Merge branch 'master' into dr-support-pip-cm

.github/workflows/stable-release.yml

@@ -8,11 +8,11 @@ on:
         description: 'Git tag'
         required: true
         type: string
-      cu:
+      cache_tag:
-        description: 'CUDA version'
+        description: 'Cached dependencies tag'
         required: true
         type: string
-        default: "129"
+        default: "cu129"
       python_minor:
         description: 'Python minor version'
         required: true
@@ -23,7 +23,11 @@ on:
         required: true
         type: string
         default: "6"
-
+      rel_name:
+        description: 'Release name'
+        required: true
+        type: string
+        default: "nvidia"
 
 jobs:
   package_comfy_windows:
@@ -42,15 +46,15 @@ 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 +69,19 @@ 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
 
+          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 ..
 
@@ -91,7 +102,7 @@ jobs:
           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 }}.7z
 
           cd ComfyUI_windows_portable
           python_embeded/python.exe -s ComfyUI/main.py --quick-test-for-ci --cpu
@@ -101,10 +112,9 @@ jobs:
           ls
 
       - name: Upload binaries to release
-        uses: svenstaro/upload-release-action@v2
+        uses: softprops/action-gh-release@v2
         with:
-          repo_token: ${{ secrets.GITHUB_TOKEN }}
+          files: ComfyUI_windows_portable_${{ inputs.rel_name }}.7z
-          file: ComfyUI_windows_portable_nvidia.7z
+          tag_name: ${{ inputs.git_tag }}
-          tag: ${{ inputs.git_tag }}
-          overwrite: true
           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

CODEOWNERS

@@ -1,25 +1,3 @@
 # Admins
 * @comfyanonymous
-
+* @kosinkadink
-# 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

README.md

@@ -233,7 +233,7 @@ 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
 

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/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/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)

comfy/samplers.py

@@ -360,7 +360,7 @@ def calc_cond_uncond_batch(model, cond, uncond, x_in, timestep, model_options):
 def cfg_function(model, cond_pred, uncond_pred, cond_scale, x, timestep, model_options={}, cond=None, uncond=None):
     if "sampler_cfg_function" in model_options:
         args = {"cond": x - cond_pred, "uncond": x - uncond_pred, "cond_scale": cond_scale, "timestep": timestep, "input": x, "sigma": timestep,
-                "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options}
+                "cond_denoised": cond_pred, "uncond_denoised": uncond_pred, "model": model, "model_options": model_options, "input_cond": cond, "input_uncond": uncond}
         cfg_result = x - model_options["sampler_cfg_function"](args)
     else:
         cfg_result = uncond_pred + (cond_pred - uncond_pred) * cond_scale

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/qwen_image.py

@@ -18,6 +18,15 @@ 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):
+        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:
             if llama_template is None:
                 if len(images) > 0:
                     llama_text = self.llama_template_images.format(text)
@@ -47,10 +56,11 @@ 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
+        if template_end == -1:
             for i, v in enumerate(tok_pairs):
                 elem = v[0]
                 if not torch.is_tensor(elem):

