import gradio as gr from modules import shared, ui_common, generation_parameters_copypaste from modules.interrogate import openclip default_task = "Short Caption" def vlm_caption_wrapper(question, system_prompt, prompt, image, model_name, prefill, thinking_mode): """Wrapper for vqa.interrogate that handles annotated image display.""" from modules.interrogate import vqa answer = vqa.interrogate(question, system_prompt, prompt, image, model_name, prefill, thinking_mode) annotated_image = vqa.get_last_annotated_image() if annotated_image is not None: return answer, gr.update(value=annotated_image, visible=True) return answer, gr.update(visible=False) def update_vlm_prompts_for_model(model_name): """Update the task dropdown choices based on selected model.""" from modules.interrogate import vqa prompts = vqa.get_prompts_for_model(model_name) return gr.update(choices=prompts, value=prompts[0] if prompts else default_task) def update_vlm_prompt_placeholder(question): """Update the prompt field placeholder based on selected task.""" from modules.interrogate import vqa placeholder = vqa.get_prompt_placeholder(question) return gr.update(placeholder=placeholder) def update_vlm_params(*args): vlm_max_tokens, vlm_num_beams, vlm_temperature, vlm_do_sample, vlm_top_k, vlm_top_p, vlm_keep_prefill, vlm_keep_thinking, vlm_thinking_mode = args shared.opts.interrogate_vlm_max_length = int(vlm_max_tokens) shared.opts.interrogate_vlm_num_beams = int(vlm_num_beams) shared.opts.interrogate_vlm_temperature = float(vlm_temperature) shared.opts.interrogate_vlm_do_sample = bool(vlm_do_sample) shared.opts.interrogate_vlm_top_k = int(vlm_top_k) shared.opts.interrogate_vlm_top_p = float(vlm_top_p) shared.opts.interrogate_vlm_keep_prefill = bool(vlm_keep_prefill) shared.opts.interrogate_vlm_keep_thinking = bool(vlm_keep_thinking) shared.opts.interrogate_vlm_thinking_mode = bool(vlm_thinking_mode) shared.opts.save() def tagger_tag_wrapper(image, model_name, general_threshold, character_threshold, include_rating, exclude_tags, max_tags, sort_alpha, use_spaces, escape_brackets): """Wrapper for tagger.tag that maps UI inputs to function parameters.""" from modules.interrogate import tagger return tagger.tag( image=image, model_name=model_name, general_threshold=general_threshold, character_threshold=character_threshold, include_rating=include_rating, exclude_tags=exclude_tags, max_tags=int(max_tags), sort_alpha=sort_alpha, use_spaces=use_spaces, escape_brackets=escape_brackets, ) def tagger_batch_wrapper(model_name, batch_files, batch_folder, batch_str, save_output, save_append, recursive, general_threshold, character_threshold, include_rating, exclude_tags, max_tags, sort_alpha, use_spaces, escape_brackets): """Wrapper for tagger.batch that maps UI inputs to function parameters.""" from modules.interrogate import tagger return tagger.batch( model_name=model_name, batch_files=batch_files, batch_folder=batch_folder, batch_str=batch_str, save_output=save_output, save_append=save_append, recursive=recursive, general_threshold=general_threshold, character_threshold=character_threshold, include_rating=include_rating, exclude_tags=exclude_tags, max_tags=int(max_tags), sort_alpha=sort_alpha, use_spaces=use_spaces, escape_brackets=escape_brackets, ) def update_tagger_ui(model_name): """Update UI controls based on selected tagger model. When DeepBooru is selected, character_threshold is disabled since DeepBooru doesn't support separate character threshold. """ from modules.interrogate import tagger is_db = tagger.is_deepbooru(model_name) return [ gr.update(interactive=not is_db), # character_threshold gr.update(), # include_rating - now supported by both taggers ] def