jaidaken
f09734b0ee
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Includes 30 custom nodes committed directly, 7 Civitai-exclusive loras stored via Git LFS, and a setup script that installs all dependencies and downloads HuggingFace-hosted models on vast.ai. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
56 lines
2.6 KiB
Python
56 lines
2.6 KiB
Python
from pathlib import Path
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import os
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import re
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#Thanks ChatGPT
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pattern = r'\bfrom_pretrained\(.*?pretrained_model_or_path\s*=\s*(.*?)(?:,|\))|filename\s*=\s*(.*?)(?:,|\))|(\w+_filename)\s*=\s*(.*?)(?:,|\))'
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aux_dir = Path(__file__).parent / 'src' / 'custom_controlnet_aux'
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VAR_DICT = dict(
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HF_MODEL_NAME = "lllyasviel/Annotators",
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DWPOSE_MODEL_NAME = "yzd-v/DWPose",
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BDS_MODEL_NAME = "bdsqlsz/qinglong_controlnet-lllite",
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DENSEPOSE_MODEL_NAME = "LayerNorm/DensePose-TorchScript-with-hint-image",
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MESH_GRAPHORMER_MODEL_NAME = "hr16/ControlNet-HandRefiner-pruned",
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SAM_MODEL_NAME = "dhkim2810/MobileSAM",
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UNIMATCH_MODEL_NAME = "hr16/Unimatch",
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DEPTH_ANYTHING_MODEL_NAME = "LiheYoung/Depth-Anything", #HF Space
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DIFFUSION_EDGE_MODEL_NAME = "hr16/Diffusion-Edge"
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)
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re_result_dict = {}
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for preprocc in os.listdir(aux_dir):
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if preprocc in ["__pycache__", 'tests']: continue
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if '.py' in preprocc: continue
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f = open(aux_dir / preprocc / '__init__.py', 'r')
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code = f.read()
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matches = re.findall(pattern, code)
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result = [match[0] or match[1] or match[3] for match in matches]
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if not len(result):
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print(preprocc)
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continue
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result = [el.replace("'", '').replace('"', '') for el in result]
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result = [VAR_DICT.get(el, el) for el in result]
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re_result_dict[preprocc] = result
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f.close()
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for preprocc, re_result in re_result_dict.items():
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model_name, filenames = re_result[0], re_result[1:]
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print(f"* {preprocc}: ", end=' ')
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assests_md = ', '.join([f"[{model_name}/{filename}](https://huggingface.co/{model_name}/blob/main/{filename})" for filename in filenames])
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print(assests_md)
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preprocc = "dwpose"
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model_name, filenames = VAR_DICT['DWPOSE_MODEL_NAME'], ["yolox_l.onnx", "dw-ll_ucoco_384.onnx"]
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print(f"* {preprocc}: ", end=' ')
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assests_md = ', '.join([f"[{model_name}/{filename}](https://huggingface.co/{model_name}/blob/main/{filename})" for filename in filenames])
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print(assests_md)
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preprocc = "yolo-nas"
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model_name, filenames = "hr16/yolo-nas-fp16", ["yolo_nas_l_fp16.onnx", "yolo_nas_m_fp16.onnx", "yolo_nas_s_fp16.onnx"]
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print(f"* {preprocc}: ", end=' ')
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assests_md = ', '.join([f"[{model_name}/{filename}](https://huggingface.co/{model_name}/blob/main/{filename})" for filename in filenames])
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print(assests_md)
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preprocc = "dwpose-torchscript"
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model_name, filenames = "hr16/DWPose-TorchScript-BatchSize5", ["dw-ll_ucoco_384_bs5.torchscript.pt", "rtmpose-m_ap10k_256_bs5.torchscript.pt"]
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print(f"* {preprocc}: ", end=' ')
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assests_md = ', '.join([f"[{model_name}/{filename}](https://huggingface.co/{model_name}/blob/main/{filename})" for filename in filenames])
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print(assests_md) |