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import spaces
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import gradio as gr
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import numpy as np
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from dc import (infer, _infer, pass_result, get_diffusers_model_list, get_samplers,
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get_vaes, enable_model_recom_prompt, enable_diffusers_model_detail,
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get_t2i_model_info, get_all_lora_tupled_list, update_loras,
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apply_lora_prompt, download_my_lora, search_civitai_lora,
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select_civitai_lora, search_civitai_lora_json,
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preset_quality, preset_styles, process_style_prompt)
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from llmdolphin import (dolphin_respond_auto, dolphin_parse_simple,
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get_llm_formats, get_dolphin_model_format, get_dolphin_models,
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get_dolphin_model_info, select_dolphin_model, select_dolphin_format, get_dolphin_sysprompt)
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from tagger.v2 import v2_upsampling_prompt, V2_ALL_MODELS
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from tagger.utils import (gradio_copy_text, gradio_copy_prompt, COPY_ACTION_JS,
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V2_ASPECT_RATIO_OPTIONS, V2_RATING_OPTIONS, V2_LENGTH_OPTIONS, V2_IDENTITY_OPTIONS)
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from tagger.tagger import (predict_tags_wd, convert_danbooru_to_e621_prompt,
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remove_specific_prompt, insert_recom_prompt, compose_prompt_to_copy,
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translate_prompt, select_random_character)
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from tagger.fl2sd3longcap import predict_tags_fl2_sd3
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def description_ui():
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gr.Markdown(
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"""
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## Danbooru Tags Transformer V2 Demo with WD Tagger & SD3 Long Captioner
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(Image =>) Prompt => Upsampled longer prompt
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- Mod of p1atdev's [Danbooru Tags Transformer V2 Demo](https://huggingface.co/spaces/p1atdev/danbooru-tags-transformer-v2) and [WD Tagger with 🤗 transformers](https://huggingface.co/spaces/p1atdev/wd-tagger-transformers).
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- Models: p1atdev's [wd-swinv2-tagger-v3-hf](https://huggingface.co/p1atdev/wd-swinv2-tagger-v3-hf), [dart-v2-moe-sft](https://huggingface.co/p1atdev/dart-v2-moe-sft), [dart-v2-sft](https://huggingface.co/p1atdev/dart-v2-sft)\
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, gokaygokay's [Florence-2-SD3-Captioner](https://huggingface.co/gokaygokay/Florence-2-SD3-Captioner)
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"""
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)
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MAX_SEED = np.iinfo(np.int32).max
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MAX_IMAGE_SIZE = 1216
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css = """
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#container { margin: 0 auto; !important; }
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#col-container { margin: 0 auto; !important; }
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#result { display: block; max-width: 520px; max-height: 520px; width: 520px; height: 520px; margin: 0px auto; !important; }
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.lora { display: block; min-width: 480px; !important; }
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#model-info { text-align: center; !important; }
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"""
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with gr.Blocks(css=css, fill_width=True, elem_id="container") as demo:
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with gr.Tab("Image Generator"):
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with gr.Column(elem_id="col-container"):
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with gr.Row():
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prompt = gr.Text(label="Prompt", show_label=False, lines=1, max_lines=8, placeholder="Enter your prompt", container=False)
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with gr.Row():
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run_button = gr.Button("Run")
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run_translate_button = gr.Button("Translate")
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result = gr.Image(label="Result", elem_id="result", show_label=False, interactive=False,
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show_download_button=True, show_share_button=False, container=True)
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with gr.Accordion("Advanced Settings", open=False):
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with gr.Row():
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negative_prompt = gr.Text(label="Negative prompt", lines=1, max_lines=6, placeholder="Enter a negative prompt",
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value="(low quality, worst quality:1.2), very displeasing, watermark, signature, ugly")
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with gr.Row():
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seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
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randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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with gr.Row():
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width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
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height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
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guidance_scale = gr.Slider(label="Guidance scale", minimum=0.0, maximum=30.0, step=0.1, value=7)
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num_inference_steps = gr.Slider(label="Number of inference steps", minimum=1, maximum=100, step=1, value=28)
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with gr.Row():
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with gr.Column(scale=4):
