polygon_count)
+        auth = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
+        }
+        task_uuid, subscription_key = await create_generate_task(
+            images=m_images,
+            seed=Seed,
+            material=Material_Type,
+            quality_override=quality_override,
+            tier=tier,
+            mesh_mode=mesh_mode,
+            TAPose=TAPose,
+            auth_kwargs=auth,
+        )
+        await poll_for_task_status(subscription_key, auth_kwargs=auth)
+        download_list = await get_rodin_download_list(task_uuid, auth_kwargs=auth)
+        model = await download_files(download_list, task_uuid)
+
+        return comfy_io.NodeOutput(model)
+
+
+class Rodin3DExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[comfy_io.ComfyNode]]:
+        return [
+            Rodin3D_Regular,
+            Rodin3D_Detail,
+            Rodin3D_Smooth,
+            Rodin3D_Sketch,
+            Rodin3D_Gen2,
+        ]
+
+
+async def comfy_entrypoint() -> Rodin3DExtension:
+    return Rodin3DExtension()
@@ -0,0 +1,749 @@
+import re
+from typing import Optional, Type, Union
+from typing_extensions import override
+
+import torch
+from pydantic import BaseModel, Field
+from comfy_api.latest import ComfyExtension, Input, io as comfy_io
+from comfy_api_nodes.apis.client import (
+    ApiEndpoint,
+    HttpMethod,
+    SynchronousOperation,
+    PollingOperation,
+    EmptyRequest,
+    R,
+    T,
+)
+from comfy_api_nodes.util.validation_utils import get_number_of_images, validate_audio_duration
+
+from comfy_api_nodes.apinode_utils import (
+    download_url_to_image_tensor,
+    download_url_to_video_output,
+    tensor_to_base64_string,
+    audio_to_base64_string,
+)
+
+class Text2ImageInputField(BaseModel):
+    prompt: str = Field(...)
+    negative_prompt: Optional[str] = Field(None)
+
+
+class Image2ImageInputField(BaseModel):
+    prompt: str = Field(...)
+    negative_prompt: Optional[str] = Field(None)
+    images: list[str] = Field(..., min_length=1, max_length=2)
+
+
+class Text2VideoInputField(BaseModel):
+    prompt: str = Field(...)
+    negative_prompt: Optional[str] = Field(None)
+    audio_url: Optional[str] = Field(None)
+
+
+class Image2VideoInputField(BaseModel):
+    prompt: str = Field(...)
+    negative_prompt: Optional[str] = Field(None)
+    img_url: str = Field(...)
+    audio_url: Optional[str] = Field(None)
+
+
+class Txt2ImageParametersField(BaseModel):
+    size: str = Field(...)
+    n: int = Field(1, description="Number of images to generate.")  # we support only value=1
+    seed: int = Field(..., ge=0, le=2147483647)
+    prompt_extend: bool = Field(True)
+    watermark: bool = Field(True)
+
+
+class Image2ImageParametersField(BaseModel):
+    size: Optional[str] = Field(None)
+    n: int = Field(1, description="Number of images to generate.")  # we support only value=1
+    seed: int = Field(..., ge=0, le=2147483647)
+    watermark: bool = Field(True)
+
+
+class Text2VideoParametersField(BaseModel):
+    size: str = Field(...)
+    seed: int = Field(..., ge=0, le=2147483647)
+    duration: int = Field(5, ge=5, le=10)
+    prompt_extend: bool = Field(True)
+    watermark: bool = Field(True)
+    audio: bool = Field(False, description="Should be audio generated automatically")
+
+
+class Image2VideoParametersField(BaseModel):
+    resolution: str = Field(...)
+    seed: int = Field(..., ge=0, le=2147483647)
+    duration: int = Field(5, ge=5, le=10)
+    prompt_extend: bool = Field(True)
+    watermark: bool = Field(True)
+    audio: bool = Field(False, description="Should be audio generated automatically")
+
+
+class Text2ImageTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Text2ImageInputField = Field(...)
+    parameters: Txt2ImageParametersField = Field(...)
+
+
+class Image2ImageTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Image2ImageInputField = Field(...)
+    parameters: Image2ImageParametersField = Field(...)
+
+
+class Text2VideoTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Text2VideoInputField = Field(...)
+    parameters: Text2VideoParametersField = Field(...)
+
+
+class Image2VideoTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Image2VideoInputField = Field(...)
+    parameters: Image2VideoParametersField = Field(...)
+
+
+class TaskCreationOutputField(BaseModel):
+    task_id: str = Field(...)
+    task_status: str = Field(...)
+
+
+class TaskCreationResponse(BaseModel):
+    output: Optional[TaskCreationOutputField] = Field(None)
+    request_id: str = Field(...)
+    code: Optional[str] = Field(None, description="The error code of the failed request.")
+    message: Optional[str] = Field(None, description="Details of the failed request.")
+
+
+class TaskResult(BaseModel):
+    url: Optional[str] = Field(None)
+    code: Optional[str] = Field(None)
+    message: Optional[str] = Field(None)
+
+
+class ImageTaskStatusOutputField(TaskCreationOutputField):
+    task_id: str = Field(...)
+    task_status: str = Field(...)
+    results: Optional[list[TaskResult]] = Field(None)
+
+
+class VideoTaskStatusOutputField(TaskCreationOutputField):
+    task_id: str = Field(...)
+    task_status: str = Field(...)
+    video_url: Optional[str] = Field(None)
+    code: Optional[str] = Field(None)
+    message: Optional[str] = Field(None)
+
+
+class ImageTaskStatusResponse(BaseModel):
+    output: Optional[ImageTaskStatusOutputField] = Field(None)
+    request_id: str = Field(...)
+
+
+class VideoTaskStatusResponse(BaseModel):
+    output: Optional[VideoTaskStatusOutputField] = Field(None)
+    request_id: str = Field(...)
+
+
+RES_IN_PARENS = re.compile(r'\((\d+)\s*[x×]\s*(\d+)\)')
+
+
+async def process_task(
+    auth_kwargs: dict[str, str],
+    url: str,
+    request_model: Type[T],
+    response_model: Type[R],
+    payload: Union[
+        Text2ImageTaskCreationRequest,
+        Image2ImageTaskCreationRequest,
+        Text2VideoTaskCreationRequest,
+        Image2VideoTaskCreationRequest,
+    ],
+    node_id: str,
+    estimated_duration: int,
+    poll_interval: int,
+) -> Type[R]:
+    initial_response = await SynchronousOperation(
+        endpoint=ApiEndpoint(
+            path=url,
+            method=HttpMethod.POST,
+            request_model=request_model,
+            response_model=TaskCreationResponse,
+        ),
+        request=payload,
+        auth_kwargs=auth_kwargs,
+    ).execute()
+
+    if not initial_response.output:
+        raise Exception(f"Unknown error occurred: {initial_response.code} - {initial_response.message}")
+
+    return await PollingOperation(
+        poll_endpoint=ApiEndpoint(
+            path=f"/proxy/wan/api/v1/tasks/{initial_response.output.task_id}",
+            method=HttpMethod.GET,
+            request_model=EmptyRequest,
+            response_model=response_model,
+        ),
+        completed_statuses=["SUCCEEDED"],
+        failed_statuses=["FAILED", "CANCELED", "UNKNOWN"],
+        status_extractor=lambda x: x.output.task_status,
+        estimated_duration=estimated_duration,
+        poll_interval=poll_interval,
+        node_id=node_id,
+        auth_kwargs=auth_kwargs,
+    ).execute()
+
+
+class WanTextToImageApi(comfy_io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return comfy_io.Schema(
+            node_id="WanTextToImageApi",
+            display_name="Wan Text to Image",
+            category="api node/image/Wan",
+            description="Generates image based on text prompt.",
+            inputs=[
+                comfy_io.Combo.Input(
+                    "model",
+                    options=["wan2.5-t2i-preview"],
+                    default="wan2.5-t2i-preview",
+                    tooltip="Model to use.",
+                ),
+                comfy_io.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt used to describe the elements and visual features, supports English/Chinese.",
+                ),
+                comfy_io.String.Input(
+                    "negative_prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Negative text prompt to guide what to avoid.",
+                    optional=True,
+                ),
+                comfy_io.Int.Input(
+                    "width",
+                    default=1024,
+                    min=768,
+                    max=1440,
+                    step=32,
+                    optional=True,
+                ),
+                comfy_io.Int.Input(
+                    "height",
+                    default=1024,
+                    min=768,
+                    max=1440,
+                    step=32,
