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import gradio as gr
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from model import models
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from multit2i import (load_models, infer_fn, infer_rand_fn, save_gallery,
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change_model, warm_model, get_model_info_md, loaded_models,
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get_positive_prefix, get_positive_suffix, get_negative_prefix, get_negative_suffix,
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get_recom_prompt_type, set_recom_prompt_preset, get_tag_type, randomize_seed, translate_to_en)
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from tagger.tagger import (predict_tags_wd, remove_specific_prompt, convert_danbooru_to_e621_prompt,
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insert_recom_prompt, compose_prompt_to_copy)
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from tagger.fl2sd3longcap import predict_tags_fl2_sd3
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from tagger.v2 import V2_ALL_MODELS, v2_random_prompt
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from tagger.utils import (V2_ASPECT_RATIO_OPTIONS, V2_RATING_OPTIONS,
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V2_LENGTH_OPTIONS, V2_IDENTITY_OPTIONS)
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max_images = 6
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MAX_SEED = 2**32-1
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load_models(models)
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css = """
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.model_info { text-align: center; }
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.output { width=112px; height=112px; max_width=112px; max_height=112px; !important; }
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.gallery { min_width=512px; min_height=512px; max_height=1024px; !important; }
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"""
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with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", fill_width=True, css=css) as demo:
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with gr.Tab("Image Generator"):
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with gr.Row():
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with gr.Column(scale=10):
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with gr.Group():
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with gr.Accordion("Prompt from Image File", open=False):
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tagger_image = gr.Image(label="Input image", type="pil", format="png", sources=["upload", "clipboard"], height=256)
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with gr.Accordion(label="Advanced options", open=False):
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with gr.Row():
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tagger_general_threshold = gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.01, interactive=True)
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tagger_character_threshold = gr.Slider(label="Character threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.01, interactive=True)
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tagger_tag_type = gr.Radio(label="Convert tags to", info="danbooru for common, e621 for Pony.", choices=["danbooru", "e621"], value="danbooru")
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with gr.Row():
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tagger_recom_prompt = gr.Radio(label="Insert reccomended prompt", choices=["None", "Animagine", "Pony"], value="None", interactive=True)
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tagger_keep_tags = gr.Radio(label="Remove tags leaving only the following", choices=["body", "dress", "all"], value="all")
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tagger_algorithms = gr.CheckboxGroup(["Use WD Tagger", "Use Florence-2-SD3-Long-Captioner"], label="Algorithms", value=["Use WD Tagger"])
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tagger_generate_from_image = gr.Button(value="Generate Tags from Image", variant="secondary")
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with gr.Accordion("Prompt Transformer", open=False):
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with gr.Row():
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v2_character = gr.Textbox(label="Character", placeholder="hatsune miku", scale=2)
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v2_series = gr.Textbox(label="Series", placeholder="vocaloid", scale=2)
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with gr.Row():
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v2_rating = gr.Radio(label="Rating", choices=list(V2_RATING_OPTIONS), value="sfw")
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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)
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v2_length = gr.Radio(label="Length", info="The total length of the tags.", choices=list(V2_LENGTH_OPTIONS), value="long")
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with gr.Row():
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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")
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v2_ban_tags = gr.Textbox(label="Ban tags", info="Tags to ban from the output.", placeholder="alternate costumen, ...", value="censored")
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v2_tag_type = gr.Radio(label="Tag Type", info="danbooru for common, e621 for Pony.", choices=["danbooru", "e621"], value="danbooru", visible=False)
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v2_model = gr.Dropdown(label="Model", choices=list(V2_ALL_MODELS.keys()), value=list(V2_ALL_MODELS.keys())[0])
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v2_copy = gr.Button(value="Copy to clipboard", variant="secondary", size="sm", interactive=False)
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random_prompt = gr.Button(value="Extend 🎲", variant="secondary")
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prompt = gr.Text(label="Prompt", lines=2, max_lines=8, placeholder="1girl, solo, ...", show_copy_button=True)
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with gr.Accordion("Advanced options", open=False):
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neg_prompt = gr.Text(label="Negative Prompt", lines=1, max_lines=8, placeholder="")
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with gr.Row():
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width = gr.Slider(label="Width", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
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height = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=1216, step=32, value=0)
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steps = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
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with gr.Row():