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model_name = gr.Dropdown(label="Model", info="You can enter a huggingface model repo_id to want to use.",
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choices=get_diffusers_model_list(), value=get_diffusers_model_list()[0],
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allow_custom_value=True, interactive=True, min_width=320)
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model_info = gr.Markdown(elem_id="model-info")
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with gr.Column(scale=1):
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model_detail = gr.Checkbox(label="Show detail of model in list", value=False)
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with gr.Row():
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sampler = gr.Dropdown(label="Sampler", choices=get_samplers(), value="Euler a")
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vae_model = gr.Dropdown(label="VAE Model", choices=get_vaes(), value=get_vaes()[0])
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with gr.Accordion("LoRA", open=True, visible=True):
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with gr.Row():
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with gr.Column():
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with gr.Row():
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lora1 = gr.Dropdown(label="LoRA 1", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True, elem_classes="lora", min_width=320)
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lora1_wt = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA 1: weight")
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with gr.Row():
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lora1_info = gr.Textbox(label="", info="Example of prompt:", value="", show_copy_button=True, interactive=False, visible=False)
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lora1_copy = gr.Button(value="Copy example to prompt", visible=False)
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lora1_md = gr.Markdown(value="", visible=False)
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with gr.Column():
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with gr.Row():
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lora2 = gr.Dropdown(label="LoRA 2", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True, elem_classes="lora", min_width=320)
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lora2_wt = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA 2: weight")
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with gr.Row():
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lora2_info = gr.Textbox(label="", info="Example of prompt:", value="", show_copy_button=True, interactive=False, visible=False)
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lora2_copy = gr.Button(value="Copy example to prompt", visible=False)
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lora2_md = gr.Markdown(value="", visible=False)
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with gr.Column():
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with gr.Row():
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lora3 = gr.Dropdown(label="LoRA 3", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True, elem_classes="lora", min_width=320)
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lora3_wt = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA 3: weight")
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with gr.Row():
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lora3_info = gr.Textbox(label="", info="Example of prompt:", value="", show_copy_button=True, interactive=False, visible=False)
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lora3_copy = gr.Button(value="Copy example to prompt", visible=False)
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lora3_md = gr.Markdown(value="", visible=False)
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with gr.Column():
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with gr.Row():
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lora4 = gr.Dropdown(label="LoRA 4", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True, elem_classes="lora", min_width=320)
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lora4_wt = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA 4: weight")
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with gr.Row():
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lora4_info = gr.Textbox(label="", info="Example of prompt:", value="", show_copy_button=True, interactive=False, visible=False)
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lora4_copy = gr.Button(value="Copy example to prompt", visible=False)
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lora4_md = gr.Markdown(value="", visible=False)
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with gr.Column():
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with gr.Row():
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lora5 = gr.Dropdown(label="LoRA 5", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True, elem_classes="lora", min_width=320)
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lora5_wt = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA 5: weight")
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with gr.Row():
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lora5_info = gr.Textbox(label="", info="Example of prompt:", value="", show_copy_button=True, interactive=False, visible=False)
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lora5_copy = gr.Button(value="Copy example to prompt", visible=False)
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lora5_md = gr.Markdown(value="", visible=False)
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with gr.Accordion("From URL", open=True, visible=True):
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with gr.Row():
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lora_search_civitai_query = gr.Textbox(label="Query", placeholder="oomuro sakurako...", lines=1)
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lora_search_civitai_basemodel = gr.CheckboxGroup(label="Search LoRA for", choices=["Pony", "SD 1.5", "SDXL 1.0"], value=["Pony", "SDXL 1.0"])
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lora_search_civitai_submit = gr.Button("Search on Civitai")
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with gr.Row():
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lora_search_civitai_result = gr.Dropdown(label="Search Results", choices=[("", "")], value="", allow_custom_value=True, visible=False)
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lora_search_civitai_json = gr.JSON(value={}, visible=False)