update_tagger_params(model_name, general_threshold, character_threshold, include_rating, max_tags, sort_alpha, use_spaces, escape_brackets, exclude_tags, show_scores): """Save all tagger parameters to shared.opts when UI controls change.""" shared.opts.waifudiffusion_model = model_name shared.opts.tagger_threshold = float(general_threshold) shared.opts.waifudiffusion_character_threshold = float(character_threshold) shared.opts.tagger_include_rating = bool(include_rating) shared.opts.tagger_max_tags = int(max_tags) shared.opts.tagger_sort_alpha = bool(sort_alpha) shared.opts.tagger_use_spaces = bool(use_spaces) shared.opts.tagger_escape_brackets = bool(escape_brackets) shared.opts.tagger_exclude_tags = str(exclude_tags) shared.opts.tagger_show_scores = bool(show_scores) shared.opts.save() def update_clip_params(*args): clip_min_length, clip_max_length, clip_chunk_size, clip_min_flavors, clip_max_flavors, clip_flavor_count, clip_num_beams = args shared.opts.interrogate_clip_min_length = int(clip_min_length) shared.opts.interrogate_clip_max_length = int(clip_max_length) shared.opts.interrogate_clip_min_flavors = int(clip_min_flavors) shared.opts.interrogate_clip_max_flavors = int(clip_max_flavors) shared.opts.interrogate_clip_num_beams = int(clip_num_beams) shared.opts.interrogate_clip_flavor_count = int(clip_flavor_count) shared.opts.interrogate_clip_chunk_size = int(clip_chunk_size) shared.opts.save() openclip.update_interrogate_params() def update_clip_model_params(clip_model, blip_model, clip_mode): """Save CLiP model settings to shared.opts when UI controls change.""" shared.opts.interrogate_clip_model = str(clip_model) shared.opts.interrogate_blip_model = str(blip_model) shared.opts.interrogate_clip_mode = str(clip_mode) shared.opts.save() def update_vlm_model_params(vlm_model, vlm_system): """Save VLM model settings to shared.opts when UI controls change.""" shared.opts.interrogate_vlm_model = str(vlm_model) shared.opts.interrogate_vlm_system = str(vlm_system) shared.opts.save() def update_default_caption_type(caption_type): """Save the default caption type to shared.opts.""" shared.opts.interrogate_default_type = str(caption_type) shared.opts.save() def create_ui(): shared.log.debug('UI initialize: tab=caption') with gr.Row(equal_height=False, variant='compact', elem_classes="caption", elem_id="caption_tab"): with gr.Column(variant='compact', elem_id='interrogate_input'): with gr.Row(): image = gr.Image(type='pil', label="Image", height=512, visible=True, image_mode='RGB', elem_id='interrogate_image') with gr.Tabs(elem_id="mode_caption"): with gr.Tab("VLM Caption", elem_id="tab_vlm_caption"): from modules.interrogate import vqa current_vlm_model = shared.opts.interrogate_vlm_model or vqa.vlm_default initial_prompts = vqa.get_prompts_for_model(current_vlm_model) with gr.Row(): vlm_system = gr.Textbox(label="System Prompt", value=vqa.vlm_system, lines=1, elem_id='vlm_system') with gr.Row(): vlm_question = gr.Dropdown(label="Task", allow_custom_value=False, choices=initial_prompts, value=default_task, elem_id='vlm_question') with gr.Row(): vlm_prompt = gr.Textbox(label="Prompt", placeholder=vqa.get_prompt_placeholder(initial_prompts[0]), lines=2, elem_id='vlm_prompt') with gr.Row(elem_id='interrogate_buttons_query'): vlm_model = gr.Dropdown(list(vqa.vlm_models), value=current_vlm_model, label='VLM Model', elem_id='vlm_model') with gr.Row(): vlm_load_btn = gr.Button(value='Load', elem_id='vlm_load', variant='secondary') vlm_unload_btn = gr.Button(value='Unload', elem_id='vlm_unload', variant='secondary') with gr.Accordion(label='VLM: Advanced Options', open=False, visible=True): with gr.Row(): vlm_max_tokens = gr.Slider(label='VLM Max Tokens', value=shared.opts.interrogate_vlm_max_length, minimum=16, maximum=4096, step=1, elem_id='vlm_max_tokens') vlm_num_beams = gr.Slider(label='VLM Num Beams', value=shared.opts.interrogate_vlm_num_beams, minimum=1, maximum=16, step=1, elem_id='vlm_num_beams') vlm_temperature = gr.Slider(label='VLM Temperature', value=shared.opts.interrogate_vlm_temperature, minimum=0.0, maximum=1.0, step=0.01, elem_id='vlm_temperature') with gr.Row(): vlm_top_k = gr.Slider(label='Top-K', value=shared.opts.interrogate_vlm_top_k, minimum=0, maximum=99, step=1, elem_id='vlm_top_k') vlm_top_p = gr.Slider(label='Top-P', value=shared.opts.interrogate_vlm_top_p, minimum=0.0, maximum=1.0, step=0.01, elem_id='vlm_top_p') with gr.Row(): vlm_do_sample = gr.Checkbox(label='Use Samplers', value=shared.opts.interrogate_vlm_do_sample, elem_id='vlm_do_sample') vlm_thinking_mode = gr.Checkbox(label='Thinking Mode', value=shared.opts.interrogate_vlm_thinking_mode, elem_id='vlm_thinking_mode') with gr.Row(): vlm_keep_thinking = gr.Checkbox(label='Keep Thinking Trace', value=shared.opts.interrogate_vlm_keep_thinking, elem_id='vlm_keep_thinking') vlm_keep_prefill = gr.Checkbox(label='Keep Prefill', value=shared.opts.interrogate_vlm_keep_prefill, elem_id='vlm_keep_prefill') with gr.Row(): vlm_prefill = gr.Textbox(label='Prefill Text', value='', lines=1, elem_id='vlm_prefill', placeholder='Optional prefill text for model to continue from') vlm_max_tokens.change(fn=update_vlm_params, inputs=[vlm_max_tokens, vlm_num_beams, vlm_temperature, vlm_do_sample, vlm_top_k, vlm_top_p, vlm_keep_prefill, vlm_keep_thinking, vlm_thinking_mode], outputs=[]) vlm_num_beams.change(fn=update_vlm_params, inputs=[vlm_max_tokens, vlm_num_beams, vlm_temperature, vlm_do_sample, vlm_top_k, vlm_top_p, vlm_keep_prefill, vlm_keep_thinking, vlm_thinking_mode], outputs=[]) vlm_temperature.change(fn=update_vlm_params, inputs=[vlm_max_tokens, vlm_num_beams, vlm_temperature, vlm_do_sample, vlm_top_k, vlm_top_p, vlm_keep_prefill, vlm_keep_thinking, vlm_thinking_mode], outputs=[]) vlm_do_sample.change(fn=update_vlm_params, inputs=[vlm_max_tokens, vlm_num_beams, vlm_temperature, vlm_do_sample, vlm_top_k, vlm_top_p, vlm_keep_prefill, vlm_keep_thinking, vlm_thinking_mode], outputs=[]) vlm_top_k.change(fn=update_vlm_params, inputs=[vlm_max_tokens, vlm_num_beams, vlm_temperature, vlm_do_sample, vlm_top_k, vlm_top_p, vlm_keep_prefill, vlm_keep_thinking, vlm_thinking_mode], outputs=[]) vlm_top_p.change(fn=update_vlm_params, inputs=[vlm_max_tokens, vlm_num_beams, vlm_temperature, vlm_do_sample, vlm_top_k, vlm_top_p, vlm_keep_prefill, vlm_keep_thinking, vlm_thinking_mode], outputs=[]) vlm_keep_prefill.change(fn=update_vlm_params, inputs=[vlm_max_tokens, vlm_num_beams, vlm_temperature, vlm_do_sample, vlm_top_k, vlm_top_p, vlm_keep_prefill, vlm_keep_thinking, vlm_thinking_mode], outputs=[]) vlm_keep_thinking.change(fn=update_vlm_params, inputs=[vlm_max_tokens, vlm_num_beams, vlm_temperature, vlm_do_sample, vlm_top_k, vlm_top_p, vlm_keep_prefill, vlm_keep_thinking, vlm_thinking_mode], outputs=[]) vlm_thinking_mode.change(fn=update_vlm_params, inputs=[vlm_max_tokens, vlm_num_beams, vlm_temperature, vlm_do_sample, vlm_top_k, vlm_top_p, vlm_keep_prefill, vlm_keep_thinking, vlm_thinking_mode], outputs=[]) with gr.Accordion(label='VLM: Batch Caption', open=False, visible=True): with gr.Row(): vlm_batch_files = gr.File(label="Files", show_label=True, file_count='multiple', file_types=['image'], interactive=True, height=100, elem_id='vlm_batch_files') with gr.Row(): vlm_batch_folder = gr.File(label="Folder", show_label=True, file_count='directory', file_types=['image'], interactive=True, height=100, elem_id='vlm_batch_folder') with gr.Row(): vlm_batch_str = gr.Textbox(label="Folder", value="", interactive=True, elem_id='vlm_batch_str') with