mirror of
https://github.com/vladmandic/automatic
synced 2026-09-20 01:31:13 +02:00
refactor(caption): consolidate interrogate settings into Caption Tab UI
Hide all CLiP, VLM, and Tagger settings from Settings > Interrogate page while keeping them in shared.opts for persistence. Caption Tab UI becomes the single control point with change handlers that save directly to config. Changes: - Hide OpenCLiP, VLM, and Tagger settings with visible=False - Add change handlers to save settings when UI controls change - Rename "Booru Tags" tab to "Tagger", update choice labels - Update interrogate.py to use unified tagger interface with all settings
This commit is contained in:
CalamitousFelicitousness
@@ -12,7 +12,7 @@ def interrogate(image):
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shared.log.error('Interrogate: no image provided')
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return ''
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t0 = time.time()
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if shared.opts.interrogate_default_type == 'OpenCLiP':
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if shared.opts.interrogate_default_type == 'CLiP':
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shared.log.info(f'Interrogate: type={shared.opts.interrogate_default_type} clip="{shared.opts.interrogate_clip_model}" blip="{shared.opts.interrogate_blip_model}" mode="{shared.opts.interrogate_clip_mode}"')
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from modules.interrogate import openclip
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openclip.load_interrogator(clip_model=shared.opts.interrogate_clip_model, blip_model=shared.opts.interrogate_blip_model)
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@@ -20,10 +20,21 @@ def interrogate(image):
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prompt = openclip.interrogate(image, mode=shared.opts.interrogate_clip_mode)
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shared.log.debug(f'Interrogate: time={time.time()-t0:.2f} answer="{prompt}"')
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return prompt
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elif shared.opts.interrogate_default_type == 'DeepBooru':
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shared.log.info(f'Interrogate: type={shared.opts.interrogate_default_type}')
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from modules.interrogate import deepbooru
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prompt = deepbooru.model.tag(image)
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elif shared.opts.interrogate_default_type == 'Tagger':
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shared.log.info(f'Interrogate: type={shared.opts.interrogate_default_type} model="{shared.opts.wd14_model}"')
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from modules.interrogate import tagger
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prompt = tagger.tag(
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image=image,
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model_name=shared.opts.wd14_model,
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general_threshold=shared.opts.wd14_general_threshold,
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character_threshold=shared.opts.wd14_character_threshold,
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include_rating=shared.opts.wd14_include_rating,
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exclude_tags=shared.opts.tagger_exclude_tags,
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max_tags=shared.opts.tagger_max_tags,
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sort_alpha=shared.opts.tagger_sort_alpha,
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use_spaces=shared.opts.tagger_use_spaces,
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escape_brackets=shared.opts.tagger_escape,
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)
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shared.log.debug(f'Interrogate: time={time.time()-t0:.2f} answer="{prompt}"')
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return prompt
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elif shared.opts.interrogate_default_type == 'VLM':
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+29
-27
@@ -673,14 +673,15 @@ options_templates.update(options_section(('postprocessing', "Postprocessing"), {
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}))
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options_templates.update(options_section(('interrogate', "Interrogate"), {
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"interrogate_default_type": OptionInfo("VLM", "Default caption type", gr.Radio, {"choices": ["OpenCLiP", "VLM", "DeepBooru"]}),
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"interrogate_default_type": OptionInfo("VLM", "Default caption type", gr.Radio, {"choices": ["VLM", "CLiP", "Tagger"]}),
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"interrogate_offload": OptionInfo(True, "Offload models "),
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"interrogate_score": OptionInfo(False, "Include scores in results when available"),
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"interrogate_score": OptionInfo(False, "Include scores in results when available", gr.Checkbox, {"visible": False}),
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"interrogate_clip_sep": OptionInfo("<h2>OpenCLiP</h2>", "", gr.HTML),
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"interrogate_clip_model": OptionInfo("ViT-L-14/openai", "CLiP: default model", gr.Dropdown, lambda: {"choices": get_clip_models()}, refresh=refresh_clip_models),
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"interrogate_clip_mode": OptionInfo(caption_types[0], "CLiP: default mode", gr.Dropdown, {"choices": caption_types}),
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"interrogate_blip_model": OptionInfo(list(caption_models)[0], "CLiP: default captioner", gr.Dropdown, {"choices": list(caption_models)}),
