Merge branch 'master' into worksplit-multigpu

.ci/windows_amd_base_files/README_VERY_IMPORTANT.txt

@@ -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.
+
+

.ci/windows_amd_base_files/run_amd_gpu_disable_smart_memory.bat

@@ -0,0 +1,2 @@
+.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build --disable-smart-memory
+pause

.ci/windows_nvidia_base_files/run_nvidia_gpu.bat

@@ -0,0 +1,2 @@
+.\python_embeded\python.exe -s ComfyUI\main.py --windows-standalone-build
+pause

.github/workflows/release-stable-all.yml

@@ -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

.github/workflows/ruff.yml

@@ -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

.github/workflows/stable-release.yml

@@ -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

.github/workflows/test-unit.yml

@@ -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:

.github/workflows/windows_release_dependencies.yml

@@ -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

.github/workflows/windows_release_dependencies_manual.yml

@@ -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 }}

.github/workflows/windows_release_nightly_pytorch.yml

@@ -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

.github/workflows/windows_release_package.yml

@@ -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 ..

CODEOWNERS

@@ -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

README.md

@@ -176,6 +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.
@@ -200,14 +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)
 
@@ -233,7 +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
 
@@ -264,12 +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:

app/frontend_management.py

@@ -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:

comfy/ldm/ace/vae/music_dcae_pipeline.py

@@ -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)

comfy/ldm/flux/math.py

@@ -37,7 +37,10 @@ def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
 
 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] + freqs_cis[..., 1] * x_[..., 1]
+
+    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):

comfy/ldm/hunyuan_video/vae_refiner.py

@@ -1,7 +1,7 @@
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
-from comfy.ldm.modules.diffusionmodules.model import ResnetBlock, AttnBlock, VideoConv3d
+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
@@ -17,11 +17,12 @@ class RMS_norm(nn.Module):
         return F.normalize(x, dim=1) * self.scale * self.gamma
 
 class DnSmpl(nn.Module):
-    def __init__(self, ic, oc, tds=True):
+    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 = VideoConv3d(ic, oc // fct, kernel_size=3)
+        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
@@ -30,7 +31,7 @@ class DnSmpl(nn.Module):
         r1 = 2 if self.tds else 1
         h = self.conv(x)
 
-        if self.tds:
+        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)
@@ -66,6 +67,7 @@ class DnSmpl(nn.Module):
             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)
@@ -83,10 +85,11 @@ class DnSmpl(nn.Module):
 
 
 class UpSmpl(nn.Module):
-    def __init__(self, ic, oc, tus=True):
+    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 = VideoConv3d(ic, oc * fct, kernel_size=3)
+        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
@@ -95,7 +98,7 @@ class UpSmpl(nn.Module):
         r1 = 2 if self.tus else 1
         h = self.conv(x)
 
-        if self.tus:
+        if self.tus and self.refiner_vae:
             hf = h[:, :, :1, :, :]
             b, c, f, ht, wd = hf.shape
             nc = c // (2 * 2)
@@ -148,43 +151,56 @@ class UpSmpl(nn.Module):
 
 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, **_):
+                 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.conv_in = VideoConv3d(in_channels, block_out_channels[0], 3, 1, 1)
+        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 // ffactor_temporal) >> 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=VideoConv3d, norm_op=RMS_norm)
+                                                     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)
+                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=VideoConv3d, norm_op=RMS_norm)
-        self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=RMS_norm)
-        self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=VideoConv3d, norm_op=RMS_norm)
+        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 = RMS_norm(ch)
-        self.conv_out = VideoConv3d(ch, z_channels << 1, 3, 1, 1)
+        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:
@@ -200,31 +216,42 @@ class Encoder(nn.Module):
         skip = x.view(b, c // grp, grp, t, h, w).mean(2)
 
         out = self.conv_out(F.silu(self.norm_out(x))) + skip
-        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()
+        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, **_):
+                 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 = VideoConv3d(z_channels, ch, 3)
+        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=VideoConv3d, norm_op=RMS_norm)
-        self.mid.attn_1 = AttnBlock(ch, conv_op=ops.Conv3d, norm_op=RMS_norm)
-        self.mid.block_2 = ResnetBlock(in_channels=ch, out_channels=ch, temb_channels=0, conv_op=VideoConv3d, norm_op=RMS_norm)
+        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()
@@ -235,25 +262,26 @@ class Decoder(nn.Module):
             stage.block = nn.ModuleList([ResnetBlock(in_channels=ch if j == 0 else tgt,
                                                      out_channels=tgt,
                                                      temb_channels=0,
-                                                     conv_op=VideoConv3d, norm_op=RMS_norm)
+                                                     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)
+                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 = RMS_norm(ch)
-        self.conv_out = VideoConv3d(ch, out_channels, 3)
+        self.norm_out = norm_op(ch)
+        self.conv_out = conv_op(ch, out_channels, 3, stride=1, padding=1)
 
     def forward(self, z):
-        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:]
+        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)))
@@ -264,4 +292,10 @@ class Decoder(nn.Module):
             if hasattr(stage, 'upsample'):
                 x = stage.upsample(x)
 
-        return self.conv_out(F.silu(self.norm_out(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

comfy/ldm/mmaudio/vae/activations.py

@@ -0,0 +1,120 @@
+# Implementation adapted from https://github.com/EdwardDixon/snake under the MIT license.
+#   LICENSE is in incl_licenses directory.
+
+import torch
+from torch import nn, sin, pow
+from torch.nn import Parameter
+import comfy.model_management
+
+class Snake(nn.Module):
+    '''
+    Implementation of a sine-based periodic activation function
+    Shape:
+        - Input: (B, C, T)
+        - Output: (B, C, T), same shape as the input
+    Parameters:
+        - alpha - trainable parameter
+    References:
+        - This activation function is from this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
+        https://arxiv.org/abs/2006.08195
+    Examples:
+        >>> a1 = snake(256)
+        >>> x = torch.randn(256)
+        >>> x = a1(x)
+    '''
+    def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
+        '''
+        Initialization.
+        INPUT:
+            - in_features: shape of the input
+            - alpha: trainable parameter
+            alpha is initialized to 1 by default, higher values = higher-frequency.
+            alpha will be trained along with the rest of your model.
+        '''
+        super(Snake, self).__init__()
+        self.in_features = in_features
+
+        # initialize alpha
+        self.alpha_logscale = alpha_logscale
+        if self.alpha_logscale:
+            self.alpha = Parameter(torch.empty(in_features))
+        else:
+            self.alpha = Parameter(torch.empty(in_features))
+
+        self.alpha.requires_grad = alpha_trainable
+
+        self.no_div_by_zero = 0.000000001
+
+    def forward(self, x):
+        '''
+        Forward pass of the function.
+        Applies the function to the input elementwise.
+        Snake ∶= x + 1/a * sin^2 (xa)
+        '''
+        alpha = comfy.model_management.cast_to(self.alpha, dtype=x.dtype, device=x.device).unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
+        if self.alpha_logscale:
+            alpha = torch.exp(alpha)
+        x = x + (1.0 / (alpha + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
+
+        return x
+
+
+class SnakeBeta(nn.Module):
+    '''
+    A modified Snake function which uses separate parameters for the magnitude of the periodic components
+    Shape:
+        - Input: (B, C, T)
+        - Output: (B, C, T), same shape as the input
+    Parameters:
+        - alpha - trainable parameter that controls frequency
+        - beta - trainable parameter that controls magnitude
+    References:
+        - This activation function is a modified version based on this paper by Liu Ziyin, Tilman Hartwig, Masahito Ueda:
+        https://arxiv.org/abs/2006.08195
+    Examples:
+        >>> a1 = snakebeta(256)
+        >>> x = torch.randn(256)
+        >>> x = a1(x)
+    '''
+    def __init__(self, in_features, alpha=1.0, alpha_trainable=True, alpha_logscale=False):
+        '''
+        Initialization.
+        INPUT:
+            - in_features: shape of the input
+            - alpha - trainable parameter that controls frequency
+            - beta - trainable parameter that controls magnitude
+            alpha is initialized to 1 by default, higher values = higher-frequency.
+            beta is initialized to 1 by default, higher values = higher-magnitude.
+            alpha will be trained along with the rest of your model.
+        '''
+        super(SnakeBeta, self).__init__()
+        self.in_features = in_features
+
+        # initialize alpha
+        self.alpha_logscale = alpha_logscale
+        if self.alpha_logscale:
+            self.alpha = Parameter(torch.empty(in_features))
+            self.beta = Parameter(torch.empty(in_features))
+        else:
+            self.alpha = Parameter(torch.empty(in_features))
+            self.beta = Parameter(torch.empty(in_features))
+
+        self.alpha.requires_grad = alpha_trainable
+        self.beta.requires_grad = alpha_trainable
+
+        self.no_div_by_zero = 0.000000001
+
+    def forward(self, x):
+        '''
+        Forward pass of the function.
+        Applies the function to the input elementwise.
+        SnakeBeta ∶= x + 1/b * sin^2 (xa)
+        '''
+        alpha = comfy.model_management.cast_to(self.alpha, dtype=x.dtype, device=x.device).unsqueeze(0).unsqueeze(-1) # line up with x to [B, C, T]
+        beta = comfy.model_management.cast_to(self.beta, dtype=x.dtype, device=x.device).unsqueeze(0).unsqueeze(-1)
+        if self.alpha_logscale:
+            alpha = torch.exp(alpha)
+            beta = torch.exp(beta)
+        x = x + (1.0 / (beta + self.no_div_by_zero)) * pow(sin(x * alpha), 2)
+
+        return x

comfy/ldm/mmaudio/vae/alias_free_torch.py

@@ -0,0 +1,157 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+import comfy.model_management
+
+if 'sinc' in dir(torch):
+    sinc = torch.sinc
+else:
+    # This code is adopted from adefossez's julius.core.sinc under the MIT License
+    # https://adefossez.github.io/julius/julius/core.html
+    #   LICENSE is in incl_licenses directory.
+    def sinc(x: torch.Tensor):
+        """
+        Implementation of sinc, i.e. sin(pi * x) / (pi * x)
+        __Warning__: Different to julius.sinc, the input is multiplied by `pi`!
+        """
+        return torch.where(x == 0,
+                           torch.tensor(1., device=x.device, dtype=x.dtype),
+                           torch.sin(math.pi * x) / math.pi / x)
+
+
+# This code is adopted from adefossez's julius.lowpass.LowPassFilters under the MIT License
+# https://adefossez.github.io/julius/julius/lowpass.html
+#   LICENSE is in incl_licenses directory.
+def kaiser_sinc_filter1d(cutoff, half_width, kernel_size): # return filter [1,1,kernel_size]
+    even = (kernel_size % 2 == 0)
+    half_size = kernel_size // 2
+
+    #For kaiser window
+    delta_f = 4 * half_width
+    A = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95
+    if A > 50.:
+        beta = 0.1102 * (A - 8.7)
+    elif A >= 21.:
+        beta = 0.5842 * (A - 21)**0.4 + 0.07886 * (A - 21.)
+    else:
+        beta = 0.
+    window = torch.kaiser_window(kernel_size, beta=beta, periodic=False)
+
+    # ratio = 0.5/cutoff -> 2 * cutoff = 1 / ratio
+    if even:
+        time = (torch.arange(-half_size, half_size) + 0.5)
+    else:
+        time = torch.arange(kernel_size) - half_size
+    if cutoff == 0:
+        filter_ = torch.zeros_like(time)
+    else:
+        filter_ = 2 * cutoff * window * sinc(2 * cutoff * time)
+        # Normalize filter to have sum = 1, otherwise we will have a small leakage
+        # of the constant component in the input signal.
+        filter_ /= filter_.sum()
+        filter = filter_.view(1, 1, kernel_size)
+
+    return filter
+
+
+class LowPassFilter1d(nn.Module):
+    def __init__(self,
+                 cutoff=0.5,
+                 half_width=0.6,
+                 stride: int = 1,
+                 padding: bool = True,
+                 padding_mode: str = 'replicate',
+                 kernel_size: int = 12):
+        # kernel_size should be even number for stylegan3 setup,
+        # in this implementation, odd number is also possible.
+        super().__init__()
+        if cutoff < -0.:
+            raise ValueError("Minimum cutoff must be larger than zero.")
+        if cutoff > 0.5:
+            raise ValueError("A cutoff above 0.5 does not make sense.")
+        self.kernel_size = kernel_size
+        self.even = (kernel_size % 2 == 0)
+        self.pad_left = kernel_size // 2 - int(self.even)
+        self.pad_right = kernel_size // 2
+        self.stride = stride
+        self.padding = padding
+        self.padding_mode = padding_mode
+        filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size)
+        self.register_buffer("filter", filter)
+
+    #input [B, C, T]
+    def forward(self, x):
+        _, C, _ = x.shape
+
+        if self.padding:
+            x = F.pad(x, (self.pad_left, self.pad_right),
+                      mode=self.padding_mode)
+        out = F.conv1d(x, comfy.model_management.cast_to(self.filter.expand(C, -1, -1), dtype=x.dtype, device=x.device),
+                       stride=self.stride, groups=C)
+
+        return out
+
+
+class UpSample1d(nn.Module):
+    def __init__(self, ratio=2, kernel_size=None):
+        super().__init__()
+        self.ratio = ratio
+        self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+        self.stride = ratio
+        self.pad = self.kernel_size // ratio - 1
+        self.pad_left = self.pad * self.stride + (self.kernel_size - self.stride) // 2
+        self.pad_right = self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2
+        filter = kaiser_sinc_filter1d(cutoff=0.5 / ratio,
+                                      half_width=0.6 / ratio,
+                                      kernel_size=self.kernel_size)
+        self.register_buffer("filter", filter)
+
+    # x: [B, C, T]
+    def forward(self, x):
+        _, C, _ = x.shape
+
+        x = F.pad(x, (self.pad, self.pad), mode='replicate')
+        x = self.ratio * F.conv_transpose1d(
+            x, comfy.model_management.cast_to(self.filter.expand(C, -1, -1), dtype=x.dtype, device=x.device), stride=self.stride, groups=C)
+        x = x[..., self.pad_left:-self.pad_right]
+
+        return x
+
+
+class DownSample1d(nn.Module):
+    def __init__(self, ratio=2, kernel_size=None):
+        super().__init__()
+        self.ratio = ratio
+        self.kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size
+        self.lowpass = LowPassFilter1d(cutoff=0.5 / ratio,
+                                       half_width=0.6 / ratio,
+                                       stride=ratio,
+                                       kernel_size=self.kernel_size)
+
+    def forward(self, x):
+        xx = self.lowpass(x)
+
+        return xx
+
+class Activation1d(nn.Module):
+    def __init__(self,
+                 activation,
+                 up_ratio: int = 2,
+                 down_ratio: int = 2,
+                 up_kernel_size: int = 12,
+                 down_kernel_size: int = 12):
+        super().__init__()
+        self.up_ratio = up_ratio
+        self.down_ratio = down_ratio
+        self.act = activation
+        self.upsample = UpSample1d(up_ratio, up_kernel_size)
+        self.downsample = DownSample1d(down_ratio, down_kernel_size)
+
+    # x: [B,C,T]
+    def forward(self, x):
+        x = self.upsample(x)
+        x = self.act(x)
+        x = self.downsample(x)
+
+        return x

comfy/ldm/mmaudio/vae/autoencoder.py

@@ -0,0 +1,156 @@
+from typing import Literal
+
+import torch
+import torch.nn as nn
+
+from .distributions import DiagonalGaussianDistribution
+from .vae import VAE_16k
+from .bigvgan import BigVGANVocoder
+import logging
+
+try:
+    import torchaudio
+except:
+    logging.warning("torchaudio missing, MMAudio VAE model will be broken")
+
+def dynamic_range_compression_torch(x, C=1, clip_val=1e-5, *, norm_fn):
+    return norm_fn(torch.clamp(x, min=clip_val) * C)
+
+
+def spectral_normalize_torch(magnitudes, norm_fn):
+    output = dynamic_range_compression_torch(magnitudes, norm_fn=norm_fn)
+    return output
+
+class MelConverter(nn.Module):
+
+    def __init__(
+        self,
+        *,
+        sampling_rate: float,
+        n_fft: int,
+        num_mels: int,
+        hop_size: int,
+        win_size: int,
+        fmin: float,
+        fmax: float,
+        norm_fn,
+    ):
+        super().__init__()
+        self.sampling_rate = sampling_rate
+        self.n_fft = n_fft
+        self.num_mels = num_mels
+        self.hop_size = hop_size
+        self.win_size = win_size
+        self.fmin = fmin
+        self.fmax = fmax
+        self.norm_fn = norm_fn
+
+        # mel = librosa_mel_fn(sr=self.sampling_rate,
+        #                      n_fft=self.n_fft,
+        #                      n_mels=self.num_mels,
+        #                      fmin=self.fmin,
+        #                      fmax=self.fmax)
+        # mel_basis = torch.from_numpy(mel).float()
+        mel_basis = torch.empty((num_mels, 1 + n_fft // 2))
+        hann_window = torch.hann_window(self.win_size)
+
+        self.register_buffer('mel_basis', mel_basis)
+        self.register_buffer('hann_window', hann_window)
+
+    @property
+    def device(self):
+        return self.mel_basis.device
+
+    def forward(self, waveform: torch.Tensor, center: bool = False) -> torch.Tensor:
+        waveform = waveform.clamp(min=-1., max=1.).to(self.device)
+
+        waveform = torch.nn.functional.pad(
+            waveform.unsqueeze(1),
+            [int((self.n_fft - self.hop_size) / 2),
+             int((self.n_fft - self.hop_size) / 2)],
+            mode='reflect')
+        waveform = waveform.squeeze(1)
+
+        spec = torch.stft(waveform,
+                          self.n_fft,
+                          hop_length=self.hop_size,
+                          win_length=self.win_size,
+                          window=self.hann_window,
+                          center=center,
+                          pad_mode='reflect',
+                          normalized=False,
+                          onesided=True,
+                          return_complex=True)
+
+        spec = torch.view_as_real(spec)
+        spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
+        spec = torch.matmul(self.mel_basis, spec)
+        spec = spectral_normalize_torch(spec, self.norm_fn)
+
+        return spec
+
+class AudioAutoencoder(nn.Module):
+
+    def __init__(
+        self,
+        *,
+        # ckpt_path: str,
+        mode=Literal['16k', '44k'],
+        need_vae_encoder: bool = True,
+    ):
+        super().__init__()
+
+        assert mode == "16k", "Only 16k mode is supported currently."
+        self.mel_converter = MelConverter(sampling_rate=16_000,
+                            n_fft=1024,
+                            num_mels=80,
+                            hop_size=256,
+                            win_size=1024,
+                            fmin=0,
+                            fmax=8_000,
+                            norm_fn=torch.log10)
+
+        self.vae = VAE_16k().eval()
+
+        bigvgan_config = {
+            "resblock": "1",
+            "num_mels": 80,
+            "upsample_rates": [4, 4, 2, 2, 2, 2],
+            "upsample_kernel_sizes": [8, 8, 4, 4, 4, 4],
+            "upsample_initial_channel": 1536,
+            "resblock_kernel_sizes": [3, 7, 11],
+            "resblock_dilation_sizes": [
+                [1, 3, 5],
+                [1, 3, 5],
+                [1, 3, 5],
+            ],
+            "activation": "snakebeta",
+            "snake_logscale": True,
+        }
+
+        self.vocoder = BigVGANVocoder(
+            bigvgan_config
+        ).eval()
+
+    @torch.inference_mode()
+    def encode_audio(self, x) -> DiagonalGaussianDistribution:
+        # x: (B * L)
+        mel = self.mel_converter(x)
+        dist = self.vae.encode(mel)
+
+        return dist
+
+    @torch.no_grad()
+    def decode(self, z):
+        mel_decoded = self.vae.decode(z)
+        audio = self.vocoder(mel_decoded)
+
+        audio = torchaudio.functional.resample(audio, 16000, 44100)
+        return audio
+
+    @torch.no_grad()
+    def encode(self, audio):
+        audio = audio.mean(dim=1)
+        audio = torchaudio.functional.resample(audio, 44100, 16000)
+        dist = self.encode_audio(audio)
+        return dist.mean

comfy/ldm/mmaudio/vae/bigvgan.py

@@ -0,0 +1,219 @@
+# Copyright (c) 2022 NVIDIA CORPORATION.
+#   Licensed under the MIT license.
+
+# Adapted from https://github.com/jik876/hifi-gan under the MIT license.
+#   LICENSE is in incl_licenses directory.
+
+import torch
+import torch.nn as nn
+from types import SimpleNamespace
+from . import activations
+from .alias_free_torch import Activation1d
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+def get_padding(kernel_size, dilation=1):
+    return int((kernel_size * dilation - dilation) / 2)
+
+class AMPBlock1(torch.nn.Module):
+
+    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5), activation=None):
+        super(AMPBlock1, self).__init__()
+        self.h = h
+
+        self.convs1 = nn.ModuleList([
+                ops.Conv1d(channels,
+                       channels,
+                       kernel_size,
+                       1,
+                       dilation=dilation[0],
+                       padding=get_padding(kernel_size, dilation[0])),
+                ops.Conv1d(channels,
+                       channels,
+                       kernel_size,
+                       1,
+                       dilation=dilation[1],
+                       padding=get_padding(kernel_size, dilation[1])),
+                ops.Conv1d(channels,
+                       channels,
+                       kernel_size,
+                       1,
+                       dilation=dilation[2],
+                       padding=get_padding(kernel_size, dilation[2]))
+        ])
+
+        self.convs2 = nn.ModuleList([
+                ops.Conv1d(channels,
+                       channels,
+                       kernel_size,
+                       1,
+                       dilation=1,
+                       padding=get_padding(kernel_size, 1)),
+                ops.Conv1d(channels,
+                       channels,
+                       kernel_size,
+                       1,
+                       dilation=1,
+                       padding=get_padding(kernel_size, 1)),
+                ops.Conv1d(channels,
+                       channels,
+                       kernel_size,
+                       1,
+                       dilation=1,
+                       padding=get_padding(kernel_size, 1))
+        ])
+
+        self.num_layers = len(self.convs1) + len(self.convs2)  # total number of conv layers
+
+        if activation == 'snake':  # periodic nonlinearity with snake function and anti-aliasing
+            self.activations = nn.ModuleList([
+                Activation1d(
+                    activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))
+                for _ in range(self.num_layers)
+            ])
+        elif activation == 'snakebeta':  # periodic nonlinearity with snakebeta function and anti-aliasing
+            self.activations = nn.ModuleList([
+                Activation1d(
+                    activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))
+                for _ in range(self.num_layers)
+            ])
+        else:
+            raise NotImplementedError(
+                "activation incorrectly specified. check the config file and look for 'activation'."
+            )
+
+    def forward(self, x):
+        acts1, acts2 = self.activations[::2], self.activations[1::2]
+        for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2):
+            xt = a1(x)
+            xt = c1(xt)
+            xt = a2(xt)
+            xt = c2(xt)
+            x = xt + x
+
+        return x
+
+
+class AMPBlock2(torch.nn.Module):
+
+    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3), activation=None):
+        super(AMPBlock2, self).__init__()
+        self.h = h
+
+        self.convs = nn.ModuleList([
+                ops.Conv1d(channels,
+                       channels,
+                       kernel_size,
+                       1,
+                       dilation=dilation[0],
+                       padding=get_padding(kernel_size, dilation[0])),
+                ops.Conv1d(channels,
+                       channels,
+                       kernel_size,
+                       1,
+                       dilation=dilation[1],
+                       padding=get_padding(kernel_size, dilation[1]))
+        ])
+
+        self.num_layers = len(self.convs)  # total number of conv layers
+
+        if activation == 'snake':  # periodic nonlinearity with snake function and anti-aliasing
+            self.activations = nn.ModuleList([
+                Activation1d(
+                    activation=activations.Snake(channels, alpha_logscale=h.snake_logscale))
+                for _ in range(self.num_layers)
+            ])
+        elif activation == 'snakebeta':  # periodic nonlinearity with snakebeta function and anti-aliasing
+            self.activations = nn.ModuleList([
+                Activation1d(
+                    activation=activations.SnakeBeta(channels, alpha_logscale=h.snake_logscale))
+                for _ in range(self.num_layers)
+            ])
+        else:
+            raise NotImplementedError(
+                "activation incorrectly specified. check the config file and look for 'activation'."
+            )
+
+    def forward(self, x):
+        for c, a in zip(self.convs, self.activations):
+            xt = a(x)
+            xt = c(xt)
+            x = xt + x
+
+        return x
+
+
+class BigVGANVocoder(torch.nn.Module):
+    # this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks.
+    def __init__(self, h):
+        super().__init__()
+        if isinstance(h, dict):
+            h = SimpleNamespace(**h)
+        self.h = h
+
+        self.num_kernels = len(h.resblock_kernel_sizes)
+        self.num_upsamples = len(h.upsample_rates)
+
+        # pre conv
+        self.conv_pre = ops.Conv1d(h.num_mels, h.upsample_initial_channel, 7, 1, padding=3)
+
+        # define which AMPBlock to use. BigVGAN uses AMPBlock1 as default
+        resblock = AMPBlock1 if h.resblock == '1' else AMPBlock2
+
+        # transposed conv-based upsamplers. does not apply anti-aliasing
+        self.ups = nn.ModuleList()
+        for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
+            self.ups.append(
+                nn.ModuleList([
+                        ops.ConvTranspose1d(h.upsample_initial_channel // (2**i),
+                                        h.upsample_initial_channel // (2**(i + 1)),
+                                        k,
+                                        u,
+                                        padding=(k - u) // 2)
+                ]))
+
+        # residual blocks using anti-aliased multi-periodicity composition modules (AMP)
+        self.resblocks = nn.ModuleList()
+        for i in range(len(self.ups)):
+            ch = h.upsample_initial_channel // (2**(i + 1))
+            for j, (k, d) in enumerate(zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
+                self.resblocks.append(resblock(h, ch, k, d, activation=h.activation))
+
+        # post conv
+        if h.activation == "snake":  # periodic nonlinearity with snake function and anti-aliasing
+            activation_post = activations.Snake(ch, alpha_logscale=h.snake_logscale)
+            self.activation_post = Activation1d(activation=activation_post)
+        elif h.activation == "snakebeta":  # periodic nonlinearity with snakebeta function and anti-aliasing
+            activation_post = activations.SnakeBeta(ch, alpha_logscale=h.snake_logscale)
+            self.activation_post = Activation1d(activation=activation_post)
+        else:
+            raise NotImplementedError(
+                "activation incorrectly specified. check the config file and look for 'activation'."
+            )
+
+        self.conv_post = ops.Conv1d(ch, 1, 7, 1, padding=3)
+
+
+    def forward(self, x):
+        # pre conv
+        x = self.conv_pre(x)
+
+        for i in range(self.num_upsamples):
+            # upsampling
+            for i_up in range(len(self.ups[i])):
+                x = self.ups[i][i_up](x)
+            # AMP blocks
+            xs = None
+            for j in range(self.num_kernels):
+                if xs is None:
+                    xs = self.resblocks[i * self.num_kernels + j](x)
+                else:
+                    xs += self.resblocks[i * self.num_kernels + j](x)
+            x = xs / self.num_kernels
+
+        # post conv
+        x = self.activation_post(x)
+        x = self.conv_post(x)
+        x = torch.tanh(x)
+
+        return x

comfy/ldm/mmaudio/vae/distributions.py

@@ -0,0 +1,92 @@
+import torch
+import numpy as np
+
+
+class AbstractDistribution:
+    def sample(self):
+        raise NotImplementedError()
+
+    def mode(self):
+        raise NotImplementedError()
+
+
+class DiracDistribution(AbstractDistribution):
+    def __init__(self, value):
+        self.value = value
+
+    def sample(self):
+        return self.value
+
+    def mode(self):
+        return self.value
+
+
+class DiagonalGaussianDistribution(object):
+    def __init__(self, parameters, deterministic=False):
+        self.parameters = parameters
+        self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
+        self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
+        self.deterministic = deterministic
+        self.std = torch.exp(0.5 * self.logvar)
+        self.var = torch.exp(self.logvar)
+        if self.deterministic:
+            self.var = self.std = torch.zeros_like(self.mean, device=self.parameters.device)
+
+    def sample(self):
+        x = self.mean + self.std * torch.randn(self.mean.shape, device=self.parameters.device)
+        return x
+
+    def kl(self, other=None):
+        if self.deterministic:
+            return torch.Tensor([0.])
+        else:
+            if other is None:
+                return 0.5 * torch.sum(torch.pow(self.mean, 2)
+                                       + self.var - 1.0 - self.logvar,
+                                       dim=[1, 2, 3])
+            else:
+                return 0.5 * torch.sum(
+                    torch.pow(self.mean - other.mean, 2) / other.var
+                    + self.var / other.var - 1.0 - self.logvar + other.logvar,
+                    dim=[1, 2, 3])
+
+    def nll(self, sample, dims=[1,2,3]):
+        if self.deterministic:
+            return torch.Tensor([0.])
+        logtwopi = np.log(2.0 * np.pi)
+        return 0.5 * torch.sum(
+            logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
+            dim=dims)
+
+    def mode(self):
+        return self.mean
+
+
+def normal_kl(mean1, logvar1, mean2, logvar2):
+    """
+    source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
+    Compute the KL divergence between two gaussians.
+    Shapes are automatically broadcasted, so batches can be compared to
+    scalars, among other use cases.
+    """
+    tensor = None
+    for obj in (mean1, logvar1, mean2, logvar2):
+        if isinstance(obj, torch.Tensor):
+            tensor = obj
+            break
+    assert tensor is not None, "at least one argument must be a Tensor"
+
+    # Force variances to be Tensors. Broadcasting helps convert scalars to
+    # Tensors, but it does not work for torch.exp().
+    logvar1, logvar2 = [
+        x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
+        for x in (logvar1, logvar2)
+    ]
+
+    return 0.5 * (
+        -1.0
+        + logvar2
+        - logvar1
+        + torch.exp(logvar1 - logvar2)
+        + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
+    )

