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import gradio as gr
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from model import models
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from multit2i import (
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load_models,
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infer_multi,
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infer_multi_random,
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save_gallery_images,
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change_model,
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get_model_info_md,
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loaded_models,
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get_positive_prefix,
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get_positive_suffix,
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get_negative_prefix,
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get_negative_suffix,
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get_recom_prompt_type,
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set_recom_prompt_preset,
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get_tag_type,
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)
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from tagger.tagger import (
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predict_tags_wd,
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remove_specific_prompt,
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convert_danbooru_to_e621_prompt,
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insert_recom_prompt,
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compose_prompt_to_copy,
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)
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from tagger.fl2sd3longcap import predict_tags_fl2_sd3
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from tagger.v2 import (
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V2_ALL_MODELS,
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v2_random_prompt,
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)
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from tagger.utils import (
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V2_ASPECT_RATIO_OPTIONS,
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V2_RATING_OPTIONS,
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V2_LENGTH_OPTIONS,
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V2_IDENTITY_OPTIONS,
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)
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load_models(models, 5)
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css = """
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#model_info { text-align: center; }
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"""
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with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", css=css) as demo:
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with gr.Column():
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with gr.Accordion("Advanced settings", open=False):
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with gr.Accordion("Recommended Prompt", open=False):
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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=[], visible=False)
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negative_suffix = gr.CheckboxGroup(label="Use Negative Suffix", choices=get_negative_suffix(), value=["Common"], visible=False)
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with gr.Accordion("Prompt Transformer", open=False):
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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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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", size="sm", interactive=False)
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with gr.Accordion("Model", open=True):
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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)
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model_info = gr.Markdown(value=get_model_info_md(list(loaded_models.keys())[0]), elem_id="model_info")
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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", sources=["upload", "clipboard"], height=256)
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with gr.Accordion(label="Advanced options", open=False):
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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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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")
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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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random_prompt = gr.Button(value="Extend Prompt 🎲", size="sm", scale=1)
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clear_prompt = gr.Button(value="Clear Prompt 🗑️", size="sm", scale=1)
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prompt = gr.Text(label="Prompt", lines=1, max_lines=8, placeholder="1girl, solo, ...", show_copy_button=True)
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neg_prompt = gr.Text(label="Negative Prompt", lines=1, max_lines=8, placeholder="", visible=False)
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with gr.Row():
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run_button = gr.Button("Generate Image", scale=6)
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random_button = gr.Button("Random Model 🎲", scale=3)
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image_num = gr.Number(label="Count", minimum=1, maximum=16, value=1, step=1, interactive=True, scale=1)
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results = gr.Gallery(label="Gallery", interactive=False, show_download_button=True, show_share_button=False,
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container=True, format="png", object_fit="contain")
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image_files = gr.Files(label="Download", interactive=False)
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clear_results = gr.Button("Clear Gallery / Download")
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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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)
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gr.Markdown(
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f"""This demo was created in reference to the following demos.
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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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"""
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)
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gr.DuplicateButton(value="Duplicate Space")
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model_name.change(change_model, [model_name], [model_info], queue=False, show_api=False)
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gr.on(
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triggers=[run_button.click, prompt.submit],
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fn=infer_multi,
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inputs=[prompt, neg_prompt, results, image_num, model_name,
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positive_prefix, positive_suffix, negative_prefix, negative_suffix],
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outputs=[results],
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queue=True,
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show_progress="full",
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show_api=True,
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).success(save_gallery_images, [results], [results, image_files], queue=False, show_api=False)
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gr.on(
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triggers=[random_button.click],
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fn=infer_multi_random,
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inputs=[prompt, neg_prompt, results, image_num,
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positive_prefix, positive_suffix, negative_prefix, negative_suffix],
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outputs=[results],
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queue=True,
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show_progress="full",
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show_api=True,
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).success(save_gallery_images, [results], [results, image_files], queue=False, show_api=False)
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clear_prompt.click(lambda: (None, None, None), None, [prompt, v2_series, v2_character], 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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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(
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get_tag_type, [positive_prefix, positive_suffix, negative_prefix, negative_suffix], [v2_tag_type], queue=False, show_api=False
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).success(
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convert_danbooru_to_e621_prompt, [prompt, v2_tag_type], [prompt], queue=False, show_api=False,
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)
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tagger_generate_from_image.click(
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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(
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predict_tags_fl2_sd3, [tagger_image, prompt, tagger_algorithms], [prompt], show_api=False,
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).success(
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remove_specific_prompt, [prompt, tagger_keep_tags], [prompt], queue=False, show_api=False,
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).success(
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convert_danbooru_to_e621_prompt, [prompt, tagger_tag_type], [prompt], queue=False, show_api=False,
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).success(
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insert_recom_prompt, [prompt, neg_prompt, tagger_recom_prompt], [prompt, neg_prompt], queue=False, show_api=False,
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).success(
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compose_prompt_to_copy, [v2_character, v2_series, prompt], [prompt], queue=False, show_api=False,
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)
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demo.queue()
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demo.launch()
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