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,)
+    @classmethod
-    RETURN_NAMES = ("luma_ref",)
+    def define_schema(cls) -> comfy_io.Schema:
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
+        return comfy_io.Schema(
-    FUNCTION = "create_luma_reference"
+            node_id="LumaReferenceNode",
-    CATEGORY = "api node/image/Luma"
+            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):
+    def execute(
-        return {
+        cls, image: torch.Tensor, weight: float, luma_ref: LumaReferenceChain = None
-            "required": {
+    ) -> comfy_io.NodeOutput:
-                "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
-    ):
         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,)
+    @classmethod
-    RETURN_NAMES = ("luma_concepts",)
+    def define_schema(cls) -> comfy_io.Schema:
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
+        return comfy_io.Schema(
-    FUNCTION = "create_concepts"
+            node_id="LumaConceptsNode",
-    CATEGORY = "api node/video/Luma"
+            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):
+    def execute(
-        return {
+        cls,
-            "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,
         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,)
+    @classmethod
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
+    def define_schema(cls) -> comfy_io.Schema:
-    FUNCTION = "api_call"
+        return comfy_io.Schema(
-    API_NODE = True
+            node_id="LumaImageNode",
-    CATEGORY = "api node/image/Luma"
+            display_name="Luma Text to Image",
+            category="api node/image/Luma",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the image generation",
+                ),
+                comfy_io.Combo.Input(
+                    "model",
+                    options=[model.value for model in LumaImageModel],
+                ),
+                comfy_io.Combo.Input(
+                    "aspect_ratio",
+                    options=[ratio.value for ratio in LumaAspectRatio],
+                    default=LumaAspectRatio.ratio_16_9,
+                ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
+                ),
+                comfy_io.Float.Input(
+                    "style_image_weight",
+                    default=1.0,
+                    min=0.0,
+                    max=1.0,
+                    step=0.01,
+                    tooltip="Weight of style image. Ignored if no style_image provided.",
+                ),
+                comfy_io.Custom(LumaIO.LUMA_REF).Input(
+                    "image_luma_ref",
+                    tooltip="Luma Reference node connection to influence generation with input images; up to 4 images can be considered.",
+                    optional=True,
+                ),
+                comfy_io.Image.Input(
+                    "style_image",
+                    tooltip="Style reference image; only 1 image will be used.",
+                    optional=True,
+                ),
+                comfy_io.Image.Input(
+                    "character_image",
+                    tooltip="Character reference images; can be a batch of multiple, up to 4 images can be considered.",
+                    optional=True,
+                ),
+            ],
+            outputs=[comfy_io.Image.Output()],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
+    async def execute(
-        return {
+        cls,
-            "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,
         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,
+    ) -> comfy_io.NodeOutput:
-        **kwargs,
-    ):
         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(
+            api_image_ref = await cls._convert_luma_refs(
-                image_luma_ref, max_refs=4, auth_kwargs=kwargs,
+                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(
+            api_style_ref = await cls._convert_style_image(
-                style_image, weight=style_image_weight, auth_kwargs=kwargs,
+                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,
+            node_id=cls.hidden.unique_id,
-            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.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,)
+    @classmethod
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
+    def define_schema(cls) -> comfy_io.Schema:
-    FUNCTION = "api_call"
+        return comfy_io.Schema(
-    API_NODE = True
+            node_id="LumaImageModifyNode",
-    CATEGORY = "api node/image/Luma"
+            display_name="Luma Image to Image",
+            category="api node/image/Luma",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.Image.Input(
+                    "image",
+                ),
+                comfy_io.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the image generation",
+                ),
+                comfy_io.Float.Input(
+                    "image_weight",
+                    default=0.1,
+                    min=0.0,
+                    max=0.98,
+                    step=0.01,
+                    tooltip="Weight of the image; the closer to 1.0, the less the image will be modified.",
+                ),
+                comfy_io.Combo.Input(
+                    "model",
+                    options=[model.value for model in LumaImageModel],
+                ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
+                ),
+            ],
+            outputs=[comfy_io.Image.Output()],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
+    async def execute(
-        return {
+        cls,
-            "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,
         prompt: str,
         model: str,
         image: torch.Tensor,
         image_weight: float,
         seed,
-        unique_id: str = None,
+    ) -> comfy_io.NodeOutput:
-        **kwargs,
+        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,
+            node_id=cls.hidden.unique_id,
-            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.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,)
+    @classmethod
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
+    def define_schema(cls) -> comfy_io.Schema:
-    FUNCTION = "api_call"
+        return comfy_io.Schema(
-    API_NODE = True
+            node_id="LumaVideoNode",
-    CATEGORY = "api node/video/Luma"
+            display_name="Luma Text to Video",
+            category="api node/video/Luma",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the video generation",
+                ),
+                comfy_io.Combo.Input(
+                    "model",
+                    options=[model.value for model in LumaVideoModel],
+                ),
+                comfy_io.Combo.Input(
+                    "aspect_ratio",
+                    options=[ratio.value for ratio in LumaAspectRatio],
+                    default=LumaAspectRatio.ratio_16_9,
+                ),
+                comfy_io.Combo.Input(
+                    "resolution",
+                    options=[resolution.value for resolution in LumaVideoOutputResolution],
+                    default=LumaVideoOutputResolution.res_540p,
+                ),
+                comfy_io.Combo.Input(
+                    "duration",
+                    options=[dur.value for dur in LumaVideoModelOutputDuration],
+                ),
+                comfy_io.Boolean.Input(
+                    "loop",
+                    default=False,
+                ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
+                ),
+                comfy_io.Custom(LumaIO.LUMA_CONCEPTS).Input(
+                    "luma_concepts",
+                    tooltip="Optional Camera Concepts to dictate camera motion via the Luma Concepts node.",
+                    optional=True,
+                )
+            ],
+            outputs=[comfy_io.Video.Output()],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
+    async def execute(
-        return {
+        cls,
-            "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,
         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,
+    ) -> comfy_io.NodeOutput:
-        **kwargs,
-    ):
         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:
+        if cls.hidden.unique_id:
-            PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", 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,)
+    @classmethod
-    DESCRIPTION = cleandoc(__doc__ or "")  # Handle potential None value
+    def define_schema(cls) -> comfy_io.Schema:
-    FUNCTION = "api_call"
+        return comfy_io.Schema(
-    API_NODE = True
+            node_id="LumaImageToVideoNode",
-    CATEGORY = "api node/video/Luma"
+            display_name="Luma Image to Video",
+            category="api node/video/Luma",
+            description=cleandoc(cls.__doc__ or ""),
+            inputs=[
+                comfy_io.String.Input(
+                    "prompt",
+                    multiline=True,
+                    default="",
+                    tooltip="Prompt for the video generation",
+                ),
+                comfy_io.Combo.Input(
+                    "model",
+                    options=[model.value for model in LumaVideoModel],
+                ),
+                # comfy_io.Combo.Input(
+                #     "aspect_ratio",
+                #     options=[ratio.value for ratio in LumaAspectRatio],
+                #     default=LumaAspectRatio.ratio_16_9,
+                # ),
+                comfy_io.Combo.Input(
+                    "resolution",
+                    options=[resolution.value for resolution in LumaVideoOutputResolution],
+                    default=LumaVideoOutputResolution.res_540p,
+                ),
+                comfy_io.Combo.Input(
+                    "duration",
+                    options=[dur.value for dur in LumaVideoModelOutputDuration],
+                ),
+                comfy_io.Boolean.Input(
+                    "loop",
+                    default=False,
+                ),
+                comfy_io.Int.Input(
+                    "seed",
+                    default=0,
+                    min=0,
+                    max=0xFFFFFFFFFFFFFFFF,
+                    control_after_generate=True,
+                    tooltip="Seed to determine if node should re-run; actual results are nondeterministic regardless of seed.",
+                ),
+                comfy_io.Image.Input(
+                    "first_image",
+                    tooltip="First frame of generated video.",
+                    optional=True,
+                ),
+                comfy_io.Image.Input(
+                    "last_image",
+                    tooltip="Last frame of generated video.",
+                    optional=True,
+                ),
+                comfy_io.Custom(LumaIO.LUMA_CONCEPTS).Input(
+                    "luma_concepts",
+                    tooltip="Optional Camera Concepts to dictate camera motion via the Luma Concepts node.",
+                    optional=True,
+                )
+            ],
+            outputs=[comfy_io.Video.Output()],
+            hidden=[
+                comfy_io.Hidden.auth_token_comfy_org,
+                comfy_io.Hidden.api_key_comfy_org,
+                comfy_io.Hidden.unique_id,
+            ],
+            is_api_node=True,
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
+    async def execute(
-        return {
+        cls,
-            "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,
         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,
+    ) -> comfy_io.NodeOutput:
-        **kwargs,
-    ):
         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:
+        if cls.hidden.unique_id:
-            PromptServer.instance.send_progress_text(f"Luma video generation started: {response_api.id}", 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
+class LumaExtension(ComfyExtension):
-# NOTE: names should be globally unique
+    @override
-NODE_CLASS_MAPPINGS = {
+    async def get_node_list(self) -> list[type[comfy_io.ComfyNode]]:
-    "LumaImageNode": LumaImageGenerationNode,
+        return [
-    "LumaImageModifyNode": LumaImageModifyNode,
+            LumaImageGenerationNode,
-    "LumaVideoNode": LumaTextToVideoGenerationNode,
+            LumaImageModifyNode,
-    "LumaImageToVideoNode": LumaImageToVideoGenerationNode,
+            LumaTextToVideoGenerationNode,
-    "LumaReferenceNode": LumaReferenceNode,
+            LumaImageToVideoGenerationNode,
-    "LumaConceptsNode": LumaConceptsNode,
+            LumaReferenceNode,
-}
+            LumaConceptsNode,
+        ]
 
-# A dictionary that contains the friendly/humanly readable titles for the nodes
+
-NODE_DISPLAY_NAME_MAPPINGS = {
+async def comfy_entrypoint() -> LumaExtension:
-    "LumaImageNode": "Luma Text to Image",
+    return LumaExtension()
-    "LumaImageModifyNode": "Luma Image to Image",
-    "LumaVideoNode": "Luma Text to Video",
-    "LumaImageToVideoNode": "Luma Image to Video",
-    "LumaReferenceNode": "Luma Reference",
-    "LumaConceptsNode": "Luma Concepts",
-}

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):
+    def define_schema(cls):
-        return {"required": {
+        return io.Schema(
-            "ascore": ("FLOAT", {"default": 6.0, "min": 0.0, "max": 1000.0, "step": 0.01}),
+            node_id="CLIPTextEncodeSDXLRefiner",
-            "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
+            category="advanced/conditioning",
-            "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
+            inputs=[
-            "text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ),
+                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),
-    RETURN_TYPES = ("CONDITIONING",)
+                io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
-    FUNCTION = "encode"
+                io.String.Input("text", multiline=True, dynamic_prompts=True),
+                io.Clip.Input("clip"),
+            ],
+            outputs=[io.Conditioning.Output()],
+        )
 
-    CATEGORY = "advanced/conditioning"
+    @classmethod
-
+    def execute(cls, clip, ascore, width, height, text) -> io.NodeOutput:
-    def encode(self, clip, ascore, width, height, text):
         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):
+    def define_schema(cls):
-        return {"required": {
+        return io.Schema(
-            "clip": ("CLIP", ),
+            node_id="CLIPTextEncodeSDXL",
-            "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
+            category="advanced/conditioning",
-            "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
+            inputs=[
-            "crop_w": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}),
+                io.Clip.Input("clip"),
-            "crop_h": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION}),
+                io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
-            "target_width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
+                io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
-            "target_height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
+                io.Int.Input("crop_w", default=0, min=0, max=nodes.MAX_RESOLUTION),
-            "text_g": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+                io.Int.Input("crop_h", default=0, min=0, max=nodes.MAX_RESOLUTION),
-            "text_l": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+                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),
-    RETURN_TYPES = ("CONDITIONING",)
+                io.String.Input("text_g", multiline=True, dynamic_prompts=True),
-    FUNCTION = "encode"
+                io.String.Input("text_l", multiline=True, dynamic_prompts=True),
+            ],
+            outputs=[io.Conditioning.Output()],
+        )
 