comfy/ldm/mmaudio/vae/vae.py

@@ -0,0 +1,358 @@
+import logging
+from typing import Optional
+
+import torch
+import torch.nn as nn
+
+from .vae_modules import (AttnBlock1D, Downsample1D, ResnetBlock1D,
+                                                 Upsample1D, nonlinearity)
+from .distributions import DiagonalGaussianDistribution
+
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+log = logging.getLogger()
+
+DATA_MEAN_80D = [
+    -1.6058, -1.3676, -1.2520, -1.2453, -1.2078, -1.2224, -1.2419, -1.2439, -1.2922, -1.2927,
+    -1.3170, -1.3543, -1.3401, -1.3836, -1.3907, -1.3912, -1.4313, -1.4152, -1.4527, -1.4728,
+    -1.4568, -1.5101, -1.5051, -1.5172, -1.5623, -1.5373, -1.5746, -1.5687, -1.6032, -1.6131,
+    -1.6081, -1.6331, -1.6489, -1.6489, -1.6700, -1.6738, -1.6953, -1.6969, -1.7048, -1.7280,
+    -1.7361, -1.7495, -1.7658, -1.7814, -1.7889, -1.8064, -1.8221, -1.8377, -1.8417, -1.8643,
+    -1.8857, -1.8929, -1.9173, -1.9379, -1.9531, -1.9673, -1.9824, -2.0042, -2.0215, -2.0436,
+    -2.0766, -2.1064, -2.1418, -2.1855, -2.2319, -2.2767, -2.3161, -2.3572, -2.3954, -2.4282,
+    -2.4659, -2.5072, -2.5552, -2.6074, -2.6584, -2.7107, -2.7634, -2.8266, -2.8981, -2.9673
+]
+
+DATA_STD_80D = [
+    1.0291, 1.0411, 1.0043, 0.9820, 0.9677, 0.9543, 0.9450, 0.9392, 0.9343, 0.9297, 0.9276, 0.9263,
+    0.9242, 0.9254, 0.9232, 0.9281, 0.9263, 0.9315, 0.9274, 0.9247, 0.9277, 0.9199, 0.9188, 0.9194,
+    0.9160, 0.9161, 0.9146, 0.9161, 0.9100, 0.9095, 0.9145, 0.9076, 0.9066, 0.9095, 0.9032, 0.9043,
+    0.9038, 0.9011, 0.9019, 0.9010, 0.8984, 0.8983, 0.8986, 0.8961, 0.8962, 0.8978, 0.8962, 0.8973,
+    0.8993, 0.8976, 0.8995, 0.9016, 0.8982, 0.8972, 0.8974, 0.8949, 0.8940, 0.8947, 0.8936, 0.8939,
+    0.8951, 0.8956, 0.9017, 0.9167, 0.9436, 0.9690, 1.0003, 1.0225, 1.0381, 1.0491, 1.0545, 1.0604,
+    1.0761, 1.0929, 1.1089, 1.1196, 1.1176, 1.1156, 1.1117, 1.1070
+]
+
+DATA_MEAN_128D = [
+    -3.3462, -2.6723, -2.4893, -2.3143, -2.2664, -2.3317, -2.1802, -2.4006, -2.2357, -2.4597,
+    -2.3717, -2.4690, -2.5142, -2.4919, -2.6610, -2.5047, -2.7483, -2.5926, -2.7462, -2.7033,
+    -2.7386, -2.8112, -2.7502, -2.9594, -2.7473, -3.0035, -2.8891, -2.9922, -2.9856, -3.0157,
+    -3.1191, -2.9893, -3.1718, -3.0745, -3.1879, -3.2310, -3.1424, -3.2296, -3.2791, -3.2782,
+    -3.2756, -3.3134, -3.3509, -3.3750, -3.3951, -3.3698, -3.4505, -3.4509, -3.5089, -3.4647,
+    -3.5536, -3.5788, -3.5867, -3.6036, -3.6400, -3.6747, -3.7072, -3.7279, -3.7283, -3.7795,
+    -3.8259, -3.8447, -3.8663, -3.9182, -3.9605, -3.9861, -4.0105, -4.0373, -4.0762, -4.1121,
+    -4.1488, -4.1874, -4.2461, -4.3170, -4.3639, -4.4452, -4.5282, -4.6297, -4.7019, -4.7960,
+    -4.8700, -4.9507, -5.0303, -5.0866, -5.1634, -5.2342, -5.3242, -5.4053, -5.4927, -5.5712,
+    -5.6464, -5.7052, -5.7619, -5.8410, -5.9188, -6.0103, -6.0955, -6.1673, -6.2362, -6.3120,
+    -6.3926, -6.4797, -6.5565, -6.6511, -6.8130, -6.9961, -7.1275, -7.2457, -7.3576, -7.4663,
+    -7.6136, -7.7469, -7.8815, -8.0132, -8.1515, -8.3071, -8.4722, -8.7418, -9.3975, -9.6628,
+    -9.7671, -9.8863, -9.9992, -10.0860, -10.1709, -10.5418, -11.2795, -11.3861
+]
+
+DATA_STD_128D = [
+    2.3804, 2.4368, 2.3772, 2.3145, 2.2803, 2.2510, 2.2316, 2.2083, 2.1996, 2.1835, 2.1769, 2.1659,
+    2.1631, 2.1618, 2.1540, 2.1606, 2.1571, 2.1567, 2.1612, 2.1579, 2.1679, 2.1683, 2.1634, 2.1557,
+    2.1668, 2.1518, 2.1415, 2.1449, 2.1406, 2.1350, 2.1313, 2.1415, 2.1281, 2.1352, 2.1219, 2.1182,
+    2.1327, 2.1195, 2.1137, 2.1080, 2.1179, 2.1036, 2.1087, 2.1036, 2.1015, 2.1068, 2.0975, 2.0991,
+    2.0902, 2.1015, 2.0857, 2.0920, 2.0893, 2.0897, 2.0910, 2.0881, 2.0925, 2.0873, 2.0960, 2.0900,
+    2.0957, 2.0958, 2.0978, 2.0936, 2.0886, 2.0905, 2.0845, 2.0855, 2.0796, 2.0840, 2.0813, 2.0817,
+    2.0838, 2.0840, 2.0917, 2.1061, 2.1431, 2.1976, 2.2482, 2.3055, 2.3700, 2.4088, 2.4372, 2.4609,
+    2.4731, 2.4847, 2.5072, 2.5451, 2.5772, 2.6147, 2.6529, 2.6596, 2.6645, 2.6726, 2.6803, 2.6812,
+    2.6899, 2.6916, 2.6931, 2.6998, 2.7062, 2.7262, 2.7222, 2.7158, 2.7041, 2.7485, 2.7491, 2.7451,
+    2.7485, 2.7233, 2.7297, 2.7233, 2.7145, 2.6958, 2.6788, 2.6439, 2.6007, 2.4786, 2.2469, 2.1877,
+    2.1392, 2.0717, 2.0107, 1.9676, 1.9140, 1.7102, 0.9101, 0.7164
+]
+
+
+class VAE(nn.Module):
+
+    def __init__(
+        self,
+        *,
+        data_dim: int,
+        embed_dim: int,
+        hidden_dim: int,
+    ):
+        super().__init__()
+
+        if data_dim == 80:
+            self.data_mean = nn.Buffer(torch.tensor(DATA_MEAN_80D, dtype=torch.float32))
+            self.data_std = nn.Buffer(torch.tensor(DATA_STD_80D, dtype=torch.float32))
+        elif data_dim == 128:
+            self.data_mean = nn.Buffer(torch.tensor(DATA_MEAN_128D, dtype=torch.float32))
+            self.data_std = nn.Buffer(torch.tensor(DATA_STD_128D, dtype=torch.float32))
+
+        self.data_mean = self.data_mean.view(1, -1, 1)
+        self.data_std = self.data_std.view(1, -1, 1)
+
+        self.encoder = Encoder1D(
+            dim=hidden_dim,
+            ch_mult=(1, 2, 4),
+            num_res_blocks=2,
+            attn_layers=[3],
+            down_layers=[0],
+            in_dim=data_dim,
+            embed_dim=embed_dim,
+        )
+        self.decoder = Decoder1D(
+            dim=hidden_dim,
+            ch_mult=(1, 2, 4),
+            num_res_blocks=2,
+            attn_layers=[3],
+            down_layers=[0],
+            in_dim=data_dim,
+            out_dim=data_dim,
+            embed_dim=embed_dim,
+        )
+
+        self.embed_dim = embed_dim
+        # self.quant_conv = nn.Conv1d(2 * embed_dim, 2 * embed_dim, 1)
+        # self.post_quant_conv = nn.Conv1d(embed_dim, embed_dim, 1)
+
+        self.initialize_weights()
+
+    def initialize_weights(self):
+        pass
+
+    def encode(self, x: torch.Tensor, normalize: bool = True) -> DiagonalGaussianDistribution:
+        if normalize:
+            x = self.normalize(x)
+        moments = self.encoder(x)
+        posterior = DiagonalGaussianDistribution(moments)
+        return posterior
+
+    def decode(self, z: torch.Tensor, unnormalize: bool = True) -> torch.Tensor:
+        dec = self.decoder(z)
+        if unnormalize:
+            dec = self.unnormalize(dec)
+        return dec
+
+    def normalize(self, x: torch.Tensor) -> torch.Tensor:
+        return (x - comfy.model_management.cast_to(self.data_mean, dtype=x.dtype, device=x.device)) / comfy.model_management.cast_to(self.data_std, dtype=x.dtype, device=x.device)
+
+    def unnormalize(self, x: torch.Tensor) -> torch.Tensor:
+        return x * comfy.model_management.cast_to(self.data_std, dtype=x.dtype, device=x.device) + comfy.model_management.cast_to(self.data_mean, dtype=x.dtype, device=x.device)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        sample_posterior: bool = True,
+        rng: Optional[torch.Generator] = None,
+        normalize: bool = True,
+        unnormalize: bool = True,
+    ) -> tuple[torch.Tensor, DiagonalGaussianDistribution]:
+
+        posterior = self.encode(x, normalize=normalize)
+        if sample_posterior:
+            z = posterior.sample(rng)
+        else:
+            z = posterior.mode()
+        dec = self.decode(z, unnormalize=unnormalize)
+        return dec, posterior
+
+    def load_weights(self, src_dict) -> None:
+        self.load_state_dict(src_dict, strict=True)
+
+    @property
+    def device(self) -> torch.device:
+        return next(self.parameters()).device
+
+    def get_last_layer(self):
+        return self.decoder.conv_out.weight
+
+    def remove_weight_norm(self):
+        return self
+
+
+class Encoder1D(nn.Module):
+
+    def __init__(self,
+                 *,
+                 dim: int,
+                 ch_mult: tuple[int] = (1, 2, 4, 8),
+                 num_res_blocks: int,
+                 attn_layers: list[int] = [],
+                 down_layers: list[int] = [],
+                 resamp_with_conv: bool = True,
+                 in_dim: int,
+                 embed_dim: int,
+                 double_z: bool = True,
+                 kernel_size: int = 3,
+                 clip_act: float = 256.0):
+        super().__init__()
+        self.dim = dim
+        self.num_layers = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.in_channels = in_dim
+        self.clip_act = clip_act
+        self.down_layers = down_layers
+        self.attn_layers = attn_layers
+        self.conv_in = ops.Conv1d(in_dim, self.dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)
+
+        in_ch_mult = (1, ) + tuple(ch_mult)
+        self.in_ch_mult = in_ch_mult
+        # downsampling
+        self.down = nn.ModuleList()
+        for i_level in range(self.num_layers):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_in = dim * in_ch_mult[i_level]
+            block_out = dim * ch_mult[i_level]
+            for i_block in range(self.num_res_blocks):
+                block.append(
+                    ResnetBlock1D(in_dim=block_in,
+                                  out_dim=block_out,
+                                  kernel_size=kernel_size,
+                                  use_norm=True))
+                block_in = block_out
+                if i_level in attn_layers:
+                    attn.append(AttnBlock1D(block_in))
+            down = nn.Module()
+            down.block = block
+            down.attn = attn
+            if i_level in down_layers:
+                down.downsample = Downsample1D(block_in, resamp_with_conv)
+            self.down.append(down)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock1D(in_dim=block_in,
+                                         out_dim=block_in,
+                                         kernel_size=kernel_size,
+                                         use_norm=True)
+        self.mid.attn_1 = AttnBlock1D(block_in)
+        self.mid.block_2 = ResnetBlock1D(in_dim=block_in,
+                                         out_dim=block_in,
+                                         kernel_size=kernel_size,
+                                         use_norm=True)
+
+        # end
+        self.conv_out = ops.Conv1d(block_in,
+                                 2 * embed_dim if double_z else embed_dim,
+                                 kernel_size=kernel_size, padding=kernel_size // 2, bias=False)
+
+        self.learnable_gain = nn.Parameter(torch.zeros([]))
+
+    def forward(self, x):
+
+        # downsampling
+        h = self.conv_in(x)
+        for i_level in range(self.num_layers):
+            for i_block in range(self.num_res_blocks):
+                h = self.down[i_level].block[i_block](h)
+                if len(self.down[i_level].attn) > 0:
+                    h = self.down[i_level].attn[i_block](h)
+                h = h.clamp(-self.clip_act, self.clip_act)
+            if i_level in self.down_layers:
+                h = self.down[i_level].downsample(h)
+
+        # middle
+        h = self.mid.block_1(h)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h)
+        h = h.clamp(-self.clip_act, self.clip_act)
+
+        # end
+        h = nonlinearity(h)
+        h = self.conv_out(h) * (self.learnable_gain + 1)
+        return h
+
+
+class Decoder1D(nn.Module):
+
+    def __init__(self,
+                 *,
+                 dim: int,
+                 out_dim: int,
+                 ch_mult: tuple[int] = (1, 2, 4, 8),
+                 num_res_blocks: int,
+                 attn_layers: list[int] = [],
+                 down_layers: list[int] = [],
+                 kernel_size: int = 3,
+                 resamp_with_conv: bool = True,
+                 in_dim: int,
+                 embed_dim: int,
+                 clip_act: float = 256.0):
+        super().__init__()
+        self.ch = dim
+        self.num_layers = len(ch_mult)
+        self.num_res_blocks = num_res_blocks
+        self.in_channels = in_dim
+        self.clip_act = clip_act
+        self.down_layers = [i + 1 for i in down_layers]  # each downlayer add one
+
+        # compute in_ch_mult, block_in and curr_res at lowest res
+        block_in = dim * ch_mult[self.num_layers - 1]
+
+        # z to block_in
+        self.conv_in = ops.Conv1d(embed_dim, block_in, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)
+
+        # middle
+        self.mid = nn.Module()
+        self.mid.block_1 = ResnetBlock1D(in_dim=block_in, out_dim=block_in, use_norm=True)
+        self.mid.attn_1 = AttnBlock1D(block_in)
+        self.mid.block_2 = ResnetBlock1D(in_dim=block_in, out_dim=block_in, use_norm=True)
+
+        # upsampling
+        self.up = nn.ModuleList()
+        for i_level in reversed(range(self.num_layers)):
+            block = nn.ModuleList()
+            attn = nn.ModuleList()
+            block_out = dim * ch_mult[i_level]
+            for i_block in range(self.num_res_blocks + 1):
+                block.append(ResnetBlock1D(in_dim=block_in, out_dim=block_out, use_norm=True))
+                block_in = block_out
+                if i_level in attn_layers:
+                    attn.append(AttnBlock1D(block_in))
+            up = nn.Module()
+            up.block = block
+            up.attn = attn
+            if i_level in self.down_layers:
+                up.upsample = Upsample1D(block_in, resamp_with_conv)
+            self.up.insert(0, up)  # prepend to get consistent order
+
+        # end
+        self.conv_out = ops.Conv1d(block_in, out_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)
+        self.learnable_gain = nn.Parameter(torch.zeros([]))
+
+    def forward(self, z):
+        # z to block_in
+        h = self.conv_in(z)
+
+        # middle
+        h = self.mid.block_1(h)
+        h = self.mid.attn_1(h)
+        h = self.mid.block_2(h)
+        h = h.clamp(-self.clip_act, self.clip_act)
+
+        # upsampling
+        for i_level in reversed(range(self.num_layers)):
+            for i_block in range(self.num_res_blocks + 1):
+                h = self.up[i_level].block[i_block](h)
+                if len(self.up[i_level].attn) > 0:
+                    h = self.up[i_level].attn[i_block](h)
+                h = h.clamp(-self.clip_act, self.clip_act)
+            if i_level in self.down_layers:
+                h = self.up[i_level].upsample(h)
+
+        h = nonlinearity(h)
+        h = self.conv_out(h) * (self.learnable_gain + 1)
+        return h
+
+
+def VAE_16k(**kwargs) -> VAE:
+    return VAE(data_dim=80, embed_dim=20, hidden_dim=384, **kwargs)
+
+
+def VAE_44k(**kwargs) -> VAE:
+    return VAE(data_dim=128, embed_dim=40, hidden_dim=512, **kwargs)
+
+
+def get_my_vae(name: str, **kwargs) -> VAE:
+    if name == '16k':
+        return VAE_16k(**kwargs)
+    if name == '44k':
+        return VAE_44k(**kwargs)
+    raise ValueError(f'Unknown model: {name}')
+

comfy/ldm/mmaudio/vae/vae_modules.py

@@ -0,0 +1,121 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from comfy.ldm.modules.diffusionmodules.model import vae_attention
+import math
+import comfy.ops
+ops = comfy.ops.disable_weight_init
+
+def nonlinearity(x):
+    # swish
+    return torch.nn.functional.silu(x) / 0.596
+
+def mp_sum(a, b, t=0.5):
+    return a.lerp(b, t) / math.sqrt((1 - t)**2 + t**2)
+
+def normalize(x, dim=None, eps=1e-4):
+    if dim is None:
+        dim = list(range(1, x.ndim))
+    norm = torch.linalg.vector_norm(x, dim=dim, keepdim=True, dtype=torch.float32)
+    norm = torch.add(eps, norm, alpha=math.sqrt(norm.numel() / x.numel()))
+    return x / norm.to(x.dtype)
+
+class ResnetBlock1D(nn.Module):
+
+    def __init__(self, *, in_dim, out_dim=None, conv_shortcut=False, kernel_size=3, use_norm=True):
+        super().__init__()
+        self.in_dim = in_dim
+        out_dim = in_dim if out_dim is None else out_dim
+        self.out_dim = out_dim
+        self.use_conv_shortcut = conv_shortcut
+        self.use_norm = use_norm
+
+        self.conv1 = ops.Conv1d(in_dim, out_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)
+        self.conv2 = ops.Conv1d(out_dim, out_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)
+        if self.in_dim != self.out_dim:
+            if self.use_conv_shortcut:
+                self.conv_shortcut = ops.Conv1d(in_dim, out_dim, kernel_size=kernel_size, padding=kernel_size // 2, bias=False)
+            else:
+                self.nin_shortcut = ops.Conv1d(in_dim, out_dim, kernel_size=1, padding=0, bias=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+
+        # pixel norm
+        if self.use_norm:
+            x = normalize(x, dim=1)
+
+        h = x
+        h = nonlinearity(h)
+        h = self.conv1(h)
+
+        h = nonlinearity(h)
+        h = self.conv2(h)
+
+        if self.in_dim != self.out_dim:
+            if self.use_conv_shortcut:
+                x = self.conv_shortcut(x)
+            else:
+                x = self.nin_shortcut(x)
+
+        return mp_sum(x, h, t=0.3)
+
+
+class AttnBlock1D(nn.Module):
+
+    def __init__(self, in_channels, num_heads=1):
+        super().__init__()
+        self.in_channels = in_channels
+
+        self.num_heads = num_heads
+        self.qkv = ops.Conv1d(in_channels, in_channels * 3, kernel_size=1, padding=0, bias=False)
+        self.proj_out = ops.Conv1d(in_channels, in_channels, kernel_size=1, padding=0, bias=False)
+        self.optimized_attention = vae_attention()
+
+    def forward(self, x):
+        h = x
+        y = self.qkv(h)
+        y = y.reshape(y.shape[0], -1, 3, y.shape[-1])
+        q, k, v = normalize(y, dim=1).unbind(2)
+
+        h = self.optimized_attention(q, k, v)
+        h = self.proj_out(h)
+
+        return mp_sum(x, h, t=0.3)
+
+
+class Upsample1D(nn.Module):
+
+    def __init__(self, in_channels, with_conv):
+        super().__init__()
+        self.with_conv = with_conv
+        if self.with_conv:
+            self.conv = ops.Conv1d(in_channels, in_channels, kernel_size=3, padding=1, bias=False)
+
+    def forward(self, x):
+        x = F.interpolate(x, scale_factor=2.0, mode='nearest-exact')  # support 3D tensor(B,C,T)
+        if self.with_conv:
+            x = self.conv(x)
+        return x
+
+
+class Downsample1D(nn.Module):
+
+    def __init__(self, in_channels, with_conv):
+        super().__init__()
+        self.with_conv = with_conv
+        if self.with_conv:
+            # no asymmetric padding in torch conv, must do it ourselves
+            self.conv1 = ops.Conv1d(in_channels, in_channels, kernel_size=1, padding=0, bias=False)
+            self.conv2 = ops.Conv1d(in_channels, in_channels, kernel_size=1, padding=0, bias=False)
+
+    def forward(self, x):
+
+        if self.with_conv:
+            x = self.conv1(x)
+
+        x = F.avg_pool1d(x, kernel_size=2, stride=2)
+
+        if self.with_conv:
+            x = self.conv2(x)
+
+        return x

comfy/ldm/wan/model.py

@@ -237,6 +237,7 @@ class WanAttentionBlock(nn.Module):
             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, transformer_options=transformer_options)
@@ -902,7 +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,

comfy/ldm/wan/vae.py

@@ -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

comfy/ldm/wan/vae2_2.py

@@ -657,51 +657,51 @@ class WanVAE(nn.Module):
         )
 
     def encode(self, x):
-        self.clear_cache()
+        conv_idx = [0]
+        feat_map = [None] * count_conv3d(self.encoder)
         x = patchify(x, patch_size=2)
         t = x.shape[2]
         iter_ = 1 + (t - 1) // 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)
         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,
                     first_chunk=True,
                 )
             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)
         out = unpatchify(out, patch_size=2)
-        self.clear_cache()
         return out
 
     def reparameterize(self, mu, log_var):
@@ -715,12 +715,3 @@ class WanVAE(nn.Module):
             return mu
         std = torch.exp(0.5 * log_var.clamp(-30.0, 20.0))
         return mu + std * torch.randn_like(std)
-
-    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

comfy/model_base.py

@@ -138,6 +138,7 @@ class BaseModel(torch.nn.Module):
             else:
                 operations = model_config.custom_operations
             self.diffusion_model = unet_model(**unet_config, device=device, operations=operations)
+            self.diffusion_model.eval()
             if comfy.model_management.force_channels_last():
                 self.diffusion_model.to(memory_format=torch.channels_last)
                 logging.debug("using channels last mode for diffusion model")
@@ -669,7 +670,6 @@ class Lotus(BaseModel):
 class StableCascade_C(BaseModel):
     def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=StageC)
-        self.diffusion_model.eval().requires_grad_(False)
 
     def extra_conds(self, **kwargs):
         out = {}
@@ -698,7 +698,6 @@ class StableCascade_C(BaseModel):
 class StableCascade_B(BaseModel):
     def __init__(self, model_config, model_type=ModelType.STABLE_CASCADE, device=None):
         super().__init__(model_config, model_type, device=device, unet_model=StageB)
-        self.diffusion_model.eval().requires_grad_(False)
 
     def extra_conds(self, **kwargs):
         out = {}

comfy/model_detection.py

@@ -365,8 +365,8 @@ def detect_unet_config(state_dict, key_prefix, metadata=None):
         dit_config["patch_size"] = 2
         dit_config["in_channels"] = 16
         dit_config["dim"] = 2304
-        dit_config["cap_feat_dim"] = 2304
-        dit_config["n_layers"] = 26
+        dit_config["cap_feat_dim"] = state_dict['{}cap_embedder.1.weight'.format(key_prefix)].shape[1]
+        dit_config["n_layers"] = count_blocks(state_dict_keys, '{}layers.'.format(key_prefix) + '{}.')
         dit_config["n_heads"] = 24
         dit_config["n_kv_heads"] = 8
         dit_config["qk_norm"] = True

comfy/model_management.py

@@ -356,6 +356,7 @@ except:
 SUPPORT_FP8_OPS = args.supports_fp8_compute
 try:
     if is_amd():
+        torch.backends.cudnn.enabled = False  # Seems to improve things a lot on AMD
         try:
             rocm_version = tuple(map(int, str(torch.version.hip).split(".")[:2]))
         except:
@@ -368,9 +369,9 @@ try:
                 if torch_version_numeric >= (2, 7):  # works on 2.6 but doesn't actually seem to improve much
                     if any((a in arch) for a in ["gfx90a", "gfx942", "gfx1100", "gfx1101", "gfx1151"]):  # TODO: more arches, TODO: gfx950
                         ENABLE_PYTORCH_ATTENTION = True
-#                if torch_version_numeric >= (2, 8):
-#                    if any((a in arch) for a in ["gfx1201"]):
-#                        ENABLE_PYTORCH_ATTENTION = True
+                if rocm_version >= (7, 0):
+                   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 ["gfx1200", "gfx1201", "gfx942", "gfx950"]):  # TODO: more arches
                 SUPPORT_FP8_OPS = True
@@ -953,11 +954,7 @@ def vae_dtype(device=None, allowed_dtypes=[]):
         if d == torch.float16 and should_use_fp16(device):
             return d
 
-        # NOTE: bfloat16 seems to work on AMD for the VAE but is extremely slow in some cases compared to fp32
-        # slowness still a problem on pytorch nightly 2.9.0.dev20250720+rocm6.4 tested on RDNA3
-        # also a problem on RDNA4 except fp32 is also slow there.
-        # This is due to large bf16 convolutions being extremely slow.
-        if d == torch.bfloat16 and ((not is_amd()) or amd_min_version(device, min_rdna_version=4)) and should_use_bf16(device):
+        if d == torch.bfloat16 and should_use_bf16(device):
             return d
 
     return torch.float32

comfy/model_patcher.py

@@ -126,16 +126,30 @@ def move_weight_functions(m, device):
     return memory
 
 class LowVramPatch:
-    def __init__(self, key, patches):
+    def __init__(self, key, patches, convert_func=None, set_func=None):
         self.key = key
         self.patches = patches
+        self.convert_func = convert_func
+        self.set_func = set_func
+
     def __call__(self, weight):
         intermediate_dtype = weight.dtype
+        if self.convert_func is not None:
+            weight = self.convert_func(weight.to(dtype=torch.float32, copy=True), inplace=True)
+
         if intermediate_dtype not in [torch.float32, torch.float16, torch.bfloat16]: #intermediate_dtype has to be one that is supported in math ops
             intermediate_dtype = torch.float32
-            return comfy.float.stochastic_rounding(comfy.lora.calculate_weight(self.patches[self.key], weight.to(intermediate_dtype), self.key, intermediate_dtype=intermediate_dtype), weight.dtype, seed=string_to_seed(self.key))
+            out = comfy.lora.calculate_weight(self.patches[self.key], weight.to(intermediate_dtype), self.key, intermediate_dtype=intermediate_dtype)
+            if self.set_func is None:
+                return comfy.float.stochastic_rounding(out, weight.dtype, seed=string_to_seed(self.key))
+            else:
+                return self.set_func(out, seed=string_to_seed(self.key), return_weight=True)
 
-        return comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=intermediate_dtype)
+        out = comfy.lora.calculate_weight(self.patches[self.key], weight, self.key, intermediate_dtype=intermediate_dtype)
+        if self.set_func is not None:
+            return self.set_func(out, seed=string_to_seed(self.key), return_weight=True).to(dtype=intermediate_dtype)
+        else:
+            return out
 
 def get_key_weight(model, key):
     set_func = None
@@ -737,13 +751,15 @@ class ModelPatcher:
                         if force_patch_weights:
                             self.patch_weight_to_device(weight_key)
                         else:
-                            m.weight_function = [LowVramPatch(weight_key, self.patches)]
+                            _, set_func, convert_func = get_key_weight(self.model, weight_key)
+                            m.weight_function = [LowVramPatch(weight_key, self.patches, convert_func, set_func)]
                             patch_counter += 1
                     if bias_key in self.patches:
                         if force_patch_weights:
                             self.patch_weight_to_device(bias_key)
                         else:
-                            m.bias_function = [LowVramPatch(bias_key, self.patches)]
+                            _, set_func, convert_func = get_key_weight(self.model, bias_key)
+                            m.bias_function = [LowVramPatch(bias_key, self.patches, convert_func, set_func)]
                             patch_counter += 1
 
                     cast_weight = True
@@ -905,10 +921,12 @@ class ModelPatcher:
                         module_mem += move_weight_functions(m, device_to)
                         if lowvram_possible:
                             if weight_key in self.patches:
-                                m.weight_function.append(LowVramPatch(weight_key, self.patches))
+                                _, set_func, convert_func = get_key_weight(self.model, weight_key)
+                                m.weight_function.append(LowVramPatch(weight_key, self.patches, convert_func, set_func))
                                 patch_counter += 1
                             if bias_key in self.patches:
-                                m.bias_function.append(LowVramPatch(bias_key, self.patches))
+                                _, set_func, convert_func = get_key_weight(self.model, bias_key)
+                                m.bias_function.append(LowVramPatch(bias_key, self.patches, convert_func, set_func))
                                 patch_counter += 1
                             cast_weight = True
 

comfy/model_sampling.py

@@ -21,17 +21,23 @@ def rescale_zero_terminal_snr_sigmas(sigmas):
     alphas_bar[-1] = 4.8973451890853435e-08
     return ((1 - alphas_bar) / alphas_bar) ** 0.5
 
+def reshape_sigma(sigma, noise_dim):
+    if sigma.nelement() == 1:
+        return sigma.view(())
+    else:
+        return sigma.view(sigma.shape[:1] + (1,) * (noise_dim - 1))
+
 class EPS:
     def calculate_input(self, sigma, noise):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
+        sigma = reshape_sigma(sigma, noise.ndim)
         return noise / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
 
     def calculate_denoised(self, sigma, model_output, model_input):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+        sigma = reshape_sigma(sigma, model_output.ndim)
         return model_input - model_output * sigma
 
     def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
+        sigma = reshape_sigma(sigma, noise.ndim)
         if max_denoise:
             noise = noise * torch.sqrt(1.0 + sigma ** 2.0)
         else:
@@ -45,12 +51,12 @@ class EPS:
 
 class V_PREDICTION(EPS):
     def calculate_denoised(self, sigma, model_output, model_input):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+        sigma = reshape_sigma(sigma, model_output.ndim)
         return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) - model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
 
 class EDM(V_PREDICTION):
     def calculate_denoised(self, sigma, model_output, model_input):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+        sigma = reshape_sigma(sigma, model_output.ndim)
         return model_input * self.sigma_data ** 2 / (sigma ** 2 + self.sigma_data ** 2) + model_output * sigma * self.sigma_data / (sigma ** 2 + self.sigma_data ** 2) ** 0.5
 
 class CONST:
@@ -58,15 +64,15 @@ class CONST:
         return noise
 
     def calculate_denoised(self, sigma, model_output, model_input):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+        sigma = reshape_sigma(sigma, model_output.ndim)
         return model_input - model_output * sigma
 
     def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
+        sigma = reshape_sigma(sigma, noise.ndim)
         return sigma * noise + (1.0 - sigma) * latent_image
 
     def inverse_noise_scaling(self, sigma, latent):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (latent.ndim - 1))
+        sigma = reshape_sigma(sigma, latent.ndim)
         return latent / (1.0 - sigma)
 
 class X0(EPS):
@@ -80,16 +86,16 @@ class IMG_TO_IMG(X0):
 class COSMOS_RFLOW:
     def calculate_input(self, sigma, noise):
         sigma = (sigma / (sigma + 1))
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
+        sigma = reshape_sigma(sigma, noise.ndim)
         return noise * (1.0 - sigma)
 
     def calculate_denoised(self, sigma, model_output, model_input):
         sigma = (sigma / (sigma + 1))
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (model_output.ndim - 1))
+        sigma = reshape_sigma(sigma, model_output.ndim)
         return model_input * (1.0 - sigma) - model_output * sigma
 
     def noise_scaling(self, sigma, noise, latent_image, max_denoise=False):
-        sigma = sigma.view(sigma.shape[:1] + (1,) * (noise.ndim - 1))
+        sigma = reshape_sigma(sigma, noise.ndim)
         noise = noise * sigma
         noise += latent_image
         return noise

comfy/ops.py

@@ -24,6 +24,8 @@ import comfy.float
 import comfy.rmsnorm
 import contextlib
 
+def run_every_op():
+    comfy.model_management.throw_exception_if_processing_interrupted()
 
 def scaled_dot_product_attention(q, k, v, *args, **kwargs):
     return torch.nn.functional.scaled_dot_product_attention(q, k, v, *args, **kwargs)
@@ -109,6 +111,7 @@ class disable_weight_init:
             return torch.nn.functional.linear(input, weight, bias)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -123,6 +126,7 @@ class disable_weight_init:
             return self._conv_forward(input, weight, bias)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -137,6 +141,7 @@ class disable_weight_init:
             return self._conv_forward(input, weight, bias)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -151,6 +156,7 @@ class disable_weight_init:
             return self._conv_forward(input, weight, bias)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -165,6 +171,7 @@ class disable_weight_init:
             return torch.nn.functional.group_norm(input, self.num_groups, weight, bias, self.eps)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -183,6 +190,7 @@ class disable_weight_init:
             return torch.nn.functional.layer_norm(input, self.normalized_shape, weight, bias, self.eps)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -202,6 +210,7 @@ class disable_weight_init:
             # return torch.nn.functional.rms_norm(input, self.normalized_shape, weight, self.eps)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -223,6 +232,7 @@ class disable_weight_init:
                 output_padding, self.groups, self.dilation)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -244,6 +254,7 @@ class disable_weight_init:
                 output_padding, self.groups, self.dilation)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -262,6 +273,7 @@ class disable_weight_init:
             return torch.nn.functional.embedding(input, weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse).to(dtype=output_dtype)
 
         def forward(self, *args, **kwargs):
+            run_every_op()
             if self.comfy_cast_weights or len(self.weight_function) > 0 or len(self.bias_function) > 0:
                 return self.forward_comfy_cast_weights(*args, **kwargs)
             else:
@@ -416,8 +428,10 @@ def scaled_fp8_ops(fp8_matrix_mult=False, scale_input=False, override_dtype=None
                 else:
                     return weight * self.scale_weight.to(device=weight.device, dtype=weight.dtype)
 
