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

comfy_api_nodes/apinode_utils.py

@@ -431,7 +431,7 @@ async def upload_video_to_comfyapi(
                     f"Video duration ({actual_duration:.2f}s) exceeds the maximum allowed ({max_duration}s)."
                 )
         except Exception as e:
-            logging.error(f"Error getting video duration: {e}")
+            logging.error("Error getting video duration: %s", str(e))
             raise ValueError(f"Could not verify video duration from source: {e}") from e
 
     upload_mime_type = f"video/{container.value.lower()}"

comfy_api_nodes/apis/client.py

@@ -359,10 +359,10 @@ class ApiClient:
         if params:
             params = {k: v for k, v in params.items() if v is not None}  # aiohttp fails to serialize None values
 
-        logging.debug(f"[DEBUG] Request Headers: {request_headers}")
+        logging.debug("[DEBUG] Request Headers: %s", request_headers)
-        logging.debug(f"[DEBUG] Files: {files}")
+        logging.debug("[DEBUG] Files: %s", files)
-        logging.debug(f"[DEBUG] Params: {params}")
+        logging.debug("[DEBUG] Params: %s", params)
-        logging.debug(f"[DEBUG] Data: {data}")
+        logging.debug("[DEBUG] Data: %s", data)
 
         if content_type == "application/x-www-form-urlencoded":
             payload_args = self._create_urlencoded_form_data_args(data or {}, request_headers)
@@ -592,9 +592,9 @@ class ApiClient:
             error_message=f"HTTP Error {exc.status}",
         )
 
-        logging.debug(f"[DEBUG] API Error: {user_friendly} (Status: {status_code})")
+        logging.debug("[DEBUG] API Error: %s (Status: %s)", user_friendly, status_code)
         if response_content:
-            logging.debug(f"[DEBUG] Response content: {response_content}")
+            logging.debug("[DEBUG] Response content: %s", response_content)
 
         # Retry if eligible
         if status_code in self.retry_status_codes and retry_count < self.max_retries:
@@ -738,11 +738,9 @@ class SynchronousOperation(Generic[T, R]):
                     if isinstance(v, Enum):
                         request_dict[k] = v.value
 
-            logging.debug(
+            logging.debug("[DEBUG] API Request: %s %s", self.endpoint.method.value, self.endpoint.path)
-                f"[DEBUG] API Request: {self.endpoint.method.value} {self.endpoint.path}"
+            logging.debug("[DEBUG] Request Data: %s", json.dumps(request_dict, indent=2))
-            )
+            logging.debug("[DEBUG] Query Params: %s", self.endpoint.query_params)
-            logging.debug(f"[DEBUG] Request Data: {json.dumps(request_dict, indent=2)}")
-            logging.debug(f"[DEBUG] Query Params: {self.endpoint.query_params}")
 
             response_json = await client.request(
                 self.endpoint.method.value,
@@ -757,11 +755,11 @@ class SynchronousOperation(Generic[T, R]):
             logging.debug("=" * 50)
             logging.debug("[DEBUG] RESPONSE DETAILS:")
             logging.debug("[DEBUG] Status Code: 200 (Success)")
-            logging.debug(f"[DEBUG] Response Body: {json.dumps(response_json, indent=2)}")
+            logging.debug("[DEBUG] Response Body: %s", json.dumps(response_json, indent=2))
             logging.debug("=" * 50)
 
             parsed_response = self.endpoint.response_model.model_validate(response_json)
-            logging.debug(f"[DEBUG] Parsed Response: {parsed_response}")
+            logging.debug("[DEBUG] Parsed Response: %s", parsed_response)
             return parsed_response
         finally:
             if owns_client:
@@ -877,7 +875,7 @@ class PollingOperation(Generic[T, R]):
         status = TaskStatus.PENDING
         for poll_count in range(1, self.max_poll_attempts + 1):
             try:
-                logging.debug(f"[DEBUG] Polling attempt #{poll_count}")
+                logging.debug("[DEBUG] Polling attempt #%s", poll_count)
 
                 request_dict = (
                     None if self.request is None else self.request.model_dump(exclude_none=True)
@@ -885,10 +883,13 @@ class PollingOperation(Generic[T, R]):
 
                 if poll_count == 1:
                     logging.debug(
-                        f"[DEBUG] Poll Request: {self.poll_endpoint.method.value} {self.poll_endpoint.path}"
+                        "[DEBUG] Poll Request: %s %s",
+                        self.poll_endpoint.method.value,
+                        self.poll_endpoint.path,
                     )
                     logging.debug(
-                        f"[DEBUG] Poll Request Data: {json.dumps(request_dict, indent=2) if request_dict else 'None'}"
+                        "[DEBUG] Poll Request Data: %s",
+                        json.dumps(request_dict, indent=2) if request_dict else "None",
                     )
 
                 # Query task status
@@ -903,7 +904,7 @@ class PollingOperation(Generic[T, R]):
 
                 # Check if task is complete
                 status = self._check_task_status(response_obj)
-                logging.debug(f"[DEBUG] Task Status: {status}")
+                logging.debug("[DEBUG] Task Status: %s", status)
 
