166 lines
6.2 KiB
Python
166 lines
6.2 KiB
Python
"""
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Custom nodes for SDXL in ComfyUI
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MIT License
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Copyright (c) 2023 Searge
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.
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"""
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from folder_paths import get_full_path
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from .controlnet import canny
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from .controlnet import leres
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from .controlnet import hed
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from .data_utils import retrieve_parameter
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from .ui import UI
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# ====================================================================================================
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# Adapter for image inputs
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# ====================================================================================================
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class SeargeControlnetAdapterV2:
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def __init__(self):
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self.expected_size = None
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self.hed_annotator = "ControlNetHED.pth"
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self.leres_annotator = "res101.pth"
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self.hed_annotator_full_path = get_full_path("annotators", self.hed_annotator)
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self.leres_annotator_full_path = get_full_path("annotators", self.leres_annotator)
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"controlnet_mode": (UI.CONTROLNET_MODES, {"default": UI.NONE},),
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"controlnet_preprocessor": ("BOOLEAN", {"default": False},),
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"strength": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.05},),
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"low_threshold": ("FLOAT", {"default": 0.25, "min": 0.0, "max": 1.0, "step": 0.05},),
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"high_threshold": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 1.0, "step": 0.05},),
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"start_percent": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.05},),
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"end_percent": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.05},),
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"noise_augmentation": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.05},),
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"revision_enhancer": ("BOOLEAN", {"default": False},),
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},
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"optional": {
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"data": ("SRG_DATA_STREAM",),
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"source_image": ("IMAGE",),
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},
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}
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RETURN_TYPES = ("SRG_DATA_STREAM", "IMAGE",)
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RETURN_NAMES = ("data", "preview",)
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FUNCTION = "get_value"
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CATEGORY = UI.CATEGORY_UI_PROMPTING
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def process_image(self, image, mode, low_threshold, high_threshold):
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if mode == UI.CN_MODE_CANNY:
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image = canny(image, low_threshold, high_threshold)
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elif mode == UI.CN_MODE_DEPTH:
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image = leres(image, low_threshold, high_threshold, self.leres_annotator_full_path)
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elif mode == UI.CN_MODE_SKETCH:
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image = hed(image, self.hed_annotator_full_path)
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else:
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# do nothing for any other mode, just use the provided image unchanged
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pass
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return image
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def create_dict(self, stack, source_image, controlnet_mode, controlnet_preprocessor, strength,
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low_threshold, high_threshold, start, end, noise_augmentation, revision_enhancer):
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if controlnet_mode is None or controlnet_mode == UI.NONE:
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cn_image = None
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else:
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cn_image = source_image
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low_threshold = round(low_threshold, 3)
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high_threshold = round(high_threshold, 3)
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# NOTE: for the modes "revision" and "custom" no image pre-processing is needed
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if controlnet_mode == UI.CN_MODE_REVISION or controlnet_mode == UI.CUSTOM:
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controlnet_preprocessor = False
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if controlnet_preprocessor and cn_image is not None:
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cn_image = self.process_image(cn_image, controlnet_mode, low_threshold, high_threshold)
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stack += [
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{
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UI.F_REV_CN_IMAGE: cn_image,
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UI.F_REV_CN_IMAGE_CHANGED: True,
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UI.F_REV_CN_MODE: controlnet_mode,
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UI.F_CN_PRE_PROCESSOR: controlnet_preprocessor,
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UI.F_REV_CN_STRENGTH: round(strength, 3),
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UI.F_CN_LOW_THRESHOLD: low_threshold,
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UI.F_CN_HIGH_THRESHOLD: high_threshold,
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UI.F_CN_START: round(start, 3),
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UI.F_CN_END: round(end, 3),
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UI.F_REV_NOISE_AUGMENTATION: round(noise_augmentation, 3),
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UI.F_REV_ENHANCER: revision_enhancer,
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}
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]
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return (
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{
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UI.F_CN_STACK: stack,
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},
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cn_image,
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)
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def get_value(self, controlnet_mode, controlnet_preprocessor, strength, low_threshold, high_threshold,
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start_percent, end_percent, noise_augmentation, revision_enhancer, source_image=None, data=None):
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if data is None:
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data = {}
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stack = retrieve_parameter(UI.F_CN_STACK, retrieve_parameter(UI.S_CONTROLNET_INPUTS, data), [])
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if self.expected_size is None:
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self.expected_size = len(stack)
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elif self.expected_size == 0:
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stack = []
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elif len(stack) > self.expected_size:
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stack = stack[:self.expected_size]
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(stack_entry, image) = self.create_dict(
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stack,
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source_image,
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controlnet_mode,
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controlnet_preprocessor,
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strength,
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low_threshold,
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high_threshold,
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start_percent,
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end_percent,
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noise_augmentation,
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revision_enhancer,
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)
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data[UI.S_CONTROLNET_INPUTS] = stack_entry
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return (data, image,)
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