-    CATEGORY = "advanced/conditioning"
+    @classmethod
-
+    def execute(cls, clip, width, height, crop_w, crop_h, target_width, target_height, text_g, text_l) -> io.NodeOutput:
-    def encode(self, clip, width, height, crop_w, crop_h, target_width, target_height, text_g, text_l):
         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,
+class ClipSdxlExtension(ComfyExtension):
-    "CLIPTextEncodeSDXL": CLIPTextEncodeSDXL,
+    @override
-}
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            CLIPTextEncodeSDXLRefiner,
+            CLIPTextEncodeSDXL,
+        ]
+
+
+async def comfy_entrypoint() -> ClipSdxlExtension:
+    return ClipSdxlExtension()

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):
+    def define_schema(cls):
-        return {
+        return io.Schema(
-            "required": {
+            node_id="FreSca",
-                "model": ("MODEL",),
+            display_name="FreSca",
-                "scale_low": ("FLOAT", {"default": 1.0, "min": 0, "max": 10, "step": 0.01,
+            category="_for_testing",
-                                        "tooltip": "Scaling factor for low-frequency components"}),
+            description="Applies frequency-dependent scaling to the guidance",
-                "scale_high": ("FLOAT", {"default": 1.25, "min": 0, "max": 10, "step": 0.01,
+            inputs=[
-                                        "tooltip": "Scaling factor for high-frequency components"}),
+                io.Model.Input("model"),
-                "freq_cutoff": ("INT", {"default": 20, "min": 1, "max": 10000, "step": 1,
+                io.Float.Input("scale_low", default=1.0, min=0, max=10, step=0.01,
-                                        "tooltip": "Number of frequency indices around center to consider as low-frequency"}),
+                               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"),
-    RETURN_TYPES = ("MODEL",)
+                io.Int.Input("freq_cutoff", default=20, min=1, max=10000, step=1,
-    FUNCTION = "patch"
+                             tooltip="Number of frequency indices around center to consider as low-frequency"),
-    CATEGORY = "_for_testing"
+            ],
-    DESCRIPTION = "Applies frequency-dependent scaling to the guidance"
+            outputs=[
-    def patch(self, model, scale_low, scale_high, freq_cutoff):
+                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 = {
+class FreScaExtension(ComfyExtension):
-    "FreSca": FreSca,
+    @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_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):
+    def define_schema(cls):
-        return {"required": { "clip_name1": (folder_paths.get_filename_list("text_encoders"), ),
+        return io.Schema(
-                              "clip_name2": (folder_paths.get_filename_list("text_encoders"), ),
+            node_id="QuadrupleCLIPLoader",
-                              "clip_name3": (folder_paths.get_filename_list("text_encoders"), ),
+            category="advanced/loaders",
-                              "clip_name4": (folder_paths.get_filename_list("text_encoders"), )
+            description="[Recipes]\n\nhidream: long clip-l, long clip-g, t5xxl, llama_8b_3.1_instruct",
-                             }}
+            inputs=[
-    RETURN_TYPES = ("CLIP",)
+                io.Combo.Input("clip_name1", options=folder_paths.get_filename_list("text_encoders")),
-    FUNCTION = "load_clip"
+                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"
+    @classmethod
-
+    def execute(cls, clip_name1, clip_name2, clip_name3, clip_name4):
-    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):
         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):
+    def define_schema(cls):
-        return {"required": {
+        return io.Schema(
-            "clip": ("CLIP", ),
+            node_id="CLIPTextEncodeHiDream",
-            "clip_l": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+            category="advanced/conditioning",
-            "clip_g": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+            inputs=[
-            "t5xxl": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+                io.Clip.Input("clip"),
-            "llama": ("STRING", {"multiline": True, "dynamicPrompts": True})
+                io.String.Input("clip_l", multiline=True, dynamic_prompts=True),
-            }}
+                io.String.Input("clip_g", multiline=True, dynamic_prompts=True),
-    RETURN_TYPES = ("CONDITIONING",)
+                io.String.Input("t5xxl", multiline=True, dynamic_prompts=True),
-    FUNCTION = "encode"
+                io.String.Input("llama", multiline=True, dynamic_prompts=True),
-
+            ],
-    CATEGORY = "advanced/conditioning"
+            outputs=[
-
+                io.Conditioning.Output(),
-    def encode(self, clip, clip_l, clip_g, t5xxl, llama):
+            ]
+        )
 
+    @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,
+class HiDreamExtension(ComfyExtension):
-    "CLIPTextEncodeHiDream": CLIPTextEncodeHiDream,
+    @override
-}
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            QuadrupleCLIPLoader,
+            CLIPTextEncodeHiDream,
+        ]
+
+
+async def comfy_entrypoint() -> HiDreamExtension:
+    return HiDreamExtension()

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):
+    def define_schema(cls):
-        return {"required": { "model": ("MODEL",),
+        return io.Schema(
-                             "tile_size": ("INT", {"default": 256, "min": 1, "max": 2048}),
+            node_id="HyperTile",
-                             "swap_size": ("INT", {"default": 2, "min": 1, "max": 128}),
+            category="model_patches/unet",
-                             "max_depth": ("INT", {"default": 0, "min": 0, "max": 10}),
+            inputs=[
-                             "scale_depth": ("BOOLEAN", {"default": False}),
+                io.Model.Input("model"),
-                              }}
+                io.Int.Input("tile_size", default=256, min=1, max=2048),
-    RETURN_TYPES = ("MODEL",)
+                io.Int.Input("swap_size", default=2, min=1, max=128),
-    FUNCTION = "patch"
+                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"
+    @classmethod
-
+    def execute(cls, model, tile_size, swap_size, max_depth, scale_depth) -> io.NodeOutput:
-    def patch(self, model, tile_size, swap_size, max_depth, scale_depth):
         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:
+            nonlocal temp
-                nh, nw, h, w = self.temp
+            if temp is not None:
-                self.temp = 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_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):
+    def define_schema(cls):
-        return {
+        return io.Schema(
-            "required": {
+            node_id="LotusConditioning",
-            },
+            category="conditioning/lotus",
-        }
+            inputs=[],
+            outputs=[io.Conditioning.Output(display_name="conditioning")],
+        )
 
-    RETURN_TYPES = ("CONDITIONING",)
+    @classmethod
-    RETURN_NAMES = ("conditioning",)
+    def execute(cls) -> io.NodeOutput:
-    FUNCTION = "conditioning"
-    CATEGORY = "conditioning/lotus"
-
-    def conditioning(self):
         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_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):
+    def define_schema(cls):
-        return {"required": { "model": ("MODEL",),
+        return io.Schema(
-                              "cfg_trunc": ("FLOAT", {"default": 100, "min": 0.0, "max": 100.0, "step": 0.01}),
+            node_id="RenormCFG",
-                              "renorm_cfg": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 100.0, "step": 0.01}),
+            category="advanced/model",
-                              }}
+            inputs=[
-    RETURN_TYPES = ("MODEL",)
+                io.Model.Input("model"),
-    FUNCTION = "patch"
+                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"
+    @classmethod
-
+    def execute(cls, model, cfg_trunc, renorm_cfg) -> io.NodeOutput:
-    def patch(self, model, cfg_trunc, renorm_cfg):
         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:
+    def define_schema(cls):
-        return {
+        return io.Schema(
-            "required": {
+            node_id="CLIPTextEncodeLumina2",
-                "system_prompt": (list(CLIPTextEncodeLumina2.SYSTEM_PROMPT.keys()), {"tooltip": CLIPTextEncodeLumina2.SYSTEM_PROMPT_TIP}),
+            display_name="CLIP Text Encode for Lumina2",
-                "user_prompt": (IO.STRING, {"multiline": True, "dynamicPrompts": True, "tooltip": "The text to be encoded."}),
+            category="conditioning",
-                "clip": (IO.CLIP, {"tooltip": "The CLIP model used for encoding the text."})
+            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=[
-    RETURN_TYPES = (IO.CONDITIONING,)
+                io.Combo.Input(
-    OUTPUT_TOOLTIPS = ("A conditioning containing the embedded text used to guide the diffusion model.",)
+                    "system_prompt",
-    FUNCTION = "encode"
+                    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"
+    @classmethod
-    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 execute(cls, clip, user_prompt, system_prompt) -> io.NodeOutput:
-
-    def encode(self, clip, user_prompt, system_prompt):
         if clip is None:
             raise RuntimeError("ERROR: clip input is invalid: None\n\nIf the clip is from a checkpoint loader node your checkpoint does not contain a valid clip or text encoder model.")
-        system_prompt = CLIPTextEncodeLumina2.SYSTEM_PROMPT[system_prompt]
+        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 = {
+class Lumina2Extension(ComfyExtension):
-    "CLIPTextEncodeLumina2": CLIPTextEncodeLumina2,
+    @override
-    "RenormCFG": RenormCFG
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
-}
+        return [
+            CLIPTextEncodeLumina2,
+            RenormCFG,
+        ]
 