+                    optional=True,
+                ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=comfy_io.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                    optional=True,
+                ),
+                comfy_io.Boolean.Input(
+                    "prompt_extend",
+                    default=True,
+                    tooltip="Whether to enhance the prompt with AI assistance.",
+                    optional=True,
+                ),
+                comfy_io.Boolean.Input(
+                    "watermark",
+                    default=True,
+                    tooltip="Whether to add an \"AI generated\" watermark to the result.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                comfy_io.Image.Output(),
+            ],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        negative_prompt: str = "",
+        width: int = 1024,
+        height: int = 1024,
+        seed: int = 0,
+        prompt_extend: bool = True,
+        watermark: bool = True,
+    ):
+        payload = Text2ImageTaskCreationRequest(
+            model=model,
+            input=Text2ImageInputField(prompt=prompt, negative_prompt=negative_prompt),
+            parameters=Txt2ImageParametersField(
+                size=f"{width}*{height}",
+                seed=seed,
+                prompt_extend=prompt_extend,
+                watermark=watermark,
+            ),
+        )
+        response = await process_task(
+            {
+                "auth_token": cls.hidden.auth_token_comfy_org,
+                "comfy_api_key": cls.hidden.api_key_comfy_org,
+            },
+            "/proxy/wan/api/v1/services/aigc/text2image/image-synthesis",
+            request_model=Text2ImageTaskCreationRequest,
+            response_model=ImageTaskStatusResponse,
+            payload=payload,
+            node_id=cls.hidden.unique_id,
+            estimated_duration=9,
+            poll_interval=3,
+        )
+        return comfy_io.NodeOutput(await download_url_to_image_tensor(str(response.output.results[0].url)))
+
+
+class WanImageToImageApi(comfy_io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return comfy_io.Schema(
+            node_id="WanImageToImageApi",
+            display_name="Wan Image to Image",
+            category="api node/image/Wan",
+            description="Generates an image from one or two input images and a text prompt. "
+                        "The output image is currently fixed at 1.6 MP; its aspect ratio matches the input image(s).",
+            inputs=[
+                comfy_io.Combo.Input(
+                    "model",
+                    options=["wan2.5-i2i-preview"],
+                    default="wan2.5-i2i-preview",
+                    tooltip="Model to use.",
+                ),
+                comfy_io.Image.Input(
+                    "image",
+                    tooltip="Single-image editing or multi-image fusion, maximum 2 images.",
+                ),
+                comfy_io.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt used to describe the elements and visual features, supports English/Chinese.",
+                ),
+                comfy_io.String.Input(
+                    "negative_prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Negative text prompt to guide what to avoid.",
+                    optional=True,
+                ),
+                # redo this later as an optional combo of recommended resolutions
+                # comfy_io.Int.Input(
+                #     "width",
+                #     default=1280,
+                #     min=384,
+                #     max=1440,
+                #     step=16,
+                #     optional=True,
+                # ),
+                # comfy_io.Int.Input(
+                #     "height",
+                #     default=1280,
+                #     min=384,
+                #     max=1440,
+                #     step=16,
+                #     optional=True,
+                # ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=comfy_io.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                    optional=True,
+                ),
+                comfy_io.Boolean.Input(
+                    "watermark",
+                    default=True,
+                    tooltip="Whether to add an \"AI generated\" watermark to the result.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                comfy_io.Image.Output(),
+            ],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        image: torch.Tensor,
+        prompt: str,
+        negative_prompt: str = "",
+        # width: int = 1024,
+        # height: int = 1024,
+        seed: int = 0,
+        watermark: bool = True,
+    ):
+        n_images = get_number_of_images(image)
+        if n_images not in (1, 2):
+            raise ValueError(f"Expected 1 or 2 input images, got {n_images}.")
+        images = []
+        for i in image:
+            images.append("data:image/png;base64," + tensor_to_base64_string(i, total_pixels=4096*4096))
+        payload = Image2ImageTaskCreationRequest(
+            model=model,
+            input=Image2ImageInputField(prompt=prompt, negative_prompt=negative_prompt, images=images),
+            parameters=Image2ImageParametersField(
+                # size=f"{width}*{height}",
+                seed=seed,
+                watermark=watermark,
+            ),
+        )
+        response = await process_task(
+            {
+                "auth_token": cls.hidden.auth_token_comfy_org,
+                "comfy_api_key": cls.hidden.api_key_comfy_org,
+            },
+            "/proxy/wan/api/v1/services/aigc/image2image/image-synthesis",
+            request_model=Image2ImageTaskCreationRequest,
+            response_model=ImageTaskStatusResponse,
+            payload=payload,
+            node_id=cls.hidden.unique_id,
+            estimated_duration=42,
+            poll_interval=3,
+        )
+        return comfy_io.NodeOutput(await download_url_to_image_tensor(str(response.output.results[0].url)))
+
+
+class WanTextToVideoApi(comfy_io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return comfy_io.Schema(
+            node_id="WanTextToVideoApi",
+            display_name="Wan Text to Video",
+            category="api node/video/Wan",
+            description="Generates video based on text prompt.",
+            inputs=[
+                comfy_io.Combo.Input(
+                    "model",
+                    options=["wan2.5-t2v-preview"],
+                    default="wan2.5-t2v-preview",
+                    tooltip="Model to use.",
+                ),
+                comfy_io.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt used to describe the elements and visual features, supports English/Chinese.",
+                ),
+                comfy_io.String.Input(
+                    "negative_prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Negative text prompt to guide what to avoid.",
+                    optional=True,
+                ),
+                comfy_io.Combo.Input(
+                    "size",
+                    options=[
+                        "480p: 1:1 (624x624)",
+                        "480p: 16:9 (832x480)",
+                        "480p: 9:16 (480x832)",
+                        "720p: 1:1 (960x960)",
+                        "720p: 16:9 (1280x720)",
+                        "720p: 9:16 (720x1280)",
+                        "720p: 4:3 (1088x832)",
+                        "720p: 3:4 (832x1088)",
+                        "1080p: 1:1 (1440x1440)",
+                        "1080p: 16:9 (1920x1080)",
+                        "1080p: 9:16 (1080x1920)",
+                        "1080p: 4:3 (1632x1248)",
+                        "1080p: 3:4 (1248x1632)",
+                    ],
+                    default="480p: 1:1 (624x624)",
+                    optional=True,
+                ),
+                comfy_io.Int.Input(
+                    "duration",
+                    default=5,
+                    min=5,
+                    max=10,
+                    step=5,
+                    display_mode=comfy_io.NumberDisplay.number,
+                    tooltip="Available durations: 5 and 10 seconds",
+                    optional=True,
+                ),
+                comfy_io.Audio.Input(
+                    "audio",
+                    optional=True,
+                    tooltip="Audio must contain a clear, loud voice, without extraneous noise, background music.",