-            def set_weight(self, weight, inplace_update=False, seed=None, **kwargs):
+            def set_weight(self, weight, inplace_update=False, seed=None, return_weight=False, **kwargs):
                 weight = comfy.float.stochastic_rounding(weight / self.scale_weight.to(device=weight.device, dtype=weight.dtype), self.weight.dtype, seed=seed)
+                if return_weight:
+                    return weight
                 if inplace_update:
                     self.weight.data.copy_(weight)
                 else:

comfy/samplers.py

@@ -564,7 +564,7 @@ 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
@@ -594,7 +594,7 @@ 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_)
 

comfy/sd.py

@@ -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.ldm.mmaudio.vae.autoencoder
 import comfy.pixel_space_convert
 import yaml
 import math
@@ -275,8 +276,13 @@ class VAE:
         if 'decoder.up_blocks.0.resnets.0.norm1.weight' in sd.keys(): #diffusers format
             sd = diffusers_convert.convert_vae_state_dict(sd)
 
-        self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) #These are for AutoencoderKL and need tweaking (should be lower)
-        self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype)
+        if model_management.is_amd():
+            VAE_KL_MEM_RATIO = 2.73
+        else:
+            VAE_KL_MEM_RATIO = 1.0
+
+        self.memory_used_encode = lambda shape, dtype: (1767 * shape[2] * shape[3]) * model_management.dtype_size(dtype) * VAE_KL_MEM_RATIO #These are for AutoencoderKL and need tweaking (should be lower)
+        self.memory_used_decode = lambda shape, dtype: (2178 * shape[2] * shape[3] * 64) * model_management.dtype_size(dtype) * VAE_KL_MEM_RATIO
         self.downscale_ratio = 8
         self.upscale_ratio = 8
         self.latent_channels = 4
@@ -291,6 +297,7 @@ class VAE:
         self.downscale_index_formula = None
         self.upscale_index_formula = None
         self.extra_1d_channel = None
+        self.crop_input = True
 
         if config is None:
             if "decoder.mid.block_1.mix_factor" in sd:
@@ -332,35 +339,51 @@ 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)
@@ -526,6 +549,25 @@ class VAE:
                 self.latent_channels = 3
                 self.latent_dim = 2
                 self.output_channels = 3
+            elif "vocoder.activation_post.downsample.lowpass.filter" in sd: #MMAudio VAE
+                sample_rate = 16000
+                if sample_rate == 16000:
+                    mode = '16k'
+                else:
+                    mode = '44k'
+
+                self.first_stage_model = comfy.ldm.mmaudio.vae.autoencoder.AudioAutoencoder(mode=mode)
+                self.memory_used_encode = lambda shape, dtype: (30 * shape[2]) * model_management.dtype_size(dtype)
+                self.memory_used_decode = lambda shape, dtype: (90 * shape[2] * 1411.2) * model_management.dtype_size(dtype)
+                self.latent_channels = 20
+                self.output_channels = 2
+                self.upscale_ratio = 512 * (44100 / sample_rate)
+                self.downscale_ratio = 512 * (44100 / sample_rate)
+                self.latent_dim = 1
+                self.process_output = lambda audio: audio
+                self.process_input = lambda audio: audio
+                self.working_dtypes = [torch.float32]
+                self.crop_input = False
             else:
                 logging.warning("WARNING: No VAE weights detected, VAE not initalized.")
                 self.first_stage_model = None
@@ -559,6 +601,9 @@ class VAE:
             raise RuntimeError("ERROR: VAE is invalid: None\n\nIf the VAE is from a checkpoint loader node your checkpoint does not contain a valid VAE.")
 
     def vae_encode_crop_pixels(self, pixels):
+        if not self.crop_input:
+            return pixels
+
         downscale_ratio = self.spacial_compression_encode()
 
         dims = pixels.shape[1:-1]
@@ -636,6 +681,7 @@ 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)
@@ -651,6 +697,13 @@ 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)
@@ -697,6 +750,7 @@ 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:
             if not self.not_video:
                 pixel_samples = pixel_samples.movedim(1, 0).unsqueeze(0)
@@ -718,6 +772,13 @@ 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
@@ -858,6 +919,7 @@ class TEModel(Enum):
     QWEN25_3B = 10
     QWEN25_7B = 11
     BYT5_SMALL_GLYPH = 12
+    GEMMA_3_4B = 13
 
 def detect_te_model(sd):
     if "text_model.encoder.layers.30.mlp.fc1.weight" in sd:
@@ -880,6 +942,8 @@ def detect_te_model(sd):
             return TEModel.BYT5_SMALL_GLYPH
         return TEModel.T5_BASE
     if 'model.layers.0.post_feedforward_layernorm.weight' in sd:
+        if 'model.layers.0.self_attn.q_norm.weight' in sd:
+            return TEModel.GEMMA_3_4B
         return TEModel.GEMMA_2_2B
     if 'model.layers.0.self_attn.k_proj.bias' in sd:
         weight = sd['model.layers.0.self_attn.k_proj.bias']
@@ -984,6 +1048,10 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data))
             clip_target.tokenizer = comfy.text_encoders.lumina2.LuminaTokenizer
             tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
+        elif te_model == TEModel.GEMMA_3_4B:
+            clip_target.clip = comfy.text_encoders.lumina2.te(**llama_detect(clip_data), model_type="gemma3_4b")
+            clip_target.tokenizer = comfy.text_encoders.lumina2.NTokenizer
+            tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
         elif te_model == TEModel.LLAMA3_8:
             clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**llama_detect(clip_data),
                                                                         clip_l=False, clip_g=False, t5=False, llama=True, dtype_t5=None, t5xxl_scaled_fp8=None)

comfy/text_encoders/hunyuan_image.py

@@ -63,7 +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]

comfy/text_encoders/llama.py

@@ -3,6 +3,7 @@ import torch.nn as nn
 from dataclasses import dataclass
 from typing import Optional, Any
 import math
+import logging
 
 from comfy.ldm.modules.attention import optimized_attention_for_device
 import comfy.model_management
@@ -28,6 +29,9 @@ class Llama2Config:
     mlp_activation = "silu"
     qkv_bias = False
     rope_dims = None
+    q_norm = None
+    k_norm = None
+    rope_scale = None
 
 @dataclass
 class Qwen25_3BConfig:
@@ -46,6 +50,9 @@ class Qwen25_3BConfig:
     mlp_activation = "silu"
     qkv_bias = True
     rope_dims = None
+    q_norm = None
+    k_norm = None
+    rope_scale = None
 
 @dataclass
 class Qwen25_7BVLI_Config:
@@ -64,6 +71,9 @@ class Qwen25_7BVLI_Config:
     mlp_activation = "silu"
     qkv_bias = True
     rope_dims = [16, 24, 24]
+    q_norm = None
+    k_norm = None
+    rope_scale = None
 
 @dataclass
 class Gemma2_2B_Config:
@@ -82,6 +92,32 @@ class Gemma2_2B_Config:
     mlp_activation = "gelu_pytorch_tanh"
     qkv_bias = False
     rope_dims = None
+    q_norm = None
+    k_norm = None
+    sliding_attention = None
+    rope_scale = None
+
+@dataclass
+class Gemma3_4B_Config:
+    vocab_size: int = 262208
+    hidden_size: int = 2560
+    intermediate_size: int = 10240
+    num_hidden_layers: int = 34
+    num_attention_heads: int = 8
+    num_key_value_heads: int = 4
+    max_position_embeddings: int = 131072
+    rms_norm_eps: float = 1e-6
+    rope_theta = [10000.0, 1000000.0]
+    transformer_type: str = "gemma3"
+    head_dim = 256
+    rms_norm_add = True
+    mlp_activation = "gelu_pytorch_tanh"
+    qkv_bias = False
+    rope_dims = None
+    q_norm = "gemma3"
+    k_norm = "gemma3"
+    sliding_attention = [False, False, False, False, False, 1024]
+    rope_scale = [1.0, 8.0]
 
 class RMSNorm(nn.Module):
     def __init__(self, dim: int, eps: float = 1e-5, add=False, device=None, dtype=None):
@@ -106,25 +142,40 @@ def rotate_half(x):
     return torch.cat((-x2, x1), dim=-1)
 
 
-def precompute_freqs_cis(head_dim, position_ids, theta, rope_dims=None, device=None):
-    theta_numerator = torch.arange(0, head_dim, 2, device=device).float()
-    inv_freq = 1.0 / (theta ** (theta_numerator / head_dim))
+def precompute_freqs_cis(head_dim, position_ids, theta, rope_scale=None, rope_dims=None, device=None):
+    if not isinstance(theta, list):
+        theta = [theta]
 
-    inv_freq_expanded = inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
-    position_ids_expanded = position_ids[:, None, :].float()
-    freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
-    emb = torch.cat((freqs, freqs), dim=-1)
-    cos = emb.cos()
-    sin = emb.sin()
-    if rope_dims is not None and position_ids.shape[0] > 1:
-        mrope_section = rope_dims * 2
-        cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze(0)
-        sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section, dim=-1))], dim=-1).unsqueeze(0)
-    else:
-        cos = cos.unsqueeze(1)
-        sin = sin.unsqueeze(1)
+    out = []
+    for index, t in enumerate(theta):
+        theta_numerator = torch.arange(0, head_dim, 2, device=device).float()
+        inv_freq = 1.0 / (t ** (theta_numerator / head_dim))
 
-    return (cos, sin)
+        if rope_scale is not None:
+            if isinstance(rope_scale, list):
+                inv_freq /= rope_scale[index]
+            else:
+                inv_freq /= rope_scale
+
+        inv_freq_expanded = inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1)
+        position_ids_expanded = position_ids[:, None, :].float()
+        freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)
+        emb = torch.cat((freqs, freqs), dim=-1)
+        cos = emb.cos()
+        sin = emb.sin()
+        if rope_dims is not None and position_ids.shape[0] > 1:
+            mrope_section = rope_dims * 2
+            cos = torch.cat([m[i % 3] for i, m in enumerate(cos.split(mrope_section, dim=-1))], dim=-1).unsqueeze(0)
+            sin = torch.cat([m[i % 3] for i, m in enumerate(sin.split(mrope_section, dim=-1))], dim=-1).unsqueeze(0)
+        else:
+            cos = cos.unsqueeze(1)
+            sin = sin.unsqueeze(1)
+        out.append((cos, sin))
+
+    if len(out) == 1:
+        return out[0]
+
+    return out
 
 
 def apply_rope(xq, xk, freqs_cis):
@@ -152,6 +203,14 @@ class Attention(nn.Module):
         self.v_proj = ops.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=config.qkv_bias, device=device, dtype=dtype)
         self.o_proj = ops.Linear(self.inner_size, config.hidden_size, bias=False, device=device, dtype=dtype)
 
+        self.q_norm = None
+        self.k_norm = None
+
+        if config.q_norm == "gemma3":
+            self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+        if config.k_norm == "gemma3":
+            self.k_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
+
     def forward(
         self,
         hidden_states: torch.Tensor,
@@ -168,6 +227,11 @@ class Attention(nn.Module):
         xk = xk.view(batch_size, seq_length, self.num_kv_heads, self.head_dim).transpose(1, 2)
         xv = xv.view(batch_size, seq_length, self.num_kv_heads, self.head_dim).transpose(1, 2)
 
+        if self.q_norm is not None:
+            xq = self.q_norm(xq)
+        if self.k_norm is not None:
+            xk = self.k_norm(xk)
+
         xq, xk = apply_rope(xq, xk, freqs_cis=freqs_cis)
 
         xk = xk.repeat_interleave(self.num_heads // self.num_kv_heads, dim=1)
@@ -192,7 +256,7 @@ class MLP(nn.Module):
         return self.down_proj(self.activation(self.gate_proj(x)) * self.up_proj(x))
 
 class TransformerBlock(nn.Module):
-    def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None):
+    def __init__(self, config: Llama2Config, index, device=None, dtype=None, ops: Any = None):
         super().__init__()
         self.self_attn = Attention(config, device=device, dtype=dtype, ops=ops)
         self.mlp = MLP(config, device=device, dtype=dtype, ops=ops)
@@ -226,7 +290,7 @@ class TransformerBlock(nn.Module):
         return x
 
 class TransformerBlockGemma2(nn.Module):
-    def __init__(self, config: Llama2Config, device=None, dtype=None, ops: Any = None):
+    def __init__(self, config: Llama2Config, index, device=None, dtype=None, ops: Any = None):
         super().__init__()
         self.self_attn = Attention(config, device=device, dtype=dtype, ops=ops)
         self.mlp = MLP(config, device=device, dtype=dtype, ops=ops)
@@ -235,6 +299,13 @@ class TransformerBlockGemma2(nn.Module):
         self.pre_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
         self.post_feedforward_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
 
+        if config.sliding_attention is not None:  # TODO: implement. (Not that necessary since models are trained on less than 1024 tokens)
+            self.sliding_attention = config.sliding_attention[index % len(config.sliding_attention)]
+        else:
+            self.sliding_attention = False
+
+        self.transformer_type = config.transformer_type
+
     def forward(
         self,
         x: torch.Tensor,
@@ -242,6 +313,14 @@ class TransformerBlockGemma2(nn.Module):
         freqs_cis: Optional[torch.Tensor] = None,
         optimized_attention=None,
     ):
+        if self.transformer_type == 'gemma3':
+            if self.sliding_attention:
+                if x.shape[1] > self.sliding_attention:
+                    logging.warning("Warning: sliding attention not implemented, results may be incorrect")
+                freqs_cis = freqs_cis[1]
+            else:
+                freqs_cis = freqs_cis[0]
+
         # Self Attention
         residual = x
         x = self.input_layernorm(x)
@@ -276,7 +355,7 @@ class Llama2_(nn.Module):
             device=device,
             dtype=dtype
         )
-        if self.config.transformer_type == "gemma2":
+        if self.config.transformer_type == "gemma2" or self.config.transformer_type == "gemma3":
             transformer = TransformerBlockGemma2
             self.normalize_in = True
         else:
@@ -284,8 +363,8 @@ class Llama2_(nn.Module):
             self.normalize_in = False
 
         self.layers = nn.ModuleList([
-            transformer(config, device=device, dtype=dtype, ops=ops)
-            for _ in range(config.num_hidden_layers)
+            transformer(config, index=i, device=device, dtype=dtype, ops=ops)
+            for i in range(config.num_hidden_layers)
         ])
         self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps, add=config.rms_norm_add, device=device, dtype=dtype)
         # self.lm_head = ops.Linear(config.hidden_size, config.vocab_size, bias=False, device=device, dtype=dtype)
@@ -305,6 +384,7 @@ class Llama2_(nn.Module):
         freqs_cis = precompute_freqs_cis(self.config.head_dim,
                                          position_ids,
                                          self.config.rope_theta,
+                                         self.config.rope_scale,
                                          self.config.rope_dims,
                                          device=x.device)
 
@@ -433,3 +513,12 @@ class Gemma2_2B(BaseLlama, torch.nn.Module):
 
         self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
         self.dtype = dtype
+
+class Gemma3_4B(BaseLlama, torch.nn.Module):
+    def __init__(self, config_dict, dtype, device, operations):
+        super().__init__()
+        config = Gemma3_4B_Config(**config_dict)
+        self.num_layers = config.num_hidden_layers
+
+        self.model = Llama2_(config, device=device, dtype=dtype, ops=operations)
+        self.dtype = dtype

comfy/text_encoders/lumina2.py

@@ -11,23 +11,41 @@ class Gemma2BTokenizer(sd1_clip.SDTokenizer):
     def state_dict(self):
         return {"spiece_model": self.tokenizer.serialize_model()}
 
+class Gemma3_4BTokenizer(sd1_clip.SDTokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        tokenizer = tokenizer_data.get("spiece_model", None)
+        super().__init__(tokenizer, pad_with_end=False, embedding_size=2560, embedding_key='gemma3_4b', tokenizer_class=SPieceTokenizer, has_end_token=False, pad_to_max_length=False, max_length=99999999, min_length=1, tokenizer_args={"add_bos": True, "add_eos": False}, tokenizer_data=tokenizer_data)
+
+    def state_dict(self):
+        return {"spiece_model": self.tokenizer.serialize_model()}
 
 class LuminaTokenizer(sd1_clip.SD1Tokenizer):
     def __init__(self, embedding_directory=None, tokenizer_data={}):
         super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="gemma2_2b", tokenizer=Gemma2BTokenizer)
 
+class NTokenizer(sd1_clip.SD1Tokenizer):
+    def __init__(self, embedding_directory=None, tokenizer_data={}):
+        super().__init__(embedding_directory=embedding_directory, tokenizer_data=tokenizer_data, name="gemma3_4b", tokenizer=Gemma3_4BTokenizer)
 
 class Gemma2_2BModel(sd1_clip.SDClipModel):
     def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
         super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma2_2B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
 
+class Gemma3_4BModel(sd1_clip.SDClipModel):
+    def __init__(self, device="cpu", layer="hidden", layer_idx=-2, dtype=None, attention_mask=True, model_options={}):
+        super().__init__(device=device, layer=layer, layer_idx=layer_idx, textmodel_json_config={}, dtype=dtype, special_tokens={"start": 2, "pad": 0}, layer_norm_hidden_state=False, model_class=comfy.text_encoders.llama.Gemma3_4B, enable_attention_masks=attention_mask, return_attention_masks=attention_mask, model_options=model_options)
 
 class LuminaModel(sd1_clip.SD1ClipModel):
-    def __init__(self, device="cpu", dtype=None, model_options={}):
-        super().__init__(device=device, dtype=dtype, name="gemma2_2b", clip_model=Gemma2_2BModel, model_options=model_options)
+    def __init__(self, device="cpu", dtype=None, model_options={}, name="gemma2_2b", clip_model=Gemma2_2BModel):
+        super().__init__(device=device, dtype=dtype, name=name, clip_model=clip_model, model_options=model_options)
 
 
-def te(dtype_llama=None, llama_scaled_fp8=None):
+def te(dtype_llama=None, llama_scaled_fp8=None, model_type="gemma2_2b"):
+    if model_type == "gemma2_2b":
+        model = Gemma2_2BModel
+    elif model_type == "gemma3_4b":
+        model = Gemma3_4BModel
+
     class LuminaTEModel_(LuminaModel):
         def __init__(self, device="cpu", dtype=None, model_options={}):
             if llama_scaled_fp8 is not None and "scaled_fp8" not in model_options:
@@ -35,5 +53,5 @@ def te(dtype_llama=None, llama_scaled_fp8=None):
                 model_options["scaled_fp8"] = llama_scaled_fp8
             if dtype_llama is not None:
                 dtype = dtype_llama
-            super().__init__(device=device, dtype=dtype, model_options=model_options)
+            super().__init__(device=device, dtype=dtype, name=model_type, model_options=model_options, clip_model=model)
     return LuminaTEModel_

comfy/text_encoders/qwen_image.py

@@ -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:]
 

comfy/utils.py

@@ -39,7 +39,11 @@ if hasattr(torch.serialization, "add_safe_globals"):  # TODO: this was added in
         pass
     ModelCheckpoint.__module__ = "pytorch_lightning.callbacks.model_checkpoint"
 
-    from numpy.core.multiarray import scalar
+    def scalar(*args, **kwargs):
+        from numpy.core.multiarray import scalar as sc
+        return sc(*args, **kwargs)
+    scalar.__module__ = "numpy.core.multiarray"
+
     from numpy import dtype
     from numpy.dtypes import Float64DType
     from _codecs import encode

comfy_api/latest/__init__.py

@@ -8,8 +8,8 @@ from comfy_api.internal.async_to_sync import create_sync_class
 from comfy_api.latest._input import ImageInput, AudioInput, MaskInput, LatentInput, VideoInput
 from comfy_api.latest._input_impl import VideoFromFile, VideoFromComponents
 from comfy_api.latest._util import VideoCodec, VideoContainer, VideoComponents
-from comfy_api.latest._io import _IO as io  #noqa: F401
-from comfy_api.latest._ui import _UI as ui  #noqa: F401
+from . import _io as io
+from . import _ui as ui
 # from comfy_api.latest._resources import _RESOURCES as resources  #noqa: F401
 from comfy_execution.utils import get_executing_context
 from comfy_execution.progress import get_progress_state, PreviewImageTuple
@@ -114,6 +114,8 @@ if TYPE_CHECKING:
     ComfyAPISync: Type[comfy_api.latest.generated.ComfyAPISyncStub.ComfyAPISyncStub]
 ComfyAPISync = create_sync_class(ComfyAPI_latest)
 
+comfy_io = io  # create the new alias for io
+
 __all__ = [
     "ComfyAPI",
     "ComfyAPISync",
@@ -121,4 +123,7 @@ __all__ = [
     "InputImpl",
     "Types",
     "ComfyExtension",
+    "io",
+    "comfy_io",
+    "ui",
 ]

comfy_api/latest/_input/video_types.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 from abc import ABC, abstractmethod
-from typing import Optional, Union
+from typing import Optional, Union, IO
 import io
 import av
 from comfy_api.util import VideoContainer, VideoCodec, VideoComponents
@@ -23,7 +23,7 @@ class VideoInput(ABC):
     @abstractmethod
     def save_to(
         self,
-        path: str,
+        path: Union[str, IO[bytes]],
         format: VideoContainer = VideoContainer.AUTO,
         codec: VideoCodec = VideoCodec.AUTO,
         metadata: Optional[dict] = None

comfy_api/latest/_io.py

@@ -336,11 +336,25 @@ class Combo(ComfyTypeIO):
     class Input(WidgetInput):
         """Combo input (dropdown)."""
         Type = str
-        def __init__(self, id: str, options: list[str]=None, display_name: str=None, optional=False, tooltip: str=None, lazy: bool=None,
-                    default: str=None, control_after_generate: bool=None,
-                    upload: UploadType=None, image_folder: FolderType=None,
-                    remote: RemoteOptions=None,
-                    socketless: bool=None):
+        def __init__(
+            self,
+            id: str,
+            options: list[str] | list[int] | type[Enum] = None,
+            display_name: str=None,
+            optional=False,
+            tooltip: str=None,
+            lazy: bool=None,
+            default: str | int | Enum = None,
+            control_after_generate: bool=None,
+            upload: UploadType=None,
+            image_folder: FolderType=None,
+            remote: RemoteOptions=None,
+            socketless: bool=None,
+        ):
+            if isinstance(options, type) and issubclass(options, Enum):
+                options = [v.value for v in options]
+            if isinstance(default, Enum):
+                default = default.value
             super().__init__(id, display_name, optional, tooltip, lazy, default, socketless)
             self.multiselect = False
             self.options = options
@@ -1568,77 +1582,78 @@ class _UIOutput(ABC):
         ...
 
 
-class _IO:
-    FolderType = FolderType
-    UploadType = UploadType
-    RemoteOptions = RemoteOptions
-    NumberDisplay = NumberDisplay
+__all__ = [
+    "FolderType",
+    "UploadType",
+    "RemoteOptions",
+    "NumberDisplay",
 
-    comfytype = staticmethod(comfytype)
-    Custom = staticmethod(Custom)
-    Input = Input
-    WidgetInput = WidgetInput
-    Output = Output
-    ComfyTypeI = ComfyTypeI
-    ComfyTypeIO = ComfyTypeIO
-    #---------------------------------
+    "comfytype",
+    "Custom",
+    "Input",
+    "WidgetInput",
+    "Output",
+    "ComfyTypeI",
+    "ComfyTypeIO",
     # Supported Types
-    Boolean = Boolean
-    Int = Int
-    Float = Float
-    String = String
-    Combo = Combo
-    MultiCombo = MultiCombo
-    Image = Image
-    WanCameraEmbedding = WanCameraEmbedding
-    Webcam = Webcam
-    Mask = Mask
-    Latent = Latent
-    Conditioning = Conditioning
-    Sampler = Sampler
-    Sigmas = Sigmas
-    Noise = Noise
-    Guider = Guider
-    Clip = Clip
-    ControlNet = ControlNet
-    Vae = Vae
-    Model = Model
-    ClipVision = ClipVision
-    ClipVisionOutput = ClipVisionOutput
-    AudioEncoderOutput = AudioEncoderOutput
-    StyleModel = StyleModel
-    Gligen = Gligen
-    UpscaleModel = UpscaleModel
-    Audio = Audio
-    Video = Video
-    SVG = SVG
-    LoraModel = LoraModel
-    LossMap = LossMap
-    Voxel = Voxel
-    Mesh = Mesh
-    Hooks = Hooks
-    HookKeyframes = HookKeyframes
-    TimestepsRange = TimestepsRange
-    LatentOperation = LatentOperation
-    FlowControl = FlowControl
-    Accumulation = Accumulation
-    Load3DCamera = Load3DCamera
-    Load3D = Load3D
-    Load3DAnimation = Load3DAnimation
-    Photomaker = Photomaker
-    Point = Point
-    FaceAnalysis = FaceAnalysis
-    BBOX = BBOX
-    SEGS = SEGS
-    AnyType = AnyType
-    MultiType = MultiType
-    #---------------------------------
-    HiddenHolder = HiddenHolder
-    Hidden = Hidden
-    NodeInfoV1 = NodeInfoV1
-    NodeInfoV3 = NodeInfoV3
-    Schema = Schema
-    ComfyNode = ComfyNode
-    NodeOutput = NodeOutput
-    add_to_dict_v1 = staticmethod(add_to_dict_v1)
-    add_to_dict_v3 = staticmethod(add_to_dict_v3)
+    "Boolean",
+    "Int",
+    "Float",
+    "String",
+    "Combo",
+    "MultiCombo",
+    "Image",
+    "WanCameraEmbedding",
+    "Webcam",
+    "Mask",
+    "Latent",
+    "Conditioning",
+    "Sampler",
+    "Sigmas",
+    "Noise",
+    "Guider",
+    "Clip",
+    "ControlNet",
+    "Vae",
+    "Model",
+    "ClipVision",
+    "ClipVisionOutput",
+    "AudioEncoder",
+    "AudioEncoderOutput",
+    "StyleModel",
+    "Gligen",
+    "UpscaleModel",
+    "Audio",
+    "Video",
+    "SVG",
+    "LoraModel",
+    "LossMap",
+    "Voxel",
+    "Mesh",
+    "Hooks",
+    "HookKeyframes",
+    "TimestepsRange",
+    "LatentOperation",
+    "FlowControl",
+    "Accumulation",
+    "Load3DCamera",
+    "Load3D",
+    "Load3DAnimation",
+    "Photomaker",
+    "Point",
+    "FaceAnalysis",
+    "BBOX",
+    "SEGS",
+    "AnyType",
+    "MultiType",
+    # Other classes
+    "HiddenHolder",
+    "Hidden",
+    "NodeInfoV1",
+    "NodeInfoV3",
+    "Schema",
+    "ComfyNode",
+    "NodeOutput",
+    "add_to_dict_v1",
+    "add_to_dict_v3",
+]

comfy_api/latest/_ui.py

@@ -449,15 +449,16 @@ class PreviewText(_UIOutput):
         return {"text": (self.value,)}
 
 
-class _UI:
-    SavedResult = SavedResult
-    SavedImages = SavedImages
-    SavedAudios = SavedAudios
-    ImageSaveHelper = ImageSaveHelper
-    AudioSaveHelper = AudioSaveHelper
-    PreviewImage = PreviewImage
-    PreviewMask = PreviewMask
-    PreviewAudio = PreviewAudio
-    PreviewVideo = PreviewVideo
-    PreviewUI3D = PreviewUI3D
-    PreviewText = PreviewText
+__all__ = [
+    "SavedResult",
+    "SavedImages",
+    "SavedAudios",
+    "ImageSaveHelper",
+    "AudioSaveHelper",
+    "PreviewImage",
+    "PreviewMask",
+    "PreviewAudio",
+    "PreviewVideo",
+    "PreviewUI3D",
+    "PreviewText",
+]

comfy_api_nodes/apinode_utils.py

@@ -18,7 +18,7 @@ from comfy_api_nodes.apis.client import (
     UploadResponse,
 )
 from server import PromptServer
-
+from comfy.cli_args import args
 
 import numpy as np
 from PIL import Image
@@ -30,7 +30,9 @@ from io import BytesIO
 import av
 
 
-async def download_url_to_video_output(video_url: str, timeout: int = None) -> VideoFromFile:
+async def download_url_to_video_output(
+    video_url: str, timeout: int = None, auth_kwargs: Optional[dict[str, str]] = None
+) -> VideoFromFile:
     """Downloads a video from a URL and returns a `VIDEO` output.
 
     Args:
@@ -39,7 +41,7 @@ async def download_url_to_video_output(video_url: str, timeout: int = None) -> V
     Returns:
         A Comfy node `VIDEO` output.
     """
-    video_io = await download_url_to_bytesio(video_url, timeout)
+    video_io = await download_url_to_bytesio(video_url, timeout, auth_kwargs=auth_kwargs)
     if video_io is None:
         error_msg = f"Failed to download video from {video_url}"
         logging.error(error_msg)
@@ -152,7 +154,7 @@ def validate_aspect_ratio(
             raise TypeError(
                 f"Aspect ratio cannot reduce to any less than {minimum_ratio_str} ({minimum_ratio}), but was {aspect_ratio} ({calculated_ratio})."
             )
-        elif calculated_ratio > maximum_ratio:
+        if calculated_ratio > maximum_ratio:
             raise TypeError(
                 f"Aspect ratio cannot reduce to any greater than {maximum_ratio_str} ({maximum_ratio}), but was {aspect_ratio} ({calculated_ratio})."
             )
@@ -164,7 +166,9 @@ def mimetype_to_extension(mime_type: str) -> str:
     return mime_type.split("/")[-1].lower()
 
 
-async def download_url_to_bytesio(url: str, timeout: int = None) -> BytesIO:
+async def download_url_to_bytesio(
+    url: str, timeout: int = None, auth_kwargs: Optional[dict[str, str]] = None
+) -> BytesIO:
     """Downloads content from a URL using requests and returns it as BytesIO.
 
     Args:
@@ -174,9 +178,18 @@ async def download_url_to_bytesio(url: str, timeout: int = None) -> BytesIO:
     Returns:
         BytesIO object containing the downloaded content.
     """
+    headers = {}
+    if url.startswith("/proxy/"):
+        url = str(args.comfy_api_base).rstrip("/") + url
+        auth_token = auth_kwargs.get("auth_token")
+        comfy_api_key = auth_kwargs.get("comfy_api_key")
+        if auth_token:
+            headers["Authorization"] = f"Bearer {auth_token}"
+        elif comfy_api_key:
+            headers["X-API-KEY"] = comfy_api_key
     timeout_cfg = aiohttp.ClientTimeout(total=timeout) if timeout else None
     async with aiohttp.ClientSession(timeout=timeout_cfg) as session:
-        async with session.get(url) as resp:
+        async with session.get(url, headers=headers) as resp:
             resp.raise_for_status()  # Raises HTTPError for bad responses (4XX or 5XX)
             return BytesIO(await resp.read())
 
@@ -256,7 +269,7 @@ def tensor_to_bytesio(
         mime_type: Target image MIME type (e.g., 'image/png', 'image/jpeg', 'image/webp', 'video/mp4').
 
