import argparse import os import gradio as gr import huggingface_hub import numpy as np import onnxruntime as rt import pandas as pd from PIL import Image import traceback import tempfile import zipfile from datetime import datetime TITLE = "WaifuDiffusion Tagger" DESCRIPTION = """ Demo for the WaifuDiffusion tagger models Example image by [ほし☆☆☆](https://www.pixiv.net/en/users/43565085) """ # Dataset v3 series of models: SWINV2_MODEL_DSV3_REPO = "SmilingWolf/wd-swinv2-tagger-v3" CONV_MODEL_DSV3_REPO = "SmilingWolf/wd-convnext-tagger-v3" VIT_MODEL_DSV3_REPO = "SmilingWolf/wd-vit-tagger-v3" VIT_LARGE_MODEL_DSV3_REPO = "SmilingWolf/wd-vit-large-tagger-v3" EVA02_LARGE_MODEL_DSV3_REPO = "SmilingWolf/wd-eva02-large-tagger-v3" # Dataset v2 series of models: MOAT_MODEL_DSV2_REPO = "SmilingWolf/wd-v1-4-moat-tagger-v2" SWIN_MODEL_DSV2_REPO = "SmilingWolf/wd-v1-4-swinv2-tagger-v2" CONV_MODEL_DSV2_REPO = "SmilingWolf/wd-v1-4-convnext-tagger-v2" CONV2_MODEL_DSV2_REPO = "SmilingWolf/wd-v1-4-convnextv2-tagger-v2" VIT_MODEL_DSV2_REPO = "SmilingWolf/wd-v1-4-vit-tagger-v2" # Files to download from the repos MODEL_FILENAME = "model.onnx" LABEL_FILENAME = "selected_tags.csv" # https://github.com/toriato/stable-diffusion-webui-wd14-tagger/blob/a9eacb1eff904552d3012babfa28b57e1d3e295c/tagger/ui.py#L368 kaomojis = [ "0_0", "(o)_(o)", "+_+", "+_-", "._.", "_", "<|>_<|>", "=_=", ">_<", "3_3", "6_9", ">_o", "@_@", "^_^", "o_o", "u_u", "x_x", "|_|", "||_||", ] def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser() parser.add_argument("--score-slider-step", type=float, default=0.05) parser.add_argument("--score-general-threshold", type=float, default=0.35) parser.add_argument("--score-character-threshold", type=float, default=0.85) parser.add_argument("--share", action="store_true") return parser.parse_args() def load_labels(dataframe) -> list[str]: name_series = dataframe["name"] name_series = name_series.map( lambda x: x.replace("_", " ") if x not in kaomojis else x ) tag_names = name_series.tolist() rating_indexes = list(np.where(dataframe["category"] == 9)[0]) general_indexes = list(np.where(dataframe["category"] == 0)[0]) character_indexes = list(np.where(dataframe["category"] == 4)[0]) return tag_names, rating_indexes, general_indexes, character_indexes def mcut_threshold(probs): """ Maximum Cut Thresholding (MCut) Largeron, C., Moulin, C., & Gery, M. (2012). MCut: A Thresholding Strategy for Multi-label Classification. In 11th International Symposium, IDA 2012 (pp. 172-183). """ sorted_probs = probs[probs.argsort()[::-1]] difs = sorted_probs[:-1] - sorted_probs[1:] t = difs.argmax() thresh = (sorted_probs[t] + sorted_probs[t + 1]) / 2 return thresh class Predictor: def __init__(self): self.model_target_size = None self.last_loaded_repo = None def download_model(self, model_repo): csv_path = huggingface_hub.hf_hub_download( model_repo, LABEL_FILENAME, ) model_path = huggingface_hub.hf_hub_download( model_repo, MODEL_FILENAME, ) return csv_path, model_path def load_model(self, model_repo): if model_repo == self.last_loaded_repo: return csv_path, model_path = self.download_model(model_repo) tags_df = pd.read_csv(csv_path) sep_tags = load_labels(tags_df) self.tag_names = sep_tags[0] self.rating_indexes = sep_tags[1] self.general_indexes = sep_tags[2] self.character_indexes = sep_tags[3] model = rt.InferenceSession(model_path) _, height, width, _ = model.get_inputs()[0].shape self.model_target_size = height self.last_loaded_repo = model_repo self.model = model def prepare_image(self, path): image = Image.open(path) image = image.convert("RGBA") target_size = self.model_target_size canvas = Image.new("RGBA", image.size, (255, 255, 255)) canvas.alpha_composite(image) image = canvas.convert("RGB") # Pad image to square image_shape = image.size max_dim = max(image_shape) pad_left = (max_dim - image_shape[0]) // 2 pad_top = (max_dim - image_shape[1]) // 2 padded_image = Image.new("RGB", (max_dim, max_dim), (255, 255, 255)) padded_image.paste(image, (pad_left, pad_top)) # Resize if max_dim != target_size: padded_image = padded_image.resize( (target_size, target_size), Image.BICUBIC, ) # Convert to numpy array image_array = np.asarray(padded_image, dtype=np.float32) # Convert PIL-native RGB to BGR image_array = image_array[:, :, ::-1] return np.expand_dims(image_array, axis=0) def create_file(self, text: str, directory: str, fileName: str) -> str: # Write the text to a file with open(os.path.join(directory, fileName), 'w+', encoding="utf-8") as file: file.write(text) return file.name def predict( self, gallery, model_repo, general_thresh, general_mcut_enabled, character_thresh, character_mcut_enabled, characters_merge_enabled, ): self.load_model(model_repo) # Result txt_infos = [] output_dir = tempfile.mkdtemp() if not os.path.exists(output_dir): os.makedirs(output_dir) sorted_general_strings = "" rating = None character_res = None general_res = None for idx, value in enumerate(gallery): try: image_path = value[0] image_name = os.path.splitext(os.path.basename(image_path))[0] image = self.prepare_image(image_path) input_name = self.model.get_inputs()[0].name label_name = self.model.get_outputs()[0].name preds = self.model.run([label_name], {input_name: image})[0] labels = list(zip(self.tag_names, preds[0].astype(float))) # First 4 labels are actually ratings: pick one with argmax ratings_names = [labels[i] for i in self.rating_indexes] rating = dict(ratings_names) # Then we have general tags: pick any where prediction confidence > threshold general_names = [labels[i] for i in self.general_indexes] if general_mcut_enabled: general_probs = np.array([x[1] for x in general_names]) general_thresh = mcut_threshold(general_probs) general_res = [x for x in general_names if x[1] > general_thresh] general_res = dict(general_res) # Everything else is characters: pick any where prediction confidence > threshold character_names = [labels[i] for i in self.character_indexes] if character_mcut_enabled: character_probs = np.array([x[1] for x in character_names]) character_thresh = mcut_threshold(character_probs) character_thresh = max(0.15, character_thresh) character_res = [x for x in character_names if x[1] > character_thresh] character_res = dict(character_res) sorted_general_list = sorted( general_res.items(), key=lambda x: x[1], reverse=True, ) sorted_general_list = [x[0] for x in sorted_general_list] sorted_general_strings = (", ".join(sorted_general_list)) #.replace("(", "\(").replace(")", "\)")) character_str = "" if character_res is not None and len(character_res) > 0: for key, value in character_res.items(): if key in sorted_general_strings: continue character_str += key + ", " txt_file = self.create_file(character_str + sorted_general_strings, output_dir, image_name + ".txt") txt_infos.append({"path":txt_file, "name": image_name + ".txt"}) if characters_merge_enabled: sorted_general_strings = character_str + sorted_general_strings except Exception as e: print(traceback.format_exc()) print("Error predict: " + str(e)) # Result download = [] if txt_infos is not None and len(txt_infos) > 0: downloadZipPath = os.path.join(output_dir, "images-tagger-" + datetime.now().strftime("%Y%m%d-%H%M%S") + ".zip") with zipfile.ZipFile(downloadZipPath, 'w', zipfile.ZIP_DEFLATED) as taggers_zip: for info in txt_infos: # Get file name from lookup taggers_zip.write(info["path"], arcname=info["name"]) download.append(downloadZipPath) return download, sorted_general_strings, rating, character_res, general_res def main(): args = parse_args() predictor = Predictor() dropdown_list = [ SWINV2_MODEL_DSV3_REPO, EVA02_LARGE_MODEL_DSV3_REPO, CONV_MODEL_DSV3_REPO, VIT_MODEL_DSV3_REPO, VIT_LARGE_MODEL_DSV3_REPO, MOAT_MODEL_DSV2_REPO, SWIN_MODEL_DSV2_REPO, CONV_MODEL_DSV2_REPO, CONV2_MODEL_DSV2_REPO, VIT_MODEL_DSV2_REPO, ] with gr.Blocks(title=TITLE) as demo: with gr.Column(): gr.Markdown( value=f"

{TITLE}

" ) gr.Markdown(value=DESCRIPTION) with gr.Row(): with gr.Column(variant="panel"): with gr.Row(): submit = gr.Button(value="Submit", variant="primary", size="lg") gallery = gr.Gallery(columns=5, rows=5, show_share_button=False, interactive=True, height="500px", label="Input") model_repo = gr.Dropdown( dropdown_list, value=SWINV2_MODEL_DSV3_REPO, label="Model", ) with gr.Row(): general_thresh = gr.Slider( 0, 1, step=args.score_slider_step, value=args.score_general_threshold, label="General Tags Threshold", scale=3, ) general_mcut_enabled = gr.Checkbox( value=False, label="Use MCut threshold", scale=1, ) with gr.Row(): character_thresh = gr.Slider( 0, 1, step=args.score_slider_step, value=args.score_character_threshold, label="Character Tags Threshold", scale=3, ) character_mcut_enabled = gr.Checkbox( value=False, label="Use MCut threshold", scale=1, ) with gr.Row(): characters_merge_enabled = gr.Checkbox( value=True, label="Merge characters into the string output", scale=1, ) with gr.Row(): clear = gr.ClearButton( components=[ gallery, model_repo, general_thresh, general_mcut_enabled, character_thresh, character_mcut_enabled, characters_merge_enabled, ], variant="secondary", size="lg", ) with gr.Column(variant="panel"): download_file = gr.File(label="Output (Download)") sorted_general_strings = gr.Textbox(label="Output (string)", show_label=True, show_copy_button=True) rating = gr.Label(label="Rating") character_res = gr.Label(label="Output (characters)") general_res = gr.Label(label="Output (tags)") clear.add( [ download_file, sorted_general_strings, rating, character_res, general_res, ] ) submit.click( predictor.predict, inputs=[ gallery, model_repo, general_thresh, general_mcut_enabled, character_thresh, character_mcut_enabled, characters_merge_enabled, ], outputs=[download_file, sorted_general_strings, rating, character_res, general_res], ) # gr.Examples( # [["power.jpg", SWINV2_MODEL_DSV3_REPO, 0.35, False, 0.85, False]], # inputs=[ # gallery, # model_repo, # general_thresh, # general_mcut_enabled, # character_thresh, # character_mcut_enabled, # characters_merge_enabled, # ], # ) demo.queue(max_size=10) demo.launch(inbrowser=True) #, ssr_mode=False if __name__ == "__main__": main()