+                ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=comfy_io.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                    optional=True,
+                ),
+                comfy_io.Boolean.Input(
+                    "generate_audio",
+                    default=False,
+                    optional=True,
+                    tooltip="If there is no audio input, generate audio automatically.",
+                ),
+                comfy_io.Boolean.Input(
+                    "prompt_extend",
+                    default=True,
+                    tooltip="Whether to enhance the prompt with AI assistance.",
+                    optional=True,
+                ),
+                comfy_io.Boolean.Input(
+                    "watermark",
+                    default=True,
+                    tooltip="Whether to add an \"AI generated\" watermark to the result.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                comfy_io.Video.Output(),
+            ],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        prompt: str,
+        negative_prompt: str = "",
+        size: str = "480p: 1:1 (624x624)",
+        duration: int = 5,
+        audio: Optional[Input.Audio] = None,
+        seed: int = 0,
+        generate_audio: bool = False,
+        prompt_extend: bool = True,
+        watermark: bool = True,
+    ):
+        width, height = RES_IN_PARENS.search(size).groups()
+        audio_url = None
+        if audio is not None:
+            validate_audio_duration(audio, 3.0, 29.0)
+            audio_url = "data:audio/mp3;base64," + audio_to_base64_string(audio, "mp3", "libmp3lame")
+        payload = Text2VideoTaskCreationRequest(
+            model=model,
+            input=Text2VideoInputField(prompt=prompt, negative_prompt=negative_prompt, audio_url=audio_url),
+            parameters=Text2VideoParametersField(
+                size=f"{width}*{height}",
+                duration=duration,
+                seed=seed,
+                audio=generate_audio,
+                prompt_extend=prompt_extend,
+                watermark=watermark,
+            ),
+        )
+        response = await process_task(
+            {
+                "auth_token": cls.hidden.auth_token_comfy_org,
+                "comfy_api_key": cls.hidden.api_key_comfy_org,
+            },
+            "/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis",
+            request_model=Text2VideoTaskCreationRequest,
+            response_model=VideoTaskStatusResponse,
+            payload=payload,
+            node_id=cls.hidden.unique_id,
+            estimated_duration=120 * int(duration / 5),
+            poll_interval=6,
+        )
+        return comfy_io.NodeOutput(await download_url_to_video_output(response.output.video_url))
+
+
+class WanImageToVideoApi(comfy_io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return comfy_io.Schema(
+            node_id="WanImageToVideoApi",
+            display_name="Wan Image to Video",
+            category="api node/video/Wan",
+            description="Generates video based on the first frame and text prompt.",
+            inputs=[
+                comfy_io.Combo.Input(
+                    "model",
+                    options=["wan2.5-i2v-preview"],
+                    default="wan2.5-i2v-preview",
+                    tooltip="Model to use.",
+                ),
+                comfy_io.Image.Input(
+                    "image",
+                ),
+                comfy_io.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt used to describe the elements and visual features, supports English/Chinese.",
+                ),
+                comfy_io.String.Input(
+                    "negative_prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Negative text prompt to guide what to avoid.",
+                    optional=True,
+                ),
+                comfy_io.Combo.Input(
+                    "resolution",
+                    options=[
+                        "480P",
+                        "720P",
+                        "1080P",
+                    ],
+                    default="480P",
+                    optional=True,
+                ),
+                comfy_io.Int.Input(
+                    "duration",
+                    default=5,
+                    min=5,
+                    max=10,
+                    step=5,
+                    display_mode=comfy_io.NumberDisplay.number,
+                    tooltip="Available durations: 5 and 10 seconds",
+                    optional=True,
+                ),
+                comfy_io.Audio.Input(
+                    "audio",
+                    optional=True,
+                    tooltip="Audio must contain a clear, loud voice, without extraneous noise, background music.",
+                ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=2147483647,
+                    step=1,
+                    display_mode=comfy_io.NumberDisplay.number,
+                    control_after_generate=True,
+                    tooltip="Seed to use for generation.",
+                    optional=True,
+                ),
+                comfy_io.Boolean.Input(
+                    "generate_audio",
+                    default=False,
+                    optional=True,
+                    tooltip="If there is no audio input, generate audio automatically.",
+                ),
+                comfy_io.Boolean.Input(
+                    "prompt_extend",
+                    default=True,
+                    tooltip="Whether to enhance the prompt with AI assistance.",
+                    optional=True,
+                ),
+                comfy_io.Boolean.Input(
+                    "watermark",
+                    default=True,
+                    tooltip="Whether to add an \"AI generated\" watermark to the result.",
+                    optional=True,
+                ),
+            ],
+            outputs=[
+                comfy_io.Video.Output(),
+            ],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
+
+    @classmethod
+    async def execute(
+        cls,
+        model: str,
+        image: torch.Tensor,
+        prompt: str,
+        negative_prompt: str = "",
+        resolution: str = "480P",
+        duration: int = 5,
+        audio: Optional[Input.Audio] = None,
+        seed: int = 0,
+        generate_audio: bool = False,
+        prompt_extend: bool = True,
+        watermark: bool = True,
+    ):
+        if get_number_of_images(image) != 1:
+            raise ValueError("Exactly one input image is required.")
+        image_url = "data:image/png;base64," + tensor_to_base64_string(image, total_pixels=2000*2000)
+        audio_url = None
+        if audio is not None:
+            validate_audio_duration(audio, 3.0, 29.0)
+            audio_url = "data:audio/mp3;base64," + audio_to_base64_string(audio, "mp3", "libmp3lame")
+        payload = Image2VideoTaskCreationRequest(
+            model=model,
+            input=Image2VideoInputField(
+                prompt=prompt, negative_prompt=negative_prompt, img_url=image_url, audio_url=audio_url
+            ),
+            parameters=Image2VideoParametersField(
+                resolution=resolution,
+                duration=duration,
+                seed=seed,
+                audio=generate_audio,
+                prompt_extend=prompt_extend,
+                watermark=watermark,
+            ),
+        )
+        response = await process_task(
+            {
+                "auth_token": cls.hidden.auth_token_comfy_org,
+                "comfy_api_key": cls.hidden.api_key_comfy_org,
+            },
+            "/proxy/wan/api/v1/services/aigc/video-generation/video-synthesis",
+            request_model=Image2VideoTaskCreationRequest,
+            response_model=VideoTaskStatusResponse,
+            payload=payload,
+            node_id=cls.hidden.unique_id,
+            estimated_duration=120 * int(duration / 5),
+            poll_interval=6,
+        )
+        return comfy_io.NodeOutput(await download_url_to_video_output(response.output.video_url))
+
+
+class WanApiExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[comfy_io.ComfyNode]]:
+        return [
+            WanTextToImageApi,
+            WanImageToImageApi,
+            WanTextToVideoApi,
+            WanImageToVideoApi,
+        ]
+
+
+async def comfy_entrypoint() -> WanApiExtension:
+    return WanApiExtension()
@@ -11,6 +11,7 @@ import json
 import random
 import hashlib
 import node_helpers
+import logging
 from comfy.cli_args import args
 from comfy.comfy_types import FileLocator