     Returns:
-        Named BytesIO object containing the image data.
+        Named BytesIO object containing the image data, with pointer set to the start of buffer.
     """
     if not mime_type:
         mime_type = "image/png"
@@ -418,7 +431,7 @@ async def upload_video_to_comfyapi(
                     f"Video duration ({actual_duration:.2f}s) exceeds the maximum allowed ({max_duration}s)."
                 )
         except Exception as e:
-            logging.error(f"Error getting video duration: {e}")
+            logging.error("Error getting video duration: %s", str(e))
             raise ValueError(f"Could not verify video duration from source: {e}") from e
 
     upload_mime_type = f"video/{container.value.lower()}"

comfy_api_nodes/apis/__init__.py

@@ -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):

comfy_api_nodes/apis/client.py

@@ -95,9 +95,10 @@ 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
+from typing import Type, Optional, Any, TypeVar, Generic, Callable
 from enum import Enum
 import json
 from urllib.parse import urljoin, urlparse
@@ -174,7 +175,7 @@ class ApiClient:
         max_retries: int = 3,
         retry_delay: float = 1.0,
         retry_backoff_factor: float = 2.0,
-        retry_status_codes: Optional[Tuple[int, ...]] = None,
+        retry_status_codes: Optional[tuple[int, ...]] = None,
         session: Optional[aiohttp.ClientSession] = None,
     ):
         self.base_url = base_url
@@ -198,9 +199,9 @@ class ApiClient:
 
     @staticmethod
     def _create_json_payload_args(
-        data: Optional[Dict[str, Any]] = None,
-        headers: Optional[Dict[str, str]] = None,
-    ) -> Dict[str, Any]:
+        data: Optional[dict[str, Any]] = None,
+        headers: Optional[dict[str, str]] = None,
+    ) -> dict[str, Any]:
         return {
             "json": data,
             "headers": headers,
@@ -208,24 +209,27 @@ class ApiClient:
 
     def _create_form_data_args(
         self,
-        data: Dict[str, Any] | None,
-        files: Dict[str, Any] | None,
-        headers: Optional[Dict[str, str]] = None,
+        data: dict[str, Any] | None,
+        files: dict[str, Any] | None,
+        headers: Optional[dict[str, str]] = None,
         multipart_parser: Callable | None = None,
-    ) -> Dict[str, Any]:
+    ) -> dict[str, Any]:
         if headers and "Content-Type" in headers:
             del headers["Content-Type"]
 
         if multipart_parser and data:
             data = multipart_parser(data)
 
-        form = aiohttp.FormData(default_to_multipart=True)
-        if data:  # regular text fields
-            for k, v in data.items():
-                if v is None:
-                    continue  # aiohttp fails to serialize "None" values
-                # aiohttp expects strings or bytes; convert enums etc.
-                form.add_field(k, str(v) if not isinstance(v, (bytes, bytearray)) else v)
+        if isinstance(data, aiohttp.FormData):
+            form = data  # If the parser already returned a FormData, pass it through
+        else:
+            form = aiohttp.FormData(default_to_multipart=True)
+            if data:  # regular text fields
+                for k, v in data.items():
+                    if v is None:
+                        continue  # aiohttp fails to serialize "None" values
+                    # aiohttp expects strings or bytes; convert enums etc.
+                    form.add_field(k, str(v) if not isinstance(v, (bytes, bytearray)) else v)
 
         if files:
             file_iter = files if isinstance(files, list) else files.items()
@@ -250,9 +254,9 @@ class ApiClient:
 
     @staticmethod
     def _create_urlencoded_form_data_args(
-        data: Dict[str, Any],
-        headers: Optional[Dict[str, str]] = None,
-    ) -> Dict[str, Any]:
+        data: dict[str, Any],
+        headers: Optional[dict[str, str]] = None,
+    ) -> dict[str, Any]:
         headers = headers or {}
         headers["Content-Type"] = "application/x-www-form-urlencoded"
         return {
@@ -260,7 +264,7 @@ class ApiClient:
             "headers": headers,
         }
 
-    def get_headers(self) -> Dict[str, str]:
+    def get_headers(self) -> dict[str, str]:
         """Get headers for API requests, including authentication if available"""
         headers = {"Content-Type": "application/json", "Accept": "application/json"}
 
@@ -271,7 +275,7 @@ class ApiClient:
 
         return headers
 
-    async def _check_connectivity(self, target_url: str) -> Dict[str, bool]:
+    async def _check_connectivity(self, target_url: str) -> dict[str, bool]:
         """
         Check connectivity to determine if network issues are local or server-related.
 
@@ -312,14 +316,14 @@ class ApiClient:
         self,
         method: str,
         path: str,
-        params: Optional[Dict[str, Any]] = None,
-        data: Optional[Dict[str, Any]] = None,
-        files: Optional[Dict[str, Any] | list[tuple[str, Any]]] = None,
-        headers: Optional[Dict[str, str]] = None,
+        params: Optional[dict[str, Any]] = None,
+        data: Optional[dict[str, Any]] = None,
+        files: Optional[dict[str, Any] | list[tuple[str, Any]]] = None,
+        headers: Optional[dict[str, str]] = None,
         content_type: str = "application/json",
         multipart_parser: Callable | None = None,
         retry_count: int = 0,  # Used internally for tracking retries
-    ) -> Dict[str, Any]:
+    ) -> dict[str, Any]:
         """
         Make an HTTP request to the API with automatic retries for transient errors.
 
@@ -355,10 +359,10 @@ class ApiClient:
         if params:
             params = {k: v for k, v in params.items() if v is not None}  # aiohttp fails to serialize None values
 
-        logging.debug(f"[DEBUG] Request Headers: {request_headers}")
-        logging.debug(f"[DEBUG] Files: {files}")
-        logging.debug(f"[DEBUG] Params: {params}")
-        logging.debug(f"[DEBUG] Data: {data}")
+        logging.debug("[DEBUG] Request Headers: %s", request_headers)
+        logging.debug("[DEBUG] Files: %s", files)
+        logging.debug("[DEBUG] Params: %s", params)
+        logging.debug("[DEBUG] Data: %s", data)
 
         if content_type == "application/x-www-form-urlencoded":
             payload_args = self._create_urlencoded_form_data_args(data or {}, request_headers)
@@ -481,7 +485,7 @@ class ApiClient:
             retry_delay: Initial delay between retries in seconds
             retry_backoff_factor: Multiplier for the delay after each retry
         """
-        headers: Dict[str, str] = {}
+        headers: dict[str, str] = {}
         skip_auto_headers: set[str] = set()
         if content_type:
             headers["Content-Type"] = content_type
@@ -499,7 +503,9 @@ 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 +538,7 @@ 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)}",
                 )
@@ -552,7 +558,7 @@ class ApiClient:
         *req_meta,
         retry_count: int,
         response_content: dict | str = "",
-    ) -> Dict[str, Any]:
+    ) -> dict[str, Any]:
         status_code = exc.status
         if status_code == 401:
             user_friendly = "Unauthorized: Please login first to use this node."
@@ -586,9 +592,9 @@ class ApiClient:
             error_message=f"HTTP Error {exc.status}",
         )
 
-        logging.debug(f"[DEBUG] API Error: {user_friendly} (Status: {status_code})")
+        logging.debug("[DEBUG] API Error: %s (Status: %s)", user_friendly, status_code)
         if response_content:
-            logging.debug(f"[DEBUG] Response content: {response_content}")
+            logging.debug("[DEBUG] Response content: %s", response_content)
 
         # Retry if eligible
         if status_code in self.retry_status_codes and retry_count < self.max_retries:
@@ -653,7 +659,7 @@ class ApiEndpoint(Generic[T, R]):
         method: HttpMethod,
         request_model: Type[T],
         response_model: Type[R],
-        query_params: Optional[Dict[str, Any]] = None,
+        query_params: Optional[dict[str, Any]] = None,
     ):
         """Initialize an API endpoint definition.
 
@@ -678,11 +684,11 @@ class SynchronousOperation(Generic[T, R]):
         self,
         endpoint: ApiEndpoint[T, R],
         request: T,
-        files: Optional[Dict[str, Any] | list[tuple[str, Any]]] = None,
+        files: Optional[dict[str, Any] | list[tuple[str, Any]]] = None,
         api_base: str | None = None,
         auth_token: Optional[str] = None,
         comfy_api_key: Optional[str] = None,
-        auth_kwargs: Optional[Dict[str, str]] = None,
+        auth_kwargs: Optional[dict[str, str]] = None,
         timeout: float = 7200.0,
         verify_ssl: bool = True,
         content_type: str = "application/json",
@@ -723,7 +729,7 @@ class SynchronousOperation(Generic[T, R]):
             )
 
         try:
-            request_dict: Optional[Dict[str, Any]]
+            request_dict: Optional[dict[str, Any]]
             if isinstance(self.request, EmptyRequest):
                 request_dict = None
             else:
@@ -732,11 +738,9 @@ class SynchronousOperation(Generic[T, R]):
                     if isinstance(v, Enum):
                         request_dict[k] = v.value
 
-            logging.debug(
-                f"[DEBUG] API Request: {self.endpoint.method.value} {self.endpoint.path}"
-            )
-            logging.debug(f"[DEBUG] Request Data: {json.dumps(request_dict, indent=2)}")
-            logging.debug(f"[DEBUG] Query Params: {self.endpoint.query_params}")
+            logging.debug("[DEBUG] API Request: %s %s", self.endpoint.method.value, self.endpoint.path)
+            logging.debug("[DEBUG] Request Data: %s", json.dumps(request_dict, indent=2))
+            logging.debug("[DEBUG] Query Params: %s", self.endpoint.query_params)
 
             response_json = await client.request(
                 self.endpoint.method.value,
@@ -751,11 +755,11 @@ class SynchronousOperation(Generic[T, R]):
             logging.debug("=" * 50)
             logging.debug("[DEBUG] RESPONSE DETAILS:")
             logging.debug("[DEBUG] Status Code: 200 (Success)")
-            logging.debug(f"[DEBUG] Response Body: {json.dumps(response_json, indent=2)}")
+            logging.debug("[DEBUG] Response Body: %s", json.dumps(response_json, indent=2))
             logging.debug("=" * 50)
 
             parsed_response = self.endpoint.response_model.model_validate(response_json)
-            logging.debug(f"[DEBUG] Parsed Response: {parsed_response}")
+            logging.debug("[DEBUG] Parsed Response: %s", parsed_response)
             return parsed_response
         finally:
             if owns_client:
@@ -778,14 +782,16 @@ class PollingOperation(Generic[T, R]):
         poll_endpoint: ApiEndpoint[EmptyRequest, R],
         completed_statuses: list[str],
         failed_statuses: list[str],
-        status_extractor: Callable[[R], str],
-        progress_extractor: Callable[[R], float] | None = None,
-        result_url_extractor: Callable[[R], str] | None = None,
+        *,
+        status_extractor: Callable[[R], Optional[str]],
+        progress_extractor: Callable[[R], Optional[float]] | None = None,
+        result_url_extractor: Callable[[R], Optional[str]] | None = None,
+        price_extractor: Callable[[R], Optional[float]] | None = None,
         request: Optional[T] = None,
         api_base: str | None = None,
         auth_token: Optional[str] = None,
         comfy_api_key: Optional[str] = None,
-        auth_kwargs: Optional[Dict[str, str]] = None,
+        auth_kwargs: Optional[dict[str, str]] = None,
         poll_interval: float = 5.0,
         max_poll_attempts: int = 120,  # Default max polling attempts (10 minutes with 5s interval)
         max_retries: int = 3,  # Max retries per individual API call
@@ -811,10 +817,12 @@ class PollingOperation(Generic[T, R]):
         self.status_extractor = status_extractor or (lambda x: getattr(x, "status", None))
         self.progress_extractor = progress_extractor
         self.result_url_extractor = result_url_extractor
+        self.price_extractor = price_extractor
         self.node_id = node_id
         self.completed_statuses = completed_statuses
         self.failed_statuses = failed_statuses
         self.final_response: Optional[R] = None
+        self.extracted_price: Optional[float] = None
 
     async def execute(self, client: Optional[ApiClient] = None) -> R:
         owns_client = client is None
@@ -836,6 +844,8 @@ class PollingOperation(Generic[T, R]):
     def _display_text_on_node(self, text: str):
         if not self.node_id:
             return
+        if self.extracted_price is not None:
+            text = f"Price: {self.extracted_price}$\n{text}"
         PromptServer.instance.send_progress_text(text, self.node_id)
 
     def _display_time_progress_on_node(self, time_completed: int | float):
@@ -871,18 +881,19 @@ class PollingOperation(Generic[T, R]):
         status = TaskStatus.PENDING
         for poll_count in range(1, self.max_poll_attempts + 1):
             try:
-                logging.debug(f"[DEBUG] Polling attempt #{poll_count}")
+                logging.debug("[DEBUG] Polling attempt #%s", poll_count)
 
-                request_dict = (
-                    None if self.request is None else self.request.model_dump(exclude_none=True)
-                )
+                request_dict = None if self.request is None else self.request.model_dump(exclude_none=True)
 
                 if poll_count == 1:
                     logging.debug(
-                        f"[DEBUG] Poll Request: {self.poll_endpoint.method.value} {self.poll_endpoint.path}"
+                        "[DEBUG] Poll Request: %s %s",
+                        self.poll_endpoint.method.value,
+                        self.poll_endpoint.path,
                     )
                     logging.debug(
-                        f"[DEBUG] Poll Request Data: {json.dumps(request_dict, indent=2) if request_dict else 'None'}"
+                        "[DEBUG] Poll Request Data: %s",
+                        json.dumps(request_dict, indent=2) if request_dict else "None",
                     )
 
                 # Query task status
@@ -897,7 +908,7 @@ class PollingOperation(Generic[T, R]):
 
                 # Check if task is complete
                 status = self._check_task_status(response_obj)
-                logging.debug(f"[DEBUG] Task Status: {status}")
+                logging.debug("[DEBUG] Task Status: %s", status)
 
                 # If progress extractor is provided, extract progress
                 if self.progress_extractor:
@@ -905,13 +916,18 @@ class PollingOperation(Generic[T, R]):
                     if new_progress is not None:
                         progress.update_absolute(new_progress, total=PROGRESS_BAR_MAX)
 
+                if self.price_extractor:
+                    price = self.price_extractor(response_obj)
+                    if price is not None:
+                        self.extracted_price = price
+
                 if status == TaskStatus.COMPLETED:
                     message = "Task completed successfully"
                     if self.result_url_extractor:
                         result_url = self.result_url_extractor(response_obj)
                         if result_url:
                             message = f"Result URL: {result_url}"
-                    logging.debug(f"[DEBUG] {message}")
+                    logging.debug("[DEBUG] %s", message)
                     self._display_text_on_node(message)
                     self.final_response = response_obj
                     if self.progress_extractor:
@@ -919,7 +935,7 @@ class PollingOperation(Generic[T, R]):
                     return self.final_response
                 if status == TaskStatus.FAILED:
                     message = f"Task failed: {json.dumps(resp)}"
-                    logging.error(f"[DEBUG] {message}")
+                    logging.error("[DEBUG] %s", message)
                     raise Exception(message)
                 logging.debug("[DEBUG] Task still pending, continuing to poll...")
                 # Task pending – wait
@@ -933,7 +949,12 @@ class PollingOperation(Generic[T, R]):
                     raise Exception(
                         f"Polling aborted after {consecutive_errors} network errors: {str(e)}"
                     ) from e
-                logging.warning("Network error (%s/%s): %s", consecutive_errors, max_consecutive_errors, str(e))
+                logging.warning(
+                    "Network error (%s/%s): %s",
+                    consecutive_errors,
+                    max_consecutive_errors,
+                    str(e),
+                )
                 await asyncio.sleep(self.poll_interval)
             except Exception as e:
                 # For other errors, increment count and potentially abort
@@ -943,10 +964,13 @@ class PollingOperation(Generic[T, R]):
                         f"Polling aborted after {consecutive_errors} consecutive errors: {str(e)}"
                     ) from e
 
-                logging.error(f"[DEBUG] Polling error: {str(e)}")
+                logging.error("[DEBUG] Polling error: %s", str(e))
                 logging.warning(
-                    f"Error during polling (attempt {poll_count}/{self.max_poll_attempts}): {str(e)}. "
-                    f"Will retry in {self.poll_interval} seconds."
+                    "Error during polling (attempt %s/%s): %s. Will retry in %s seconds.",
+                    poll_count,
+                    self.max_poll_attempts,
+                    str(e),
+                    self.poll_interval,
                 )
                 await asyncio.sleep(self.poll_interval)
 

comfy_api_nodes/apis/gemini_api.py

@@ -1,19 +1,22 @@
-from __future__ import annotations
-
-from typing import List, Optional
+from typing import Optional
 
 from comfy_api_nodes.apis import GeminiGenerationConfig, GeminiContent, GeminiSafetySetting, GeminiSystemInstructionContent, GeminiTool, GeminiVideoMetadata
 from pydantic import BaseModel
 
 
+class GeminiImageConfig(BaseModel):
+    aspectRatio: Optional[str] = None
+
+
 class GeminiImageGenerationConfig(GeminiGenerationConfig):
-    responseModalities: Optional[List[str]] = None
+    responseModalities: Optional[list[str]] = None
+    imageConfig: Optional[GeminiImageConfig] = None
 
 
 class GeminiImageGenerateContentRequest(BaseModel):
-    contents: List[GeminiContent]
+    contents: list[GeminiContent]
     generationConfig: Optional[GeminiImageGenerationConfig] = None
-    safetySettings: Optional[List[GeminiSafetySetting]] = None
+    safetySettings: Optional[list[GeminiSafetySetting]] = None
     systemInstruction: Optional[GeminiSystemInstructionContent] = None
-    tools: Optional[List[GeminiTool]] = None
+    tools: Optional[list[GeminiTool]] = None
     videoMetadata: Optional[GeminiVideoMetadata] = None

comfy_api_nodes/apis/pika_defs.py

@@ -0,0 +1,100 @@
+from typing import Optional
+from enum import Enum
+from pydantic import BaseModel, Field
+
+
+class Pikaffect(str, Enum):
+    Cake_ify = "Cake-ify"
+    Crumble = "Crumble"
+    Crush = "Crush"
+    Decapitate = "Decapitate"
+    Deflate = "Deflate"
+    Dissolve = "Dissolve"
+    Explode = "Explode"
+    Eye_pop = "Eye-pop"
+    Inflate = "Inflate"
+    Levitate = "Levitate"
+    Melt = "Melt"
+    Peel = "Peel"
+    Poke = "Poke"
+    Squish = "Squish"
+    Ta_da = "Ta-da"
+    Tear = "Tear"
+
+
+class PikaBodyGenerate22C2vGenerate22PikascenesPost(BaseModel):
+    aspectRatio: Optional[float] = Field(None, description='Aspect ratio (width / height)')
+    duration: Optional[int] = Field(5)
+    ingredientsMode: str = Field(...)
+    negativePrompt: Optional[str] = Field(None)
+    promptText: Optional[str] = Field(None)
+    resolution: Optional[str] = Field('1080p')
+    seed: Optional[int] = Field(None)
+
+
+class PikaGenerateResponse(BaseModel):
+    video_id: str = Field(...)
+
+
+class PikaBodyGenerate22I2vGenerate22I2vPost(BaseModel):
+    duration: Optional[int] = 5
+    negativePrompt: Optional[str] = Field(None)
+    promptText: Optional[str] = Field(None)
+    resolution: Optional[str] = '1080p'
+    seed: Optional[int] = Field(None)
+
+
+class PikaBodyGenerate22KeyframeGenerate22PikaframesPost(BaseModel):
+    duration: Optional[int] = Field(None, ge=5, le=10)
+    negativePrompt: Optional[str] = Field(None)
+    promptText: str = Field(...)
+    resolution: Optional[str] = '1080p'
+    seed: Optional[int] = Field(None)
+
+
+class PikaBodyGenerate22T2vGenerate22T2vPost(BaseModel):
+    aspectRatio: Optional[float] = Field(
+        1.7777777777777777,
+        description='Aspect ratio (width / height)',
+        ge=0.4,
+        le=2.5,
+    )
+    duration: Optional[int] = 5
+    negativePrompt: Optional[str] = Field(None)
+    promptText: str = Field(...)
+    resolution: Optional[str] = '1080p'
+    seed: Optional[int] = Field(None)
+
+
+class PikaBodyGeneratePikadditionsGeneratePikadditionsPost(BaseModel):
+    negativePrompt: Optional[str] = Field(None)
+    promptText: Optional[str] = Field(None)
+    seed: Optional[int] = Field(None)
+
+
+class PikaBodyGeneratePikaffectsGeneratePikaffectsPost(BaseModel):
+    negativePrompt: Optional[str] = Field(None)
+    pikaffect: Optional[str] = None
+    promptText: Optional[str] = Field(None)
+    seed: Optional[int] = Field(None)
+
+
+class PikaBodyGeneratePikaswapsGeneratePikaswapsPost(BaseModel):
+    negativePrompt: Optional[str] = Field(None)
+    promptText: Optional[str] = Field(None)
+    seed: Optional[int] = Field(None)
+    modifyRegionRoi: Optional[str] = Field(None)
+
+
+class PikaStatusEnum(str, Enum):
+    queued = "queued"
+    started = "started"
+    finished = "finished"
+    failed = "failed"
+
+
+class PikaVideoResponse(BaseModel):
+    id: str = Field(...)
+    progress: Optional[int] = Field(None)
+    status: PikaStatusEnum
+    url: Optional[str] = Field(None)

comfy_api_nodes/apis/request_logger.py

@@ -4,62 +4,99 @@ 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:
         os.makedirs(log_dir, exist_ok=True)
     except Exception as e:
-        logger.error(f"Error creating API log directory {log_dir}: {e}")
+        logger.error("Error creating API log directory %s: %s", log_dir, str(e))
         # Fallback to base temp directory if sub-directory creation fails
         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}")
@@ -85,9 +122,10 @@ def log_request_response(
     try:
         with open(filepath, "w", encoding="utf-8") as f:
             f.write("\n".join(log_content))
-        logger.debug(f"API log saved to: {filepath}")
+        logger.debug("API log saved to: %s", filepath)
     except Exception as e:
-        logger.error(f"Error writing API log to {filepath}: {e}")
+        logger.error("Error writing API log to %s: %s", filepath, str(e))
+
 
 if __name__ == '__main__':
     # Example usage (for testing the logger directly)

comfy_api_nodes/apis/rodin_api.py

@@ -52,7 +52,3 @@ class RodinResourceItem(BaseModel):
 
 class Rodin3DDownloadResponse(BaseModel):
     list: List[RodinResourceItem] = Field(..., description="Source List")
-
-
-
-

comfy_api_nodes/nodes_bytedance.py

@@ -249,8 +249,8 @@ class ByteDanceImageNode(comfy_io.ComfyNode):
             inputs=[
                 comfy_io.Combo.Input(
                     "model",
-                    options=[model.value for model in Text2ImageModelName],
-                    default=Text2ImageModelName.seedream_3.value,
+                    options=Text2ImageModelName,
+                    default=Text2ImageModelName.seedream_3,
                     tooltip="Model name",
                 ),
                 comfy_io.String.Input(
@@ -382,8 +382,8 @@ class ByteDanceImageEditNode(comfy_io.ComfyNode):
             inputs=[
                 comfy_io.Combo.Input(
                     "model",
-                    options=[model.value for model in Image2ImageModelName],
-                    default=Image2ImageModelName.seededit_3.value,
+                    options=Image2ImageModelName,
+                    default=Image2ImageModelName.seededit_3,
                     tooltip="Model name",
                 ),
                 comfy_io.Image.Input(
@@ -676,8 +676,8 @@ class ByteDanceTextToVideoNode(comfy_io.ComfyNode):
             inputs=[
                 comfy_io.Combo.Input(
                     "model",
-                    options=[model.value for model in Text2VideoModelName],
-                    default=Text2VideoModelName.seedance_1_pro.value,
+                    options=Text2VideoModelName,
+                    default=Text2VideoModelName.seedance_1_pro,
                     tooltip="Model name",
                 ),
                 comfy_io.String.Input(
@@ -793,8 +793,8 @@ class ByteDanceImageToVideoNode(comfy_io.ComfyNode):
             inputs=[
                 comfy_io.Combo.Input(
                     "model",
-                    options=[model.value for model in Image2VideoModelName],
-                    default=Image2VideoModelName.seedance_1_pro.value,
+                    options=Image2VideoModelName,
+                    default=Image2VideoModelName.seedance_1_pro,
                     tooltip="Model name",
                 ),
                 comfy_io.String.Input(
@@ -920,7 +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",
                 ),

comfy_api_nodes/nodes_gemini.py

@@ -26,7 +26,7 @@ from comfy_api_nodes.apis import (
     GeminiPart,
     GeminiMimeType,
 )
-from comfy_api_nodes.apis.gemini_api import GeminiImageGenerationConfig, GeminiImageGenerateContentRequest
+from comfy_api_nodes.apis.gemini_api import GeminiImageGenerationConfig, GeminiImageGenerateContentRequest, GeminiImageConfig
 from comfy_api_nodes.apis.client import (
     ApiEndpoint,
     HttpMethod,
@@ -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"
@@ -62,6 +63,7 @@ class GeminiImageModel(str, Enum):
     """
 
     gemini_2_5_flash_image_preview = "gemini-2.5-flash-image-preview"
+    gemini_2_5_flash_image = "gemini-2.5-flash-image"
 
 
 def get_gemini_endpoint(
@@ -310,7 +312,7 @@ 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 +492,6 @@ 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(
@@ -538,7 +539,7 @@ class GeminiImage(ComfyNodeABC):
                     {
                         "tooltip": "The Gemini model to use for generating responses.",
                         "options": [model.value for model in GeminiImageModel],
-                        "default": GeminiImageModel.gemini_2_5_flash_image_preview.value,
+                        "default": GeminiImageModel.gemini_2_5_flash_image.value,
                     },
                 ),
                 "seed": (
@@ -579,6 +580,14 @@ class GeminiImage(ComfyNodeABC):
                 #         "tooltip": "How many images to generate",
                 #     },
                 # ),
+                "aspect_ratio": (
+                    IO.COMBO,
+                    {
+                        "tooltip": "Defaults to matching the output image size to that of your input image, or otherwise generates 1:1 squares.",
+                        "options": ["auto", "1:1", "2:3", "3:2", "3:4", "4:3", "4:5", "5:4", "9:16", "16:9", "21:9"],
+                        "default": "auto",
+                    },
+                ),
             },
             "hidden": {
                 "auth_token": "AUTH_TOKEN_COMFY_ORG",
@@ -600,15 +609,17 @@ class GeminiImage(ComfyNodeABC):
         images: Optional[IO.IMAGE] = None,
         files: Optional[list[GeminiPart]] = None,
         n=1,
+        aspect_ratio: str = "auto",
         unique_id: Optional[str] = None,
         **kwargs,
     ):
-        # Validate inputs
         validate_string(prompt, strip_whitespace=True, min_length=1)
-        # Create parts list with text prompt as the first part
         parts: list[GeminiPart] = [create_text_part(prompt)]
 
-        # Add other modal parts
+        if not aspect_ratio:
+            aspect_ratio = "auto"  # for backward compatability with old workflows; to-do remove this in December
+        image_config = GeminiImageConfig(aspectRatio=aspect_ratio)
+
         if images is not None:
             image_parts = create_image_parts(images)
             parts.extend(image_parts)
@@ -625,7 +636,8 @@ class GeminiImage(ComfyNodeABC):
                     ),
                 ],
                 generationConfig=GeminiImageGenerationConfig(
-                    responseModalities=["TEXT","IMAGE"]
+                    responseModalities=["TEXT","IMAGE"],
+                    imageConfig=None if aspect_ratio == "auto" else image_config,
                 )
             ),
             auth_kwargs=kwargs,

comfy_api_nodes/nodes_luma.py

@@ -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=LumaImageModel,
+                ),
+                comfy_io.Combo.Input(
+                    "aspect_ratio",
+                    options=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=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=LumaVideoModel,
+                ),
+                comfy_io.Combo.Input(
+                    "aspect_ratio",
+                    options=LumaAspectRatio,
+                    default=LumaAspectRatio.ratio_16_9,
+                ),
+                comfy_io.Combo.Input(
+                    "resolution",
+                    options=LumaVideoOutputResolution,
+                    default=LumaVideoOutputResolution.res_540p,
+                ),
+                comfy_io.Combo.Input(
+                    "duration",
+                    options=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=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=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()

comfy_api_nodes/nodes_minimax.py

@@ -500,7 +500,7 @@ class MinimaxHailuoVideoNode(comfy_io.ComfyNode):
             raise Exception(
                 f"No video was found in the response. Full response: {file_result.model_dump()}"
             )
-        logging.info(f"Generated video URL: {file_url}")
+        logging.info("Generated video URL: %s", file_url)
         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}"

comfy_api_nodes/nodes_moonvalley.py

@@ -2,11 +2,7 @@ import logging
 from typing import Any, Callable, Optional, TypeVar
 import torch
 from typing_extensions import override
-from comfy_api_nodes.util.validation_utils import (
-    get_image_dimensions,
-    validate_image_dimensions,
-)
-
+from comfy_api_nodes.util.validation_utils import validate_image_dimensions
 
 from comfy_api_nodes.apis import (
     MoonvalleyTextToVideoRequest,
@@ -132,47 +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.
@@ -282,7 +237,7 @@ def trim_video(video: VideoInput, duration_sec: float) -> VideoInput:
         audio_stream = None
 
         for stream in input_container.streams:
-            logging.info(f"Found stream: type={stream.type}, class={type(stream)}")
+            logging.info("Found stream: type=%s, class=%s", stream.type, type(stream))
             if isinstance(stream, av.VideoStream):
                 # Create output video stream with same parameters
                 video_stream = output_container.add_stream(
@@ -292,7 +247,7 @@ def trim_video(video: VideoInput, duration_sec: float) -> VideoInput:
                 video_stream.height = stream.height
                 video_stream.pix_fmt = "yuv420p"
                 logging.info(
-                    f"Added video stream: {stream.width}x{stream.height} @ {stream.average_rate}fps"
+                    "Added video stream: %sx%s @ %sfps", stream.width, stream.height, stream.average_rate
                 )
             elif isinstance(stream, av.AudioStream):
                 # Create output audio stream with same parameters
@@ -301,9 +256,7 @@ def trim_video(video: VideoInput, duration_sec: float) -> VideoInput:
                 )
                 audio_stream.sample_rate = stream.sample_rate
                 audio_stream.layout = stream.layout
-                logging.info(
-                    f"Added audio stream: {stream.sample_rate}Hz, {stream.channels} channels"
-                )
+                logging.info("Added audio stream: %sHz, %s channels", stream.sample_rate, stream.channels)
 
         # Calculate target frame count that's divisible by 16
         fps = input_container.streams.video[0].average_rate
@@ -333,9 +286,7 @@ def trim_video(video: VideoInput, duration_sec: float) -> VideoInput:
             for packet in video_stream.encode():
                 output_container.mux(packet)
 
-            logging.info(
-                f"Encoded {frame_count} video frames (target: {target_frames})"
-            )
+            logging.info("Encoded %s video frames (target: %s)", frame_count, target_frames)
 
         # Decode and re-encode audio frames
         if audio_stream:
@@ -353,7 +304,7 @@ def trim_video(video: VideoInput, duration_sec: float) -> VideoInput:
             for packet in audio_stream.encode():
                 output_container.mux(packet)
 
-            logging.info(f"Encoded {audio_frame_count} audio frames")
+            logging.info("Encoded %s audio frames", audio_frame_count)
 
         # Close containers
         output_container.close()
@@ -380,7 +331,7 @@ def parse_width_height_from_res(resolution: str):
         "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},
+        # "21:9 (2560 x 1080)": {"width": 2560, "height": 1080},
     }
     return res_map.get(resolution, {"width": 1920, "height": 1080})
 
