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

.ci/windows_amd_base_files/README_VERY_IMPORTANT.txt

@@ -3,10 +3,13 @@ https://www.amd.com/en/resources/support-articles/release-notes/RN-AMDGPU-WINDOW
 
 HOW TO RUN:
 
-if you have a AMD gpu:
+If you have a AMD gpu:
 
 run_amd_gpu.bat
 
+If you have memory issues you can try disabling the smart memory management by running comfyui with:
+
+run_amd_gpu_disable_smart_memory.bat
 
 IF YOU GET A RED ERROR IN THE UI MAKE SURE YOU HAVE A MODEL/CHECKPOINT IN: ComfyUI\models\checkpoints
 

.ci/windows_amd_base_files/run_amd_gpu_disable_smart_memory.bat

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

.github/workflows/ruff.yml

@@ -21,3 +21,28 @@ jobs:
 
     - name: Run Ruff
       run: ruff check .
+
+  pylint:
+    name: Run Pylint
+    runs-on: ubuntu-latest
+
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+
+    - name: Set up Python
+      uses: actions/setup-python@v4
+      with:
+        python-version: '3.12'
+
+    - name: Install requirements
+      run: |
+        python -m pip install --upgrade pip
+        pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
+        pip install -r requirements.txt
+
+    - name: Install Pylint
+      run: pip install pylint
+
+    - name: Run Pylint
+      run: pylint comfy_api_nodes

comfy_api_nodes/apis/__init__.py

@@ -2,6 +2,7 @@
 #   filename:  filtered-openapi.yaml
 #   timestamp: 2025-07-30T08:54:00+00:00
 
+# pylint: disable
 from __future__ import annotations
 
 from datetime import date, datetime

comfy_api_nodes/apis/client.py

@@ -95,6 +95,7 @@ import aiohttp
 import asyncio
 import logging
 import io
+import os
 import socket
 from aiohttp.client_exceptions import ClientError, ClientResponseError
 from typing import Dict, Type, Optional, Any, TypeVar, Generic, Callable, Tuple
@@ -499,7 +500,9 @@ class ApiClient:
         else:
             raise ValueError("File must be BytesIO or str path")
 
-        operation_id = f"upload_{upload_url.split('/')[-1]}_{uuid.uuid4().hex[:8]}"
+        parsed = urlparse(upload_url)
+        basename = os.path.basename(parsed.path) or parsed.netloc or "upload"
+        operation_id = f"upload_{basename}_{uuid.uuid4().hex[:8]}"
         request_logger.log_request_response(
             operation_id=operation_id,
             request_method="PUT",
@@ -532,7 +535,7 @@ class ApiClient:
                     request_method="PUT",
                     request_url=upload_url,
                     response_status_code=e.status if hasattr(e, "status") else None,
-                    response_headers=dict(e.headers) if getattr(e, "headers") else None,
+                    response_headers=dict(e.headers) if hasattr(e, "headers") else None,
                     response_content=None,
                     error_message=f"{type(e).__name__}: {str(e)}",
                 )

comfy_api_nodes/apis/request_logger.py

@@ -4,16 +4,18 @@ import os
 import datetime
 import json
 import logging
+import re
+import hashlib
+from typing import Any
+
 import folder_paths
 
 # Get the logger instance
 logger = logging.getLogger(__name__)
 
+
 def get_log_directory():
-    """
-    Ensures the API log directory exists within ComfyUI's temp directory
-    and returns its path.
-    """
+    """Ensures the API log directory exists within ComfyUI's temp directory and returns its path."""
     base_temp_dir = folder_paths.get_temp_directory()
     log_dir = os.path.join(base_temp_dir, "api_logs")
     try:
@@ -24,42 +26,77 @@ def get_log_directory():
         return base_temp_dir
     return log_dir
 