 
-NODE_DISPLAY_NAME_MAPPINGS = {
+async def comfy_entrypoint() -> Lumina2Extension:
-    "CLIPTextEncodeLumina2": "CLIP Text Encode for Lumina2",
+    return Lumina2Extension()
-}

comfy_extras/nodes_photomaker.py

@@ -4,6 +4,8 @@ import folder_paths
 import comfy.clip_model
 import comfy.clip_vision
 import comfy.ops
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
 
 # code for model from: https://github.com/TencentARC/PhotoMaker/blob/main/photomaker/model.py under Apache License Version 2.0
 VISION_CONFIG_DICT = {
@@ -116,41 +118,52 @@ class PhotoMakerIDEncoder(comfy.clip_model.CLIPVisionModelProjection):
         return updated_prompt_embeds
 
 
-class PhotoMakerLoader:
+class PhotoMakerLoader(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls):
-        return {"required": { "photomaker_model_name": (folder_paths.get_filename_list("photomaker"), )}}
+        return io.Schema(
+            node_id="PhotoMakerLoader",
+            category="_for_testing/photomaker",
+            inputs=[
+                io.Combo.Input("photomaker_model_name", options=folder_paths.get_filename_list("photomaker")),
+            ],
+            outputs=[
+                io.Photomaker.Output(),
+            ],
+            is_experimental=True,
+        )
 
-    RETURN_TYPES = ("PHOTOMAKER",)
+    @classmethod
-    FUNCTION = "load_photomaker_model"
+    def execute(cls, photomaker_model_name):
-
-    CATEGORY = "_for_testing/photomaker"
-
-    def load_photomaker_model(self, photomaker_model_name):
         photomaker_model_path = folder_paths.get_full_path_or_raise("photomaker", photomaker_model_name)
         photomaker_model = PhotoMakerIDEncoder()
         data = comfy.utils.load_torch_file(photomaker_model_path, safe_load=True)
         if "id_encoder" in data:
             data = data["id_encoder"]
         photomaker_model.load_state_dict(data)
-        return (photomaker_model,)
+        return io.NodeOutput(photomaker_model)
 
 
-class PhotoMakerEncode:
+class PhotoMakerEncode(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls):
-        return {"required": { "photomaker": ("PHOTOMAKER",),
+        return io.Schema(
-                              "image": ("IMAGE",),
+            node_id="PhotoMakerEncode",
-                              "clip": ("CLIP", ),
+            category="_for_testing/photomaker",
-                              "text": ("STRING", {"multiline": True, "dynamicPrompts": True, "default": "photograph of photomaker"}),
+            inputs=[
-                             }}
+                io.Photomaker.Input("photomaker"),
+                io.Image.Input("image"),
+                io.Clip.Input("clip"),
+                io.String.Input("text", multiline=True, dynamic_prompts=True, default="photograph of photomaker"),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+            is_experimental=True,
+        )
 
-    RETURN_TYPES = ("CONDITIONING",)
+    @classmethod
-    FUNCTION = "apply_photomaker"
+    def execute(cls, photomaker, image, clip, text):
-
-    CATEGORY = "_for_testing/photomaker"
-
-    def apply_photomaker(self, photomaker, image, clip, text):
         special_token = "photomaker"
         pixel_values = comfy.clip_vision.clip_preprocess(image.to(photomaker.load_device)).float()
         try:
@@ -178,11 +191,16 @@ class PhotoMakerEncode:
         else:
             out = cond
 
-        return ([[out, {"pooled_output": pooled}]], )
+        return io.NodeOutput([[out, {"pooled_output": pooled}]])
 
 
-NODE_CLASS_MAPPINGS = {
+class PhotomakerExtension(ComfyExtension):
-    "PhotoMakerLoader": PhotoMakerLoader,
+    @override
-    "PhotoMakerEncode": PhotoMakerEncode,
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
-}
+        return [
+            PhotoMakerLoader,
+            PhotoMakerEncode,
+        ]
 
+async def comfy_entrypoint() -> PhotomakerExtension:
+    return PhotomakerExtension()

comfy_extras/nodes_pixart.py

@@ -1,24 +1,38 @@
-from nodes import MAX_RESOLUTION
+from typing_extensions import override
+import nodes
+from comfy_api.latest import ComfyExtension, io
 
-class CLIPTextEncodePixArtAlpha:
+class CLIPTextEncodePixArtAlpha(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls):
-        return {"required": {
+        return io.Schema(
-            "width": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
+            node_id="CLIPTextEncodePixArtAlpha",
-            "height": ("INT", {"default": 1024.0, "min": 0, "max": MAX_RESOLUTION}),
+            category="advanced/conditioning",
+            description="Encodes text and sets the resolution conditioning for PixArt Alpha. Does not apply to PixArt Sigma.",
+            inputs=[
+                io.Int.Input("width", default=1024, min=0, max=nodes.MAX_RESOLUTION),
+                io.Int.Input("height", default=1024, min=0, max=nodes.MAX_RESOLUTION),
                 # "aspect_ratio": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
-            "text": ("STRING", {"multiline": True, "dynamicPrompts": True}), "clip": ("CLIP", ),
+                io.String.Input("text", multiline=True, dynamic_prompts=True),
-            }}
+                io.Clip.Input("clip"),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("CONDITIONING",)
+    @classmethod
-    FUNCTION = "encode"
+    def execute(cls, clip, width, height, text):
-    CATEGORY = "advanced/conditioning"
-    DESCRIPTION = "Encodes text and sets the resolution conditioning for PixArt Alpha. Does not apply to PixArt Sigma."
-
-    def encode(self, clip, width, height, text):
         tokens = clip.tokenize(text)
-        return (clip.encode_from_tokens_scheduled(tokens, add_dict={"width": width, "height": height}),)
+        return io.NodeOutput(clip.encode_from_tokens_scheduled(tokens, add_dict={"width": width, "height": height}))
 
-NODE_CLASS_MAPPINGS = {
+
-    "CLIPTextEncodePixArtAlpha": CLIPTextEncodePixArtAlpha,
+class PixArtExtension(ComfyExtension):
-}
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            CLIPTextEncodePixArtAlpha,
+        ]
+
+async def comfy_entrypoint() -> PixArtExtension:
+    return PixArtExtension()

comfy_extras/nodes_post_processing.py

@@ -1,3 +1,4 @@
+from typing_extensions import override
 import numpy as np
 import torch
 import torch.nn.functional as F
@@ -7,33 +8,27 @@ import math
 import comfy.utils
 import comfy.model_management
 import node_helpers
+from comfy_api.latest import ComfyExtension, io
 
