mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-05-20 22:17:31 +08:00
d71cc1c8f2
* Move detection category under image category * Add missing categories * Move detection nodes to detection category * Move save nodes to image root catefory * Rename postprocessors * Move mask category under image * Move guiders category to parent level at root of sampling category * Move custom_sampling category to parent level at the root of sampling category * Modify description of LoRA loaders * Fix node id SolidMask * Move VOID Quadmask under image/mask * Group compositing nodes under image/compositing * Move load image as mask to image category for consistency with other load image nodes * Align display name with Load Checkpoint * Move dataset category under training category * Rename Number Convert to Conver Number (verb first) * Rename Canny node * Revert wanBlockSwap + description * Add description to RemoveBackground node * Revert category update of dataset
46 lines
1.7 KiB
Python
46 lines
1.7 KiB
Python
from kornia.filters import canny
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from typing_extensions import override
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import comfy.model_management
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from comfy_api.latest import ComfyExtension, io
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import torch
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class Canny(io.ComfyNode):
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@classmethod
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def define_schema(cls):
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return io.Schema(
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node_id="Canny",
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display_name="Detect Edges (Canny)",
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search_aliases=["edge detection", "outline", "contour detection", "line art"],
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category="image/filters",
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essentials_category="Image Tools",
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inputs=[
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io.Image.Input("image"),
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io.Float.Input("low_threshold", default=0.4, min=0.01, max=0.99, step=0.01),
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io.Float.Input("high_threshold", default=0.8, min=0.01, max=0.99, step=0.01),
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],
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outputs=[io.Image.Output()],
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)
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@classmethod
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def detect_edge(cls, image, low_threshold, high_threshold):
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# Deprecated: use the V3 schema's `execute` method instead of this.
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return cls.execute(image, low_threshold, high_threshold)
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@classmethod
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def execute(cls, image, low_threshold, high_threshold) -> io.NodeOutput:
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output = canny(image.to(device=comfy.model_management.get_torch_device(), dtype=torch.float32).movedim(-1, 1), low_threshold, high_threshold)
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img_out = output[1].to(device=comfy.model_management.intermediate_device(), dtype=comfy.model_management.intermediate_dtype()).repeat(1, 3, 1, 1).movedim(1, -1)
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return io.NodeOutput(img_out)
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class CannyExtension(ComfyExtension):
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@override
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async def get_node_list(self) -> list[type[io.ComfyNode]]:
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return [Canny]
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async def comfy_entrypoint() -> CannyExtension:
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return CannyExtension()
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