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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, save_image_history, |
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get_vaes, enable_diffusers_model_detail, extract_exif_data, process_upscale, UPSCALER_KEYS, FACE_RESTORATION_MODELS, |
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preset_quality, preset_styles, process_style_prompt, get_all_lora_tupled_list, update_loras, apply_lora_prompt, |
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download_my_lora, search_civitai_lora, update_civitai_selection, select_civitai_lora, search_civitai_lora_json, |
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get_t2i_model_info, get_civitai_tag, CIVITAI_SORT, CIVITAI_PERIOD, CIVITAI_BASEMODEL, |
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SCHEDULE_TYPE_OPTIONS, SCHEDULE_PREDICTION_TYPE_OPTIONS, preprocessor_tab, SDXL_TASK, TASK_MODEL_LIST, |
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PROMPT_W_OPTIONS, POST_PROCESSING_SAMPLER, DIFFUSERS_CONTROLNET_MODEL, IP_MODELS, MODE_IP_OPTIONS, |
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TASK_AND_PREPROCESSORS, update_task_options, change_preprocessor_choices, get_ti_choices, |
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update_textual_inversion, set_textual_inversion_prompt, create_mask_now) |
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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 = 4096 |
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MIN_IMAGE_SIZE = 256 |
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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 { max-width: 520px; max-height: 520px; width: 520px; height: 520px; margin: 0px auto; object-fit: contain; !important; } |
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.lora { min-width: 480px; !important; } |
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.title { font-size: 3em; align-items: center; text-align: center; } |
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.info { align-items: center; text-align: center; } |
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.desc [src$='#float'] { float: right; margin: 20px; } |
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.image { margin: 0px auto; object-fit: contain; } |
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""" |
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with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60, 3600)) as demo: |
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gr.Markdown("# Votepurchase Multiple Model", elem_classes="title") |
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state = gr.State(value={}) |
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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", variant="primary", scale=5) |
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run_translate_button = gr.Button("Run with LLM Enhance", variant="secondary", scale=3) |
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auto_trans = gr.Checkbox(label="Auto translate to English", value=False, scale=2) |
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|
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result = gr.Image(label="Result", elem_id="result", format="png", type="filepath", 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("History", open=False): |
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history_files = gr.Files(interactive=False, visible=False) |
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history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", format="png", interactive=False, show_share_button=False, |
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show_download_button=True) |
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history_clear_button = gr.Button(value="Clear History", variant="secondary") |
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history_clear_button.click(lambda: ([], []), None, [history_gallery, history_files], queue=False, show_api=False) |
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with gr.Accordion("Advanced Settings", open=True): |
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task = gr.Dropdown(label="Task", choices=SDXL_TASK, value=TASK_MODEL_LIST[0]) |
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with gr.Tab("Generation Settings"): |
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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", show_copy_button=True, |
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value="(low quality, worst quality:1.2), very displeasing, watermark, signature, ugly") |
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with gr.Accordion("Prompt Settings", open=False): |
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with gr.Row(): |
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quality_selector = gr.Radio(label="Quality Tag Presets", interactive=True, choices=list(preset_quality.keys()), value="None", scale=3) |
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style_selector = gr.Radio(label="Style Presets", interactive=True, choices=list(preset_styles.keys()), value="None", scale=3) |
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with gr.Row(): |
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recom_prompt = gr.Checkbox(label="Recommended prompt", value=True, scale=1) |
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prompt_syntax = gr.Dropdown(label="Prompt Syntax", choices=PROMPT_W_OPTIONS, value=PROMPT_W_OPTIONS[1][1]) |