-class Blend:
+class Blend(io.ComfyNode):
-    def __init__(self):
+    @classmethod
-        pass
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ImageBlend",
+            category="image/postprocessing",
+            inputs=[
+                io.Image.Input("image1"),
+                io.Image.Input("image2"),
+                io.Float.Input("blend_factor", default=0.5, min=0.0, max=1.0, step=0.01),
+                io.Combo.Input("blend_mode", options=["normal", "multiply", "screen", "overlay", "soft_light", "difference"]),
+            ],
+            outputs=[
+                io.Image.Output(),
+            ],
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
+    def execute(cls, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str) -> io.NodeOutput:
-        return {
-            "required": {
-                "image1": ("IMAGE",),
-                "image2": ("IMAGE",),
-                "blend_factor": ("FLOAT", {
-                    "default": 0.5,
-                    "min": 0.0,
-                    "max": 1.0,
-                    "step": 0.01
-                }),
-                "blend_mode": (["normal", "multiply", "screen", "overlay", "soft_light", "difference"],),
-            },
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "blend_images"
-
-    CATEGORY = "image/postprocessing"
-
-    def blend_images(self, image1: torch.Tensor, image2: torch.Tensor, blend_factor: float, blend_mode: str):
         image1, image2 = node_helpers.image_alpha_fix(image1, image2)
         image2 = image2.to(image1.device)
         if image1.shape != image2.shape:
@@ -41,12 +36,13 @@ class Blend:
             image2 = comfy.utils.common_upscale(image2, image1.shape[2], image1.shape[1], upscale_method='bicubic', crop='center')
             image2 = image2.permute(0, 2, 3, 1)
 
-        blended_image = self.blend_mode(image1, image2, blend_mode)
+        blended_image = cls.blend_mode(image1, image2, blend_mode)
         blended_image = image1 * (1 - blend_factor) + blended_image * blend_factor
         blended_image = torch.clamp(blended_image, 0, 1)
-        return (blended_image,)
+        return io.NodeOutput(blended_image)
 
-    def blend_mode(self, img1, img2, mode):
+    @classmethod
+    def blend_mode(cls, img1, img2, mode):
         if mode == "normal":
             return img2
         elif mode == "multiply":
@@ -56,13 +52,13 @@ class Blend:
         elif mode == "overlay":
             return torch.where(img1 <= 0.5, 2 * img1 * img2, 1 - 2 * (1 - img1) * (1 - img2))
         elif mode == "soft_light":
-            return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (self.g(img1) - img1))
+            return torch.where(img2 <= 0.5, img1 - (1 - 2 * img2) * img1 * (1 - img1), img1 + (2 * img2 - 1) * (cls.g(img1) - img1))
         elif mode == "difference":
             return img1 - img2
-        else:
         raise ValueError(f"Unsupported blend mode: {mode}")
 
-    def g(self, x):
+    @classmethod
+    def g(cls, x):
         return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x))
 
 def gaussian_kernel(kernel_size: int, sigma: float, device=None):
@@ -71,38 +67,26 @@ def gaussian_kernel(kernel_size: int, sigma: float, device=None):
     g = torch.exp(-(d * d) / (2.0 * sigma * sigma))
     return g / g.sum()
 
-class Blur:
+class Blur(io.ComfyNode):
-    def __init__(self):
+    @classmethod
-        pass
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ImageBlur",
+            category="image/postprocessing",
+            inputs=[
+                io.Image.Input("image"),
+                io.Int.Input("blur_radius", default=1, min=1, max=31, step=1),
+                io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.1),
+            ],
+            outputs=[
+                io.Image.Output(),
+            ],
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
+    def execute(cls, image: torch.Tensor, blur_radius: int, sigma: float) -> io.NodeOutput:
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "blur_radius": ("INT", {
-                    "default": 1,
-                    "min": 1,
-                    "max": 31,
-                    "step": 1
-                }),
-                "sigma": ("FLOAT", {
-                    "default": 1.0,
-                    "min": 0.1,
-                    "max": 10.0,
-                    "step": 0.1
-                }),
-            },
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "blur"
-
-    CATEGORY = "image/postprocessing"
-
-    def blur(self, image: torch.Tensor, blur_radius: int, sigma: float):
         if blur_radius == 0:
-            return (image,)
+            return io.NodeOutput(image)
 
         image = image.to(comfy.model_management.get_torch_device())
         batch_size, height, width, channels = image.shape
@@ -115,31 +99,24 @@ class Blur:
         blurred = F.conv2d(padded_image, kernel, padding=kernel_size // 2, groups=channels)[:,:,blur_radius:-blur_radius, blur_radius:-blur_radius]
         blurred = blurred.permute(0, 2, 3, 1)
 
-        return (blurred.to(comfy.model_management.intermediate_device()),)
+        return io.NodeOutput(blurred.to(comfy.model_management.intermediate_device()))
 
-class Quantize:
-    def __init__(self):
-        pass
 
+class Quantize(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls):
-        return {
+        return io.Schema(
-            "required": {
+            node_id="ImageQuantize",
-                "image": ("IMAGE",),
+            category="image/postprocessing",
-                "colors": ("INT", {
+            inputs=[
-                    "default": 256,
+                io.Image.Input("image"),
-                    "min": 1,
+                io.Int.Input("colors", default=256, min=1, max=256, step=1),
-                    "max": 256,
+                io.Combo.Input("dither", options=["none", "floyd-steinberg", "bayer-2", "bayer-4", "bayer-8", "bayer-16"]),
-                    "step": 1
+            ],
-                }),
+            outputs=[
-                "dither": (["none", "floyd-steinberg", "bayer-2", "bayer-4", "bayer-8", "bayer-16"],),
+                io.Image.Output(),
-            },
+            ],
-        }
+        )
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "quantize"
-
-    CATEGORY = "image/postprocessing"
 
     @staticmethod
     def bayer(im, pal_im, order):
@@ -167,7 +144,8 @@ class Quantize:
         im = im.quantize(palette=pal_im, dither=Image.Dither.NONE)
         return im
 
-    def quantize(self, image: torch.Tensor, colors: int, dither: str):
+    @classmethod
+    def execute(cls, image: torch.Tensor, colors: int, dither: str) -> io.NodeOutput:
         batch_size, height, width, _ = image.shape
         result = torch.zeros_like(image)
 
@@ -187,46 +165,29 @@ class Quantize:
             quantized_array = torch.tensor(np.array(quantized_image.convert("RGB"))).float() / 255
             result[b] = quantized_array
 
-        return (result,)
+        return io.NodeOutput(result)
 
-class Sharpen:
+class Sharpen(io.ComfyNode):
-    def __init__(self):
+    @classmethod
-        pass
+    def define_schema(cls):
+        return io.Schema(
+            node_id="ImageSharpen",
+            category="image/postprocessing",
+            inputs=[
+                io.Image.Input("image"),
+                io.Int.Input("sharpen_radius", default=1, min=1, max=31, step=1),
+                io.Float.Input("sigma", default=1.0, min=0.1, max=10.0, step=0.01),
+                io.Float.Input("alpha", default=1.0, min=0.0, max=5.0, step=0.01),
+            ],
+            outputs=[
+                io.Image.Output(),
+            ],
+        )
 
     @classmethod
-    def INPUT_TYPES(s):
+    def execute(cls, image: torch.Tensor, sharpen_radius: int, sigma:float, alpha: float) -> io.NodeOutput:
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "sharpen_radius": ("INT", {
-                    "default": 1,
-                    "min": 1,
-                    "max": 31,
-                    "step": 1
-                }),
-                "sigma": ("FLOAT", {
-                    "default": 1.0,
-                    "min": 0.1,
-                    "max": 10.0,
-                    "step": 0.01
-                }),
-                "alpha": ("FLOAT", {
-                    "default": 1.0,
-                    "min": 0.0,
-                    "max": 5.0,
-                    "step": 0.01
-                }),
-            },
-        }
-
-    RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "sharpen"
-
-    CATEGORY = "image/postprocessing"
-
-    def sharpen(self, image: torch.Tensor, sharpen_radius: int, sigma:float, alpha: float):
         if sharpen_radius == 0:
-            return (image,)
+            return io.NodeOutput(image)
 
         batch_size, height, width, channels = image.shape
         image = image.to(comfy.model_management.get_torch_device())
@@ -245,23 +206,29 @@ class Sharpen:
 
         result = torch.clamp(sharpened, 0, 1)
 
