99 lines
3.4 KiB
Python
99 lines
3.4 KiB
Python
# https://huggingface.co/spaces/SmilingWolf/wd-v1-4-tags
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# https://github.com/pythongosssss/ComfyUI-WD14-Tagger/blob/main/wd14tagger.py
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# {
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# "wd-v1-4-moat-tagger-v2": "https://huggingface.co/SmilingWolf/wd-v1-4-moat-tagger-v2",
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# "wd-v1-4-convnextv2-tagger-v2": "https://huggingface.co/SmilingWolf/wd-v1-4-convnextv2-tagger-v2",
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# "wd-v1-4-convnext-tagger-v2": "https://huggingface.co/SmilingWolf/wd-v1-4-convnext-tagger-v2",
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# "wd-v1-4-convnext-tagger": "https://huggingface.co/SmilingWolf/wd-v1-4-convnext-tagger",
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# "wd-v1-4-vit-tagger-v2": "https://huggingface.co/SmilingWolf/wd-v1-4-vit-tagger-v2"
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# }
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import numpy as np
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import csv
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import onnxruntime as ort
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from PIL import Image
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from onnxruntime import InferenceSession
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from modules.config import path_clip_vision
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from modules.model_loader import load_file_from_url
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global_model = None
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global_csv = None
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def default_interrogator(image_rgb, threshold=0.35, character_threshold=0.85, exclude_tags=""):
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global global_model, global_csv
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model_name = "wd-v1-4-moat-tagger-v2"
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model_onnx_filename = load_file_from_url(
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url=f'https://huggingface.co/SmilingWolf/{model_name}/resolve/main/model.onnx',
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model_dir=path_clip_vision,
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file_name=f'{model_name}.onnx',
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)
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model_csv_filename = load_file_from_url(
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url=f'https://huggingface.co/SmilingWolf/{model_name}/resolve/main/selected_tags.csv',
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model_dir=path_clip_vision,
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file_name=f'{model_name}.csv',
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)
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if global_model is not None:
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model = global_model
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else:
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model = InferenceSession(model_onnx_filename, providers=ort.get_available_providers())
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global_model = model
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input = model.get_inputs()[0]
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height = input.shape[1]
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image = Image.fromarray(image_rgb) # RGB
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ratio = float(height)/max(image.size)
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new_size = tuple([int(x*ratio) for x in image.size])
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image = image.resize(new_size, Image.LANCZOS)
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square = Image.new("RGB", (height, height), (255, 255, 255))
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square.paste(image, ((height-new_size[0])//2, (height-new_size[1])//2))
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image = np.array(square).astype(np.float32)
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image = image[:, :, ::-1] # RGB -> BGR
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image = np.expand_dims(image, 0)
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if global_csv is not None:
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csv_lines = global_csv
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else:
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csv_lines = []
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with open(model_csv_filename) as f:
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reader = csv.reader(f)
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next(reader)
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for row in reader:
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csv_lines.append(row)
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global_csv = csv_lines
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tags = []
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general_index = None
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character_index = None
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for line_num, row in enumerate(csv_lines):
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if general_index is None and row[2] == "0":
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general_index = line_num
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elif character_index is None and row[2] == "4":
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character_index = line_num
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tags.append(row[1])
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label_name = model.get_outputs()[0].name
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probs = model.run([label_name], {input.name: image})[0]
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result = list(zip(tags, probs[0]))
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general = [item for item in result[general_index:character_index] if item[1] > threshold]
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character = [item for item in result[character_index:] if item[1] > character_threshold]
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all = character + general
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remove = [s.strip() for s in exclude_tags.lower().split(",")]
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all = [tag for tag in all if tag[0] not in remove]
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res = ", ".join((item[0].replace("(", "\\(").replace(")", "\\)") for item in all)).replace('_', ' ')
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return res
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