                 # If progress extractor is provided, extract progress
                 if self.progress_extractor:
@@ -917,7 +918,7 @@ class PollingOperation(Generic[T, R]):
                         result_url = self.result_url_extractor(response_obj)
                         if result_url:
                             message = f"Result URL: {result_url}"
-                    logging.debug(f"[DEBUG] {message}")
+                    logging.debug("[DEBUG] %s", message)
                     self._display_text_on_node(message)
                     self.final_response = response_obj
                     if self.progress_extractor:
@@ -925,7 +926,7 @@ class PollingOperation(Generic[T, R]):
                     return self.final_response
                 if status == TaskStatus.FAILED:
                     message = f"Task failed: {json.dumps(resp)}"
-                    logging.error(f"[DEBUG] {message}")
+                    logging.error("[DEBUG] %s", message)
                     raise Exception(message)
                 logging.debug("[DEBUG] Task still pending, continuing to poll...")
                 # Task pending – wait
@@ -939,7 +940,12 @@ class PollingOperation(Generic[T, R]):
                     raise Exception(
                         f"Polling aborted after {consecutive_errors} network errors: {str(e)}"
                     ) from e
-                logging.warning("Network error (%s/%s): %s", consecutive_errors, max_consecutive_errors, str(e))
+                logging.warning(
+                    "Network error (%s/%s): %s",
+                    consecutive_errors,
+                    max_consecutive_errors,
+                    str(e),
+                )
                 await asyncio.sleep(self.poll_interval)
             except Exception as e:
                 # For other errors, increment count and potentially abort
@@ -949,10 +955,13 @@ class PollingOperation(Generic[T, R]):
                         f"Polling aborted after {consecutive_errors} consecutive errors: {str(e)}"
                     ) from e
 
-                logging.error(f"[DEBUG] Polling error: {str(e)}")
+                logging.error("[DEBUG] Polling error: %s", str(e))
                 logging.warning(
-                    f"Error during polling (attempt {poll_count}/{self.max_poll_attempts}): {str(e)}. "
+                    "Error during polling (attempt %s/%s): %s. Will retry in %s seconds.",
-                    f"Will retry in {self.poll_interval} seconds."
+                    poll_count,
+                    self.max_poll_attempts,
+                    str(e),
+                    self.poll_interval,
                 )
                 await asyncio.sleep(self.poll_interval)
 

comfy_api_nodes/apis/request_logger.py

@@ -21,7 +21,7 @@ def get_log_directory():
     try:
         os.makedirs(log_dir, exist_ok=True)
     except Exception as e:
-        logger.error(f"Error creating API log directory {log_dir}: {e}")
+        logger.error("Error creating API log directory %s: %s", log_dir, str(e))
         # Fallback to base temp directory if sub-directory creation fails
         return base_temp_dir
     return log_dir
@@ -122,9 +122,9 @@ def log_request_response(
     try:
         with open(filepath, "w", encoding="utf-8") as f:
             f.write("\n".join(log_content))
-        logger.debug(f"API log saved to: {filepath}")
+        logger.debug("API log saved to: %s", filepath)
     except Exception as e:
-        logger.error(f"Error writing API log to {filepath}: {e}")
+        logger.error("Error writing API log to %s: %s", filepath, str(e))
 
 
 if __name__ == '__main__':

comfy_api_nodes/nodes_kling.py

@@ -296,7 +296,7 @@ def validate_video_result_response(response) -> None:
     """Validates that the Kling task result contains a video."""
     if not is_valid_video_response(response):
         error_msg = f"Kling task {response.data.task_id} succeeded but no video data found in response."
-        logging.error(f"Error: {error_msg}.\nResponse: {response}")
+        logging.error("Error: %s.\nResponse: %s", error_msg, response)
         raise Exception(error_msg)
 
 
@@ -304,7 +304,7 @@ def validate_image_result_response(response) -> None:
     """Validates that the Kling task result contains an image."""
     if not is_valid_image_response(response):
         error_msg = f"Kling task {response.data.task_id} succeeded but no image data found in response."
-        logging.error(f"Error: {error_msg}.\nResponse: {response}")
+        logging.error("Error: %s.\nResponse: %s", error_msg, response)
         raise Exception(error_msg)
 