-        return (result.to(comfy.model_management.intermediate_device()),)
+        return io.NodeOutput(result.to(comfy.model_management.intermediate_device()))
 
-class ImageScaleToTotalPixels:
+class ImageScaleToTotalPixels(io.ComfyNode):
     upscale_methods = ["nearest-exact", "bilinear", "area", "bicubic", "lanczos"]
     crop_methods = ["disabled", "center"]
 
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls):
-        return {"required": { "image": ("IMAGE",), "upscale_method": (s.upscale_methods,),
+        return io.Schema(
-                              "megapixels": ("FLOAT", {"default": 1.0, "min": 0.01, "max": 16.0, "step": 0.01}),
+            node_id="ImageScaleToTotalPixels",
-                            }}
+            category="image/upscaling",
-    RETURN_TYPES = ("IMAGE",)
+            inputs=[
-    FUNCTION = "upscale"
+                io.Image.Input("image"),
+                io.Combo.Input("upscale_method", options=cls.upscale_methods),
+                io.Float.Input("megapixels", default=1.0, min=0.01, max=16.0, step=0.01),
+            ],
+            outputs=[
+                io.Image.Output(),
+            ],
+        )
 
-    CATEGORY = "image/upscaling"
+    @classmethod
-
+    def execute(cls, image, upscale_method, megapixels) -> io.NodeOutput:
-    def upscale(self, image, upscale_method, megapixels):
         samples = image.movedim(-1,1)
         total = int(megapixels * 1024 * 1024)
 
@@ -271,12 +238,18 @@ class ImageScaleToTotalPixels:
 
         s = comfy.utils.common_upscale(samples, width, height, upscale_method, "disabled")
         s = s.movedim(1,-1)
-        return (s,)
+        return io.NodeOutput(s)
 
-NODE_CLASS_MAPPINGS = {
+class PostProcessingExtension(ComfyExtension):
-    "ImageBlend": Blend,
+    @override
-    "ImageBlur": Blur,
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
-    "ImageQuantize": Quantize,
+        return [
-    "ImageSharpen": Sharpen,
+            Blend,
-    "ImageScaleToTotalPixels": ImageScaleToTotalPixels,
+            Blur,
-}
+            Quantize,
+            Sharpen,
+            ImageScaleToTotalPixels,
+        ]
+
+async def comfy_entrypoint() -> PostProcessingExtension:
+    return PostProcessingExtension()

comfy_extras/nodes_qwen.py

@@ -1,24 +1,29 @@
 import node_helpers
 import comfy.utils
 import math
+from typing_extensions import override
+from comfy_api.latest import ComfyExtension, io
 
 
-class TextEncodeQwenImageEdit:
+class TextEncodeQwenImageEdit(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls):
-        return {"required": {
+        return io.Schema(
-            "clip": ("CLIP", ),
+            node_id="TextEncodeQwenImageEdit",
-            "prompt": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+            category="advanced/conditioning",
-            },
+            inputs=[
-            "optional": {"vae": ("VAE", ),
+                io.Clip.Input("clip"),
-                         "image": ("IMAGE", ),}}
+                io.String.Input("prompt", multiline=True, dynamic_prompts=True),
+                io.Vae.Input("vae", optional=True),
+                io.Image.Input("image", optional=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("CONDITIONING",)
+    @classmethod
-    FUNCTION = "encode"
+    def execute(cls, clip, prompt, vae=None, image=None) -> io.NodeOutput:
-
-    CATEGORY = "advanced/conditioning"
-
-    def encode(self, clip, prompt, vae=None, image=None):
         ref_latent = None
         if image is None:
             images = []
@@ -40,28 +45,30 @@ class TextEncodeQwenImageEdit:
         conditioning = clip.encode_from_tokens_scheduled(tokens)
         if ref_latent is not None:
             conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": [ref_latent]}, append=True)
-        return (conditioning, )
+        return io.NodeOutput(conditioning)
 
 
-class TextEncodeQwenImageEditPlus:
+class TextEncodeQwenImageEditPlus(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls):
-        return {"required": {
+        return io.Schema(
-            "clip": ("CLIP", ),
+            node_id="TextEncodeQwenImageEditPlus",
-            "prompt": ("STRING", {"multiline": True, "dynamicPrompts": True}),
+            category="advanced/conditioning",
-            },
+            inputs=[
-            "optional": {"vae": ("VAE", ),
+                io.Clip.Input("clip"),
-                         "image1": ("IMAGE", ),
+                io.String.Input("prompt", multiline=True, dynamic_prompts=True),
-                         "image2": ("IMAGE", ),
+                io.Vae.Input("vae", optional=True),
-                         "image3": ("IMAGE", ),
+                io.Image.Input("image1", optional=True),
-                         }}
+                io.Image.Input("image2", optional=True),
+                io.Image.Input("image3", optional=True),
+            ],
+            outputs=[
+                io.Conditioning.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("CONDITIONING",)
+    @classmethod
-    FUNCTION = "encode"
+    def execute(cls, clip, prompt, vae=None, image1=None, image2=None, image3=None) -> io.NodeOutput:
-
-    CATEGORY = "advanced/conditioning"
-
-    def encode(self, clip, prompt, vae=None, image1=None, image2=None, image3=None):
         ref_latents = []
         images = [image1, image2, image3]
         images_vl = []
@@ -94,10 +101,17 @@ class TextEncodeQwenImageEditPlus:
         conditioning = clip.encode_from_tokens_scheduled(tokens)
         if len(ref_latents) > 0:
             conditioning = node_helpers.conditioning_set_values(conditioning, {"reference_latents": ref_latents}, append=True)
-        return (conditioning, )
+        return io.NodeOutput(conditioning)
 
 
-NODE_CLASS_MAPPINGS = {
+class QwenExtension(ComfyExtension):
-    "TextEncodeQwenImageEdit": TextEncodeQwenImageEdit,
+    @override
-    "TextEncodeQwenImageEditPlus": TextEncodeQwenImageEditPlus,
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
-}
+        return [
+            TextEncodeQwenImageEdit,
+            TextEncodeQwenImageEditPlus,
+        ]
+
+
+async def comfy_entrypoint() -> QwenExtension:
+    return QwenExtension()

comfy_extras/nodes_rebatch.py

@@ -1,18 +1,25 @@
+from typing_extensions import override
 import torch
 
-class LatentRebatch:
+from comfy_api.latest import ComfyExtension, io
+
+
+class LatentRebatch(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls):
-        return {"required": { "latents": ("LATENT",),
+        return io.Schema(
-                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
+            node_id="RebatchLatents",
-                              }}
+            display_name="Rebatch Latents",
-    RETURN_TYPES = ("LATENT",)
+            category="latent/batch",
-    INPUT_IS_LIST = True
+            is_input_list=True,
-    OUTPUT_IS_LIST = (True, )
+            inputs=[
-
+                io.Latent.Input("latents"),
-    FUNCTION = "rebatch"
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
-
+            ],
-    CATEGORY = "latent/batch"
+            outputs=[
+                io.Latent.Output(is_output_list=True),
+            ],
+        )
 
     @staticmethod
     def get_batch(latents, list_ind, offset):
@@ -53,7 +60,8 @@ class LatentRebatch:
         result = [torch.cat((b1, b2)) if torch.is_tensor(b1) else b1 + b2 for b1, b2 in zip(batch1, batch2)]
         return result
 