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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_classes="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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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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gpu_duration = gr.Slider(label="GPU time duration (seconds)", minimum=5, maximum=240, value=59) |
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with gr.Row(): |
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width = gr.Slider(label="Width", minimum=MIN_IMAGE_SIZE, maximum=MAX_IMAGE_SIZE, step=32, value=1024) |
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height = gr.Slider(label="Height", minimum=MIN_IMAGE_SIZE, 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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guidance_rescale = gr.Slider(label="CFG rescale", value=0., step=0.01, minimum=0., maximum=1.5) |
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with gr.Row(): |
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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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pag_scale = gr.Slider(minimum=0.0, maximum=10.0, step=0.1, value=0.0, label="PAG Scale") |
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clip_skip = gr.Checkbox(value=True, label="Layer 2 Clip Skip") |
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free_u = gr.Checkbox(value=False, label="FreeU") |
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with gr.Row(): |
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sampler = gr.Dropdown(label="Sampler", choices=get_samplers(), value="Euler") |
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schedule_type = gr.Dropdown(label="Schedule type", choices=SCHEDULE_TYPE_OPTIONS, value=SCHEDULE_TYPE_OPTIONS[0]) |
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schedule_prediction_type = gr.Dropdown(label="Discrete Sampling Type", choices=SCHEDULE_PREDICTION_TYPE_OPTIONS, value=SCHEDULE_PREDICTION_TYPE_OPTIONS[0]) |
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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("Other Settings", open=False): |
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with gr.Accordion("Textual inversion", open=True): |
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active_textual_inversion = gr.Checkbox(value=False, label="Active Textual Inversion in prompt") |
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use_textual_inversion = gr.CheckboxGroup(choices=get_ti_choices(model_name.value) if active_textual_inversion.value else [], value=None, label="Use Textual Invertion in prompt") |
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with gr.Tab("LoRA"): |
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def lora_dropdown(label, visible=True): |
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return gr.Dropdown(label=label, choices=get_all_lora_tupled_list(), value="", allow_custom_value=True, elem_classes="lora", min_width=320, visible=visible) |
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def lora_scale_slider(label, visible=True): |
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return gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label=label, visible=visible) |
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def lora_textbox(): |
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return gr.Textbox(label="", info="Example of prompt:", value="", show_copy_button=True, interactive=False, visible=False) |
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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 = lora_dropdown("LoRA 1") |
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lora1_wt = lora_scale_slider("LoRA 1: weight") |
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with gr.Row(): |
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lora1_info = lora_textbox() |
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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 = lora_dropdown("LoRA 2") |
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lora2_wt = lora_scale_slider("LoRA 2: weight") |
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with gr.Row(): |
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lora2_info = lora_textbox() |
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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 = lora_dropdown("LoRA 3") |
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lora3_wt = lora_scale_slider("LoRA 3: weight") |
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with gr.Row(): |
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lora3_info = lora_textbox() |
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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 = lora_dropdown("LoRA 4") |
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lora4_wt = lora_scale_slider("LoRA 4: weight") |
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with gr.Row(): |
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lora4_info = lora_textbox() |
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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 = lora_dropdown("LoRA 5") |
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lora5_wt = lora_scale_slider("LoRA 5: weight") |
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with gr.Row(): |
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lora5_info = lora_textbox() |
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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.Column(): |
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with gr.Row(): |
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lora6 = lora_dropdown("LoRA 6", visible=False) |
