| from kornia.filters import canny |
| from typing_extensions import override |
|
|
| import comfy.model_management |
| from comfy_api.latest import ComfyExtension, io |
|
|
|
|
| class Canny(io.ComfyNode): |
| @classmethod |
| def define_schema(cls): |
| return io.Schema( |
| node_id="Canny", |
| category="image/preprocessors", |
| inputs=[ |
| io.Image.Input("image"), |
| io.Float.Input("low_threshold", default=0.4, min=0.01, max=0.99, step=0.01), |
| io.Float.Input("high_threshold", default=0.8, min=0.01, max=0.99, step=0.01), |
| ], |
| outputs=[io.Image.Output()], |
| ) |
|
|
| @classmethod |
| def detect_edge(cls, image, low_threshold, high_threshold): |
| |
| return cls.execute(image, low_threshold, high_threshold) |
|
|
| @classmethod |
| def execute(cls, image, low_threshold, high_threshold) -> io.NodeOutput: |
| output = canny(image.to(comfy.model_management.get_torch_device()).movedim(-1, 1), low_threshold, high_threshold) |
| img_out = output[1].to(comfy.model_management.intermediate_device()).repeat(1, 3, 1, 1).movedim(1, -1) |
| return io.NodeOutput(img_out) |
|
|
|
|
| class CannyExtension(ComfyExtension): |
| @override |
| async def get_node_list(self) -> list[type[io.ComfyNode]]: |
| return [Canny] |
|
|
|
|
| async def comfy_entrypoint() -> CannyExtension: |
| return CannyExtension() |
|
|