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cfg = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
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seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
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seed_rand = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary")
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recom_prompt_preset = gr.Radio(label="Set Presets", choices=get_recom_prompt_type(), value="Common")
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with gr.Row():
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positive_prefix = gr.CheckboxGroup(label="Use Positive Prefix", choices=get_positive_prefix(), value=[])
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positive_suffix = gr.CheckboxGroup(label="Use Positive Suffix", choices=get_positive_suffix(), value=["Common"])
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negative_prefix = gr.CheckboxGroup(label="Use Negative Prefix", choices=get_negative_prefix(), value=[])
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negative_suffix = gr.CheckboxGroup(label="Use Negative Suffix", choices=get_negative_suffix(), value=["Common"])
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with gr.Row():
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image_num = gr.Slider(label="Number of images", minimum=1, maximum=max_images, value=1, step=1, interactive=True, scale=2)
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trans_prompt = gr.Button(value="Translate 📝", variant="secondary", size="sm", scale=2)
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clear_prompt = gr.Button(value="Clear 🗑️", variant="secondary", size="sm", scale=1)
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with gr.Row():
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run_button = gr.Button("Generate Image", variant="primary", scale=6)
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random_button = gr.Button("Random Model 🎲", variant="secondary", scale=3)
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with gr.Group():
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model_name = gr.Dropdown(label="Select Model", choices=list(loaded_models.keys()), value=list(loaded_models.keys())[0], allow_custom_value=True)
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model_info = gr.Markdown(value=get_model_info_md(list(loaded_models.keys())[0]), elem_classes="model_info")
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with gr.Column(scale=10):
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with gr.Group():
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with gr.Row():
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output = [gr.Image(label='', elem_classes="output", type="filepath", format="png",
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show_download_button=True, show_share_button=False, show_label=False,
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interactive=False, min_width=80, visible=True, width=112, height=112) for _ in range(max_images)]
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with gr.Group():
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results = gr.Gallery(label="Gallery", elem_classes="gallery", interactive=False, show_download_button=True, show_share_button=False,
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container=True, format="png", object_fit="cover", columns=2, rows=2)
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image_files = gr.Files(label="Download", interactive=False)
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clear_results = gr.Button("Clear Gallery / Download 🗑️", variant="secondary")
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with gr.Column():
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examples = gr.Examples(
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examples = [
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["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"],
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["sailor moon, magical girl transformation, sparkles and ribbons, soft pastel colors, crescent moon motif, starry night sky background, shoujo manga style"],
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["kafuu chino, 1girl, solo"],
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["1girl"],
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["beautiful sunset"],
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],
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inputs=[prompt],
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cache_examples=False,
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)
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with gr.Tab("PNG Info"):
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def extract_exif_data(image):
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if image is None: return ""
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try:
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metadata_keys = ['parameters', 'metadata', 'prompt', 'Comment']
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for key in metadata_keys:
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if key in image.info:
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return image.info[key]
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return str(image.info)
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except Exception as e:
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return f"Error extracting metadata: {str(e)}"
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with gr.Row():
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with gr.Column():
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image_metadata = gr.Image(label="Image with metadata", type="pil", sources=["upload"])
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with gr.Column():
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result_metadata = gr.Textbox(label="Metadata", show_label=True, show_copy_button=True, interactive=False, container=True, max_lines=99)
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image_metadata.change(
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fn=extract_exif_data,
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inputs=[image_metadata],
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outputs=[result_metadata],
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)
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gr.Markdown(
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f"""This demo was created in reference to the following demos.<br>
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[Nymbo/Flood](https://huggingface.co/spaces/Nymbo/Flood),
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[Yntec/ToyWorldXL](https://huggingface.co/spaces/Yntec/ToyWorldXL),
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[Yntec/Diffusion80XX](https://huggingface.co/spaces/Yntec/Diffusion80XX).
