app.py

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

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"

MODEL_FILENAME = "model.onnx"
LABEL_FILENAME = "selected_tags.csv"

kaomojis = [
    "0_0", "(o)_(o)", "+_+", "+_-", "._.", "<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):
    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, image):
        target_size = self.model_target_size
        
        canvas = Image.new("RGBA", image.size, (255, 255, 255))
        canvas.alpha_composite(image)
        image = canvas.convert("RGB")

        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))

        if max_dim != target_size:
            padded_image = padded_image.resize((target_size, target_size), Image.BICUBIC)

        image_array = np.asarray(padded_image, dtype=np.float32)
        image_array = image_array[:, :, ::-1]
        
        return np.expand_dims(image_array, axis=0)

    def predict(
        self,
        image,
        model_repo,
        general_thresh,
        general_mcut_enabled,
        character_thresh,
        character_mcut_enabled,
    ):
        self.load_model(model_repo)
        
        image = self.prepare_image(image)
        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)))
        
        ratings_names = [labels[i] for i in self.rating_indexes]
        rating = dict(ratings_names)

        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)

        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 = sorted(general_res.items(), key=lambda x: x[1], reverse=True)
        sorted_general_strings = [x[0] for x in sorted_general]
        sorted_general_strings = ", ".join(sorted_general_strings).replace("(", "\(").replace(")", "\)")

        return sorted_general_strings, rating, character_res, general_res

def main():
    args = parse_args()
    predictor = Predictor()

    dropdown_list = [
        SWINV2_MODEL_DSV3_REPO,
        CONV_MODEL_DSV3_REPO,
        VIT_MODEL_DSV3_REPO,
        VIT_LARGE_MODEL_DSV3_REPO,
        EVA02_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(f"<h1 style='text-align: center; margin-bottom: 1rem'>{TITLE}</h1>")
            gr.Markdown(DESCRIPTION)
            with gr.Row():
                with gr.Column(variant="panel"):
                    image = gr.Image(type="pil", image_mode="RGBA", 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():
                        clear = gr.ClearButton(
                            components=[
                                image,
                                model_repo,
                                general_thresh,
                                general_mcut_enabled,
                                character_thresh,
                                character_mcut_enabled,
                            ],
                            variant="secondary",
                            size="lg",
                        )
                        submit = gr.Button(value="Submit", variant="primary", size="lg")
                with gr.Column(variant="panel"):
                    sorted_general_strings = gr.Textbox(label="Output (string)")
                    rating = gr.Label(label="Rating")
                    character_res = gr.Label(label="Output (characters)")
                    general_res = gr.Label(label="Output (tags)")
                    clear.add([sorted_general_strings, rating, character_res, general_res])

        submit.click(
            predictor.predict,
            inputs=[
                image,
                model_repo,
                general_thresh,
                general_mcut_enabled,
                character_thresh,
                character_mcut_enabled,
            ],
            outputs=[sorted_general_strings, rating, character_res, general_res],
        )
        
        gr.Examples(
            [["power.jpg", SWINV2_MODEL_DSV3_REPO, 0.35, False, 0.85, False]],
            inputs=[
                image,
                model_repo,
                general_thresh,
                general_mcut_enabled,
                character_thresh,
                character_mcut_enabled,
            ],
        )
        
        demo.queue(max_size=10)
    
    demo.launch()

if __name__ == "__main__":
    main()