app.py

from transformers import CLIPImageProcessor, AutoModel
import torch
import json
import torch.nn as nn
from PIL import Image
import gradio as gr
import os
from huggingface_hub import login, snapshot_download

TITLE = "Danbooru Tagger"
DESCRIPTION = """
## Dataset
- Source: Danbooru
- Cutoff Date: 2025-11-27
- Validation Split: 10% of Dataset

## Validation Results

### General
Tags Count: 11046
| Metric          | Value       |
|-----------------|-------------|
| Macro F1        | 0.4439      |
| Macro Precision | 0.4168      |
| Macro Recall    | 0.4964      |
| Micro F1        | 0.6595      |
| Micro Precision | 0.5982      |
| Micro Recall    | 0.7349      |

### Character
Tags Count: 9148
| Metric          | Value       |
|-----------------|-------------|
| Macro F1        | 0.8646      |
| Macro Precision | 0.8897      |
| Macro Recall    | 0.8492      |
| Micro F1        | 0.9092      |
| Micro Precision | 0.9195      |
| Micro Recall    | 0.8991      |

### Artist
Tags Count: 17171
| Metric          | Value       |
|-----------------|-------------|
| Macro F1        | 0.8008      |
| Macro Precision | 0.8669      |
| Macro Recall    | 0.7641      |
| Micro F1        | 0.8596      |
| Micro Precision | 0.8948      |
| Micro Recall    | 0.8271      |
"""

kaomojis = [
    "0_0",
    "(o)_(o)",
    "+_+",
    "+_-",
    "._.",
    "<o>_<o>",
    "<|>_<|>",
    "=_=",
    ">_<",
    "3_3",
    "6_9",
    ">_o",
    "@_@",
    "^_^",
    "o_o",
    "u_u",
    "x_x",
    "|_|",
    "||_||",
]

device = torch.device('cpu')
dtype = torch.float32

hf_token = os.getenv("HF_TOKEN")
if hf_token:
    login(token=hf_token)
else:
    raise ValueError("environment variable HF_TOKEN not found.")

repo_id = "Johnny-Z/danbooru_vfm"
repo_dir = snapshot_download(repo_id)
model = AutoModel.from_pretrained(repo_id, dtype=dtype, trust_remote_code=True, device_map=device)

processor = CLIPImageProcessor.from_pretrained(repo_id)

class MultiheadAttentionPoolingHead(nn.Module):
    def __init__(self, input_size):
        super().__init__()

        self.map_probe = nn.Parameter(torch.randn(1, 1, input_size))
        self.map_layernorm0 = nn.LayerNorm(input_size, eps=1e-08)
        self.map_attention = torch.nn.MultiheadAttention(input_size, input_size // 64, batch_first=True)
        self.map_layernorm1 = nn.LayerNorm(input_size, eps=1e-08)
        self.map_ffn = nn.Sequential(
            nn.Linear(input_size, input_size * 4),
            nn.SiLU(),
            nn.Linear(input_size * 4, input_size)
        )

    def forward(self, hidden_state: torch.Tensor) -> torch.Tensor:
        batch_size = hidden_state.shape[0]
        probe = self.map_probe.repeat(batch_size, 1, 1)

        hidden_state = self.map_layernorm0(hidden_state)
        hidden_state = self.map_attention(probe, hidden_state, hidden_state)[0]
        hidden_state = self.map_layernorm1(hidden_state)

        residual = hidden_state
        hidden_state = residual + self.map_ffn(hidden_state)
        return hidden_state[:, 0]

class MLP(nn.Module):
    def __init__(self, input_size, class_num):
        super().__init__()
        self.mlp_layer0 = nn.Sequential(
            nn.LayerNorm(input_size, eps=1e-08),
            nn.Linear(input_size, input_size // 2),
            nn.SiLU()
        )
        self.mlp_layer1 = nn.Linear(input_size // 2, class_num)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.mlp_layer0(x)
        x = self.mlp_layer1(x)
        x = self.sigmoid(x)
        return x

with open(os.path.join(repo_dir, 'general_tag_dict.json'), 'r', encoding='utf-8') as f:
    general_dict = json.load(f)

with open(os.path.join(repo_dir, 'character_tag_dict.json'), 'r', encoding='utf-8') as f:
    character_dict = json.load(f)
    
with open(os.path.join(repo_dir, 'artist_tag_dict.json'), 'r', encoding='utf-8') as f:
    artist_dict = json.load(f)

with open(os.path.join(repo_dir, 'implications_list.json'), 'r', encoding='utf-8') as f:
    implications_list = json.load(f)

with open(os.path.join(repo_dir, 'artist_threshold.json'), 'r', encoding='utf-8') as f:
    artist_thresholds = json.load(f)

with open(os.path.join(repo_dir, 'character_threshold.json'), 'r', encoding='utf-8') as f:
    character_thresholds = json.load(f)

with open(os.path.join(repo_dir, 'general_threshold.json'), 'r', encoding='utf-8') as f:
    general_thresholds = json.load(f)

with open(os.path.join(repo_dir, 'character_feature.json'), 'r', encoding='utf-8') as f:
    character_features = json.load(f)

model_map = MultiheadAttentionPoolingHead(2048)
model_map.load_state_dict(torch.load(os.path.join(repo_dir, "map_head.pth"), map_location=device, weights_only=True))
model_map.to(device).to(dtype).eval()

general_class = 11046
mlp_general = MLP(2048, general_class)
mlp_general.load_state_dict(torch.load(os.path.join(repo_dir, "cls_predictor_general.pth"), map_location=device, weights_only=True))
mlp_general.to(device).to(dtype).eval()