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lora6_wt = lora_scale_slider("LoRA 6: weight", visible=False) |
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with gr.Row(): |
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lora6_info = lora_textbox() |
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lora6_copy = gr.Button(value="Copy example to prompt", visible=False) |
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lora6_md = gr.Markdown(value="", visible=False) |
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with gr.Column(): |
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with gr.Row(): |
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lora7 = lora_dropdown("LoRA 7", visible=False) |
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lora7_wt = lora_scale_slider("LoRA 7: weight", visible=False) |
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with gr.Row(): |
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lora7_info = lora_textbox() |
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lora7_copy = gr.Button(value="Copy example to prompt", visible=False) |
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lora7_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_basemodel = gr.CheckboxGroup(label="Search LoRA for", choices=CIVITAI_BASEMODEL, value=["Pony", "Illustrious", "SDXL 1.0"]) |
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lora_search_civitai_sort = gr.Radio(label="Sort", choices=CIVITAI_SORT, value="Highest Rated") |
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lora_search_civitai_period = gr.Radio(label="Period", choices=CIVITAI_PERIOD, value="AllTime") |
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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_tag = gr.Dropdown(label="Tag", choices=get_civitai_tag(), value=get_civitai_tag()[0], allow_custom_value=True) |
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lora_search_civitai_user = gr.Textbox(label="Username", lines=1) |
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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_json = gr.JSON(value={}, visible=False) |
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lora_search_civitai_desc = gr.Markdown(value="", visible=False, elem_classes="desc") |
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with gr.Accordion("Select from Gallery", open=False): |
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lora_search_civitai_gallery = gr.Gallery([], label="Results", allow_preview=False, columns=5, show_share_button=False, interactive=False) |
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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_download_url = gr.Textbox(label="LoRA's download URL", placeholder="https://civitai.com/api/download/models/28907", info="It has to be .safetensors files, and you can also download them from Hugging Face.", lines=1) |
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lora_download = gr.Button("Get and set LoRA and apply to prompt") |
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with gr.Tab("ControlNet / Img2img / Inpaint"): |
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task_sel = gr.Radio(label="Task Selector", choices=SDXL_TASK, value=TASK_MODEL_LIST[0]) |
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with gr.Row(): |
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with gr.Column(): |
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image_control = gr.ImageEditor(label="Image ControlNet / Inpaint / Img2img", type="filepath", sources=["upload", "clipboard", "webcam"], image_mode='RGB', |
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show_share_button=False, show_fullscreen_button=False, layers=False, canvas_size=(384, 384), width=384, height=512, |
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brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed", default_size=32), eraser=gr.Eraser(default_size="32"), elem_classes="image") |
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result_to_ic_button = gr.Button("Get image from generated result") |
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image_mask = gr.Image(label="Image Mask", type="filepath", height=384, sources=["upload", "clipboard"], show_share_button=False, elem_classes="image") |
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with gr.Row(): |
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strength = gr.Slider(minimum=0.01, maximum=1.0, step=0.01, value=0.55, label="Strength", |
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info="This option adjusts the level of changes for img2img, repaint and inpaint.") |
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image_resolution = gr.Slider(minimum=64, maximum=2048, step=64, value=1024, label="Image Resolution", |
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info="The maximum proportional size of the generated image based on the uploaded image.") |
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with gr.Row(): |
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controlnet_model = gr.Dropdown(label="ControlNet model", choices=DIFFUSERS_CONTROLNET_MODEL, value=DIFFUSERS_CONTROLNET_MODEL[0], allow_custom_value=True) |
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with gr.Row(): |
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control_net_output_scaling = gr.Slider(minimum=0, maximum=5.0, step=0.1, value=1, label="ControlNet Output Scaling in UNet") |
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control_net_start_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, value=0, label="ControlNet Start Threshold (%)") |
