Super-squash branch 'main' using huggingface_hub

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+title: Votepurchase Multiple Model (SD1.5/SDXL Text-to-Image)
+emoji: 🖼🖼️📦
+colorFrom: purple
+colorTo: red
+sdk: gradio
+sdk_version: 4.41.0
+app_file: app.py
+license: mit
+short_description: Text-to-Image
+pinned: true
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import spaces
+import gradio as gr
+import numpy as np
+
+# DiffuseCraft
+from dc import (
+    infer,
+    _infer,
+    pass_result,
+    get_diffusers_model_list,
+    get_samplers,
+    get_vaes,
+    enable_model_recom_prompt,
+    enable_diffusers_model_detail,
+    get_t2i_model_info,
+    get_all_lora_tupled_list,
+    update_loras,
+    apply_lora_prompt,
+    download_my_lora,
+    search_civitai_lora,
+    select_civitai_lora,
+    search_civitai_lora_json,
+    preset_quality,
+    preset_styles,
+    process_style_prompt,
+)
+# Translator
+from llmdolphin import (
+    dolphin_respond_auto,
+    dolphin_parse_simple,
+    get_llm_formats,
+    get_dolphin_model_format,
+    get_dolphin_models,
+    get_dolphin_model_info,
+    select_dolphin_model,
+    select_dolphin_format,
+    get_dolphin_sysprompt,
+)
+# Tagger
+from tagger.v2 import (
+    v2_upsampling_prompt,
+    V2_ALL_MODELS,
+)
+from tagger.utils import (
+    gradio_copy_text,
+    gradio_copy_prompt,
+    COPY_ACTION_JS,
+    V2_ASPECT_RATIO_OPTIONS,
+    V2_RATING_OPTIONS,
+    V2_LENGTH_OPTIONS,
+    V2_IDENTITY_OPTIONS
+)
+from tagger.tagger import (
+    predict_tags_wd,
+    convert_danbooru_to_e621_prompt,
+    remove_specific_prompt,
+    insert_recom_prompt,
+    compose_prompt_to_copy,
+    translate_prompt,
+    select_random_character,
+)
+from tagger.fl2sd3longcap import (
+    predict_tags_fl2_sd3,
+)
+def description_ui():
+    gr.Markdown(
+        """
+## Danbooru Tags Transformer V2 Demo with WD Tagger & SD3 Long Captioner
+(Image =>) Prompt => Upsampled longer prompt
+- 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).
+- 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)\
+, gokaygokay's [Florence-2-SD3-Captioner](https://huggingface.co/gokaygokay/Florence-2-SD3-Captioner)
+"""
+    )
+
+
+MAX_SEED = np.iinfo(np.int32).max
+MAX_IMAGE_SIZE = 1216
+
+css = """
+#container {
+    margin: 0 auto;
+}
+#col-container {
+    margin: 0 auto;
+    max-width: 520px;
+}
+#model-info { text-align: center; }
+"""
+
+with gr.Blocks(css=css, fill_width=True, elem_id="container") as demo:
+    with gr.Tab("Image Generator"):
+        with gr.Column(elem_id="col-container"):
+
+            prompt = gr.Text(
+                label="Prompt",
+                show_label=False,
+                lines=1,
+                max_lines=8,
+                placeholder="Enter your prompt",
+                container=False,
+            )
+            
+            with gr.Row():
+                run_button = gr.Button("Run")
+                run_translate_button = gr.Button("Translate")
+
+            result = gr.Image(label="Result", show_label=False, interactive=False, show_download_button=True, show_share_button=False, container=True)
+            
+            with gr.Accordion("Advanced Settings", open=False):
+
+                negative_prompt = gr.Text(
+                    label="Negative prompt",
+                    lines=1,
+                    max_lines=6,
+                    placeholder="Enter a negative prompt",
+                    value="(low quality, worst quality:1.2), very displeasing, watermark, signature, ugly"
+                )
+
+                seed = gr.Slider(
+                    label="Seed",
+                    minimum=0,
+                    maximum=MAX_SEED,
+                    step=1,
+                    value=0,
+                )
+
+                randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+
+                with gr.Row():
+                    width = gr.Slider(
+                        label="Width",
+                        minimum=256,
+                        maximum=MAX_IMAGE_SIZE,
+                        step=32,
+                        value=1024,#832,
+                    )
+
+                    height = gr.Slider(
+                        label="Height",
+                        minimum=256,
+                        maximum=MAX_IMAGE_SIZE,
+                        step=32,
+                        value=1024,#1216,
+                    )
+
+                with gr.Row():
+                    guidance_scale = gr.Slider(
+                        label="Guidance scale",
+                        minimum=0.0,
+                        maximum=30.0,
+                        step=0.1,
+                        value=7,
+                    )
+
+                    num_inference_steps = gr.Slider(
+                        label="Number of inference steps",
+                        minimum=1,
+                        maximum=100,
+                        step=1,
+                        value=28,
+                    )
+
+                with gr.Group():
+                    model_name = gr.Dropdown(label="Model", info="You can enter a huggingface model repo_id to want to use.", choices=get_diffusers_model_list(), value=get_diffusers_model_list()[0], allow_custom_value=True, interactive=True)
+                    model_info = gr.Markdown(elem_id="model-info")
+                    model_detail = gr.Checkbox(label="Show detail of model in list", value=False)
+
+                sampler = gr.Dropdown(label="Sampler", choices=get_samplers(), value="Euler a")
+
+                chatbot = gr.Chatbot(likeable=False, render_markdown=False, visible=False) # component for auto-translation
+
+                with gr.Accordion("LoRA", open=True, visible=True):
+                    with gr.Group():
+                        lora1 = gr.Dropdown(label="LoRA 1", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True)
+                        lora1_wt = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA 1: weight")
+                        with gr.Row():
+                            lora1_info = gr.Textbox(label="", info="Example of prompt:", value="", show_copy_button=True, interactive=False, visible=False)
+                            lora1_copy = gr.Button(value="Copy example to prompt", visible=False)
+                        lora1_md = gr.Markdown(value="", visible=False)
+                    with gr.Group():
+                        lora2 = gr.Dropdown(label="LoRA 2", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True)
+                        lora2_wt = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA 2: weight")
+                        with gr.Row():
+                            lora2_info = gr.Textbox(label="", info="Example of prompt:", value="", show_copy_button=True, interactive=False, visible=False)
+                            lora2_copy = gr.Button(value="Copy example to prompt", visible=False)
+                        lora2_md = gr.Markdown(value="", visible=False)
+                    with gr.Group():
+                        lora3 = gr.Dropdown(label="LoRA 3", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True)
+                        lora3_wt = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA 3: weight")
+                        with gr.Row():
+                            lora3_info = gr.Textbox(label="", info="Example of prompt:", value="", show_copy_button=True, interactive=False, visible=False)
+                            lora3_copy = gr.Button(value="Copy example to prompt", visible=False)
+                        lora3_md = gr.Markdown(value="", visible=False)
+                    with gr.Group():
+                        lora4 = gr.Dropdown(label="LoRA 4", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True)
+                        lora4_wt = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA 4: weight")
+                        with gr.Row():
+                            lora4_info = gr.Textbox(label="", info="Example of prompt:", value="", show_copy_button=True, interactive=False, visible=False)
+                            lora4_copy = gr.Button(value="Copy example to prompt", visible=False)
+                        lora4_md = gr.Markdown(value="", visible=False)
+                    with gr.Group():
+                        lora5 = gr.Dropdown(label="LoRA 5", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True)
+                        lora5_wt = gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label="LoRA 5: weight")
+                        with gr.Row():
+                            lora5_info = gr.Textbox(label="", info="Example of prompt:", value="", show_copy_button=True, interactive=False, visible=False)
+                            lora5_copy = gr.Button(value="Copy example to prompt", visible=False)
+                        lora5_md = gr.Markdown(value="", visible=False)
+                    with gr.Accordion("From URL", open=True, visible=True):
+                        with gr.Row():
+                            lora_search_civitai_query = gr.Textbox(label="Query", placeholder="oomuro sakurako...", lines=1)
+                            lora_search_civitai_basemodel = gr.CheckboxGroup(label="Search LoRA for", choices=["Pony", "SD 1.5", "SDXL 1.0"], value=["Pony", "SDXL 1.0"])
+                            lora_search_civitai_submit = gr.Button("Search on Civitai")
+                        lora_search_civitai_result = gr.Dropdown(label="Search Results", choices=[("", "")], value="", allow_custom_value=True, visible=False)
+                        lora_search_civitai_json = gr.JSON(value={}, visible=False)
+                        lora_search_civitai_desc = gr.Markdown(value="", visible=False)
+                        lora_download_url = gr.Textbox(label="URL", placeholder="http://...my_lora_url.safetensors", lines=1)
+                        lora_download = gr.Button("Get and set LoRA and apply to prompt")
+                
+                vae_model = gr.Dropdown(label="VAE Model", choices=get_vaes(), value=get_vaes()[0])
+                recom_prompt = gr.Checkbox(label="Recommended prompt", value=True)
+                quality_selector = gr.Dropdown(label="Quality Tags Presets", interactive=True, choices=list(preset_quality.keys()), value="None")
+                style_selector = gr.Dropdown(label="Style Preset", interactive=True, choices=list(preset_styles.keys()), value="None")
+
+                with gr.Accordion("Translation Settings", open=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")
+                    chat_sysmsg = gr.Textbox(value=get_dolphin_sysprompt(), label="System message")
+                    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")
+
+            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],
+            )
+
+    gr.on( #lambda x: None, inputs=None, outputs=result).then(
+        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,
+                sampler, vae_model],
+        outputs=[result],
+        queue=True,
+        show_progress="full",
+        show_api=True,
+    )
+
+    gr.on( #lambda x: None, inputs=None, outputs=result).then(
+        triggers=[run_translate_button.click],
+        fn=_infer, # dummy fn for api
+        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,
+                sampler, vae_model],
+        outputs=[result],
+        queue=False,
+        show_api=True,
+        api_name="infer_translate",
+    ).success(
+        fn=dolphin_respond_auto,
+        inputs=[prompt, chatbot],
+        outputs=[chatbot],
+        queue=True,
+        show_progress="full",
+        show_api=False,
+    ).success(
+        fn=dolphin_parse_simple,
+        inputs=[prompt, chatbot],
+        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,
+                sampler, vae_model],
+        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) # dummy fn for api
+
+    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],
+        fn=update_loras,
+        inputs=[prompt, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_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],
+        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)
+
+    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],
+        outputs=[lora_search_civitai_result, lora_search_civitai_desc, lora_search_civitai_submit, lora_search_civitai_query],
+        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)  # fn for api
+    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],
+        outputs=[lora1, lora2, lora3, lora4, lora5],
+        scroll_to_output=True,
+        queue=True,
+        show_api=False,
+    )
+
+    recom_prompt.change(enable_model_recom_prompt, [recom_prompt], [recom_prompt], 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",
+    )
+
+    model_detail.change(enable_diffusers_model_detail, [model_detail, model_name], [model_detail, model_name], 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], [chat_model, chat_format, chat_model_info], 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], [chat_format], queue=False, show_api=False)\
+    .success(lambda: None, None, chatbot, queue=False, show_api=False)
+
+    # Tagger
+    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)
+
+demo.queue()
+demo.launch()

dc.py

@@ -0,0 +1,1328 @@
+import spaces
+import os
+from stablepy import Model_Diffusers
+from stablepy.diffusers_vanilla.model import scheduler_names
+from stablepy.diffusers_vanilla.style_prompt_config import STYLE_NAMES
+import torch
+import re
+import shutil
+import random
+from stablepy import (
+    CONTROLNET_MODEL_IDS,
+    VALID_TASKS,
+    T2I_PREPROCESSOR_NAME,
+    FLASH_LORA,
+    SCHEDULER_CONFIG_MAP,
+    scheduler_names,
+    IP_ADAPTER_MODELS,
+    IP_ADAPTERS_SD,
+    IP_ADAPTERS_SDXL,
+    REPO_IMAGE_ENCODER,
+    ALL_PROMPT_WEIGHT_OPTIONS,
+    SD15_TASKS,
+    SDXL_TASKS,
+)
+import urllib.parse
+import gradio as gr
+from PIL import Image
+import IPython.display
+import time, json
+from IPython.utils import capture
+import logging
+logging.getLogger("diffusers").setLevel(logging.ERROR)
+import diffusers
+diffusers.utils.logging.set_verbosity(40)
+import warnings
+warnings.filterwarnings(action="ignore", category=FutureWarning, module="diffusers")
+warnings.filterwarnings(action="ignore", category=UserWarning, module="diffusers")
+warnings.filterwarnings(action="ignore", category=FutureWarning, module="transformers")
+from stablepy import logger
+logger.setLevel(logging.CRITICAL)
+
+from env import (
+    hf_token,
+    hf_read_token, # to use only for private repos
+    CIVITAI_API_KEY,
+    HF_LORA_PRIVATE_REPOS1,
+    HF_LORA_PRIVATE_REPOS2,
+    HF_LORA_ESSENTIAL_PRIVATE_REPO,
+    HF_VAE_PRIVATE_REPO,
+    HF_SDXL_EMBEDS_NEGATIVE_PRIVATE_REPO,
+    HF_SDXL_EMBEDS_POSITIVE_PRIVATE_REPO,
+    directory_models,
+    directory_loras,
+    directory_vaes,
+    directory_embeds,
+    directory_embeds_sdxl,
+    directory_embeds_positive_sdxl,
+    load_diffusers_format_model,
+    download_model_list,
+    download_lora_list,
+    download_vae_list,
+    download_embeds,
+)
+
+preprocessor_controlnet = {
+  "openpose": [
+    "Openpose",
+    "None",
+  ],
+  "scribble": [
+    "HED",
+    "Pidinet",
+    "None",
+  ],
+  "softedge": [
+    "Pidinet",
+    "HED",
+    "HED safe",
+    "Pidinet safe",
+    "None",
+  ],
+  "segmentation": [
+    "UPerNet",
+    "None",
+  ],
+  "depth": [
+    "DPT",
+    "Midas",
+    "None",
+  ],
+  "normalbae": [
+    "NormalBae",
+    "None",
+  ],
+  "lineart": [
+    "Lineart",
+    "Lineart coarse",
+    "Lineart (anime)",
+    "None",
+    "None (anime)",
+  ],
+  "shuffle": [
+    "ContentShuffle",
+    "None",
+  ],
+  "canny": [
+    "Canny"
+  ],
+  "mlsd": [
+    "MLSD"
+  ],
+  "ip2p": [
+    "ip2p"
+  ],
+}
+
+task_stablepy = {
+    'txt2img': 'txt2img',
+    'img2img': 'img2img',
+    'inpaint': 'inpaint',
+    # 'canny T2I Adapter': 'sdxl_canny_t2i',  # NO HAVE STEP CALLBACK PARAMETERS SO NOT WORKS WITH DIFFUSERS 0.29.0
+    # 'sketch  T2I Adapter': 'sdxl_sketch_t2i',
+    # 'lineart  T2I Adapter': 'sdxl_lineart_t2i',
+    # 'depth-midas  T2I Adapter': 'sdxl_depth-midas_t2i',
+    # 'openpose  T2I Adapter': 'sdxl_openpose_t2i',
+    'openpose ControlNet': 'openpose',
+    'canny ControlNet': 'canny',
+    'mlsd ControlNet': 'mlsd',
+    'scribble ControlNet': 'scribble',
+    'softedge ControlNet': 'softedge',
+    'segmentation ControlNet': 'segmentation',
+    'depth ControlNet': 'depth',
+    'normalbae ControlNet': 'normalbae',
+    'lineart ControlNet': 'lineart',
+    # 'lineart_anime ControlNet': 'lineart_anime',
+    'shuffle ControlNet': 'shuffle',
+    'ip2p ControlNet': 'ip2p',
+    'optical pattern ControlNet': 'pattern',
+    'tile realistic': 'sdxl_tile_realistic',
+}
+
+task_model_list = list(task_stablepy.keys())
+
+
+def download_things(directory, url, hf_token="", civitai_api_key=""):
+    url = url.strip()
+    
+    if "drive.google.com" in url:
+        original_dir = os.getcwd()
+        os.chdir(directory)
+        os.system(f"gdown --fuzzy {url}")
+        os.chdir(original_dir)
+    elif "huggingface.co" in url:
+        url = url.replace("?download=true", "")
+        # url = urllib.parse.quote(url, safe=':/')  # fix encoding
+        if "/blob/" in url:
+            url = url.replace("/blob/", "/resolve/")
+        user_header = f'"Authorization: Bearer {hf_token}"'
+        if hf_token:
+            os.system(f"aria2c --console-log-level=error --summary-interval=10 --header={user_header} -c -x 16 -k 1M -s 16 {url} -d {directory}  -o {url.split('/')[-1]}")
+        else:
+            os.system (f"aria2c --optimize-concurrent-downloads --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 {url} -d {directory}  -o {url.split('/')[-1]}")
+    elif "civitai.com" in url:
+        if "?" in url:
+            url = url.split("?")[0]
+        if civitai_api_key:
+            url = url + f"?token={civitai_api_key}"
+            os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
+        else:
+            print("\033[91mYou need an API key to download Civitai models.\033[0m")
+    else:
+        os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
+
+
+def get_model_list(directory_path):
+    model_list = []
+    valid_extensions = {'.ckpt' , '.pt', '.pth', '.safetensors', '.bin'}
+
+    for filename in os.listdir(directory_path):
+        if os.path.splitext(filename)[1] in valid_extensions:
+            name_without_extension = os.path.splitext(filename)[0]
+            file_path = os.path.join(directory_path, filename)
+            # model_list.append((name_without_extension, file_path))
+            model_list.append(file_path)
+            print('\033[34mFILE: ' + file_path + '\033[0m')
+    return model_list
+
+
+def process_string(input_string):
+    parts = input_string.split('/')
+
+    if len(parts) == 2:
+        first_element = parts[1]
+        complete_string = input_string
+        result = (first_element, complete_string)
+        return result
+    else:
+        return None
+
+## BEGIN MOD
+from modutils import (
+    to_list,
+    list_uniq,
+    list_sub,
+    get_model_id_list,
+    get_tupled_embed_list,
+    get_tupled_model_list,
+    get_lora_model_list,
+    download_private_repo,
+)
+
+# - **Download Models**
+download_model = ", ".join(download_model_list)
+# - **Download VAEs**
+download_vae = ", ".join(download_vae_list)
+# - **Download LoRAs**
+download_lora = ", ".join(download_lora_list)
+
+#download_private_repo(HF_LORA_ESSENTIAL_PRIVATE_REPO, directory_loras, True)
+download_private_repo(HF_VAE_PRIVATE_REPO, directory_vaes, False)
+
+load_diffusers_format_model = list_uniq(load_diffusers_format_model + get_model_id_list())
+## END MOD
+
+CIVITAI_API_KEY = os.environ.get("CIVITAI_API_KEY")
+hf_token = os.environ.get("HF_TOKEN")
+
+# Download stuffs
+for url in [url.strip() for url in download_model.split(',')]:
+    if not os.path.exists(f"./models/{url.split('/')[-1]}"):
+        download_things(directory_models, url, hf_token, CIVITAI_API_KEY)
+for url in [url.strip() for url in download_vae.split(',')]:
+    if not os.path.exists(f"./vaes/{url.split('/')[-1]}"):
+        download_things(directory_vaes, url, hf_token, CIVITAI_API_KEY)
+for url in [url.strip() for url in download_lora.split(',')]:
+    if not os.path.exists(f"./loras/{url.split('/')[-1]}"):
+        download_things(directory_loras, url, hf_token, CIVITAI_API_KEY)
+
+# Download Embeddings
+for url_embed in download_embeds:
+    if not os.path.exists(f"./embedings/{url_embed.split('/')[-1]}"):
+        download_things(directory_embeds, url_embed, hf_token, CIVITAI_API_KEY)
+
+# Build list models
+embed_list = get_model_list(directory_embeds)
+model_list = get_model_list(directory_models)
+model_list = load_diffusers_format_model + model_list
+## BEGIN MOD
+lora_model_list = get_lora_model_list()
+vae_model_list = get_model_list(directory_vaes)
+vae_model_list.insert(0, "None")
+
+#download_private_repo(HF_SDXL_EMBEDS_NEGATIVE_PRIVATE_REPO, directory_embeds_sdxl, False)
+#download_private_repo(HF_SDXL_EMBEDS_POSITIVE_PRIVATE_REPO, directory_embeds_positive_sdxl, False)
+embed_sdxl_list = get_model_list(directory_embeds_sdxl) + get_model_list(directory_embeds_positive_sdxl)
+
+def get_embed_list(pipeline_name):
+    return get_tupled_embed_list(embed_sdxl_list if pipeline_name == "StableDiffusionXLPipeline" else embed_list)
+
+
+## END MOD
+
+print('\033[33m🏁 Download and listing of valid models completed.\033[0m')
+
+upscaler_dict_gui = {
+    None : None,
+    "Lanczos" : "Lanczos",
+    "Nearest" : "Nearest",
+    "RealESRGAN_x4plus" : "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
+    "RealESRNet_x4plus" : "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth",
+    "RealESRGAN_x4plus_anime_6B": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
+    "RealESRGAN_x2plus": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
+    "realesr-animevideov3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
+    "realesr-general-x4v3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
+    "realesr-general-wdn-x4v3" : "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
+    "4x-UltraSharp" : "https://huggingface.co/Shandypur/ESRGAN-4x-UltraSharp/resolve/main/4x-UltraSharp.pth",
+    "4x_foolhardy_Remacri" : "https://huggingface.co/FacehugmanIII/4x_foolhardy_Remacri/resolve/main/4x_foolhardy_Remacri.pth",
+    "Remacri4xExtraSmoother" : "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/Remacri%204x%20ExtraSmoother.pth",
+    "AnimeSharp4x" : "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/AnimeSharp%204x.pth",
+    "lollypop" : "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/lollypop.pth",
+    "RealisticRescaler4x" : "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/RealisticRescaler%204x.pth",
+    "NickelbackFS4x" : "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/NickelbackFS%204x.pth"
+}
+
+
+def extract_parameters(input_string):
+    parameters = {}
+    input_string = input_string.replace("\n", "")
+
+    if not "Negative prompt:" in input_string:
+        print("Negative prompt not detected")
+        parameters["prompt"] = input_string
+        return parameters
+
+    parm = input_string.split("Negative prompt:")
+    parameters["prompt"] = parm[0]
+    if not "Steps:" in parm[1]:
+        print("Steps not detected")
+        parameters["neg_prompt"] = parm[1]
+        return parameters
+    parm = parm[1].split("Steps:")
+    parameters["neg_prompt"] = parm[0]
+    input_string = "Steps:" + parm[1]
+
+    # Extracting Steps
+    steps_match = re.search(r'Steps: (\d+)', input_string)
+    if steps_match:
+        parameters['Steps'] = int(steps_match.group(1))
+
+    # Extracting Size
+    size_match = re.search(r'Size: (\d+x\d+)', input_string)
+    if size_match:
+        parameters['Size'] = size_match.group(1)
+        width, height = map(int, parameters['Size'].split('x'))
+        parameters['width'] = width
+        parameters['height'] = height
+
+    # Extracting other parameters
+    other_parameters = re.findall(r'(\w+): (.*?)(?=, \w+|$)', input_string)
+    for param in other_parameters:
+        parameters[param[0]] = param[1].strip('"')
+
+    return parameters
+
+
+## BEGIN MOD
+class GuiSD:
+    def __init__(self):
+        self.model = None
+    
+        print("Loading model...")
