app.py

import gradio as gr
from model import models
from multit2i import (load_models, infer_fn, infer_rand_fn, save_gallery,
    change_model, warm_model, get_model_info_md, loaded_models, warm_models,
    get_positive_prefix, get_positive_suffix, get_negative_prefix, get_negative_suffix,
    get_recom_prompt_type, set_recom_prompt_preset, get_tag_type, randomize_seed, translate_to_en)
from tagger.tagger import (predict_tags_wd, remove_specific_prompt, convert_danbooru_to_e621_prompt,
    insert_recom_prompt, compose_prompt_to_copy)
from tagger.fl2sd3longcap import predict_tags_fl2_sd3
from tagger.v2 import V2_ALL_MODELS, v2_random_prompt
from tagger.utils import (V2_ASPECT_RATIO_OPTIONS, V2_RATING_OPTIONS,
    V2_LENGTH_OPTIONS, V2_IDENTITY_OPTIONS)

max_images = 6
MAX_SEED = 2**32-1
load_models(models)
warm_models(models[0:max_images])

css = """
.title { font-size: 3em; align-items: center; text-align: center; }
.info { align-items: center; text-align: center; }
.model_info { text-align: center; }
.output { width=112px; height=112px; max_width=112px; max_height=112px; !important; }
.gallery { min_width=512px; min_height=512px; max_height=1024px; !important; }
"""

with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", fill_width=True, css=css) as demo:
    with gr.Tab(""):
        with gr.Row():
            with gr.Column(scale=10): 
                with gr.Group():
                    with gr.Accordion("Prompt Transformer", open=False):
                        with gr.Row(equal_height=True):
                            v2_rating = gr.Radio(label="Rating", choices=list(V2_RATING_OPTIONS), value="sfw")
                            v2_aspect_ratio = gr.Radio(label="Aspect ratio", info="The aspect ratio of the image.", choices=list(V2_ASPECT_RATIO_OPTIONS), value="square", visible=False)
                            v2_length = gr.Radio(label="Length", info="The total length of the tags.", choices=list(V2_LENGTH_OPTIONS), value="long")
                       
                        
                    prompt = gr.Text(label="Prompt", lines=2, max_lines=8, placeholder="1girl, solo, ...", show_copy_button=True)
                    with gr.Accordion("Advanced options", open=False):
                        neg_prompt = gr.Text(label="Negative Prompt", lines=1, max_lines=8, placeholder="")
                        with gr.Row(equal_height=True):
                            width = gr.Slider(label="Width",  maximum=1216, step=32, value=0)
                            height = gr.Slider(label="Height",  maximum=1216, step=32, value=0)
                            steps = gr.Slider(label="Number of inference steps", maximum=100, step=1, value=0)
                        with gr.Row(equal_height=True):
                            cfg = gr.Slider(label="Guidance scale", maximum=30.0, step=0.1, value=0)
                            seed = gr.Slider(label="Seed",  minimum=-1, maximum=MAX_SEED, step=1, value=-1)
                            seed_rand = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary")
                        recom_prompt_preset = gr.Radio(label="Set Presets", choices=get_recom_prompt_type(), value="Common")
                        with gr.Row(equal_height=True):
                            positive_prefix = gr.CheckboxGroup(label="Use Positive Prefix", choices=get_positive_prefix(), value=[])
                            positive_suffix = gr.CheckboxGroup(label="Use Positive Suffix", choices=get_positive_suffix(), value=["Common"])
                            negative_prefix = gr.CheckboxGroup(label="Use Negative Prefix", choices=get_negative_prefix(), value=[])
                            negative_suffix = gr.CheckboxGroup(label="Use Negative Suffix", choices=get_negative_suffix(), value=["Common"])
                    with gr.Row(equal_height=True):
                        image_num = gr.Slider(label="Number of images", minimum=1, maximum=max_images, value=1, step=1, interactive=True, scale=2)
                                       
                with gr.Row(equal_height=True):
                    run_button = gr.Button("Generate Image", variant="primary", scale=6)
                    random_button = gr.Button("Random Model 🎲", variant="secondary", scale=3)
                    #stop_button = gr.Button('Stop', variant="stop", interactive=False, scale=1)
                with gr.Group():
                    model_name = gr.Dropdown(label="Select Model", choices=list(loaded_models.keys()), value=list(loaded_models.keys())[0], allow_custom_value=True)
                    model_info = gr.Markdown(value=get_model_info_md(list(loaded_models.keys())[0]), elem_classes="model_info")
            with gr.Column(scale=10): 
                with gr.Group():
                    with gr.Row():
                        output = [gr.Image(label='', elem_classes="output", type="filepath", format="png",
                                show_download_button=True, show_share_button=False, show_label=False,
                                interactive=False, min_width=80, visible=True, width=112, height=112) for _ in range(max_images)]
                with gr.Group():
                    results = gr.Gallery(label="Gallery", elem_classes="gallery", interactive=False, show_download_button=True, show_share_button=False,
                                        container=True, format="png", object_fit="cover", columns=2, rows=2)
                    image_files = gr.Files(label="Download", interactive=False)
                    clear_results = gr.Button("Clear Gallery / Download 🗑️", variant="secondary")
       