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lora_search_civitai_desc = gr.Markdown(value="", visible=False)
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lora_download_url = gr.Textbox(label="URL", placeholder="http://...my_lora_url.safetensors", lines=1)
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lora_download = gr.Button("Get and set LoRA and apply to prompt")
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with gr.Row():
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recom_prompt = gr.Checkbox(label="Recommended prompt", value=True)
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quality_selector = gr.Radio(label="Quality Tag Presets", interactive=True, choices=list(preset_quality.keys()), value="None")
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style_selector = gr.Radio(label="Style Presets", interactive=True, choices=list(preset_styles.keys()), value="None")
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with gr.Accordion("Translation Settings", open=False):
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chatbot = gr.Chatbot(likeable=False, render_markdown=False, visible=False)
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chat_model = gr.Dropdown(choices=get_dolphin_models(), value=get_dolphin_models()[0][1], allow_custom_value=True, label="Model")
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chat_model_info = gr.Markdown(value=get_dolphin_model_info(get_dolphin_models()[0][1]), label="Model info")
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chat_format = gr.Dropdown(choices=get_llm_formats(), value=get_dolphin_model_format(get_dolphin_models()[0][1]), label="Message format")
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with gr.Row():
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chat_tokens = gr.Slider(minimum=1, maximum=4096, value=512, step=1, label="Max tokens")
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chat_temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
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chat_topp = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p")
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chat_topk = gr.Slider(minimum=0, maximum=100, value=40, step=1, label="Top-k")
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chat_rp = gr.Slider(minimum=0.0, maximum=2.0, value=1.1, step=0.1, label="Repetition penalty")
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chat_sysmsg = gr.Textbox(value=get_dolphin_sysprompt(), label="System message")
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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.on(
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triggers=[run_button.click, prompt.submit],
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fn=infer,
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inputs=[prompt, negative_prompt, seed, randomize_seed, width, height,
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guidance_scale, num_inference_steps, model_name,
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lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt,
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sampler, vae_model],
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outputs=[result],
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queue=True,
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show_progress="full",
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show_api=True,
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)
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gr.on(
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triggers=[run_translate_button.click],
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fn=_infer,
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inputs=[prompt, negative_prompt, seed, randomize_seed, width, height,
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guidance_scale, num_inference_steps, model_name,
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lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt,
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sampler, vae_model],
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outputs=[result],
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queue=False,
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show_api=True,
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api_name="infer_translate",
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).success(
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fn=dolphin_respond_auto,
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inputs=[prompt, chatbot],
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outputs=[chatbot],
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queue=True,
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show_progress="full",
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show_api=False,
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).success(
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fn=dolphin_parse_simple,
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inputs=[prompt, chatbot],
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outputs=[prompt],
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queue=False,
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show_api=False,
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).success(
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fn=infer,
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inputs=[prompt, negative_prompt, seed, randomize_seed, width, height,
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guidance_scale, num_inference_steps, model_name,
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lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt,
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sampler, vae_model],
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outputs=[result],
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queue=True,
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show_progress="full",
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show_api=False,
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).success(lambda: None, None, chatbot, queue=False, show_api=False)\
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.success(pass_result, [result], [result], queue=False, show_api=False)
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gr.on(
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triggers=[lora1.change, lora1_wt.change, lora2.change, lora2_wt.change, lora3.change, lora3_wt.change,
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lora4.change, lora4_wt.change, lora5.change, lora5_wt.change],
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fn=update_loras,