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control_net_stop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, value=1, label="ControlNet Stop Threshold (%)") |
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with gr.Row(): |
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preprocessor_name = gr.Dropdown(label="Preprocessor Name", choices=TASK_AND_PREPROCESSORS["canny"]) |
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with gr.Row(): |
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preprocess_resolution = gr.Slider(minimum=64, maximum=2048, step=64, value=512, label="Preprocessor Resolution") |
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low_threshold = gr.Slider(minimum=1, maximum=255, step=1, value=100, label="'CANNY' low threshold") |
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high_threshold = gr.Slider(minimum=1, maximum=255, step=1, value=200, label="'CANNY' high threshold") |
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with gr.Row(): |
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value_threshold = gr.Slider(minimum=1, maximum=2.0, step=0.01, value=0.1, label="'MLSD' Hough value threshold") |
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distance_threshold = gr.Slider(minimum=1, maximum=20.0, step=0.01, value=0.1, label="'MLSD' Hough distance threshold") |
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recolor_gamma_correction = gr.Number(minimum=0., maximum=25., value=1., step=0.001, label="'RECOLOR' gamma correction") |
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tile_blur_sigma = gr.Number(minimum=0, maximum=100, value=9, step=1, label="'TILE' blur sigma") |
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with gr.Tab("IP-Adapter"): |
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with gr.Accordion("IP-Adapter 1", open=True, visible=True): |
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with gr.Row(): |
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with gr.Column(): |
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image_ip1 = gr.ImageEditor(label="IP Image", type="filepath", sources=["upload", "clipboard", "webcam"], image_mode='RGB', |
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show_share_button=False, show_fullscreen_button=False, layers=False, canvas_size=(384, 384), width=384, height=512, |
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brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed", default_size=32), eraser=gr.Eraser(default_size="32"), elem_classes="image") |
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result_to_ip1_button = gr.Button("Get image from generated result") |
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mask_ip1 = gr.Image(label="IP Mask (optional)", type="filepath", height=384, sources=["upload", "clipboard"], show_share_button=False, elem_classes="image") |
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with gr.Row(): |
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model_ip1 = gr.Dropdown(value="plus_face", label="Model", choices=IP_MODELS) |
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mode_ip1 = gr.Dropdown(value="original", label="Mode", choices=MODE_IP_OPTIONS) |
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scale_ip1 = gr.Slider(minimum=0., maximum=2., step=0.01, value=0.7, label="Scale") |
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with gr.Accordion("IP-Adapter 2", open=True, visible=True): |
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with gr.Row(): |
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with gr.Column(): |
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image_ip2 = gr.ImageEditor(label="IP Image", type="filepath", sources=["upload", "clipboard", "webcam"], image_mode='RGB', |
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show_share_button=False, show_fullscreen_button=False, layers=False, canvas_size=(384, 384), width=384, height=512, |
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brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed", default_size=32), eraser=gr.Eraser(default_size="32"), elem_classes="image") |
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result_to_ip2_button = gr.Button("Get image from generated result") |
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mask_ip2 = gr.Image(label="IP Mask (optional)", type="filepath", height=384, sources=["upload", "clipboard"], show_share_button=False, elem_classes="image") |
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with gr.Row(): |
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model_ip2 = gr.Dropdown(value="base", label="Model", choices=IP_MODELS) |
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mode_ip2 = gr.Dropdown(value="style", label="Mode", choices=MODE_IP_OPTIONS) |
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scale_ip2 = gr.Slider(minimum=0., maximum=2., step=0.01, value=0.7, label="Scale") |
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|
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with gr.Tab("Inpaint Mask Maker"): |
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with gr.Row(): |
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with gr.Column(): |
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image_base = gr.ImageEditor(sources=["upload", "clipboard", "webcam"], |
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brush=gr.Brush(default_size="32", color_mode="fixed", colors=["rgba(0, 0, 0, 1)", "rgba(0, 0, 0, 0.1)", "rgba(255, 255, 255, 0.1)"]), |
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eraser=gr.Eraser(default_size="32"), show_share_button=False, show_fullscreen_button=False, |