-def _format_data_for_logging(data):
+
+def _sanitize_filename_component(name: str) -> str:
+    if not name:
+        return "log"
+    sanitized = re.sub(r"[^A-Za-z0-9._-]+", "_", name)  # Replace disallowed characters with underscore
+    sanitized = sanitized.strip(" ._")  # Windows: trailing dots or spaces are not allowed
+    if not sanitized:
+        sanitized = "log"
+    return sanitized
+
+
+def _short_hash(*parts: str, length: int = 10) -> str:
+    return hashlib.sha1(("|".join(parts)).encode("utf-8")).hexdigest()[:length]
+
+
+def _build_log_filepath(log_dir: str, operation_id: str, request_url: str) -> str:
+    """Build log filepath. We keep it well under common path length limits aiming for <= 240 characters total."""
+    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
+    slug = _sanitize_filename_component(operation_id)  # Best-effort human-readable slug from operation_id
+    h = _short_hash(operation_id or "", request_url or "")  # Short hash ties log to the full operation and URL
+
+    # Compute how much room we have for the slug given the directory length
+    # Keep total path length reasonably below ~260 on Windows.
+    max_total_path = 240
+    prefix = f"{timestamp}_"
+    suffix = f"_{h}.log"
+    if not slug:
+        slug = "op"
+    max_filename_len = max(60, max_total_path - len(log_dir) - 1)
+    max_slug_len = max(8, max_filename_len - len(prefix) - len(suffix))
+    if len(slug) > max_slug_len:
+        slug = slug[:max_slug_len].rstrip(" ._-")
+    return os.path.join(log_dir, f"{prefix}{slug}{suffix}")
+
+
+def _format_data_for_logging(data: Any) -> str:
     """Helper to format data (dict, str, bytes) for logging."""
     if isinstance(data, bytes):
         try:
-            return data.decode('utf-8')  # Try to decode as text
+            return data.decode("utf-8")  # Try to decode as text
         except UnicodeDecodeError:
             return f"[Binary data of length {len(data)} bytes]"
     elif isinstance(data, (dict, list)):
         try:
             return json.dumps(data, indent=2, ensure_ascii=False)
         except TypeError:
-            return str(data) # Fallback for non-serializable objects
+            return str(data)  # Fallback for non-serializable objects
     return str(data)
 
+
 def log_request_response(
     operation_id: str,
     request_method: str,
     request_url: str,
     request_headers: dict | None = None,
     request_params: dict | None = None,
-    request_data: any = None,
+    request_data: Any = None,
     response_status_code: int | None = None,
     response_headers: dict | None = None,
-    response_content: any = None,
-    error_message: str | None = None
+    response_content: Any = None,
+    error_message: str | None = None,
 ):
     """
     Logs API request and response details to a file in the temp/api_logs directory.
+    Filenames are sanitized and length-limited for cross-platform safety.
+    If we still fail to write, we fall back to appending into api.log.
     """
     log_dir = get_log_directory()
-    timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
-    filename = f"{timestamp}_{operation_id.replace('/', '_').replace(':', '_')}.log"
-    filepath = os.path.join(log_dir, filename)
-
-    log_content = []
+    filepath = _build_log_filepath(log_dir, operation_id, request_url)
 
+    log_content: list[str] = []
     log_content.append(f"Timestamp: {datetime.datetime.now().isoformat()}")
     log_content.append(f"Operation ID: {operation_id}")
     log_content.append("-" * 30 + " REQUEST " + "-" * 30)
@@ -69,7 +106,7 @@ def log_request_response(
         log_content.append(f"Headers:\n{_format_data_for_logging(request_headers)}")
     if request_params:
         log_content.append(f"Params:\n{_format_data_for_logging(request_params)}")
-    if request_data:
+    if request_data is not None:
         log_content.append(f"Data/Body:\n{_format_data_for_logging(request_data)}")
 
     log_content.append("\n" + "-" * 30 + " RESPONSE " + "-" * 30)
@@ -77,7 +114,7 @@ def log_request_response(
         log_content.append(f"Status Code: {response_status_code}")
     if response_headers:
         log_content.append(f"Headers:\n{_format_data_for_logging(response_headers)}")
-    if response_content:
+    if response_content is not None:
         log_content.append(f"Content:\n{_format_data_for_logging(response_content)}")
     if error_message:
         log_content.append(f"Error:\n{error_message}")
@@ -89,6 +126,7 @@ def log_request_response(
     except Exception as e:
         logger.error(f"Error writing API log to {filepath}: {e}")
 
+
 if __name__ == '__main__':
     # Example usage (for testing the logger directly)
     logger.setLevel(logging.DEBUG)

comfy_api_nodes/apis/rodin_api.py

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

comfy_extras/nodes_morphology.py

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

custom_nodes/example_node.py.example

@@ -1,96 +1,70 @@
-class Example:
+from typing_extensions import override
+
+from comfy_api.latest import ComfyExtension, io
+
+
+class Example(io.ComfyNode):
     """
-    A example node
+    An example node
 
     Class methods
     -------------
-    INPUT_TYPES (dict):
-        Tell the main program input parameters of nodes.
-    IS_CHANGED:
+    define_schema (io.Schema):
+        Tell the main program the metadata, input, output parameters of nodes.
+    fingerprint_inputs:
         optional method to control when the node is re executed.
+    check_lazy_status:
+        optional method to control list of input names that need to be evaluated.
 