gr.Row(): vlm_save_output = gr.Checkbox(label='Save Caption Files', value=True, elem_id="vlm_save_output") vlm_save_append = gr.Checkbox(label='Append Caption Files', value=False, elem_id="vlm_save_append") vlm_folder_recursive = gr.Checkbox(label='Recursive', value=False, elem_id="vlm_folder_recursive") with gr.Row(elem_id='interrogate_buttons_batch'): btn_vlm_caption_batch = gr.Button("Batch Caption", variant='primary', elem_id="btn_vlm_caption_batch") with gr.Row(): btn_vlm_caption = gr.Button("Caption", variant='primary', elem_id="btn_vlm_caption") with gr.Tab("OpenCLiP", elem_id='tab_clip_interrogate'): with gr.Row(): clip_model = gr.Dropdown([], value=shared.opts.interrogate_clip_model, label='CLiP Model', elem_id='clip_clip_model') ui_common.create_refresh_button(clip_model, openclip.refresh_clip_models, lambda: {"choices": openclip.refresh_clip_models()}, 'clip_models_refresh') blip_model = gr.Dropdown(list(openclip.caption_models), value=shared.opts.interrogate_blip_model, label='Caption Model', elem_id='btN_clip_blip_model') clip_mode = gr.Dropdown(openclip.caption_types, label='Mode', value='fast', elem_id='clip_clip_mode') with gr.Accordion(label='CLiP: Advanced Options', open=False, visible=True): with gr.Row(): clip_min_length = gr.Slider(label='clip: min length', value=shared.opts.interrogate_clip_min_length, minimum=8, maximum=75, step=1, elem_id='clip_caption_min_length') clip_max_length = gr.Slider(label='clip: max length', value=shared.opts.interrogate_clip_max_length, minimum=16, maximum=1024, step=1, elem_id='clip_caption_max_length') clip_chunk_size = gr.Slider(label='clip: chunk size', value=shared.opts.interrogate_clip_chunk_size, minimum=256, maximum=4096, step=8, elem_id='clip_chunk_size') with gr.Row(): clip_min_flavors = gr.Slider(label='clip: min flavors', value=shared.opts.interrogate_clip_min_flavors, minimum=1, maximum=16, step=1, elem_id='clip_min_flavors') clip_max_flavors = gr.Slider(label='clip: max flavors', value=shared.opts.interrogate_clip_max_flavors, minimum=1, maximum=64, step=1, elem_id='clip_max_flavors') clip_flavor_count = gr.Slider(label='clip: intermediates', value=shared.opts.interrogate_clip_flavor_count, minimum=256, maximum=4096, step=8, elem_id='clip_flavor_intermediate_count') with gr.Row(): clip_num_beams = gr.Slider(label='clip: num beams', value=shared.opts.interrogate_clip_num_beams, minimum=1, maximum=16, step=1, elem_id='clip_num_beams') clip_min_length.change(fn=update_clip_params, inputs=[clip_min_length, clip_max_length, clip_chunk_size, clip_min_flavors, clip_max_flavors, clip_flavor_count, clip_num_beams], outputs=[]) clip_max_length.change(fn=update_clip_params, inputs=[clip_min_length, clip_max_length, clip_chunk_size, clip_min_flavors, clip_max_flavors, clip_flavor_count, clip_num_beams], outputs=[]) clip_chunk_size.change(fn=update_clip_params, inputs=[clip_min_length, clip_max_length, clip_chunk_size, clip_min_flavors, clip_max_flavors, clip_flavor_count, clip_num_beams], outputs=[]) clip_min_flavors.change(fn=update_clip_params, inputs=[clip_min_length, clip_max_length, clip_chunk_size, clip_min_flavors, clip_max_flavors, clip_flavor_count, clip_num_beams], outputs=[]) clip_max_flavors.change(fn=update_clip_params, inputs=[clip_min_length, clip_max_length, clip_chunk_size, clip_min_flavors, clip_max_flavors, clip_flavor_count, clip_num_beams], outputs=[]) clip_flavor_count.change(fn=update_clip_params, inputs=[clip_min_length, clip_max_length, clip_chunk_size, clip_min_flavors, clip_max_flavors, clip_flavor_count, clip_num_beams], outputs=[]) clip_num_beams.change(fn=update_clip_params, inputs=[clip_min_length, clip_max_length, clip_chunk_size, clip_min_flavors, clip_max_flavors, clip_flavor_count, clip_num_beams], outputs=[]) with