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canvas_size=(384, 384), width=384, height=512, elem_classes="image") |
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result_to_cm_button = gr.Button("Get image from generated result") |
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invert_mask = gr.Checkbox(value=False, label="Invert mask") |
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cm_btn = gr.Button("Create mask") |
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with gr.Column(): |
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img_source = gr.Image(interactive=False, height=384, show_share_button=False, elem_classes="image") |
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img_result = gr.Image(label="Mask image", show_label=True, interactive=False, height=384, show_share_button=False, elem_classes="image") |
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cm_btn_send = gr.Button("Send to ControlNet / Img2img / Inpaint") |
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with gr.Row(): |
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cm_btn_send_ip1 = gr.Button("Send to IP-Adapter 1") |
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cm_btn_send_ip2 = gr.Button("Send to IP-Adapter 2") |
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cm_btn.click(create_mask_now, [image_base, invert_mask], [img_source, img_result], show_api=False) |
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def send_img(img_source, img_result): |
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return img_source, img_result |
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cm_btn_send.click(send_img, [img_source, img_result], [image_control, image_mask], queue=False, show_api=False) |
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cm_btn_send_ip1.click(send_img, [img_source, img_result], [image_ip1, mask_ip1], queue=False, show_api=False) |
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cm_btn_send_ip2.click(send_img, [img_source, img_result], [image_ip2, mask_ip2], queue=False, show_api=False) |
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|
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with gr.Tab("Hires fix / Detailfix / Face restoration"): |
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with gr.Accordion("Hires fix", open=True): |
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with gr.Row(): |
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upscaler_model_path = gr.Dropdown(label="Upscaler", choices=UPSCALER_KEYS, value=UPSCALER_KEYS[0]) |
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upscaler_increases_size = gr.Slider(minimum=1.1, maximum=4., step=0.1, value=1.2, label="Upscale by") |
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upscaler_tile_size = gr.Slider(minimum=0, maximum=512, step=16, value=0, label="Upscaler Tile Size", info="0 = no tiling") |
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upscaler_tile_overlap = gr.Slider(minimum=0, maximum=48, step=1, value=8, label="Upscaler Tile Overlap") |
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with gr.Row(): |
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hires_steps = gr.Slider(minimum=0, value=30, maximum=100, step=1, label="Hires Steps") |
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hires_denoising_strength = gr.Slider(minimum=0.1, maximum=1.0, step=0.01, value=0.55, label="Hires Denoising Strength") |
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hires_sampler = gr.Dropdown(label="Hires Sampler", choices=POST_PROCESSING_SAMPLER, value=POST_PROCESSING_SAMPLER[0]) |
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hires_schedule_list = ["Use same schedule type"] + SCHEDULE_TYPE_OPTIONS |
|
hires_schedule_type = gr.Dropdown(label="Hires Schedule type", choices=hires_schedule_list, value=hires_schedule_list[0]) |
|
hires_guidance_scale = gr.Slider(minimum=-1., maximum=30., step=0.5, value=-1., label="Hires CFG", info="If the value is -1, the main CFG will be used") |
|
with gr.Row(): |
|
hires_prompt = gr.Textbox(label="Hires Prompt", placeholder="Main prompt will be use", lines=3) |
|
hires_negative_prompt = gr.Textbox(label="Hires Negative Prompt", placeholder="Main negative prompt will be use", lines=3) |
|
with gr.Accordion("Detail fix", open=True): |
|
with gr.Row(): |
|
|
|
adetailer_inpaint_only = gr.Checkbox(label="Inpaint only", value=True) |
|
|
|
adetailer_verbose = gr.Checkbox(label="Verbose", value=False) |
|
|
|
adetailer_sampler = gr.Dropdown(label="Adetailer sampler:", choices=POST_PROCESSING_SAMPLER, value=POST_PROCESSING_SAMPLER[0]) |
|
with gr.Accordion("Detailfix A", open=True, visible=True): |
|
|
|
adetailer_active_a = gr.Checkbox(label="Enable Adetailer A", value=False) |
|
prompt_ad_a = gr.Textbox(label="Main prompt", placeholder="Main prompt will be use", lines=3) |
|
negative_prompt_ad_a = gr.Textbox(label="Negative prompt", placeholder="Main negative prompt will be use", lines=3) |
|
with gr.Row(): |
|
strength_ad_a = gr.Number(label="Strength:", value=0.35, step=0.01, minimum=0.01, maximum=1.0) |
|
face_detector_ad_a = gr.Checkbox(label="Face detector", value=False) |
|
person_detector_ad_a = gr.Checkbox(label="Person detector", value=True) |
|
hand_detector_ad_a = gr.Checkbox(label="Hand detector", value=False) |
|
with gr.Row(): |
|
mask_dilation_a = gr.Number(label="Mask dilation:", value=4, minimum=1) |
|
mask_blur_a = gr.Number(label="Mask blur:", value=4, minimum=1) |
|
mask_padding_a = gr.Number(label="Mask padding:", value=32, minimum=1) |
|
with gr.Accordion("Detailfix B", open=True, visible=True): |
|
|
|
adetailer_active_b = gr.Checkbox(label="Enable Adetailer B", value=False) |
|