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inputs=[prompt, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt],
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outputs=[prompt, lora1, lora1_wt, lora1_info, lora1_copy, lora1_md,
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lora2, lora2_wt, lora2_info, lora2_copy, lora2_md, lora3, lora3_wt, lora3_info, lora3_copy, lora3_md,
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lora4, lora4_wt, lora4_info, lora4_copy, lora4_md, lora5, lora5_wt, lora5_info, lora5_copy, lora5_md],
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queue=False,
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trigger_mode="once",
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show_api=False,
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)
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lora1_copy.click(apply_lora_prompt, [prompt, lora1_info], [prompt], queue=False, show_api=False)
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lora2_copy.click(apply_lora_prompt, [prompt, lora2_info], [prompt], queue=False, show_api=False)
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lora3_copy.click(apply_lora_prompt, [prompt, lora3_info], [prompt], queue=False, show_api=False)
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lora4_copy.click(apply_lora_prompt, [prompt, lora4_info], [prompt], queue=False, show_api=False)
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lora5_copy.click(apply_lora_prompt, [prompt, lora5_info], [prompt], queue=False, show_api=False)
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gr.on(
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triggers=[lora_search_civitai_submit.click, lora_search_civitai_query.submit],
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fn=search_civitai_lora,
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inputs=[lora_search_civitai_query, lora_search_civitai_basemodel],
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outputs=[lora_search_civitai_result, lora_search_civitai_desc, lora_search_civitai_submit, lora_search_civitai_query],
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scroll_to_output=True,
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queue=True,
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show_api=False,
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)
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lora_search_civitai_json.change(search_civitai_lora_json, [lora_search_civitai_query, lora_search_civitai_basemodel], [lora_search_civitai_json], queue=True, show_api=True)
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lora_search_civitai_result.change(select_civitai_lora, [lora_search_civitai_result], [lora_download_url, lora_search_civitai_desc], scroll_to_output=True, queue=False, show_api=False)
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gr.on(
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triggers=[lora_download.click, lora_download_url.submit],
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fn=download_my_lora,
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inputs=[lora_download_url,lora1, lora2, lora3, lora4, lora5],
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outputs=[lora1, lora2, lora3, lora4, lora5],
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scroll_to_output=True,
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queue=True,
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show_api=False,
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)
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recom_prompt.change(enable_model_recom_prompt, [recom_prompt], [recom_prompt], queue=False, show_api=False)
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gr.on(
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triggers=[quality_selector.change, style_selector.change],
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fn=process_style_prompt,
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inputs=[prompt, negative_prompt, style_selector, quality_selector],
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outputs=[prompt, negative_prompt],
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queue=False,
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trigger_mode="once",
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)
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model_detail.change(enable_diffusers_model_detail, [model_detail, model_name], [model_detail, model_name], queue=False, show_api=False)
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model_name.change(get_t2i_model_info, [model_name], [model_info], queue=False, show_api=False)
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chat_model.change(select_dolphin_model, [chat_model], [chat_model, chat_format, chat_model_info], queue=True, show_progress="full", show_api=False)\
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.success(lambda: None, None, chatbot, queue=False, show_api=False)
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chat_format.change(select_dolphin_format, [chat_format], [chat_format], queue=False, show_api=False)\
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.success(lambda: None, None, chatbot, queue=False, show_api=False)
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with gr.Tab("Tags Transformer with Tagger"):
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with gr.Column():
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with gr.Group():
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input_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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general_threshold = gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.01, interactive=True)
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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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input_tag_type = gr.Radio(label="Convert tags to", info="danbooru for Animagine, e621 for Pony.", choices=["danbooru", "e621"], value="danbooru")
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recom_prompt = gr.Radio(label="Insert reccomended prompt", choices=["None", "Animagine", "Pony"], value="None", interactive=True)
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image_algorithms = gr.CheckboxGroup(["Use WD Tagger", "Use Florence-2-SD3-Long-Captioner"], label="Algorithms", value=["Use WD Tagger"])
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keep_tags = gr.Radio(label="Remove tags leaving only the following", choices=["body", "dress", "all"], value="all")
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generate_from_image_btn = gr.Button(value="GENERATE TAGS FROM IMAGE", size="lg", variant="primary")
