app.py

import spaces
import gradio as gr

from v2 import v2_upsampling_prompt, V2_ALL_MODELS
from utils import (gradio_copy_text, COPY_ACTION_JS, V2_ASPECT_RATIO_OPTIONS,
    V2_RATING_OPTIONS, V2_LENGTH_OPTIONS, V2_IDENTITY_OPTIONS)
from tagger import (predict_tags_wd, convert_danbooru_to_e621_prompt,
    remove_specific_prompt, insert_recom_prompt, compose_prompt_to_copy,
    translate_prompt, sort_tags, select_random_character)
from z3de621conv import predict_tags_e621
from fl2sd3longcap import predict_tags_fl2_sd3
from fl2basepromptgen import predict_tags_fl2_base_prompt_gen
from fl2cog import predict_tags_fl2_cog
from fl2flux import predict_tags_fl2_flux
from wdtagger import predict_tags_wd_large, get_wdtagger_models, VIT_LARGE_MODEL_DSV3_REPO
from dgb import predict_tags_dgb

def description_ui():
    gr.Markdown(
        """

## Danbooru Tags Transformer V2 Demo with WD Tagger

(Image =>) Prompt => Upsampled longer prompt

"""
    )
def description_ui2():
    gr.Markdown(
        """

- Mod of p1atdev's [Danbooru Tags Transformer V2 Demo](https://huggingface.co/spaces/p1atdev/danbooru-tags-transformer-v2) and [WD Tagger with 🤗 transformers](https://huggingface.co/spaces/p1atdev/wd-tagger-transformers).

- Models: p1atdev's [wd-swinv2-tagger-v3-hf](https://huggingface.co/p1atdev/wd-swinv2-tagger-v3-hf), [dart-v2-moe-sft](https://huggingface.co/p1atdev/dart-v2-moe-sft), [dart-v2-sft](https://huggingface.co/p1atdev/dart-v2-sft)\

, toynya's [Z3D-E621-Convnext](https://huggingface.co/toynya/Z3D-E621-Convnext), gokaygokay's [Florence-2-SD3-Captioner](https://huggingface.co/gokaygokay/Florence-2-SD3-Captioner),\

 MiaoshouAI's [Florence-2-base-PromptGen-v1.5](https://huggingface.co/MiaoshouAI/Florence-2-base-PromptGen-v1.5),\

 SmilingWolf's [wd-vit-large-tagger-v3](https://huggingface.co/SmilingWolf/wd-vit-large-tagger-v3),\

 thwri's [CogFlorence-2.1-Large](https://huggingface.co/thwri/CogFlorence-2.1-Large/),\

 gokaygokay's [Florence-2-Flux](https://huggingface.co/gokaygokay/Florence-2-Flux),\

 gokaygokay's [Florence-2-Flux-Large](https://huggingface.co/gokaygokay/Florence-2-Flux-Large),\

 v2ray's [deepgelbooru](https://huggingface.co/v2ray/deepgelbooru).

"""
    )


def main():
    with gr.Blocks() as ui:
        description_ui()
        with gr.Column():
            with gr.Group():
                input_image = gr.Image(label="Input image", type="pil", sources=["upload", "clipboard"], height=256)
                with gr.Accordion(label="Advanced options", open=False):
                    wdtagger_model = gr.Dropdown(label="Original WD Tagger Model", choices=get_wdtagger_models(), value=VIT_LARGE_MODEL_DSV3_REPO)
                    general_threshold = gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.01, interactive=True)
                    character_threshold = gr.Slider(label="Character threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.01, interactive=True)
                    e621_threshold = gr.Slider(label="Threshold (Z3D-E621-Convnext)", minimum=0.0, maximum=1.0, value=0.5, step=0.01, interactive=True)
                    input_tag_type = gr.Radio(label="Convert tags to", info="danbooru for Animagine, e621 for Pony.", choices=["danbooru", "e621"], value="danbooru")
                    recom_prompt = gr.Radio(label="Insert reccomended prompt", choices=["None", "Animagine", "Pony"], value="None", interactive=True)
                image_algorithms = gr.CheckboxGroup(["Use WD Tagger", "Use Original WD Tagger", "Use Z3D-E621-Convnext", "Use Florence-2-SD3-Long-Captioner",
                    "Use Florence-2-base-PromptGen", "Use CogFlorence-2.1-Large", "Use Florence-2-Flux","Use Florence-2-Flux-Large", "Use DeepGelbooru"], label="Algorithms", value=["Use WD Tagger"])
                keep_tags = gr.Radio(label="Remove tags leaving only the following", choices=["body", "dress", "all"], value="all")
                generate_from_image_btn = gr.Button(value="GENERATE TAGS FROM IMAGE", size="lg", variant="primary")
            with gr.Group():
                with gr.Row():
                    input_character = gr.Textbox(label="Character tags", placeholder="hatsune miku")
                    input_copyright = gr.Textbox(label="Copyright tags", placeholder="vocaloid")
                    random_character = gr.Button(value="Random character 🎲", size="sm")
                input_general = gr.TextArea(label="General tags", lines=4, placeholder="1girl, ...", value="")
                input_tags_to_copy = gr.Textbox(value="", visible=False)
                copy_input_btn = gr.Button(value="Copy to clipboard", size="sm", interactive=False)
                translate_input_prompt_button = gr.Button(value="Translate prompt to English", size="sm", variant="secondary")
                tag_type = gr.Radio(label="Output tag conversion", info="danbooru for Animagine, e621 for Pony.", choices=["danbooru", "e621"], value="e621", visible=False)
                input_rating = gr.Radio(label="Rating", choices=list(V2_RATING_OPTIONS), value="explicit")
                with gr.Accordion(label="Advanced options", open=False):
                    input_aspect_ratio = gr.Radio(label="Aspect ratio", info="The aspect ratio of the image.", choices=list(V2_ASPECT_RATIO_OPTIONS), value="square")
                    input_length = gr.Radio(label="Length", info="The total length of the tags.", choices=list(V2_LENGTH_OPTIONS), value="very_long")
                    input_identity = gr.Radio(label="Keep identity", info="How strictly to keep the identity of the character or subject. If you specify the detail of subject in the prompt, you should choose `strict`. Otherwise, choose `none` or `lax`. `none` is very creative but sometimes ignores the input prompt.", choices=list(V2_IDENTITY_OPTIONS), value="lax")                    
                    input_ban_tags = gr.Textbox(label="Ban tags", info="Tags to ban from the output.", placeholder="alternate costumen, ...", value="censored")
                    model_name = gr.Dropdown(label="Model", choices=list(V2_ALL_MODELS.keys()), value=list(V2_ALL_MODELS.keys())[0])
                    dummy_np = gr.Textbox(label="Negative prompt", value="", visible=False)
                    recom_animagine = gr.Textbox(label="Animagine reccomended prompt", value="Animagine", visible=False)
                    recom_pony = gr.Textbox(label="Pony reccomended prompt", value="Pony", visible=False)
                generate_btn = gr.Button(value="GENERATE TAGS", size="lg", variant="primary")
            with gr.Row():
                with gr.Group():
                    output_text = gr.TextArea(label="Output tags", interactive=False, show_copy_button=True)
                    copy_btn = gr.Button(value="Copy to clipboard", size="sm", interactive=False)
                with gr.Group():
                    output_text_pony = gr.TextArea(label="Output tags (Pony e621 style)", interactive=False, show_copy_button=True)
                    copy_btn_pony = gr.Button(value="Copy to clipboard", size="sm", interactive=False)
        description_ui2()