-    def rebatch(self, latents, batch_size):
+    @classmethod
+    def execute(cls, latents, batch_size):
         batch_size = batch_size[0]
 
         output_list = []
@@ -63,24 +71,24 @@ class LatentRebatch:
         for i in range(len(latents)):
             # fetch new entry of list
             #samples, masks, indices = self.get_batch(latents, i)
-            next_batch = self.get_batch(latents, i, processed)
+            next_batch = cls.get_batch(latents, i, processed)
             processed += len(next_batch[2])
             # set to current if current is None
             if current_batch[0] is None:
                 current_batch = next_batch
             # add previous to list if dimensions do not match
             elif next_batch[0].shape[-1] != current_batch[0].shape[-1] or next_batch[0].shape[-2] != current_batch[0].shape[-2]:
-                sliced, _ = self.slice_batch(current_batch, 1, batch_size)
+                sliced, _ = cls.slice_batch(current_batch, 1, batch_size)
                 output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]})
                 current_batch = next_batch
             # cat if everything checks out
             else:
-                current_batch = self.cat_batch(current_batch, next_batch)
+                current_batch = cls.cat_batch(current_batch, next_batch)
 
             # add to list if dimensions gone above target batch size
             if current_batch[0].shape[0] > batch_size:
                 num = current_batch[0].shape[0] // batch_size
-                sliced, remainder = self.slice_batch(current_batch, num, batch_size)
+                sliced, remainder = cls.slice_batch(current_batch, num, batch_size)
 
                 for i in range(num):
                     output_list.append({'samples': sliced[0][i], 'noise_mask': sliced[1][i], 'batch_index': sliced[2][i]})
@@ -89,7 +97,7 @@ class LatentRebatch:
 
         #add remainder
         if current_batch[0] is not None:
-            sliced, _ = self.slice_batch(current_batch, 1, batch_size)
+            sliced, _ = cls.slice_batch(current_batch, 1, batch_size)
             output_list.append({'samples': sliced[0][0], 'noise_mask': sliced[1][0], 'batch_index': sliced[2][0]})
 
         #get rid of empty masks
@@ -97,23 +105,27 @@ class LatentRebatch:
             if s['noise_mask'].mean() == 1.0:
                 del s['noise_mask']
 
-        return (output_list,)
+        return io.NodeOutput(output_list)
 
-class ImageRebatch:
+class ImageRebatch(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls):
-        return {"required": { "images": ("IMAGE",),
+        return io.Schema(
-                              "batch_size": ("INT", {"default": 1, "min": 1, "max": 4096}),
+            node_id="RebatchImages",
-                              }}
+            display_name="Rebatch Images",
-    RETURN_TYPES = ("IMAGE",)
+            category="image/batch",
-    INPUT_IS_LIST = True
+            is_input_list=True,
-    OUTPUT_IS_LIST = (True, )
+            inputs=[
+                io.Image.Input("images"),
+                io.Int.Input("batch_size", default=1, min=1, max=4096),
+            ],
+            outputs=[
+                io.Image.Output(is_output_list=True),
+            ],
+        )
 
-    FUNCTION = "rebatch"
+    @classmethod
-
+    def execute(cls, images, batch_size):
-    CATEGORY = "image/batch"
-
-    def rebatch(self, images, batch_size):
         batch_size = batch_size[0]
 
         output_list = []
@@ -125,14 +137,17 @@ class ImageRebatch:
         for i in range(0, len(all_images), batch_size):
             output_list.append(torch.cat(all_images[i:i+batch_size], dim=0))
 
-        return (output_list,)
+        return io.NodeOutput(output_list)
 
-NODE_CLASS_MAPPINGS = {
-    "RebatchLatents": LatentRebatch,
-    "RebatchImages": ImageRebatch,
-}
 
-NODE_DISPLAY_NAME_MAPPINGS = {
+class RebatchExtension(ComfyExtension):
-    "RebatchLatents": "Rebatch Latents",
+    @override
-    "RebatchImages": "Rebatch Images",
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
-}
+        return [
+            LatentRebatch,
+            ImageRebatch,
+        ]
+
+
+async def comfy_entrypoint() -> RebatchExtension:
+    return RebatchExtension()

comfy_extras/nodes_sag.py

@@ -2,10 +2,13 @@ import torch
 from torch import einsum
 import torch.nn.functional as F
 import math
+from typing_extensions import override
 
 from einops import rearrange, repeat
 from comfy.ldm.modules.attention import optimized_attention
 import comfy.samplers
+from comfy_api.latest import ComfyExtension, io
+
 
 # from comfy/ldm/modules/attention.py
 # but modified to return attention scores as well as output
@@ -104,19 +107,26 @@ def gaussian_blur_2d(img, kernel_size, sigma):
     img = F.conv2d(img, kernel2d, groups=img.shape[-3])
     return img
 
-class SelfAttentionGuidance:
+class SelfAttentionGuidance(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls):
-        return {"required": { "model": ("MODEL",),
+        return io.Schema(
-                             "scale": ("FLOAT", {"default": 0.5, "min": -2.0, "max": 5.0, "step": 0.01}),
+            node_id="SelfAttentionGuidance",
-                             "blur_sigma": ("FLOAT", {"default": 2.0, "min": 0.0, "max": 10.0, "step": 0.1}),
+            display_name="Self-Attention Guidance",
-                              }}
+            category="_for_testing",
-    RETURN_TYPES = ("MODEL",)
+            inputs=[
-    FUNCTION = "patch"
+                io.Model.Input("model"),
+                io.Float.Input("scale", default=0.5, min=-2.0, max=5.0, step=0.01),
+                io.Float.Input("blur_sigma", default=2.0, min=0.0, max=10.0, step=0.1),
+            ],
+            outputs=[
+                io.Model.Output(),
+            ],
+            is_experimental=True,
+        )
 
-    CATEGORY = "_for_testing"
+    @classmethod
-
+    def execute(cls, model, scale, blur_sigma):
-    def patch(self, model, scale, blur_sigma):
         m = model.clone()
 
         attn_scores = None
@@ -170,12 +180,16 @@ class SelfAttentionGuidance:
         # unet.mid_block.attentions[0].transformer_blocks[0].attn1.patch
         m.set_model_attn1_replace(attn_and_record, "middle", 0, 0)
 
-        return (m, )
+        return io.NodeOutput(m)
 
-NODE_CLASS_MAPPINGS = {
-    "SelfAttentionGuidance": SelfAttentionGuidance,
-}
 
-NODE_DISPLAY_NAME_MAPPINGS = {
+class SagExtension(ComfyExtension):
-    "SelfAttentionGuidance": "Self-Attention Guidance",
+    @override
-}
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            SelfAttentionGuidance,
+        ]
+
+
+async def comfy_entrypoint() -> SagExtension:
+    return SagExtension()

comfy_extras/nodes_sdupscale.py

@@ -1,23 +1,31 @@
+from typing_extensions import override
+
 import torch
 import comfy.utils
+from comfy_api.latest import ComfyExtension, io
 
-class SD_4XUpscale_Conditioning:
+class SD_4XUpscale_Conditioning(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls):
-        return {"required": { "images": ("IMAGE",),
+        return io.Schema(
-                              "positive": ("CONDITIONING",),
+            node_id="SD_4XUpscale_Conditioning",
-                              "negative": ("CONDITIONING",),
+            category="conditioning/upscale_diffusion",
-                              "scale_ratio": ("FLOAT", {"default": 4.0, "min": 0.0, "max": 10.0, "step": 0.01}),
+            inputs=[
-                              "noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
+                io.Image.Input("images"),
-                             }}
+                io.Conditioning.Input("positive"),
-    RETURN_TYPES = ("CONDITIONING", "CONDITIONING", "LATENT")
+                io.Conditioning.Input("negative"),
-    RETURN_NAMES = ("positive", "negative", "latent")
+                io.Float.Input("scale_ratio", default=4.0, min=0.0, max=10.0, step=0.01),
+                io.Float.Input("noise_augmentation", default=0.0, min=0.0, max=1.0, step=0.001),
+            ],
+            outputs=[
+                io.Conditioning.Output(display_name="positive"),
+                io.Conditioning.Output(display_name="negative"),
+                io.Latent.Output(display_name="latent"),
+            ],
+        )
 