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with gr.Group():
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with gr.Row():
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input_character = gr.Textbox(label="Character tags", placeholder="hatsune miku")
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input_copyright = gr.Textbox(label="Copyright tags", placeholder="vocaloid")
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random_character = gr.Button(value="Random character 🎲", size="sm")
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input_general = gr.TextArea(label="General tags", lines=4, placeholder="1girl, ...", value="")
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input_tags_to_copy = gr.Textbox(value="", visible=False)
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with gr.Row():
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copy_input_btn = gr.Button(value="Copy to clipboard", size="sm", interactive=False)
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copy_prompt_btn_input = gr.Button(value="Copy to primary prompt", size="sm", interactive=False)
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translate_input_prompt_button = gr.Button(value="Translate prompt to English", size="sm", variant="secondary")
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tag_type = gr.Radio(label="Output tag conversion", info="danbooru for Animagine, e621 for Pony.", choices=["danbooru", "e621"], value="e621", visible=False)
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input_rating = gr.Radio(label="Rating", choices=list(V2_RATING_OPTIONS), value="explicit")
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with gr.Accordion(label="Advanced options", open=False):
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input_aspect_ratio = gr.Radio(label="Aspect ratio", info="The aspect ratio of the image.", choices=list(V2_ASPECT_RATIO_OPTIONS), value="square")
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input_length = gr.Radio(label="Length", info="The total length of the tags.", choices=list(V2_LENGTH_OPTIONS), value="very_long")
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|
input_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")
|
|
input_ban_tags = gr.Textbox(label="Ban tags", info="Tags to ban from the output.", placeholder="alternate costumen, ...", value="censored")
|
|
model_name = gr.Dropdown(label="Model", choices=list(V2_ALL_MODELS.keys()), value=list(V2_ALL_MODELS.keys())[0])
|
|
dummy_np = gr.Textbox(label="Negative prompt", value="", visible=False)
|
|
recom_animagine = gr.Textbox(label="Animagine reccomended prompt", value="Animagine", visible=False)
|
|
recom_pony = gr.Textbox(label="Pony reccomended prompt", value="Pony", visible=False)
|
|
generate_btn = gr.Button(value="GENERATE TAGS", size="lg", variant="primary")
|
|
with gr.Row():
|
|
with gr.Group():
|
|
output_text = gr.TextArea(label="Output tags", interactive=False, show_copy_button=True)
|
|
with gr.Row():
|
|
copy_btn = gr.Button(value="Copy to clipboard", size="sm", interactive=False)
|
|
copy_prompt_btn = gr.Button(value="Copy to primary prompt", size="sm", interactive=False)
|
|
with gr.Group():
|
|
output_text_pony = gr.TextArea(label="Output tags (Pony e621 style)", interactive=False, show_copy_button=True)
|
|
with gr.Row():
|
|
copy_btn_pony = gr.Button(value="Copy to clipboard", size="sm", interactive=False)
|
|
copy_prompt_btn_pony = gr.Button(value="Copy to primary prompt", size="sm", interactive=False)
|
|
|
|
random_character.click(select_random_character, [input_copyright, input_character], [input_copyright, input_character], queue=False, show_api=False)
|
|
|
|
translate_input_prompt_button.click(translate_prompt, [input_general], [input_general], queue=False, show_api=False)
|
|
translate_input_prompt_button.click(translate_prompt, [input_character], [input_character], queue=False, show_api=False)
|
|
translate_input_prompt_button.click(translate_prompt, [input_copyright], [input_copyright], queue=False, show_api=False)
|
|
|
|
generate_from_image_btn.click(
|
|
lambda: ("", "", ""), None, [input_copyright, input_character, input_general], queue=False, show_api=False,
|
|
).success(
|
|
predict_tags_wd,
|
|
[input_image, input_general, image_algorithms, general_threshold, character_threshold],
|
|
[input_copyright, input_character, input_general, copy_input_btn],
|
|
show_api=False,
|
|
).success(
|
|
predict_tags_fl2_sd3, [input_image, input_general, image_algorithms], [input_general], show_api=False,
|
|
).success(
|
|
remove_specific_prompt, [input_general, keep_tags], [input_general], queue=False, show_api=False,
|
|
).success(
|
|
convert_danbooru_to_e621_prompt, [input_general, input_tag_type], [input_general], queue=False, show_api=False,
|
|
).success(
|
|
insert_recom_prompt, [input_general, dummy_np, recom_prompt], [input_general, dummy_np], queue=False, show_api=False,
|
|
).success(lambda: gr.update(interactive=True), None, [copy_prompt_btn_input], queue=False, show_api=False)
|
|
copy_input_btn.click(compose_prompt_to_copy, [input_character, input_copyright, input_general], [input_tags_to_copy], show_api=False)\
|
|
.success(gradio_copy_text, [input_tags_to_copy], js=COPY_ACTION_JS, show_api=False)
|
|
copy_prompt_btn_input.click(compose_prompt_to_copy, inputs=[input_character, input_copyright, input_general], outputs=[input_tags_to_copy], show_api=False)\
|
|
.success(gradio_copy_prompt, inputs=[input_tags_to_copy], outputs=[prompt], show_api=False)
|
|
|
|
generate_btn.click(
|
|
v2_upsampling_prompt,
|
|
[model_name, input_copyright, input_character, input_general,
|
|
input_rating, input_aspect_ratio, input_length, input_identity, input_ban_tags],
|
|
[output_text],
|
|
show_api=False,
|
|
).success(
|
|
convert_danbooru_to_e621_prompt, [output_text, tag_type], [output_text_pony], queue=False, show_api=False,
|
|
).success(
|
|
insert_recom_prompt, [output_text, dummy_np, recom_animagine], [output_text, dummy_np], queue=False, show_api=False,
|
|
).success(
|
|
insert_recom_prompt, [output_text_pony, dummy_np, recom_pony], [output_text_pony, dummy_np], queue=False, show_api=False,
|
|
).success(lambda: (gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=True)),
|
|
None, [copy_btn, copy_btn_pony, copy_prompt_btn, copy_prompt_btn_pony], queue=False, show_api=False)
|
|
copy_btn.click(gradio_copy_text, [output_text], js=COPY_ACTION_JS, show_api=False)
|
|
copy_btn_pony.click(gradio_copy_text, [output_text_pony], js=COPY_ACTION_JS, show_api=False)
|
|
copy_prompt_btn.click(gradio_copy_prompt, inputs=[output_text], outputs=[prompt], show_api=False)
|
|
copy_prompt_btn_pony.click(gradio_copy_prompt, inputs=[output_text_pony], outputs=[prompt], show_api=False)
|
|
|
|
demo.queue()
|
|
demo.launch()
|
|
|
