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Running
on
Zero
| # https://huggingface.co/spaces/SmilingWolf/wd-v1-4-tags | |
| import os | |
| import csv | |
| import numpy as np | |
| import onnxruntime as ort | |
| from PIL import Image | |
| from onnxruntime import InferenceSession | |
| from torch.hub import download_url_to_file | |
| global_model = None | |
| global_csv = None | |
| def download_model(url, local_path): | |
| if os.path.exists(local_path): | |
| return local_path | |
| temp_path = local_path + '.tmp' | |
| download_url_to_file(url=url, dst=temp_path) | |
| os.rename(temp_path, local_path) | |
| return local_path | |
| def default_interrogator(image, threshold=0.35, character_threshold=0.85, exclude_tags=""): | |
| global global_model, global_csv | |
| model_name = "wd-v1-4-moat-tagger-v2" | |
| model_onnx_filename = download_model( | |
| url=f'https://huggingface.co/lllyasviel/misc/resolve/main/{model_name}.onnx', | |
| local_path=f'./{model_name}.onnx', | |
| ) | |
| model_csv_filename = download_model( | |
| url=f'https://huggingface.co/lllyasviel/misc/resolve/main/{model_name}.csv', | |
| local_path=f'./{model_name}.csv', | |
| ) | |
| if global_model is not None: | |
| model = global_model | |
| else: | |
| # assert 'CUDAExecutionProvider' in ort.get_available_providers(), 'CUDA Install Failed!' | |
| # model = InferenceSession(model_onnx_filename, providers=['CUDAExecutionProvider']) | |
| model = InferenceSession(model_onnx_filename, providers=['CPUExecutionProvider']) | |
| global_model = model | |
| input = model.get_inputs()[0] | |
| height = input.shape[1] | |
| if isinstance(image, str): | |
| image = Image.open(image) # RGB | |
| elif isinstance(image, np.ndarray): | |
| image = Image.fromarray(image) | |
| else: | |
| image = image | |
| ratio = float(height) / max(image.size) | |
| new_size = tuple([int(x*ratio) for x in image.size]) | |
| image = image.resize(new_size, Image.LANCZOS) | |
| square = Image.new("RGB", (height, height), (255, 255, 255)) | |
| square.paste(image, ((height-new_size[0])//2, (height-new_size[1])//2)) | |
| image = np.array(square).astype(np.float32) | |
| image = image[:, :, ::-1] # RGB -> BGR | |
| image = np.expand_dims(image, 0) | |
| if global_csv is not None: | |
| csv_lines = global_csv | |
| else: | |
| csv_lines = [] | |
| with open(model_csv_filename) as f: | |
| reader = csv.reader(f) | |
| next(reader) | |
| for row in reader: | |
| csv_lines.append(row) | |
| global_csv = csv_lines | |
| tags = [] | |
| general_index = None | |
| character_index = None | |
| for line_num, row in enumerate(csv_lines): | |
| if general_index is None and row[2] == "0": | |
| general_index = line_num | |
| elif character_index is None and row[2] == "4": | |
| character_index = line_num | |
| tags.append(row[1]) | |
| label_name = model.get_outputs()[0].name | |
| probs = model.run([label_name], {input.name: image})[0] | |
| result = list(zip(tags, probs[0])) | |
| general = [item for item in result[general_index:character_index] if item[1] > threshold] | |
| character = [item for item in result[character_index:] if item[1] > character_threshold] | |
| all = character + general | |
| remove = [s.strip() for s in exclude_tags.lower().split(",")] | |
| all = [tag for tag in all if tag[0] not in remove] | |
| res = ", ".join((item[0].replace("(", "\\(").replace(")", "\\)") for item in all)).replace('_', ' ') | |
| return res | |