-    FUNCTION = "encode"
+    @classmethod
-
+    def execute(cls, images, positive, negative, scale_ratio, noise_augmentation):
-    CATEGORY = "conditioning/upscale_diffusion"
-
-    def encode(self, images, positive, negative, scale_ratio, noise_augmentation):
         width = max(1, round(images.shape[-2] * scale_ratio))
         height = max(1, round(images.shape[-3] * scale_ratio))
 
@@ -39,8 +47,16 @@ class SD_4XUpscale_Conditioning:
             out_cn.append(n)
 
         latent = torch.zeros([images.shape[0], 4, height // 4, width // 4])
-        return (out_cp, out_cn, {"samples":latent})
+        return io.NodeOutput(out_cp, out_cn, {"samples":latent})
 
-NODE_CLASS_MAPPINGS = {
+
-    "SD_4XUpscale_Conditioning": SD_4XUpscale_Conditioning,
+class SdUpscaleExtension(ComfyExtension):
-}
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            SD_4XUpscale_Conditioning,
+        ]
+
+
+async def comfy_entrypoint() -> SdUpscaleExtension:
+    return SdUpscaleExtension()

comfy_extras/nodes_tcfg.py

@@ -1,8 +1,9 @@
 # TCFG: Tangential Damping Classifier-free Guidance - (arXiv: https://arxiv.org/abs/2503.18137)
 
+from typing_extensions import override
 import torch
 
-from comfy.comfy_types import IO, ComfyNodeABC, InputTypeDict
+from comfy_api.latest import ComfyExtension, io
 
 
 def score_tangential_damping(cond_score: torch.Tensor, uncond_score: torch.Tensor) -> torch.Tensor:
@@ -26,23 +27,24 @@ def score_tangential_damping(cond_score: torch.Tensor, uncond_score: torch.Tenso
     return uncond_score_td.reshape_as(uncond_score).to(uncond_score.dtype)
 
 
-class TCFG(ComfyNodeABC):
+class TCFG(io.ComfyNode):
     @classmethod
-    def INPUT_TYPES(cls) -> InputTypeDict:
+    def define_schema(cls):
-        return {
+        return io.Schema(
-            "required": {
+            node_id="TCFG",
-                "model": (IO.MODEL, {}),
+            display_name="Tangential Damping CFG",
-            }
+            category="advanced/guidance",
-        }
+            description="TCFG – Tangential Damping CFG (2503.18137)\n\nRefine the uncond (negative) to align with the cond (positive) for improving quality.",
+            inputs=[
+                io.Model.Input("model"),
+            ],
+            outputs=[
+                io.Model.Output(display_name="patched_model"),
+            ],
+        )
 
-    RETURN_TYPES = (IO.MODEL,)
+    @classmethod
-    RETURN_NAMES = ("patched_model",)
+    def execute(cls, model):
-    FUNCTION = "patch"
-
-    CATEGORY = "advanced/guidance"
-    DESCRIPTION = "TCFG – Tangential Damping CFG (2503.18137)\n\nRefine the uncond (negative) to align with the cond (positive) for improving quality."
-
-    def patch(self, model):
         m = model.clone()
 
         def tangential_damping_cfg(args):
@@ -59,13 +61,16 @@ class TCFG(ComfyNodeABC):
             return [cond_pred, uncond_pred_td] + conds_out[2:]
 
         m.set_model_sampler_pre_cfg_function(tangential_damping_cfg)
-        return (m,)
+        return io.NodeOutput(m)
 
 
-NODE_CLASS_MAPPINGS = {
+class TcfgExtension(ComfyExtension):
-    "TCFG": TCFG,
+    @override
-}
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            TCFG,
+        ]
 
-NODE_DISPLAY_NAME_MAPPINGS = {
+
-    "TCFG": "Tangential Damping CFG",
+async def comfy_entrypoint() -> TcfgExtension:
-}
+    return TcfgExtension()

main.py

@@ -153,6 +153,7 @@ if __name__ == "__main__":
     if args.cuda_device is not None:
         os.environ['CUDA_VISIBLE_DEVICES'] = str(args.cuda_device)
         os.environ['HIP_VISIBLE_DEVICES'] = str(args.cuda_device)
+        os.environ["ASCEND_RT_VISIBLE_DEVICES"] = str(args.cuda_device)
         logging.info("Set cuda device to: {}".format(args.cuda_device))
 
     if args.oneapi_device_selector is not None:

requirements.txt

@@ -1,5 +1,5 @@
 comfyui-frontend-package==1.26.13
-comfyui-workflow-templates==0.1.86
+comfyui-workflow-templates==0.1.88
 comfyui-embedded-docs==0.2.6
 comfyui_manager==4.0.2
 torch

server.py

@@ -556,6 +556,8 @@ class PromptServer():
             vram_total, torch_vram_total = comfy.model_management.get_total_memory(device, torch_total_too=True)
             vram_free, torch_vram_free = comfy.model_management.get_free_memory(device, torch_free_too=True)
             required_frontend_version = FrontendManager.get_required_frontend_version()
+            installed_templates_version = FrontendManager.get_installed_templates_version()
+            required_templates_version = FrontendManager.get_required_templates_version()
 
             system_stats = {
                 "system": {
@@ -564,6 +566,8 @@ class PromptServer():
                     "ram_free": ram_free,
                     "comfyui_version": __version__,
                     "required_frontend_version": required_frontend_version,
+                    "installed_templates_version": installed_templates_version,
+                    "required_templates_version": required_templates_version,
                     "python_version": sys.version,
                     "pytorch_version": comfy.model_management.torch_version,
                     "embedded_python": os.path.split(os.path.split(sys.executable)[0])[1] == "python_embeded",

tests-unit/app_test/frontend_manager_test.py

@@ -205,3 +205,74 @@ numpy"""
 
     # Assert
     assert version is None
+
+
+def test_get_templates_version():
+    # Arrange
+    expected_version = "0.1.41"
+    mock_requirements_content = """torch
+torchsde
+comfyui-frontend-package==1.25.0
+comfyui-workflow-templates==0.1.41
+other-package==1.0.0
+numpy"""
+
+    # Act
+    with patch("builtins.open", mock_open(read_data=mock_requirements_content)):
+        version = FrontendManager.get_required_templates_version()
+
+    # Assert
+    assert version == expected_version
+
+
+def test_get_templates_version_not_found():
+    # Arrange
+    mock_requirements_content = """torch
+torchsde
+comfyui-frontend-package==1.25.0
+other-package==1.0.0
+numpy"""
+
+    # Act
+    with patch("builtins.open", mock_open(read_data=mock_requirements_content)):
+        version = FrontendManager.get_required_templates_version()
+
+    # Assert
+    assert version is None
+
+
+def test_get_templates_version_invalid_semver():
+    # Arrange
+    mock_requirements_content = """torch
+torchsde
+comfyui-workflow-templates==1.0.0.beta
+other-package==1.0.0
+numpy"""
+
+    # Act
+    with patch("builtins.open", mock_open(read_data=mock_requirements_content)):
+        version = FrontendManager.get_required_templates_version()
+
+    # Assert
+    assert version is None
+
+
+def test_get_installed_templates_version():
+    # Arrange
+    expected_version = "0.1.40"
+
+    # Act
+    with patch("app.frontend_management.version", return_value=expected_version):
+        version = FrontendManager.get_installed_templates_version()
+
+    # Assert
+    assert version == expected_version
+
+
+def test_get_installed_templates_version_not_installed():
+    # Act
+    with patch("app.frontend_management.version", side_effect=Exception("Package not found")):
+        version = FrontendManager.get_installed_templates_version()
+
+    # Assert
+    assert version is None