+        self.model = Model_Diffusers(
+            base_model_id="cagliostrolab/animagine-xl-3.1",
+            task_name="txt2img",
+            vae_model=None,
+            type_model_precision=torch.float16,
+            retain_task_model_in_cache=False,
+        )
+
+    def infer_short(self, model, pipe_params, progress=gr.Progress(track_tqdm=True)):
+        progress(0, desc="Start inference...")
+        images, image_list = model(**pipe_params)
+        progress(1, desc="Inference completed.")
+        if not isinstance(images, list): images = [images]
+        img = []
+        for image in images:
+            img.append((image, None))
+        return img
+
+    def load_new_model(self, model_name, vae_model, task, progress=gr.Progress(track_tqdm=True)):
+
+        yield f"Loading model: {model_name}"
+        
+        vae_model = vae_model if vae_model != "None" else None
+
+        if model_name in model_list:
+            model_is_xl = "xl" in model_name.lower()
+            sdxl_in_vae = vae_model and "sdxl" in vae_model.lower()
+            model_type = "SDXL" if model_is_xl else "SD 1.5"
+            incompatible_vae = (model_is_xl and vae_model and not sdxl_in_vae) or (not model_is_xl and sdxl_in_vae)
+
+            if incompatible_vae:
+                vae_model = None
+
+        
+        self.model.load_pipe(
+            model_name,
+            task_name=task_stablepy[task],
+            vae_model=vae_model if vae_model != "None" else None,
+            type_model_precision=torch.float16,
+            retain_task_model_in_cache=False,
+        )
+        yield f"Model loaded: {model_name}"
+
+    @spaces.GPU
+    def generate_pipeline(
+        self,
+        prompt,
+        neg_prompt,
+        num_images,
+        steps,
+        cfg,
+        clip_skip,
+        seed,
+        lora1,
+        lora_scale1,
+        lora2,
+        lora_scale2,
+        lora3,
+        lora_scale3,
+        lora4,
+        lora_scale4,
+        lora5,
+        lora_scale5,
+        sampler,
+        img_height,
+        img_width,
+        model_name,
+        vae_model,
+        task,
+        image_control,
+        preprocessor_name,
+        preprocess_resolution,
+        image_resolution,
+        style_prompt,  # list []
+        style_json_file,
+        image_mask,
+        strength,
+        low_threshold,
+        high_threshold,
+        value_threshold,
+        distance_threshold,
+        controlnet_output_scaling_in_unet,
+        controlnet_start_threshold,
+        controlnet_stop_threshold,
+        textual_inversion,
+        syntax_weights,
+        upscaler_model_path,
+        upscaler_increases_size,
+        esrgan_tile,
+        esrgan_tile_overlap,
+        hires_steps,
+        hires_denoising_strength,
+        hires_sampler,
+        hires_prompt,
+        hires_negative_prompt,
+        hires_before_adetailer,
+        hires_after_adetailer,
+        loop_generation,
+        leave_progress_bar,
+        disable_progress_bar,
+        image_previews,
+        display_images,
+        save_generated_images,
+        image_storage_location,
+        retain_compel_previous_load,
+        retain_detailfix_model_previous_load,
+        retain_hires_model_previous_load,
+        t2i_adapter_preprocessor,
+        t2i_adapter_conditioning_scale,
+        t2i_adapter_conditioning_factor,
+        xformers_memory_efficient_attention,
+        freeu,
+        generator_in_cpu,
+        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,
+        retain_task_cache_gui,
+        image_ip1,
+        mask_ip1,
+        model_ip1,
+        mode_ip1,
+        scale_ip1,
+        image_ip2,
+        mask_ip2,
+        model_ip2,
+        mode_ip2,
+        scale_ip2,
+        progress=gr.Progress(track_tqdm=True),
+    ):
+        progress(0, desc="Preparing inference...")
+        
+        vae_model = vae_model if vae_model != "None" else None
+        loras_list = [lora1, lora2, lora3, lora4, lora5]
+        vae_msg = f"VAE: {vae_model}" if vae_model else ""
+        msg_lora = []
+
+## BEGIN MOD
+        prompt, neg_prompt = insert_model_recom_prompt(prompt, neg_prompt, model_name)
+        global lora_model_list
+        lora_model_list = get_lora_model_list()
+## END MOD
+        
+        if model_name in model_list:
+            model_is_xl = "xl" in model_name.lower()
+            sdxl_in_vae = vae_model and "sdxl" in vae_model.lower()
+            model_type = "SDXL" if model_is_xl else "SD 1.5"
+            incompatible_vae = (model_is_xl and vae_model and not sdxl_in_vae) or (not model_is_xl and sdxl_in_vae)
+
+            if incompatible_vae:
+                msg_inc_vae = (
+                    f"The selected VAE is for a { 'SD 1.5' if model_is_xl else 'SDXL' } model, but you"
+                    f" are using a { model_type } model. The default VAE "
+                    "will be used."
+                )
+                gr.Info(msg_inc_vae)
+                vae_msg = msg_inc_vae
+                vae_model = None
+
+            for la in loras_list:
+                if la is not None and la != "None" and la in lora_model_list:
+                    print(la)
+                    lora_type = ("animetarot" in la.lower() or "Hyper-SD15-8steps".lower() in la.lower())
+                    if (model_is_xl and lora_type) or (not model_is_xl and not lora_type):
+                        msg_inc_lora = f"The LoRA {la} is for { 'SD 1.5' if model_is_xl else 'SDXL' }, but you are using { model_type }."
+                        gr.Info(msg_inc_lora)
+                        msg_lora.append(msg_inc_lora)
+
+        task = task_stablepy[task]
+
+        params_ip_img = []
+        params_ip_msk = []
+        params_ip_model = []
+        params_ip_mode = []
+        params_ip_scale = []
+
+        all_adapters = [
+            (image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1),
+            (image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2),
+        ]
+
+        for imgip, mskip, modelip, modeip, scaleip in all_adapters:
+            if imgip:
+                params_ip_img.append(imgip)
+                if mskip:
+                    params_ip_msk.append(mskip)
+                params_ip_model.append(modelip)
+                params_ip_mode.append(modeip)
+                params_ip_scale.append(scaleip)
+
+        # First load
+        model_precision = torch.float16
+        if not self.model:
+            from stablepy import Model_Diffusers
+
+            print("Loading model...")
+            self.model = Model_Diffusers(
+                base_model_id=model_name,
+                task_name=task,
+                vae_model=vae_model if vae_model != "None" else None,
+                type_model_precision=model_precision,
+                retain_task_model_in_cache=retain_task_cache_gui,
+            )
+
+        if task != "txt2img" and not image_control:
+            raise ValueError("No control image found: To use this function, you have to upload an image in 'Image ControlNet/Inpaint/Img2img'")
+
+        if task == "inpaint" and not image_mask:
+            raise ValueError("No mask image found: Specify one in 'Image Mask'")
+
+        if upscaler_model_path in [None, "Lanczos", "Nearest"]:
+            upscaler_model = upscaler_model_path
+        else:
+            directory_upscalers = 'upscalers'
+            os.makedirs(directory_upscalers, exist_ok=True)
+
+            url_upscaler = upscaler_dict_gui[upscaler_model_path]
+
+            if not os.path.exists(f"./upscalers/{url_upscaler.split('/')[-1]}"):
+                download_things(directory_upscalers, url_upscaler, hf_token)
+
+            upscaler_model = f"./upscalers/{url_upscaler.split('/')[-1]}"
+
+        logging.getLogger("ultralytics").setLevel(logging.INFO if adetailer_verbose else logging.ERROR)
+
+        print("Config model:", model_name, vae_model, loras_list)
+
+        self.model.load_pipe(
+            model_name,
+            task_name=task,
+            vae_model=vae_model if vae_model != "None" else None,
+            type_model_precision=model_precision,
+            retain_task_model_in_cache=retain_task_cache_gui,
+        )
+
+## BEGIN MOD
+#        if textual_inversion and self.model.class_name == "StableDiffusionXLPipeline":
+#            print("No Textual inversion for SDXL")
+## END MOD
+
+        adetailer_params_A = {
+            "face_detector_ad" : face_detector_ad_a,
+            "person_detector_ad" : person_detector_ad_a,
+            "hand_detector_ad" : hand_detector_ad_a,
+            "prompt": prompt_ad_a,
+            "negative_prompt" : negative_prompt_ad_a,
+            "strength" : strength_ad_a,
+            # "image_list_task" : None,
+            "mask_dilation" : mask_dilation_a,
+            "mask_blur" : mask_blur_a,
+            "mask_padding" : mask_padding_a,
+            "inpaint_only" : adetailer_inpaint_only,
+            "sampler" : adetailer_sampler,
+        }
+
+        adetailer_params_B = {
+            "face_detector_ad" : face_detector_ad_b,
+            "person_detector_ad" : person_detector_ad_b,
+            "hand_detector_ad" : hand_detector_ad_b,
+            "prompt": prompt_ad_b,
+            "negative_prompt" : negative_prompt_ad_b,
+            "strength" : strength_ad_b,
+            # "image_list_task" : None,
+            "mask_dilation" : mask_dilation_b,
+            "mask_blur" : mask_blur_b,
+            "mask_padding" : mask_padding_b,
+        }
+        pipe_params = {
+            "prompt": prompt,
+            "negative_prompt": neg_prompt,
+            "img_height": img_height,
+            "img_width": img_width,
+            "num_images": num_images,
+            "num_steps": steps,
+            "guidance_scale": cfg,
+            "clip_skip": clip_skip,
+            "seed": seed,
+            "image": image_control,
+            "preprocessor_name": preprocessor_name,
+            "preprocess_resolution": preprocess_resolution,
+            "image_resolution": image_resolution,
+            "style_prompt": style_prompt if style_prompt else "",
+            "style_json_file": "",
+            "image_mask": image_mask,  # only for Inpaint
+            "strength": strength,  # only for Inpaint or ...
+            "low_threshold": low_threshold,
+            "high_threshold": high_threshold,
+            "value_threshold": value_threshold,
+            "distance_threshold": distance_threshold,
+            "lora_A": lora1 if lora1 != "None" else None,
+            "lora_scale_A": lora_scale1,
+            "lora_B": lora2 if lora2 != "None" else None,
+            "lora_scale_B": lora_scale2,
+            "lora_C": lora3 if lora3 != "None" else None,
+            "lora_scale_C": lora_scale3,
+            "lora_D": lora4 if lora4 != "None" else None,
+            "lora_scale_D": lora_scale4,
+            "lora_E": lora5 if lora5 != "None" else None,
+            "lora_scale_E": lora_scale5,
+## BEGIN MOD
+            "textual_inversion": get_embed_list(self.model.class_name) if textual_inversion else [],
+## END MOD
+            "syntax_weights": syntax_weights,  # "Classic"
+            "sampler": sampler,
+            "xformers_memory_efficient_attention": xformers_memory_efficient_attention,
+            "gui_active": True,
+            "loop_generation": loop_generation,
+            "controlnet_conditioning_scale": float(controlnet_output_scaling_in_unet),
+            "control_guidance_start": float(controlnet_start_threshold),
+            "control_guidance_end": float(controlnet_stop_threshold),
+            "generator_in_cpu": generator_in_cpu,
+            "FreeU": freeu,
+            "adetailer_A": adetailer_active_a,
+            "adetailer_A_params": adetailer_params_A,
+            "adetailer_B": adetailer_active_b,
+            "adetailer_B_params": adetailer_params_B,
+            "leave_progress_bar": leave_progress_bar,
+            "disable_progress_bar": disable_progress_bar,
+            "image_previews": image_previews,
+            "display_images": display_images,
+            "save_generated_images": save_generated_images,
+            "image_storage_location": image_storage_location,
+            "retain_compel_previous_load": retain_compel_previous_load,
+            "retain_detailfix_model_previous_load": retain_detailfix_model_previous_load,
+            "retain_hires_model_previous_load": retain_hires_model_previous_load,
+            "t2i_adapter_preprocessor": t2i_adapter_preprocessor,
+            "t2i_adapter_conditioning_scale": float(t2i_adapter_conditioning_scale),
+            "t2i_adapter_conditioning_factor": float(t2i_adapter_conditioning_factor),
+            "upscaler_model_path": upscaler_model,
+            "upscaler_increases_size": upscaler_increases_size,
+            "esrgan_tile": esrgan_tile,
+            "esrgan_tile_overlap": esrgan_tile_overlap,
+            "hires_steps": hires_steps,
+            "hires_denoising_strength": hires_denoising_strength,
+            "hires_prompt": hires_prompt,
+            "hires_negative_prompt": hires_negative_prompt,
+            "hires_sampler": hires_sampler,
+            "hires_before_adetailer": hires_before_adetailer,
+            "hires_after_adetailer": hires_after_adetailer,
+            "ip_adapter_image": params_ip_img,
+            "ip_adapter_mask": params_ip_msk,
+            "ip_adapter_model": params_ip_model,
+            "ip_adapter_mode": params_ip_mode,
+            "ip_adapter_scale": params_ip_scale,
+        }
+
+        # Maybe fix lora issue: 'Cannot copy out of meta tensor; no data!''
+        self.model.pipe.to("cuda:0" if torch.cuda.is_available() else "cpu")
+
+        progress(1, desc="Inference preparation completed. Starting inference...")
+
+        info_state = f"PROCESSING "
+        info_state += ">"
+        info_state = f"COMPLETED. Seeds: {str(seed)}"
+        if vae_msg:
+            info_state = info_state + "<br>" + vae_msg
+        if msg_lora:
+            info_state = info_state + "<br>" + "<br>".join(msg_lora)
+        return self.infer_short(self.model, pipe_params), info_state
+## END MOD
+
+
+from pathlib import Path
+from modutils import (
+    safe_float,
+    escape_lora_basename,
+    to_lora_key,
+    to_lora_path,
+    get_local_model_list,
+    get_private_lora_model_lists,
+    get_valid_lora_name,
+    get_valid_lora_path,
+    get_valid_lora_wt,
+    get_lora_info,
+    normalize_prompt_list,
+    get_civitai_info,
+    search_lora_on_civitai,
+)
+
+sd_gen = GuiSD()
+def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
+           model_name = load_diffusers_format_model[0], lora1 = None, lora1_wt = 1.0, lora2 = None, lora2_wt = 1.0,
+           lora3 = None, lora3_wt = 1.0, lora4 = None, lora4_wt = 1.0, lora5 = None, lora5_wt = 1.0,
+           sampler = "Euler a", vae = None, progress=gr.Progress(track_tqdm=True)):
+    import PIL
+    import numpy as np
+    MAX_SEED = np.iinfo(np.int32).max
+
+    images: list[tuple[PIL.Image.Image, str | None]] = []
+    info: str = ""
+    progress(0, desc="Preparing...")
+
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+
+    generator = torch.Generator().manual_seed(seed).seed()
+
+    prompt, negative_prompt = insert_model_recom_prompt(prompt, negative_prompt, model_name)
+    progress(0.5, desc="Preparing...")
+    lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt = \
+        set_prompt_loras(prompt, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt)
+    lora1 = get_valid_lora_path(lora1)
+    lora2 = get_valid_lora_path(lora2)
+    lora3 = get_valid_lora_path(lora3)
+    lora4 = get_valid_lora_path(lora4)
+    lora5 = get_valid_lora_path(lora5)
+    progress(1, desc="Preparation completed. Starting inference preparation...")
+
+    sd_gen.load_new_model(model_name, vae, task_model_list[0])
+    images, info = sd_gen.generate_pipeline(prompt, negative_prompt, 1, num_inference_steps,
+        guidance_scale, True, generator, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt,
+        lora4, lora4_wt, lora5, lora5_wt, sampler,
+        height, width, model_name, vae, task_model_list[0], None, "Canny", 512, 1024,
+        None, None, None, 0.35, 100, 200, 0.1, 0.1, 1.0, 0., 1., False, "Classic", None,
+        1.0, 100, 10, 30, 0.55, "Use same sampler", "", "",
+        False, True, 1, True, False, False, False, False, "./images", False, False, False, True, 1, 0.55,
+        False, False, False, True, False, "Use same sampler", False, "", "", 0.35, True, True, False, 4, 4, 32,
+        False, "", "", 0.35, True, True, False, 4, 4, 32,
+        True, None, None, "plus_face", "original", 0.7, None, None, "base", "style", 0.7
+    )
+
+    progress(1, desc="Inference completed.")
