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https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
fix(nodes): fix constraints in cnet processors
There were some invalid constraints with the processors - minimum of 0 for resolution or multiple of 64 for resolution. Made minimum 1px and no multiple ofs.
This commit is contained in:
psychedelicious
parent
c35625eb44
commit
33a9f9a4dc
@ -165,13 +165,13 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithBoard):
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title="Canny Processor",
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tags=["controlnet", "canny"],
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category="controlnet",
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version="1.3.2",
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version="1.3.3",
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)
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class CannyImageProcessorInvocation(ImageProcessorInvocation):
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"""Canny edge detection for ControlNet"""
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detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
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image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
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detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
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image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
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low_threshold: int = InputField(
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default=100, ge=0, le=255, description="The low threshold of the Canny pixel gradient (0-255)"
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)
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@ -199,13 +199,13 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
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title="HED (softedge) Processor",
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tags=["controlnet", "hed", "softedge"],
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category="controlnet",
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version="1.2.2",
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version="1.2.3",
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)
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class HedImageProcessorInvocation(ImageProcessorInvocation):
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"""Applies HED edge detection to image"""
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detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
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image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
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detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
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image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
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# safe not supported in controlnet_aux v0.0.3
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# safe: bool = InputField(default=False, description=FieldDescriptions.safe_mode)
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scribble: bool = InputField(default=False, description=FieldDescriptions.scribble_mode)
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@ -228,13 +228,13 @@ class HedImageProcessorInvocation(ImageProcessorInvocation):
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title="Lineart Processor",
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tags=["controlnet", "lineart"],
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category="controlnet",
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version="1.2.2",
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version="1.2.3",
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)
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class LineartImageProcessorInvocation(ImageProcessorInvocation):
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"""Applies line art processing to image"""
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detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
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image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
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detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
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image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
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coarse: bool = InputField(default=False, description="Whether to use coarse mode")
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def run_processor(self, image: Image.Image) -> Image.Image:
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@ -250,13 +250,13 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation):
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title="Lineart Anime Processor",
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tags=["controlnet", "lineart", "anime"],
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category="controlnet",
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version="1.2.2",
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version="1.2.3",
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)
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class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
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"""Applies line art anime processing to image"""
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detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
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image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
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detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
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image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
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def run_processor(self, image: Image.Image) -> Image.Image:
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processor = LineartAnimeProcessor()
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@ -273,15 +273,15 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
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title="Midas Depth Processor",
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tags=["controlnet", "midas"],
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category="controlnet",
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version="1.2.3",
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version="1.2.4",
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)
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class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
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"""Applies Midas depth processing to image"""
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a_mult: float = InputField(default=2.0, ge=0, description="Midas parameter `a_mult` (a = a_mult * PI)")
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bg_th: float = InputField(default=0.1, ge=0, description="Midas parameter `bg_th`")
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detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
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image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
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detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
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image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
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# depth_and_normal not supported in controlnet_aux v0.0.3
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# depth_and_normal: bool = InputField(default=False, description="whether to use depth and normal mode")
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@ -304,13 +304,13 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
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title="Normal BAE Processor",
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tags=["controlnet"],
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category="controlnet",
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version="1.2.2",
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version="1.2.3",
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)
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class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
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"""Applies NormalBae processing to image"""
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detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
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image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
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detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
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image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
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def run_processor(self, image):
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normalbae_processor = NormalBaeDetector.from_pretrained("lllyasviel/Annotators")
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@ -321,13 +321,13 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
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@invocation(
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"mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.2"
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"mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.3"
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)
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class MlsdImageProcessorInvocation(ImageProcessorInvocation):
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"""Applies MLSD processing to image"""
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detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
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image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
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detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
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image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
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thr_v: float = InputField(default=0.1, ge=0, description="MLSD parameter `thr_v`")
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thr_d: float = InputField(default=0.1, ge=0, description="MLSD parameter `thr_d`")
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@ -344,13 +344,13 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation):
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@invocation(
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"pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.2"
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"pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.3"
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)
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class PidiImageProcessorInvocation(ImageProcessorInvocation):
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"""Applies PIDI processing to image"""
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detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
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image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
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detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
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image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
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safe: bool = InputField(default=False, description=FieldDescriptions.safe_mode)
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scribble: bool = InputField(default=False, description=FieldDescriptions.scribble_mode)
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@ -371,13 +371,13 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation):
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title="Content Shuffle Processor",
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tags=["controlnet", "contentshuffle"],
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category="controlnet",
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version="1.2.2",
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version="1.2.3",
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)
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class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
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"""Applies content shuffle processing to image"""
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detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
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image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
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detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
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image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