@@ -433,11 +384,11 @@ class MoonvalleyImg2VideoNode(comfy_io.ComfyNode):
                     "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",
+                    "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(
@@ -448,14 +399,14 @@ class MoonvalleyImg2VideoNode(comfy_io.ComfyNode):
                         "1:1 (1152 x 1152)",
                         "4:3 (1536 x 1152)",
                         "3:4 (1152 x 1536)",
-                        "21:9 (2560 x 1080)",
+                        # "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,
+                    default=4.5,
                     min=1.0,
                     max=20.0,
                     step=1.0,
@@ -469,10 +420,11 @@ class MoonvalleyImg2VideoNode(comfy_io.ComfyNode):
                     step=1,
                     display_mode=comfy_io.NumberDisplay.number,
                     tooltip="Random seed value",
+                    control_after_generate=True,
                 ),
                 comfy_io.Int.Input(
                     "steps",
-                    default=100,
+                    default=33,
                     min=1,
                     max=100,
                     step=1,
@@ -499,7 +451,7 @@ class MoonvalleyImg2VideoNode(comfy_io.ComfyNode):
         seed: int,
         steps: int,
     ) -> comfy_io.NodeOutput:
-        validate_input_image(image, True)
+        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 = parse_width_height_from_res(resolution)
 
@@ -513,12 +465,11 @@ class MoonvalleyImg2VideoNode(comfy_io.ComfyNode):
             steps=steps,
             seed=seed,
             guidance_scale=prompt_adherence,
-            num_frames=128,
             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"""
+
         # Get MIME type from tensor - assuming PNG format for image tensors
         mime_type = "image/png"
 
@@ -571,11 +522,11 @@ class MoonvalleyVideo2VideoNode(comfy_io.ComfyNode):
                     "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",
+                    "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.Int.Input(
@@ -591,7 +542,7 @@ class MoonvalleyVideo2VideoNode(comfy_io.ComfyNode):
                 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.",
+                    "Videos longer than 5s will be automatically trimmed. Only MP4 format supported.",
                 ),
                 comfy_io.Combo.Input(
                     "control_type",
@@ -608,6 +559,15 @@ class MoonvalleyVideo2VideoNode(comfy_io.ComfyNode):
                     tooltip="Only used if control_type is 'Motion Transfer'",
                     optional=True,
                 ),
+                comfy_io.Int.Input(
+                    "steps",
+                    default=33,
+                    min=1,
+                    max=100,
+                    step=1,
+                    display_mode=comfy_io.NumberDisplay.number,
+                    tooltip="Number of inference steps",
+                ),
             ],
             outputs=[comfy_io.Video.Output()],
             hidden=[
@@ -627,6 +587,8 @@ class MoonvalleyVideo2VideoNode(comfy_io.ComfyNode):
         video: Optional[VideoInput] = None,
         control_type: str = "Motion Transfer",
         motion_intensity: Optional[int] = 100,
+        steps=33,
+        prompt_adherence=4.5,
     ) -> comfy_io.NodeOutput:
         auth = {
             "auth_token": cls.hidden.auth_token_comfy_org,
@@ -636,7 +598,6 @@ class MoonvalleyVideo2VideoNode(comfy_io.ComfyNode):
         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)
 
         # Only include motion_intensity for Motion Transfer
@@ -648,6 +609,8 @@ class MoonvalleyVideo2VideoNode(comfy_io.ComfyNode):
             negative_prompt=negative_prompt,
             seed=seed,
             control_params=control_params,
+            steps=steps,
+            guidance_scale=prompt_adherence,
         )
 
         control = parse_control_parameter(control_type)
@@ -699,11 +662,11 @@ class MoonvalleyTxt2VideoNode(comfy_io.ComfyNode):
                     "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",
+                    "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(
@@ -721,7 +684,7 @@ class MoonvalleyTxt2VideoNode(comfy_io.ComfyNode):
                 ),
                 comfy_io.Float.Input(
                     "prompt_adherence",
-                    default=10.0,
+                    default=4.0,
                     min=1.0,
                     max=20.0,
                     step=1.0,
@@ -734,11 +697,12 @@ class MoonvalleyTxt2VideoNode(comfy_io.ComfyNode):
                     max=4294967295,
                     step=1,
                     display_mode=comfy_io.NumberDisplay.number,
+                    control_after_generate=True,
                     tooltip="Random seed value",
                 ),
                 comfy_io.Int.Input(
                     "steps",
-                    default=100,
+                    default=33,
                     min=1,
                     max=100,
                     step=1,

comfy_api_nodes/nodes_pixverse.py

@@ -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=PixverseAspectRatio,
+                ),
+                comfy_io.Combo.Input(
+                    "quality",
+                    options=PixverseQuality,
+                    default=PixverseQuality.res_540p,
+                ),
+                comfy_io.Combo.Input(
+                    "duration_seconds",
+                    options=PixverseDuration,
+                ),
+                comfy_io.Combo.Input(
+                    "motion_mode",
+                    options=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="",
+                    multiline=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=PixverseQuality,
+                    default=PixverseQuality.res_540p,
+                ),
+                comfy_io.Combo.Input(
+                    "duration_seconds",
+                    options=PixverseDuration,
+                ),
+                comfy_io.Combo.Input(
+                    "motion_mode",
+                    options=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="",
+                    multiline=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=PixverseQuality,
+                    default=PixverseQuality.res_540p,
+                ),
+                comfy_io.Combo.Input(
+                    "duration_seconds",
+                    options=PixverseDuration,
+                ),
+                comfy_io.Combo.Input(
+                    "motion_mode",
+                    options=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="",
+                    multiline=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()

comfy_api_nodes/nodes_recraft.py

@@ -35,57 +35,64 @@ from server import PromptServer
 import torch
 from io import BytesIO
 from PIL import UnidentifiedImageError
+import aiohttp
 
 
 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()
-
-        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))
-
-        return all_bytesio
-
-
-def recraft_multipart_parser(data, parent_key=None, formatter: callable=None, converted_to_check: list[list]=None, is_list=False) -> dict:
+    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]:
     """
-    Formats data such that multipart/form-data will work with requests library
-    when both files and data are present.
+    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()
+
+    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
+
+
+def recraft_multipart_parser(
+    data,
+    parent_key=None,
+    formatter: callable = None,
+    converted_to_check: list[list] = None,
+    is_list: bool = False,
+    return_mode: str = "formdata"  # "dict" | "formdata"
+) -> dict | aiohttp.FormData:
+    """
+    Formats data such that multipart/form-data will work with aiohttp library when both files and data are present.
 
     The OpenAI client that Recraft uses has a bizarre way of serializing lists:
 
@@ -103,23 +110,23 @@ def recraft_multipart_parser(data, parent_key=None, formatter: callable=None, co
     # Modification of a function that handled a different type of multipart parsing, big ups:
     # https://gist.github.com/kazqvaizer/4cebebe5db654a414132809f9f88067b
 
-    def handle_converted_lists(data, parent_key, lists_to_check=tuple[list]):
+    def handle_converted_lists(item, parent_key, lists_to_check=tuple[list]):
         # if list already exists exists, just extend list with data
         for check_list in lists_to_check:
             for conv_tuple in check_list:
-                if conv_tuple[0] == parent_key and type(conv_tuple[1]) is list:
-                    conv_tuple[1].append(formatter(data))
+                if conv_tuple[0] == parent_key and isinstance(conv_tuple[1], list):
+                    conv_tuple[1].append(formatter(item))
                     return True
         return False
 
     if converted_to_check is None:
         converted_to_check = []
 
-
+    effective_mode = return_mode if parent_key is None else "dict"
     if formatter is None:
         formatter = lambda v: v  # Multipart representation of value
 
-    if type(data) is not dict:
+    if not isinstance(data, dict):
         # if list already exists exists, just extend list with data
         added = handle_converted_lists(data, parent_key, converted_to_check)
         if added:
@@ -136,15 +143,24 @@ def recraft_multipart_parser(data, parent_key=None, formatter: callable=None, co
 
     for key, value in data.items():
         current_key = key if parent_key is None else f"{parent_key}[{key}]"
-        if type(value) is dict:
+        if isinstance(value, dict):
             converted.extend(recraft_multipart_parser(value, current_key, formatter, next_check).items())
-        elif type(value) is list:
+        elif isinstance(value, list):
             for ind, list_value in enumerate(value):
                 iter_key = f"{current_key}[]"
                 converted.extend(recraft_multipart_parser(list_value, iter_key, formatter, next_check, is_list=True).items())
         else:
             converted.append((current_key, formatter(value)))
 
+    if effective_mode == "formdata":
+        fd = aiohttp.FormData()
+        for k, v in dict(converted).items():
+            if isinstance(v, list):
+                for item in v:
+                    fd.add_field(k, str(item))
+            else:
+                fd.add_field(k, str(v))
+        return fd
     return dict(converted)
 
 

comfy_api_nodes/nodes_runway.py

@@ -200,11 +200,11 @@ class RunwayImageToVideoNodeGen3a(comfy_io.ComfyNode):
                 ),
                 comfy_io.Combo.Input(
                     "duration",
-                    options=[model.value for model in Duration],
+                    options=Duration,
                 ),
                 comfy_io.Combo.Input(
                     "ratio",
-                    options=[model.value for model in RunwayGen3aAspectRatio],
+                    options=RunwayGen3aAspectRatio,
                 ),
                 comfy_io.Int.Input(
                     "seed",
@@ -300,11 +300,11 @@ class RunwayImageToVideoNodeGen4(comfy_io.ComfyNode):
                 ),
                 comfy_io.Combo.Input(
                     "duration",
-                    options=[model.value for model in Duration],
+                    options=Duration,
                 ),
                 comfy_io.Combo.Input(
                     "ratio",
-                    options=[model.value for model in RunwayGen4TurboAspectRatio],
+                    options=RunwayGen4TurboAspectRatio,
                 ),
                 comfy_io.Int.Input(
                     "seed",
@@ -408,11 +408,11 @@ class RunwayFirstLastFrameNode(comfy_io.ComfyNode):
                 ),
                 comfy_io.Combo.Input(
                     "duration",
-                    options=[model.value for model in Duration],
+                    options=Duration,
                 ),
                 comfy_io.Combo.Input(
                     "ratio",
-                    options=[model.value for model in RunwayGen3aAspectRatio],
+                    options=RunwayGen3aAspectRatio,
                 ),
                 comfy_io.Int.Input(
                     "seed",

comfy_api_nodes/nodes_sora.py

@@ -0,0 +1,175 @@
+from typing import Optional
+from typing_extensions import override
+
+import torch
+from pydantic import BaseModel, Field
+from comfy_api.latest import ComfyExtension, io as comfy_io
+from comfy_api_nodes.apis.client import (
+    ApiEndpoint,
+    HttpMethod,
+    SynchronousOperation,
+    PollingOperation,
+    EmptyRequest,
+)
+from comfy_api_nodes.util.validation_utils import get_number_of_images
+
+from comfy_api_nodes.apinode_utils import (
+    download_url_to_video_output,
+    tensor_to_bytesio,
+)
+
+class Sora2GenerationRequest(BaseModel):
+    prompt: str = Field(...)
+    model: str = Field(...)
+    seconds: str = Field(...)
+    size: str = Field(...)
+
+
+class Sora2GenerationResponse(BaseModel):
+    id: str = Field(...)
+    error: Optional[dict] = Field(None)
+    status: Optional[str] = Field(None)
+
+
+class OpenAIVideoSora2(comfy_io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return comfy_io.Schema(
+            node_id="OpenAIVideoSora2",
+            display_name="OpenAI Sora - Video",
+            category="api node/video/Sora",
+            description="OpenAI video and audio generation.",
+            inputs=[
+                comfy_io.Combo.Input(
+                    "model",
+                    options=["sora-2", "sora-2-pro"],
+                    default="sora-2",
+                ),
+                comfy_io.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Guiding text; may be empty if an input image is present.",
+                ),
+                comfy_io.Combo.Input(
+                    "size",
+                    options=[
+                        "720x1280",
+                        "1280x720",
+                        "1024x1792",
+                        "1792x1024",
+                    ],
+                    default="1280x720",
+                ),
+                comfy_io.Combo.Input(
+                    "duration",
+                    options=[4, 8, 12],
+                    default=8,
+                ),
+                comfy_io.Image.Input(
+                    "image",
+                    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,
+                    optional=True,
+                    tooltip="Seed to determine if node should re-run; "
+                            "actual results are nondeterministic regardless of seed.",
+                ),
+            ],
+            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,
+        size: str = "1280x720",
+        duration: int = 8,
+        seed: int = 0,
+        image: Optional[torch.Tensor] = None,
+    ):
+        if model == "sora-2" and size not in ("720x1280", "1280x720"):
+            raise ValueError("Invalid size for sora-2 model, only 720x1280 and 1280x720 are supported.")
+        files_input = None
+        if image is not None:
+            if get_number_of_images(image) != 1:
+                raise ValueError("Currently only one input image is supported.")
+            files_input = {"input_reference": ("image.png", tensor_to_bytesio(image), "image/png")}
+        auth = {
+            "auth_token": cls.hidden.auth_token_comfy_org,
+            "comfy_api_key": cls.hidden.api_key_comfy_org,
+        }
+        payload = Sora2GenerationRequest(
+            model=model,
+            prompt=prompt,
+            seconds=str(duration),
+            size=size,
+        )
+        initial_operation = SynchronousOperation(
+            endpoint=ApiEndpoint(
+                path="/proxy/openai/v1/videos",
+                method=HttpMethod.POST,
+                request_model=Sora2GenerationRequest,
+                response_model=Sora2GenerationResponse
+            ),
+            request=payload,
+            files=files_input,
+            auth_kwargs=auth,
+            content_type="multipart/form-data",
+        )
+        initial_response = await initial_operation.execute()
+        if initial_response.error:
+            raise Exception(initial_response.error.message)
+
+        model_time_multiplier = 1 if model == "sora-2" else 2
+        poll_operation = PollingOperation(
+            poll_endpoint=ApiEndpoint(
+                path=f"/proxy/openai/v1/videos/{initial_response.id}",
+                method=HttpMethod.GET,
+                request_model=EmptyRequest,
+                response_model=Sora2GenerationResponse
+            ),
+            completed_statuses=["completed"],
+            failed_statuses=["failed"],
+            status_extractor=lambda x: x.status,
+            auth_kwargs=auth,
+            poll_interval=8.0,
+            max_poll_attempts=160,
+            node_id=cls.hidden.unique_id,
+            estimated_duration=45 * (duration / 4) * model_time_multiplier,
+        )
+        await poll_operation.execute()
+        return comfy_io.NodeOutput(
+            await download_url_to_video_output(
+                f"/proxy/openai/v1/videos/{initial_response.id}/content",
+                auth_kwargs=auth,
+            )
+        )
+
+
+class OpenAISoraExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[comfy_io.ComfyNode]]:
+        return [
+            OpenAIVideoSora2,
+        ]
+
+
+async def comfy_entrypoint() -> OpenAISoraExtension:
+    return OpenAISoraExtension()

comfy_api_nodes/nodes_stability.py

@@ -82,8 +82,8 @@ class StabilityStableImageUltraNode(comfy_io.ComfyNode):
                 ),
                 comfy_io.Combo.Input(
                     "aspect_ratio",
-                    options=[x.value for x in StabilityAspectRatio],
-                    default=StabilityAspectRatio.ratio_1_1.value,
+                    options=StabilityAspectRatio,
+                    default=StabilityAspectRatio.ratio_1_1,
                     tooltip="Aspect ratio of generated image.",
                 ),
                 comfy_io.Combo.Input(
@@ -217,12 +217,12 @@ class StabilityStableImageSD_3_5Node(comfy_io.ComfyNode):
                 ),
                 comfy_io.Combo.Input(
                     "model",
-                    options=[x.value for x in Stability_SD3_5_Model],
+                    options=Stability_SD3_5_Model,
                 ),
                 comfy_io.Combo.Input(
                     "aspect_ratio",
-                    options=[x.value for x in StabilityAspectRatio],
-                    default=StabilityAspectRatio.ratio_1_1.value,
+                    options=StabilityAspectRatio,
+                    default=StabilityAspectRatio.ratio_1_1,
                     tooltip="Aspect ratio of generated image.",
                 ),
                 comfy_io.Combo.Input(

comfy_api_nodes/nodes_veo2.py

@@ -215,7 +215,7 @@ class VeoVideoGenerationNode(comfy_io.ComfyNode):
         initial_response = await initial_operation.execute()
         operation_name = initial_response.name
 
-        logging.info(f"Veo generation started with operation name: {operation_name}")
+        logging.info("Veo generation started with operation name: %s", operation_name)
 
         # Define status extractor function
         def status_extractor(response):

comfy_api_nodes/nodes_vidu.py

@@ -173,8 +173,8 @@ class ViduTextToVideoNode(comfy_io.ComfyNode):
             inputs=[
                 comfy_io.Combo.Input(
                     "model",
-                    options=[model.value for model in VideoModelName],
-                    default=VideoModelName.vidu_q1.value,
+                    options=VideoModelName,
+                    default=VideoModelName.vidu_q1,
                     tooltip="Model name",
                 ),
                 comfy_io.String.Input(
@@ -205,22 +205,22 @@ class ViduTextToVideoNode(comfy_io.ComfyNode):
                 ),
                 comfy_io.Combo.Input(
                     "aspect_ratio",
-                    options=[model.value for model in AspectRatio],
-                    default=AspectRatio.r_16_9.value,
+                    options=AspectRatio,
+                    default=AspectRatio.r_16_9,
                     tooltip="The aspect ratio of the output video",
                     optional=True,
                 ),
                 comfy_io.Combo.Input(
                     "resolution",
-                    options=[model.value for model in Resolution],
-                    default=Resolution.r_1080p.value,
+                    options=Resolution,
+                    default=Resolution.r_1080p,
                     tooltip="Supported values may vary by model & duration",
                     optional=True,
                 ),
                 comfy_io.Combo.Input(
                     "movement_amplitude",
-                    options=[model.value for model in MovementAmplitude],
-                    default=MovementAmplitude.auto.value,
+                    options=MovementAmplitude,
+                    default=MovementAmplitude.auto,
                     tooltip="The movement amplitude of objects in the frame",
                     optional=True,
                 ),
@@ -278,8 +278,8 @@ class ViduImageToVideoNode(comfy_io.ComfyNode):
             inputs=[
                 comfy_io.Combo.Input(
                     "model",
-                    options=[model.value for model in VideoModelName],
-                    default=VideoModelName.vidu_q1.value,
+                    options=VideoModelName,
+                    default=VideoModelName.vidu_q1,
                     tooltip="Model name",
                 ),
                 comfy_io.Image.Input(
@@ -316,14 +316,14 @@ class ViduImageToVideoNode(comfy_io.ComfyNode):
                 ),
                 comfy_io.Combo.Input(
                     "resolution",
-                    options=[model.value for model in Resolution],
-                    default=Resolution.r_1080p.value,
+                    options=Resolution,
+                    default=Resolution.r_1080p,
                     tooltip="Supported values may vary by model & duration",
                     optional=True,
                 ),
                 comfy_io.Combo.Input(
                     "movement_amplitude",
-                    options=[model.value for model in MovementAmplitude],
+                    options=MovementAmplitude,
                     default=MovementAmplitude.auto.value,
                     tooltip="The movement amplitude of objects in the frame",
                     optional=True,
@@ -388,8 +388,8 @@ class ViduReferenceVideoNode(comfy_io.ComfyNode):
             inputs=[
                 comfy_io.Combo.Input(
                     "model",
-                    options=[model.value for model in VideoModelName],
-                    default=VideoModelName.vidu_q1.value,
+                    options=VideoModelName,
+                    default=VideoModelName.vidu_q1,
                     tooltip="Model name",
                 ),
                 comfy_io.Image.Input(
@@ -424,8 +424,8 @@ class ViduReferenceVideoNode(comfy_io.ComfyNode):
                 ),
                 comfy_io.Combo.Input(
                     "aspect_ratio",
-                    options=[model.value for model in AspectRatio],
-                    default=AspectRatio.r_16_9.value,
+                    options=AspectRatio,
+                    default=AspectRatio.r_16_9,
                     tooltip="The aspect ratio of the output video",
                     optional=True,
                 ),

comfy_api_nodes/nodes_wan.py

@@ -28,6 +28,12 @@ class Text2ImageInputField(BaseModel):
     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)
@@ -49,6 +55,13 @@ class Txt2ImageParametersField(BaseModel):
     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)
@@ -73,6 +86,12 @@ class Text2ImageTaskCreationRequest(BaseModel):
     parameters: Txt2ImageParametersField = Field(...)
 
 
+class Image2ImageTaskCreationRequest(BaseModel):
+    model: str = Field(...)
+    input: Image2ImageInputField = Field(...)
+    parameters: Image2ImageParametersField = Field(...)
+
+
 class Text2VideoTaskCreationRequest(BaseModel):
     model: str = Field(...)
     input: Text2VideoInputField = Field(...)
@@ -135,7 +154,12 @@ async def process_task(
     url: str,
     request_model: Type[T],
     response_model: Type[R],
-    payload: Union[Text2ImageTaskCreationRequest, Text2VideoTaskCreationRequest, Image2VideoTaskCreationRequest],
+    payload: Union[
+        Text2ImageTaskCreationRequest,
+        Image2ImageTaskCreationRequest,
+        Text2VideoTaskCreationRequest,
+        Image2VideoTaskCreationRequest,
+    ],
     node_id: str,
     estimated_duration: int,
     poll_interval: int,
@@ -288,6 +312,128 @@ class WanTextToImageApi(comfy_io.ComfyNode):
         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):
@@ -593,6 +739,7 @@ class WanApiExtension(ComfyExtension):
     async def get_node_list(self) -> list[type[comfy_io.ComfyNode]]:
         return [
             WanTextToImageApi,
+            WanImageToImageApi,
             WanTextToVideoApi,
             WanImageToVideoApi,
         ]

comfy_extras/nodes_audio.py

@@ -142,9 +142,10 @@ def save_audio(self, audio, filename_prefix="ComfyUI", format="flac", prompt=Non
         for key, value in metadata.items():
             output_container.metadata[key] = value
 
+        layout = 'mono' if waveform.shape[0] == 1 else 'stereo'
         # Set up the output stream with appropriate properties
         if format == "opus":
-            out_stream = output_container.add_stream("libopus", rate=sample_rate)
+            out_stream = output_container.add_stream("libopus", rate=sample_rate, layout=layout)
             if quality == "64k":
                 out_stream.bit_rate = 64000
             elif quality == "96k":
@@ -156,7 +157,7 @@ def save_audio(self, audio, filename_prefix="ComfyUI", format="flac", prompt=Non
             elif quality == "320k":
                 out_stream.bit_rate = 320000
         elif format == "mp3":
-            out_stream = output_container.add_stream("libmp3lame", rate=sample_rate)
+            out_stream = output_container.add_stream("libmp3lame", rate=sample_rate, layout=layout)
             if quality == "V0":
                 #TODO i would really love to support V3 and V5 but there doesn't seem to be a way to set the qscale level, the property below is a bool
                 out_stream.codec_context.qscale = 1
@@ -165,9 +166,9 @@ def save_audio(self, audio, filename_prefix="ComfyUI", format="flac", prompt=Non
             elif quality == "320k":
                 out_stream.bit_rate = 320000
         else: #format == "flac":
-            out_stream = output_container.add_stream("flac", rate=sample_rate)
+            out_stream = output_container.add_stream("flac", rate=sample_rate, layout=layout)
 
-        frame = av.AudioFrame.from_ndarray(waveform.movedim(0, 1).reshape(1, -1).float().numpy(), format='flt', layout='mono' if waveform.shape[0] == 1 else 'stereo')
+        frame = av.AudioFrame.from_ndarray(waveform.movedim(0, 1).reshape(1, -1).float().numpy(), format='flt', layout=layout)
         frame.sample_rate = sample_rate
         frame.pts = 0
         output_container.mux(out_stream.encode(frame))
@@ -360,7 +361,7 @@ 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, )
 

comfy_extras/nodes_audio_encoder.py

@@ -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()

comfy_extras/nodes_clip_sdxl.py

@@ -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()

comfy_extras/nodes_compositing.py

@@ -1,6 +1,9 @@
 import torch
 import comfy.utils
 from enum import Enum
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
 
 def resize_mask(mask, shape):
     return torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(shape[0], shape[1]), mode="bilinear").squeeze(1)
@@ -101,24 +104,28 @@ def porter_duff_composite(src_image: torch.Tensor, src_alpha: torch.Tensor, dst_
     return out_image, out_alpha
 
 
-class PorterDuffImageComposite:
+class PorterDuffImageComposite(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "source": ("IMAGE",),
-                "source_alpha": ("MASK",),
-                "destination": ("IMAGE",),
-                "destination_alpha": ("MASK",),
-                "mode": ([mode.name for mode in PorterDuffMode], {"default": PorterDuffMode.DST.name}),
-            },
-        }
+    def define_schema(cls):
+        return io.Schema(
+            node_id="PorterDuffImageComposite",
+            display_name="Porter-Duff Image Composite",
+            category="mask/compositing",
+            inputs=[
+                io.Image.Input("source"),
+                io.Mask.Input("source_alpha"),
+                io.Image.Input("destination"),
+                io.Mask.Input("destination_alpha"),
+                io.Combo.Input("mode", options=[mode.name for mode in PorterDuffMode], default=PorterDuffMode.DST.name),
+            ],
+            outputs=[
+                io.Image.Output(),
+                io.Mask.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("IMAGE", "MASK")
-    FUNCTION = "composite"
-    CATEGORY = "mask/compositing"
-
-    def composite(self, source: torch.Tensor, source_alpha: torch.Tensor, destination: torch.Tensor, destination_alpha: torch.Tensor, mode):
+    @classmethod
+    def execute(cls, source: torch.Tensor, source_alpha: torch.Tensor, destination: torch.Tensor, destination_alpha: torch.Tensor, mode) -> io.NodeOutput:
         batch_size = min(len(source), len(source_alpha), len(destination), len(destination_alpha))
         out_images = []
         out_alphas = []
@@ -150,45 +157,48 @@ class PorterDuffImageComposite:
             out_images.append(out_image)
             out_alphas.append(out_alpha.squeeze(2))
 
-        result = (torch.stack(out_images), torch.stack(out_alphas))
-        return result
+        return io.NodeOutput(torch.stack(out_images), torch.stack(out_alphas))
 
 
-class SplitImageWithAlpha:
+class SplitImageWithAlpha(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-                "required": {
-                    "image": ("IMAGE",),
-                }
-        }
+    def define_schema(cls):
+        return io.Schema(
+            node_id="SplitImageWithAlpha",
+            display_name="Split Image with Alpha",
+            category="mask/compositing",
+            inputs=[
+                io.Image.Input("image"),
+            ],
+            outputs=[
+                io.Image.Output(),
+                io.Mask.Output(),
+            ],
+        )
 
-    CATEGORY = "mask/compositing"
-    RETURN_TYPES = ("IMAGE", "MASK")
-    FUNCTION = "split_image_with_alpha"
-
-    def split_image_with_alpha(self, image: torch.Tensor):
+    @classmethod
+    def execute(cls, image: torch.Tensor) -> io.NodeOutput:
         out_images = [i[:,:,:3] for i in image]
         out_alphas = [i[:,:,3] if i.shape[2] > 3 else torch.ones_like(i[:,:,0]) for i in image]
-        result = (torch.stack(out_images), 1.0 - torch.stack(out_alphas))
-        return result
+        return io.NodeOutput(torch.stack(out_images), 1.0 - torch.stack(out_alphas))
 
 
-class JoinImageWithAlpha:
+class JoinImageWithAlpha(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-                "required": {
-                    "image": ("IMAGE",),
-                    "alpha": ("MASK",),
-                }
-        }
+    def define_schema(cls):
+        return io.Schema(
+            node_id="JoinImageWithAlpha",
+            display_name="Join Image with Alpha",
+            category="mask/compositing",
+            inputs=[
+                io.Image.Input("image"),
+                io.Mask.Input("alpha"),
+            ],
+            outputs=[io.Image.Output()],
+        )
 
-    CATEGORY = "mask/compositing"
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "join_image_with_alpha"
-
-    def join_image_with_alpha(self, image: torch.Tensor, alpha: torch.Tensor):
+    @classmethod
+    def execute(cls, image: torch.Tensor, alpha: torch.Tensor) -> io.NodeOutput:
         batch_size = min(len(image), len(alpha))
         out_images = []
 
@@ -196,19 +206,18 @@ class JoinImageWithAlpha:
         for i in range(batch_size):
            out_images.append(torch.cat((image[i][:,:,:3], alpha[i].unsqueeze(2)), dim=2))
 
-        result = (torch.stack(out_images),)
-        return result
+        return io.NodeOutput(torch.stack(out_images))
 
 
-NODE_CLASS_MAPPINGS = {
-    "PorterDuffImageComposite": PorterDuffImageComposite,
-    "SplitImageWithAlpha": SplitImageWithAlpha,
-    "JoinImageWithAlpha": JoinImageWithAlpha,
-}
+class CompositingExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            PorterDuffImageComposite,
+            SplitImageWithAlpha,
+            JoinImageWithAlpha,
+        ]
 
 
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "PorterDuffImageComposite": "Porter-Duff Image Composite",
-    "SplitImageWithAlpha": "Split Image with Alpha",
-    "JoinImageWithAlpha": "Join Image with Alpha",
-}
+async def comfy_entrypoint() -> CompositingExtension:
+    return CompositingExtension()

comfy_extras/nodes_differential_diffusion.py

@@ -1,34 +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", ),
-            },
-            "optional": {
-                "strength": ("FLOAT", {
-                    "default": 1.0,
-                    "min": 0.0,
-                    "max": 1.0,
-                    "step": 0.01,
-                }),
-            }
-        }
-    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, strength=1.0):
+    @classmethod
+    def execute(cls, model, strength=1.0) -> io.NodeOutput:
         model = model.clone()
-        model.set_model_denoise_mask_function(lambda *args, **kwargs: self.forward(*args, **kwargs, strength=strength))
-        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, strength: float):
+    @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
@@ -53,9 +60,13 @@ class DifferentialDiffusion():
             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()

comfy_extras/nodes_edit_model.py

@@ -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()

comfy_extras/nodes_eps.py

@@ -0,0 +1,74 @@
+from typing_extensions import override
+
+from comfy_api.latest import ComfyExtension, io
+
+
+class EpsilonScaling(io.ComfyNode):
+    """
+    Implements the Epsilon Scaling method from 'Elucidating the Exposure Bias in Diffusion Models'
+    (https://arxiv.org/abs/2308.15321v6).
+
+    This method mitigates exposure bias by scaling the predicted noise during sampling,
+    which can significantly improve sample quality. This implementation uses the "uniform schedule"
+    recommended by the paper for its practicality and effectiveness.
+    """
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="Epsilon Scaling",
+            category="model_patches/unet",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input(
+                    "scaling_factor",
+                    default=1.005,
+                    min=0.5,
+                    max=1.5,
+                    step=0.001,
+                    display_mode=io.NumberDisplay.number,
+                ),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+        )
+
+    @classmethod
+    def execute(cls, model, scaling_factor) -> io.NodeOutput:
+        # Prevent division by zero, though the UI's min value should prevent this.
+        if scaling_factor == 0:
+            scaling_factor = 1e-9
+
+        def epsilon_scaling_function(args):
+            """
+            This function is applied after the CFG guidance has been calculated.
+            It recalculates the denoised latent by scaling the predicted noise.
+            """
+            denoised = args["denoised"]
+            x = args["input"]
+
+            noise_pred = x - denoised
+
+            scaled_noise_pred = noise_pred / scaling_factor
+
+            new_denoised = x - scaled_noise_pred
+
+            return new_denoised
+
+        # Clone the model patcher to avoid modifying the original model in place
+        model_clone = model.clone()
+
+        model_clone.set_model_sampler_post_cfg_function(epsilon_scaling_function)
+
+        return io.NodeOutput(model_clone)
+
+
+class EpsilonScalingExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            EpsilonScaling,
+        ]
+
+async def comfy_entrypoint() -> EpsilonScalingExtension:
+    return EpsilonScalingExtension()

comfy_extras/nodes_flux.py

@@ -1,60 +1,80 @@
 import node_helpers
 import comfy.utils
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
 
