import gradio as gr from model import models from multit2i import ( load_models, infer_multi, infer_multi_random, save_gallery_images, change_model, get_model_info_md, loaded_models, get_positive_prefix, get_positive_suffix, get_negative_prefix, get_negative_suffix, get_recom_prompt_type, set_recom_prompt_preset, get_tag_type, ) from tagger.tagger import ( predict_tags_wd, remove_specific_prompt, convert_danbooru_to_e621_prompt, insert_recom_prompt, ) 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, ) load_models(models, 5) #load_models(models, 20) # Fetching 20 models at the same time. default: 5 css = """ #model_info { text-align: center; display:block; } """ with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", css=css) as demo: with gr.Column(): with gr.Accordion("Advanced settings", open=True): with gr.Accordion("Recommended Prompt", open=False): recom_prompt_preset = gr.Radio(label="Set Presets", choices=get_recom_prompt_type(), value="Common") with gr.Row(): 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=[], visible=False) negative_suffix = gr.CheckboxGroup(label="Use Negative Suffix", choices=get_negative_suffix(), value=["Common"], visible=False) with gr.Accordion("Prompt Transformer", open=False): 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") v2_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") v2_ban_tags = gr.Textbox(label="Ban tags", info="Tags to ban from the output.", placeholder="alternate costumen, ...", value="censored") v2_tag_type = gr.Radio(label="Tag Type", info="danbooru for common, e621 for Pony.", choices=["danbooru", "e621"], value="danbooru", visible=False) v2_model = gr.Dropdown(label="Model", choices=list(V2_ALL_MODELS.keys()), value=list(V2_ALL_MODELS.keys())[0]) with gr.Accordion("Model", open=True): model_name = gr.Dropdown(label="Select Model", show_label=False, 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_id="model_info") with gr.Group(): with gr.Accordion("Prompt from Image File", open=False): tagger_image = gr.Image(label="Input image", type="pil", sources=["upload", "clipboard"], height=256) with gr.Accordion(label="Advanced options", open=False): tagger_general_threshold = gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.01, interactive=True) tagger_character_threshold = gr.Slider(label="Character threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.01, interactive=True) tagger_tag_type = gr.Radio(label="Convert tags to", info="danbooru for common, e621 for Pony.", choices=["danbooru", "e621"], value="danbooru") tagger_recom_prompt = gr.Radio(label="Insert reccomended prompt", choices=["None", "Animagine", "Pony"], value="None", interactive=True) tagger_keep_tags = gr.Radio(label="Remove tags leaving only the following", choices=["body", "dress", "all"], value="all") tagger_algorithms = gr.CheckboxGroup(["Use WD Tagger", "Use Florence-2-SD3-Long-Captioner"], label="Algorithms", value=["Use WD Tagger"]) tagger_generate_from_image = gr.Button(value="Generate Tags from Image") with gr.Row(): v2_character = gr.Textbox(label="Character", placeholder="hatsune miku", scale=2) v2_series = gr.Textbox(label="Series", placeholder="vocaloid", scale=2) random_prompt = gr.Button(value="Extend Prompt 🎲", size="sm", scale=1) clear_prompt = gr.Button(value="Clear Prompt 🗑️", size="sm", scale=1) prompt = gr.Text(label="Prompt", lines=1, max_lines=8, placeholder="1girl, solo, ...", show_copy_button=True) neg_prompt = gr.Text(label="Negative Prompt", lines=1, max_lines=8, placeholder="", visible=False) with gr.Row(): run_button = gr.Button("Generate Image", scale=6) random_button = gr.Button("Random Model 🎲", scale=3) image_num = gr.Number(label="Count", minimum=1, maximum=16, value=1, step=1, interactive=True, scale=1) results = gr.Gallery(label="Gallery", interactive=False, show_download_button=True, show_share_button=False, container=True, format="png", object_fit="contain") image_files = gr.Files(label="Download", interactive=False) clear_results = gr.Button("Clear Gallery / Download") examples = gr.Examples( examples = [ ["souryuu asuka langley, 1girl, neon genesis evangelion, plugsuit, pilot suit, red bodysuit, sitting, crossing legs, black eye patch, cat hat, throne, symmetrical, looking down, from bottom, looking at viewer, outdoors"], ["sailor moon, magical girl transformation, sparkles and ribbons, soft pastel colors, crescent moon motif, starry night sky background, shoujo manga style"], ["kafuu chino, 1girl, solo"], ["1girl"], ["beautiful sunset"], ], inputs=[prompt], ) gr.Markdown( f"""This demo was created in reference to the following demos. - [Nymbo/Flood](https://huggingface.co/spaces/Nymbo/Flood). - [Yntec/ToyWorldXL](https://huggingface.co/spaces/Yntec/ToyWorldXL). """ ) gr.DuplicateButton(value="Duplicate Space") model_name.change(change_model, [model_name], [model_info], queue=False, show_api=False) gr.on( triggers=[run_button.click, prompt.submit], fn=infer_multi, inputs=[prompt, neg_prompt, results, image_num, model_name, positive_prefix, positive_suffix, negative_prefix, negative_suffix], outputs=[results], queue=True, show_progress="full", show_api=True, ).success(save_gallery_images, [results], [results, image_files], queue=False, show_api=False) gr.on( triggers=[random_button.click], fn=infer_multi_random, inputs=[prompt, neg_prompt, results, image_num, positive_prefix, positive_suffix, negative_prefix, negative_suffix], outputs=[results], queue=True, show_progress="full", show_api=True, ).success(save_gallery_images, [results], [results, image_files], queue=False, show_api=False) clear_prompt.click(lambda: (None, None, None), None, [prompt, v2_series, v2_character], 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) 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( predict_tags_wd, [tagger_image, prompt, tagger_algorithms, tagger_general_threshold, tagger_character_threshold], [v2_series, v2_character, prompt, gr.Button(visible=False)], 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, ) demo.queue() demo.launch()