prompt_ad_b = gr.Textbox(label="Main prompt", placeholder="Main prompt will be use", lines=3) |
|
negative_prompt_ad_b = gr.Textbox(label="Negative prompt", placeholder="Main negative prompt will be use", lines=3) |
|
with gr.Row(): |
|
strength_ad_b = gr.Number(label="Strength:", value=0.35, step=0.01, minimum=0.01, maximum=1.0) |
|
face_detector_ad_b = gr.Checkbox(label="Face detector", value=False) |
|
person_detector_ad_b = gr.Checkbox(label="Person detector", value=True) |
|
hand_detector_ad_b = gr.Checkbox(label="Hand detector", value=False) |
|
with gr.Row(): |
|
mask_dilation_b = gr.Number(label="Mask dilation:", value=4, minimum=1) |
|
mask_blur_b = gr.Number(label="Mask blur:", value=4, minimum=1) |
|
mask_padding_b = gr.Number(label="Mask padding:", value=32, minimum=1) |
|
with gr.Accordion("Face restoration", open=True, visible=True): |
|
face_rest_options = [None] + FACE_RESTORATION_MODELS |
|
with gr.Row(): |
|
face_restoration_model = gr.Dropdown(label="Face restoration model", choices=face_rest_options, value=face_rest_options[0]) |
|
face_restoration_visibility = gr.Slider(minimum=0., maximum=1., step=0.001, value=1., label="Visibility") |
|
face_restoration_weight = gr.Slider(minimum=0., maximum=1., step=0.001, value=.5, label="Weight", info="(0 = maximum effect, 1 = minimum effect)") |
|
|
|
with gr.Tab("Translation Settings"): |
|
chatbot = gr.Chatbot(render_markdown=False, visible=False) |
|
chat_model = gr.Dropdown(choices=get_dolphin_models(), value=get_dolphin_models()[0][1], allow_custom_value=True, label="Model") |
|
chat_model_info = gr.Markdown(value=get_dolphin_model_info(get_dolphin_models()[0][1]), label="Model info") |
|
chat_format = gr.Dropdown(choices=get_llm_formats(), value=get_dolphin_model_format(get_dolphin_models()[0][1]), label="Message format") |
|
with gr.Row(): |
|
chat_tokens = gr.Slider(minimum=1, maximum=4096, value=512, step=1, label="Max tokens") |
|
chat_temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature") |
|
chat_topp = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p") |
|
chat_topk = gr.Slider(minimum=0, maximum=100, value=40, step=1, label="Top-k") |
|
chat_rp = gr.Slider(minimum=0.0, maximum=2.0, value=1.1, step=0.1, label="Repetition penalty") |
|
chat_sysmsg = gr.Textbox(value=get_dolphin_sysprompt(), label="System message") |
|
|
|
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, |
|
) |
|
|
|
model_name.change(update_task_options, [model_name, task], [task], queue=False, show_api=False)\ |
|
.success(update_task_options, [model_name, task_sel], [task_sel], queue=False, show_api=False) |
|
task_sel.select(lambda x: x, [task_sel], [task], queue=False, show_api=False) |
|
task.change(change_preprocessor_choices, [task], [preprocessor_name], queue=False, show_api=False)\ |
|
.success(lambda x: x, [task], [task_sel], queue=False, show_api=False) |
|
active_textual_inversion.change(update_textual_inversion, [active_textual_inversion, model_name], [use_textual_inversion], queue=False, show_api=False) |
|
model_name.change(update_textual_inversion, [active_textual_inversion, model_name], [use_textual_inversion], queue=False, show_api=False) |
|
use_textual_inversion.change(set_textual_inversion_prompt, [use_textual_inversion, prompt, negative_prompt, prompt_syntax], [prompt, negative_prompt]) |
|
result_to_cm_button.click(lambda x: x, [result], [image_base], queue=False, show_api=False) |
|
result_to_ic_button.click(lambda x: x, [result], [image_control], queue=False, show_api=False) |
|
result_to_ip1_button.click(lambda x: x, [result], [image_ip1], queue=False, show_api=False) |
|
result_to_ip2_button.click(lambda x: x, [result], [image_ip2], queue=False, show_api=False) |
|
|
|
gr.on( |
|
triggers=[run_button.click, prompt.submit], |
|
fn=infer, |
|
inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, |
|
guidance_scale, num_inference_steps, model_name, |
|
lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, |
|
lora5, lora5_wt, lora6, lora6_wt, lora7, lora7_wt, task, prompt_syntax, |
|
sampler, vae_model, schedule_type, schedule_prediction_type, |
|
clip_skip, pag_scale, free_u, guidance_rescale, |
|
image_control, image_mask, strength, image_resolution, |
|
controlnet_model, control_net_output_scaling, control_net_start_threshold, control_net_stop_threshold, |
|
preprocessor_name, preprocess_resolution, low_threshold, high_threshold, |
|
value_threshold, distance_threshold, recolor_gamma_correction, tile_blur_sigma, |
|
image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1, |
|
image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2, |
|
upscaler_model_path, upscaler_increases_size, upscaler_tile_size, upscaler_tile_overlap, hires_steps, hires_denoising_strength, |
|
hires_sampler, hires_schedule_type, hires_guidance_scale, hires_prompt, hires_negative_prompt, |
|
adetailer_inpaint_only, adetailer_verbose, adetailer_sampler, adetailer_active_a, |
|
prompt_ad_a, negative_prompt_ad_a, strength_ad_a, face_detector_ad_a, person_detector_ad_a, hand_detector_ad_a, |
|
mask_dilation_a, mask_blur_a, mask_padding_a, adetailer_active_b, prompt_ad_b, negative_prompt_ad_b, strength_ad_b, |
|
face_detector_ad_b, person_detector_ad_b, hand_detector_ad_b, mask_dilation_b, mask_blur_b, mask_padding_b, |
|
active_textual_inversion, face_restoration_model, face_restoration_visibility, face_restoration_weight, gpu_duration, auto_trans, recom_prompt], |
|
outputs=[result], |
|
queue=True, |
|
show_progress="full", |
|
show_api=True, |
|
) |
|
|
|
gr.on( |
|
triggers=[run_translate_button.click], |
|
fn=_infer, |
|
inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, |
|
guidance_scale, num_inference_steps, model_name, |
|
lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, |