+    output_image = images[0][0] if images else None
+
+    return output_image
+
+
+def _infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
+            model_name = load_diffusers_format_model[0], lora1 = None, lora1_wt = 1.0, lora2 = None, lora2_wt = 1.0,
+            lora3 = None, lora3_wt = 1.0, lora4 = None, lora4_wt = 1.0, lora5 = None, lora5_wt = 1.0,
+            sampler = "Euler a", vae = None, progress=gr.Progress(track_tqdm=True)):
+    return gr.update(visible=True)
+
+
+def pass_result(result):
+    return result
+
+
+def get_samplers():
+    return scheduler_names
+
+
+def get_vaes():
+    return vae_model_list
+
+
+show_diffusers_model_list_detail = False
+cached_diffusers_model_tupled_list = get_tupled_model_list(load_diffusers_format_model)
+def get_diffusers_model_list():
+    if show_diffusers_model_list_detail:
+        return cached_diffusers_model_tupled_list
+    else:
+        return load_diffusers_format_model
+
+
+def enable_diffusers_model_detail(is_enable: bool = False, model_name: str = ""):
+    global show_diffusers_model_list_detail
+    show_diffusers_model_list_detail = is_enable
+    new_value = model_name
+    index = 0
+    if model_name in set(load_diffusers_format_model):
+        index = load_diffusers_format_model.index(model_name)
+    if is_enable:
+        new_value = cached_diffusers_model_tupled_list[index][1]
+    else:
+        new_value = load_diffusers_format_model[index]
+    return gr.update(value=is_enable), gr.update(value=new_value, choices=get_diffusers_model_list())
+
+
+def get_t2i_model_info(repo_id: str):
+    from huggingface_hub import HfApi
+    api = HfApi()
+    try:
+        if " " in repo_id or not api.repo_exists(repo_id): return ""
+        model = api.model_info(repo_id=repo_id)
+    except Exception as e:
+        print(f"Error: Failed to get {repo_id}'s info. ")
+        return ""
+    if model.private or model.gated: return ""
+    tags = model.tags
+    info = []
+    url = f"https://huggingface.co/{repo_id}/"
+    if not 'diffusers' in tags: return ""
+    if 'diffusers:StableDiffusionXLPipeline' in tags:
+        info.append("SDXL")
+    elif 'diffusers:StableDiffusionPipeline' in tags:
+        info.append("SD1.5")
+    if model.card_data and model.card_data.tags:
+        info.extend(list_sub(model.card_data.tags, ['text-to-image', 'stable-diffusion', 'stable-diffusion-api', 'safetensors', 'stable-diffusion-xl']))
+    info.append(f"DLs: {model.downloads}")
+    info.append(f"likes: {model.likes}")
+    info.append(model.last_modified.strftime("lastmod: %Y-%m-%d"))
+    md = f"Model Info: {', '.join(info)}, [Model Repo]({url})"
+    return gr.update(value=md)
+
+
+def load_model_prompt_dict():
+    import json
+    dict = {}
+    try:
+        with open('model_dict.json', encoding='utf-8') as f:
+            dict = json.load(f)
+    except Exception:
+        pass
+    return dict
+
+
+model_prompt_dict = load_model_prompt_dict()
+
+
+model_recom_prompt_enabled = True
+animagine_ps = to_list("masterpiece, best quality, very aesthetic, absurdres")
+animagine_nps = to_list("lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]")
+pony_ps = to_list("score_9, score_8_up, score_7_up, masterpiece, best quality, very aesthetic, absurdres")
+pony_nps = to_list("source_pony, score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends")
+other_ps = to_list("anime artwork, anime style, studio anime, highly detailed, cinematic photo, 35mm photograph, film, bokeh, professional, 4k, highly detailed")
+other_nps = to_list("photo, deformed, black and white, realism, disfigured, low contrast, drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly")
+default_ps = to_list("highly detailed, masterpiece, best quality, very aesthetic, absurdres")
+default_nps = to_list("score_6, score_5, score_4, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]")
+def insert_model_recom_prompt(prompt: str = "", neg_prompt: str = "", model_name: str = "None"):
+    if not model_recom_prompt_enabled or not model_name: return prompt, neg_prompt
+    prompts = to_list(prompt)
+    neg_prompts = to_list(neg_prompt)
+    prompts = list_sub(prompts, animagine_ps + pony_ps + other_ps)
+    neg_prompts = list_sub(neg_prompts, animagine_nps + pony_nps + other_nps)
+    last_empty_p = [""] if not prompts and type != "None" else []
+    last_empty_np = [""] if not neg_prompts and type != "None" else []
+    ps = []
+    nps = []
+    if model_name in model_prompt_dict.keys(): 
+        ps = to_list(model_prompt_dict[model_name]["prompt"])
+        nps = to_list(model_prompt_dict[model_name]["negative_prompt"])
+    else:
+        ps = default_ps
+        nps = default_nps
+    prompts = prompts + ps
+    neg_prompts = neg_prompts + nps
+    prompt = ", ".join(list_uniq(prompts) + last_empty_p)
+    neg_prompt = ", ".join(list_uniq(neg_prompts) + last_empty_np)
+    return prompt, neg_prompt
+
+
+def enable_model_recom_prompt(is_enable: bool = True):
+    global model_recom_prompt_enabled
+    model_recom_prompt_enabled = is_enable
+    return is_enable
+
+
+private_lora_dict = {}
+try:
+    with open('lora_dict.json', encoding='utf-8') as f:
+        d = json.load(f)
+        for k, v in d.items():
+            private_lora_dict[escape_lora_basename(k)] = v
+except Exception:
+    pass
+
+
+private_lora_model_list = get_private_lora_model_lists()
+loras_dict = {"None": ["", "", "", "", ""], "": ["", "", "", "", ""]} | private_lora_dict.copy()
+loras_url_to_path_dict = {} # {"URL to download": "local filepath", ...}
+civitai_lora_last_results = {}  # {"URL to download": {search results}, ...}
+all_lora_list = []
+
+
+def get_all_lora_list():
+    global all_lora_list
+    loras = get_lora_model_list()
+    all_lora_list = loras.copy()
+    return loras
+
+
+def get_all_lora_tupled_list():
+    global loras_dict
+    models = get_all_lora_list()
+    if not models: return []
+    tupled_list = []
+    for model in models:
+        #if not model: continue # to avoid GUI-related bug
+        basename = Path(model).stem
+        key = to_lora_key(model)
+        items = None
+        if key in loras_dict.keys():
+            items = loras_dict.get(key, None)
+        else:
+            items = get_civitai_info(model)
+            if items != None:
+                loras_dict[key] = items
+        name = basename
+        value = model
+        if items and items[2] != "":
+            if items[1] == "Pony":
+                name = f"{basename} (for {items[1]}🐴, {items[2]})"
+            else:
+                name = f"{basename} (for {items[1]}, {items[2]})"
+        tupled_list.append((name, value))
+    return tupled_list
+
+
+def update_lora_dict(path: str):
+    global loras_dict
+    key = to_lora_key(path)
+    if key in loras_dict.keys(): return
+    items = get_civitai_info(path)
+    if items == None: return
+    loras_dict[key] = items
+
+
+def download_lora(dl_urls: str):
+    global loras_url_to_path_dict
+    dl_path = ""
+    before = get_local_model_list(directory_loras)
+    urls = []
+    for url in [url.strip() for url in dl_urls.split(',')]:
+        local_path = f"{directory_loras}/{url.split('/')[-1]}"
+        if not Path(local_path).exists():
+            download_things(directory_loras, url, hf_token, CIVITAI_API_KEY)
+            urls.append(url)
+    after = get_local_model_list(directory_loras)
+    new_files = list_sub(after, before)
+    i = 0
+    for file in new_files:
+        path = Path(file)
+        if path.exists():
+            new_path = Path(f'{path.parent.name}/{escape_lora_basename(path.stem)}{path.suffix}')
+            path.resolve().rename(new_path.resolve())
+            loras_url_to_path_dict[urls[i]] = str(new_path)
+            update_lora_dict(str(new_path))
+            dl_path = str(new_path)
+        i += 1
+    return dl_path
+
+
+def copy_lora(path: str, new_path: str):
+    import shutil
+    if path == new_path: return new_path
+    cpath = Path(path)
+    npath = Path(new_path)
+    if cpath.exists():
+        try:
+            shutil.copy(str(cpath.resolve()), str(npath.resolve()))
+        except Exception:
+            return None
+        update_lora_dict(str(npath))
+        return new_path
+    else:
+        return None
+
+
+def download_my_lora(dl_urls: str, lora1: str, lora2: str, lora3: str, lora4: str, lora5: str):
+    path = download_lora(dl_urls)
+    if path:
+        if not lora1 or lora1 == "None":
+            lora1 = path
+        elif not lora2 or lora2 == "None":
+            lora2 = path
+        elif not lora3 or lora3 == "None":
+            lora3 = path
+        elif not lora4 or lora4 == "None":
+            lora4 = path
+        elif not lora5 or lora5 == "None":
+            lora5 = path
+    choices = get_all_lora_tupled_list()
+    return gr.update(value=lora1, choices=choices), gr.update(value=lora2, choices=choices), gr.update(value=lora3, choices=choices),\
+        gr.update(value=lora4, choices=choices), gr.update(value=lora5, choices=choices)
+
+
+def set_prompt_loras(prompt, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt):
+    import re
+    lora1 = get_valid_lora_name(lora1)
+    lora2 = get_valid_lora_name(lora2)
+    lora3 = get_valid_lora_name(lora3)
+    lora4 = get_valid_lora_name(lora4)
+    lora5 = get_valid_lora_name(lora5)
+    if not "<lora" in prompt: return lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt
+    lora1_wt = get_valid_lora_wt(prompt, lora1, lora1_wt)
+    lora2_wt = get_valid_lora_wt(prompt, lora2, lora2_wt)
+    lora3_wt = get_valid_lora_wt(prompt, lora3, lora3_wt)
+    lora4_wt = get_valid_lora_wt(prompt, lora4, lora4_wt)
+    lora5_wt = get_valid_lora_wt(prompt, lora5, lora5_wt)
+    on1, label1, tag1, md1 = get_lora_info(lora1)
+    on2, label2, tag2, md2 = get_lora_info(lora2)
+    on3, label3, tag3, md3 = get_lora_info(lora3)
+    on4, label4, tag4, md4 = get_lora_info(lora4)
+    on5, label5, tag5, md5 = get_lora_info(lora5)
+    lora_paths = [lora1, lora2, lora3, lora4, lora5]
+    prompts = prompt.split(",") if prompt else []
+    for p in prompts:
+        p = str(p).strip()
+        if "<lora" in p:
+            result = re.findall(r'<lora:(.+?):(.+?)>', p)
+            if not result: continue
+            key = result[0][0]
+            wt = result[0][1]
+            path = to_lora_path(key)
+            if not key in loras_dict.keys() or not path:
+                path = get_valid_lora_name(path)
+                if not path or path == "None": continue
+            if path in lora_paths:
+                continue
+            elif not on1:
+                lora1 = path
+                lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                lora1_wt = safe_float(wt)
+                on1 = True
+            elif not on2:
+                lora2 = path
+                lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                lora2_wt = safe_float(wt)
+                on2 = True
+            elif not on3:
+                lora3 = path
+                lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                lora3_wt = safe_float(wt)
+                on3 = True
+            elif not on4:
+                lora4 = path
+                lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                lora4_wt = safe_float(wt)
+                on4, label4, tag4, md4 = get_lora_info(lora4)
+            elif not on5:
+                lora5 = path
+                lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                lora5_wt = safe_float(wt)
+                on5 = True
+    return lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt
+
+
+def apply_lora_prompt(prompt: str, lora_info: str):
+    if lora_info == "None": return gr.update(value=prompt)
+    tags = prompt.split(",") if prompt else []
+    prompts = normalize_prompt_list(tags)
+    lora_tag = lora_info.replace("/",",")
+    lora_tags = lora_tag.split(",") if str(lora_info) != "None" else []
+    lora_prompts = normalize_prompt_list(lora_tags)
+    empty = [""]
+    prompt = ", ".join(list_uniq(prompts + lora_prompts) + empty)
+    return gr.update(value=prompt)
+
+
+def update_loras(prompt, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt):
+    import re
+    on1, label1, tag1, md1 = get_lora_info(lora1)
+    on2, label2, tag2, md2 = get_lora_info(lora2)
+    on3, label3, tag3, md3 = get_lora_info(lora3)
+    on4, label4, tag4, md4 = get_lora_info(lora4)
+    on5, label5, tag5, md5 = get_lora_info(lora5)
+    lora_paths = [lora1, lora2, lora3, lora4, lora5]
+    prompts = prompt.split(",") if prompt else []
+    output_prompts = []
+    for p in prompts:
+        p = str(p).strip()
+        if "<lora" in p:
+            result = re.findall(r'<lora:(.+?):(.+?)>', p)
+            if not result: continue
+            key = result[0][0]
+            wt = result[0][1]
+            path = to_lora_path(key)
+            if not key in loras_dict.keys() or not path: continue
+            if path in lora_paths:
+                output_prompts.append(f"<lora:{to_lora_key(path)}:{safe_float(wt):.2f}>")
+        elif p:
+            output_prompts.append(p)
+    lora_prompts = []
+    if on1: lora_prompts.append(f"<lora:{to_lora_key(lora1)}:{lora1_wt:.2f}>")
+    if on2: lora_prompts.append(f"<lora:{to_lora_key(lora2)}:{lora2_wt:.2f}>")
+    if on3: lora_prompts.append(f"<lora:{to_lora_key(lora3)}:{lora3_wt:.2f}>")
+    if on4: lora_prompts.append(f"<lora:{to_lora_key(lora4)}:{lora4_wt:.2f}>")
+    if on5: lora_prompts.append(f"<lora:{to_lora_key(lora5)}:{lora5_wt:.2f}>")
+    output_prompt = ", ".join(list_uniq(output_prompts + lora_prompts + [""]))
+    choices = get_all_lora_tupled_list()
+    return gr.update(value=output_prompt), gr.update(value=lora1, choices=choices), gr.update(value=lora1_wt),\
+     gr.update(value=tag1, label=label1, visible=on1), gr.update(visible=on1), gr.update(value=md1, visible=on1),\
+     gr.update(value=lora2, choices=choices), gr.update(value=lora2_wt),\
+     gr.update(value=tag2, label=label2, visible=on2), gr.update(visible=on2), gr.update(value=md2, visible=on2),\
+     gr.update(value=lora3, choices=choices), gr.update(value=lora3_wt),\
+     gr.update(value=tag3, label=label3, visible=on3), gr.update(visible=on3), gr.update(value=md3, visible=on3),\
+     gr.update(value=lora4, choices=choices), gr.update(value=lora4_wt),\
+     gr.update(value=tag4, label=label4, visible=on4), gr.update(visible=on4), gr.update(value=md4, visible=on4),\
+     gr.update(value=lora5, choices=choices), gr.update(value=lora5_wt),\
+     gr.update(value=tag5, label=label5, visible=on5), gr.update(visible=on5), gr.update(value=md5, visible=on5)
+
+
+def search_civitai_lora(query, base_model):
+    global civitai_lora_last_results
+    items = search_lora_on_civitai(query, base_model)
+    if not items: return gr.update(choices=[("", "")], value="", visible=False),\
+          gr.update(value="", visible=False), gr.update(visible=True), gr.update(visible=True)
+    civitai_lora_last_results = {}
+    choices = []
+    for item in items:
+        base_model_name = "Pony🐴" if item['base_model'] == "Pony" else item['base_model']
+        name = f"{item['name']} (for {base_model_name} / By: {item['creator']} / Tags: {', '.join(item['tags'])})"
+        value = item['dl_url']
+        choices.append((name, value))
+        civitai_lora_last_results[value] = item
+    if not choices: return gr.update(choices=[("", "")], value="", visible=False),\
+          gr.update(value="", visible=False), gr.update(visible=True), gr.update(visible=True)
+    result = civitai_lora_last_results.get(choices[0][1], "None")
+    md = result['md'] if result else ""
+    return gr.update(choices=choices, value=choices[0][1], visible=True), gr.update(value=md, visible=True),\
+          gr.update(visible=True), gr.update(visible=True)
+
+
+def select_civitai_lora(search_result):
+    if not "http" in search_result: return gr.update(value=""), gr.update(value="None", visible=True)
+    result = civitai_lora_last_results.get(search_result, "None")
+    md = result['md'] if result else ""
+    return gr.update(value=search_result), gr.update(value=md, visible=True)
+
+
+def search_civitai_lora_json(query, base_model):
+    results = {}
+    items = search_lora_on_civitai(query, base_model)
+    if not items: return gr.update(value=results)
+    for item in items:
+        results[item['dl_url']] = item
+    return gr.update(value=results)
+
+
+quality_prompt_list = [
+    {
+        "name": "None",
+        "prompt": "",
+        "negative_prompt": "lowres",
+    },
+    {
+        "name": "Animagine Common",
+        "prompt": "anime artwork, anime style, vibrant, studio anime, highly detailed, masterpiece, best quality, very aesthetic, absurdres",
+        "negative_prompt": "lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]",
+    },
+    {
+        "name": "Pony Anime Common",
+        "prompt": "source_anime, score_9, score_8_up, score_7_up, masterpiece, best quality, very aesthetic, absurdres",
+        "negative_prompt": "source_pony, source_furry, source_cartoon, score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends",
+    },
+    {
+        "name": "Pony Common",
+        "prompt": "source_anime, score_9, score_8_up, score_7_up",
+        "negative_prompt": "source_pony, source_furry, source_cartoon, score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends",
+    },
+    {
+        "name": "Animagine Standard v3.0",
+        "prompt": "masterpiece, best quality",
+        "negative_prompt": "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name",
+    },
+    {
+        "name": "Animagine Standard v3.1",
+        "prompt": "masterpiece, best quality, very aesthetic, absurdres",
+        "negative_prompt": "lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]",
+    },
+    {
+        "name": "Animagine Light v3.1",
+        "prompt": "(masterpiece), best quality, very aesthetic, perfect face",
+        "negative_prompt": "(low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn",
+    },
+    {
+        "name": "Animagine Heavy v3.1",
+        "prompt": "(masterpiece), (best quality), (ultra-detailed), very aesthetic, illustration, disheveled hair, perfect composition, moist skin, intricate details",
