app.py

import os
from time import time_ns

import gradio as gr
import torch
from huggingface_hub import Repository
from llama_cpp import Llama, LLAMA_SPLIT_MODE_NONE
from transformers import LlamaForCausalLM, LlamaTokenizer

from kgen.generate import tag_gen
from kgen.metainfo import SPECIAL, TARGET


MODEL_PATH = "KBlueLeaf/DanTagGen"


@torch.no_grad()
def get_result(
    text_model: LlamaForCausalLM,
    tokenizer: LlamaTokenizer,
    rating: str = "",
    artist: str = "",
    characters: str = "",
    copyrights: str = "",
    target: str = "long",
    special_tags: list[str] = ["1girl"],
    general: str = "",
    aspect_ratio: float = 0.0,
    blacklist: str = "",
    escape_bracket: bool = False,
    temperature: float = 1.35,
):
    start = time_ns()
    print("=" * 50, "\n")
    # Use LLM to predict possible summary
    # This prompt allow model itself to make request longer based on what it learned
    # Which will be better for preference sim and pref-sum contrastive scorer
    prompt = f"""
rating: {rating or '<|empty|>'}
artist: {artist.strip() or '<|empty|>'}
characters: {characters.strip() or '<|empty|>'}
copyrights: {copyrights.strip() or '<|empty|>'}
aspect ratio: {f"{aspect_ratio:.1f}" or '<|empty|>'}
target: {'<|' + target + '|>' if target else '<|long|>'}
general: {", ".join(special_tags)}, {general.strip().strip(",")}<|input_end|>
""".strip()

    artist = artist.strip().strip(",").replace("_", " ")
    characters = characters.strip().strip(",").replace("_", " ")
    copyrights = copyrights.strip().strip(",").replace("_", " ")
    special_tags = [tag.strip().replace("_", " ") for tag in special_tags]
    general = general.strip().strip(",")
    black_list = set(
        [tag.strip().replace("_", " ") for tag in blacklist.strip().split(",")]
    )

    prompt_tags = special_tags + general.strip().strip(",").split(",")
    len_target = TARGET[target]
    llm_gen = ""

    for llm_gen, extra_tokens in tag_gen(
        text_model,
        tokenizer,
        prompt,
        prompt_tags,
        len_target,
        black_list,
        temperature=temperature,
        top_p=0.95,
        top_k=100,
        max_new_tokens=256,
        max_retry=5,
    ):
        yield "", llm_gen, f"Total cost time: {(time_ns()-start)/1e9:.2f}s"
    print()
    print("-" * 50)

    general = f"{general.strip().strip(',')}, {','.join(extra_tokens)}"
    tags = general.strip().split(",")
    tags = [tag.strip() for tag in tags if tag.strip()]
    special = special_tags + [tag for tag in tags if tag in SPECIAL]
    tags = [tag for tag in tags if tag not in special]

    final_prompt = ", ".join(special)
    if characters:
        final_prompt += f", \n\n{characters}"
    if copyrights:
        final_prompt += ", "
        if not characters:
            final_prompt += "\n\n"
        final_prompt += copyrights
    if artist:
        final_prompt += f", \n\n{artist}"
    final_prompt += f""", \n\n{', '.join(tags)},

masterpiece, newest, absurdres, {rating}"""

    print(final_prompt)
    print("=" * 50)

    if escape_bracket:
        final_prompt = (
            final_prompt.replace("[", "\\[")
            .replace("]", "\\]")
            .replace("(", "\\(")
            .replace(")", "\\)")
        )

    yield final_prompt, llm_gen, f"Total cost time: {(time_ns()-start)/1e9:.2f}s  |  Total general tags: {len(special+tags)}"


if __name__ == "__main__":
    tokenizer = LlamaTokenizer.from_pretrained(MODEL_PATH)
    text_model = LlamaForCausalLM.from_pretrained(MODEL_PATH)
    text_model = text_model.eval()

    def wrapper(
        rating: str,
        artist: str,
        characters: str,
        copyrights: str,
        target: str,
        special_tags: list[str],
        general: str,
        width: float,
        height: float,
        blacklist: str,
        escape_bracket: bool,
        temperature: float = 1.35,
    ):
        yield from get_result(
            text_model,
            tokenizer,
            rating,
            artist,
            characters,
            copyrights,
            target,
            special_tags,
            general,
            width / height,
            blacklist,
            escape_bracket,
            temperature,
        )

    with gr.Blocks(theme=gr.themes.Soft()) as demo:
        with gr.Row():
            with gr.Column(scale=4):
                with gr.Row():
                    with gr.Column(scale=2):
                        rating = gr.Radio(
                            ["safe", "sensitive", "nsfw", "nsfw, explicit"],
                            label="Rating",
                        )
                        special_tags = gr.Dropdown(
                            SPECIAL,
                            value=["1girl"],
                            label="Special tags",
                            multiselect=True,
                        )
                        characters = gr.Textbox(label="Characters")
                        copyrights = gr.Textbox(label="Copyrights(Series)")
                        artist = gr.Textbox(label="Artist")
                        target = gr.Radio(
                            ["very_short", "short", "long", "very_long"],
                            label="Target length",
                        )
                    with gr.Column(scale=2):
                        general = gr.TextArea(label="Input your general tags")
                        black_list = gr.TextArea(
                            label="tag Black list (seperated by comma)"
                        )
                        with gr.Row():
                            width = gr.Slider(
                                value=1024,
                                minimum=256,
                                maximum=4096,
                                step=32,
                                label="Width",
                            )
                            height = gr.Slider(
                                value=1024,
                                minimum=256,
                                maximum=4096,
                                step=32,
                                label="Height",
                            )
                        with gr.Row():
                            temperature = gr.Slider(
                                value=1.35,
                                minimum=0.1,
                                maximum=2,
                                step=0.05,
                                label="Temperature",
                            )
                            escape_bracket = gr.Checkbox(
                                value=False,
                                label="Escape bracket",
                            )
                submit = gr.Button("Submit")
            with gr.Column(scale=3):
                formated_result = gr.TextArea(
                    label="Final output", lines=14, show_copy_button=True
                )
                llm_result = gr.TextArea(label="LLM output", lines=10)
                cost_time = gr.Markdown()
        submit.click(
            wrapper,
            inputs=[
                rating,
                artist,
                characters,
                copyrights,
                target,
                special_tags,
                general,
                width,
                height,
                black_list,
                temperature,
                escape_bracket,
            ],
            outputs=[
                formated_result,
                llm_result,
                cost_time,
            ],
            show_progress=True,
        )

    demo.launch()