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# OpenCLiP settings (hidden - controlled via Caption Tab)
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"interrogate_clip_sep": OptionInfo("<h2>OpenCLiP</h2>", "", gr.HTML, {"visible": False}),
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"interrogate_clip_model": OptionInfo("ViT-L-14/openai", "CLiP: default model", gr.Dropdown, lambda: {"choices": get_clip_models(), "visible": False}, refresh=refresh_clip_models),
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"interrogate_clip_mode": OptionInfo(caption_types[0], "CLiP: default mode", gr.Dropdown, {"choices": caption_types, "visible": False}),
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"interrogate_blip_model": OptionInfo(list(caption_models)[0], "CLiP: default captioner", gr.Dropdown, {"choices": list(caption_models), "visible": False}),
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"interrogate_clip_num_beams": OptionInfo(1, "CLiP: num beams", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1, "visible": False}),
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"interrogate_clip_min_length": OptionInfo(32, "CLiP: min length", gr.Slider, {"minimum": 1, "maximum": 128, "step": 1, "visible": False}),
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"interrogate_clip_max_length": OptionInfo(74, "CLiP: max length", gr.Slider, {"minimum": 1, "maximum": 512, "step": 1, "visible": False}),
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@@ -689,13 +690,14 @@ options_templates.update(options_section(('interrogate', "Interrogate"), {
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"interrogate_clip_flavor_count": OptionInfo(1024, "CLiP: intermediate flavors", gr.Slider, {"minimum": 256, "maximum": 4096, "step": 64, "visible": False}),
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"interrogate_clip_chunk_size": OptionInfo(1024, "CLiP: chunk size", gr.Slider, {"minimum": 256, "maximum": 4096, "step": 64, "visible": False}),
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"interrogate_vlm_sep": OptionInfo("<h2>VLM</h2>", "", gr.HTML),
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"interrogate_vlm_model": OptionInfo(vlm_default, "VLM: default model", gr.Dropdown, {"choices": list(vlm_models)}),
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"interrogate_vlm_prompt": OptionInfo(vlm_prompts[2], "VLM: default prompt", DropdownEditable, {"choices": vlm_prompts }),
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"interrogate_vlm_system": OptionInfo(vlm_system, "VLM: default prompt"),
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# VLM settings (hidden - controlled via Caption Tab)
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"interrogate_vlm_sep": OptionInfo("<h2>VLM</h2>", "", gr.HTML, {"visible": False}),
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"interrogate_vlm_model": OptionInfo(vlm_default, "VLM: default model", gr.Dropdown, {"choices": list(vlm_models), "visible": False}),
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"interrogate_vlm_prompt": OptionInfo(vlm_prompts[2], "VLM: default prompt", DropdownEditable, {"choices": vlm_prompts, "visible": False}),
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"interrogate_vlm_system": OptionInfo(vlm_system, "VLM: default prompt", gr.Textbox, {"visible": False}),
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"interrogate_vlm_num_beams": OptionInfo(1, "VLM: num beams", gr.Slider, {"minimum": 1, "maximum": 16, "step": 1, "visible": False}),
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"interrogate_vlm_max_length": OptionInfo(512, "VLM: max length", gr.Slider, {"minimum": 1, "maximum": 4096, "step": 1, "visible": False}),
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"interrogate_vlm_do_sample": OptionInfo(True, "VLM: use sample method"),
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"interrogate_vlm_do_sample": OptionInfo(True, "VLM: use sample method", gr.Checkbox, {"visible": False}),
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"interrogate_vlm_temperature": OptionInfo(0.8, "VLM: temperature", gr.Slider, {"minimum": 0, "maximum": 1.0, "step": 0.01, "visible": False}),
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"interrogate_vlm_top_k": OptionInfo(0, "VLM: top-k", gr.Slider, {"minimum": 0, "maximum": 99, "step": 1, "visible": False}),
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"interrogate_vlm_top_p": OptionInfo(0, "VLM: top-p", gr.Slider, {"minimum": 0, "maximum": 1.0, "step": 0.01, "visible": False}),
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@@ -703,24 +705,24 @@ options_templates.update(options_section(('interrogate', "Interrogate"), {
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"interrogate_vlm_keep_thinking": OptionInfo(False, "VLM: keep reasoning trace in output", gr.Checkbox, {"visible": False}),
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"interrogate_vlm_thinking_mode": OptionInfo(False, "VLM: enable thinking/reasoning mode", gr.Checkbox, {"visible": False}),
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# Common tagger settings (shared by DeepBooru and WD14)
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"tagger_sep": OptionInfo("<h2>Tagger Settings</h2>", "", gr.HTML),
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"tagger_max_tags": OptionInfo(74, "Tagger: max tags", gr.Slider, {"minimum": 1, "maximum": 512, "step": 1}),
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"tagger_sort_alpha": OptionInfo(False, "Tagger: sort alphabetically"),
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"tagger_use_spaces": OptionInfo(False, "Tagger: use spaces for tags"),
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"tagger_escape": OptionInfo(True, "Tagger: escape brackets"),
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"tagger_exclude_tags": OptionInfo("", "Tagger: exclude tags"),
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# Common tagger settings (hidden - controlled via Caption Tab)