+        "negative_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair, extra digit, fewer digits, cropped, worst quality, low quality, very displeasing",
+    },
+]
+
+
+style_list = [
+    {
+        "name": "None",
+        "prompt": "",
+        "negative_prompt": "",
+    },
+    {
+        "name": "Cinematic",
+        "prompt": "cinematic still, emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy",
+        "negative_prompt": "cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
+    },
+    {
+        "name": "Photographic",
+        "prompt": "cinematic photo, 35mm photograph, film, bokeh, professional, 4k, highly detailed",
+        "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
+    },
+    {
+        "name": "Anime",
+        "prompt": "anime artwork, anime style, vibrant, studio anime, highly detailed",
+        "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast",
+    },
+    {
+        "name": "Manga",
+        "prompt": "manga style, vibrant, high-energy, detailed, iconic, Japanese comic style",
+        "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style",
+    },
+    {
+        "name": "Digital Art",
+        "prompt": "concept art, digital artwork, illustrative, painterly, matte painting, highly detailed",
+        "negative_prompt": "photo, photorealistic, realism, ugly",
+    },
+    {
+        "name": "Pixel art",
+        "prompt": "pixel-art, low-res, blocky, pixel art style, 8-bit graphics",
+        "negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic",
+    },
+    {
+        "name": "Fantasy art",
+        "prompt": "ethereal fantasy concept art, magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy",
+        "negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white",
+    },
+    {
+        "name": "Neonpunk",
+        "prompt": "neonpunk style, cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional",
+        "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured",
+    },
+    {
+        "name": "3D Model",
+        "prompt": "professional 3d model, octane render, highly detailed, volumetric, dramatic lighting",
+        "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
+    },
+]
+
+
+preset_styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
+preset_quality = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in quality_prompt_list}
+
+
+def process_style_prompt(prompt: str, neg_prompt: str, styles_key: str = "None", quality_key: str = "None"):
+    def to_list(s):
+        return [x.strip() for x in s.split(",") if not s == ""]
+    
+    def list_sub(a, b):
+        return [e for e in a if e not in b]
+    
+    def list_uniq(l):
+        return sorted(set(l), key=l.index)
+
+    animagine_ps = to_list("anime artwork, anime style, vibrant, studio anime, highly detailed, masterpiece, best quality, very aesthetic, absurdres")
+    animagine_nps = to_list("lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]")
+    pony_ps = to_list("source_anime, score_9, score_8_up, score_7_up, masterpiece, best quality, very aesthetic, absurdres")
+    pony_nps = to_list("source_pony, source_furry, source_cartoon, score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends")
+    prompts = to_list(prompt)
+    neg_prompts = to_list(neg_prompt)
+
+    all_styles_ps = []
+    all_styles_nps = []
+    for d in style_list:
+        all_styles_ps.extend(to_list(str(d.get("prompt", ""))))
+        all_styles_nps.extend(to_list(str(d.get("negative_prompt", ""))))
+
+    all_quality_ps = []
+    all_quality_nps = []
+    for d in quality_prompt_list:
+        all_quality_ps.extend(to_list(str(d.get("prompt", ""))))
+        all_quality_nps.extend(to_list(str(d.get("negative_prompt", ""))))
+
+    quality_ps = to_list(preset_quality[quality_key][0])
+    quality_nps = to_list(preset_quality[quality_key][1])
+    styles_ps = to_list(preset_styles[styles_key][0])
+    styles_nps = to_list(preset_styles[styles_key][1])
+
+    prompts = list_sub(prompts, animagine_ps + pony_ps + all_styles_ps + all_quality_ps)
+    neg_prompts = list_sub(neg_prompts, animagine_nps + pony_nps + all_styles_nps + all_quality_nps)
+
+    last_empty_p = [""] if not prompts and type != "None" and type != "Auto" and styles_key != "None" and quality_key != "None" else []
+    last_empty_np = [""] if not neg_prompts and type != "None" and type != "Auto" and styles_key != "None" and quality_key != "None" else []
+
+    if type == "Animagine":
+        prompts = prompts + animagine_ps
+        neg_prompts = neg_prompts + animagine_nps
+    elif type == "Pony":
+        prompts = prompts + pony_ps
+        neg_prompts = neg_prompts + pony_nps
+
+    prompts = prompts + styles_ps + quality_ps
+    neg_prompts = neg_prompts + styles_nps + quality_nps
+
+    prompt = ", ".join(list_uniq(prompts) + last_empty_p)
+    neg_prompt = ", ".join(list_uniq(neg_prompts) + last_empty_np)
+
+    return gr.update(value=prompt), gr.update(value=neg_prompt)
+
+

env.py

@@ -0,0 +1,129 @@
+import os
+
+CIVITAI_API_KEY = os.environ.get("CIVITAI_API_KEY")
+hf_token = os.environ.get("HF_TOKEN")
+hf_read_token = os.environ.get('HF_READ_TOKEN') # only use for private repo
+
+# - **List Models**
+load_diffusers_format_model = [
+    'votepurchase/animagine-xl-3.1',
+    'votepurchase/NSFW-GEN-ANIME-v2',
+    'votepurchase/kivotos-xl-2.0',
+    'votepurchase/holodayo-xl-2.1',
+    'votepurchase/ponyDiffusionV6XL',
+    'votepurchase/AnythingXL_xl',
+    'votepurchase/7thAnimeXLPonyA_v10',
+    'votepurchase/ChilloutMix',
+    'votepurchase/NovelAIRemix',
+    'votepurchase/NSFW-gen-v2',
+    'votepurchase/PerfectDeliberate-Anime_v2',
+    'votepurchase/realpony-xl',
+    'votepurchase/artiwaifu-diffusion-1.0',
+    'votepurchase/Starry-XL-v5.2',
+    'votepurchase/Yaki-Dofu-Mix',
+    'votepurchase/ebara-pony-v1-sdxl',
+    'votepurchase/waiANIMIXPONYXL_v10',
+    'votepurchase/counterfeitV30_v30',
+    'votepurchase/ebara-pony',
+    'votepurchase/Realistic_Vision_V1.4',
+    'votepurchase/pony',
+    'votepurchase/ponymatureSDXL_ponyeclipse10',
+    'votepurchase/waiREALMIX_v70',
+    'votepurchase/waiREALCN_v10',
+    'votepurchase/PVCStyleModelMovable_pony151',
+    'votepurchase/PVCStyleModelMovable_beta27Realistic',
+    'votepurchase/PVCStyleModelFantasy_beta12',
+    'votepurchase/pvcxl-v1-lora',
+    'votepurchase/Realistic_Vision_V2.0',
+    'votepurchase/RealVisXL_V4.0',
+    'votepurchase/juggernautXL_hyper_8step_sfw',
+    'votepurchase/ponyRealism_v21MainVAE',
+    'stabilityai/stable-diffusion-xl-base-1.0',
+    'cagliostrolab/animagine-xl-3.1',
+    'misri/epicrealismXL_v7FinalDestination',
+    'misri/juggernautXL_juggernautX',
+    'misri/zavychromaxl_v80',
+    'SG161222/RealVisXL_V4.0',
+    'misri/newrealityxlAllInOne_Newreality40',
+    'eienmojiki/Anything-XL',
+    'eienmojiki/Starry-XL-v5.2',
+    'gsdf/CounterfeitXL',
+    'kitty7779/ponyDiffusionV6XL',
+    'yodayo-ai/clandestine-xl-1.0',
+    'yodayo-ai/kivotos-xl-2.0',
+    'yodayo-ai/holodayo-xl-2.1',
+    'digiplay/majicMIX_sombre_v2',
+    'digiplay/majicMIX_realistic_v6',
+    'digiplay/majicMIX_realistic_v7',
+    'digiplay/DreamShaper_8',
+    'digiplay/BeautifulArt_v1',
+    'digiplay/DarkSushi2.5D_v1',
+    'digiplay/darkphoenix3D_v1.1',
+    'digiplay/BeenYouLiteL11_diffusers',
+    'rubbrband/revAnimated_v2Rebirth',
+    'youknownothing/cyberrealistic_v50',
+    'votepurchase/counterfeitV30_v30',
+    'Meina/MeinaMix_V11',
+    'Meina/MeinaUnreal_V5',
+    'Meina/MeinaPastel_V7',
+    'rubbrband/realcartoon3d_v16',
+    'rubbrband/realcartoonRealistic_v14',
+    'KBlueLeaf/Kohaku-XL-Epsilon-rev2',
+    'KBlueLeaf/Kohaku-XL-Epsilon-rev3',
+    'KBlueLeaf/Kohaku-XL-Zeta',
+    'kayfahaarukku/UrangDiffusion-1.2',
+    'Eugeoter/artiwaifu-diffusion-1.0',
+    'Raelina/Rae-Diffusion-XL-V2',
+    'Raelina/Raemu-XL-V4',
+]
+
+# List all Models for specified user
+HF_MODEL_USER_LIKES = ["votepurchase"] # sorted by number of likes
+HF_MODEL_USER_EX = ["John6666"] # sorted by a special rule
+
+
+# - **Download Models**
+download_model_list = [
+]
+
+# - **Download VAEs**
+download_vae_list = [
+    'https://huggingface.co/madebyollin/sdxl-vae-fp16-fix/resolve/main/sdxl.vae.safetensors?download=true',
+    'https://huggingface.co/nubby/blessed-sdxl-vae-fp16-fix/resolve/main/sdxl_vae-fp16fix-c-1.1-b-0.5.safetensors?download=true',
+    "https://huggingface.co/nubby/blessed-sdxl-vae-fp16-fix/blob/main/sdxl_vae-fp16fix-blessed.safetensors",
+    "https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt",
+    "https://huggingface.co/stabilityai/sd-vae-ft-ema-original/resolve/main/vae-ft-ema-560000-ema-pruned.ckpt",
+]
+
+# - **Download LoRAs**
+download_lora_list = [
+]
+
+# Download Embeddings
+download_embeds = [
+    'https://huggingface.co/datasets/Nerfgun3/bad_prompt/blob/main/bad_prompt_version2.pt',
+    'https://huggingface.co/embed/negative/resolve/main/EasyNegativeV2.safetensors',
+    'https://huggingface.co/embed/negative/resolve/main/bad-hands-5.pt',
+]
+
+directory_models = 'models'
+os.makedirs(directory_models, exist_ok=True)
+directory_loras = 'loras'
+os.makedirs(directory_loras, exist_ok=True)
+directory_vaes = 'vaes'
+os.makedirs(directory_vaes, exist_ok=True)
+directory_embeds = 'embedings'
+os.makedirs(directory_embeds, exist_ok=True)
+
+directory_embeds_sdxl = 'embedings_xl'
+os.makedirs(directory_embeds_sdxl, exist_ok=True)
+directory_embeds_positive_sdxl = 'embedings_xl/positive'
+os.makedirs(directory_embeds_positive_sdxl, exist_ok=True)
+
+HF_LORA_PRIVATE_REPOS1 = ['John6666/loratest1', 'John6666/loratest3', 'John6666/loratest4', 'John6666/loratest6']
+HF_LORA_PRIVATE_REPOS2 = ['John6666/loratest10', 'John6666/loratest11','John6666/loratest'] # to be sorted as 1 repo
+HF_LORA_PRIVATE_REPOS = HF_LORA_PRIVATE_REPOS1 + HF_LORA_PRIVATE_REPOS2
+HF_LORA_ESSENTIAL_PRIVATE_REPO = 'John6666/loratest1' # to be downloaded on run app
+HF_VAE_PRIVATE_REPO = 'John6666/vaetest'
+HF_SDXL_EMBEDS_NEGATIVE_PRIVATE_REPO = 'John6666/embeddingstest'
+HF_SDXL_EMBEDS_POSITIVE_PRIVATE_REPO = 'John6666/embeddingspositivetest'

ja_to_danbooru/ja_to_danbooru.py

@@ -0,0 +1,87 @@
+import argparse
+import re
+from pathlib import Path
+
+
+def load_json_dict(path: str):
+    import json
+    from pathlib import Path
+    dict = {}
+    if not Path(path).exists(): return dict
+    try:
+        with open(path, encoding='utf-8') as f:
+            dict = json.load(f)
+    except Exception:
+        print(f"Failed to open dictionary file: {path}")
+        return dict
+    return dict
+
+
+ja_danbooru_dict = load_json_dict('ja_danbooru_dict.json')
+char_series_dict = load_json_dict('character_series_dict.json')
+tagtype_dict = load_json_dict('danbooru_tagtype_dict.json')
+
+
+def jatags_to_danbooru_tags(jatags: list[str]):
+    from rapidfuzz.process import extractOne
+    from rapidfuzz.utils import default_process
+    keys = list(ja_danbooru_dict.keys())
+    ckeys = list(char_series_dict.keys())
+    tags = []
+    for jatag in jatags:
+        jatag = str(jatag).strip()
+        s = default_process(str(jatag))
+        e1 = extractOne(s, keys, processor=default_process, score_cutoff=90.0)
+        if e1:
+            tag = str(ja_danbooru_dict[e1[0]])
+            tags.append(tag)
+            if tag in tagtype_dict.keys() and tagtype_dict[tag] == "character":
+                cs = default_process(tag)
+                ce1 = extractOne(cs, ckeys, processor=default_process, score_cutoff=95.0)
+                if ce1:
+                    series = str(char_series_dict[ce1[0]])
+                    tags.append(series)
+    return tags
+
+
+def jatags_to_danbooru(input_tag, input_file, output_file, is_append):
+    if input_file and Path(input_file).exists():
+        try:
+            with open(input_file, 'r', encoding='utf-8') as f:
+                input_tag = f.read()
+        except Exception:
+            print(f"Failed to open input file: {input_file}")
+    ja_tags = [tag.strip() for tag in input_tag.split(",")] if input_tag else []
+    tags = jatags_to_danbooru_tags(ja_tags)
+    output_tags = ja_tags + tags if is_append else tags
+    output_tag = ", ".join(output_tags)
+    if output_file:
+        try:
+            with open(output_file, mode='w', encoding="utf-8") as f:
+                f.write(output_tag)
+        except Exception:
+            print(f"Failed to write output file: {output_file}")
+    else:
+        print(output_tag)
+    return output_tag
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--tags", default=None, type=str, required=False, help="Input tags.")
+    parser.add_argument("--file", default=None, type=str, required=False, help="Input tags from a text file.")
+    parser.add_argument("--out", default=None, type=str, help="Output to text file.")
+    parser.add_argument("--append", default=False, type=bool, help="Whether the output contains the input tags or not.")
+
+    args = parser.parse_args()
+    assert (args.tags, args.file) != (None, None), "Must provide --tags or --file!"
+
+    jatags_to_danbooru(args.tags, args.file, args.out, args.append)
+
+
+# Usage:
+# python ja_to_danbooru.py --tags "女の子, 大室櫻子"
+# python danbooru_to_ja.py --file inputtag.txt
+# python danbooru_to_ja.py --file inputtag.txt --append True
+# Datasets: https://huggingface.co/datasets/p1atdev/danbooru-ja-tag-pair-20240715
+# Datasets: https://github.com/ponapon280/danbooru-e621-converter

llmdolphin.py

@@ -0,0 +1,884 @@
+import gradio as gr
+import spaces
+from llama_cpp import Llama
+from llama_cpp_agent import LlamaCppAgent, MessagesFormatterType
+from llama_cpp_agent.providers import LlamaCppPythonProvider
+from llama_cpp_agent.chat_history import BasicChatHistory
+from llama_cpp_agent.chat_history.messages import Roles
+from ja_to_danbooru.ja_to_danbooru import jatags_to_danbooru_tags
+import wrapt_timeout_decorator
+
+
+llm_models_dir = "./llm_models"
+llm_models = {
+    #"": ["", MessagesFormatterType.LLAMA_3],
+    #"": ["", MessagesFormatterType.MISTRAL],
+    #"": ["", MessagesFormatterType.ALPACA],
+    #"": ["", MessagesFormatterType.OPEN_CHAT],
+    #"": ["", MessagesFormatterType.CHATML],
+    #"": ["", MessagesFormatterType.PHI_3],
+    "mn-12b-lyra-v2a1-q5_k_m.gguf": ["HalleyStarbun/MN-12B-Lyra-v2a1-Q5_K_M-GGUF", MessagesFormatterType.CHATML],
+    "L3-8B-Tamamo-v1.i1-Q5_K_M.gguf": ["mradermacher/L3-8B-Tamamo-v1-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Llama-3.1-8B-EZO-1.1-it.Q5_K_M.gguf": ["mradermacher/Llama-3.1-8B-EZO-1.1-it-GGUF", MessagesFormatterType.MISTRAL],
+    "MN-12B-Starcannon-v1.i1-Q4_K_M.gguf": ["mradermacher/MN-12B-Starcannon-v1-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "MN-12B-Starcannon-v2.i1-Q4_K_M.gguf": ["mradermacher/MN-12B-Starcannon-v2-i1-GGUF", MessagesFormatterType.CHATML],
+    "MN-12B-Starcannon-v3.i1-Q4_K_M.gguf": ["mradermacher/MN-12B-Starcannon-v3-i1-GGUF", MessagesFormatterType.CHATML],
+    "MN-12B-Starcannon-v4-unofficial.i1-Q4_K_M.gguf": ["mradermacher/MN-12B-Starcannon-v4-unofficial-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "MN-12B-Starsong-v1.i1-Q4_K_M.gguf": ["mradermacher/MN-12B-Starsong-v1-i1-GGUF", MessagesFormatterType.CHATML],
+    "Lumimaid-Magnum-12B.i1-Q4_K_M.gguf": ["mradermacher/Lumimaid-Magnum-12B-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Nemo-12B-Marlin-v1.i1-Q4_K_M.gguf": ["mradermacher/Nemo-12B-Marlin-v1-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Nemo-12B-Marlin-v2.i1-Q4_K_M.gguf": ["mradermacher/Nemo-12B-Marlin-v2-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Nemo-12B-Marlin-v3.Q4_K_M.gguf": ["mradermacher/Nemo-12B-Marlin-v3-GGUF", MessagesFormatterType.MISTRAL],
+    "Nemo-12B-Marlin-v4.i1-Q4_K_M.gguf": ["mradermacher/Nemo-12B-Marlin-v4-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Nemo-12B-Marlin-v5-Q4_K_M.gguf": ["starble-dev/Nemo-12B-Marlin-v5-GGUF", MessagesFormatterType.CHATML],
+    "Nemo-12B-Marlin-v7.Q4_K_M.gguf": ["mradermacher/Nemo-12B-Marlin-v7-GGUF", MessagesFormatterType.MISTRAL],
+    "Nemo-12B-Marlin-v8.Q4_K_S.gguf": ["mradermacher/Nemo-12B-Marlin-v8-GGUF", MessagesFormatterType.MISTRAL],
+    "NemoDori-v0.2-Upscaled.1-14B.Q4_K_M.gguf": ["mradermacher/NemoDori-v0.2-Upscaled.1-14B-GGUF", MessagesFormatterType.MISTRAL],
+    "Fireball-12B-v1.0.i1-Q4_K_M.gguf": ["mradermacher/Fireball-12B-v1.0-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Fireball-Mistral-Nemo-Base-2407-sft-v2.2a.Q4_K_M.gguf": ["mradermacher/Fireball-Mistral-Nemo-Base-2407-sft-v2.2a-GGUF", MessagesFormatterType.MISTRAL],
+    "T-III-12B.Q4_K_M.gguf": ["mradermacher/T-III-12B-GGUF", MessagesFormatterType.CHATML],
+    "T-IIIa-12B.Q4_K_S.gguf": ["mradermacher/T-IIIa-12B-GGUF", MessagesFormatterType.MISTRAL],
+    "StorieCreative.i1-Q4_K_S.gguf": ["mradermacher/StorieCreative-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Deutscher-Pantheon-12B.Q4_K_M.gguf": ["mradermacher/Deutscher-Pantheon-12B-GGUF", MessagesFormatterType.MISTRAL],
+    "guns-and-roses-r1-Q4_K_L-imat.gguf": ["Reiterate3680/guns-and-roses-r1-GGUF", MessagesFormatterType.MISTRAL],
+    "Trinas_Nectar-8B-model_stock.i1-Q4_K_M.gguf": ["mradermacher/Trinas_Nectar-8B-model_stock-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "nemo-12b-hiwaifu-Q4_K_L-imat.gguf": ["Reiterate3680/nemo-12b-hiwaifu-GGUF", MessagesFormatterType.MISTRAL],
+    "Soliloquy-7B-v3-Q4_K_L-imat.gguf": ["Reiterate3680/Soliloquy-7B-v3-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Lyra-Gutenberg-mistral-nemo-12B.Q4_K_M.gguf": ["mradermacher/Lyra-Gutenberg-mistral-nemo-12B-GGUF", MessagesFormatterType.MISTRAL],
+    "Gutensuppe-mistral-nemo-12B.Q4_K_M.gguf": ["mradermacher/Gutensuppe-mistral-nemo-12B-GGUF", MessagesFormatterType.MISTRAL],
+    "IceTea21EnergyDrinkRPV13-dpo240-Q8_0.gguf": ["icefog72/IceTea21EnergyDrinkRPV13-dpo240-gguf", MessagesFormatterType.MISTRAL],
+    "Instant-RP-Noodles-12B.Q4_K_M.gguf": ["mradermacher/Instant-RP-Noodles-12B-GGUF", MessagesFormatterType.MISTRAL],
+    "Violet_Twilight-v0.1_q4_K_M.gguf": ["Epiculous/Violet_Twilight-v0.1-GGUF", MessagesFormatterType.MISTRAL],
+    "Llama3.1-vodka.Q4_K_S.gguf": ["mradermacher/Llama3.1-vodka-GGUF", MessagesFormatterType.MISTRAL],