 

comfy_api_nodes/nodes_minimax.py

@@ -500,7 +500,7 @@ class MinimaxHailuoVideoNode(comfy_io.ComfyNode):
             raise Exception(
                 f"No video was found in the response. Full response: {file_result.model_dump()}"
             )
-        logging.info(f"Generated video URL: {file_url}")
+        logging.info("Generated video URL: %s", file_url)
         if cls.hidden.unique_id:
             if hasattr(file_result.file, "backup_download_url"):
                 message = f"Result URL: {file_url}\nBackup URL: {file_result.file.backup_download_url}"

comfy_api_nodes/nodes_moonvalley.py

@@ -237,7 +237,7 @@ def trim_video(video: VideoInput, duration_sec: float) -> VideoInput:
         audio_stream = None
 
         for stream in input_container.streams:
-            logging.info(f"Found stream: type={stream.type}, class={type(stream)}")
+            logging.info("Found stream: type=%s, class=%s", stream.type, type(stream))
             if isinstance(stream, av.VideoStream):
                 # Create output video stream with same parameters
                 video_stream = output_container.add_stream(
@@ -247,7 +247,7 @@ def trim_video(video: VideoInput, duration_sec: float) -> VideoInput:
                 video_stream.height = stream.height
                 video_stream.pix_fmt = "yuv420p"
                 logging.info(
-                    f"Added video stream: {stream.width}x{stream.height} @ {stream.average_rate}fps"
+                    "Added video stream: %sx%s @ %sfps", stream.width, stream.height, stream.average_rate
                 )
             elif isinstance(stream, av.AudioStream):
                 # Create output audio stream with same parameters
@@ -256,9 +256,7 @@ def trim_video(video: VideoInput, duration_sec: float) -> VideoInput:
                 )
                 audio_stream.sample_rate = stream.sample_rate
                 audio_stream.layout = stream.layout
-                logging.info(
+                logging.info("Added audio stream: %sHz, %s channels", stream.sample_rate, stream.channels)
-                    f"Added audio stream: {stream.sample_rate}Hz, {stream.channels} channels"
-                )
 
         # Calculate target frame count that's divisible by 16
         fps = input_container.streams.video[0].average_rate
@@ -288,9 +286,7 @@ def trim_video(video: VideoInput, duration_sec: float) -> VideoInput:
             for packet in video_stream.encode():
                 output_container.mux(packet)
 
-            logging.info(
+            logging.info("Encoded %s video frames (target: %s)", frame_count, target_frames)
-                f"Encoded {frame_count} video frames (target: {target_frames})"
-            )
 
         # Decode and re-encode audio frames
         if audio_stream:
@@ -308,7 +304,7 @@ def trim_video(video: VideoInput, duration_sec: float) -> VideoInput:
             for packet in audio_stream.encode():
                 output_container.mux(packet)
 
-            logging.info(f"Encoded {audio_frame_count} audio frames")
+            logging.info("Encoded %s audio frames", audio_frame_count)
 
         # Close containers
         output_container.close()

comfy_api_nodes/nodes_rodin.py

@@ -172,16 +172,16 @@ async def create_generate_task(
     logging.info("[ Rodin3D API - Submit Jobs ] Submit Generate Task Success!")
     subscription_key = response.jobs.subscription_key
     task_uuid = response.uuid
-    logging.info(f"[ Rodin3D API - Submit Jobs ] UUID: {task_uuid}")
+    logging.info("[ Rodin3D API - Submit Jobs ] UUID: %s", task_uuid)
     return task_uuid, subscription_key
 
 
 def check_rodin_status(response: Rodin3DCheckStatusResponse) -> str:
     all_done = all(job.status == JobStatus.Done for job in response.jobs)
     status_list = [str(job.status) for job in response.jobs]
-    logging.info(f"[ Rodin3D API - CheckStatus ] Generate Status: {status_list}")
+    logging.info("[ Rodin3D API - CheckStatus ] Generate Status: %s", status_list)
     if any(job.status == JobStatus.Failed for job in response.jobs):
-        logging.error(f"[ Rodin3D API - CheckStatus ] Generate Failed: {status_list}, Please try again.")
+        logging.error("[ Rodin3D API - CheckStatus ] Generate Failed: %s, Please try again.", status_list)
         raise Exception("[ Rodin3D API ] Generate Failed, Please Try again.")
     if all_done:
         return "DONE"
@@ -235,7 +235,7 @@ async def download_files(url_list, task_uuid):
             file_path = os.path.join(save_path, file_name)
             if file_path.endswith(".glb"):
                 model_file_path = file_path
-            logging.info(f"[ Rodin3D API - download_files ] Downloading file: {file_path}")
+            logging.info("[ Rodin3D API - download_files ] Downloading file: %s", file_path)
             max_retries = 5
             for attempt in range(max_retries):
                 try:
@@ -246,7 +246,7 @@ async def download_files(url_list, task_uuid):
                                 f.write(chunk)
                     break
                 except Exception as e:
-                    logging.info(f"[ Rodin3D API - download_files ] Error downloading {file_path}:{e}")
+                    logging.info("[ Rodin3D API - download_files ] Error downloading %s:%s", file_path, str(e))
                     if attempt < max_retries - 1:
                         logging.info("Retrying...")
                         await asyncio.sleep(2)

comfy_api_nodes/nodes_veo2.py

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

comfy_extras/nodes_compositing.py

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

comfy_extras/nodes_latent.py

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

comfy_extras/nodes_lora_extract.py

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

comfy_extras/nodes_model_downscale.py

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

pyproject.toml

@@ -61,7 +61,6 @@ messages_control.disable = [
   # 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",
   "unnecessary-lambda-assignment",