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"tagger_sep": OptionInfo("<h2>Tagger Settings</h2>", "", gr.HTML, {"visible": False}),
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"tagger_max_tags": OptionInfo(74, "Tagger: max tags", gr.Slider, {"minimum": 1, "maximum": 512, "step": 1, "visible": False}),
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"tagger_sort_alpha": OptionInfo(False, "Tagger: sort alphabetically", gr.Checkbox, {"visible": False}),
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"tagger_use_spaces": OptionInfo(False, "Tagger: use spaces for tags", gr.Checkbox, {"visible": False}),
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"tagger_escape": OptionInfo(True, "Tagger: escape brackets", gr.Checkbox, {"visible": False}),
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"tagger_exclude_tags": OptionInfo("", "Tagger: exclude tags", gr.Textbox, {"visible": False}),
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# DeepBooru-specific settings
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"deepbooru_sep": OptionInfo("<h2>DeepBooru</h2>", "", gr.HTML),
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"deepbooru_score_threshold": OptionInfo(0.65, "DeepBooru: score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
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# DeepBooru-specific settings (hidden - controlled via Caption Tab)
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"deepbooru_sep": OptionInfo("<h2>DeepBooru</h2>", "", gr.HTML, {"visible": False}),
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"deepbooru_score_threshold": OptionInfo(0.65, "DeepBooru: score threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01, "visible": False}),
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# WD14-specific settings
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"wd14_sep": OptionInfo("<h2>WD14 Tagger</h2>", "", gr.HTML),
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"wd14_model": OptionInfo("wd-eva02-large-tagger-v3", "WD14: default model", gr.Dropdown, {"choices": []}),
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"wd14_general_threshold": OptionInfo(0.35, "WD14: general tag threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
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"wd14_character_threshold": OptionInfo(0.85, "WD14: character tag threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01}),
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"wd14_include_rating": OptionInfo(False, "WD14: include rating tags"),
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# WD14-specific settings (hidden - controlled via Caption Tab)
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"wd14_sep": OptionInfo("<h2>WD14 Tagger</h2>", "", gr.HTML, {"visible": False}),
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"wd14_model": OptionInfo("wd-eva02-large-tagger-v3", "WD14: default model", gr.Dropdown, {"choices": [], "visible": False}),
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"wd14_general_threshold": OptionInfo(0.35, "WD14: general tag threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01, "visible": False}),
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"wd14_character_threshold": OptionInfo(0.85, "WD14: character tag threshold", gr.Slider, {"minimum": 0, "maximum": 1, "step": 0.01, "visible": False}),
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"wd14_include_rating": OptionInfo(False, "WD14: include rating tags", gr.Checkbox, {"visible": False}),
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}))
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options_templates.update(options_section(('huggingface', "Huggingface"), {
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+53
-1
@@ -96,6 +96,20 @@ def update_tagger_ui(model_name):
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]
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def update_tagger_params(model_name, general_threshold, character_threshold, include_rating, max_tags, sort_alpha, use_spaces, escape_brackets, exclude_tags):
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"""Save all tagger parameters to shared.opts when UI controls change."""
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shared.opts.wd14_model = model_name
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shared.opts.wd14_general_threshold = float(general_threshold)
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shared.opts.wd14_character_threshold = float(character_threshold)
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shared.opts.wd14_include_rating = bool(include_rating)
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shared.opts.tagger_max_tags = int(max_tags)
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shared.opts.tagger_sort_alpha = bool(sort_alpha)
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shared.opts.tagger_use_spaces = bool(use_spaces)
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shared.opts.tagger_escape = bool(escape_brackets)
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shared.opts.tagger_exclude_tags = str(exclude_tags)
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shared.opts.save()
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def update_clip_params(*args):
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clip_min_length, clip_max_length, clip_chunk_size, clip_min_flavors, clip_max_flavors, clip_flavor_count, clip_num_beams = args
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shared.opts.interrogate_clip_min_length = int(clip_min_length)
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@@ -109,6 +123,21 @@ def update_clip_params(*args):
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openclip.update_interrogate_params()
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def update_clip_model_params(clip_model, blip_model, clip_mode):
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"""Save CLiP model settings to shared.opts when UI controls change."""