-    Attributes
-    ----------
-    RETURN_TYPES (`tuple`):
-        The type of each element in the output tuple.
-    RETURN_NAMES (`tuple`):
-        Optional: The name of each output in the output tuple.
-    FUNCTION (`str`):
-        The name of the entry-point method. For example, if `FUNCTION = "execute"` then it will run Example().execute()
-    OUTPUT_NODE ([`bool`]):
-        If this node is an output node that outputs a result/image from the graph. The SaveImage node is an example.
-        The backend iterates on these output nodes and tries to execute all their parents if their parent graph is properly connected.
-        Assumed to be False if not present.
-    CATEGORY (`str`):
-        The category the node should appear in the UI.
-    DEPRECATED (`bool`):
-        Indicates whether the node is deprecated. Deprecated nodes are hidden by default in the UI, but remain
-        functional in existing workflows that use them.
-    EXPERIMENTAL (`bool`):
-        Indicates whether the node is experimental. Experimental nodes are marked as such in the UI and may be subject to
-        significant changes or removal in future versions. Use with caution in production workflows.
-    execute(s) -> tuple || None:
-        The entry point method. The name of this method must be the same as the value of property `FUNCTION`.
-        For example, if `FUNCTION = "execute"` then this method's name must be `execute`, if `FUNCTION = "foo"` then it must be `foo`.
     """
-    def __init__(self):
-        pass
 
     @classmethod
-    def INPUT_TYPES(s):
+    def define_schema(cls) -> io.Schema:
         """
-            Return a dictionary which contains config for all input fields.
-            Some types (string): "MODEL", "VAE", "CLIP", "CONDITIONING", "LATENT", "IMAGE", "INT", "STRING", "FLOAT".
-            Input types "INT", "STRING" or "FLOAT" are special values for fields on the node.
-            The type can be a list for selection.
-
-            Returns: `dict`:
-                - Key input_fields_group (`string`): Can be either required, hidden or optional. A node class must have property `required`
-                - Value input_fields (`dict`): Contains input fields config:
-                    * Key field_name (`string`): Name of a entry-point method's argument
-                    * Value field_config (`tuple`):
-                        + First value is a string indicate the type of field or a list for selection.
-                        + Second value is a config for type "INT", "STRING" or "FLOAT".
+            Return a schema which contains all information about the node.
+            Some types: "Model", "Vae", "Clip", "Conditioning", "Latent", "Image", "Int", "String", "Float", "Combo".
+            For outputs the "io.Model.Output" should be used, for inputs the "io.Model.Input" can be used.
+            The type can be a "Combo" - this will be a list for selection.
         """
-        return {
-            "required": {
-                "image": ("IMAGE",),
-                "int_field": ("INT", {
-                    "default": 0, 
-                    "min": 0, #Minimum value
-                    "max": 4096, #Maximum value
-                    "step": 64, #Slider's step
-                    "display": "number", # Cosmetic only: display as "number" or "slider"
-                    "lazy": True # Will only be evaluated if check_lazy_status requires it
-                }),
-                "float_field": ("FLOAT", {
-                    "default": 1.0,
-                    "min": 0.0,
-                    "max": 10.0,
-                    "step": 0.01,
-                    "round": 0.001, #The value representing the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
-                    "display": "number",
-                    "lazy": True
-                }),
-                "print_to_screen": (["enable", "disable"],),
-                "string_field": ("STRING", {
-                    "multiline": False, #True if you want the field to look like the one on the ClipTextEncode node
-                    "default": "Hello World!",
-                    "lazy": True
-                }),
-            },
-        }
+        return io.Schema(
+            node_id="Example",
+            display_name="Example Node",
+            category="Example",
+            inputs=[
+                io.Image.Input("image"),
+                io.Int.Input(
+                    "int_field",
+                    min=0,
+                    max=4096,
+                    step=64, # Slider's step
+                    display_mode=io.NumberDisplay.number,  # Cosmetic only: display as "number" or "slider"
+                    lazy=True,  # Will only be evaluated if check_lazy_status requires it
+                ),
+                io.Float.Input(
+                    "float_field",
+                    default=1.0,
+                    min=0.0,
+                    max=10.0,
+                    step=0.01,
+                    round=0.001, #The value representing the precision to round to, will be set to the step value by default. Can be set to False to disable rounding.
+                    display_mode=io.NumberDisplay.number,
+                    lazy=True,
+                ),
+                io.Combo.Input("print_to_screen", options=["enable", "disable"]),
+                io.String.Input(
+                    "string_field",
+                    multiline=False,  # True if you want the field to look like the one on the ClipTextEncode node
+                    default="Hello world!",
+                    lazy=True,
+                )
+            ],
+            outputs=[
+                io.Image.Output(),
+            ],
+        )
 
-    RETURN_TYPES = ("IMAGE",)
-    #RETURN_NAMES = ("image_output_name",)
-
-    FUNCTION = "test"
-
-    #OUTPUT_NODE = False
-
-    CATEGORY = "Example"
-
-    def check_lazy_status(self, image, string_field, int_field, float_field, print_to_screen):
+    @classmethod
+    def check_lazy_status(cls, image, string_field, int_field, float_field, print_to_screen):
         """
             Return a list of input names that need to be evaluated.
 
