make canny preview works
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+36
-2
@@ -73,6 +73,40 @@ class StableDiffusionPipeline:
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return (pipeline,)
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class MakeCanny:
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def __init__(self) -> None:
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pass
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@classmethod
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def INPUT_TYPES(cls):
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return {"required": {
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"image_path" : ("STRING", {"multiline": True}),
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"high_threshold": ("INT", {"default": 200, "min":0, "max":255}),
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"low_threshold": ("INT", {"default": 100, "min":0, "max":255}),
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}}
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RETURN_TYPES = ("IMAGE", "IMAGE")
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RETURN_NAMES = ("Canny", "Canny Preview")
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FUNCTION = "create_canny"
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CATEGORY = "Diffusers-in-Comfy"
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def convert_images_to_tensors(self, images):
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return torch.stack([np.transpose(ToTensor()(image), (1, 2, 0)) for image in images])
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def create_canny(self, image_path, low_threshold, high_threshold):
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original_image = load_image(image_path)
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image = np.array(original_image)
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image = cv2.Canny(image, low_threshold, high_threshold)
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image = image[:, :, None]
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image = np.concatenate([image, image, image], axis=2)
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canny = Image.fromarray(image)
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return (canny, self.convert_images_to_tensors([canny]),)
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class ImageInference:
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def __init__(self) -> None:
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pass
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@@ -329,20 +363,20 @@ class BLoRALoader:
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NODE_CLASS_MAPPINGS = {
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"CreatePipeline": StableDiffusionPipeline,
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"CreateControlNetPipeline": StableDiffusionControlNetPipeline,
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"GenerateImage": ImageInference,
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"GenerateControlledImage": ImageControlNetInference,
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"LoRALoader" : LoRALoader,
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"BLoRALoader" : BLoRALoader,
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"MakeCanny": MakeCanny,
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"CreatePipeline" : "StableDiffusionPipeline",
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"CreateControlNetPipeline": "StableDiffusionControlNetPipeline",
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"GenerateImage" : "ImageInference",
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"GenerateControlledImage": "ImageControlNetInference",
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"LoRALoader" : "LoRALoader",
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"BLoRALoader" : "BLoRALoader",
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"MakeCanny": "MakeCanny",
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}
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