+    "L3.1-Pyro-Mantus-v0.1c-8B.q5_k_m.gguf": ["kromquant/L3.1-Pyro-Mantus-v0.1c-8B-GGUFs", MessagesFormatterType.MISTRAL],
+    "Llama-3.1-8B-ArliAI-RPMax-v1.1-Q5_K_M.gguf": ["ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1-GGUF", MessagesFormatterType.MISTRAL],
+    "l3-notcrazy-8b-q4_k_m.gguf": ["bunnycore/L3-NotCrazy-8B-Q4_K_M-GGUF", MessagesFormatterType.LLAMA_3],
+    "Maverick-8B.Q5_K_M.gguf": ["RichardErkhov/bunnycore_-_Maverick-8B-gguf", MessagesFormatterType.LLAMA_3],
+    "Fireball-12B-v1.01a.Q4_K_M.gguf": ["mradermacher/Fireball-12B-v1.01a-GGUF", MessagesFormatterType.CHATML],
+    "Loki-v5.2.Q5_K_M.gguf": ["mradermacher/Loki-v5.2-GGUF", MessagesFormatterType.MISTRAL],
+    "Loki-v5.1.Q5_K_M.gguf": ["mradermacher/Loki-v5.1-GGUF", MessagesFormatterType.MISTRAL],
+    "GracieRP-freefallenLora-Gemma2-Inst-9B.i1-Q4_K_M.gguf": ["mradermacher/GracieRP-freefallenLora-Gemma2-Inst-9B-i1-GGUF", MessagesFormatterType.ALPACA],
+    "mistral-nemo-gutenberg-12B-v4.Q4_K_M.gguf": ["mradermacher/mistral-nemo-gutenberg-12B-v4-GGUF", MessagesFormatterType.MISTRAL],
+    "FunkyMerge-12b-0.1.Q4_K_M.gguf": ["mradermacher/FunkyMerge-12b-0.1-GGUF", MessagesFormatterType.MISTRAL],
+    "NemoMix-Unleashed-12B-Q4_K_M.gguf": ["bartowski/NemoMix-Unleashed-12B-GGUF", MessagesFormatterType.MISTRAL],
+    "IceTea21EnergyDrinkRPV13.Q4_K_S.gguf": ["mradermacher/IceTea21EnergyDrinkRPV13-GGUF", MessagesFormatterType.MISTRAL],
+    "MegaBeam-Mistral-7B-512k-Q5_K_M.gguf": ["bartowski/MegaBeam-Mistral-7B-512k-GGUF", MessagesFormatterType.MISTRAL],
+    "azur-8b-model_stock-q4_k_m.gguf": ["DreadPoor/Azur-8B-model_stock-Q4_K_M-GGUF", MessagesFormatterType.LLAMA_3],
+    "Chronos-Gold-12B-1.0-Q4_K_M.gguf": ["bartowski/Chronos-Gold-12B-1.0-GGUF", MessagesFormatterType.MISTRAL],
+    "L3.1-Romes-Ninomos-Maxxing.Q5_K_M.gguf": ["mradermacher/L3.1-Romes-Ninomos-Maxxing-GGUF", MessagesFormatterType.LLAMA_3],
+    "mistral-nemo-minitron-8b-base-q4_k_m.gguf": ["Daemontatox/Mistral-NeMo-Minitron-8B-Base-Q4_K_M-GGUF", MessagesFormatterType.MISTRAL],
+    "Nokstella_coder-8B-model_stock.i1-Q4_K_S.gguf": ["mradermacher/Nokstella_coder-8B-model_stock-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "vtion_model_v1.Q5_K_M.gguf": ["mradermacher/vtion_model_v1-GGUF", MessagesFormatterType.LLAMA_3],
+    "storiecreative-q5_k_m.gguf": ["ClaudioItaly/StorieCreative-Q5_K_M-GGUF", MessagesFormatterType.MISTRAL],
+    "L3.1-gramamax.Q5_K_M.gguf": ["mradermacher/L3.1-gramamax-GGUF", MessagesFormatterType.MISTRAL],
+    "Evolutionstory128.Q5_K_M.gguf": ["mradermacher/Evolutionstory128-GGUF", MessagesFormatterType.CHATML],
+    "sellen-8b-model_stock-q4_k_m.gguf": ["DreadPoor/Sellen-8B-model_stock-Q4_K_M-GGUF", MessagesFormatterType.MISTRAL],
+    "nokstella_coder-8b-model_stock-q4_k_m.gguf": ["DreadPoor/Nokstella_coder-8B-model_stock-Q4_K_M-GGUF", MessagesFormatterType.LLAMA_3],
+    "Ultra-Instruct-12B-Q4_K_M.gguf": ["bartowski/Ultra-Instruct-12B-GGUF", MessagesFormatterType.MISTRAL],
+    "L3.1-Sithamo-v0.4-8B.q5_k_m.gguf": ["kromquant/L3.1-Siithamo-v0.4-8B-GGUFs", MessagesFormatterType.MISTRAL],
+    "Berry-Spark-7B-Fix.Q5_K_M.gguf": ["mradermacher/Berry-Spark-7B-Fix-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "llama3.1-gutenberg-8B.Q4_K_S.gguf": ["mradermacher/llama3.1-gutenberg-8B-GGUF", MessagesFormatterType.LLAMA_3],
+    "L3.1-Romes-Ninomos.Q4_K_S.gguf": ["mradermacher/L3.1-Romes-Ninomos-GGUF", MessagesFormatterType.LLAMA_3],
+    "nemo-12b-summarizer-de-v3.Q4_K_M.gguf": ["mradermacher/nemo-12b-summarizer-de-v3-GGUF", MessagesFormatterType.MISTRAL],
+    "suzume-llama-3-8B-multilingual-orpo-borda-top25.Q5_K_M.gguf": ["darkshapes/suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf", MessagesFormatterType.LLAMA_3],
+    "Fireball-Mistral-Nemo-Base-2407-sft-v2.1.Q4_K_M.gguf": ["mradermacher/Fireball-Mistral-Nemo-Base-2407-sft-v2.1-GGUF", MessagesFormatterType.MISTRAL],
+    "gemma-2-9B-it-advanced-v2.1-Q5_K_M.gguf": ["jsgreenawalt/gemma-2-9B-it-advanced-v2.1-GGUF", MessagesFormatterType.ALPACA],
+    "mistral-12b-neptune-6k-instruct.Q4_K_M.gguf": ["mradermacher/mistral-12b-neptune-6k-instruct-GGUF", MessagesFormatterType.MISTRAL],
+    "evolutionstory-q5_k_m.gguf": ["ClaudioItaly/Evolutionstory-Q5_K_M-GGUF", MessagesFormatterType.MISTRAL],
+    "AuraFinal12B-Q4_K_L-imat.gguf": ["Reiterate3680/AuraFinal12B-GGUF", MessagesFormatterType.MISTRAL],
+    "Hollow-Tail-V1-12B-Q5_K_M.gguf": ["starble-dev/Hollow-Tail-V1-12B-GGUF", MessagesFormatterType.MISTRAL],
+    "IceSakeRPTrainingTestV1-7b.Q5_K_M.gguf": ["mradermacher/IceSakeRPTrainingTestV1-7b-GGUF", MessagesFormatterType.MISTRAL],
+    "IceTea21EnergyDrinkRPV10.Q5_K_M.gguf": ["mradermacher/IceTea21EnergyDrinkRPV10-GGUF", MessagesFormatterType.MISTRAL],
+    "MN-LooseCannon-12B-v2-Q4_K_L-imat.gguf": ["Reiterate3680/MN-LooseCannon-12B-v2-GGUF", MessagesFormatterType.CHATML],
+    "MN-MT3-m4-12B-Q4_K_L-imat.gguf": ["Reiterate3680/MN-MT3-m4-12B-GGUF", MessagesFormatterType.CHATML],
+    "Mahou-Gutenberg-Nemo-12B.Q4_K_M.gguf": ["mradermacher/Mahou-Gutenberg-Nemo-12B-GGUF", MessagesFormatterType.MISTRAL],
+    "Mahou-1.3-llama3.1-8B.Q5_K_M.gguf": ["mradermacher/Mahou-1.3-llama3.1-8B-GGUF", MessagesFormatterType.CHATML],
+    "gemma-advanced-v1.Q4_K_M.gguf": ["QuantFactory/gemma-advanced-v1-GGUF", MessagesFormatterType.ALPACA],
+    "flammen21X-mistral-7B-Q5_K_M.gguf": ["duyntnet/flammen21X-mistral-7B-imatrix-GGUF", MessagesFormatterType.MISTRAL],
+    "Magnum-Instruct-DPO-12B.Q4_K_M.gguf": ["mradermacher/Magnum-Instruct-DPO-12B-GGUF", MessagesFormatterType.MISTRAL],
+    "Carasique-v0.3b.Q4_K_S.gguf": ["mradermacher/Carasique-v0.3b-GGUF", MessagesFormatterType.MISTRAL],
+    "MN-12b-Sunrose-Q4_K_L-imat.gguf": ["Reiterate3680/MN-12b-Sunrose-GGUF", MessagesFormatterType.MISTRAL],
+    "OpenChat-3.5-7B-SOLAR-v2.0.i1-Q4_K_M.gguf": ["mradermacher/OpenChat-3.5-7B-SOLAR-v2.0-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Carasique-v0.3.Q4_K_M.gguf": ["mradermacher/Carasique-v0.3-GGUF", MessagesFormatterType.MISTRAL],
+    "Crimson_Dawn-V0.1.Q4_K_M.gguf": ["mradermacher/Crimson_Dawn-V0.1-GGUF", MessagesFormatterType.MISTRAL],
+    "Samantha-hermes3-8b-model-fixed.i1-Q5_K_M.gguf": ["mradermacher/Samantha-hermes3-8b-model-fixed-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Hermes-3-Llama-3.1-8B-lorablated-Q5_K_M.gguf": ["bartowski/Hermes-3-Llama-3.1-8B-lorablated-GGUF", MessagesFormatterType.LLAMA_3],
+    "stratagem-instruct-12b.i1-Q4_K_M.gguf": ["mradermacher/stratagem-instruct-12b-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "omed-llama3.1-8b.Q5_K_M.gguf": ["mradermacher/omed-llama3.1-8b-GGUF", MessagesFormatterType.LLAMA_3],
+    "omed-gemma2-9b.i1-Q4_K_M.gguf": ["mradermacher/omed-gemma2-9b-i1-GGUF", MessagesFormatterType.ALPACA],
+    "L3.1-Siithamo-v0.3-8B.q5_k_m.gguf": ["kromquant/L3.1-Siithamo-v0.3-8B-GGUFs", MessagesFormatterType.LLAMA_3],
+    "mistral-nemo-gutenberg-12B-v3.i1-Q4_K_M.gguf": ["mradermacher/mistral-nemo-gutenberg-12B-v3-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "MN-12B-Tarsus-Q4_K_L-imat.gguf": ["Reiterate3680/MN-12B-Tarsus-GGUF", MessagesFormatterType.MISTRAL],
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+    "gemma-2-9b-it-SimPO.i1-Q4_K_M.gguf": ["mradermacher/gemma-2-9b-it-SimPO-i1-GGUF", MessagesFormatterType.ALPACA],
+    "Gemma-2-9B-It-SPPO-Iter3.Q4_K_M.iMatrix.gguf": ["MCZK/Gemma-2-9B-It-SPPO-Iter3-GGUF", MessagesFormatterType.ALPACA],
+    "Llama-3-NeuralPaca-8b.Q4_K_M.gguf": ["RichardErkhov/NeuralNovel_-_Llama-3-NeuralPaca-8b-gguf", MessagesFormatterType.ALPACA],
+    "SaoRPM-2x8B.i1-Q4_K_M.gguf": ["mradermacher/SaoRPM-2x8B-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "L3-Hecate-8B-v1.2.Q4_K_M.gguf": ["mradermacher/L3-Hecate-8B-v1.2-GGUF", MessagesFormatterType.LLAMA_3],
+    "Mahou-1.3b-llama3-8B.i1-Q4_K_M.gguf": ["mradermacher/Mahou-1.3b-llama3-8B-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "SwallowMaid-8B-L3-SPPO-abliterated.i1-Q5_K_M.gguf": ["mradermacher/SwallowMaid-8B-L3-SPPO-abliterated-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "L3-8B-Lunar-Stheno.i1-Q5_K_M.gguf": ["mradermacher/L3-8B-Lunar-Stheno-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "llama3_Loradent.Q4_K_M.gguf": ["mradermacher/llama3_Loradent-GGUF", MessagesFormatterType.LLAMA_3],
+    "Llama-3-8B-Stroganoff.i1-Q4_K_M.gguf": ["mradermacher/Llama-3-8B-Stroganoff-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "L3-8B-EnchantedForest-v0.5.i1-Q4_K_M.gguf": ["mradermacher/L3-8B-EnchantedForest-v0.5-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "gemma-radiation-rp-9b-q5_k_m.gguf": ["pegasus912/Gemma-Radiation-RP-9B-Q5_K_M-GGUF", MessagesFormatterType.MISTRAL],
+    "Magic-Dolphin-7b.Q4_K_M.gguf": ["mradermacher/Magic-Dolphin-7b-GGUF", MessagesFormatterType.MISTRAL],
+    "mathstral-7B-v0.1-Q5_K_M.gguf": ["bartowski/mathstral-7B-v0.1-GGUF", MessagesFormatterType.MISTRAL],
+    "Gemma2-9B-it-Boku-v1.Q5_K_M.gguf": ["mradermacher/Gemma2-9B-it-Boku-v1-GGUF", MessagesFormatterType.MISTRAL],
+    "Gemma-2-9B-It-SPPO-Iter3-Q5_K_M.gguf": ["grapevine-AI/Gemma-2-9B-It-SPPO-Iter3-GGUF", MessagesFormatterType.MISTRAL],
+    "L3-8B-Niitama-v1.i1-Q4_K_M.gguf": ["mradermacher/L3-8B-Niitama-v1-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Maidphin-Kunoichi-7B.Q5_K_M.gguf": ["RichardErkhov/nbeerbower_-_Maidphin-Kunoichi-7B-gguf", MessagesFormatterType.MISTRAL],
+    "L3-15B-EtherealMaid-t0.0001.i1-Q4_K_M.gguf": ["mradermacher/L3-15B-EtherealMaid-t0.0001-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "L3-15B-MythicalMaid-t0.0001.i1-Q4_K_M.gguf": ["mradermacher/L3-15B-MythicalMaid-t0.0001-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "llama-3-Nephilim-v3-8B.Q5_K_M.gguf": ["grimjim/llama-3-Nephilim-v3-8B-GGUF", MessagesFormatterType.LLAMA_3],
+    "NarutoDolphin-10B.Q5_K_M.gguf": ["RichardErkhov/FelixChao_-_NarutoDolphin-10B-gguf", MessagesFormatterType.MISTRAL],
+    "l3-8b-tamamo-v1-q8_0.gguf": ["Ransss/L3-8B-Tamamo-v1-Q8_0-GGUF", MessagesFormatterType.LLAMA_3],
+    "Tiger-Gemma-9B-v1-Q4_K_M.gguf": ["bartowski/Tiger-Gemma-9B-v1-GGUF", MessagesFormatterType.LLAMA_3],
+    "TooManyMixRolePlay-7B-Story_V3.5.Q4_K_M.gguf": ["mradermacher/TooManyMixRolePlay-7B-Story_V3.5-GGUF", MessagesFormatterType.LLAMA_3],
+    "natsumura-llama3-v1.1-8b.Q4_K_M.gguf": ["mradermacher/natsumura-llama3-v1.1-8b-GGUF", MessagesFormatterType.LLAMA_3],
+    "natsumura-llama3-v1-8b.i1-Q4_K_M.gguf": ["mradermacher/natsumura-llama3-v1-8b-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "nephra_v1.0.Q5_K_M.gguf": ["PrunaAI/yodayo-ai-nephra_v1.0-GGUF-smashed", MessagesFormatterType.LLAMA_3],
+    "DPO-ONLY-Zephyr-7B.Q6_K.gguf": ["mradermacher/DPO-ONLY-Zephyr-7B-GGUF", MessagesFormatterType.LLAMA_3],
+    "L3-Deluxe-Scrambled-Eggs-On-Toast-8B.Q8_0.gguf": ["mradermacher/L3-Deluxe-Scrambled-Eggs-On-Toast-8B-GGUF", MessagesFormatterType.LLAMA_3],
+    "L3-Scrambled-Eggs-On-Toast-8B.i1-Q6_K.gguf": ["mradermacher/L3-Scrambled-Eggs-On-Toast-8B-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Llama-3-uncensored-Dare-1.Q4_K_M.gguf": ["mradermacher/Llama-3-uncensored-Dare-1-GGUF", MessagesFormatterType.LLAMA_3],
+    "llama3-8B-DarkIdol-2.2-Uncensored-1048K.i1-Q6_K.gguf": ["mradermacher/llama3-8B-DarkIdol-2.2-Uncensored-1048K-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "dolphin-2.9.3-mistral-7b-32k-q4_k_m.gguf": ["huggingkot/dolphin-2.9.3-mistral-7B-32k-Q4_K_M-GGUF", MessagesFormatterType.MISTRAL],
+    "dolphin-2.9.3-mistral-7B-32k-Q5_K_M.gguf": ["bartowski/dolphin-2.9.3-mistral-7B-32k-GGUF", MessagesFormatterType.MISTRAL],
+    "Lexi-Llama-3-8B-Uncensored_Q5_K_M.gguf": ["Orenguteng/Llama-3-8B-Lexi-Uncensored-GGUF", MessagesFormatterType.LLAMA_3],
+    "Llama3-Sophie.Q8_0.gguf": ["mradermacher/Llama3-Sophie-GGUF", MessagesFormatterType.LLAMA_3],
+    "Aura-Uncensored-OAS-8B-L3.i1-Q4_K_M.gguf": ["mradermacher/Aura-Uncensored-OAS-8B-L3-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "L3-Uncen-Merger-Omelette-RP-v0.2-8B-Q5_K_S-imat.gguf": ["LWDCLS/L3-Uncen-Merger-Omelette-RP-v0.2-8B-GGUF-IQ-Imatrix-Request", MessagesFormatterType.LLAMA_3],
+    "qwen2-diffusion-prompter-v01-q6_k.gguf": ["trollek/Qwen2-0.5B-DiffusionPrompter-v0.1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Smegmma-Deluxe-9B-v1-Q6_K.gguf": ["bartowski/Smegmma-Deluxe-9B-v1-GGUF", MessagesFormatterType.MISTRAL],
+    "Mahou-1.3c-mistral-7B.i1-Q6_K.gguf": ["mradermacher/Mahou-1.3c-mistral-7B-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Silicon-Maid-7B-Q8_0_X.gguf": ["duyntnet/Silicon-Maid-7B-imatrix-GGUF", MessagesFormatterType.ALPACA],
+    "l3-umbral-mind-rp-v3.0-8b-q5_k_m-imat.gguf": ["Casual-Autopsy/L3-Umbral-Mind-RP-v3.0-8B-Q5_K_M-GGUF", MessagesFormatterType.LLAMA_3],
+    "Meta-Llama-3.1-8B-Claude-iMat-Q5_K_M.gguf": ["InferenceIllusionist/Meta-Llama-3.1-8B-Claude-iMat-GGUF", MessagesFormatterType.LLAMA_3],
+    "Phi-3.1-mini-128k-instruct-Q6_K_L.gguf": ["bartowski/Phi-3.1-mini-128k-instruct-GGUF", MessagesFormatterType.PHI_3],
+    "tifa-7b-qwen2-v0.1.q4_k_m.gguf": ["Tifa-RP/Tifa-7B-Qwen2-v0.1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Oumuamua-7b-RP_Q5_K_M.gguf": ["Aratako/Oumuamua-7b-RP-GGUF", MessagesFormatterType.MISTRAL],
+    "Japanese-TextGen-Kage-v0.1.2-2x7B-NSFW_iMat_Ch200_IQ4_XS.gguf": ["dddump/Japanese-TextGen-Kage-v0.1.2-2x7B-NSFW-gguf", MessagesFormatterType.VICUNA],
+    "ChatWaifu_v1.2.1.Q5_K_M.gguf": ["mradermacher/ChatWaifu_v1.2.1-GGUF", MessagesFormatterType.MISTRAL],
+    "ChatWaifu_v1.1.Q5_K_M.gguf": ["mradermacher/ChatWaifu_v1.1-GGUF", MessagesFormatterType.MISTRAL],
+    "Ninja-V2-7B_Q4_K_M.gguf": ["Local-Novel-LLM-project/Ninja-V2-7B-GGUF", MessagesFormatterType.VICUNA],
+    "Yamase-12B.Q4_K_M.gguf": ["mradermacher/Yamase-12B-GGUF", MessagesFormatterType.MISTRAL],
+    "borea-phi-3.5-mini-instruct-common.Q5_K_M.gguf": ["keitokei1994/Borea-Phi-3.5-mini-Instruct-Common-GGUF", MessagesFormatterType.PHI_3],
+    "Llama-3-Nymeria-ELYZA-8B.i1-Q4_K_M.gguf": ["mradermacher/Llama-3-Nymeria-ELYZA-8B-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "suzume-llama-3-8B-japanese.Q4_K_M.gguf": ["PrunaAI/lightblue-suzume-llama-3-8B-japanese-GGUF-smashed", MessagesFormatterType.LLAMA_3],
+    "suzume-llama-3-8B-multilingual-orpo-borda-top25.Q4_K_M.gguf": ["RichardErkhov/lightblue_-_suzume-llama-3-8B-multilingual-orpo-borda-top25-gguf", MessagesFormatterType.LLAMA_3],
+    "Bungo-L3-8B.Q5_K_M.gguf": ["backyardai/Bungo-L3-8B-GGUF", MessagesFormatterType.LLAMA_3],
+    "ezo-common-t2-2b-gemma-2-it.Q6_K.gguf": ["keitokei1994/EZO-Common-T2-2B-gemma-2-it-GGUF", MessagesFormatterType.ALPACA],
+    "Llama-3-EZO-8b-Common-it.Q5_K_M.iMatrix.gguf": ["MCZK/Llama-3-EZO-8b-Common-it-GGUF", MessagesFormatterType.MISTRAL],
+    "EZO-Common-9B-gemma-2-it.i1-Q4_K_M.gguf": ["mradermacher/EZO-Common-9B-gemma-2-it-i1-GGUF", MessagesFormatterType.MISTRAL],
+}
+llm_formats = {
+    "MISTRAL": MessagesFormatterType.MISTRAL,
+    "CHATML": MessagesFormatterType.CHATML,
+    "VICUNA": MessagesFormatterType.VICUNA,
+    "LLAMA 2": MessagesFormatterType.LLAMA_2,
+    "SYNTHIA": MessagesFormatterType.SYNTHIA,
+    "NEURAL CHAT": MessagesFormatterType.NEURAL_CHAT,
+    "SOLAR": MessagesFormatterType.SOLAR,
+    "OPEN CHAT": MessagesFormatterType.OPEN_CHAT,
+    "ALPACA": MessagesFormatterType.ALPACA,
+    "CODE DS": MessagesFormatterType.CODE_DS,
+    "B22": MessagesFormatterType.B22,
+    "LLAMA 3": MessagesFormatterType.LLAMA_3,
+    "PHI 3": MessagesFormatterType.PHI_3,
+    "Autocoder": MessagesFormatterType.AUTOCODER,
+    "DeepSeek Coder v2": MessagesFormatterType.DEEP_SEEK_CODER_2,
+    "Gemma 2": MessagesFormatterType.ALPACA,
+    "Qwen2": MessagesFormatterType.OPEN_CHAT,
+}
+# https://github.com/Maximilian-Winter/llama-cpp-agent
+llm_languages = ["English", "Japanese", "Chinese"]
+llm_models_tupled_list = []
+default_llm_model_filename = list(llm_models.keys())[0]
+override_llm_format = None
+
+
+def to_list(s):
+    return [x.strip() for x in s.split(",") if not s == ""]
+
+
+def list_uniq(l):
+    return sorted(set(l), key=l.index)
+
+
+@wrapt_timeout_decorator.timeout(dec_timeout=3.5)
+def to_list_ja(s):
+    import re
+    s = re.sub(r'[、。]', ',', s)
+    return [x.strip() for x in s.split(",") if not s == ""]
+
+
+def is_japanese(s):
+    import unicodedata
+    for ch in s:
+        name = unicodedata.name(ch, "") 
+        if "CJK UNIFIED" in name or "HIRAGANA" in name or "KATAKANA" in name:
+            return True
+    return False
+
+
+def update_llm_model_tupled_list():
+    from pathlib import Path
+    global llm_models_tupled_list
+    llm_models_tupled_list = []
+    for k, v in llm_models.items():
+        name = k
+        value = k
+        llm_models_tupled_list.append((name, value))
+    model_files = Path(llm_models_dir).glob('*.gguf')
+    for path in model_files:
+        name = path.name
+        value = path.name
+        llm_models_tupled_list.append((name, value))
+    llm_models_tupled_list = list_uniq(llm_models_tupled_list)
+    return llm_models_tupled_list
+
+
+def download_llm_models():
+    from huggingface_hub import hf_hub_download
+    global llm_models_tupled_list
+    llm_models_tupled_list = []
+    for k, v in llm_models.items():
+        try:
+            hf_hub_download(repo_id = v[0], filename = k, local_dir = llm_models_dir)
+        except Exception:
+            continue
+        name = k
+        value = k
+        llm_models_tupled_list.append((name, value))
+
+
+def download_llm_model(filename):
+    from huggingface_hub import hf_hub_download
+    if not filename in llm_models.keys(): return default_llm_model_filename
+    try:
+        hf_hub_download(repo_id = llm_models[filename][0], filename = filename, local_dir = llm_models_dir)
+    except Exception:
+        return default_llm_model_filename
+    update_llm_model_tupled_list()
+    return filename
+
+
+def get_dolphin_model_info(filename):
+    md = "None"
+    items = llm_models.get(filename, None)
+    if items:
+        md = f'Repo: [{items[0]}](https://huggingface.co/{items[0]})'
+    return md
+
+
+def select_dolphin_model(filename, progress=gr.Progress(track_tqdm=True)):
+    global override_llm_format
+    override_llm_format = None
+    progress(0, desc="Loading model...")