@@ -107,7 +81,8 @@ class Example:
         else:
             return []
 
-    def test(self, image, string_field, int_field, float_field, print_to_screen):
+    @classmethod
+    def execute(cls, image, string_field, int_field, float_field, print_to_screen) -> io.NodeOutput:
         if print_to_screen == "enable":
             print(f"""Your input contains:
                 string_field aka input text: {string_field}
@@ -116,7 +91,7 @@ class Example:
             """)
         #do some processing on the image, in this example I just invert it
         image = 1.0 - image
-        return (image,)
+        return io.NodeOutput(image)
 
     """
         The node will always be re executed if any of the inputs change but
@@ -127,7 +102,7 @@ class Example:
         changes between executions the LoadImage node is executed again.
     """
     #@classmethod
-    #def IS_CHANGED(s, image, string_field, int_field, float_field, print_to_screen):
+    #def fingerprint_inputs(s, image, string_field, int_field, float_field, print_to_screen):
     #    return ""
 
 # Set the web directory, any .js file in that directory will be loaded by the frontend as a frontend extension
@@ -143,13 +118,13 @@ async def get_hello(request):
     return web.json_response("hello")
 
 
-# A dictionary that contains all nodes you want to export with their names
-# NOTE: names should be globally unique
-NODE_CLASS_MAPPINGS = {
-    "Example": Example
-}
+class ExampleExtension(ComfyExtension):
+    @override
+    async def get_node_list(self) -> list[type[io.ComfyNode]]:
+        return [
+            Example,
+        ]
 
-# A dictionary that contains the friendly/humanly readable titles for the nodes
-NODE_DISPLAY_NAME_MAPPINGS = {
-    "Example": "Example Node"
-}
+
+async def comfy_entrypoint() -> ExampleExtension:  # ComfyUI calls this to load your extension and its nodes.
+    return ExampleExtension()

main.py

@@ -141,6 +141,7 @@ if os.name == "nt":
     os.environ['MIMALLOC_PURGE_DELAY'] = '0'
 
 if __name__ == "__main__":
+    os.environ['TORCH_ROCM_AOTRITON_ENABLE_EXPERIMENTAL'] = '1'
     if args.default_device is not None:
         default_dev = args.default_device
         devices = list(range(32))

pyproject.toml

@@ -22,3 +22,57 @@ lint.select = [
   "F",
 ]
 exclude = ["*.ipynb", "**/generated/*.pyi"]
+
+[tool.pylint]
+master.py-version = "3.9"
+master.extension-pkg-allow-list = [
+  "pydantic",
+]
+reports.output-format = "colorized"
+similarities.ignore-imports = "yes"
+messages_control.disable = [
+  "missing-module-docstring",
+  "missing-class-docstring",
+  "missing-function-docstring",
+  "line-too-long",
+  "too-few-public-methods",
+  "too-many-public-methods",
+  "too-many-instance-attributes",
+  "too-many-positional-arguments",
+  "broad-exception-raised",
+  "too-many-lines",
+  "invalid-name",
+  "unused-argument",
+  "broad-exception-caught",
+  "consider-using-with",
+  "fixme",
+  "too-many-statements",
+  "too-many-branches",
+  "too-many-locals",
+  "too-many-arguments",
+  "duplicate-code",
+  "abstract-method",
+  "superfluous-parens",
+  "arguments-differ",
+  "redefined-builtin",
+  "unnecessary-lambda",
+  "dangerous-default-value",
+  # next warnings should be fixed in future
+  "bad-classmethod-argument",  # Class method should have 'cls' as first argument
+  "wrong-import-order",  # Standard imports should be placed before third party imports
+  "logging-fstring-interpolation", # Use lazy % formatting in logging functions
+  "ungrouped-imports",
+  "unnecessary-pass",
+  "unidiomatic-typecheck",
+  "unnecessary-lambda-assignment",
+  "bad-indentation",
+  "no-else-return",
+  "no-else-raise",
+  "invalid-overridden-method",
+  "unused-variable",
+  "pointless-string-statement",
+  "inconsistent-return-statements",
+  "import-outside-toplevel",
+  "reimported",
+  "redefined-outer-name",
+]