|
lora5, lora5_wt, lora6, lora6_wt, lora7, lora7_wt, task, prompt_syntax, |
|
sampler, vae_model, schedule_type, schedule_prediction_type, |
|
clip_skip, pag_scale, free_u, guidance_rescale, |
|
image_control, image_mask, strength, image_resolution, |
|
controlnet_model, control_net_output_scaling, control_net_start_threshold, control_net_stop_threshold, |
|
preprocessor_name, preprocess_resolution, low_threshold, high_threshold, |
|
value_threshold, distance_threshold, recolor_gamma_correction, tile_blur_sigma, |
|
image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1, |
|
image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2, |
|
upscaler_model_path, upscaler_increases_size, upscaler_tile_size, upscaler_tile_overlap, hires_steps, hires_denoising_strength, |
|
hires_sampler, hires_schedule_type, hires_guidance_scale, hires_prompt, hires_negative_prompt, |
|
adetailer_inpaint_only, adetailer_verbose, adetailer_sampler, adetailer_active_a, |
|
prompt_ad_a, negative_prompt_ad_a, strength_ad_a, face_detector_ad_a, person_detector_ad_a, hand_detector_ad_a, |
|
mask_dilation_a, mask_blur_a, mask_padding_a, adetailer_active_b, prompt_ad_b, negative_prompt_ad_b, strength_ad_b, |
|
face_detector_ad_b, person_detector_ad_b, hand_detector_ad_b, mask_dilation_b, mask_blur_b, mask_padding_b, |
|
active_textual_inversion, face_restoration_model, face_restoration_visibility, face_restoration_weight, gpu_duration, auto_trans, recom_prompt], |
|
outputs=[result], |
|
queue=False, |
|
show_api=True, |
|
api_name="infer_translate", |
|
).success( |
|
fn=dolphin_respond_auto, |
|
inputs=[prompt, chatbot, chat_model, chat_sysmsg, chat_tokens, chat_temperature, chat_topp, chat_topk, chat_rp, state], |
|
outputs=[chatbot, result, prompt], |
|
queue=True, |
|
show_progress="full", |
|
show_api=False, |
|
).success( |
|
fn=dolphin_parse_simple, |
|
inputs=[prompt, chatbot, state], |
|
outputs=[prompt], |
|
queue=False, |
|
show_api=False, |
|
).success( |
|
fn=infer, |
|
inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, |
|
guidance_scale, num_inference_steps, model_name, |
|
lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, |
|
lora5, lora5_wt, lora6, lora6_wt, lora7, lora7_wt, task, prompt_syntax, |
|
sampler, vae_model, schedule_type, schedule_prediction_type, |
|
clip_skip, pag_scale, free_u, guidance_rescale, |
|
image_control, image_mask, strength, image_resolution, |
|
controlnet_model, control_net_output_scaling, control_net_start_threshold, control_net_stop_threshold, |
|
preprocessor_name, preprocess_resolution, low_threshold, high_threshold, |
|
value_threshold, distance_threshold, recolor_gamma_correction, tile_blur_sigma, |
|
image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1, |
|
image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2, |
|
upscaler_model_path, upscaler_increases_size, upscaler_tile_size, upscaler_tile_overlap, hires_steps, hires_denoising_strength, |
|
hires_sampler, hires_schedule_type, hires_guidance_scale, hires_prompt, hires_negative_prompt, |
|
adetailer_inpaint_only, adetailer_verbose, adetailer_sampler, adetailer_active_a, |
|
prompt_ad_a, negative_prompt_ad_a, strength_ad_a, face_detector_ad_a, person_detector_ad_a, hand_detector_ad_a, |
|
mask_dilation_a, mask_blur_a, mask_padding_a, adetailer_active_b, prompt_ad_b, negative_prompt_ad_b, strength_ad_b, |
|
face_detector_ad_b, person_detector_ad_b, hand_detector_ad_b, mask_dilation_b, mask_blur_b, mask_padding_b, |
|
active_textual_inversion, face_restoration_model, face_restoration_visibility, face_restoration_weight, gpu_duration, auto_trans, recom_prompt], |
|
outputs=[result], |
|
queue=True, |
|
show_progress="full", |
|
show_api=False, |
|
).success(lambda: None, None, chatbot, queue=False, show_api=False)\ |
|
.success(pass_result, [result], [result], queue=False, show_api=False) |
|
|
|
result.change(save_image_history, [result, history_gallery, history_files, model_name], [history_gallery, history_files], queue=False, show_api=False) |
|
|
|
gr.on( |
|
triggers=[lora1.change, lora1_wt.change, lora2.change, lora2_wt.change, lora3.change, lora3_wt.change, |
|
lora4.change, lora4_wt.change, lora5.change, lora5_wt.change, lora6.change, lora6_wt.change, lora7.change, lora7_wt.change, prompt_syntax.change], |
|
fn=update_loras, |
|
inputs=[prompt, prompt_syntax, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt, lora6, lora6_wt, lora7, lora7_wt], |
|
outputs=[prompt, lora1, lora1_wt, lora1_info, lora1_copy, lora1_md, |
|
lora2, lora2_wt, lora2_info, lora2_copy, lora2_md, lora3, lora3_wt, lora3_info, lora3_copy, lora3_md, |
|
lora4, lora4_wt, lora4_info, lora4_copy, lora4_md, lora5, lora5_wt, lora5_info, lora5_copy, lora5_md, |
|
lora6, lora6_wt, lora6_info, lora6_copy, lora6_md, lora7, lora7_wt, lora7_info, lora7_copy, lora7_md], |
|
queue=False, |
|
trigger_mode="once", |
|
show_api=False, |
|
) |
|
lora1_copy.click(apply_lora_prompt, [prompt, lora1_info], [prompt], queue=False, show_api=False) |
|
lora2_copy.click(apply_lora_prompt, [prompt, lora2_info], [prompt], queue=False, show_api=False) |
|
lora3_copy.click(apply_lora_prompt, [prompt, lora3_info], [prompt], queue=False, show_api=False) |
|
lora4_copy.click(apply_lora_prompt, [prompt, lora4_info], [prompt], queue=False, show_api=False) |
|
lora5_copy.click(apply_lora_prompt, [prompt, lora5_info], [prompt], queue=False, show_api=False) |
|
lora6_copy.click(apply_lora_prompt, [prompt, lora6_info], [prompt], queue=False, show_api=False) |
|
lora7_copy.click(apply_lora_prompt, [prompt, lora7_info], [prompt], queue=False, show_api=False) |
|
|
|
gr.on( |
|
triggers=[lora_search_civitai_submit.click, lora_search_civitai_query.submit], |