        random_character.click(select_random_character, [input_copyright, input_character], [input_copyright, input_character], queue=False)

        translate_input_prompt_button.click(translate_prompt, [input_general], [input_general], queue=False)
        translate_input_prompt_button.click(translate_prompt, [input_character], [input_character], queue=False)
        translate_input_prompt_button.click(translate_prompt, [input_copyright], [input_copyright], queue=False)

        generate_from_image_btn.click(
            lambda: ("", "", ""), None, [input_copyright, input_character, input_general], queue=False,
        ).success(
            predict_tags_wd,
            [input_image, input_general, image_algorithms, general_threshold, character_threshold, input_copyright, input_character],
            [input_copyright, input_character, input_general, copy_input_btn],
        ).success(
            predict_tags_wd_large,
            [input_image, input_general, image_algorithms, general_threshold, character_threshold, input_copyright, input_character, wdtagger_model],
            [input_copyright, input_character, input_general, copy_input_btn],
        ).success(
            predict_tags_e621, [input_image, input_general, image_algorithms, e621_threshold], [input_general],
        ).success(
            predict_tags_fl2_sd3, [input_image, input_general, image_algorithms], [input_general],
        ).success(
            predict_tags_fl2_base_prompt_gen, [input_image, input_general, image_algorithms], [input_general],
        ).success(
            predict_tags_fl2_cog, [input_image, input_general, image_algorithms], [input_general],
        ).success(
            predict_tags_fl2_flux, [input_image, input_general, image_algorithms], [input_general],
        ).success(
            predict_tags_dgb, [input_image, input_general, image_algorithms, general_threshold], [input_general],
        ).success(
            remove_specific_prompt, [input_general, keep_tags], [input_general], queue=False,
        ).success(
            convert_danbooru_to_e621_prompt, [input_general, input_tag_type], [input_general], queue=False,
        ).success(
            sort_tags, [input_general], [input_general], queue=False,
        ).success(
            insert_recom_prompt, [input_general, dummy_np, recom_prompt], [input_general, dummy_np], queue=False,
        )
        copy_input_btn.click(compose_prompt_to_copy, [input_character, input_copyright, input_general], [input_tags_to_copy], queue=False)\
            .success(gradio_copy_text, [input_tags_to_copy], js=COPY_ACTION_JS)

        generate_btn.click(
            v2_upsampling_prompt,
            [model_name, input_copyright, input_character, input_general,
            input_rating, input_aspect_ratio, input_length, input_identity, input_ban_tags],
            [output_text],
        ).success(
            sort_tags, [output_text], [output_text], queue=False,
        ).success(
            convert_danbooru_to_e621_prompt, [output_text, tag_type], [output_text_pony], queue=False,
        ).success(
            insert_recom_prompt, [output_text, dummy_np, recom_animagine], [output_text, dummy_np], queue=False,
        ).success(
            insert_recom_prompt, [output_text_pony, dummy_np, recom_pony], [output_text_pony, dummy_np], queue=False,
        ).success(lambda: (gr.update(interactive=True), gr.update(interactive=True)), None, [copy_btn, copy_btn_pony], queue=False)
        copy_btn.click(gradio_copy_text, [output_text], js=COPY_ACTION_JS)
        copy_btn_pony.click(gradio_copy_text, [output_text_pony], js=COPY_ACTION_JS)

    ui.launch()


if __name__ == "__main__":
    main()