@@ -359,11 +360,221 @@ class RecordAudio:
    def load(self, audio):
        audio_path = folder_paths.get_annotated_filepath(audio)

-        waveform, sample_rate = torchaudio.load(audio_path)
+        waveform, sample_rate = load(audio_path)
        audio = {"waveform": waveform.unsqueeze(0), "sample_rate": sample_rate}
        return (audio, )


+class TrimAudioDuration:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "audio": ("AUDIO",),
+                "start_index": ("FLOAT", {"default": 0.0, "min": -0xffffffffffffffff, "max": 0xffffffffffffffff, "step": 0.01, "tooltip": "Start time in seconds, can be negative to count from the end (supports sub-seconds)."}),
+                "duration": ("FLOAT", {"default": 60.0, "min": 0.0, "step": 0.01, "tooltip": "Duration in seconds"}),
+            },
+        }
+
+    FUNCTION = "trim"
+    RETURN_TYPES = ("AUDIO",)
+    CATEGORY = "audio"
+    DESCRIPTION = "Trim audio tensor into chosen time range."
+
+    def trim(self, audio, start_index, duration):
+        waveform = audio["waveform"]
+        sample_rate = audio["sample_rate"]
+        audio_length = waveform.shape[-1]
+
+        if start_index < 0:
+            start_frame = audio_length + int(round(start_index * sample_rate))
+        else:
+            start_frame = int(round(start_index * sample_rate))
+        start_frame = max(0, min(start_frame, audio_length - 1))
+
+        end_frame = start_frame + int(round(duration * sample_rate))
+        end_frame = max(0, min(end_frame, audio_length))
+
+        if start_frame >= end_frame:
+            raise ValueError("AudioTrim: Start time must be less than end time and be within the audio length.")
+
+        return ({"waveform": waveform[..., start_frame:end_frame], "sample_rate": sample_rate},)
+
+
+class SplitAudioChannels:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "audio": ("AUDIO",),
+        }}
+
+    RETURN_TYPES = ("AUDIO", "AUDIO")
+    RETURN_NAMES = ("left", "right")
+    FUNCTION = "separate"
+    CATEGORY = "audio"
+    DESCRIPTION = "Separates the audio into left and right channels."
+
+    def separate(self, audio):
+        waveform = audio["waveform"]
+        sample_rate = audio["sample_rate"]
+
+        if waveform.shape[1] != 2:
+            raise ValueError("AudioSplit: Input audio has only one channel.")
+
+        left_channel = waveform[..., 0:1, :]
+        right_channel = waveform[..., 1:2, :]
+
+        return ({"waveform": left_channel, "sample_rate": sample_rate}, {"waveform": right_channel, "sample_rate": sample_rate})
+
+
+def match_audio_sample_rates(waveform_1, sample_rate_1, waveform_2, sample_rate_2):
+    if sample_rate_1 != sample_rate_2:
+        if sample_rate_1 > sample_rate_2:
+            waveform_2 = torchaudio.functional.resample(waveform_2, sample_rate_2, sample_rate_1)
+            output_sample_rate = sample_rate_1
+            logging.info(f"Resampling audio2 from {sample_rate_2}Hz to {sample_rate_1}Hz for merging.")
+        else:
+            waveform_1 = torchaudio.functional.resample(waveform_1, sample_rate_1, sample_rate_2)
+            output_sample_rate = sample_rate_2
+            logging.info(f"Resampling audio1 from {sample_rate_1}Hz to {sample_rate_2}Hz for merging.")
+    else:
+        output_sample_rate = sample_rate_1
+    return waveform_1, waveform_2, output_sample_rate
+
+
+class AudioConcat:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "audio1": ("AUDIO",),
+            "audio2": ("AUDIO",),
+            "direction": (['after', 'before'], {"default": 'after', "tooltip": "Whether to append audio2 after or before audio1."}),
+        }}
+
+    RETURN_TYPES = ("AUDIO",)
+    FUNCTION = "concat"
+    CATEGORY = "audio"
+    DESCRIPTION = "Concatenates the audio1 to audio2 in the specified direction."
+
+    def concat(self, audio1, audio2, direction):
+        waveform_1 = audio1["waveform"]
+        waveform_2 = audio2["waveform"]
+        sample_rate_1 = audio1["sample_rate"]
+        sample_rate_2 = audio2["sample_rate"]
+
+        if waveform_1.shape[1] == 1:
+            waveform_1 = waveform_1.repeat(1, 2, 1)
+            logging.info("AudioConcat: Converted mono audio1 to stereo by duplicating the channel.")
+        if waveform_2.shape[1] == 1:
+            waveform_2 = waveform_2.repeat(1, 2, 1)
+            logging.info("AudioConcat: Converted mono audio2 to stereo by duplicating the channel.")
+
+        waveform_1, waveform_2, output_sample_rate = match_audio_sample_rates(waveform_1, sample_rate_1, waveform_2, sample_rate_2)
+
+        if direction == 'after':
+            concatenated_audio = torch.cat((waveform_1, waveform_2), dim=2)
+        elif direction == 'before':
+            concatenated_audio = torch.cat((waveform_2, waveform_1), dim=2)
+
+        return ({"waveform": concatenated_audio, "sample_rate": output_sample_rate},)
+
+
+class AudioMerge:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "audio1": ("AUDIO",),
+                "audio2": ("AUDIO",),
+                "merge_method": (["add", "mean", "subtract", "multiply"], {"tooltip": "The method used to combine the audio waveforms."}),
+            },
+        }
+
+    FUNCTION = "merge"
+    RETURN_TYPES = ("AUDIO",)
+    CATEGORY = "audio"
+    DESCRIPTION = "Combine two audio tracks by overlaying their waveforms."
+
+    def merge(self, audio1, audio2, merge_method):
+        waveform_1 = audio1["waveform"]
+        waveform_2 = audio2["waveform"]
+        sample_rate_1 = audio1["sample_rate"]
+        sample_rate_2 = audio2["sample_rate"]
+
+        waveform_1, waveform_2, output_sample_rate = match_audio_sample_rates(waveform_1, sample_rate_1, waveform_2, sample_rate_2)
+
+        length_1 = waveform_1.shape[-1]
+        length_2 = waveform_2.shape[-1]
+
+        if length_2 > length_1:
+            logging.info(f"AudioMerge: Trimming audio2 from {length_2} to {length_1} samples to match audio1 length.")
+            waveform_2 = waveform_2[..., :length_1]
+        elif length_2 < length_1:
+            logging.info(f"AudioMerge: Padding audio2 from {length_2} to {length_1} samples to match audio1 length.")
+            pad_shape = list(waveform_2.shape)
+            pad_shape[-1] = length_1 - length_2
+            pad_tensor = torch.zeros(pad_shape, dtype=waveform_2.dtype, device=waveform_2.device)
+            waveform_2 = torch.cat((waveform_2, pad_tensor), dim=-1)
+
+        if merge_method == "add":
+            waveform = waveform_1 + waveform_2
+        elif merge_method == "subtract":
+            waveform = waveform_1 - waveform_2
+        elif merge_method == "multiply":
+            waveform = waveform_1 * waveform_2
+        elif merge_method == "mean":
+            waveform = (waveform_1 + waveform_2) / 2
+
+        max_val = waveform.abs().max()
+        if max_val > 1.0:
+            waveform = waveform / max_val
+
+        return ({"waveform": waveform, "sample_rate": output_sample_rate},)
+
+
+class AudioAdjustVolume:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "audio": ("AUDIO",),
+            "volume": ("INT", {"default": 1.0, "min": -100, "max": 100, "tooltip": "Volume adjustment in decibels (dB). 0 = no change, +6 = double, -6 = half, etc"}),
+        }}
+
+    RETURN_TYPES = ("AUDIO",)
+    FUNCTION = "adjust_volume"
+    CATEGORY = "audio"
+
+    def adjust_volume(self, audio, volume):
+        if volume == 0:
+            return (audio,)
+        waveform = audio["waveform"]
+        sample_rate = audio["sample_rate"]
+
+        gain = 10 ** (volume / 20)
+        waveform = waveform * gain
+
+        return ({"waveform": waveform, "sample_rate": sample_rate},)
+
+
+class EmptyAudio:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required": {
+            "duration": ("FLOAT", {"default": 60.0, "min": 0.0, "max": 0xffffffffffffffff, "step": 0.01, "tooltip": "Duration of the empty audio clip in seconds"}),
+            "sample_rate": ("INT", {"default": 44100, "tooltip": "Sample rate of the empty audio clip."}),
+            "channels": ("INT", {"default": 2, "min": 1, "max": 2, "tooltip": "Number of audio channels (1 for mono, 2 for stereo)."}),
+        }}
+
+    RETURN_TYPES = ("AUDIO",)
+    FUNCTION = "create_empty_audio"
+    CATEGORY = "audio"
+
+    def create_empty_audio(self, duration, sample_rate, channels):
+        num_samples = int(round(duration * sample_rate))
+        waveform = torch.zeros((1, channels, num_samples), dtype=torch.float32)
+        return ({"waveform": waveform, "sample_rate": sample_rate},)
+
+
 NODE_CLASS_MAPPINGS = {
    "EmptyLatentAudio": EmptyLatentAudio,
    "VAEEncodeAudio": VAEEncodeAudio,
@@ -375,6 +586,12 @@ NODE_CLASS_MAPPINGS = {
    "PreviewAudio": PreviewAudio,
    "ConditioningStableAudio": ConditioningStableAudio,
    "RecordAudio": RecordAudio,
+    "TrimAudioDuration": TrimAudioDuration,
+    "SplitAudioChannels": SplitAudioChannels,
+    "AudioConcat": AudioConcat,
+    "AudioMerge": AudioMerge,
+    "AudioAdjustVolume": AudioAdjustVolume,
+    "EmptyAudio": EmptyAudio,
 }

