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, 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) css = """ .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("Image Generator"): with gr.Row(): with gr.Column(scale=10): with gr.Group(): with gr.Accordion("Prompt from Image File", open=False): tagger_image = gr.Image(label="Input image", type="pil", format="png", sources=["upload", "clipboard"], height=256) with gr.Accordion(label="Advanced options", open=False): with gr.Row(): 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") with gr.Row(): 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", variant="secondary") with gr.Accordion("Prompt Transformer", open=False): with gr.Row(): v2_character = gr.Textbox(label="Character", placeholder="hatsune miku", scale=2) v2_series = gr.Textbox(label="Series", placeholder="vocaloid", scale=2) with gr.Row(): 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") with gr.Row(): 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]) v2_copy = gr.Button(value="Copy to clipboard", variant="secondary", size="sm", interactive=False) random_prompt = gr.Button(value="Extend 🎲", variant="secondary") 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(): width = gr.Slider(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0) height = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0) steps = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0) with gr.Row(): cfg = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0) seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", 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(): 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(): image_num = gr.Slider(label="Number of images", minimum=1, maximum=max_images, value=1, step=1, interactive=True, scale=2) trans_prompt = gr.Button(value="Translate 📝", variant="secondary", size="sm", scale=2) clear_prompt = gr.Button(value="Clear 🗑️", variant="secondary", size="sm", scale=1) with gr.Row(): 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") with gr.Column(): 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], cache_examples=False, ) with gr.Tab("PNG Info"): def extract_exif_data(image): if image is None: return "" try: metadata_keys = ['parameters', 'metadata', 'prompt', 'Comment'] for key in metadata_keys: if key in image.info: return image.info[key] return str(image.info) except Exception as e: return f"Error extracting metadata: {str(e)}" with gr.Row(): with gr.Column(): image_metadata = gr.Image(label="Image with metadata", type="pil", sources=["upload"]) with gr.Column(): result_metadata = gr.Textbox(label="Metadata", show_label=True, show_copy_button=True, interactive=False, container=True, max_lines=99) image_metadata.change( fn=extract_exif_data, inputs=[image_metadata], outputs=[result_metadata], ) 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), [Yntec/Diffusion80XX](https://huggingface.co/spaces/Yntec/Diffusion80XX). """ ) gr.DuplicateButton(value="Duplicate Space") gr.Markdown(f"Just a few edits to *model.py* are all it takes to complete your own collection.") #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)