+    value = download_llm_model(filename)
+    progress(1, desc="Model loaded.")
+    md = get_dolphin_model_info(filename)
+    return gr.update(value=value, choices=get_dolphin_models()), gr.update(value=get_dolphin_model_format(value)), gr.update(value=md)
+
+
+def select_dolphin_format(format_name):
+    global override_llm_format
+    override_llm_format = llm_formats[format_name]
+    return gr.update(value=format_name)
+
+
+#download_llm_models()
+download_llm_model(default_llm_model_filename)
+
+
+def get_dolphin_models():
+    return update_llm_model_tupled_list()
+
+
+def get_llm_formats():
+    return list(llm_formats.keys())
+
+
+def get_key_from_value(d, val):
+    keys = [k for k, v in d.items() if v == val]
+    if keys:
+        return keys[0]
+    return None
+
+
+def get_dolphin_model_format(filename):
+    if not filename in llm_models.keys(): filename = default_llm_model_filename
+    format = llm_models[filename][1]
+    format_name = get_key_from_value(llm_formats, format)
+    return format_name
+
+
+def add_dolphin_models(query, format_name):
+    import re
+    from huggingface_hub import HfApi
+    global llm_models
+    api = HfApi()
+    add_models = {}
+    format = llm_formats[format_name]
+    filename = ""
+    repo = ""
+    try:
+        s = list(re.findall(r'^(?:https?://huggingface.co/)?(.+?/.+?)(?:/.*/(.+?.gguf).*?)?$', query)[0])
+        if s and  "" in s: s.remove("")
+        if len(s) == 1:
+            repo = s[0]
+            if not api.repo_exists(repo_id = repo): return gr.update(visible=True)
+            files = api.list_repo_files(repo_id = repo)
+            for file in files:
+                if str(file).endswith(".gguf"): add_models[filename] = [repo, format]
+        elif len(s) >= 2:
+            repo = s[0]
+            filename = s[1]
+            if not api.repo_exists(repo_id = repo) or not api.file_exists(repo_id = repo, filename = filename): return gr.update(visible=True)
+            add_models[filename] = [repo, format]
+        else: return gr.update(visible=True)
+    except Exception:
+        return gr.update(visible=True)
+    print(add_models)
+    llm_models = (llm_models | add_models).copy()
+    return gr.update(choices=get_dolphin_models())
+
+
+dolphin_output_language = "English"
+dolphin_sysprompt_mode = "Default"
+dolphin_system_prompt = {"Default": r'''You are a helpful AI assistant to generate messages for AI that outputs an image when I enter a message.
+The message must have the following [Tags] generated in strict accordance with the following [Rules]:
+```
+[Tags]
+- Words to describe full names of characters and names of series in which they appear.
+- Words to describe names of the people there and their numbers, such as 2girls, 1boy.
+- Words to describe their hair color, hairstyle, hair length, hair accessory, eye color, eye shape, facial expression, breast size, and clothing of them in detail, such as long hair.
+- Words to describe their external features, ornaments and belongings (also specify colors, patterns, shapes) in detail.
+- Words to describe their stance from head to toe in detail.
+- Words to describe their acting, especially with sexual activity in detail.
+- Words to describe their surroundings in detail.
+- Words to describe background details, such as inside room, forest, starry sky.
+[Rules]
+- Any output should be plain text in English and don't use line breaks.
+- Output only composed of Tags in 1 line, separated by commas with spaces between Tags, in lower case English.
+- Output should be in the format: "//GENBEGIN//://1girl, Tag, Tag, ..., Tag//://GENEND//".
+- Preferably refer to and describe the information obtained from Danbooru. If not, describe it in own way.
+- It's preferable that each Tag is a plain phrase, word, caption, Danbooru tag, or E621 tag.
+- Convert any nicknames to full names first.
+- If a sexual theme is given, priority should be given to specific and rich descriptions of sexual activity, especially about genitals, fluids.
+- Assemble a short story internally which is developed from the themes provided, then describe a scene into an detailed English sentences based on the central character internally.
+- Split sentences into short phrases or words, and then convert them to Tags.
+- Use associated Danbooru tags, E621 tags.
+- Same Tags should be used only once per output.
+- Anyway, keep processing until you've finished outputting a message.
+```
+Based on these Rules, please tell me a message within 40 Tags that can generate an image for the following themes:''',
+"Strictly on themes": r'''You are a helpful AI assistant to generate messages for AI that outputs an image when I enter a message.
+The message must have the following [Tags] generated in strict accordance with the following [Rules]:
+```
+[Tags]
+- Words to describe full names of characters and names of series in which they appear.
+- Words to describe names of the people there and their numbers, such as 2girls, 1boy.
+- Words to describe their hair color, hairstyle, hair length, hair accessory, eye color, eye shape, facial expression, breast size, and clothing of them in detail, such as long hair.
+- Words to describe their external features, ornaments and belongings (also specify colors, patterns, shapes) in detail.
+- Words to describe their stance from head to toe in detail.
+- Words to describe their acting, especially with sexual activity in detail.
+- Words to describe their surroundings in detail.
+- Words to describe background details, such as inside room, forest, starry sky.
+[Rules]
+- Any output should be plain text in English and don't use line breaks.
+- Output only composed of Tags in 1 line, separated by commas with spaces between Tags, in lower case English.
+- Output should be in the format: "//GENBEGIN//://1girl, Tag, Tag, ..., Tag//://GENEND//".
+- Preferably refer to and describe the information obtained from Danbooru. If not, describe it in own way.
+- It's preferable that each Tag is a plain phrase, word, caption, Danbooru tag, or E621 tag.
+- Convert any nicknames to full names first.
+- If a sexual theme is given, priority should be given to specific and rich descriptions of sexual activity, especially about genitals, fluids.
+- Rewrite the given themes in plain English without changing the main idea.
+- Split sentences into short phrases or words, and then convert them to Tags.
+- Use associated Danbooru tags, E621 tags.
+- Same Tags should be used only once per output.
+- Anyway, keep processing until you've finished outputting a message.
+```
+Based on these Rules, please tell me a message within 40 Tags that can generate an image for the following themes:''',
+"With description": r'''You are a helpful AI assistant to generate messages for AI that outputs an image when I enter a message.
+The message must have the following [Tags] generated in strict accordance with the following [Rules]:
+```
+[Tags]
+- Words to describe full names of characters and names of series in which they appear.
+- Words to describe names of the people there and their numbers, such as 2girls, 1boy.
+- Words to describe their hair color, hairstyle, hair length, hair accessory, eye color, eye shape, facial expression, breast size, and clothing of them in detail, such as long hair.
+- Words to describe their external features, ornaments and belongings (also specify colors, patterns, shapes) in detail.
+- Words to describe their stance from head to toe in detail.
+- Words to describe their acting, especially with sexual activity in detail.
+- Words to describe their surroundings in detail.
+- Words to describe background details, such as inside room, forest, starry sky.
+[Rules]
+- Any Tags should be plain text in English and don't use line breaks.
+- Message is only composed of Tags in 1 line, separated by commas with spaces between Tags, in lower case English.
+- Message should be in the format: "//GENBEGIN//://1girl, Tag, Tag, ..., Tag//://GENEND//".
+- Preferably refer to and describe the information obtained from Danbooru. If not, describe it in own way.
+- It's preferable that each Tag is a plain phrase, word, caption, Danbooru tag, or E621 tag.
+- Convert any nicknames to full names first.
+- If a sexual theme is given, priority should be given to specific and rich descriptions of sexual activity, especially about genitals, fluids.
+- Assemble a short story internally which is developed from the themes provided, then describe a scene into an detailed English sentences based on the central character internally.
+- Split sentences into short phrases or words, and then convert them to Tags.
+- Use associated Danbooru tags, E621 tags.
+- Same Tags should be used only once per output.
+- Anyway, keep processing until you've finished outputting a message.
+```
+Based on these Rules, please tell me a message within 40 Tags that can generate an image for the following themes,
+ then describe the message you've generated in short, in <LANGUAGE>.:''',
+"With dialogue and description": r'''You are a helpful AI assistant to generate messages for AI that outputs an image when I enter a message.
+The message must have the following [Tags] generated in strict accordance with the following [Rules]:
+```
+[Tags]
+- Words to describe full names of characters and names of series in which they appear.
+- Words to describe names of the people there and their numbers, such as 2girls, 1boy.
+- Words to describe their hair color, hairstyle, hair length, hair accessory, eye color, eye shape, facial expression, breast size, and clothing of them in detail, such as long hair.
+- Words to describe their external features, ornaments and belongings (also specify colors, patterns, shapes) in detail.
+- Words to describe their stance from head to toe in detail.
+- Words to describe their acting, especially with sexual activity in detail.
+- Words to describe their surroundings in detail.
+- Words to describe background details, such as inside room, forest, starry sky.
+[Rules]
+- Any Tags should be plain text in English and don't use line breaks.
+- Message is only composed of Tags in 1 line, separated by commas with spaces between Tags, in lower case English.
+- Message should be in the format: "//GENBEGIN//://1girl, Tag, Tag, ..., Tag//://GENEND//".
+- Preferably refer to and describe the information obtained from Danbooru. If not, describe it in own way.
+- It's preferable that each Tag is a plain phrase, word, caption, Danbooru tag, or E621 tag.
+- Convert any nicknames to full names first.
+- If a sexual theme is given, priority should be given to specific and rich descriptions of sexual activity, especially about genitals, fluids.
+- Assemble a short story internally which is developed from the themes provided, then describe a scene into an detailed English sentences based on the central character internally.
+- Split sentences into short phrases or words, and then convert them to Tags.
+- Use associated Danbooru tags, E621 tags.
+- Same Tags should be used only once per output.
+- Anyway, keep processing until you've finished outputting a message.
+```
+Based on these Rules, please tell me message within 40 Tags that can generate an image for the following themes,
+ then write the character's long actor's line composed of one's voices and moaning and voices in thought, based on the story you have assembled, in <LANGUAGE>,
+ enclosed in //VOICEBEGIN//:// and //://VOICEEND//, then describe the message you've generated in short, in <LANGUAGE>.:''',
+"Longer prompt": r'''You are a helpful AI assistant to generate messages for AI that outputs an image when I enter a message.
+The message must have the following [Tags] generated in strict accordance with the following [Rules]:
+```
+[Tags]
+- Words to describe full names of characters and names of series in which they appear.
+- Words to describe names of the people there and their numbers, such as 2girls, 1boy.
+- Words to describe their hair color, hairstyle, hair length, hair accessory, eye color, eye shape, facial expression, breast size, and clothing of them in detail.
+- Words to describe their external features, ornaments and belongings (also specify colors, patterns, shapes) in detail.
+- Words to describe their stance from head to toe in detail.
+- Words to describe their acting, especially with sexual activity in detail.
+- Words to describe their surroundings in detail.
+- Words to describe background details.
+[Rules]
+- Any Tags should be plain text in English and don't use line breaks.
+- Message is only composed of Tags in 1 line, separated by commas with spaces between Tags, in lower case English.
+- Message should be enclosed in //GENBEGIN//:// and //://GENEND//.
+- Convert any nicknames to full names first.
+- If a sexual theme is given, priority should be given to specific and rich descriptions of sexual activity, especially about genitals, fluids.
+- Assemble a short story internally which is developed from the themes provided, then describe a scene into detailed English text based on the central character internally.
+- Tags can be in the form of sentences.
+- You can also use Danbooru tags, E621 tags as Tags.
+- Anyway, keep processing until you've finished outputting a message.
+```
+Based on these Rules, please tell me a message within 40 Tags that can generate an image for the following themes:''',
+"Longer prompt strictly on themes": r'''You are a helpful AI assistant to generate messages for AI that outputs an image when I enter a message.
+The message must have the following [Tags] generated in strict accordance with the following [Rules]:
+```
+[Tags]
+- Words to describe full names of characters and names of series in which they appear.
+- Words to describe names of the people there and their numbers, such as 2girls, 1boy.
+- Words to describe their hair color, hairstyle, hair length, hair accessory, eye color, eye shape, facial expression, breast size, and clothing of them in detail.
+- Words to describe their external features, ornaments and belongings (also specify colors, patterns, shapes) in detail.
+- Words to describe their stance from head to toe in detail.
+- Words to describe their acting, especially with sexual activity in detail.
+- Words to describe their surroundings in detail.
+- Words to describe background details.
+[Rules]
+- Any Tags should be plain text in English and don't use line breaks.
+- Message is only composed of Tags in 1 line, separated by commas with spaces between Tags, in lower case English.
+- Message should be enclosed in //GENBEGIN//:// and //://GENEND//.
+- Convert any nicknames to full names first.
+- If a sexual theme is given, priority should be given to specific and rich descriptions of sexual activity, especially about genitals, fluids.
+- Rewrite the given themes in plain English without changing the main idea.
+- Tags can be in the form of sentences.
+- You can also use Danbooru tags, E621 tags as Tags.
+- Anyway, keep processing until you've finished outputting a message.
+```
+Based on these Rules, please tell me a message within 40 Tags that can generate an image for the following themes:''',
+"Longer prompt with description": r'''You are a helpful AI assistant to generate messages for AI that outputs an image when I enter a message.
+The message must have the following [Tags] generated in strict accordance with the following [Rules]:
+```
+[Tags]
+- Words to describe full names of characters and names of series in which they appear.
+- Words to describe names of the people there and their numbers, such as 2girls, 1boy.
+- Words to describe their hair color, hairstyle, hair length, hair accessory, eye color, eye shape, facial expression, breast size, and clothing of them in detail.
+- Words to describe their external features, ornaments and belongings (also specify colors, patterns, shapes) in detail.
+- Words to describe their stance from head to toe in detail.
+- Words to describe their acting, especially with sexual activity in detail.
+- Words to describe their surroundings in detail.
+- Words to describe background details.
+[Rules]
+- Any Tags should be plain text in English and don't use line breaks.
+- Message is only composed of Tags in 1 line, separated by commas with spaces between Tags, in lower case English.
+- Message should be enclosed in //GENBEGIN//:// and //://GENEND//.
+- Convert any nicknames to full names first.
+- If a sexual theme is given, priority should be given to specific and rich descriptions of sexual activity, especially about genitals, fluids.
+- Assemble a short story internally which is developed from the themes provided, then describe a scene into detailed English text based on the central character internally.
+- Tags can be in the form of sentences.
+- You can also use Danbooru tags, E621 tags as Tags.
+- Anyway, keep processing until you've finished outputting a message.
+```
+Based on these Rules, please tell me a message within 40 Tags that can generate an image for the following themes,
+ then describe the message you've generated in short, in <LANGUAGE>.:''',
+"Japanese to Danbooru Dictionary": r"""You are a helpful AI assistant.
+Extract Japanese words from the following sentences and output them separated by commas. Convert words in their original forms.
+Output should be enclosed in //GENBEGIN//:// and //://GENEND//. The text to be given is as follows:""",
+"Chat with LLM": r"You are a helpful AI assistant. Respond in <LANGUAGE>."}
+
+
+def get_dolphin_sysprompt():
+    import re
+    prompt = re.sub('<LANGUAGE>', dolphin_output_language, dolphin_system_prompt.get(dolphin_sysprompt_mode, ""))
+    return prompt
+
+
+def get_dolphin_sysprompt_mode():
+    return list(dolphin_system_prompt.keys())
+
+
+def select_dolphin_sysprompt(key: str):
+    global dolphin_sysprompt_mode
+    if not key in dolphin_system_prompt.keys():
+        dolphin_sysprompt_mode = "Default"
+    else:
+        dolphin_sysprompt_mode = key
+    return gr.update(value=get_dolphin_sysprompt())
+
+
+def get_dolphin_languages():
+    return llm_languages
+
+
+def select_dolphin_language(lang: str):
+    global dolphin_output_language
+    dolphin_output_language = lang
+    return gr.update(value=get_dolphin_sysprompt())
+
+
+@wrapt_timeout_decorator.timeout(dec_timeout=5.0)
+def get_raw_prompt(msg: str):
+    import re
+    m = re.findall(r'/GENBEGIN/(.+?)/GENEND/', msg, re.DOTALL)
+    return re.sub(r'[*/:_"#\n]', ' ', ", ".join(m)).lower() if m else ""
+
+
+@spaces.GPU(duration=60)
+def dolphin_respond(
+    message: str,
+    history: list[tuple[str, str]],
+    model: str = default_llm_model_filename,
+    system_message: str = get_dolphin_sysprompt(),
+    max_tokens: int = 1024,
+    temperature: float = 0.7,
+    top_p: float = 0.95,
+    top_k: int = 40,
+    repeat_penalty: float = 1.1,
+    progress=gr.Progress(track_tqdm=True),
+):
+    from pathlib import Path
+    progress(0, desc="Processing...")
+
+    if override_llm_format:
+        chat_template = override_llm_format
+    else:
+        chat_template = llm_models[model][1]
+
+    llm = Llama(
+        model_path=str(Path(f"{llm_models_dir}/{model}")),
+        flash_attn=True,
+        n_gpu_layers=81, # 81
+        n_batch=1024,
+        n_ctx=8192, #8192
+    )
+    provider = LlamaCppPythonProvider(llm)
+
+    agent = LlamaCppAgent(
+        provider,
+        system_prompt=f"{system_message}",
+        predefined_messages_formatter_type=chat_template,
+        debug_output=False
+    )
+    
+    settings = provider.get_provider_default_settings()
+    settings.temperature = temperature
+    settings.top_k = top_k
+    settings.top_p = top_p
+    settings.max_tokens = max_tokens
+    settings.repeat_penalty = repeat_penalty
+    settings.stream = True
+
+    messages = BasicChatHistory()
+
+    for msn in history:
+        user = {
+            'role': Roles.user,
+            'content': msn[0]
+        }
+        assistant = {
+            'role': Roles.assistant,
+            'content': msn[1]
+        }
+        messages.add_message(user)
+        messages.add_message(assistant)
+    
+    stream = agent.get_chat_response(
+        message,
+        llm_sampling_settings=settings,
+        chat_history=messages,
+        returns_streaming_generator=True,
+        print_output=False
+    )
+    
+    progress(0.5, desc="Processing...")