character_class = 9148
mlp_character = MLP(2048, character_class)
mlp_character.load_state_dict(torch.load(os.path.join(repo_dir, "cls_predictor_character.pth"), map_location=device, weights_only=True))
mlp_character.to(device).to(dtype).eval()

artist_class = 17171
mlp_artist = MLP(2048, artist_class)
mlp_artist.load_state_dict(torch.load(os.path.join(repo_dir, "cls_predictor_artist.pth"), map_location=device, weights_only=True))
mlp_artist.to(device).to(dtype).eval()

def prediction_to_tag(prediction, tag_dict, class_num):
    prediction = prediction.view(class_num)
    predicted_ids = (prediction >= 0.2).nonzero(as_tuple=True)[0].cpu().numpy() + 1
    
    general = {}
    character = {}
    artist = {}
    date = {}
    rating = {}
    
    for tag, value in tag_dict.items():
        if value[2] in predicted_ids:
            tag_value = round(prediction[value[2] - 1].item(), 6)
            if value[1] == "general" and tag_value >= general_thresholds.get(tag, {}).get("Threshold", 0.75):
                general[tag] = tag_value
            elif value[1] == "character" and tag_value >= character_thresholds.get(tag, {}).get("Threshold", 0.75):
                character[tag] = tag_value
            elif value[1] == "artist" and tag_value >= artist_thresholds.get(tag, {}).get("Threshold", 0.75):
                artist[tag] = tag_value
            elif value[1] == "rating":
                rating[tag] = tag_value
            elif value[1] == "date":
                date[tag] = tag_value

    general = dict(sorted(general.items(), key=lambda item: item[1], reverse=True))
    character = dict(sorted(character.items(), key=lambda item: item[1], reverse=True))
    artist = dict(sorted(artist.items(), key=lambda item: item[1], reverse=True))

    if date:
        date = {max(date, key=date.get): date[max(date, key=date.get)]}
    if rating:
        rating = {max(rating, key=rating.get): rating[max(rating, key=rating.get)]}
    
    return general, character, artist, date, rating

def process_image(image):
    try:
        image = image.convert('RGBA')
        background = Image.new('RGBA', image.size, (255, 255, 255, 255))
        image = Image.alpha_composite(background, image).convert('RGB')

        image_inputs = processor(images=[image], return_tensors="pt").to(device).to(dtype)

    except (OSError, IOError) as e:
        print(f"Error opening image: {e}")
        return
    with torch.no_grad():
        embedding = model(image_inputs.pixel_values)

        embedding = model_map(embedding)

        general_prediction = mlp_general(embedding)
        general_ = prediction_to_tag(general_prediction, general_dict, general_class)
        general_tags = general_[0]
        rating = general_[4]

        character_prediction = mlp_character(embedding)
        character_ = prediction_to_tag(character_prediction, character_dict, character_class)
        character_tags = character_[1]

        remove_list = []
        for tag in character_tags:
            if tag in implications_list:
                remove_list.extend([implication for implication in implications_list[tag]])

        character_tags_list = [tag for tag in character_tags if tag not in remove_list]

        artist_prediction = mlp_artist(embedding)
        artist_ = prediction_to_tag(artist_prediction, artist_dict, artist_class)
        artist_tags = artist_[2]
        date = artist_[3]

    combined_tags = {**general_tags}

    tags_list = [tag for tag in combined_tags]
    remove_list = []
    for tag in tags_list:
        if tag in implications_list:
            for implication in implications_list[tag]:
                remove_list.append(implication)
    for char_tag in character_tags_list:
        if char_tag in character_features:
            for character_feature in character_features[char_tag]:
                remove_list.append(character_feature)
    tags_list = [tag for tag in tags_list if tag not in remove_list]
    tags_list = [tag.replace("_", " ") if tag not in kaomojis else tag for tag in tags_list]

    tags_str = ", ".join(character_tags_list + tags_list).replace("(", r"\(").replace(")", r"\)")

    return tags_str, artist_tags, character_tags, general_tags, rating, date

def main():
    with gr.Blocks(title=TITLE) as demo:
        with gr.Column():
            gr.Markdown(
                value=f"<h1 style='text-align: center; margin-bottom: 1rem'>{TITLE}</h1>"
            )
            with gr.Row():
                with gr.Column(variant="panel"):
                    submit = gr.Button(value="Submit", variant="primary", size="lg")
                    image = gr.Image(type="pil", image_mode="RGBA", label="Input")
                    with gr.Row():
                        clear = gr.ClearButton(
                            components=[
                                image,
                            ],
                            variant="secondary",
                            size="lg",
                        )
                    gr.Markdown(value=DESCRIPTION)
                with gr.Column(variant="panel"):
                    tags_str = gr.Textbox(label="Output", lines=4)
                    with gr.Row():
                        rating = gr.Label(label="Rating")
                        date = gr.Label(label="Year")
                    artist_tags = gr.Label(label="Artist")
                    character_tags = gr.Label(label="Character")
                    general_tags = gr.Label(label="General")
                    clear.add(
                        [
                            tags_str,
                            artist_tags,
                            general_tags,
                            character_tags,
                            rating,
                            date,
                        ]
                    )

        submit.click(
            process_image,
            inputs=[
                image
            ],
            outputs=[tags_str, artist_tags, character_tags, general_tags, rating, date],
        )

    demo.queue(max_size=10)
    demo.launch()

if __name__ == "__main__":
    main()