 NODE_DISPLAY_NAME_MAPPINGS = {
@@ -387,4 +604,10 @@ NODE_DISPLAY_NAME_MAPPINGS = {
    "SaveAudioMP3": "Save Audio (MP3)",
    "SaveAudioOpus": "Save Audio (Opus)",
    "RecordAudio": "Record Audio",
+    "TrimAudioDuration": "Trim Audio Duration",
+    "SplitAudioChannels": "Split Audio Channels",
+    "AudioConcat": "Audio Concat",
+    "AudioMerge": "Audio Merge",
+    "AudioAdjustVolume": "Audio Adjust Volume",
+    "EmptyAudio": "Empty Audio",
 }
@@ -1,44 +1,62 @@
 import folder_paths
 import comfy.audio_encoders.audio_encoders
 import comfy.utils
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io


-class AudioEncoderLoader:
+class AudioEncoderLoader(io.ComfyNode):
    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "audio_encoder_name": (folder_paths.get_filename_list("audio_encoders"), ),
-                             }}
-    RETURN_TYPES = ("AUDIO_ENCODER",)
-    FUNCTION = "load_model"
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="AudioEncoderLoader",
+            category="loaders",
+            inputs=[
+                io.Combo.Input(
+                    "audio_encoder_name",
+                    options=folder_paths.get_filename_list("audio_encoders"),
+                ),
+            ],
+            outputs=[io.AudioEncoder.Output()],
+        )

-    CATEGORY = "loaders"
-
-    def load_model(self, audio_encoder_name):
+    @classmethod
+    def execute(cls, audio_encoder_name) -> io.NodeOutput:
        audio_encoder_name = folder_paths.get_full_path_or_raise("audio_encoders", audio_encoder_name)
        sd = comfy.utils.load_torch_file(audio_encoder_name, safe_load=True)
        audio_encoder = comfy.audio_encoders.audio_encoders.load_audio_encoder_from_sd(sd)
        if audio_encoder is None:
            raise RuntimeError("ERROR: audio encoder file is invalid and does not contain a valid model.")
-        return (audio_encoder,)
+        return io.NodeOutput(audio_encoder)


-class AudioEncoderEncode:
+class AudioEncoderEncode(io.ComfyNode):
    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "audio_encoder": ("AUDIO_ENCODER",),
-                              "audio": ("AUDIO",),
-                             }}
-    RETURN_TYPES = ("AUDIO_ENCODER_OUTPUT",)
-    FUNCTION = "encode"
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="AudioEncoderEncode",
+            category="conditioning",
+            inputs=[
+                io.AudioEncoder.Input("audio_encoder"),
+                io.Audio.Input("audio"),
+            ],
+            outputs=[io.AudioEncoderOutput.Output()],
+        )

-    CATEGORY = "conditioning"
-
-    def encode(self, audio_encoder, audio):
+    @classmethod
+    def execute(cls, audio_encoder, audio) -> io.NodeOutput:
        output = audio_encoder.encode_audio(audio["waveform"], audio["sample_rate"])
-        return (output,)
+        return io.NodeOutput(output)


-NODE_CLASS_MAPPINGS = {
-    "AudioEncoderLoader": AudioEncoderLoader,
-    "AudioEncoderEncode": AudioEncoderEncode,
-}
+class AudioEncoder(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            AudioEncoderLoader,
+            AudioEncoderEncode,
+        ]
+
+
+async def comfy_entrypoint() -> AudioEncoder:
+    return AudioEncoder()
@@ -2,12 +2,12 @@ import nodes
 import torch
 import numpy as np
 from einops import rearrange
+from typing_extensions import override
 import comfy.model_management

+from comfy_api.latest import ComfyExtension, io


-MAX_RESOLUTION = nodes.MAX_RESOLUTION
-
 CAMERA_DICT = {
    "base_T_norm": 1.5,
    "base_angle": np.pi/3,
@@ -148,32 +148,47 @@ def get_camera_motion(angle, T, speed, n=81):
    RT = np.stack(RT)
    return RT

-class WanCameraEmbedding:
+class WanCameraEmbedding(io.ComfyNode):
    @classmethod
-    def INPUT_TYPES(cls):
-        return {
-            "required": {
-                "camera_pose":(["Static","Pan Up","Pan Down","Pan Left","Pan Right","Zoom In","Zoom Out","Anti Clockwise (ACW)", "ClockWise (CW)"],{"default":"Static"}),
-                "width": ("INT", {"default": 832, "min": 16, "max": MAX_RESOLUTION, "step": 16}),
-                "height": ("INT", {"default": 480, "min": 16, "max": MAX_RESOLUTION, "step": 16}),
-                "length": ("INT", {"default": 81, "min": 1, "max": MAX_RESOLUTION, "step": 4}),
-            },
-            "optional":{
-                "speed":("FLOAT",{"default":1.0, "min": 0, "max": 10.0, "step": 0.1}),
-                "fx":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.000000001}),
-                "fy":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.000000001}),
-                "cx":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.01}),
-                "cy":("FLOAT",{"default":0.5, "min": 0, "max": 1, "step": 0.01}),
-            }
+    def define_schema(cls):
+        return io.Schema(
+            node_id="WanCameraEmbedding",
+            category="camera",
+            inputs=[
+                io.Combo.Input(
+                    "camera_pose",
+                    options=[
+                        "Static",
+                        "Pan Up",
+                        "Pan Down",
+                        "Pan Left",
+                        "Pan Right",
+                        "Zoom In",
+                        "Zoom Out",
+                        "Anti Clockwise (ACW)",
+                        "ClockWise (CW)",
+                    ],
+                    default="Static",
+                ),
+                io.Int.Input("width", default=832, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("length", default=81, min=1, max=nodes.MAX_RESOLUTION, step=4),
+                io.Float.Input("speed", default=1.0, min=0, max=10.0, step=0.1, optional=True),
+                io.Float.Input("fx", default=0.5, min=0, max=1, step=0.000000001, optional=True),
+                io.Float.Input("fy", default=0.5, min=0, max=1, step=0.000000001, optional=True),
+                io.Float.Input("cx", default=0.5, min=0, max=1, step=0.01, optional=True),
+                io.Float.Input("cy", default=0.5, min=0, max=1, step=0.01, optional=True),
+            ],
+            outputs=[
+                io.WanCameraEmbedding.Output(display_name="camera_embedding"),
+                io.Int.Output(display_name="width"),
+                io.Int.Output(display_name="height"),
+                io.Int.Output(display_name="length"),
+            ],
+        )

-        }
-
-    RETURN_TYPES = ("WAN_CAMERA_EMBEDDING","INT","INT","INT")
-    RETURN_NAMES = ("camera_embedding","width","height","length")
-    FUNCTION = "run"
-    CATEGORY = "camera"
-
-    def run(self, camera_pose, width, height, length, speed=1.0,  fx=0.5, fy=0.5, cx=0.5, cy=0.5):
+    @classmethod
+    def execute(cls, camera_pose, width, height, length, speed=1.0, fx=0.5, fy=0.5, cx=0.5, cy=0.5) -> io.NodeOutput:
        """
        Use Camera trajectory as extrinsic parameters to calculate Plücker embeddings (Sitzmannet al., 2021)
        Adapted from https://github.com/aigc-apps/VideoX-Fun/blob/main/comfyui/comfyui_nodes.py
@@ -210,9 +225,15 @@ class WanCameraEmbedding:
        control_camera_video = control_camera_video.contiguous().view(b, f // 4, 4, c, h, w).transpose(2, 3)
        control_camera_video = control_camera_video.contiguous().view(b, f // 4, c * 4, h, w).transpose(1, 2)

-        return (control_camera_video, width, height, length)
+        return io.NodeOutput(control_camera_video, width, height, length)


-NODE_CLASS_MAPPINGS = {
-    "WanCameraEmbedding": WanCameraEmbedding,
-}
+class CameraTrajectoryExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            WanCameraEmbedding,
+        ]
+
+async def comfy_entrypoint() -> CameraTrajectoryExtension:
+    return CameraTrajectoryExtension()
@@ -1,25 +1,41 @@
 from kornia.filters import canny
+from typing_extensions import override
+
 import comfy.model_management
+from comfy_api.latest import ComfyExtension, io