-class CLIPTextEncodeFlux:
+
+class CLIPTextEncodeFlux(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "clip": ("CLIP", ),
-            "clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}),
-            "t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}),
-            "guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1}),
-            }}
-    RETURN_TYPES = ("CONDITIONING",)
-    FUNCTION = "encode"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CLIPTextEncodeFlux",
+            category="advanced/conditioning/flux",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
+                io.String.Input("t5xxl", multiline=True, dynamic_prompts=True),
+                io.Float.Input("guidance", default=3.5, min=0.0, max=100.0, step=0.1),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+        )
 
-    CATEGORY = "advanced/conditioning/flux"
-
-    def encode(self, clip, clip_l, t5xxl, guidance):
+    @classmethod
+    def execute(cls, clip, clip_l, t5xxl, guidance) -> io.NodeOutput:
         tokens = clip.tokenize(clip_l)
         tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"]
 
-        return (clip.encode_from_tokens_scheduled(tokens, add_dict={"guidance": guidance}), )
+        return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens, add_dict={"guidance": guidance}))
 
-class FluxGuidance:
+    encode = execute  # TODO: remove
+
+
+class FluxGuidance(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "conditioning": ("CONDITIONING", ),
-            "guidance": ("FLOAT", {"default": 3.5, "min": 0.0, "max": 100.0, "step": 0.1}),
-            }}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="FluxGuidance",
+            category="advanced/conditioning/flux",
+            inputs=[
+                io.Conditioning.Input("conditioning"),
+                io.Float.Input("guidance", default=3.5, min=0.0, max=100.0, step=0.1),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("CONDITIONING",)
-    FUNCTION = "append"
-
-    CATEGORY = "advanced/conditioning/flux"
-
-    def append(self, conditioning, guidance):
+    @classmethod
+    def execute(cls, conditioning, guidance) -> io.NodeOutput:
         c = node_helpers.conditioning_set_values(conditioning, {"guidance": guidance})
-        return (c, )
+        return io.NodeOutput(c)
+
+    append = execute  # TODO: remove
 
 
-class FluxDisableGuidance:
+class FluxDisableGuidance(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "conditioning": ("CONDITIONING", ),
-            }}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="FluxDisableGuidance",
+            category="advanced/conditioning/flux",
+            description="This node completely disables the guidance embed on Flux and Flux like models",
+            inputs=[
+                io.Conditioning.Input("conditioning"),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("CONDITIONING",)
-    FUNCTION = "append"
-
-    CATEGORY = "advanced/conditioning/flux"
-    DESCRIPTION = "This node completely disables the guidance embed on Flux and Flux like models"
-
-    def append(self, conditioning):
+    @classmethod
+    def execute(cls, conditioning) -> io.NodeOutput:
         c = node_helpers.conditioning_set_values(conditioning, {"guidance": None})
-        return (c, )
+        return io.NodeOutput(c)
+
+    append = execute  # TODO: remove
 
 
 PREFERED_KONTEXT_RESOLUTIONS = [
@@ -78,52 +98,73 @@ PREFERED_KONTEXT_RESOLUTIONS = [
 ]
 
 
-class FluxKontextImageScale:
+class FluxKontextImageScale(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"image": ("IMAGE", ),
-                            },
-               }
+    def define_schema(cls):
+        return io.Schema(
+            node_id="FluxKontextImageScale",
+            category="advanced/conditioning/flux",
+            description="This node resizes the image to one that is more optimal for flux kontext.",
+            inputs=[
+                io.Image.Input("image"),
+            ],
+            outputs=[
+                io.Image.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "scale"
-
-    CATEGORY = "advanced/conditioning/flux"
-    DESCRIPTION = "This node resizes the image to one that is more optimal for flux kontext."
-
-    def scale(self, image):
+    @classmethod
+    def execute(cls, image) -> io.NodeOutput:
         width = image.shape[2]
         height = image.shape[1]
         aspect_ratio = width / height
         _, width, height = min((abs(aspect_ratio - w / h), w, h) for w, h in PREFERED_KONTEXT_RESOLUTIONS)
         image = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "lanczos", "center").movedim(1, -1)
-        return (image, )
+        return io.NodeOutput(image)
+
+    scale = execute  # TODO: remove
 
 
-class FluxKontextMultiReferenceLatentMethod:
+class FluxKontextMultiReferenceLatentMethod(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "conditioning": ("CONDITIONING", ),
-            "reference_latents_method": (("offset", "index", "uxo/uno"), ),
-            }}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="FluxKontextMultiReferenceLatentMethod",
+            category="advanced/conditioning/flux",
+            inputs=[
+                io.Conditioning.Input("conditioning"),
+                io.Combo.Input(
+                    "reference_latents_method",
+                    options=["offset", "index", "uxo/uno"],
+                ),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+            is_experimental=True,
+        )
 
-    RETURN_TYPES = ("CONDITIONING",)
-    FUNCTION = "append"
-    EXPERIMENTAL = True
-
-    CATEGORY = "advanced/conditioning/flux"
-
-    def append(self, conditioning, reference_latents_method):
+    @classmethod
+    def execute(cls, conditioning, reference_latents_method) -> io.NodeOutput:
         if "uxo" in reference_latents_method or "uso" in reference_latents_method:
             reference_latents_method = "uxo"
         c = node_helpers.conditioning_set_values(conditioning, {"reference_latents_method": reference_latents_method})
-        return (c, )
+        return io.NodeOutput(c)
 
-NODE_CLASS_MAPPINGS = {
-    "CLIPTextEncodeFlux": CLIPTextEncodeFlux,
-    "FluxGuidance": FluxGuidance,
-    "FluxDisableGuidance": FluxDisableGuidance,
-    "FluxKontextImageScale": FluxKontextImageScale,
-    "FluxKontextMultiReferenceLatentMethod": FluxKontextMultiReferenceLatentMethod,
-}
+    append = execute  # TODO: remove
+
+
+class FluxExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            CLIPTextEncodeFlux,
+            FluxGuidance,
+            FluxDisableGuidance,
+            FluxKontextImageScale,
+            FluxKontextMultiReferenceLatentMethod,
+        ]
+
+
+async def comfy_entrypoint() -> FluxExtension:
+    return FluxExtension()

comfy_extras/nodes_fresca.py

@@ -1,6 +1,8 @@
 # Code based on https://github.com/WikiChao/FreSca (MIT License)
 import torch
 import torch.fft as fft
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
 
 
 def Fourier_filter(x, scale_low=1.0, scale_high=1.5, freq_cutoff=20):
@@ -51,25 +53,31 @@ def Fourier_filter(x, scale_low=1.0, scale_high=1.5, freq_cutoff=20):
     return x_filtered
 
 
-class FreSca:
+class FreSca(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "model": ("MODEL",),
-                "scale_low": ("FLOAT", {"default": 1.0, "min": 0, "max": 10, "step": 0.01,
-                                        "tooltip": "Scaling factor for low-frequency components"}),
-                "scale_high": ("FLOAT", {"default": 1.25, "min": 0, "max": 10, "step": 0.01,
-                                        "tooltip": "Scaling factor for high-frequency components"}),
-                "freq_cutoff": ("INT", {"default": 20, "min": 1, "max": 10000, "step": 1,
-                                        "tooltip": "Number of frequency indices around center to consider as low-frequency"}),
-            }
-        }
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "patch"
-    CATEGORY = "_for_testing"
-    DESCRIPTION = "Applies frequency-dependent scaling to the guidance"
-    def patch(self, model, scale_low, scale_high, freq_cutoff):
+    def define_schema(cls):
+        return io.Schema(
+            node_id="FreSca",
+            display_name="FreSca",
+            category="_for_testing",
+            description="Applies frequency-dependent scaling to the guidance",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("scale_low", default=1.0, min=0, max=10, step=0.01,
+                               tooltip="Scaling factor for low-frequency components"),
+                io.Float.Input("scale_high", default=1.25, min=0, max=10, step=0.01,
+                               tooltip="Scaling factor for high-frequency components"),
+                io.Int.Input("freq_cutoff", default=20, min=1, max=10000, step=1,
+                             tooltip="Number of frequency indices around center to consider as low-frequency"),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, model, scale_low, scale_high, freq_cutoff):
         def custom_cfg_function(args):
             conds_out = args["conds_out"]
             if len(conds_out) <= 1 or None in args["conds"][:2]:
@@ -91,13 +99,16 @@ class FreSca:
         m = model.clone()
         m.set_model_sampler_pre_cfg_function(custom_cfg_function)
 
-        return (m,)
+        return io.NodeOutput(m)
 
 
-NODE_CLASS_MAPPINGS = {
-    "FreSca": FreSca,
-}
+class FreScaExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            FreSca,
+        ]
 
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "FreSca": "FreSca",
-}
+
+async def comfy_entrypoint() -> FreScaExtension:
+    return FreScaExtension()

comfy_extras/nodes_gits.py

@@ -1,6 +1,8 @@
 # from https://github.com/zju-pi/diff-sampler/tree/main/gits-main
 import numpy as np
 import torch
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
 
 def loglinear_interp(t_steps, num_steps):
     """
@@ -333,25 +335,28 @@ NOISE_LEVELS = {
     ],
 }
 
-class GITSScheduler:
+class GITSScheduler(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required":
-                    {"coeff": ("FLOAT", {"default": 1.20, "min": 0.80, "max": 1.50, "step": 0.05}),
-                     "steps": ("INT", {"default": 10, "min": 2, "max": 1000}),
-                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                      }
-               }
-    RETURN_TYPES = ("SIGMAS",)
-    CATEGORY = "sampling/custom_sampling/schedulers"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="GITSScheduler",
+            category="sampling/custom_sampling/schedulers",
+            inputs=[
+                io.Float.Input("coeff", default=1.20, min=0.80, max=1.50, step=0.05),
+                io.Int.Input("steps", default=10, min=2, max=1000),
+                io.Float.Input("denoise", default=1.0, min=0.0, max=1.0, step=0.01),
+            ],
+            outputs=[
+                io.Sigmas.Output(),
+            ],
+        )
 
-    FUNCTION = "get_sigmas"
-
-    def get_sigmas(self, coeff, steps, denoise):
+    @classmethod
+    def execute(cls, coeff, steps, denoise):
         total_steps = steps
         if denoise < 1.0:
             if denoise <= 0.0:
-                return (torch.FloatTensor([]),)
+                return io.NodeOutput(torch.FloatTensor([]))
             total_steps = round(steps * denoise)
 
         if steps <= 20:
@@ -362,8 +367,16 @@ class GITSScheduler:
 
         sigmas = sigmas[-(total_steps + 1):]
         sigmas[-1] = 0
-        return (torch.FloatTensor(sigmas), )
+        return io.NodeOutput(torch.FloatTensor(sigmas))
 
-NODE_CLASS_MAPPINGS = {
-    "GITSScheduler": GITSScheduler,
-}
+
+class GITSSchedulerExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            GITSScheduler,
+        ]
+
+
+async def comfy_entrypoint() -> GITSSchedulerExtension:
+    return GITSSchedulerExtension()

comfy_extras/nodes_hidream.py

@@ -1,55 +1,73 @@
+from typing_extensions import override
+
 import folder_paths
 import comfy.sd
 import comfy.model_management
+from comfy_api.latest import ComfyExtension, io
 
 
-class QuadrupleCLIPLoader:
+class QuadrupleCLIPLoader(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ),
-                              "clip_name2": (folder_paths.get_filename_list("text_encoders"), ),
-                              "clip_name3": (folder_paths.get_filename_list("text_encoders"), ),
-                              "clip_name4": (folder_paths.get_filename_list("text_encoders"), )
-                             }}
-    RETURN_TYPES = ("CLIP",)
-    FUNCTION = "load_clip"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="QuadrupleCLIPLoader",
+            category="advanced/loaders",
+            description="[Recipes]\n\nhidream: long clip-l, long clip-g, t5xxl, llama_8b_3.1_instruct",
+            inputs=[
+                io.Combo.Input("clip_name1", options=folder_paths.get_filename_list("text_encoders")),
+                io.Combo.Input("clip_name2", options=folder_paths.get_filename_list("text_encoders")),
+                io.Combo.Input("clip_name3", options=folder_paths.get_filename_list("text_encoders")),
+                io.Combo.Input("clip_name4", options=folder_paths.get_filename_list("text_encoders")),
+            ],
+            outputs=[
+                io.Clip.Output(),
+            ]
+        )
 
-    CATEGORY = "advanced/loaders"
-
-    DESCRIPTION = "[Recipes]\n\nhidream: long clip-l, long clip-g, t5xxl, llama_8b_3.1_instruct"
-
-    def load_clip(self, clip_name1, clip_name2, clip_name3, clip_name4):
+    @classmethod
+    def execute(cls, clip_name1, clip_name2, clip_name3, clip_name4):
         clip_path1 = folder_paths.get_full_path_or_raise("text_encoders", clip_name1)
         clip_path2 = folder_paths.get_full_path_or_raise("text_encoders", clip_name2)
         clip_path3 = folder_paths.get_full_path_or_raise("text_encoders", clip_name3)
         clip_path4 = folder_paths.get_full_path_or_raise("text_encoders", clip_name4)
         clip = comfy.sd.load_clip(ckpt_paths=[clip_path1, clip_path2, clip_path3, clip_path4], embedding_directory=folder_paths.get_folder_paths("embeddings"))
-        return (clip,)
+        return io.NodeOutput(clip)
 
-class CLIPTextEncodeHiDream:
+class CLIPTextEncodeHiDream(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "clip": ("CLIP", ),
-            "clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}),
-            "clip_g": ("STRING", {"multiline": True, "dynamicPrompts": True}),
-            "t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}),
-            "llama": ("STRING", {"multiline": True, "dynamicPrompts": True})
-            }}
-    RETURN_TYPES = ("CONDITIONING",)
-    FUNCTION = "encode"
-
-    CATEGORY = "advanced/conditioning"
-
-    def encode(self, clip, clip_l, clip_g, t5xxl, llama):
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CLIPTextEncodeHiDream",
+            category="advanced/conditioning",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
+                io.String.Input("clip_g", multiline=True, dynamic_prompts=True),
+                io.String.Input("t5xxl", multiline=True, dynamic_prompts=True),
+                io.String.Input("llama", multiline=True, dynamic_prompts=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ]
+        )
 
+    @classmethod
+    def execute(cls, clip, clip_l, clip_g, t5xxl, llama):
         tokens = clip.tokenize(clip_g)
         tokens["l"] = clip.tokenize(clip_l)["l"]
         tokens["t5xxl"] = clip.tokenize(t5xxl)["t5xxl"]
         tokens["llama"] = clip.tokenize(llama)["llama"]
-        return (clip.encode_from_tokens_scheduled(tokens), )
+        return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens))
 
-NODE_CLASS_MAPPINGS = {
-    "QuadrupleCLIPLoader": QuadrupleCLIPLoader,
-    "CLIPTextEncodeHiDream": CLIPTextEncodeHiDream,
-}
+
+class HiDreamExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            QuadrupleCLIPLoader,
+            CLIPTextEncodeHiDream,
+        ]
+
+
+async def comfy_entrypoint() -> HiDreamExtension:
+    return HiDreamExtension()

comfy_extras/nodes_hunyuan.py

@@ -2,42 +2,60 @@ import nodes
 import node_helpers
 import torch
 import comfy.model_management
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
 
 
-class CLIPTextEncodeHunyuanDiT:
+class CLIPTextEncodeHunyuanDiT(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "clip": ("CLIP", ),
-            "bert": ("STRING", {"multiline": True, "dynamicPrompts": True}),
-            "mt5xl": ("STRING", {"multiline": True, "dynamicPrompts": True}),
-            }}
-    RETURN_TYPES = ("CONDITIONING",)
-    FUNCTION = "encode"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CLIPTextEncodeHunyuanDiT",
+            category="advanced/conditioning",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.String.Input("bert", multiline=True, dynamic_prompts=True),
+                io.String.Input("mt5xl", multiline=True, dynamic_prompts=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+        )
 
-    CATEGORY = "advanced/conditioning"
-
-    def encode(self, clip, bert, mt5xl):
+    @classmethod
+    def execute(cls, clip, bert, mt5xl) -> io.NodeOutput:
         tokens = clip.tokenize(bert)
         tokens["mt5xl"] = clip.tokenize(mt5xl)["mt5xl"]
 
-        return (clip.encode_from_tokens_scheduled(tokens), )
+        return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens))
 
-class EmptyHunyuanLatentVideo:
+    encode = execute  # TODO: remove
+
+
+class EmptyHunyuanLatentVideo(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "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": 25, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
-                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
-    RETURN_TYPES = ("LATENT",)
-    FUNCTION = "generate"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="EmptyHunyuanLatentVideo",
+            category="latent/video",
+            inputs=[
+                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=25, min=1, max=nodes.MAX_RESOLUTION, step=4),
+                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) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
-        return ({"samples":latent}, )
+        return io.NodeOutput({"samples":latent})
+
+    generate = execute  # TODO: remove
+
 
 PROMPT_TEMPLATE_ENCODE_VIDEO_I2V = (
     "<|start_header_id|>system<|end_header_id|>\n\n<image>\nDescribe the video by detailing the following aspects according to the reference image: "
@@ -50,45 +68,61 @@ PROMPT_TEMPLATE_ENCODE_VIDEO_I2V = (
     "<|start_header_id|>assistant<|end_header_id|>\n\n"
 )
 
-class TextEncodeHunyuanVideo_ImageToVideo:
+class TextEncodeHunyuanVideo_ImageToVideo(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-            "clip": ("CLIP", ),
-            "clip_vision_output": ("CLIP_VISION_OUTPUT", ),
-            "prompt": ("STRING", {"multiline": True, "dynamicPrompts": True}),
-            "image_interleave": ("INT", {"default": 2, "min": 1, "max": 512, "tooltip": "How much the image influences things vs the text prompt. Higher number means more influence from the text prompt."}),
-            }}
-    RETURN_TYPES = ("CONDITIONING",)
-    FUNCTION = "encode"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="TextEncodeHunyuanVideo_ImageToVideo",
+            category="advanced/conditioning",
+            inputs=[
+                io.Clip.Input("clip"),
+                io.ClipVisionOutput.Input("clip_vision_output"),
+                io.String.Input("prompt", multiline=True, dynamic_prompts=True),
+                io.Int.Input(
+                    "image_interleave",
+                    default=2,
+                    min=1,
+                    max=512,
+                    tooltip="How much the image influences things vs the text prompt. Higher number means more influence from the text prompt.",
+                ),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+        )
 
-    CATEGORY = "advanced/conditioning"
-
-    def encode(self, clip, clip_vision_output, prompt, image_interleave):
+    @classmethod
+    def execute(cls, clip, clip_vision_output, prompt, image_interleave) -> io.NodeOutput:
         tokens = clip.tokenize(prompt, llama_template=PROMPT_TEMPLATE_ENCODE_VIDEO_I2V, image_embeds=clip_vision_output.mm_projected, image_interleave=image_interleave)
-        return (clip.encode_from_tokens_scheduled(tokens), )
+        return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens))
 
-class HunyuanImageToVideo:
+    encode = execute  # TODO: remove
+
+
+class HunyuanImageToVideo(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"positive": ("CONDITIONING", ),
-                             "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": 53, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 4}),
-                             "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
-                             "guidance_type": (["v1 (concat)", "v2 (replace)", "custom"], )
-                },
-                "optional": {"start_image": ("IMAGE", ),
-                }}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="HunyuanImageToVideo",
+            category="conditioning/video_models",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                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=53, min=1, max=nodes.MAX_RESOLUTION, step=4),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+                io.Combo.Input("guidance_type", options=["v1 (concat)", "v2 (replace)", "custom"]),
+                io.Image.Input("start_image", optional=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
 
-    RETURN_TYPES = ("CONDITIONING", "LATENT")
-    RETURN_NAMES = ("positive", "latent")
-    FUNCTION = "encode"
-
-    CATEGORY = "conditioning/video_models"
-
-    def encode(self, positive, vae, width, height, length, batch_size, guidance_type, start_image=None):
+    @classmethod
+    def execute(cls, positive, vae, width, height, length, batch_size, guidance_type, start_image=None) -> io.NodeOutput:
         latent = torch.zeros([batch_size, 16, ((length - 1) // 4) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
         out_latent = {}
 
@@ -111,51 +145,76 @@ class HunyuanImageToVideo:
             positive = node_helpers.conditioning_set_values(positive, cond)
 
         out_latent["samples"] = latent
-        return (positive, out_latent)
+        return io.NodeOutput(positive, out_latent)
 
-class EmptyHunyuanImageLatent:
+    encode = execute  # TODO: remove
+
+
+class EmptyHunyuanImageLatent(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "width": ("INT", {"default": 2048, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}),
-                              "height": ("INT", {"default": 2048, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}),
-                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
-    RETURN_TYPES = ("LATENT",)
-    FUNCTION = "generate"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="EmptyHunyuanImageLatent",
+            category="latent",
+            inputs=[
+                io.Int.Input("width", default=2048, min=64, max=nodes.MAX_RESOLUTION, step=32),
+                io.Int.Input("height", default=2048, min=64, max=nodes.MAX_RESOLUTION, step=32),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
 
-    CATEGORY = "latent"
-
-    def generate(self, width, height, batch_size=1):
+    @classmethod
+    def execute(cls, width, height, batch_size=1) -> io.NodeOutput:
         latent = torch.zeros([batch_size, 64, height // 32, width // 32], device=comfy.model_management.intermediate_device())
-        return ({"samples":latent}, )
+        return io.NodeOutput({"samples":latent})
 
-class HunyuanRefinerLatent:
+    generate = execute  # TODO: remove
+
+
+class HunyuanRefinerLatent(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"positive": ("CONDITIONING", ),
-                             "negative": ("CONDITIONING", ),
-                             "latent": ("LATENT", ),
-                             "noise_augmentation": ("FLOAT", {"default": 0.10, "min": 0.0, "max": 1.0, "step": 0.01}),
-                             }}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="HunyuanRefinerLatent",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Latent.Input("latent"),
+                io.Float.Input("noise_augmentation", default=0.10, min=0.0, max=1.0, step=0.01),
 
-    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
-    RETURN_NAMES = ("positive", "negative", "latent")
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
 
-    FUNCTION = "execute"
-
-    def execute(self, positive, negative, latent, noise_augmentation):
+    @classmethod
+    def execute(cls, positive, negative, latent, noise_augmentation) -> io.NodeOutput:
         latent = latent["samples"]
         positive = node_helpers.conditioning_set_values(positive, {"concat_latent_image": latent, "noise_augmentation": noise_augmentation})
         negative = node_helpers.conditioning_set_values(negative, {"concat_latent_image": latent, "noise_augmentation": noise_augmentation})
         out_latent = {}
         out_latent["samples"] = torch.zeros([latent.shape[0], 32, latent.shape[-3], latent.shape[-2], latent.shape[-1]], device=comfy.model_management.intermediate_device())
-        return (positive, negative, out_latent)
+        return io.NodeOutput(positive, negative, out_latent)
 
 
-NODE_CLASS_MAPPINGS = {
-    "CLIPTextEncodeHunyuanDiT": CLIPTextEncodeHunyuanDiT,
-    "TextEncodeHunyuanVideo_ImageToVideo": TextEncodeHunyuanVideo_ImageToVideo,
-    "EmptyHunyuanLatentVideo": EmptyHunyuanLatentVideo,
-    "HunyuanImageToVideo": HunyuanImageToVideo,
-    "EmptyHunyuanImageLatent": EmptyHunyuanImageLatent,
-    "HunyuanRefinerLatent": HunyuanRefinerLatent,
-}
+class HunyuanExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            CLIPTextEncodeHunyuanDiT,
+            TextEncodeHunyuanVideo_ImageToVideo,
+            EmptyHunyuanLatentVideo,
+            HunyuanImageToVideo,
+            EmptyHunyuanImageLatent,
+            HunyuanRefinerLatent,
+        ]
+
+
+async def comfy_entrypoint() -> HunyuanExtension:
+    return HunyuanExtension()

comfy_extras/nodes_hypertile.py

@@ -1,9 +1,11 @@
 #Taken from: https://github.com/tfernd/HyperTile/
 
 import math
+from typing_extensions import override
 from einops import rearrange
 # Use torch rng for consistency across generations
 from torch import randint
+from comfy_api.latest import ComfyExtension, io
 
 def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int:
     min_value = min(min_value, value)
@@ -20,25 +22,31 @@ def random_divisor(value: int, min_value: int, /, max_options: int = 1) -> int:
 
     return ns[idx]
 
-class HyperTile:
+class HyperTile(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "model": ("MODEL",),
-                             "tile_size": ("INT", {"default": 256, "min": 1, "max": 2048}),
-                             "swap_size": ("INT", {"default": 2, "min": 1, "max": 128}),
-                             "max_depth": ("INT", {"default": 0, "min": 0, "max": 10}),
-                             "scale_depth": ("BOOLEAN", {"default": False}),
-                              }}
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "patch"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="HyperTile",
+            category="model_patches/unet",
+            inputs=[
+                io.Model.Input("model"),
+                io.Int.Input("tile_size", default=256, min=1, max=2048),
+                io.Int.Input("swap_size", default=2, min=1, max=128),
+                io.Int.Input("max_depth", default=0, min=0, max=10),
+                io.Boolean.Input("scale_depth", default=False),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+        )
 
-    CATEGORY = "model_patches/unet"
-
-    def patch(self, model, tile_size, swap_size, max_depth, scale_depth):
+    @classmethod
+    def execute(cls, model, tile_size, swap_size, max_depth, scale_depth) -> io.NodeOutput:
         latent_tile_size = max(32, tile_size) // 8
-        self.temp = None
+        temp = None
 
         def hypertile_in(q, k, v, extra_options):
+            nonlocal temp
             model_chans = q.shape[-2]
             orig_shape = extra_options['original_shape']
             apply_to = []
@@ -58,14 +66,15 @@ class HyperTile:
 
                 if nh * nw > 1:
                     q = rearrange(q, "b (nh h nw w) c -> (b nh nw) (h w) c", h=h // nh, w=w // nw, nh=nh, nw=nw)
-                    self.temp = (nh, nw, h, w)
+                    temp = (nh, nw, h, w)
                 return q, k, v
 
             return q, k, v
         def hypertile_out(out, extra_options):
-            if self.temp is not None:
-                nh, nw, h, w = self.temp
-                self.temp = None
+            nonlocal temp
+            if temp is not None:
+                nh, nw, h, w = temp
+                temp = None
                 out = rearrange(out, "(b nh nw) hw c -> b nh nw hw c", nh=nh, nw=nw)
                 out = rearrange(out, "b nh nw (h w) c -> b (nh h nw w) c", h=h // nh, w=w // nw)
             return out
@@ -76,6 +85,14 @@ class HyperTile:
         m.set_model_attn1_output_patch(hypertile_out)
         return (m, )
 
-NODE_CLASS_MAPPINGS = {
-    "HyperTile": HyperTile,
-}
+
+class HyperTileExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            HyperTile,
+        ]
+
+
+async def comfy_entrypoint() -> HyperTileExtension:
+    return HyperTileExtension()

comfy_extras/nodes_ip2p.py

@@ -1,21 +1,30 @@
 import torch
 
-class InstructPixToPixConditioning:
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
+
+class InstructPixToPixConditioning(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"positive": ("CONDITIONING", ),
-                             "negative": ("CONDITIONING", ),
-                             "vae": ("VAE", ),
-                             "pixels": ("IMAGE", ),
-                             }}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="InstructPixToPixConditioning",
+            category="conditioning/instructpix2pix",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Vae.Input("vae"),
+                io.Image.Input("pixels"),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
 
-    RETURN_TYPES = ("CONDITIONING","CONDITIONING","LATENT")
-    RETURN_NAMES = ("positive", "negative", "latent")
-    FUNCTION = "encode"
-
-    CATEGORY = "conditioning/instructpix2pix"
-
-    def encode(self, positive, negative, pixels, vae):
+    @classmethod
+    def execute(cls, positive, negative, pixels, vae) -> io.NodeOutput:
         x = (pixels.shape[1] // 8) * 8
         y = (pixels.shape[2] // 8) * 8
 
@@ -38,8 +47,17 @@ class InstructPixToPixConditioning:
                 n = [t[0], d]
                 c.append(n)
             out.append(c)
-        return (out[0], out[1], out_latent)
+        return io.NodeOutput(out[0], out[1], out_latent)
+
+
+class InstructPix2PixExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            InstructPixToPixConditioning,
+        ]
+
+
+async def comfy_entrypoint() -> InstructPix2PixExtension:
+    return InstructPix2PixExtension()
 
-NODE_CLASS_MAPPINGS = {
-    "InstructPixToPixConditioning": InstructPixToPixConditioning,
-}

comfy_extras/nodes_latent.py

@@ -2,6 +2,8 @@ import comfy.utils
 import comfy_extras.nodes_post_processing
 import torch
 import nodes
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
 
 
 def reshape_latent_to(target_shape, latent, repeat_batch=True):
@@ -13,17 +15,23 @@ def reshape_latent_to(target_shape, latent, repeat_batch=True):
         return latent
 
 
-class LatentAdd:
+class LatentAdd(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentAdd",
+            category="latent/advanced",
+            inputs=[
+                io.Latent.Input("samples1"),
+                io.Latent.Input("samples2"),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("LATENT",)
-    FUNCTION = "op"
-
-    CATEGORY = "latent/advanced"
-
-    def op(self, samples1, samples2):
+    @classmethod
+    def execute(cls, samples1, samples2) -> io.NodeOutput:
         samples_out = samples1.copy()
 
         s1 = samples1["samples"]
@@ -31,19 +39,25 @@ class LatentAdd:
 
         s2 = reshape_latent_to(s1.shape, s2)
         samples_out["samples"] = s1 + s2
-        return (samples_out,)
+        return io.NodeOutput(samples_out)
 
-class LatentSubtract:
+class LatentSubtract(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentSubtract",
+            category="latent/advanced",
+            inputs=[
+                io.Latent.Input("samples1"),
+                io.Latent.Input("samples2"),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("LATENT",)
-    FUNCTION = "op"
-
-    CATEGORY = "latent/advanced"
-
-    def op(self, samples1, samples2):
+    @classmethod
+    def execute(cls, samples1, samples2) -> io.NodeOutput:
         samples_out = samples1.copy()
 
         s1 = samples1["samples"]
@@ -51,41 +65,49 @@ class LatentSubtract:
 
         s2 = reshape_latent_to(s1.shape, s2)
         samples_out["samples"] = s1 - s2
-        return (samples_out,)
+        return io.NodeOutput(samples_out)
 
-class LatentMultiply:
+class LatentMultiply(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "samples": ("LATENT",),
-                              "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
-                             }}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentMultiply",
+            category="latent/advanced",
+            inputs=[
+                io.Latent.Input("samples"),
+                io.Float.Input("multiplier", default=1.0, min=-10.0, max=10.0, step=0.01),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("LATENT",)
-    FUNCTION = "op"
-
-    CATEGORY = "latent/advanced"
-
-    def op(self, samples, multiplier):
+    @classmethod
+    def execute(cls, samples, multiplier) -> io.NodeOutput:
         samples_out = samples.copy()
 
         s1 = samples["samples"]
         samples_out["samples"] = s1 * multiplier
-        return (samples_out,)
+        return io.NodeOutput(samples_out)
 