+
+    outputs = ""
+    for output in stream:
+        outputs += output
+        yield [(outputs, None)]
+
+
+def dolphin_parse(
+    history: list[tuple[str, str]],
+):
+    if dolphin_sysprompt_mode == "Chat with LLM" or not history or len(history) < 1:
+        return "", gr.update(visible=True), gr.update(visible=True)
+    try:
+        msg = history[-1][0]
+        raw_prompt = get_raw_prompt(msg)
+    except Exception:
+        return "", gr.update(visible=True), gr.update(visible=True)
+    prompts = []
+    if dolphin_sysprompt_mode == "Japanese to Danbooru Dictionary" and is_japanese(raw_prompt):
+        prompts = list_uniq(jatags_to_danbooru_tags(to_list_ja(raw_prompt)) + ["nsfw", "explicit"])
+    else:
+        prompts = list_uniq(to_list(raw_prompt) + ["nsfw", "explicit"])
+    return ", ".join(prompts), gr.update(interactive=True), gr.update(interactive=True)
+
+
+@spaces.GPU(duration=60)
+def dolphin_respond_auto(
+    message: str,
+    history: list[tuple[str, str]],
+    model: str = default_llm_model_filename,
+    system_message: str = get_dolphin_sysprompt(),
+    max_tokens: int = 1024,
+    temperature: float = 0.7,
+    top_p: float = 0.95,
+    top_k: int = 40,
+    repeat_penalty: float = 1.1,
+    progress=gr.Progress(track_tqdm=True),
+):
+    #if not is_japanese(message): return [(None, None)]
+    from pathlib import Path
+    progress(0, desc="Processing...")
+
+    if override_llm_format:
+        chat_template = override_llm_format
+    else:
+        chat_template = llm_models[model][1]
+
+    llm = Llama(
+        model_path=str(Path(f"{llm_models_dir}/{model}")),
+        flash_attn=True,
+        n_gpu_layers=81, # 81
+        n_batch=1024,
+        n_ctx=8192, #8192
+    )
+    provider = LlamaCppPythonProvider(llm)
+
+    agent = LlamaCppAgent(
+        provider,
+        system_prompt=f"{system_message}",
+        predefined_messages_formatter_type=chat_template,
+        debug_output=False
+    )
+    
+    settings = provider.get_provider_default_settings()
+    settings.temperature = temperature
+    settings.top_k = top_k
+    settings.top_p = top_p
+    settings.max_tokens = max_tokens
+    settings.repeat_penalty = repeat_penalty
+    settings.stream = True
+
+    messages = BasicChatHistory()
+
+    for msn in history:
+        user = {
+            'role': Roles.user,
+            'content': msn[0]
+        }
+        assistant = {
+            'role': Roles.assistant,
+            'content': msn[1]
+        }
+        messages.add_message(user)
+        messages.add_message(assistant)
+    
+    progress(0, desc="Translating...")
+    stream = agent.get_chat_response(
+        message,
+        llm_sampling_settings=settings,
+        chat_history=messages,
+        returns_streaming_generator=True,
+        print_output=False
+    )
+
+    progress(0.5, desc="Processing...")
+    
+    outputs = ""
+    for output in stream:
+        outputs += output
+        yield [(outputs, None)]
+
+
+def dolphin_parse_simple(
+    message: str,
+    history: list[tuple[str, str]],
+):
+    #if not is_japanese(message): return message
+    if dolphin_sysprompt_mode == "Chat with LLM" or not history or len(history) < 1: return message
+    try:
+        msg = history[-1][0]
+        raw_prompt = get_raw_prompt(msg)
+    except Exception:
+        return ""
+    prompts = []
+    if dolphin_sysprompt_mode == "Japanese to Danbooru Dictionary" and is_japanese(raw_prompt):
+        prompts = list_uniq(jatags_to_danbooru_tags(to_list_ja(raw_prompt)) + ["nsfw", "explicit", "rating_explicit"])
+    else:
+        prompts = list_uniq(to_list(raw_prompt) + ["nsfw", "explicit", "rating_explicit"])
+    return ", ".join(prompts)

modutils.py

@@ -0,0 +1,1224 @@
+import json
+import gradio as gr
+from huggingface_hub import HfApi
+import os
+from pathlib import Path
+
+from env import (
+    HF_LORA_PRIVATE_REPOS1,
+    HF_LORA_PRIVATE_REPOS2,
+    HF_MODEL_USER_EX,
+    HF_MODEL_USER_LIKES,
+    directory_loras,
+    hf_read_token,
+    hf_token,
+    CIVITAI_API_KEY,
+)
+
+
+def get_user_agent():
+    return 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:127.0) Gecko/20100101 Firefox/127.0'
+
+
+def to_list(s):
+    return [x.strip() for x in s.split(",") if not s == ""]
+
+
+def list_uniq(l):
+        return sorted(set(l), key=l.index)
+
+
+def list_sub(a, b):
+    return [e for e in a if e not in b]
+
+
+def get_local_model_list(dir_path):
+    model_list = []
+    valid_extensions = ('.ckpt', '.pt', '.pth', '.safetensors', '.bin')
+    for file in Path(dir_path).glob("*"):
+        if file.suffix in valid_extensions:
+            file_path = str(Path(f"{dir_path}/{file.name}"))
+            model_list.append(file_path)
+    return model_list
+
+
+def download_things(directory, url, hf_token="", civitai_api_key=""):
+    url = url.strip()
+    
+    if "drive.google.com" in url:
+        original_dir = os.getcwd()
+        os.chdir(directory)
+        os.system(f"gdown --fuzzy {url}")
+        os.chdir(original_dir)
+    elif "huggingface.co" in url:
+        url = url.replace("?download=true", "")
+        # url = urllib.parse.quote(url, safe=':/')  # fix encoding
+        if "/blob/" in url:
+            url = url.replace("/blob/", "/resolve/")
+        user_header = f'"Authorization: Bearer {hf_token}"'
+        if hf_token:
+            os.system(f"aria2c --console-log-level=error --summary-interval=10 --header={user_header} -c -x 16 -k 1M -s 16 {url} -d {directory}  -o {url.split('/')[-1]}")
+        else:
+            os.system (f"aria2c --optimize-concurrent-downloads --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 {url} -d {directory}  -o {url.split('/')[-1]}")
+    elif "civitai.com" in url:
+        if "?" in url:
+            url = url.split("?")[0]
+        if civitai_api_key:
+            url = url + f"?token={civitai_api_key}"
+            os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
+        else:
+            print("\033[91mYou need an API key to download Civitai models.\033[0m")
+    else:
+        os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
+
+
+def escape_lora_basename(basename: str):
+    return basename.replace(".", "_").replace(" ", "_").replace(",", "")
+
+
+def to_lora_key(path: str):
+    return escape_lora_basename(Path(path).stem)
+
+
+def to_lora_path(key: str):
+    if Path(key).is_file(): return key
+    path = Path(f"{directory_loras}/{escape_lora_basename(key)}.safetensors")
+    return str(path)
+
+
+def safe_float(input):
+    output = 1.0
+    try:
+        output = float(input)
+    except Exception:
+        output = 1.0
+    return output
+
+
+def save_gallery_images(images, progress=gr.Progress(track_tqdm=True)):
+    from datetime import datetime, timezone, timedelta
+    progress(0, desc="Updating gallery...")
+    dt_now = datetime.now(timezone(timedelta(hours=9)))
+    basename = dt_now.strftime('%Y%m%d_%H%M%S_')
+    i = 1
+    if not images: return images
+    output_images = []
+    output_paths = []
+    for image in images:
+        filename = basename + str(i) + ".png"
+        i += 1
+        oldpath = Path(image[0])
+        newpath = oldpath
+        try:
+            if oldpath.exists():
+                newpath = oldpath.resolve().rename(Path(filename).resolve())
+        except Exception:
+           pass
+        finally: 
+            output_paths.append(str(newpath))
+            output_images.append((str(newpath), str(filename)))
+    progress(1, desc="Gallery updated.")
+    return gr.update(value=output_images), gr.update(value=output_paths), gr.update(visible=True)
+
+
+def download_private_repo(repo_id, dir_path, is_replace):
+    from huggingface_hub import snapshot_download
+    if not hf_read_token: return
+    try:
+        snapshot_download(repo_id=repo_id, local_dir=dir_path, allow_patterns=['*.ckpt', '*.pt', '*.pth', '*.safetensors', '*.bin'], use_auth_token=hf_read_token)
+    except Exception as e:
+        print(f"Error: Failed to download {repo_id}. ")
+        return
+    if is_replace:
+        for file in Path(dir_path).glob("*"):
+            if file.exists() and "." in file.stem or " " in file.stem and file.suffix in ['.ckpt', '.pt', '.pth', '.safetensors', '.bin']:
+                newpath = Path(f'{file.parent.name}/{escape_lora_basename(file.stem)}{file.suffix}')
+                file.resolve().rename(newpath.resolve())
+
+
+private_model_path_repo_dict = {} # {"local filepath": "huggingface repo_id", ...}
+
+
+def get_private_model_list(repo_id, dir_path):
+    global private_model_path_repo_dict
+    api = HfApi()
+    if not hf_read_token: return []
+    try:
+        files = api.list_repo_files(repo_id, token=hf_read_token)
+    except Exception as e:
+        print(f"Error: Failed to list {repo_id}. ")
+        return []
+    model_list = []
+    for file in files:
+        path = Path(f"{dir_path}/{file}")
+        if path.suffix in ['.ckpt', '.pt', '.pth', '.safetensors', '.bin']:
+            model_list.append(str(path))
+    for model in model_list:
+        private_model_path_repo_dict[model] = repo_id
+    return model_list
+
+
+def download_private_file(repo_id, path, is_replace):
+    from huggingface_hub import hf_hub_download
+    file = Path(path)
+    newpath = Path(f'{file.parent.name}/{escape_lora_basename(file.stem)}{file.suffix}') if is_replace else file
+    if not hf_read_token or newpath.exists(): return
+    filename = file.name
+    dirname = file.parent.name
+    try:
+        hf_hub_download(repo_id=repo_id, filename=filename, local_dir=dirname, use_auth_token=hf_read_token)
+    except Exception as e:
+        print(f"Error: Failed to download {filename}. ")
+        return
+    if is_replace:
+        file.resolve().rename(newpath.resolve())
+
+
+def download_private_file_from_somewhere(path, is_replace):
+    if not path in private_model_path_repo_dict.keys(): return
+    repo_id = private_model_path_repo_dict.get(path, None)
+    download_private_file(repo_id, path, is_replace)
+
+
+model_id_list = []
+def get_model_id_list():
+    global model_id_list
+    if len(model_id_list) != 0: return model_id_list
+    api = HfApi()
+    model_ids = []
+    try:
+        models_likes = []
+        for author in HF_MODEL_USER_LIKES:
+            models_likes.extend(api.list_models(author=author, cardData=True, sort="likes"))
+        models_ex = []
+        for author in HF_MODEL_USER_EX:
+            models_ex = api.list_models(author=author, cardData=True, sort="last_modified")
+    except Exception as e:
+        print(f"Error: Failed to list {author}'s models. ")
+        return model_ids
+    for model in models_likes:
+        model_ids.append(model.id) if not model.private else ""
+    anime_models = []
+    real_models = []
+    for model in models_ex:
+        if not model.private:
+            anime_models.append(model.id) if 'anime' in model.tags else real_models.append(model.id)
+    model_ids.extend(anime_models)
+    model_ids.extend(real_models)
+    model_id_list = model_ids.copy()
+    return model_ids
+
+
+model_id_list = get_model_id_list()
+
+
+def get_t2i_model_info(repo_id: str):
+    api = HfApi()
+    try:
+        if " " in repo_id or not api.repo_exists(repo_id): return ""
+        model = api.model_info(repo_id=repo_id)
+    except Exception as e:
+        print(f"Error: Failed to get {repo_id}'s info. ")
+        return ""
+    if model.private or model.gated: return ""
+    tags = model.tags
+    info = []
+    url = f"https://huggingface.co/{repo_id}/"
+    if not 'diffusers' in tags: return ""
+    if 'diffusers:StableDiffusionXLPipeline' in tags:
+        info.append("SDXL")
+    elif 'diffusers:StableDiffusionPipeline' in tags:
+        info.append("SD1.5")
+    if model.card_data and model.card_data.tags:
+        info.extend(list_sub(model.card_data.tags, ['text-to-image', 'stable-diffusion', 'stable-diffusion-api', 'safetensors', 'stable-diffusion-xl']))
+    info.append(f"DLs: {model.downloads}")
+    info.append(f"likes: {model.likes}")
+    info.append(model.last_modified.strftime("lastmod: %Y-%m-%d"))
+    md = f"Model Info: {', '.join(info)}, [Model Repo]({url})"
+    return gr.update(value=md)
+
+
+def get_tupled_model_list(model_list):
+    if not model_list: return []
+    tupled_list = []
+    for repo_id in model_list:
+        api = HfApi()
+        try:
+            if not api.repo_exists(repo_id): continue
+            model = api.model_info(repo_id=repo_id)
+        except Exception as e:
+            continue
+        if model.private or model.gated: continue
+        tags = model.tags
+        info = []
+        if not 'diffusers' in tags: continue
+        if 'diffusers:StableDiffusionXLPipeline' in tags:
+            info.append("SDXL")
+        elif 'diffusers:StableDiffusionPipeline' in tags:
+            info.append("SD1.5")
+        if model.card_data and model.card_data.tags:
+            info.extend(list_sub(model.card_data.tags, ['text-to-image', 'stable-diffusion', 'stable-diffusion-api', 'safetensors', 'stable-diffusion-xl']))
+        if "pony" in info:
+            info.remove("pony")
+            name = f"{repo_id} (Pony🐴, {', '.join(info)})"
+        else:
+            name = f"{repo_id} ({', '.join(info)})"
+        tupled_list.append((name, repo_id))
+    return tupled_list
+
+
+private_lora_dict = {}
+try:
+    with open('lora_dict.json', encoding='utf-8') as f:
+        d = json.load(f)
+        for k, v in d.items():
+            private_lora_dict[escape_lora_basename(k)] = v
+except Exception:
+    pass
+loras_dict = {"None": ["", "", "", "", ""], "": ["", "", "", "", ""]} | private_lora_dict.copy()
+civitai_not_exists_list = []
+loras_url_to_path_dict = {} # {"URL to download": "local filepath", ...}
+civitai_lora_last_results = {}  # {"URL to download": {search results}, ...}
+all_lora_list = []
+
+
+private_lora_model_list = []
+def get_private_lora_model_lists():
+    global private_lora_model_list
+    if len(private_lora_model_list) != 0: return private_lora_model_list
+    models1 = []
+    models2 = []
+    for repo in HF_LORA_PRIVATE_REPOS1:
+        models1.extend(get_private_model_list(repo, directory_loras))
+    for repo in HF_LORA_PRIVATE_REPOS2:
+        models2.extend(get_private_model_list(repo, directory_loras))
+    models = list_uniq(models1 + sorted(models2))
+    private_lora_model_list = models.copy()
+    return models
+
+
+private_lora_model_list = get_private_lora_model_lists()
+
+
+def get_civitai_info(path):
+    global civitai_not_exists_list
+    import requests
+    from urllib3.util import Retry
+    from requests.adapters import HTTPAdapter
+    if path in set(civitai_not_exists_list): return ["", "", "", "", ""]
+    if not Path(path).exists(): return None
+    user_agent = get_user_agent()
+    headers = {'User-Agent': user_agent, 'content-type': 'application/json'}
+    base_url = 'https://civitai.com/api/v1/model-versions/by-hash/'
+    params = {}
+    session = requests.Session()
+    retries = Retry(total=5, backoff_factor=1, status_forcelist=[500, 502, 503, 504])
+    session.mount("https://", HTTPAdapter(max_retries=retries))
+    import hashlib
+    with open(path, 'rb') as file:
+        file_data = file.read()
+    hash_sha256 = hashlib.sha256(file_data).hexdigest()
+    url = base_url + hash_sha256
+    try:
+        r = session.get(url, params=params, headers=headers, stream=True, timeout=(3.0, 15))
+    except Exception as e:
+        return ["", "", "", "", ""]
+    if not r.ok: return None
+    json = r.json()
+    if not 'baseModel' in json:
+        civitai_not_exists_list.append(path)
+        return ["", "", "", "", ""]
+    items = []
+    items.append(" / ".join(json['trainedWords']))
+    items.append(json['baseModel'])
+    items.append(json['model']['name'])
+    items.append(f"https://civitai.com/models/{json['modelId']}")
+    items.append(json['images'][0]['url'])
+    return items
+
+
+def get_lora_model_list():
+    loras = list_uniq(get_private_lora_model_lists() + get_local_model_list(directory_loras))
+    loras.insert(0, "None")
+    loras.insert(0, "")
+    return loras
+
+
+def get_all_lora_list():
+    global all_lora_list
+    loras = get_lora_model_list()
+    all_lora_list = loras.copy()
+    return loras
+
+
+def get_all_lora_tupled_list():
+    global loras_dict
+    models = get_all_lora_list()
+    if not models: return []
+    tupled_list = []
+    for model in models:
+        #if not model: continue # to avoid GUI-related bug
+        basename = Path(model).stem
+        key = to_lora_key(model)
+        items = None
+        if key in loras_dict.keys():
+            items = loras_dict.get(key, None)
+        else:
+            items = get_civitai_info(model)
+            if items != None:
+                loras_dict[key] = items
+        name = basename
+        value = model
+        if items and items[2] != "":
+            if items[1] == "Pony":
+                name = f"{basename} (for {items[1]}🐴, {items[2]})"
+            else:
+                name = f"{basename} (for {items[1]}, {items[2]})"
+        tupled_list.append((name, value))
+    return tupled_list
+
+
+def update_lora_dict(path):
+    global loras_dict
+    key = escape_lora_basename(Path(path).stem)
+    if key in loras_dict.keys(): return
+    items = get_civitai_info(path)
+    if items == None: return
+    loras_dict[key] = items
+
+
+def download_lora(dl_urls: str):
+    global loras_url_to_path_dict
+    dl_path = ""
+    before = get_local_model_list(directory_loras)
+    urls = []
+    for url in [url.strip() for url in dl_urls.split(',')]:
+        local_path = f"{directory_loras}/{url.split('/')[-1]}"
+        if not Path(local_path).exists():
+            download_things(directory_loras, url, hf_token, CIVITAI_API_KEY)
+            urls.append(url)
+    after = get_local_model_list(directory_loras)
+    new_files = list_sub(after, before)
+    i = 0
+    for file in new_files:
+        path = Path(file)
+        if path.exists():
+            new_path = Path(f'{path.parent.name}/{escape_lora_basename(path.stem)}{path.suffix}')
+            path.resolve().rename(new_path.resolve())
+            loras_url_to_path_dict[urls[i]] = str(new_path)
+            update_lora_dict(str(new_path))
+            dl_path = str(new_path)
+        i += 1
+    return dl_path
+
+
+def copy_lora(path: str, new_path: str):
+    import shutil
+    if path == new_path: return new_path
+    cpath = Path(path)
+    npath = Path(new_path)
+    if cpath.exists():
+        try:
+            shutil.copy(str(cpath.resolve()), str(npath.resolve()))
+        except Exception:
+            return None
+        update_lora_dict(str(npath))
+        return new_path
+    else:
+        return None
+
+
+def download_my_lora(dl_urls: str, lora1: str, lora2: str, lora3: str, lora4: str, lora5: str):
+    path = download_lora(dl_urls)
+    if path:
+        if not lora1 or lora1 == "None":
+            lora1 = path
+        elif not lora2 or lora2 == "None":
+            lora2 = path
+        elif not lora3 or lora3 == "None":
+            lora3 = path
+        elif not lora4 or lora4 == "None":
+            lora4 = path
+        elif not lora5 or lora5 == "None":
+            lora5 = path
+    choices = get_all_lora_tupled_list()
+    return gr.update(value=lora1, choices=choices), gr.update(value=lora2, choices=choices), gr.update(value=lora3, choices=choices),\
+        gr.update(value=lora4, choices=choices), gr.update(value=lora5, choices=choices)
+
+
+def get_valid_lora_name(query: str):
+    path = "None"
+    if not query or query == "None": return "None"
+    if to_lora_key(query) in loras_dict.keys(): return query
+    if query in loras_url_to_path_dict.keys():
+        path = loras_url_to_path_dict[query]
+    else:
+        path = to_lora_path(query.strip().split('/')[-1])
+    if Path(path).exists():
+        return path
+    elif "http" in query:
+        dl_file = download_lora(query)
+        if dl_file and Path(dl_file).exists(): return dl_file
+    else:
+        dl_file = find_similar_lora(query)
+        if dl_file and Path(dl_file).exists(): return dl_file
+    return "None"
+
+
+def get_valid_lora_path(query: str):
+    path = None
+    if not query or query == "None": return None
+    if to_lora_key(query) in loras_dict.keys(): return query
+    if Path(path).exists():
+        return path
+    else:
+        return None
+
+
+def get_valid_lora_wt(prompt: str, lora_path: str, lora_wt: float):
+    import re
+    wt = lora_wt
+    result = re.findall(f'<lora:{to_lora_key(lora_path)}:(.+?)>', prompt)
+    if not result: return wt
+    wt = safe_float(result[0][0])
+    return wt
+
+
+def set_prompt_loras(prompt, prompt_syntax, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt):
+    import re
+    if not "Classic" in str(prompt_syntax):  return lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt
+    lora1 = get_valid_lora_name(lora1)
+    lora2 = get_valid_lora_name(lora2)
+    lora3 = get_valid_lora_name(lora3)
+    lora4 = get_valid_lora_name(lora4)
+    lora5 = get_valid_lora_name(lora5)
+    if not "<lora" in prompt: return lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt
+    lora1_wt = get_valid_lora_wt(prompt, lora1, lora1_wt)
+    lora2_wt = get_valid_lora_wt(prompt, lora2, lora2_wt)
+    lora3_wt = get_valid_lora_wt(prompt, lora3, lora3_wt)
+    lora4_wt = get_valid_lora_wt(prompt, lora4, lora4_wt)
+    lora5_wt = get_valid_lora_wt(prompt, lora5, lora5_wt)
+    on1, label1, tag1, md1 = get_lora_info(lora1)
+    on2, label2, tag2, md2 = get_lora_info(lora2)
+    on3, label3, tag3, md3 = get_lora_info(lora3)
+    on4, label4, tag4, md4 = get_lora_info(lora4)
+    on5, label5, tag5, md5 = get_lora_info(lora5)
+    lora_paths = [lora1, lora2, lora3, lora4, lora5]
+    prompts = prompt.split(",") if prompt else []
+    for p in prompts:
+        p = str(p).strip()
+        if "<lora" in p:
+            result = re.findall(r'<lora:(.+?):(.+?)>', p)
+            if not result: continue
+            key = result[0][0]
+            wt = result[0][1]
+            path = to_lora_path(key)
+            if not key in loras_dict.keys() or not path:
+                path = get_valid_lora_name(path)
+                if not path or path == "None": continue
+            if path in lora_paths:
+                continue
+            elif not on1:
+                lora1 = path
+                lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                lora1_wt = safe_float(wt)
+                on1 = True
+            elif not on2:
+                lora2 = path
+                lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                lora2_wt = safe_float(wt)
+                on2 = True
+            elif not on3:
+                lora3 = path
+                lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                lora3_wt = safe_float(wt)
+                on3 = True
+            elif not on4:
+                lora4 = path
+                lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                lora4_wt = safe_float(wt)
+                on4, label4, tag4, md4 = get_lora_info(lora4)
+            elif not on5:
+                lora5 = path
+                lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                lora5_wt = safe_float(wt)
+                on5 = True
+    return lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt
+
+
+def get_lora_info(lora_path: str):
+    is_valid = False
+    tag = ""
+    label = ""
+    md = "None"
+    if not lora_path or lora_path == "None":
+        print("LoRA file not found.")