-class Canny:
+class Canny(io.ComfyNode):
    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"image": ("IMAGE",),
-                                "low_threshold": ("FLOAT", {"default": 0.4, "min": 0.01, "max": 0.99, "step": 0.01}),
-                                "high_threshold": ("FLOAT", {"default": 0.8, "min": 0.01, "max": 0.99, "step": 0.01})
-                                }}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="Canny",
+            category="image/preprocessors",
+            inputs=[
+                io.Image.Input("image"),
+                io.Float.Input("low_threshold", default=0.4, min=0.01, max=0.99, step=0.01),
+                io.Float.Input("high_threshold", default=0.8, min=0.01, max=0.99, step=0.01),
+            ],
+            outputs=[io.Image.Output()],
+        )

-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "detect_edge"
+    @classmethod
+    def detect_edge(cls, image, low_threshold, high_threshold):
+        # Deprecated: use the V3 schema's `execute` method instead of this.
+        return cls.execute(image, low_threshold, high_threshold)

-    CATEGORY = "image/preprocessors"
-
-    def detect_edge(self, image, low_threshold, high_threshold):
+    @classmethod
+    def execute(cls, image, low_threshold, high_threshold) -> io.NodeOutput:
        output = canny(image.to(comfy.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold)
        img_out = output[1].to(comfy.model_management.intermediate_device()).repeat(1, 3, 1, 1).movedim(1, -1)
-        return (img_out,)
+        return io.NodeOutput(img_out)

-NODE_CLASS_MAPPINGS = {
-    "Canny": Canny,
-}
+
+class CannyExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [Canny]
+
+
+async def comfy_entrypoint() -> CannyExtension:
+    return CannyExtension()
@@ -1,5 +1,10 @@
+from typing_extensions import override
+
 import torch

+from comfy_api.latest import ComfyExtension, io
+
+
 # https://github.com/WeichenFan/CFG-Zero-star
 def optimized_scale(positive, negative):
    positive_flat = positive.reshape(positive.shape[0], -1)
@@ -16,17 +21,20 @@ def optimized_scale(positive, negative):

    return st_star.reshape([positive.shape[0]] + [1] * (positive.ndim - 1))

-class CFGZeroStar:
+class CFGZeroStar(io.ComfyNode):
    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"model": ("MODEL",),
-                            }}
-    RETURN_TYPES = ("MODEL",)
-    RETURN_NAMES = ("patched_model",)
-    FUNCTION = "patch"
-    CATEGORY = "advanced/guidance"
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="CFGZeroStar",
+            category="advanced/guidance",
+            inputs=[
+                io.Model.Input("model"),
+            ],
+            outputs=[io.Model.Output(display_name="patched_model")],
+        )

-    def patch(self, model):
+    @classmethod
+    def execute(cls, model) -> io.NodeOutput:
        m = model.clone()
        def cfg_zero_star(args):
            guidance_scale = args['cond_scale']
@@ -38,21 +46,24 @@ class CFGZeroStar:

            return out + uncond_p * (alpha - 1.0)  + guidance_scale * uncond_p * (1.0 - alpha)
        m.set_model_sampler_post_cfg_function(cfg_zero_star)
-        return (m, )
+        return io.NodeOutput(m)

-class CFGNorm:
+class CFGNorm(io.ComfyNode):
    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"model": ("MODEL",),
-                             "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
-                            }}
-    RETURN_TYPES = ("MODEL",)
-    RETURN_NAMES = ("patched_model",)
-    FUNCTION = "patch"
-    CATEGORY = "advanced/guidance"
-    EXPERIMENTAL = True
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="CFGNorm",
+            category="advanced/guidance",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("strength", default=1.0, min=0.0, max=100.0, step=0.01),
+            ],
+            outputs=[io.Model.Output(display_name="patched_model")],
+            is_experimental=True,
+        )

-    def patch(self, model, strength):
+    @classmethod
+    def execute(cls, model, strength) -> io.NodeOutput:
        m = model.clone()
        def cfg_norm(args):
            cond_p = args['cond_denoised']
@@ -64,9 +75,17 @@ class CFGNorm:
            return pred_text_ * scale * strength

        m.set_model_sampler_post_cfg_function(cfg_norm)
-        return (m, )
+        return io.NodeOutput(m)

-NODE_CLASS_MAPPINGS = {
-    "CFGZeroStar": CFGZeroStar,
-    "CFGNorm": CFGNorm,
-}
+
+class CfgExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            CFGZeroStar,
+            CFGNorm,
+        ]
+
+
+async def comfy_entrypoint() -> CfgExtension:
+    return CfgExtension()
@@ -0,0 +1,114 @@
+from typing_extensions import override
+from typing import Callable
+
+import torch
+
+import comfy.model_management
+from comfy_api.latest import ComfyExtension, io
+
+import nodes
+
+class EmptyChromaRadianceLatentImage(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="EmptyChromaRadianceLatentImage",
+            category="latent/chroma_radiance",
+            inputs=[
+                io.Int.Input(id="width", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input(id="height", default=1024, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input(id="batch_size", default=1, min=1, max=4096),
+            ],
+            outputs=[io.Latent().Output()],
+        )
+
+    @classmethod
+    def execute(cls, *, width: int, height: int, batch_size: int=1) -> io.NodeOutput:
+        latent = torch.zeros((batch_size, 3, height, width), device=comfy.model_management.intermediate_device())
+        return io.NodeOutput({"samples":latent})
+
+
+class ChromaRadianceOptions(io.ComfyNode):
+    @classmethod
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="ChromaRadianceOptions",
+            category="model_patches/chroma_radiance",
+            description="Allows setting advanced options for the Chroma Radiance model.",
+            inputs=[
+                io.Model.Input(id="model"),
+                io.Boolean.Input(
+                    id="preserve_wrapper",
+                    default=True,
+                    tooltip="When enabled, will delegate to an existing model function wrapper if it exists. Generally should be left enabled.",
+                ),
+                io.Float.Input(
+                    id="start_sigma",
+                    default=1.0,
+                    min=0.0,
+                    max=1.0,
+                    tooltip="First sigma that these options will be in effect.",
+                ),
+                io.Float.Input(
+                    id="end_sigma",
+                    default=0.0,
+                    min=0.0,
+                    max=1.0,
+                    tooltip="Last sigma that these options will be in effect.",
+                ),
+                io.Int.Input(
+                    id="nerf_tile_size",
+                    default=-1,
+                    min=-1,
+                    tooltip="Allows overriding the default NeRF tile size. -1 means use the default (32). 0 means use non-tiling mode (may require a lot of VRAM).",
+                ),
+            ],
+            outputs=[io.Model.Output()],
+        )
+
+    @classmethod
+    def execute(
+        cls,
+        *,
+        model: io.Model.Type,
+        preserve_wrapper: bool,
+        start_sigma: float,
+        end_sigma: float,
+        nerf_tile_size: int,
+    ) -> io.NodeOutput:
+        radiance_options = {}
+        if nerf_tile_size >= 0:
+            radiance_options["nerf_tile_size"] = nerf_tile_size
+
+        if not radiance_options:
+            return io.NodeOutput(model)
+
+        old_wrapper = model.model_options.get("model_function_wrapper")
+
+        def model_function_wrapper(apply_model: Callable, args: dict) -> torch.Tensor:
+            c = args["c"].copy()
+            sigma = args["timestep"].max().detach().cpu().item()
+            if end_sigma <= sigma <= start_sigma:
+                transformer_options = c.get("transformer_options", {}).copy()
+                transformer_options["chroma_radiance_options"] = radiance_options.copy()
+                c["transformer_options"] = transformer_options
+            if not (preserve_wrapper and old_wrapper):
+                return apply_model(args["input"], args["timestep"], **c)
+            return old_wrapper(apply_model, args | {"c": c})
+
+        model = model.clone()
+        model.set_model_unet_function_wrapper(model_function_wrapper)
+        return io.NodeOutput(model)
+
+
+class ChromaRadianceExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            EmptyChromaRadianceLatentImage,
+            ChromaRadianceOptions,
+        ]
+
+
+async def comfy_entrypoint() -> ChromaRadianceExtension:
+    return ChromaRadianceExtension()
@@ -1,43 +1,52 @@
-from nodes import MAX_RESOLUTION
+from typing_extensions import override