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shared.opts.interrogate_clip_model = str(clip_model)
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shared.opts.interrogate_blip_model = str(blip_model)
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shared.opts.interrogate_clip_mode = str(clip_mode)
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shared.opts.save()
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def update_vlm_model_params(vlm_model, vlm_system):
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"""Save VLM model settings to shared.opts when UI controls change."""
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shared.opts.interrogate_vlm_model = str(vlm_model)
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shared.opts.interrogate_vlm_system = str(vlm_system)
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shared.opts.save()
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def create_ui():
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shared.log.debug('UI initialize: tab=caption')
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with gr.Row(equal_height=False, variant='compact', elem_classes="caption", elem_id="caption_tab"):
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@@ -211,7 +240,7 @@ def create_ui():
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with gr.Row():
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btn_clip_interrogate_img = gr.Button("Interrogate", variant='primary', elem_id="btn_clip_interrogate_img")
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btn_clip_analyze_img = gr.Button("Analyze", variant='primary', elem_id="btn_clip_analyze_img")
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with gr.Tab("Booru Tags", elem_id='tab_booru_tags'):
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with gr.Tab("Tagger", elem_id='tab_tagger'):
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from modules.interrogate import tagger
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with gr.Row():
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wd_model = gr.Dropdown(tagger.get_models(), value=shared.opts.wd14_model, label='Tagger Model', elem_id='wd_model')
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@@ -290,6 +319,29 @@ def create_ui():
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# Dynamic UI update when tagger model changes (disable controls for DeepBooru)
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wd_model.change(fn=update_tagger_ui, inputs=[wd_model], outputs=[wd_character_threshold, wd_include_rating], show_progress=False)
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# Save tagger parameters to shared.opts when UI controls change
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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]
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wd_model.change(fn=update_tagger_params, inputs=tagger_inputs, outputs=[], show_progress=False)
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wd_general_threshold.change(fn=update_tagger_params, inputs=tagger_inputs, outputs=[], show_progress=False)
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wd_character_threshold.change(fn=update_tagger_params, inputs=tagger_inputs, outputs=[], show_progress=False)
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wd_include_rating.change(fn=update_tagger_params, inputs=tagger_inputs, outputs=[], show_progress=False)
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wd_max_tags.change(fn=update_tagger_params, inputs=tagger_inputs, outputs=[], show_progress=False)
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wd_sort_alpha.change(fn=update_tagger_params, inputs=tagger_inputs, outputs=[], show_progress=False)
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wd_use_spaces.change(fn=update_tagger_params, inputs=tagger_inputs, outputs=[], show_progress=False)
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wd_escape.change(fn=update_tagger_params, inputs=tagger_inputs, outputs=[], show_progress=False)
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wd_exclude_tags.change(fn=update_tagger_params, inputs=tagger_inputs, outputs=[], show_progress=False)
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# Save CLiP model parameters to shared.opts when UI controls change
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clip_model_inputs = [clip_model, blip_model, clip_mode]
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clip_model.change(fn=update_clip_model_params, inputs=clip_model_inputs, outputs=[], show_progress=False)
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blip_model.change(fn=update_clip_model_params, inputs=clip_model_inputs, outputs=[], show_progress=False)
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clip_mode.change(fn=update_clip_model_params, inputs=clip_model_inputs, outputs=[], show_progress=False)
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# Save VLM model parameters to shared.opts when UI controls change
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vlm_model_inputs = [vlm_model, vlm_system]
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vlm_model.change(fn=update_vlm_model_params, inputs=vlm_model_inputs, outputs=[], show_progress=False)
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vlm_system.change(fn=update_vlm_model_params, inputs=vlm_model_inputs, outputs=[], show_progress=False)
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for tabname, button in copy_interrogate_buttons.items():
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generation_parameters_copypaste.register_paste_params_button(generation_parameters_copypaste.ParamBinding(paste_button=button, tabname=tabname, source_text_component=prompt, source_image_component=image,))
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generation_parameters_copypaste.add_paste_fields("caption", image, None)
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