gr.Accordion(label='CLiP: Batch Interrogate', open=False, visible=True): with gr.Row(): clip_batch_files = gr.File(label="Files", show_label=True, file_count='multiple', file_types=['image'], interactive=True, height=100, elem_id='clip_batch_files') with gr.Row(): clip_batch_folder = gr.File(label="Folder", show_label=True, file_count='directory', file_types=['image'], interactive=True, height=100, elem_id='clip_batch_folder') with gr.Row(): clip_batch_str = gr.Textbox(label="Folder", value="", interactive=True, elem_id='clip_batch_str') with gr.Row(): clip_save_output = gr.Checkbox(label='Save Caption Files', value=True, elem_id="clip_save_output") clip_save_append = gr.Checkbox(label='Append Caption Files', value=False, elem_id="clip_save_append") clip_folder_recursive = gr.Checkbox(label='Recursive', value=False, elem_id="clip_folder_recursive") with gr.Row(): btn_clip_interrogate_batch = gr.Button("Batch Interrogate", variant='primary', elem_id="btn_clip_interrogate_batch") with gr.Row(): btn_clip_interrogate_img = gr.Button("Interrogate", variant='primary', elem_id="btn_clip_interrogate_img") btn_clip_analyze_img = gr.Button("Analyze", variant='primary', elem_id="btn_clip_analyze_img") with gr.Tab("Tagger", elem_id='tab_tagger'): from modules.interrogate import tagger with gr.Row(): wd_model = gr.Dropdown(tagger.get_models(), value=shared.opts.waifudiffusion_model, label='Tagger Model', elem_id='wd_model') ui_common.create_refresh_button(wd_model, tagger.refresh_models, lambda: {"choices": tagger.get_models()}, 'wd_models_refresh') with gr.Row(): wd_load_btn = gr.Button(value='Load', elem_id='wd_load', variant='secondary') wd_unload_btn = gr.Button(value='Unload', elem_id='wd_unload', variant='secondary') with gr.Accordion(label='Tagger: Advanced Options', open=True, visible=True): with gr.Row(): wd_general_threshold = gr.Slider(label='General threshold', value=shared.opts.tagger_threshold, minimum=0.0, maximum=1.0, step=0.01, elem_id='wd_general_threshold') wd_character_threshold = gr.Slider(label='Character threshold', value=shared.opts.waifudiffusion_character_threshold, minimum=0.0, maximum=1.0, step=0.01, elem_id='wd_character_threshold') with gr.Row(): wd_max_tags = gr.Slider(label='Max tags', value=shared.opts.tagger_max_tags, minimum=1, maximum=512, step=1, elem_id='wd_max_tags') wd_include_rating = gr.Checkbox(label='Include rating', value=shared.opts.tagger_include_rating, elem_id='wd_include_rating') with gr.Row(): wd_sort_alpha = gr.Checkbox(label='Sort alphabetically', value=shared.opts.tagger_sort_alpha, elem_id='wd_sort_alpha') wd_use_spaces = gr.Checkbox(label='Use spaces', value=shared.opts.tagger_use_spaces, elem_id='wd_use_spaces') wd_escape = gr.Checkbox(label='Escape brackets', value=shared.opts.tagger_escape_brackets, elem_id='wd_escape') with gr.Row(): wd_exclude_tags = gr.Textbox(label='Exclude tags', value=shared.opts.tagger_exclude_tags, placeholder='Comma-separated tags to exclude', elem_id='wd_exclude_tags') with gr.Row(): wd_show_scores = gr.Checkbox(label='Show confidence scores', value=shared.opts.tagger_show_scores, elem_id='wd_show_scores') gr.HTML('') with gr.Accordion(label='Tagger: Batch', open=False, visible=True): with gr.Row(): wd_batch_files = gr.File(label="Files", show_label=True, file_count='multiple', file_types=['image'], interactive=True, height=100, elem_id='wd_batch_files') with gr.Row(): wd_batch_folder = gr.File(label="Folder", show_label=True, file_count='directory', file_types=['image'], interactive=True, height=100, elem_id='wd_batch_folder') with gr.Row(): wd_batch_str = gr.Textbox(label="Folder", value="", interactive=True, elem_id='wd_batch_str') with gr.Row(): wd_save_output = gr.Checkbox(label='Save