-class CLIPTextEncodeSDXLRefiner:
+import nodes
+from comfy_api.latest import ComfyExtension, io
+
+
+class CLIPTextEncodeSDXLRefiner(io.ComfyNode):
    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "ascore": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
-            "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
-            "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
-            "text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ),
-            }}
-    RETURN_TYPES = ("CONDITIONING",)
-    FUNCTION = "encode"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CLIPTextEncodeSDXLRefiner",
+            category="advanced/conditioning",
+            inputs=[
+                io.Float.Input("ascore", default=6.0, min=0.0, max=1000.0, step=0.01),
+                io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
+                io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
+                io.String.Input("text", multiline=True, dynamic_prompts=True),
+                io.Clip.Input("clip"),
+            ],
+            outputs=[io.Conditioning.Output()],
+        )

-    CATEGORY = "advanced/conditioning"
-
-    def encode(self, clip, ascore, width, height, text):
+    @classmethod
+    def execute(cls, clip, ascore, width, height, text) -> io.NodeOutput:
        tokens = clip.tokenize(text)
-        return (clip.encode_from_tokens_scheduled(tokens, add_dict={"aesthetic_score": ascore, "width": width, "height": height}), )
+        return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens, add_dict={"aesthetic_score": ascore, "width": width, "height": height}))

-class CLIPTextEncodeSDXL:
+class CLIPTextEncodeSDXL(io.ComfyNode):
    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "clip": ("CLIP", ),
-            "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
-            "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
-            "crop_w": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}),
-            "crop_h": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}),
-            "target_width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
-            "target_height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
-            "text_g": ("STRING", {"multiline": True, "dynamicPrompts": True}),
-            "text_l": ("STRING", {"multiline": True, "dynamicPrompts": True}),
-            }}
-    RETURN_TYPES = ("CONDITIONING",)
-    FUNCTION = "encode"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CLIPTextEncodeSDXL",
+            category="advanced/conditioning",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
+                io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
+                io.Int.Input("crop_w", default=0, min=0, max=nodes.MAX_RESOLUTION),
+                io.Int.Input("crop_h", default=0, min=0, max=nodes.MAX_RESOLUTION),
+                io.Int.Input("target_width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
+                io.Int.Input("target_height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
+                io.String.Input("text_g", multiline=True, dynamic_prompts=True),
+                io.String.Input("text_l", multiline=True, dynamic_prompts=True),
+            ],
+            outputs=[io.Conditioning.Output()],
+        )

-    CATEGORY = "advanced/conditioning"
-
-    def encode(self, clip, width, height, crop_w, crop_h, target_width, target_height, text_g, text_l):
+    @classmethod
+    def execute(cls, clip, width, height, crop_w, crop_h, target_width, target_height, text_g, text_l) -> io.NodeOutput:
        tokens = clip.tokenize(text_g)
        tokens["l"] = clip.tokenize(text_l)["l"]
        if len(tokens["l"]) != len(tokens["g"]):
@@ -46,9 +55,17 @@ class CLIPTextEncodeSDXL:
                tokens["l"] += empty["l"]
            while len(tokens["l"]) > len(tokens["g"]):
                tokens["g"] += empty["g"]
-        return (clip.encode_from_tokens_scheduled(tokens, add_dict={"width": width, "height": height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}), )
+        return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens, add_dict={"width": width, "height": height, "crop_w": crop_w, "crop_h": crop_h, "target_width": target_width, "target_height": target_height}))

-NODE_CLASS_MAPPINGS = {
-    "CLIPTextEncodeSDXLRefiner": CLIPTextEncodeSDXLRefiner,
-    "CLIPTextEncodeSDXL": CLIPTextEncodeSDXL,
-}
+
+class ClipSdxlExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            CLIPTextEncodeSDXLRefiner,
+            CLIPTextEncodeSDXL,
+        ]
+
+
+async def comfy_entrypoint() -> ClipSdxlExtension:
+    return ClipSdxlExtension()
@@ -1,15 +1,25 @@
+from typing_extensions import override
+
+from comfy_api.latest import ComfyExtension, io


-class CLIPTextEncodeControlnet:
+class CLIPTextEncodeControlnet(io.ComfyNode):
    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"clip": ("CLIP", ), "conditioning": ("CONDITIONING", ), "text": ("STRING", {"multiline": True, "dynamicPrompts": True})}}
-    RETURN_TYPES = ("CONDITIONING",)
-    FUNCTION = "encode"
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="CLIPTextEncodeControlnet",
+            category="_for_testing/conditioning",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.Conditioning.Input("conditioning"),
+                io.String.Input("text", multiline=True, dynamic_prompts=True),
+            ],
+            outputs=[io.Conditioning.Output()],
+            is_experimental=True,
+        )

-    CATEGORY = "_for_testing/conditioning"
-
-    def encode(self, clip, conditioning, text):
+    @classmethod
+    def execute(cls, clip, conditioning, text) -> io.NodeOutput:
        tokens = clip.tokenize(text)
        cond, pooled = clip.encode_from_tokens(tokens, return_pooled=True)
        c = []
@@ -18,32 +28,41 @@ class CLIPTextEncodeControlnet:
            n[1]['cross_attn_controlnet'] = cond
            n[1]['pooled_output_controlnet'] = pooled
            c.append(n)
-        return (c, )
+        return io.NodeOutput(c)

-class T5TokenizerOptions:
+class T5TokenizerOptions(io.ComfyNode):
    @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "clip": ("CLIP", ),
-                "min_padding": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1}),
-                "min_length": ("INT", {"default": 0, "min": 0, "max": 10000, "step": 1}),
-            }
-        }
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="T5TokenizerOptions",
+            category="_for_testing/conditioning",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.Int.Input("min_padding", default=0, min=0, max=10000, step=1),
+                io.Int.Input("min_length", default=0, min=0, max=10000, step=1),
+            ],
+            outputs=[io.Clip.Output()],
+            is_experimental=True,
+        )

-    CATEGORY = "_for_testing/conditioning"
-    RETURN_TYPES = ("CLIP",)
-    FUNCTION = "set_options"
-
-    def set_options(self, clip, min_padding, min_length):
+    @classmethod
+    def execute(cls, clip, min_padding, min_length) -> io.NodeOutput:
        clip = clip.clone()
        for t5_type in ["t5xxl", "pile_t5xl", "t5base", "mt5xl", "umt5xxl"]:
            clip.set_tokenizer_option("{}_min_padding".format(t5_type), min_padding)
            clip.set_tokenizer_option("{}_min_length".format(t5_type), min_length)

-        return (clip, )
+        return io.NodeOutput(clip)

-NODE_CLASS_MAPPINGS = {
-    "CLIPTextEncodeControlnet": CLIPTextEncodeControlnet,
-    "T5TokenizerOptions": T5TokenizerOptions,
-}
+
+class CondExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            CLIPTextEncodeControlnet,
+            T5TokenizerOptions,
+        ]
+
+
+async def comfy_entrypoint() -> CondExtension:
+    return CondExtension()
@@ -1,25 +1,32 @@
+from typing_extensions import override
 import nodes
 import torch
 import comfy.model_management
 import comfy.utils
 import comfy.latent_formats

+from comfy_api.latest import ComfyExtension, io

-class EmptyCosmosLatentVideo:
+
+class EmptyCosmosLatentVideo(io.ComfyNode):
    @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"
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="EmptyCosmosLatentVideo",
+            category="latent/video",
+            inputs=[
+                io.Int.Input("width", default=1280, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("height", default=704, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("length", default=121, min=1, max=nodes.MAX_RESOLUTION, step=8),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+            ],
+            outputs=[io.Latent.Output()],
+        )