-class LatentInterpolate:
+class LatentInterpolate(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "samples1": ("LATENT",),
-                              "samples2": ("LATENT",),
-                              "ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                              }}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentInterpolate",
+            category="latent/advanced",
+            inputs=[
+                io.Latent.Input("samples1"),
+                io.Latent.Input("samples2"),
+                io.Float.Input("ratio", default=1.0, min=0.0, max=1.0, step=0.01),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("LATENT",)
-    FUNCTION = "op"
-
-    CATEGORY = "latent/advanced"
-
-    def op(self, samples1, samples2, ratio):
+    @classmethod
+    def execute(cls, samples1, samples2, ratio) -> io.NodeOutput:
         samples_out = samples1.copy()
 
         s1 = samples1["samples"]
@@ -104,19 +126,26 @@ class LatentInterpolate:
         st = torch.nan_to_num(t / mt)
 
         samples_out["samples"] = st * (m1 * ratio + m2 * (1.0 - ratio))
-        return (samples_out,)
+        return io.NodeOutput(samples_out)
 
-class LatentConcat:
+class LatentConcat(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",), "dim": (["x", "-x", "y", "-y", "t", "-t"], )}}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentConcat",
+            category="latent/advanced",
+            inputs=[
+                io.Latent.Input("samples1"),
+                io.Latent.Input("samples2"),
+                io.Combo.Input("dim", options=["x", "-x", "y", "-y", "t", "-t"]),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("LATENT",)
-    FUNCTION = "op"
-
-    CATEGORY = "latent/advanced"
-
-    def op(self, samples1, samples2, dim):
+    @classmethod
+    def execute(cls, samples1, samples2, dim) -> io.NodeOutput:
         samples_out = samples1.copy()
 
         s1 = samples1["samples"]
@@ -136,22 +165,27 @@ class LatentConcat:
             dim = -3
 
         samples_out["samples"] = torch.cat(c, dim=dim)
-        return (samples_out,)
+        return io.NodeOutput(samples_out)
 
-class LatentCut:
+class LatentCut(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"samples": ("LATENT",),
-                             "dim": (["x", "y", "t"], ),
-                             "index": ("INT", {"default": 0, "min": -nodes.MAX_RESOLUTION, "max": nodes.MAX_RESOLUTION, "step": 1}),
-                             "amount": ("INT", {"default": 1, "min": 1, "max": nodes.MAX_RESOLUTION, "step": 1})}}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentCut",
+            category="latent/advanced",
+            inputs=[
+                io.Latent.Input("samples"),
+                io.Combo.Input("dim", options=["x", "y", "t"]),
+                io.Int.Input("index", default=0, min=-nodes.MAX_RESOLUTION, max=nodes.MAX_RESOLUTION, step=1),
+                io.Int.Input("amount", default=1, min=1, max=nodes.MAX_RESOLUTION, step=1),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("LATENT",)
-    FUNCTION = "op"
-
-    CATEGORY = "latent/advanced"
-
-    def op(self, samples, dim, index, amount):
+    @classmethod
+    def execute(cls, samples, dim, index, amount) -> io.NodeOutput:
         samples_out = samples.copy()
 
         s1 = samples["samples"]
@@ -171,19 +205,25 @@ class LatentCut:
             amount = min(-index, amount)
 
         samples_out["samples"] = torch.narrow(s1, dim, index, amount)
-        return (samples_out,)
+        return io.NodeOutput(samples_out)
 
-class LatentBatch:
+class LatentBatch(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentBatch",
+            category="latent/batch",
+            inputs=[
+                io.Latent.Input("samples1"),
+                io.Latent.Input("samples2"),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("LATENT",)
-    FUNCTION = "batch"
-
-    CATEGORY = "latent/batch"
-
-    def batch(self, samples1, samples2):
+    @classmethod
+    def execute(cls, samples1, samples2) -> io.NodeOutput:
         samples_out = samples1.copy()
         s1 = samples1["samples"]
         s2 = samples2["samples"]
@@ -192,20 +232,25 @@ class LatentBatch:
         s = torch.cat((s1, s2), dim=0)
         samples_out["samples"] = s
         samples_out["batch_index"] = samples1.get("batch_index", [x for x in range(0, s1.shape[0])]) + samples2.get("batch_index", [x for x in range(0, s2.shape[0])])
-        return (samples_out,)
+        return io.NodeOutput(samples_out)
 
-class LatentBatchSeedBehavior:
+class LatentBatchSeedBehavior(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "samples": ("LATENT",),
-                              "seed_behavior": (["random", "fixed"],{"default": "fixed"}),}}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentBatchSeedBehavior",
+            category="latent/advanced",
+            inputs=[
+                io.Latent.Input("samples"),
+                io.Combo.Input("seed_behavior", options=["random", "fixed"], default="fixed"),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("LATENT",)
-    FUNCTION = "op"
-
-    CATEGORY = "latent/advanced"
-
-    def op(self, samples, seed_behavior):
+    @classmethod
+    def execute(cls, samples, seed_behavior) -> io.NodeOutput:
         samples_out = samples.copy()
         latent = samples["samples"]
         if seed_behavior == "random":
@@ -215,41 +260,50 @@ class LatentBatchSeedBehavior:
             batch_number = samples_out.get("batch_index", [0])[0]
             samples_out["batch_index"] = [batch_number] * latent.shape[0]
 
-        return (samples_out,)
+        return io.NodeOutput(samples_out)
 
-class LatentApplyOperation:
+class LatentApplyOperation(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "samples": ("LATENT",),
-                             "operation": ("LATENT_OPERATION",),
-                             }}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentApplyOperation",
+            category="latent/advanced/operations",
+            is_experimental=True,
+            inputs=[
+                io.Latent.Input("samples"),
+                io.LatentOperation.Input("operation"),
+            ],
+            outputs=[
+                io.Latent.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("LATENT",)
-    FUNCTION = "op"
-
-    CATEGORY = "latent/advanced/operations"
-    EXPERIMENTAL = True
-
-    def op(self, samples, operation):
+    @classmethod
+    def execute(cls, samples, operation) -> io.NodeOutput:
         samples_out = samples.copy()
 
         s1 = samples["samples"]
         samples_out["samples"] = operation(latent=s1)
-        return (samples_out,)
+        return io.NodeOutput(samples_out)
 
-class LatentApplyOperationCFG:
+class LatentApplyOperationCFG(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "model": ("MODEL",),
-                             "operation": ("LATENT_OPERATION",),
-                              }}
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "patch"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentApplyOperationCFG",
+            category="latent/advanced/operations",
+            is_experimental=True,
+            inputs=[
+                io.Model.Input("model"),
+                io.LatentOperation.Input("operation"),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+        )
 
-    CATEGORY = "latent/advanced/operations"
-    EXPERIMENTAL = True
-
-    def patch(self, model, operation):
+    @classmethod
+    def execute(cls, model, operation) -> io.NodeOutput:
         m = model.clone()
 
         def pre_cfg_function(args):
@@ -261,21 +315,25 @@ class LatentApplyOperationCFG:
             return conds_out
 
         m.set_model_sampler_pre_cfg_function(pre_cfg_function)
-        return (m, )
+        return io.NodeOutput(m)
 
-class LatentOperationTonemapReinhard:
+class LatentOperationTonemapReinhard(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
-                              }}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentOperationTonemapReinhard",
+            category="latent/advanced/operations",
+            is_experimental=True,
+            inputs=[
+                io.Float.Input("multiplier", default=1.0, min=0.0, max=100.0, step=0.01),
+            ],
+            outputs=[
+                io.LatentOperation.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("LATENT_OPERATION",)
-    FUNCTION = "op"
-
-    CATEGORY = "latent/advanced/operations"
-    EXPERIMENTAL = True
-
-    def op(self, multiplier):
+    @classmethod
+    def execute(cls, multiplier) -> io.NodeOutput:
         def tonemap_reinhard(latent, **kwargs):
             latent_vector_magnitude = (torch.linalg.vector_norm(latent, dim=(1)) + 0.0000000001)[:,None]
             normalized_latent = latent / latent_vector_magnitude
@@ -291,39 +349,27 @@ class LatentOperationTonemapReinhard:
             new_magnitude *= top
 
             return normalized_latent * new_magnitude
-        return (tonemap_reinhard,)
+        return io.NodeOutput(tonemap_reinhard)
 
-class LatentOperationSharpen:
+class LatentOperationSharpen(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {
-                "sharpen_radius": ("INT", {
-                    "default": 9,
-                    "min": 1,
-                    "max": 31,
-                    "step": 1
-                }),
-                "sigma": ("FLOAT", {
-                    "default": 1.0,
-                    "min": 0.1,
-                    "max": 10.0,
-                    "step": 0.1
-                }),
-                "alpha": ("FLOAT", {
-                    "default": 0.1,
-                    "min": 0.0,
-                    "max": 5.0,
-                    "step": 0.01
-                }),
-                              }}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LatentOperationSharpen",
+            category="latent/advanced/operations",
+            is_experimental=True,
+            inputs=[
+                io.Int.Input("sharpen_radius", default=9, min=1, max=31, step=1),
+                io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.1),
+                io.Float.Input("alpha", default=0.1, min=0.0, max=5.0, step=0.01),
+            ],
+            outputs=[
+                io.LatentOperation.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("LATENT_OPERATION",)
-    FUNCTION = "op"
-
-    CATEGORY = "latent/advanced/operations"
-    EXPERIMENTAL = True
-
-    def op(self, sharpen_radius, sigma, alpha):
+    @classmethod
+    def execute(cls, sharpen_radius, sigma, alpha) -> io.NodeOutput:
         def sharpen(latent, **kwargs):
             luminance = (torch.linalg.vector_norm(latent, dim=(1)) + 1e-6)[:,None]
             normalized_latent = latent / luminance
@@ -340,19 +386,27 @@ class LatentOperationSharpen:
             sharpened = torch.nn.functional.conv2d(padded_image, kernel.repeat(channels, 1, 1).unsqueeze(1), padding=kernel_size // 2, groups=channels)[:,:,sharpen_radius:-sharpen_radius, sharpen_radius:-sharpen_radius]
 
             return luminance * sharpened
-        return (sharpen,)
+        return io.NodeOutput(sharpen)
 
-NODE_CLASS_MAPPINGS = {
-    "LatentAdd": LatentAdd,
-    "LatentSubtract": LatentSubtract,
-    "LatentMultiply": LatentMultiply,
-    "LatentInterpolate": LatentInterpolate,
-    "LatentConcat": LatentConcat,
-    "LatentCut": LatentCut,
-    "LatentBatch": LatentBatch,
-    "LatentBatchSeedBehavior": LatentBatchSeedBehavior,
-    "LatentApplyOperation": LatentApplyOperation,
-    "LatentApplyOperationCFG": LatentApplyOperationCFG,
-    "LatentOperationTonemapReinhard": LatentOperationTonemapReinhard,
-    "LatentOperationSharpen": LatentOperationSharpen,
-}
+
+class LatentExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            LatentAdd,
+            LatentSubtract,
+            LatentMultiply,
+            LatentInterpolate,
+            LatentConcat,
+            LatentCut,
+            LatentBatch,
+            LatentBatchSeedBehavior,
+            LatentApplyOperation,
+            LatentApplyOperationCFG,
+            LatentOperationTonemapReinhard,
+            LatentOperationSharpen,
+        ]
+
+
+async def comfy_entrypoint() -> LatentExtension:
+    return LatentExtension()

comfy_extras/nodes_lora_extract.py

@@ -5,6 +5,8 @@ import folder_paths
 import os
 import logging
 from enum import Enum
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
 
 CLAMP_QUANTILE = 0.99
 
@@ -71,32 +73,40 @@ def calc_lora_model(model_diff, rank, prefix_model, prefix_lora, output_sd, lora
             output_sd["{}{}.diff_b".format(prefix_lora, k[len(prefix_model):-5])] = sd[k].contiguous().half().cpu()
     return output_sd
 
-class LoraSave:
-    def __init__(self):
-        self.output_dir = folder_paths.get_output_directory()
+class LoraSave(io.ComfyNode):
+    @classmethod
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LoraSave",
+            display_name="Extract and Save Lora",
+            category="_for_testing",
+            inputs=[
+                io.String.Input("filename_prefix", default="loras/ComfyUI_extracted_lora"),
+                io.Int.Input("rank", default=8, min=1, max=4096, step=1),
+                io.Combo.Input("lora_type", options=tuple(LORA_TYPES.keys())),
+                io.Boolean.Input("bias_diff", default=True),
+                io.Model.Input(
+                    "model_diff",
+                    tooltip="The ModelSubtract output to be converted to a lora.",
+                    optional=True,
+                ),
+                io.Clip.Input(
+                  "text_encoder_diff",
+                    tooltip="The CLIPSubtract output to be converted to a lora.",
+                    optional=True,
+                ),
+            ],
+            is_experimental=True,
+            is_output_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"filename_prefix": ("STRING", {"default": "loras/ComfyUI_extracted_lora"}),
-                              "rank": ("INT", {"default": 8, "min": 1, "max": 4096, "step": 1}),
-                              "lora_type": (tuple(LORA_TYPES.keys()),),
-                              "bias_diff": ("BOOLEAN", {"default": True}),
-                            },
-                "optional": {"model_diff": ("MODEL", {"tooltip": "The ModelSubtract output to be converted to a lora."}),
-                             "text_encoder_diff": ("CLIP", {"tooltip": "The CLIPSubtract output to be converted to a lora."})},
-    }
-    RETURN_TYPES = ()
-    FUNCTION = "save"
-    OUTPUT_NODE = True
-
-    CATEGORY = "_for_testing"
-
-    def save(self, filename_prefix, rank, lora_type, bias_diff, model_diff=None, text_encoder_diff=None):
+    def execute(cls, filename_prefix, rank, lora_type, bias_diff, model_diff=None, text_encoder_diff=None) -> io.NodeOutput:
         if model_diff is None and text_encoder_diff is None:
-            return {}
+            return io.NodeOutput()
 
         lora_type = LORA_TYPES.get(lora_type)
-        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, self.output_dir)
+        full_output_folder, filename, counter, subfolder, filename_prefix = folder_paths.get_save_image_path(filename_prefix, folder_paths.get_output_directory())
 
         output_sd = {}
         if model_diff is not None:
@@ -108,12 +118,16 @@ class LoraSave:
         output_checkpoint = os.path.join(full_output_folder, output_checkpoint)
 
         comfy.utils.save_torch_file(output_sd, output_checkpoint, metadata=None)
-        return {}
+        return io.NodeOutput()
 
-NODE_CLASS_MAPPINGS = {
-    "LoraSave": LoraSave
-}
 
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "LoraSave": "Extract and Save Lora"
-}
+class LoraSaveExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            LoraSave,
+        ]
+
+
+async def comfy_entrypoint() -> LoraSaveExtension:
+    return LoraSaveExtension()

comfy_extras/nodes_lotus.py

@@ -1,20 +1,22 @@
+from typing_extensions import override
+
 import torch
 import comfy.model_management as mm
+from comfy_api.latest import ComfyExtension, io
 
-class LotusConditioning:
+
+class LotusConditioning(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-            },
-        }
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LotusConditioning",
+            category="conditioning/lotus",
+            inputs=[],
+            outputs=[io.Conditioning.Output(display_name="conditioning")],
+        )
 
-    RETURN_TYPES = ("CONDITIONING",)
-    RETURN_NAMES = ("conditioning",)
-    FUNCTION = "conditioning"
-    CATEGORY = "conditioning/lotus"
-
-    def conditioning(self):
+    @classmethod
+    def execute(cls) -> io.NodeOutput:
         device = mm.get_torch_device()
         #lotus uses a frozen encoder and null conditioning, i'm just inlining the results of that operation since it doesn't change
         #and getting parity with the reference implementation would otherwise require inference and 800mb of tensors
@@ -22,8 +24,16 @@ class LotusConditioning:
 
         cond = [[prompt_embeds, {}]]
 
-        return (cond,)
+        return io.NodeOutput(cond)
 
-NODE_CLASS_MAPPINGS = {
-    "LotusConditioning" : LotusConditioning,
-}
+
+class LotusExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            LotusConditioning,
+        ]
+
+
+async def comfy_entrypoint() -> LotusExtension:
+    return LotusExtension()

comfy_extras/nodes_lt.py

@@ -1,4 +1,3 @@
-import io
 import nodes
 import node_helpers
 import torch
@@ -8,46 +7,61 @@ import comfy.utils
 import math
 import numpy as np
 import av
+from io import BytesIO
+from typing_extensions import override
 from comfy.ldm.lightricks.symmetric_patchifier import SymmetricPatchifier, latent_to_pixel_coords
+from comfy_api.latest import ComfyExtension, io
 
-class EmptyLTXVLatentVideo:
+class EmptyLTXVLatentVideo(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "width": ("INT", {"default": 768, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}),
-                              "height": ("INT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}),
-                              "length": ("INT", {"default": 97, "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):
+        return io.Schema(
+            node_id="EmptyLTXVLatentVideo",
+            category="latent/video/ltxv",
+            inputs=[
+                io.Int.Input("width", default=768, min=64, max=nodes.MAX_RESOLUTION, step=32),
+                io.Int.Input("height", default=512, min=64, max=nodes.MAX_RESOLUTION, step=32),
+                io.Int.Input("length", default=97, 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/ltxv"
-
-    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, 128, ((length - 1) // 8) + 1, height // 32, width // 32], device=comfy.model_management.intermediate_device())
-        return ({"samples": latent}, )
+        return io.NodeOutput({"samples": latent})
 
+    generate = execute  # TODO: remove
 
-class LTXVImgToVideo:
+class LTXVImgToVideo(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"positive": ("CONDITIONING", ),
-                             "negative": ("CONDITIONING", ),
-                             "vae": ("VAE",),
-                             "image": ("IMAGE",),
-                             "width": ("INT", {"default": 768, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}),
-                             "height": ("INT", {"default": 512, "min": 64, "max": nodes.MAX_RESOLUTION, "step": 32}),
-                             "length": ("INT", {"default": 97, "min": 9, "max": nodes.MAX_RESOLUTION, "step": 8}),
-                             "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
-                             "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0}),
-                             }}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVImgToVideo",
+            category="conditioning/video_models",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Vae.Input("vae"),
+                io.Image.Input("image"),
+                io.Int.Input("width", default=768, min=64, max=nodes.MAX_RESOLUTION, step=32),
+                io.Int.Input("height", default=512, min=64, max=nodes.MAX_RESOLUTION, step=32),
+                io.Int.Input("length", default=97, min=9, max=nodes.MAX_RESOLUTION, step=8),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+                io.Float.Input("strength", default=1.0, min=0.0, max=1.0),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
 
-    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
-    RETURN_NAMES = ("positive", "negative", "latent")
-
-    CATEGORY = "conditioning/video_models"
-    FUNCTION = "generate"
-
-    def generate(self, positive, negative, image, vae, width, height, length, batch_size, strength):
+    @classmethod
+    def execute(cls, positive, negative, image, vae, width, height, length, batch_size, strength) -> io.NodeOutput:
         pixels = comfy.utils.common_upscale(image.movedim(-1, 1), width, height, "bilinear", "center").movedim(1, -1)
         encode_pixels = pixels[:, :, :, :3]
         t = vae.encode(encode_pixels)
@@ -62,7 +76,9 @@ class LTXVImgToVideo:
         )
         conditioning_latent_frames_mask[:, :, :t.shape[2]] = 1.0 - strength
 
-        return (positive, negative, {"samples": latent, "noise_mask": conditioning_latent_frames_mask}, )
+        return io.NodeOutput(positive, negative, {"samples": latent, "noise_mask": conditioning_latent_frames_mask})
+
+    generate = execute  # TODO: remove
 
 
 def conditioning_get_any_value(conditioning, key, default=None):
@@ -93,35 +109,46 @@ def get_keyframe_idxs(cond):
     num_keyframes = torch.unique(keyframe_idxs[:, 0]).shape[0]
     return keyframe_idxs, num_keyframes
 
-class LTXVAddGuide:
+class LTXVAddGuide(io.ComfyNode):
+    NUM_PREFIX_FRAMES = 2
+    PATCHIFIER = SymmetricPatchifier(1)
+
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"positive": ("CONDITIONING", ),
-                             "negative": ("CONDITIONING", ),
-                             "vae": ("VAE",),
-                             "latent": ("LATENT",),
-                             "image": ("IMAGE", {"tooltip": "Image or video to condition the latent video on. Must be 8*n + 1 frames."
-                                                 "If the video is not 8*n + 1 frames, it will be cropped to the nearest 8*n + 1 frames."}),
-                             "frame_idx": ("INT", {"default": 0, "min": -9999, "max": 9999,
-                                                   "tooltip": "Frame index to start the conditioning at. For single-frame images or "
-                                                   "videos with 1-8 frames, any frame_idx value is acceptable. For videos with 9+ "
-                                                   "frames, frame_idx must be divisible by 8, otherwise it will be rounded down to "
-                                                   "the nearest multiple of 8. Negative values are counted from the end of the video."}),
-                             "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                             }
-            }
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVAddGuide",
+            category="conditioning/video_models",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Vae.Input("vae"),
+                io.Latent.Input("latent"),
+                io.Image.Input(
+                    "image",
+                    tooltip="Image or video to condition the latent video on. Must be 8*n + 1 frames. "
+                            "If the video is not 8*n + 1 frames, it will be cropped to the nearest 8*n + 1 frames.",
+                ),
+                io.Int.Input(
+                    "frame_idx",
+                    default=0,
+                    min=-9999,
+                    max=9999,
+                    tooltip="Frame index to start the conditioning at. "
+                            "For single-frame images or videos with 1-8 frames, any frame_idx value is acceptable. "
+                            "For videos with 9+ frames, frame_idx must be divisible by 8, otherwise it will be rounded "
+                            "down to the nearest multiple of 8. Negative values are counted from the end of the video.",
+                ),
+                io.Float.Input("strength", default=1.0, min=0.0, max=1.0, step=0.01),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
 
-    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
-    RETURN_NAMES = ("positive", "negative", "latent")
-
-    CATEGORY = "conditioning/video_models"
-    FUNCTION = "generate"
-
-    def __init__(self):
-        self._num_prefix_frames = 2
-        self._patchifier = SymmetricPatchifier(1)
-
-    def encode(self, vae, latent_width, latent_height, images, scale_factors):
+    @classmethod
+    def encode(cls, vae, latent_width, latent_height, images, scale_factors):
         time_scale_factor, width_scale_factor, height_scale_factor = scale_factors
         images = images[:(images.shape[0] - 1) // time_scale_factor * time_scale_factor + 1]
         pixels = comfy.utils.common_upscale(images.movedim(-1, 1), latent_width * width_scale_factor, latent_height * height_scale_factor, "bilinear", crop="disabled").movedim(1, -1)
@@ -129,7 +156,8 @@ class LTXVAddGuide:
         t = vae.encode(encode_pixels)
         return encode_pixels, t
 
-    def get_latent_index(self, cond, latent_length, guide_length, frame_idx, scale_factors):
+    @classmethod
+    def get_latent_index(cls, cond, latent_length, guide_length, frame_idx, scale_factors):
         time_scale_factor, _, _ = scale_factors
         _, num_keyframes = get_keyframe_idxs(cond)
         latent_count = latent_length - num_keyframes
@@ -141,9 +169,10 @@ class LTXVAddGuide:
 
         return frame_idx, latent_idx
 
-    def add_keyframe_index(self, cond, frame_idx, guiding_latent, scale_factors):
+    @classmethod
+    def add_keyframe_index(cls, cond, frame_idx, guiding_latent, scale_factors):
         keyframe_idxs, _ = get_keyframe_idxs(cond)
-        _, latent_coords = self._patchifier.patchify(guiding_latent)
+        _, latent_coords = cls.PATCHIFIER.patchify(guiding_latent)
         pixel_coords = latent_to_pixel_coords(latent_coords, scale_factors, causal_fix=frame_idx == 0)  # we need the causal fix only if we're placing the new latents at index 0
         pixel_coords[:, 0] += frame_idx
         if keyframe_idxs is None:
@@ -152,8 +181,9 @@ class LTXVAddGuide:
             keyframe_idxs = torch.cat([keyframe_idxs, pixel_coords], dim=2)
         return node_helpers.conditioning_set_values(cond, {"keyframe_idxs": keyframe_idxs})
 
-    def append_keyframe(self, positive, negative, frame_idx, latent_image, noise_mask, guiding_latent, strength, scale_factors):
-        _, latent_idx = self.get_latent_index(
+    @classmethod
+    def append_keyframe(cls, positive, negative, frame_idx, latent_image, noise_mask, guiding_latent, strength, scale_factors):
+        _, latent_idx = cls.get_latent_index(
             cond=positive,
             latent_length=latent_image.shape[2],
             guide_length=guiding_latent.shape[2],
@@ -162,8 +192,8 @@ class LTXVAddGuide:
         )
         noise_mask[:, :, latent_idx:latent_idx + guiding_latent.shape[2]] = 1.0
 
-        positive = self.add_keyframe_index(positive, frame_idx, guiding_latent, scale_factors)
-        negative = self.add_keyframe_index(negative, frame_idx, guiding_latent, scale_factors)
+        positive = cls.add_keyframe_index(positive, frame_idx, guiding_latent, scale_factors)
+        negative = cls.add_keyframe_index(negative, frame_idx, guiding_latent, scale_factors)
 
         mask = torch.full(
             (noise_mask.shape[0], 1, guiding_latent.shape[2], noise_mask.shape[3], noise_mask.shape[4]),
@@ -176,7 +206,8 @@ class LTXVAddGuide:
         noise_mask = torch.cat([noise_mask, mask], dim=2)
         return positive, negative, latent_image, noise_mask
 
-    def replace_latent_frames(self, latent_image, noise_mask, guiding_latent, latent_idx, strength):
+    @classmethod
+    def replace_latent_frames(cls, latent_image, noise_mask, guiding_latent, latent_idx, strength):
         cond_length = guiding_latent.shape[2]
         assert latent_image.shape[2] >= latent_idx + cond_length, "Conditioning frames exceed the length of the latent sequence."
 
@@ -195,20 +226,21 @@ class LTXVAddGuide:
 
         return latent_image, noise_mask
 
-    def generate(self, positive, negative, vae, latent, image, frame_idx, strength):
+    @classmethod
+    def execute(cls, positive, negative, vae, latent, image, frame_idx, strength) -> io.NodeOutput:
         scale_factors = vae.downscale_index_formula
         latent_image = latent["samples"]
         noise_mask = get_noise_mask(latent)
 
         _, _, latent_length, latent_height, latent_width = latent_image.shape
-        image, t = self.encode(vae, latent_width, latent_height, image, scale_factors)
+        image, t = cls.encode(vae, latent_width, latent_height, image, scale_factors)
 
-        frame_idx, latent_idx = self.get_latent_index(positive, latent_length, len(image), frame_idx, scale_factors)
+        frame_idx, latent_idx = cls.get_latent_index(positive, latent_length, len(image), frame_idx, scale_factors)
         assert latent_idx + t.shape[2] <= latent_length, "Conditioning frames exceed the length of the latent sequence."
 
-        num_prefix_frames = min(self._num_prefix_frames, t.shape[2])
+        num_prefix_frames = min(cls.NUM_PREFIX_FRAMES, t.shape[2])
 
-        positive, negative, latent_image, noise_mask = self.append_keyframe(
+        positive, negative, latent_image, noise_mask = cls.append_keyframe(
             positive,
             negative,
             frame_idx,
@@ -223,9 +255,9 @@ class LTXVAddGuide:
 
         t = t[:, :, num_prefix_frames:]
         if t.shape[2] == 0:
-            return (positive, negative, {"samples": latent_image, "noise_mask": noise_mask},)
+            return io.NodeOutput(positive, negative, {"samples": latent_image, "noise_mask": noise_mask})
 
-        latent_image, noise_mask = self.replace_latent_frames(
+        latent_image, noise_mask = cls.replace_latent_frames(
             latent_image,
             noise_mask,
             t,
@@ -233,34 +265,37 @@ class LTXVAddGuide:
             strength,
         )
 
-        return (positive, negative, {"samples": latent_image, "noise_mask": noise_mask},)
+        return io.NodeOutput(positive, negative, {"samples": latent_image, "noise_mask": noise_mask})
+
+    generate = execute  # TODO: remove
 
 
-class LTXVCropGuides:
+class LTXVCropGuides(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"positive": ("CONDITIONING", ),
-                             "negative": ("CONDITIONING", ),
-                             "latent": ("LATENT",),
-                             }
-            }
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVCropGuides",
+            category="conditioning/video_models",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Latent.Input("latent"),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
 
-    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
-    RETURN_NAMES = ("positive", "negative", "latent")
-
-    CATEGORY = "conditioning/video_models"
-    FUNCTION = "crop"
-
-    def __init__(self):
-        self._patchifier = SymmetricPatchifier(1)
-
-    def crop(self, positive, negative, latent):
+    @classmethod
+    def execute(cls, positive, negative, latent) -> io.NodeOutput:
         latent_image = latent["samples"].clone()
         noise_mask = get_noise_mask(latent)
 
         _, num_keyframes = get_keyframe_idxs(positive)
         if num_keyframes == 0:
-            return (positive, negative, {"samples": latent_image, "noise_mask": noise_mask},)
+            return io.NodeOutput(positive, negative, {"samples": latent_image, "noise_mask": noise_mask},)
 
         latent_image = latent_image[:, :, :-num_keyframes]
         noise_mask = noise_mask[:, :, :-num_keyframes]
@@ -268,44 +303,54 @@ class LTXVCropGuides:
         positive = node_helpers.conditioning_set_values(positive, {"keyframe_idxs": None})
         negative = node_helpers.conditioning_set_values(negative, {"keyframe_idxs": None})
 
-        return (positive, negative, {"samples": latent_image, "noise_mask": noise_mask},)
+        return io.NodeOutput(positive, negative, {"samples": latent_image, "noise_mask": noise_mask})
+
+    crop = execute  # TODO: remove
 
 
-class LTXVConditioning:
+class LTXVConditioning(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"positive": ("CONDITIONING", ),
-                             "negative": ("CONDITIONING", ),
-                             "frame_rate": ("FLOAT", {"default": 25.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
-                             }}
-    RETURN_TYPES = ("CONDITIONING", "CONDITIONING")
-    RETURN_NAMES = ("positive", "negative")
-    FUNCTION = "append"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVConditioning",
+            category="conditioning/video_models",
+            inputs=[
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Float.Input("frame_rate", default=25.0, min=0.0, max=1000.0, step=0.01),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+            ],
+        )
 
-    CATEGORY = "conditioning/video_models"
-
-    def append(self, positive, negative, frame_rate):
+    @classmethod
+    def execute(cls, positive, negative, frame_rate) -> io.NodeOutput:
         positive = node_helpers.conditioning_set_values(positive, {"frame_rate": frame_rate})
         negative = node_helpers.conditioning_set_values(negative, {"frame_rate": frame_rate})
-        return (positive, negative)
+        return io.NodeOutput(positive, negative)
 
 
-class ModelSamplingLTXV:
+class ModelSamplingLTXV(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "model": ("MODEL",),
-                              "max_shift": ("FLOAT", {"default": 2.05, "min": 0.0, "max": 100.0, "step":0.01}),
-                              "base_shift": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 100.0, "step":0.01}),
-                              },
-                "optional": {"latent": ("LATENT",), }
-                }
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ModelSamplingLTXV",
+            category="advanced/model",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("max_shift", default=2.05, min=0.0, max=100.0, step=0.01),
+                io.Float.Input("base_shift", default=0.95, min=0.0, max=100.0, step=0.01),
+                io.Latent.Input("latent", optional=True),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "patch"
-
-    CATEGORY = "advanced/model"
-
-    def patch(self, model, max_shift, base_shift, latent=None):
+    @classmethod
+    def execute(cls, model, max_shift, base_shift, latent=None) -> io.NodeOutput:
         m = model.clone()
 
         if latent is None:
@@ -329,37 +374,41 @@ class ModelSamplingLTXV:
         model_sampling.set_parameters(shift=shift)
         m.add_object_patch("model_sampling", model_sampling)
 
-        return (m, )
+        return io.NodeOutput(m)
 
