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20f2cac6f0 |
@@ -49,7 +49,7 @@ This custom node helps to conveniently enhance images through Detector, Detailer
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### ControlNet, IPAdapter
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* `ControlNetApply (SEGS)` - To apply ControlNet in SEGS, you need to use the Preprocessor Provider node from the Inspire Pack to utilize this node.
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* `segs_preprocessor` and `control_image` can be selectively applied. If an `control_image` is given, `segs_preprocessor` will be ignored.
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* `segs_preprocessor` and `control_image` can be selectively applied. If a `control_image` is given, `segs_preprocessor` will be ignored.
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* If set to `control_image`, you can preview the cropped cnet image through `SEGSPreview (CNET Image)`. Images generated by `segs_preprocessor` should be verified through the `cnet_images` output of each Detailer.
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* The `segs_preprocessor` operates by applying preprocessing on-the-fly based on the cropped image during the detailing process, while `control_image` will be cropped and used as input to `ControlNetApply (SEGS)`.
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* `ControlNetClear (SEGS)` - Clear applied ControlNet in SEGS
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@@ -1,7 +1,7 @@
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import configparser
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import os
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version_code = [7, 14]
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version_code = [7, 14, 1]
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version = f"V{version_code[0]}.{version_code[1]}" + (f'.{version_code[2]}' if len(version_code) > 2 else '')
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dependency_version = 24
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@@ -52,7 +52,10 @@ class GeneralSwitch:
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print(f"SELECTED: {input_name}")
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return [input_name]
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if input_name in kwargs:
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return [input_name]
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else:
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return []
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@staticmethod
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def doit(*args, **kwargs):
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+1
-1
@@ -1,7 +1,7 @@
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[project]
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name = "comfyui-impact-pack"
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description = "This extension offers various detector nodes and detailer nodes that allow you to configure a workflow that automatically enhances facial details. And provide iterative upscaler."
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version = "7.14"
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version = "7.14.1"
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license = { file = "LICENSE.txt" }
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dependencies = ["segment-anything", "scikit-image", "piexif", "transformers", "opencv-python-headless", "GitPython", "scipy>=1.11.4"]
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