-    CATEGORY = "latent/video"
-
-    def generate(self, width, height, length, batch_size=1):
+    @classmethod
+    def execute(cls, width, height, length, batch_size=1) -> io.NodeOutput:
        latent = torch.zeros([batch_size, 16, ((length - 1) // 8) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
-        return ({"samples": latent}, )
+        return io.NodeOutput({"samples": latent})


 def vae_encode_with_padding(vae, image, width, height, length, padding=0):
@@ -33,31 +40,31 @@ def vae_encode_with_padding(vae, image, width, height, length, padding=0):
    return latent_temp[:, :, :latent_len]


-class CosmosImageToVideoLatent:
+class CosmosImageToVideoLatent(io.ComfyNode):
    @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", ),
-                }}
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="CosmosImageToVideoLatent",
+            category="conditioning/inpaint",
+            inputs=[
+                io.Vae.Input("vae"),
+                io.Int.Input("width", default=1280, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("height", default=704, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("length", default=121, min=1, max=nodes.MAX_RESOLUTION, step=8),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+                io.Image.Input("start_image", optional=True),
+                io.Image.Input("end_image", optional=True),
+            ],
+            outputs=[io.Latent.Output()],
+        )

-
-    RETURN_TYPES = ("LATENT",)
-    FUNCTION = "encode"
-
-    CATEGORY = "conditioning/inpaint"
-
-    def encode(self, vae, width, height, length, batch_size, start_image=None, end_image=None):
+    @classmethod
+    def execute(cls, vae, width, height, length, batch_size, start_image=None, end_image=None) -> io.NodeOutput:
        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,)
+            return io.NodeOutput(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())

@@ -74,33 +81,33 @@ class CosmosImageToVideoLatent:
        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,)
+        return io.NodeOutput(out_latent)

-class CosmosPredict2ImageToVideoLatent:
+class CosmosPredict2ImageToVideoLatent(io.ComfyNode):
    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"vae": ("VAE", ),
-                             "width": ("INT", {"default": 848, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
-                             "height": ("INT", {"default": 480, "min": 16, "max": nodes.MAX_RESOLUTION, "step": 16}),
-                             "length": ("INT", {"default": 93, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
-                             "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
-                },
-                "optional": {"start_image": ("IMAGE", ),
-                             "end_image": ("IMAGE", ),
-                }}
+    def define_schema(cls) -> io.Schema:
+        return io.Schema(
+            node_id="CosmosPredict2ImageToVideoLatent",
+            category="conditioning/inpaint",
+            inputs=[
+                io.Vae.Input("vae"),
+                io.Int.Input("width", default=848, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("height", default=480, min=16, max=nodes.MAX_RESOLUTION, step=16),
+                io.Int.Input("length", default=93, min=1, max=nodes.MAX_RESOLUTION, step=4),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+                io.Image.Input("start_image", optional=True),
+                io.Image.Input("end_image", optional=True),
+            ],
+            outputs=[io.Latent.Output()],
+        )

-
-    RETURN_TYPES = ("LATENT",)
-    FUNCTION = "encode"
-
-    CATEGORY = "conditioning/inpaint"
-
-    def encode(self, vae, width, height, length, batch_size, start_image=None, end_image=None):
+    @classmethod
+    def execute(cls, vae, width, height, length, batch_size, start_image=None, end_image=None) -> io.NodeOutput:
        latent = torch.zeros([1, 16, ((length - 1) // 4) + 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,)
+            return io.NodeOutput(out_latent)

        mask = torch.ones([latent.shape[0], 1, ((length - 1) // 4) + 1, latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device())

@@ -119,10 +126,18 @@ class CosmosPredict2ImageToVideoLatent:
        latent = latent_format.process_out(latent) * mask + latent * (1.0 - mask)
        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,)
+        return io.NodeOutput(out_latent)

-NODE_CLASS_MAPPINGS = {
-    "EmptyCosmosLatentVideo": EmptyCosmosLatentVideo,
-    "CosmosImageToVideoLatent": CosmosImageToVideoLatent,
-    "CosmosPredict2ImageToVideoLatent": CosmosPredict2ImageToVideoLatent,
-}
+
+class CosmosExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            EmptyCosmosLatentVideo,
+            CosmosImageToVideoLatent,
+            CosmosPredict2ImageToVideoLatent,
+        ]
+
+
+async def comfy_entrypoint() -> CosmosExtension:
+    return CosmosExtension()
@@ -1,23 +1,41 @@
 # code adapted from https://github.com/exx8/differential-diffusion

+from typing_extensions import override
+
 import torch
+from comfy_api.latest import ComfyExtension, io

-class DifferentialDiffusion():
+
+class DifferentialDiffusion(io.ComfyNode):
    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"model": ("MODEL", ),
-                            }}
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "apply"
-    CATEGORY = "_for_testing"
-    INIT = False
+    def define_schema(cls):
+        return io.Schema(
+            node_id="DifferentialDiffusion",
+            display_name="Differential Diffusion",
+            category="_for_testing",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input(
+                    "strength",
+                    default=1.0,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    optional=True,
+                ),
+            ],
+            outputs=[io.Model.Output()],
+            is_experimental=True,
+        )

-    def apply(self, model):
+    @classmethod
+    def execute(cls, model, strength=1.0) -> io.NodeOutput:
        model = model.clone()
-        model.set_model_denoise_mask_function(self.forward)
-        return (model,)
+        model.set_model_denoise_mask_function(lambda *args, **kwargs: cls.forward(*args, **kwargs, strength=strength))
+        return io.NodeOutput(model)

-    def forward(self, sigma: torch.Tensor, denoise_mask: torch.Tensor, extra_options: dict):
+    @classmethod
+    def forward(cls, sigma: torch.Tensor, denoise_mask: torch.Tensor, extra_options: dict, strength: float):
        model = extra_options["model"]
        step_sigmas = extra_options["sigmas"]
        sigma_to = model.inner_model.model_sampling.sigma_min
@@ -31,12 +49,24 @@ class DifferentialDiffusion():

        threshold = (current_ts - ts_to) / (ts_from - ts_to)

-        return (denoise_mask >= threshold).to(denoise_mask.dtype)
+        # Generate the binary mask based on the threshold
+        binary_mask = (denoise_mask >= threshold).to(denoise_mask.dtype)
+
+        # Blend binary mask with the original denoise_mask using strength
+        if strength and strength < 1:
+            blended_mask = strength * binary_mask + (1 - strength) * denoise_mask
+            return blended_mask
+        else:
+            return binary_mask


-NODE_CLASS_MAPPINGS = {
-    "DifferentialDiffusion": DifferentialDiffusion,
-}
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "DifferentialDiffusion": "Differential Diffusion",
-}
+class DifferentialDiffusionExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            DifferentialDiffusion,
+        ]
+
+
+async def comfy_entrypoint() -> DifferentialDiffusionExtension:
+    return DifferentialDiffusionExtension()
@@ -1,26 +1,38 @@
 import node_helpers
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io


-class ReferenceLatent:
+class ReferenceLatent(io.ComfyNode):
    @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"conditioning": ("CONDITIONING", ),
-                            },
-                "optional": {"latent": ("LATENT", ),}
-               }
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ReferenceLatent",
+            category="advanced/conditioning/edit_models",
+            description="This node sets the guiding latent for an edit model. If the model supports it you can chain multiple to set multiple reference images.",
+            inputs=[
+                io.Conditioning.Input("conditioning"),
+                io.Latent.Input("latent", optional=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ]
+        )

-    RETURN_TYPES = ("CONDITIONING",)
-    FUNCTION = "append"
-
-    CATEGORY = "advanced/conditioning/edit_models"
-    DESCRIPTION = "This node sets the guiding latent for an edit model. If the model supports it you can chain multiple to set multiple reference images."
-
-    def append(self, conditioning, latent=None):
+    @classmethod
+    def execute(cls, conditioning, latent=None) -> io.NodeOutput:
        if latent is not None:
            conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": [latent["samples"]]}, append=True)
-        return (conditioning, )
+        return io.NodeOutput(conditioning)


-NODE_CLASS_MAPPINGS = {
-    "ReferenceLatent": ReferenceLatent,
-}
+class EditModelExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            ReferenceLatent,
+        ]
+
+
+def comfy_entrypoint() -> EditModelExtension:
+    return EditModelExtension()
--- a/Show More
+++ b/Show More