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"""
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)
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gr.DuplicateButton(value="Duplicate Space")
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gr.Markdown(f"Just a few edits to *model.py* are all it takes to complete your own collection.")
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model_name.change(change_model, [model_name], [model_info], queue=False, show_api=False)\
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.success(warm_model, [model_name], None, queue=False, show_api=False)
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for i, o in enumerate(output):
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img_i = gr.Number(i, visible=False)
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image_num.change(lambda i, n: gr.update(visible = (i < n)), [img_i, image_num], o, show_api=False)
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gen_event = gr.on(triggers=[run_button.click, prompt.submit],
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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,
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inputs=[img_i, image_num, model_name, prompt, neg_prompt, height, width, steps, cfg, seed,
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positive_prefix, positive_suffix, negative_prefix, negative_suffix],
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outputs=[o], queue=False, show_api=False)
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gen_event2 = gr.on(triggers=[random_button.click],
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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,
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inputs=[img_i, image_num, model_name, prompt, neg_prompt, height, width, steps, cfg, seed,
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positive_prefix, positive_suffix, negative_prefix, negative_suffix],
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outputs=[o], queue=False, show_api=False)
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o.change(save_gallery, [o, results], [results, image_files], show_api=False)
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clear_prompt.click(lambda: (None, None, None, None), None, [prompt, neg_prompt, v2_character, v2_series], queue=False, show_api=False)
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clear_results.click(lambda: (None, None), None, [results, image_files], queue=False, show_api=False)
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recom_prompt_preset.change(set_recom_prompt_preset, [recom_prompt_preset],
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[positive_prefix, positive_suffix, negative_prefix, negative_suffix], queue=False, show_api=False)
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seed_rand.click(randomize_seed, None, [seed], queue=False, show_api=False)
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trans_prompt.click(translate_to_en, [prompt], [prompt], queue=False, show_api=False)\
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.then(translate_to_en, [neg_prompt], [neg_prompt], queue=False, show_api=False)
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random_prompt.click(
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v2_random_prompt, [prompt, v2_series, v2_character, v2_rating, v2_aspect_ratio, v2_length,
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v2_identity, v2_ban_tags, v2_model], [prompt, v2_series, v2_character], show_api=False,
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).success(get_tag_type, [positive_prefix, positive_suffix, negative_prefix, negative_suffix], [v2_tag_type], queue=False, show_api=False
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).success(convert_danbooru_to_e621_prompt, [prompt, v2_tag_type], [prompt], queue=False, show_api=False)
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tagger_generate_from_image.click(lambda: ("", "", ""), None, [v2_series, v2_character, prompt], queue=False, show_api=False,
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).success(
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predict_tags_wd,
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[tagger_image, prompt, tagger_algorithms, tagger_general_threshold, tagger_character_threshold],
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[v2_series, v2_character, prompt, v2_copy],
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show_api=False,
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).success(predict_tags_fl2_sd3, [tagger_image, prompt, tagger_algorithms], [prompt], show_api=False,
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).success(remove_specific_prompt, [prompt, tagger_keep_tags], [prompt], queue=False, show_api=False,
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).success(convert_danbooru_to_e621_prompt, [prompt, tagger_tag_type], [prompt], queue=False, show_api=False,
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).success(insert_recom_prompt, [prompt, neg_prompt, tagger_recom_prompt], [prompt, neg_prompt], queue=False, show_api=False,
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).success(compose_prompt_to_copy, [v2_character, v2_series, prompt], [prompt], queue=False, show_api=False)
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demo.launch(max_threads=400, ssr_mode=False)
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