Caption Files', value=True, elem_id="wd_save_output") wd_save_append = gr.Checkbox(label='Append Caption Files', value=False, elem_id="wd_save_append") wd_folder_recursive = gr.Checkbox(label='Recursive', value=False, elem_id="wd_folder_recursive") with gr.Row(): btn_wd_tag_batch = gr.Button("Batch Tag", variant='primary', elem_id="btn_wd_tag_batch") with gr.Row(): btn_wd_tag = gr.Button("Tag", variant='primary', elem_id="btn_wd_tag") with gr.Tab("Interrogate", elem_id='tab_interrogate'): with gr.Row(): default_caption_type = gr.Radio( choices=["VLM", "OpenCLiP", "Tagger"], value=shared.opts.interrogate_default_type, label="Default Caption Type", elem_id="default_caption_type" ) with gr.Column(variant='compact', elem_id='interrogate_output'): with gr.Row(elem_id='interrogate_output_prompt'): prompt = gr.Textbox(label="Answer", lines=12, placeholder="ai generated image description") with gr.Row(elem_id='interrogate_output_image'): output_image = gr.Image(type='pil', label="Annotated Image", interactive=False, visible=False, elem_id='interrogate_output_image_display') with gr.Row(elem_id='interrogate_output_classes'): medium = gr.Label(elem_id="interrogate_label_medium", label="Medium", num_top_classes=5, visible=False) artist = gr.Label(elem_id="interrogate_label_artist", label="Artist", num_top_classes=5, visible=False) movement = gr.Label(elem_id="interrogate_label_movement", label="Movement", num_top_classes=5, visible=False) trending = gr.Label(elem_id="interrogate_label_trending", label="Trending", num_top_classes=5, visible=False) flavor = gr.Label(elem_id="interrogate_label_flavor", label="Flavor", num_top_classes=5, visible=False) clip_labels_text = gr.Textbox(elem_id="interrogate_clip_labels_text", label="CLIP Analysis", lines=15, interactive=False, visible=False, show_label=False) with gr.Row(elem_id='copy_buttons_interrogate'): copy_interrogate_buttons = generation_parameters_copypaste.create_buttons(["txt2img", "img2img", "control", "extras"]) btn_clip_interrogate_img.click(openclip.interrogate_image, inputs=[image, clip_model, blip_model, clip_mode], outputs=[prompt]).then(fn=lambda: gr.update(visible=False), inputs=[], outputs=[output_image]) btn_clip_analyze_img.click(openclip.analyze_image, inputs=[image, clip_model, blip_model], outputs=[medium, artist, movement, trending, flavor, clip_labels_text]).then(fn=lambda: gr.update(visible=False), inputs=[], outputs=[output_image]) btn_clip_interrogate_batch.click(fn=openclip.interrogate_batch, inputs=[clip_batch_files, clip_batch_folder, clip_batch_str, clip_model, blip_model, clip_mode, clip_save_output, clip_save_append, clip_folder_recursive], outputs=[prompt]).then(fn=lambda: gr.update(visible=False), inputs=[], outputs=[output_image]) btn_vlm_caption.click(fn=vlm_caption_wrapper, inputs=[vlm_question, vlm_system, vlm_prompt, image, vlm_model, vlm_prefill, vlm_thinking_mode], outputs=[prompt, output_image]) btn_vlm_caption_batch.click(fn=vqa.batch, inputs=[vlm_model, vlm_system, vlm_batch_files, vlm_batch_folder, vlm_batch_str, vlm_question, vlm_prompt, vlm_save_output, vlm_save_append, vlm_folder_recursive, vlm_prefill, vlm_thinking_mode], outputs=[prompt]).then(fn=lambda: gr.update(visible=False), inputs=[], outputs=[output_image]) btn_wd_tag.click(fn=tagger_tag_wrapper, inputs=[image, wd_model, wd_general_threshold, wd_character_threshold, wd_include_rating, wd_exclude_tags, wd_max_tags, wd_sort_alpha, wd_use_spaces, wd_escape], outputs=[prompt]).then(fn=lambda: gr.update(visible=False), inputs=[], outputs=[output_image]) btn_wd_tag_batch.click(fn=tagger_batch_wrapper, inputs=[wd_model, wd_batch_files, wd_batch_folder, wd_batch_str, wd_save_output, wd_save_append, wd_folder_recursive, wd_general_threshold, wd_character_threshold, wd_include_rating, wd_exclude_tags, wd_max_tags, wd_sort_alpha, wd_use_spaces, wd_escape], outputs=[prompt]).then(fn=lambda: gr.update(visible=False), inputs=[], outputs=[output_image]) # Dynamic UI updates based on selected model and task vlm_model.change(fn=update_vlm_prompts_for_model, inputs=[vlm_model], outputs=[vlm_question]) vlm_question.change(fn=update_vlm_prompt_placeholder, inputs=[vlm_question], outputs=[vlm_prompt]) # Load/Unload model buttons vlm_load_btn.click(fn=vqa.load_model, inputs=[vlm_model], outputs=[]) vlm_unload_btn.click(fn=vqa.unload_model, inputs=[], outputs=[]) def tagger_load_wrapper(model_name): from modules.interrogate import tagger return tagger.load_model(model_name) def tagger_unload_wrapper(): from modules.interrogate import tagger return tagger.unload_model() wd_load_btn.click(fn=tagger_load_wrapper, inputs=[wd_model], outputs=[]) wd_unload_btn.click(fn=tagger_unload_wrapper, inputs=[], outputs=[]) # Dynamic UI update when tagger model changes (disable controls for DeepBooru) wd_model.change(fn=update_tagger_ui, inputs=[wd_model], outputs=[wd_character_threshold, wd_include_rating], show_progress=False) # Save tagger parameters to shared.opts when UI controls change tagger_inputs = [wd_model, wd_general_threshold, wd_character_threshold, wd_include_rating, wd_max_tags, wd_sort_alpha, wd_use_spaces, wd_escape, wd_exclude_tags, wd_show_scores] wd_model.change(fn=update_tagger_params, inputs=tagger_inputs, outputs=[], show_progress=False) wd_general_threshold.change(fn=update_tagger_params, inputs=tagger_inputs, outputs=[], show_progress=False) wd_character_threshold.change(fn=update_tagger_params, inputs=tagger_inputs, outputs=[], show_progress=False) wd_include_rating.change(fn=update_tagger_params, inputs=tagger_inputs, outputs=[], show_progress=False) wd_max_tags.change(fn=update_tagger_params, inputs=tagger_inputs, outputs=[], show_progress=False) wd_sort_alpha.change(fn=update_tagger_params, inputs=tagger_inputs, outputs=[], show_progress=False) wd_use_spaces.change(fn=update_tagger_params, inputs=tagger_inputs, outputs=[], show_progress=False) wd_escape.change(fn=update_tagger_params, inputs=tagger_inputs, outputs=[], show_progress=False) wd_exclude_tags.change(fn=update_tagger_params, inputs=tagger_inputs, outputs=[], show_progress=False) wd_show_scores.change(fn=update_tagger_params, inputs=tagger_inputs, outputs=[], show_progress=False) # Save CLiP model parameters to shared.opts when UI controls change clip_model_inputs = [clip_model, blip_model, clip_mode] clip_model.change(fn=update_clip_model_params, inputs=clip_model_inputs, outputs=[], show_progress=False) blip_model.change(fn=update_clip_model_params, inputs=clip_model_inputs, outputs=[], show_progress=False) clip_mode.change(fn=update_clip_model_params, inputs=clip_model_inputs, outputs=[], show_progress=False) # Save VLM model parameters to shared.opts when UI controls change vlm_model_inputs = [vlm_model, vlm_system] vlm_model.change(fn=update_vlm_model_params, inputs=vlm_model_inputs, outputs=[], show_progress=False) vlm_system.change(fn=update_vlm_model_params, inputs=vlm_model_inputs, outputs=[], show_progress=False) # Save default caption type to shared.opts when UI control changes default_caption_type.change(fn=update_default_caption_type, inputs=[default_caption_type], outputs=[], show_progress=False) for tabname, button in copy_interrogate_buttons.items(): generation_parameters_copypaste.register_paste_params_button(generation_parameters_copypaste.ParamBinding(paste_button=button, tabname=tabname, source_text_component=prompt, source_image_component=image,)) generation_parameters_copypaste.add_paste_fields("caption", image, None)