 
-class LTXVScheduler:
+class LTXVScheduler(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required":
-                    {"steps": ("INT", {"default": 20, "min": 1, "max": 10000}),
-                     "max_shift": ("FLOAT", {"default": 2.05, "min": 0.0, "max": 100.0, "step":0.01}),
-                     "base_shift": ("FLOAT", {"default": 0.95, "min": 0.0, "max": 100.0, "step":0.01}),
-                     "stretch": ("BOOLEAN", {
-                        "default": True,
-                        "tooltip": "Stretch the sigmas to be in the range [terminal, 1]."
-                    }),
-                     "terminal": (
-                        "FLOAT",
-                        {
-                            "default": 0.1, "min": 0.0, "max": 0.99, "step": 0.01,
-                            "tooltip": "The terminal value of the sigmas after stretching."
-                        },
-                    ),
-                    },
-                "optional": {"latent": ("LATENT",), }
-               }
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVScheduler",
+            category="sampling/custom_sampling/schedulers",
+            inputs=[
+                io.Int.Input("steps", default=20, min=1, max=10000),
+                io.Float.Input("max_shift", default=2.05, min=0.0, max=100.0, step=0.01),
+                io.Float.Input("base_shift", default=0.95, min=0.0, max=100.0, step=0.01),
+                io.Boolean.Input(
+                    id="stretch",
+                    default=True,
+                    tooltip="Stretch the sigmas to be in the range [terminal, 1].",
+                ),
+                io.Float.Input(
+                    id="terminal",
+                    default=0.1,
+                    min=0.0,
+                    max=0.99,
+                    step=0.01,
+                    tooltip="The terminal value of the sigmas after stretching.",
+                ),
+                io.Latent.Input("latent", optional=True),
+            ],
+            outputs=[
+                io.Sigmas.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("SIGMAS",)
-    CATEGORY = "sampling/custom_sampling/schedulers"
-
-    FUNCTION = "get_sigmas"
-
-    def get_sigmas(self, steps, max_shift, base_shift, stretch, terminal, latent=None):
+    @classmethod
+    def execute(cls, steps, max_shift, base_shift, stretch, terminal, latent=None) -> io.NodeOutput:
         if latent is None:
             tokens = 4096
         else:
@@ -389,7 +438,7 @@ class LTXVScheduler:
             stretched = 1.0 - (one_minus_z / scale_factor)
             sigmas[non_zero_mask] = stretched
 
-        return (sigmas,)
+        return io.NodeOutput(sigmas)
 
 def encode_single_frame(output_file, image_array: np.ndarray, crf):
     container = av.open(output_file, "w", format="mp4")
@@ -423,52 +472,55 @@ def preprocess(image: torch.Tensor, crf=29):
         return image
 
     image_array = (image[:(image.shape[0] // 2) * 2, :(image.shape[1] // 2) * 2] * 255.0).byte().cpu().numpy()
-    with io.BytesIO() as output_file:
+    with BytesIO() as output_file:
         encode_single_frame(output_file, image_array, crf)
         video_bytes = output_file.getvalue()
-    with io.BytesIO(video_bytes) as video_file:
+    with BytesIO(video_bytes) as video_file:
         image_array = decode_single_frame(video_file)
     tensor = torch.tensor(image_array, dtype=image.dtype, device=image.device) / 255.0
     return tensor
 
 
-class LTXVPreprocess:
+class LTXVPreprocess(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "img_compression": (
-                    "INT",
-                    {
-                        "default": 35,
-                        "min": 0,
-                        "max": 100,
-                        "tooltip": "Amount of compression to apply on image.",
-                    },
+    def define_schema(cls):
+        return io.Schema(
+            node_id="LTXVPreprocess",
+            category="image",
+            inputs=[
+                io.Image.Input("image"),
+                io.Int.Input(
+                    id="img_compression", default=35, min=0, max=100, tooltip="Amount of compression to apply on image."
                 ),
-            }
-        }
+            ],
+            outputs=[
+                io.Image.Output(display_name="output_image"),
+            ],
+        )
 
-    FUNCTION = "preprocess"
-    RETURN_TYPES = ("IMAGE",)
-    RETURN_NAMES = ("output_image",)
-    CATEGORY = "image"
-
-    def preprocess(self, image, img_compression):
+    @classmethod
+    def execute(cls, image, img_compression) -> io.NodeOutput:
         output_images = []
         for i in range(image.shape[0]):
             output_images.append(preprocess(image[i], img_compression))
-        return (torch.stack(output_images),)
+        return io.NodeOutput(torch.stack(output_images))
+
+    preprocess = execute  # TODO: remove
+
+class LtxvExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            EmptyLTXVLatentVideo,
+            LTXVImgToVideo,
+            ModelSamplingLTXV,
+            LTXVConditioning,
+            LTXVScheduler,
+            LTXVAddGuide,
+            LTXVPreprocess,
+            LTXVCropGuides,
+        ]
 
 
-NODE_CLASS_MAPPINGS = {
-    "EmptyLTXVLatentVideo": EmptyLTXVLatentVideo,
-    "LTXVImgToVideo": LTXVImgToVideo,
-    "ModelSamplingLTXV": ModelSamplingLTXV,
-    "LTXVConditioning": LTXVConditioning,
-    "LTXVScheduler": LTXVScheduler,
-    "LTXVAddGuide": LTXVAddGuide,
-    "LTXVPreprocess": LTXVPreprocess,
-    "LTXVCropGuides": LTXVCropGuides,
-}
+async def comfy_entrypoint() -> LtxvExtension:
+    return LtxvExtension()

comfy_extras/nodes_lumina2.py

@@ -1,20 +1,27 @@
-from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict
+from typing_extensions import override
 import torch
 
+from comfy_api.latest import ComfyExtension, io
 
-class RenormCFG:
+
+class RenormCFG(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "model": ("MODEL",),
-                              "cfg_trunc": ("FLOAT", {"default": 100, "min": 0.0, "max": 100.0, "step": 0.01}),
-                              "renorm_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
-                              }}
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "patch"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="RenormCFG",
+            category="advanced/model",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("cfg_trunc", default=100, min=0.0, max=100.0, step=0.01),
+                io.Float.Input("renorm_cfg", default=1.0, min=0.0, max=100.0, step=0.01),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+        )
 
-    CATEGORY = "advanced/model"
-
-    def patch(self, model, cfg_trunc, renorm_cfg):
+    @classmethod
+    def execute(cls, model, cfg_trunc, renorm_cfg) -> io.NodeOutput:
         def renorm_cfg_func(args):
             cond_denoised = args["cond_denoised"]
             uncond_denoised = args["uncond_denoised"]
@@ -53,10 +60,10 @@ class RenormCFG:
 
         m = model.clone()
         m.set_model_sampler_cfg_function(renorm_cfg_func)
-        return (m, )
+        return io.NodeOutput(m)
 
 
-class CLIPTextEncodeLumina2(ComfyNodeABC):
+class CLIPTextEncodeLumina2(io.ComfyNode):
     SYSTEM_PROMPT = {
         "superior": "You are an assistant designed to generate superior images with the superior "\
             "degree of image-text alignment based on textual prompts or user prompts.",
@@ -69,36 +76,52 @@ class CLIPTextEncodeLumina2(ComfyNodeABC):
         "Alignment: You are an assistant designed to generate high-quality images with the highest "\
         "degree of image-text alignment based on textual prompts."
     @classmethod
-    def INPUT_TYPES(s) -> InputTypeDict:
-        return {
-            "required": {
-                "system_prompt": (list(CLIPTextEncodeLumina2.SYSTEM_PROMPT.keys()), {"tooltip": CLIPTextEncodeLumina2.SYSTEM_PROMPT_TIP}),
-                "user_prompt": (IO.STRING, {"multiline": True, "dynamicPrompts": True, "tooltip": "The text to be encoded."}),
-                "clip": (IO.CLIP, {"tooltip": "The CLIP model used for encoding the text."})
-            }
-        }
-    RETURN_TYPES = (IO.CONDITIONING,)
-    OUTPUT_TOOLTIPS = ("A conditioning containing the embedded text used to guide the diffusion model.",)
-    FUNCTION = "encode"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="CLIPTextEncodeLumina2",
+            display_name="CLIP Text Encode for Lumina2",
+            category="conditioning",
+            description="Encodes a system prompt and a user prompt using a CLIP model into an embedding "
+                        "that can be used to guide the diffusion model towards generating specific images.",
+            inputs=[
+                io.Combo.Input(
+                    "system_prompt",
+                    options=list(cls.SYSTEM_PROMPT.keys()),
+                    tooltip=cls.SYSTEM_PROMPT_TIP,
+                ),
+                io.String.Input(
+                    "user_prompt",
+                    multiline=True,
+                    dynamic_prompts=True,
+                    tooltip="The text to be encoded.",
+                ),
+                io.Clip.Input("clip", tooltip="The CLIP model used for encoding the text."),
+            ],
+            outputs=[
+                io.Conditioning.Output(
+                    tooltip="A conditioning containing the embedded text used to guide the diffusion model.",
+                ),
+            ],
+        )
 
-    CATEGORY = "conditioning"
-    DESCRIPTION = "Encodes a system prompt and a user prompt using a CLIP model into an embedding that can be used to guide the diffusion model towards generating specific images."
-
-    def encode(self, clip, user_prompt, system_prompt):
+    @classmethod
+    def execute(cls, clip, user_prompt, system_prompt) -> io.NodeOutput:
         if clip is None:
             raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.")
-        system_prompt = CLIPTextEncodeLumina2.SYSTEM_PROMPT[system_prompt]
+        system_prompt = cls.SYSTEM_PROMPT[system_prompt]
         prompt = f'{system_prompt} <Prompt Start> {user_prompt}'
         tokens = clip.tokenize(prompt)
-        return (clip.encode_from_tokens_scheduled(tokens), )
+        return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens))
 
 
-NODE_CLASS_MAPPINGS = {
-    "CLIPTextEncodeLumina2": CLIPTextEncodeLumina2,
-    "RenormCFG": RenormCFG
-}
+class Lumina2Extension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            CLIPTextEncodeLumina2,
+            RenormCFG,
+        ]
 
 
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "CLIPTextEncodeLumina2": "CLIP Text Encode for Lumina2",
-}
+async def comfy_entrypoint() -> Lumina2Extension:
+    return Lumina2Extension()

comfy_extras/nodes_mahiro.py

@@ -1,17 +1,29 @@
+from typing_extensions import override
 import torch
 import torch.nn.functional as F
 
-class Mahiro:
+from comfy_api.latest import ComfyExtension, io
+
+
+class Mahiro(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"model": ("MODEL",),
-                            }}
-    RETURN_TYPES = ("MODEL",)
-    RETURN_NAMES = ("patched_model",)
-    FUNCTION = "patch"
-    CATEGORY = "_for_testing"
-    DESCRIPTION = "Modify the guidance to scale more on the 'direction' of the positive prompt rather than the difference between the negative prompt."
-    def patch(self, model):
+    def define_schema(cls):
+        return io.Schema(
+            node_id="Mahiro",
+            display_name="Mahiro is so cute that she deserves a better guidance function!! (。・ω・。)",
+            category="_for_testing",
+            description="Modify the guidance to scale more on the 'direction' of the positive prompt rather than the difference between the negative prompt.",
+            inputs=[
+                io.Model.Input("model"),
+            ],
+            outputs=[
+                io.Model.Output(display_name="patched_model"),
+            ],
+            is_experimental=True,
+        )
+
+    @classmethod
+    def execute(cls, model) -> io.NodeOutput:
         m = model.clone()
         def mahiro_normd(args):
             scale: float = args['cond_scale']
@@ -30,12 +42,16 @@ class Mahiro:
             wm = (simsc*cfg + (4-simsc)*leap) / 4
             return wm
         m.set_model_sampler_post_cfg_function(mahiro_normd)
-        return (m, )
+        return io.NodeOutput(m)
 
-NODE_CLASS_MAPPINGS = {
-    "Mahiro": Mahiro
-}
 
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "Mahiro": "Mahiro is so cute that she deserves a better guidance function!! (。・ω・。)",
-}
+class MahiroExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            Mahiro,
+        ]
+
+
+async def comfy_entrypoint() -> MahiroExtension:
+    return MahiroExtension()

comfy_extras/nodes_mask.py

@@ -12,35 +12,38 @@ from nodes import MAX_RESOLUTION
 def composite(destination, source, x, y, mask = None, multiplier = 8, resize_source = False):
     source = source.to(destination.device)
     if resize_source:
-        source = torch.nn.functional.interpolate(source, size=(destination.shape[2], destination.shape[3]), mode="bilinear")
+        source = torch.nn.functional.interpolate(source, size=(destination.shape[-2], destination.shape[-1]), mode="bilinear")
 
     source = comfy.utils.repeat_to_batch_size(source, destination.shape[0])
 
-    x = max(-source.shape[3] * multiplier, min(x, destination.shape[3] * multiplier))
-    y = max(-source.shape[2] * multiplier, min(y, destination.shape[2] * multiplier))
+    x = max(-source.shape[-1] * multiplier, min(x, destination.shape[-1] * multiplier))
+    y = max(-source.shape[-2] * multiplier, min(y, destination.shape[-2] * multiplier))
 
     left, top = (x // multiplier, y // multiplier)
-    right, bottom = (left + source.shape[3], top + source.shape[2],)
+    right, bottom = (left + source.shape[-1], top + source.shape[-2],)
 
     if mask is None:
         mask = torch.ones_like(source)
     else:
         mask = mask.to(destination.device, copy=True)
-        mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[2], source.shape[3]), mode="bilinear")
+        mask = torch.nn.functional.interpolate(mask.reshape((-1, 1, mask.shape[-2], mask.shape[-1])), size=(source.shape[-2], source.shape[-1]), mode="bilinear")
         mask = comfy.utils.repeat_to_batch_size(mask, source.shape[0])
 
     # calculate the bounds of the source that will be overlapping the destination
     # this prevents the source trying to overwrite latent pixels that are out of bounds
     # of the destination
-    visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),)
+    visible_width, visible_height = (destination.shape[-1] - left + min(0, x), destination.shape[-2] - top + min(0, y),)
 
     mask = mask[:, :, :visible_height, :visible_width]
+    if mask.ndim < source.ndim:
+        mask = mask.unsqueeze(1)
+
     inverse_mask = torch.ones_like(mask) - mask
 
-    source_portion = mask * source[:, :, :visible_height, :visible_width]
-    destination_portion = inverse_mask  * destination[:, :, top:bottom, left:right]
+    source_portion = mask * source[..., :visible_height, :visible_width]
+    destination_portion = inverse_mask  * destination[..., top:bottom, left:right]
 
-    destination[:, :, top:bottom, left:right] = source_portion + destination_portion
+    destination[..., top:bottom, left:right] = source_portion + destination_portion
     return destination
 
 class LatentCompositeMasked:

comfy_extras/nodes_mochi.py

@@ -1,23 +1,40 @@
-import nodes
+from typing_extensions import override
 import torch
 import comfy.model_management
+import nodes
+from comfy_api.latest import ComfyExtension, io
 
-class EmptyMochiLatentVideo:
+
+class EmptyMochiLatentVideo(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "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": 25, "min": 7, "max": nodes.MAX_RESOLUTION, "step": 6}),
-                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096})}}
-    RETURN_TYPES = ("LATENT",)
-    FUNCTION = "generate"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="EmptyMochiLatentVideo",
+            category="latent/video",
+            inputs=[
+                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=25, min=7, max=nodes.MAX_RESOLUTION, step=6),
+                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, 12, ((length - 1) // 6) + 1, height // 8, width // 8], device=comfy.model_management.intermediate_device())
-        return ({"samples":latent}, )
+        return io.NodeOutput({"samples": latent})
 
-NODE_CLASS_MAPPINGS = {
-    "EmptyMochiLatentVideo": EmptyMochiLatentVideo,
-}
+
+class MochiExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            EmptyMochiLatentVideo,
+        ]
+
+
+async def comfy_entrypoint() -> MochiExtension:
+    return MochiExtension()

comfy_extras/nodes_model_downscale.py

@@ -1,24 +1,33 @@
+from typing_extensions import override
 import comfy.utils
+from comfy_api.latest import ComfyExtension, io
 
-class PatchModelAddDownscale:
-    upscale_methods = ["bicubic", "nearest-exact", "bilinear", "area", "bislerp"]
+
+class PatchModelAddDownscale(io.ComfyNode):
+    UPSCALE_METHODS = ["bicubic", "nearest-exact", "bilinear", "area", "bislerp"]
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "model": ("MODEL",),
-                              "block_number": ("INT", {"default": 3, "min": 1, "max": 32, "step": 1}),
-                              "downscale_factor": ("FLOAT", {"default": 2.0, "min": 0.1, "max": 9.0, "step": 0.001}),
-                              "start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
-                              "end_percent": ("FLOAT", {"default": 0.35, "min": 0.0, "max": 1.0, "step": 0.001}),
-                              "downscale_after_skip": ("BOOLEAN", {"default": True}),
-                              "downscale_method": (s.upscale_methods,),
-                              "upscale_method": (s.upscale_methods,),
-                              }}
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "patch"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="PatchModelAddDownscale",
+            display_name="PatchModelAddDownscale (Kohya Deep Shrink)",
+            category="model_patches/unet",
+            inputs=[
+                io.Model.Input("model"),
+                io.Int.Input("block_number", default=3, min=1, max=32, step=1),
+                io.Float.Input("downscale_factor", default=2.0, min=0.1, max=9.0, step=0.001),
+                io.Float.Input("start_percent", default=0.0, min=0.0, max=1.0, step=0.001),
+                io.Float.Input("end_percent", default=0.35, min=0.0, max=1.0, step=0.001),
+                io.Boolean.Input("downscale_after_skip", default=True),
+                io.Combo.Input("downscale_method", options=cls.UPSCALE_METHODS),
+                io.Combo.Input("upscale_method", options=cls.UPSCALE_METHODS),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+        )
 
-    CATEGORY = "model_patches/unet"
-
-    def patch(self, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method):
+    @classmethod
+    def execute(cls, model, block_number, downscale_factor, start_percent, end_percent, downscale_after_skip, downscale_method, upscale_method) -> io.NodeOutput:
         model_sampling = model.get_model_object("model_sampling")
         sigma_start = model_sampling.percent_to_sigma(start_percent)
         sigma_end = model_sampling.percent_to_sigma(end_percent)
@@ -41,13 +50,21 @@ class PatchModelAddDownscale:
         else:
             m.set_model_input_block_patch(input_block_patch)
         m.set_model_output_block_patch(output_block_patch)
-        return (m, )
+        return io.NodeOutput(m)
 
-NODE_CLASS_MAPPINGS = {
-    "PatchModelAddDownscale": PatchModelAddDownscale,
-}
 
 NODE_DISPLAY_NAME_MAPPINGS = {
     # Sampling
-    "PatchModelAddDownscale": "PatchModelAddDownscale (Kohya Deep Shrink)",
+    "PatchModelAddDownscale": "",
 }
+
+class ModelDownscaleExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            PatchModelAddDownscale,
+        ]
+
+
+async def comfy_entrypoint() -> ModelDownscaleExtension:
+    return ModelDownscaleExtension()

comfy_extras/nodes_morphology.py

@@ -1,24 +1,34 @@
 import torch
 import comfy.model_management
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
 
 from kornia.morphology import dilation, erosion, opening, closing, gradient, top_hat, bottom_hat
 import kornia.color
 
 
-class Morphology:
+class Morphology(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"image": ("IMAGE",),
-                                "operation": (["erode",  "dilate", "open", "close", "gradient", "bottom_hat", "top_hat"],),
-                                "kernel_size": ("INT", {"default": 3, "min": 3, "max": 999, "step": 1}),
-                                }}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="Morphology",
+            display_name="ImageMorphology",
+            category="image/postprocessing",
+            inputs=[
+                io.Image.Input("image"),
+                io.Combo.Input(
+                    "operation",
+                    options=["erode", "dilate", "open", "close", "gradient", "bottom_hat", "top_hat"],
+                ),
+                io.Int.Input("kernel_size", default=3, min=3, max=999, step=1),
+            ],
+            outputs=[
+                io.Image.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "process"
-
-    CATEGORY = "image/postprocessing"
-
-    def process(self, image, operation, kernel_size):
+    @classmethod
+    def execute(cls, image, operation, kernel_size) -> io.NodeOutput:
         device = comfy.model_management.get_torch_device()
         kernel = torch.ones(kernel_size, kernel_size, device=device)
         image_k = image.to(device).movedim(-1, 1)
@@ -39,49 +49,63 @@ class Morphology:
         else:
             raise ValueError(f"Invalid operation {operation} for morphology. Must be one of 'erode', 'dilate', 'open', 'close', 'gradient', 'tophat', 'bottomhat'")
         img_out = output.to(comfy.model_management.intermediate_device()).movedim(1, -1)
-        return (img_out,)
+        return io.NodeOutput(img_out)
 
 
-class ImageRGBToYUV:
+class ImageRGBToYUV(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": { "image": ("IMAGE",),
-                              }}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ImageRGBToYUV",
+            category="image/batch",
+            inputs=[
+                io.Image.Input("image"),
+            ],
+            outputs=[
+                io.Image.Output(display_name="Y"),
+                io.Image.Output(display_name="U"),
+                io.Image.Output(display_name="V"),
+            ],
+        )
 
-    RETURN_TYPES = ("IMAGE", "IMAGE", "IMAGE")
-    RETURN_NAMES = ("Y", "U", "V")
-    FUNCTION = "execute"
-
-    CATEGORY = "image/batch"
-
-    def execute(self, image):
+    @classmethod
+    def execute(cls, image) -> io.NodeOutput:
         out = kornia.color.rgb_to_ycbcr(image.movedim(-1, 1)).movedim(1, -1)
-        return (out[..., 0:1].expand_as(image), out[..., 1:2].expand_as(image), out[..., 2:3].expand_as(image))
+        return io.NodeOutput(out[..., 0:1].expand_as(image), out[..., 1:2].expand_as(image), out[..., 2:3].expand_as(image))
 
-class ImageYUVToRGB:
+class ImageYUVToRGB(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"Y": ("IMAGE",),
-                             "U": ("IMAGE",),
-                             "V": ("IMAGE",),
-                              }}
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ImageYUVToRGB",
+            category="image/batch",
+            inputs=[
+                io.Image.Input("Y"),
+                io.Image.Input("U"),
+                io.Image.Input("V"),
+            ],
+            outputs=[
+                io.Image.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "execute"
-
-    CATEGORY = "image/batch"
-
-    def execute(self, Y, U, V):
+    @classmethod
+    def execute(cls, Y, U, V) -> io.NodeOutput:
         image = torch.cat([torch.mean(Y, dim=-1, keepdim=True), torch.mean(U, dim=-1, keepdim=True), torch.mean(V, dim=-1, keepdim=True)], dim=-1)
         out = kornia.color.ycbcr_to_rgb(image.movedim(-1, 1)).movedim(1, -1)
-        return (out,)
+        return io.NodeOutput(out)
 
-NODE_CLASS_MAPPINGS = {
-    "Morphology": Morphology,
-    "ImageRGBToYUV": ImageRGBToYUV,
-    "ImageYUVToRGB": ImageYUVToRGB,
-}
 
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "Morphology": "ImageMorphology",
-}
+class MorphologyExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            Morphology,
+            ImageRGBToYUV,
+            ImageYUVToRGB,
+        ]
+
+
+async def comfy_entrypoint() -> MorphologyExtension:
+    return MorphologyExtension()
+

comfy_extras/nodes_optimalsteps.py

@@ -1,9 +1,12 @@
 # from https://github.com/bebebe666/OptimalSteps
 
-
 import numpy as np
 import torch
 
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
+
 def loglinear_interp(t_steps, num_steps):
     """
     Performs log-linear interpolation of a given array of decreasing numbers.
@@ -23,25 +26,28 @@ NOISE_LEVELS = {"FLUX": [0.9968, 0.9886, 0.9819, 0.975, 0.966, 0.9471, 0.9158, 0
 "Chroma": [0.992, 0.99, 0.988, 0.985, 0.982, 0.978, 0.973, 0.968, 0.961, 0.953, 0.943, 0.931, 0.917, 0.9, 0.881, 0.858, 0.832, 0.802, 0.769, 0.731, 0.69, 0.646, 0.599, 0.55, 0.501, 0.451, 0.402, 0.355, 0.311, 0.27, 0.232, 0.199, 0.169, 0.143, 0.12, 0.101, 0.084, 0.07, 0.058, 0.048, 0.001],
 }
 
-class OptimalStepsScheduler:
+class OptimalStepsScheduler(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required":
-                    {"model_type": (["FLUX", "Wan", "Chroma"], ),
-                     "steps": ("INT", {"default": 20, "min": 3, "max": 1000}),
-                     "denoise": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
-                      }
-               }
-    RETURN_TYPES = ("SIGMAS",)
-    CATEGORY = "sampling/custom_sampling/schedulers"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="OptimalStepsScheduler",
+            category="sampling/custom_sampling/schedulers",
+            inputs=[
+                io.Combo.Input("model_type", options=["FLUX", "Wan", "Chroma"]),
+                io.Int.Input("steps", default=20, min=3, max=1000),
+                io.Float.Input("denoise", default=1.0, min=0.0, max=1.0, step=0.01),
+            ],
+            outputs=[
+                io.Sigmas.Output(),
+            ],
+        )
 
-    FUNCTION = "get_sigmas"
-
-    def get_sigmas(self, model_type, steps, denoise):
+    @classmethod
+    def execute(cls, model_type, steps, denoise) ->io.NodeOutput:
         total_steps = steps
         if denoise < 1.0:
             if denoise <= 0.0:
-                return (torch.FloatTensor([]),)
+                return io.NodeOutput(torch.FloatTensor([]))
             total_steps = round(steps * denoise)
 
         sigmas = NOISE_LEVELS[model_type][:]
@@ -50,8 +56,16 @@ class OptimalStepsScheduler:
 
         sigmas = sigmas[-(total_steps + 1):]
         sigmas[-1] = 0
-        return (torch.FloatTensor(sigmas), )
+        return io.NodeOutput(torch.FloatTensor(sigmas))
 
-NODE_CLASS_MAPPINGS = {
-    "OptimalStepsScheduler": OptimalStepsScheduler,
-}
+
+class OptimalStepsExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            OptimalStepsScheduler,
+        ]
+
+
+async def comfy_entrypoint() -> OptimalStepsExtension:
+    return OptimalStepsExtension()

comfy_extras/nodes_pag.py

@@ -3,25 +3,30 @@
 
 #My modified one here is more basic but has less chances of breaking with ComfyUI updates.
 
+from typing_extensions import override
+
 import comfy.model_patcher
 import comfy.samplers
+from comfy_api.latest import ComfyExtension, io
 
-class PerturbedAttentionGuidance:
+
+class PerturbedAttentionGuidance(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {
-            "required": {
-                "model": ("MODEL",),
-                "scale": ("FLOAT", {"default": 3.0, "min": 0.0, "max": 100.0, "step": 0.01, "round": 0.01}),
-            }
-        }
+    def define_schema(cls):
+        return io.Schema(
+            node_id="PerturbedAttentionGuidance",
+            category="model_patches/unet",
+            inputs=[
+                io.Model.Input("model"),
+                io.Float.Input("scale", default=3.0, min=0.0, max=100.0, step=0.01, round=0.01),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "patch"
-
-    CATEGORY = "model_patches/unet"
-
-    def patch(self, model, scale):
+    @classmethod
+    def execute(cls, model, scale) -> io.NodeOutput:
         unet_block = "middle"
         unet_block_id = 0
         m = model.clone()
@@ -49,8 +54,16 @@ class PerturbedAttentionGuidance:
 
         m.set_model_sampler_post_cfg_function(post_cfg_function)
 
-        return (m,)
+        return io.NodeOutput(m)
 
-NODE_CLASS_MAPPINGS = {
-    "PerturbedAttentionGuidance": PerturbedAttentionGuidance,
-}
+
+class PAGExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            PerturbedAttentionGuidance,
+        ]
+
+
+async def comfy_entrypoint() -> PAGExtension:
+    return PAGExtension()

comfy_extras/nodes_perpneg.py

@@ -5,6 +5,9 @@ import comfy.samplers
 import comfy.utils
 import node_helpers
 import math
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
+
 
 def perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_nocond, neg_scale, cond_scale):
     pos = noise_pred_pos - noise_pred_nocond
@@ -16,20 +19,27 @@ def perp_neg(x, noise_pred_pos, noise_pred_neg, noise_pred_nocond, neg_scale, co
     return cfg_result
 
 #TODO: This node should be removed, it has been replaced with PerpNegGuider
-class PerpNeg:
+class PerpNeg(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required": {"model": ("MODEL", ),
-                             "empty_conditioning": ("CONDITIONING", ),
-                             "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
-                            }}
-    RETURN_TYPES = ("MODEL",)
-    FUNCTION = "patch"
+    def define_schema(cls):
+        return io.Schema(
+            node_id="PerpNeg",
+            display_name="Perp-Neg (DEPRECATED by PerpNegGuider)",
+            category="_for_testing",
+            inputs=[
+                io.Model.Input("model"),
+                io.Conditioning.Input("empty_conditioning"),
+                io.Float.Input("neg_scale", default=1.0, min=0.0, max=100.0, step=0.01),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+            is_experimental=True,
+            is_deprecated=True,
+        )
 
-    CATEGORY = "_for_testing"
-    DEPRECATED = True
-
-    def patch(self, model, empty_conditioning, neg_scale):
+    @classmethod
+    def execute(cls, model, empty_conditioning, neg_scale) -> io.NodeOutput:
         m = model.clone()
         nocond = comfy.sampler_helpers.convert_cond(empty_conditioning)
 
@@ -50,7 +60,7 @@ class PerpNeg:
 
         m.set_model_sampler_cfg_function(cfg_function)
 
-        return (m, )
+        return io.NodeOutput(m)
 
 
 class Guider_PerpNeg(comfy.samplers.CFGGuider):
@@ -112,35 +122,42 @@ class Guider_PerpNeg(comfy.samplers.CFGGuider):
 
         return cfg_result
 
-class PerpNegGuider:
+class PerpNegGuider(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
-        return {"required":
-                    {"model": ("MODEL",),
-                    "positive": ("CONDITIONING", ),
-                    "negative": ("CONDITIONING", ),
-                    "empty_conditioning": ("CONDITIONING", ),
-                    "cfg": ("FLOAT", {"default": 8.0, "min": 0.0, "max": 100.0, "step":0.1, "round": 0.01}),
-                    "neg_scale": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
-                     }
-                }
+    def define_schema(cls):
+        return io.Schema(
+            node_id="PerpNegGuider",
+            category="_for_testing",
+            inputs=[
+                io.Model.Input("model"),
+                io.Conditioning.Input("positive"),
+                io.Conditioning.Input("negative"),
+                io.Conditioning.Input("empty_conditioning"),
+                io.Float.Input("cfg", default=8.0, min=0.0, max=100.0, step=0.1, round=0.01),
+                io.Float.Input("neg_scale", default=1.0, min=0.0, max=100.0, step=0.01),
+            ],
+            outputs=[
+                io.Guider.Output(),
+            ],
+            is_experimental=True,
+        )
 
-    RETURN_TYPES = ("GUIDER",)
-
-    FUNCTION = "get_guider"
-    CATEGORY = "_for_testing"
-
-    def get_guider(self, model, positive, negative, empty_conditioning, cfg, neg_scale):
+    @classmethod
+    def execute(cls, model, positive, negative, empty_conditioning, cfg, neg_scale) -> io.NodeOutput:
         guider = Guider_PerpNeg(model)
         guider.set_conds(positive, negative, empty_conditioning)
         guider.set_cfg(cfg, neg_scale)
-        return (guider,)
+        return io.NodeOutput(guider)
 
-NODE_CLASS_MAPPINGS = {
-    "PerpNeg": PerpNeg,
-    "PerpNegGuider": PerpNegGuider,
-}
 
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "PerpNeg": "Perp-Neg (DEPRECATED by PerpNegGuider)",
-}
+class PerpNegExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            PerpNeg,
+            PerpNegGuider,
+        ]
+
+
+async def comfy_entrypoint() -> PerpNegExtension:
+    return PerpNegExtension()