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Final name changes
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@ -32,7 +32,7 @@ class CannyEdgePreproces:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "detect_edge"
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CATEGORY = "preprocessor"
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CATEGORY = "preprocessors"
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def detect_edge(self, image, low_threshold, high_threshold, l2gradient):
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#Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_canny2image.py
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@ -46,7 +46,7 @@ class HEDPreproces:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "detect_boundary"
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CATEGORY = "preprocessor"
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CATEGORY = "preprocessors"
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def detect_boundary(self, image):
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#Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_hed2image.py
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@ -60,7 +60,7 @@ class ScribblePreprocess:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "transform_scribble"
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CATEGORY = "preprocessor"
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CATEGORY = "preprocessors"
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def transform_scribble(self, image):
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#Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_scribble2image.py
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@ -76,7 +76,7 @@ class FakeScribblePreprocess:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "transform_scribble"
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CATEGORY = "preprocessor"
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CATEGORY = "preprocessors"
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def transform_scribble(self, image):
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#Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_fake_scribble2image.py
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@ -87,7 +87,7 @@ class FakeScribblePreprocess:
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np_detected_map[np_detected_map < 255] = 0
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return (img_np_to_tensor(np_detected_map),)
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class MIDASDepthPreprocess:
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class MIDASDepthMapPreprocess:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "image": ("IMAGE", ) ,
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@ -97,14 +97,14 @@ class MIDASDepthPreprocess:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "estimate_depth"
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CATEGORY = "preprocessor"
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CATEGORY = "preprocessors"
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def estimate_depth(self, image, a, bg_threshold):
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#Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_depth2image.py
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depth_map_np, normal_map_np = common_annotator_call(midas.MidasDetector(), image, a, bg_threshold)
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return (img_np_to_tensor(depth_map_np),)
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class MIDASNormalPreprocess:
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class MIDASNormalMapPreprocess:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "image": ("IMAGE", ) ,
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@ -114,7 +114,7 @@ class MIDASNormalPreprocess:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "estimate_normal"
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CATEGORY = "preprocessor"
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CATEGORY = "preprocessors"
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def estimate_normal(self, image, a, bg_threshold):
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#Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_depth2image.py
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@ -132,7 +132,7 @@ class MLSDPreprocess:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "detect_edge"
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CATEGORY = "preprocessor"
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CATEGORY = "preprocessors"
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def detect_edge(self, image, score_threshold, dist_threshold):
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#Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_hough2image.py
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@ -148,7 +148,7 @@ class OpenposePreprocess:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "estimate_pose"
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CATEGORY = "preprocessor"
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CATEGORY = "preprocessors"
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def estimate_pose(self, image, detect_hand):
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#Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_pose2image.py
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@ -163,7 +163,7 @@ class UniformerPreprocess:
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "semantic_segmentate"
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CATEGORY = "preprocessor"
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CATEGORY = "preprocessors"
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def semantic_segmentate(self, image):
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#Ref: https://github.com/lllyasviel/ControlNet/blob/main/gradio_seg2image.py
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@ -177,8 +177,8 @@ NODE_CLASS_MAPPINGS = {
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"ScribblePreprocess": ScribblePreprocess,
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"FakeScribblePreprocess": FakeScribblePreprocess,
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"OpenposePreprocess": OpenposePreprocess,
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"MiDaS-DepthPreprocess": MIDASDepthPreprocess,
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"MiDaS-NormalPreprocess": MIDASNormalPreprocess,
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"MiDaS-DepthMapPreprocess": MIDASDepthMapPreprocess,
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"MiDaS-NormalMapPreprocess": MIDASNormalMapPreprocess,
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"SemSegPreprocess": UniformerPreprocess
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}
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