|
fn=search_civitai_lora, |
|
inputs=[lora_search_civitai_query, lora_search_civitai_basemodel, lora_search_civitai_sort, lora_search_civitai_period, lora_search_civitai_tag, lora_search_civitai_user, lora_search_civitai_gallery], |
|
outputs=[lora_search_civitai_result, lora_search_civitai_desc, lora_search_civitai_submit, lora_search_civitai_query, lora_search_civitai_gallery], |
|
scroll_to_output=True, |
|
queue=True, |
|
show_api=False, |
|
) |
|
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) |
|
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) |
|
gr.on( |
|
triggers=[lora_download.click, lora_download_url.submit], |
|
fn=download_my_lora, |
|
inputs=[lora_download_url, lora1, lora2, lora3, lora4, lora5, lora6, lora7], |
|
outputs=[lora1, lora2, lora3, lora4, lora5, lora6, lora7], |
|
scroll_to_output=True, |
|
queue=True, |
|
show_api=False, |
|
) |
|
lora_search_civitai_gallery.select(update_civitai_selection, None, [lora_search_civitai_result], queue=False, show_api=False) |
|
|
|
|
|
gr.on( |
|
triggers=[quality_selector.change, style_selector.change], |
|
fn=process_style_prompt, |
|
inputs=[prompt, negative_prompt, style_selector, quality_selector], |
|
outputs=[prompt, negative_prompt], |
|
queue=False, |
|
trigger_mode="once", |
|
show_api=False, |
|
) |
|
|
|
model_detail.change(enable_diffusers_model_detail, [model_detail, model_name, state], [model_detail, model_name, state], queue=False, show_api=False) |
|
model_name.change(get_t2i_model_info, [model_name], [model_info], queue=False, show_api=False) |
|
|
|
chat_model.change(select_dolphin_model, [chat_model, state], [chat_model, chat_format, chat_model_info, state], queue=True, show_progress="full", show_api=False)\ |
|
.success(lambda: None, None, chatbot, queue=False, show_api=False) |
|
chat_format.change(select_dolphin_format, [chat_format, state], [chat_format, state], queue=False, show_api=False)\ |
|
.success(lambda: None, None, chatbot, queue=False, show_api=False) |
|
|
|
|
|
with gr.Tab("Tags Transformer with Tagger"): |
|
with gr.Column(): |
|
with gr.Group(): |
|
input_image = gr.Image(label="Input image", type="pil", sources=["upload", "clipboard"], height=256) |
|
with gr.Accordion(label="Advanced options", open=False): |
|
general_threshold = gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.3, step=0.01, interactive=True) |
|
character_threshold = gr.Slider(label="Character threshold", minimum=0.0, maximum=1.0, value=0.8, step=0.01, interactive=True) |
|
input_tag_type = gr.Radio(label="Convert tags to", info="danbooru for Animagine, e621 for Pony.", choices=["danbooru", "e621"], value="danbooru") |
|
recom_prompt = gr.Radio(label="Insert reccomended prompt", choices=["None", "Animagine", "Pony"], value="None", interactive=True) |
|
image_algorithms = gr.CheckboxGroup(["Use WD Tagger", "Use Florence-2-SD3-Long-Captioner"], label="Algorithms", value=["Use WD Tagger"]) |
|
keep_tags = gr.Radio(label="Remove tags leaving only the following", choices=["body", "dress", "all"], value="all") |
|
generate_from_image_btn = gr.Button(value="GENERATE TAGS FROM IMAGE", size="lg", variant="primary") |
|
with gr.Group(): |
|
with gr.Row(): |
|
input_character = gr.Textbox(label="Character tags", placeholder="hatsune miku") |
|
input_copyright = gr.Textbox(label="Copyright tags", placeholder="vocaloid") |
|
random_character = gr.Button(value="Random character 🎲", size="sm") |
|
input_general = gr.TextArea(label="General tags", lines=4, placeholder="1girl, ...", value="") |
|
input_tags_to_copy = gr.Textbox(value="", visible=False) |
|
with gr.Row(): |
|
copy_input_btn = gr.Button(value="Copy to clipboard", size="sm", interactive=False) |
|
copy_prompt_btn_input = gr.Button(value="Copy to primary prompt", size="sm", interactive=False) |
|
translate_input_prompt_button = gr.Button(value="Translate prompt to English", size="sm", variant="secondary") |
|
tag_type = gr.Radio(label="Output tag conversion", info="danbooru for Animagine, e621 for Pony.", choices=["danbooru", "e621"], value="e621", visible=False) |
|
input_rating = gr.Radio(label="Rating", choices=list(V2_RATING_OPTIONS), value="explicit") |
|
with gr.Accordion(label="Advanced options", open=False): |
|
input_aspect_ratio = gr.Radio(label="Aspect ratio", info="The aspect ratio of the image.", choices=list(V2_ASPECT_RATIO_OPTIONS), value="square") |
|
input_length = gr.Radio(label="Length", info="The total length of the tags.", choices=list(V2_LENGTH_OPTIONS), value="very_long") |
|
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) |
|
|
|
with gr.Tab("PNG Info"): |
|
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], |
|
) |
|
|
|
with gr.Tab("Upscaler"): |
|
with gr.Row(): |
|
with gr.Column(): |
|
USCALER_TAB_KEYS = [name for name in UPSCALER_KEYS[9:]] |
|
image_up_tab = gr.Image(label="Image", type="pil", sources=["upload"]) |
|
upscaler_tab = gr.Dropdown(label="Upscaler", choices=USCALER_TAB_KEYS, value=USCALER_TAB_KEYS[5]) |
|
upscaler_size_tab = gr.Slider(minimum=1., maximum=4., step=0.1, value=1.1, label="Upscale by") |
|
generate_button_up_tab = gr.Button(value="START UPSCALE", variant="primary") |
|
with gr.Column(): |
|
result_up_tab = gr.Image(label="Result", type="pil", interactive=False, format="png") |
|
generate_button_up_tab.click( |
|
fn=process_upscale, |
|
inputs=[image_up_tab, upscaler_tab, upscaler_size_tab], |
|
outputs=[result_up_tab], |
|
) |
|
|
|
with gr.Tab("Preprocessor", render=True): |
|
preprocessor_tab() |
|
|
|
gr.LoginButton() |
|
gr.DuplicateButton(value="Duplicate Space for private use (This demo does not work on CPU. Requires GPU Space)") |
|
|
|
demo.queue() |
|
demo.launch(show_error=True, share=True, debug=True) |
|
|