   
    #gr.on(triggers=[run_button.click, prompt.submit, random_button.click], fn=lambda: gr.update(interactive=True), inputs=None, outputs=stop_button, show_api=False)
    model_name.change(change_model, [model_name], [model_info], queue=False, show_api=False)\
    .success(warm_model, [model_name], None, queue=False, show_api=False)
    for i, o in enumerate(output):
        img_i = gr.Number(i, visible=False)
        image_num.change(lambda i, n: gr.update(visible = (i < n)), [img_i, image_num], o, show_api=False)
        gen_event = gr.on(triggers=[run_button.click, prompt.submit],
         fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4: infer_fn(m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4) if (i < n) else None,
         inputs=[img_i, image_num, model_name, prompt, neg_prompt, height, width, steps, cfg, seed,
                  positive_prefix, positive_suffix, negative_prefix, negative_suffix],
         outputs=[o], queue=False, show_api=False)  # Be sure to delete ", queue=False" when activating the stop button
        gen_event2 = gr.on(triggers=[random_button.click],
         fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4: infer_rand_fn(m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4) if (i < n) else None,
         inputs=[img_i, image_num, model_name, prompt, neg_prompt, height, width, steps, cfg, seed,
                  positive_prefix, positive_suffix, negative_prefix, negative_suffix],
         outputs=[o], queue=False, show_api=False)  # Be sure to delete ", queue=False" when activating the stop button
        o.change(save_gallery, [o, results], [results, image_files], show_api=False)
        #stop_button.click(lambda: gr.update(interactive=False), None, stop_button, cancels=[gen_event, gen_event2], show_api=False)

    clear_prompt.click(lambda: (None, None, None, None), None, [prompt, neg_prompt, v2_character, v2_series], queue=False, show_api=False)
    clear_results.click(lambda: (None, None), None, [results, image_files], queue=False, show_api=False)
    recom_prompt_preset.change(set_recom_prompt_preset, [recom_prompt_preset],
     [positive_prefix, positive_suffix, negative_prefix, negative_suffix], queue=False, show_api=False)
    seed_rand.click(randomize_seed, None, [seed], queue=False, show_api=False)
    trans_prompt.click(translate_to_en, [prompt], [prompt], queue=False, show_api=False)\
    .then(translate_to_en, [neg_prompt], [neg_prompt], queue=False, show_api=False)

    random_prompt.click(
        v2_random_prompt, [prompt, v2_series, v2_character, v2_rating, v2_aspect_ratio, v2_length,
          v2_identity, v2_ban_tags, v2_model], [prompt, v2_series, v2_character], show_api=False,
    ).success(get_tag_type, [positive_prefix, positive_suffix, negative_prefix, negative_suffix], [v2_tag_type], queue=False, show_api=False
    ).success(convert_danbooru_to_e621_prompt, [prompt, v2_tag_type], [prompt], queue=False, show_api=False)
    tagger_generate_from_image.click(lambda: ("", "", ""), None, [v2_series, v2_character, prompt], queue=False, show_api=False,
    ).success(
        predict_tags_wd,
        [tagger_image, prompt, tagger_algorithms, tagger_general_threshold, tagger_character_threshold],
        [v2_series, v2_character, prompt, v2_copy],
        show_api=False,
    ).success(predict_tags_fl2_sd3, [tagger_image, prompt, tagger_algorithms], [prompt], show_api=False,
    ).success(remove_specific_prompt, [prompt, tagger_keep_tags], [prompt], queue=False, show_api=False,
    ).success(convert_danbooru_to_e621_prompt, [prompt, tagger_tag_type], [prompt], queue=False, show_api=False,
    ).success(insert_recom_prompt, [prompt, neg_prompt, tagger_recom_prompt], [prompt, neg_prompt], queue=False, show_api=False,
    ).success(compose_prompt_to_copy, [v2_character, v2_series, prompt], [prompt], queue=False, show_api=False)

#demo.queue(default_concurrency_limit=200, max_size=200)
demo.launch(max_threads=400, ssr_mode=False)