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h: int = InputField(default=512, ge=0, description="Content shuffle `h` parameter")
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w: int = InputField(default=512, ge=0, description="Content shuffle `w` parameter")
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f: int = InputField(default=256, ge=0, description="Content shuffle `f` parameter")
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@ -401,7 +401,7 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
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title="Zoe (Depth) Processor",
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tags=["controlnet", "zoe", "depth"],
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category="controlnet",
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version="1.2.2",
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version="1.2.3",
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)
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class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
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"""Applies Zoe depth processing to image"""
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@ -417,15 +417,15 @@ class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
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title="Mediapipe Face Processor",
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tags=["controlnet", "mediapipe", "face"],
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category="controlnet",
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version="1.2.3",
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version="1.2.4",
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)
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class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
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"""Applies mediapipe face processing to image"""
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max_faces: int = InputField(default=1, ge=1, description="Maximum number of faces to detect")
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min_confidence: float = InputField(default=0.5, ge=0, le=1, description="Minimum confidence for face detection")
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detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
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image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
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detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
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image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
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def run_processor(self, image):
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mediapipe_face_processor = MediapipeFaceDetector()
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@ -444,7 +444,7 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
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title="Leres (Depth) Processor",
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tags=["controlnet", "leres", "depth"],
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category="controlnet",
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version="1.2.2",
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version="1.2.3",
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)
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class LeresImageProcessorInvocation(ImageProcessorInvocation):
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"""Applies leres processing to image"""
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@ -452,8 +452,8 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
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thr_a: float = InputField(default=0, description="Leres parameter `thr_a`")
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thr_b: float = InputField(default=0, description="Leres parameter `thr_b`")
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boost: bool = InputField(default=False, description="Whether to use boost mode")
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detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
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image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
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detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
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image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
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def run_processor(self, image):
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leres_processor = LeresDetector.from_pretrained("lllyasviel/Annotators")
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@ -473,7 +473,7 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
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title="Tile Resample Processor",
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tags=["controlnet", "tile"],
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category="controlnet",
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version="1.2.2",
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version="1.2.3",
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)
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class TileResamplerProcessorInvocation(ImageProcessorInvocation):
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"""Tile resampler processor"""
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@ -513,13 +513,13 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation):
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title="Segment Anything Processor",
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tags=["controlnet", "segmentanything"],
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category="controlnet",
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version="1.2.3",
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version="1.2.4",
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)
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class SegmentAnythingProcessorInvocation(ImageProcessorInvocation):
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"""Applies segment anything processing to image"""
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detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
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image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
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detect_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.detect_res)
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image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
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def run_processor(self, image):
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# segment_anything_processor = SamDetector.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
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@ -560,12 +560,12 @@ class SamDetectorReproducibleColors(SamDetector):
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title="Color Map Processor",
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tags=["controlnet"],
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category="controlnet",
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version="1.2.2",
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version="1.2.3",
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)
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class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
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"""Generates a color map from the provided image"""
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color_map_tile_size: int = InputField(default=64, ge=0, description=FieldDescriptions.tile_size)
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color_map_tile_size: int = InputField(default=64, ge=1, description=FieldDescriptions.tile_size)
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def run_processor(self, image: Image.Image):
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np_image = np.array(image, dtype=np.uint8)
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@ -592,7 +592,7 @@ DEPTH_ANYTHING_MODEL_SIZES = Literal["large", "base", "small"]
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title="Depth Anything Processor",
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tags=["controlnet", "depth", "depth anything"],
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category="controlnet",
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version="1.1.1",
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version="1.1.2",
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)
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class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
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"""Generates a depth map based on the Depth Anything algorithm"""
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@ -600,7 +600,7 @@ class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
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model_size: DEPTH_ANYTHING_MODEL_SIZES = InputField(
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default="small", description="The size of the depth model to use"
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)
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resolution: int = InputField(default=512, ge=64, multiple_of=64, description=FieldDescriptions.image_res)
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resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
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def run_processor(self, image: Image.Image):
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depth_anything_detector = DepthAnythingDetector()
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@ -615,7 +615,7 @@ class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
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title="DW Openpose Image Processor",
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tags=["controlnet", "dwpose", "openpose"],
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category="controlnet",
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version="1.1.0",
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version="1.1.1",
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)
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class DWOpenposeImageProcessorInvocation(ImageProcessorInvocation):
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"""Generates an openpose pose from an image using DWPose"""
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@ -623,7 +623,7 @@ class DWOpenposeImageProcessorInvocation(ImageProcessorInvocation):
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draw_body: bool = InputField(default=True)
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draw_face: bool = InputField(default=False)
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draw_hands: bool = InputField(default=False)
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image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
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image_resolution: int = InputField(default=512, ge=1, description=FieldDescriptions.image_res)
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def run_processor(self, image: Image.Image):
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dw_openpose = DWOpenposeDetector()
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@ -642,15 +642,15 @@ class DWOpenposeImageProcessorInvocation(ImageProcessorInvocation):
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title="Heuristic Resize",
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tags=["image, controlnet"],
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category="image",
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version="1.0.0",
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version="1.0.1",
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classification=Classification.Prototype,
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)
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class HeuristicResizeInvocation(BaseInvocation):
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"""Resize an image using a heuristic method. Preserves edge maps."""
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image: ImageField = InputField(description="The image to resize")
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width: int = InputField(default=512, gt=0, description="The width to resize to (px)")
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height: int = InputField(default=512, gt=0, description="The height to resize to (px)")
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width: int = InputField(default=512, ge=1, description="The width to resize to (px)")
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height: int = InputField(default=512, ge=1, description="The height to resize to (px)")
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def invoke(self, context: InvocationContext) -> ImageOutput:
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image = context.images.get_pil(self.image.image_name, "RGB")
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