+        return is_valid, label, tag, md
+    path = Path(lora_path)
+    new_path = Path(f'{path.parent.name}/{escape_lora_basename(path.stem)}{path.suffix}')
+    if not to_lora_key(str(new_path)) in loras_dict.keys() and str(path) not in set(get_all_lora_list()):
+        print("LoRA file is not registered.")
+        return tag, label, tag, md
+    if not new_path.exists():
+        download_private_file_from_somewhere(str(path), True)
+    basename = new_path.stem
+    label = f'Name: {basename}'
+    items = loras_dict.get(basename, None)
+    if items == None:
+        items = get_civitai_info(str(new_path))
+        if items != None:
+            loras_dict[basename] = items
+    if items and items[2] != "":
+        tag = items[0]
+        label = f'Name: {basename}'
+        if items[1] == "Pony":
+            label = f'Name: {basename} (for Pony🐴)'
+        if items[4]:
+            md = f'<img src="{items[4]}" alt="thumbnail" width="150" height="240"><br>[LoRA Model URL]({items[3]})'
+        elif items[3]:
+            md = f'[LoRA Model URL]({items[3]})'
+    is_valid = True
+    return is_valid, label, tag, md
+
+
+def normalize_prompt_list(tags: list[str]):
+    prompts = []
+    for tag in tags:
+        tag = str(tag).strip()
+        if tag:
+            prompts.append(tag)
+    return prompts
+
+
+def apply_lora_prompt(prompt: str = "", lora_info: str = ""):
+    if lora_info == "None": return gr.update(value=prompt)
+    tags = prompt.split(",") if prompt else []
+    prompts = normalize_prompt_list(tags)
+
+    lora_tag = lora_info.replace("/",",")
+    lora_tags = lora_tag.split(",") if str(lora_info) != "None" else []
+    lora_prompts = normalize_prompt_list(lora_tags)
+ 
+    empty = [""]
+    prompt = ", ".join(list_uniq(prompts + lora_prompts) + empty)
+    return gr.update(value=prompt)
+
+
+def update_loras(prompt, prompt_syntax, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt):
+    import re
+    on1, label1, tag1, md1 = get_lora_info(lora1)
+    on2, label2, tag2, md2 = get_lora_info(lora2)
+    on3, label3, tag3, md3 = get_lora_info(lora3)
+    on4, label4, tag4, md4 = get_lora_info(lora4)
+    on5, label5, tag5, md5 = get_lora_info(lora5)
+    lora_paths = [lora1, lora2, lora3, lora4, lora5]
+
+    output_prompt = prompt
+    if "Classic" in str(prompt_syntax):
+        prompts = prompt.split(",") if prompt else []
+        output_prompts = []
+        for p in prompts:
+            p = str(p).strip()
+            if "<lora" in p:
+                result = re.findall(r'<lora:(.+?):(.+?)>', p)
+                if not result: continue
+                key = result[0][0]
+                wt = result[0][1]
+                path = to_lora_path(key)
+                if not key in loras_dict.keys() or not path: continue
+                if path in lora_paths:
+                    output_prompts.append(f"<lora:{to_lora_key(path)}:{safe_float(wt):.2f}>")
+            elif p:
+                output_prompts.append(p)
+        lora_prompts = []
+        if on1: lora_prompts.append(f"<lora:{to_lora_key(lora1)}:{lora1_wt:.2f}>")
+        if on2: lora_prompts.append(f"<lora:{to_lora_key(lora2)}:{lora2_wt:.2f}>")
+        if on3: lora_prompts.append(f"<lora:{to_lora_key(lora3)}:{lora3_wt:.2f}>")
+        if on4: lora_prompts.append(f"<lora:{to_lora_key(lora4)}:{lora4_wt:.2f}>")
+        if on5: lora_prompts.append(f"<lora:{to_lora_key(lora5)}:{lora5_wt:.2f}>")
+        output_prompt = ", ".join(list_uniq(output_prompts + lora_prompts + [""]))
+    choices = get_all_lora_tupled_list()
+
+    return gr.update(value=output_prompt), gr.update(value=lora1, choices=choices), gr.update(value=lora1_wt),\
+     gr.update(value=tag1, label=label1, visible=on1), gr.update(visible=on1), gr.update(value=md1, visible=on1),\
+     gr.update(value=lora2, choices=choices), gr.update(value=lora2_wt),\
+     gr.update(value=tag2, label=label2, visible=on2), gr.update(visible=on2), gr.update(value=md2, visible=on2),\
+     gr.update(value=lora3, choices=choices), gr.update(value=lora3_wt),\
+     gr.update(value=tag3, label=label3, visible=on3), gr.update(visible=on3), gr.update(value=md3, visible=on3),\
+     gr.update(value=lora4, choices=choices), gr.update(value=lora4_wt),\
+     gr.update(value=tag4, label=label4, visible=on4), gr.update(visible=on4), gr.update(value=md4, visible=on4),\
+     gr.update(value=lora5, choices=choices), gr.update(value=lora5_wt),\
+     gr.update(value=tag5, label=label5, visible=on5), gr.update(visible=on5), gr.update(value=md5, visible=on5)
+
+
+def get_my_lora(link_url):
+    from pathlib import Path
+    before = get_local_model_list(directory_loras)
+    for url in [url.strip() for url in link_url.split(',')]:
+        if not Path(f"{directory_loras}/{url.split('/')[-1]}").exists():
+            download_things(directory_loras, url, hf_token, CIVITAI_API_KEY)
+    after = get_local_model_list(directory_loras)
+    new_files = list_sub(after, before)
+    for file in new_files:
+        path = Path(file)
+        if path.exists():
+            new_path = Path(f'{path.parent.name}/{escape_lora_basename(path.stem)}{path.suffix}')
+            path.resolve().rename(new_path.resolve())
+            update_lora_dict(str(new_path))
+    new_lora_model_list = get_lora_model_list()
+    new_lora_tupled_list = get_all_lora_tupled_list()
+    
+    return gr.update(
+        choices=new_lora_tupled_list, value=new_lora_model_list[-1]
+    ), gr.update(
+        choices=new_lora_tupled_list
+    ), gr.update(
+        choices=new_lora_tupled_list
+    ), gr.update(
+        choices=new_lora_tupled_list
+    ), gr.update(
+        choices=new_lora_tupled_list
+    )
+
+
+def upload_file_lora(files, progress=gr.Progress(track_tqdm=True)):
+    progress(0, desc="Uploading...")
+    file_paths = [file.name for file in files]
+    progress(1, desc="Uploaded.")
+    return gr.update(value=file_paths, visible=True), gr.update(visible=True)
+
+
+def move_file_lora(filepaths):
+    import shutil
+    for file in filepaths:
+        path = Path(shutil.move(Path(file).resolve(), Path(f"./{directory_loras}").resolve()))
+        newpath = Path(f'{path.parent.name}/{escape_lora_basename(path.stem)}{path.suffix}')
+        path.resolve().rename(newpath.resolve())
+        update_lora_dict(str(newpath))
+
+    new_lora_model_list = get_lora_model_list()
+    new_lora_tupled_list = get_all_lora_tupled_list()
+    
+    return gr.update(
+        choices=new_lora_tupled_list, value=new_lora_model_list[-1]
+    ), gr.update(
+        choices=new_lora_tupled_list
+    ), gr.update(
+        choices=new_lora_tupled_list
+    ), gr.update(
+        choices=new_lora_tupled_list
+    ), gr.update(
+        choices=new_lora_tupled_list
+    )
+
+
+def get_civitai_info(path):
+    global civitai_not_exists_list
+    global loras_url_to_path_dict
+    import requests
+    from requests.adapters import HTTPAdapter
+    from urllib3.util import Retry
+    default = ["", "", "", "", ""]
+    if path in set(civitai_not_exists_list): return default
+    if not Path(path).exists(): return None
+    user_agent = get_user_agent()
+    headers = {'User-Agent': user_agent, 'content-type': 'application/json'}
+    base_url = 'https://civitai.com/api/v1/model-versions/by-hash/'
+    params = {}
+    session = requests.Session()
+    retries = Retry(total=5, backoff_factor=1, status_forcelist=[500, 502, 503, 504])
+    session.mount("https://", HTTPAdapter(max_retries=retries))
+    import hashlib
+    with open(path, 'rb') as file:
+        file_data = file.read()
+    hash_sha256 = hashlib.sha256(file_data).hexdigest()
+    url = base_url + hash_sha256
+    try:
+        r = session.get(url, params=params, headers=headers, stream=True, timeout=(3.0, 15))
+    except Exception as e:
+        print(e)
+        return default
+    else:
+        if not r.ok: return None
+        json = r.json()
+        if 'baseModel' not in json:
+            civitai_not_exists_list.append(path)
+            return default
+        items = []
+        items.append(" / ".join(json['trainedWords']))                  # The words (prompts) used to trigger the model
+        items.append(json['baseModel'])                                 # Base model (SDXL1.0, Pony, ...)
+        items.append(json['model']['name'])                             # The name of the model version
+        items.append(f"https://civitai.com/models/{json['modelId']}")   # The repo url for the model
+        items.append(json['images'][0]['url'])                          # The url for a sample image
+        loras_url_to_path_dict[path] = json['downloadUrl']              # The download url to get the model file for this specific version
+        return items
+
+
+def search_lora_on_civitai(query: str, allow_model: list[str] = ["Pony", "SDXL 1.0"], limit: int = 100):
+    import requests
+    from requests.adapters import HTTPAdapter
+    from urllib3.util import Retry
+    if not query: return None
+    user_agent = get_user_agent()
+    headers = {'User-Agent': user_agent, 'content-type': 'application/json'}
+    base_url = 'https://civitai.com/api/v1/models'
+    params = {'query': query, 'types': ['LORA'], 'sort': 'Highest Rated', 'period': 'AllTime',
+              'nsfw': 'true', 'supportsGeneration ': 'true', 'limit': limit}
+    session = requests.Session()
+    retries = Retry(total=5, backoff_factor=1, status_forcelist=[500, 502, 503, 504])
+    session.mount("https://", HTTPAdapter(max_retries=retries))
+    try:
+        r = session.get(base_url, params=params, headers=headers, stream=True, timeout=(3.0, 30))
+    except Exception as e:
+        print(e)
+        return None
+    else:
+        if not r.ok: return None
+        json = r.json()
+        if 'items' not in json: return None
+        items = []
+        for j in json['items']:
+            for model in j['modelVersions']:
+                item = {}
+                if model['baseModel'] not in set(allow_model): continue
+                item['name'] = j['name']
+                item['creator'] = j['creator']['username']
+                item['tags'] = j['tags']
+                item['model_name'] = model['name']
+                item['base_model'] = model['baseModel']
+                item['dl_url'] = model['downloadUrl']
+                item['md'] = f'<img src="{model["images"][0]["url"]}" alt="thumbnail" width="150" height="240"><br>[LoRA Model URL](https://civitai.com/models/{j["id"]})'
+                items.append(item)
+        return items
+
+
+def search_civitai_lora(query, base_model):
+    global civitai_lora_last_results
+    items = search_lora_on_civitai(query, base_model)
+    if not items: return gr.update(choices=[("", "")], value="", visible=False),\
+          gr.update(value="", visible=False), gr.update(visible=True), gr.update(visible=True)
+    civitai_lora_last_results = {}
+    choices = []
+    for item in items:
+        base_model_name = "Pony🐴" if item['base_model'] == "Pony" else item['base_model']
+        name = f"{item['name']} (for {base_model_name} / By: {item['creator']} / Tags: {', '.join(item['tags'])})"
+        value = item['dl_url']
+        choices.append((name, value))
+        civitai_lora_last_results[value] = item
+    if not choices: return gr.update(choices=[("", "")], value="", visible=False),\
+          gr.update(value="", visible=False), gr.update(visible=True), gr.update(visible=True)
+    result = civitai_lora_last_results.get(choices[0][1], "None")
+    md = result['md'] if result else ""
+    return gr.update(choices=choices, value=choices[0][1], visible=True), gr.update(value=md, visible=True),\
+          gr.update(visible=True), gr.update(visible=True)
+
+
+def select_civitai_lora(search_result):
+    if not "http" in search_result: return gr.update(value=""), gr.update(value="None", visible=True)
+    result = civitai_lora_last_results.get(search_result, "None")
+    md = result['md'] if result else ""
+    return gr.update(value=search_result), gr.update(value=md, visible=True)
+
+
+def find_similar_lora(q: str):
+    from rapidfuzz.process import extractOne
+    from rapidfuzz.utils import default_process
+    query = to_lora_key(q)
+    print(f"Finding <lora:{query}:...>...")
+    keys = list(private_lora_dict.keys())
+    values = [x[2] for x in list(private_lora_dict.values())]
+    s = default_process(query)
+    e1 = extractOne(s, keys + values, processor=default_process, score_cutoff=80.0)
+    key = ""
+    if e1:
+        e = e1[0]
+        if e in set(keys): key = e
+        elif e in set(values): key = keys[values.index(e)]
+    if key:
+        path = to_lora_path(key)
+        new_path = to_lora_path(query)
+        if not Path(path).exists():
+            if not Path(new_path).exists(): download_private_file_from_somewhere(path, True)
+            if Path(path).exists() and copy_lora(path, new_path): return new_path
+    print(f"Finding <lora:{query}:...> on Civitai...")
+    civitai_query = Path(query).stem if Path(query).is_file() else query
+    civitai_query = civitai_query.replace("_", " ").replace("-", " ")
+    base_model = ["Pony", "SDXL 1.0"]
+    items = search_lora_on_civitai(civitai_query, base_model, 1)
+    if items:
+        item = items[0]
+        path = download_lora(item['dl_url'])
+        new_path = query if Path(query).is_file() else to_lora_path(query)
+        if path and copy_lora(path, new_path): return new_path
+    return None
+
+
+def change_interface_mode(mode: str):
+    if mode == "Fast":
+        return gr.update(open=False), gr.update(visible=True), gr.update(open=False), gr.update(open=False),\
+        gr.update(visible=True), gr.update(open=False), gr.update(visible=True), gr.update(open=False),\
+        gr.update(visible=True), gr.update(value="Fast")
+    elif mode == "Simple": # t2i mode
+        return gr.update(open=True), gr.update(visible=True), gr.update(open=False), gr.update(open=False),\
+        gr.update(visible=True), gr.update(open=False), gr.update(visible=False), gr.update(open=True),\
+        gr.update(visible=False), gr.update(value="Standard")
+    elif mode == "LoRA": # t2i LoRA  mode
+        return gr.update(open=True), gr.update(visible=True), gr.update(open=True), gr.update(open=False),\
+        gr.update(visible=True), gr.update(open=True), gr.update(visible=True), gr.update(open=False),\
+        gr.update(visible=False), gr.update(value="Standard")
+    else: # Standard
+        return gr.update(open=False), gr.update(visible=True), gr.update(open=False), gr.update(open=False),\
+        gr.update(visible=True), gr.update(open=False), gr.update(visible=True), gr.update(open=False),\
+        gr.update(visible=True), gr.update(value="Standard")
+
+
+quality_prompt_list = [
+    {
+        "name": "None",
+        "prompt": "",
+        "negative_prompt": "lowres",
+    },
+    {
+        "name": "Animagine Common",
+        "prompt": "anime artwork, anime style, vibrant, studio anime, highly detailed, masterpiece, best quality, very aesthetic, absurdres",
+        "negative_prompt": "lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]",
+    },
+    {
+        "name": "Pony Anime Common",
+        "prompt": "source_anime, score_9, score_8_up, score_7_up, masterpiece, best quality, very aesthetic, absurdres",
+        "negative_prompt": "source_pony, source_furry, source_cartoon, score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends",
+    },
+    {
+        "name": "Pony Common",
+        "prompt": "source_anime, score_9, score_8_up, score_7_up",
+        "negative_prompt": "source_pony, source_furry, source_cartoon, score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends",
+    },
+    {
+        "name": "Animagine Standard v3.0",
+        "prompt": "masterpiece, best quality",
+        "negative_prompt": "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name",
+    },
+    {
+        "name": "Animagine Standard v3.1",
+        "prompt": "masterpiece, best quality, very aesthetic, absurdres",
+        "negative_prompt": "lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]",
+    },
+    {
+        "name": "Animagine Light v3.1",
+        "prompt": "(masterpiece), best quality, very aesthetic, perfect face",
+        "negative_prompt": "(low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn",
+    },
+    {
+        "name": "Animagine Heavy v3.1",
+        "prompt": "(masterpiece), (best quality), (ultra-detailed), very aesthetic, illustration, disheveled hair, perfect composition, moist skin, intricate details",
+        "negative_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair, extra digit, fewer digits, cropped, worst quality, low quality, very displeasing",
+    },
+]
+
+
+style_list = [
+    {
+        "name": "None",
+        "prompt": "",
+        "negative_prompt": "",
+    },
+    {
+        "name": "Cinematic",
+        "prompt": "cinematic still, emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy",
+        "negative_prompt": "cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
+    },
+    {
+        "name": "Photographic",
+        "prompt": "cinematic photo, 35mm photograph, film, bokeh, professional, 4k, highly detailed",
+        "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
+    },
+    {
+        "name": "Anime",
+        "prompt": "anime artwork, anime style, vibrant, studio anime, highly detailed",
+        "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast",
+    },
+    {
+        "name": "Manga",
+        "prompt": "manga style, vibrant, high-energy, detailed, iconic, Japanese comic style",
+        "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style",
+    },
+    {
+        "name": "Digital Art",
+        "prompt": "concept art, digital artwork, illustrative, painterly, matte painting, highly detailed",
+        "negative_prompt": "photo, photorealistic, realism, ugly",
+    },
+    {
+        "name": "Pixel art",
+        "prompt": "pixel-art, low-res, blocky, pixel art style, 8-bit graphics",
+        "negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic",
+    },
+    {
+        "name": "Fantasy art",
+        "prompt": "ethereal fantasy concept art, magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy",
+        "negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white",
+    },
+    {
+        "name": "Neonpunk",
+        "prompt": "neonpunk style, cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional",
+        "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured",
+    },
+    {
+        "name": "3D Model",
+        "prompt": "professional 3d model, octane render, highly detailed, volumetric, dramatic lighting",
+        "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
+    },
+]
+
+
+optimization_list = {
+    "None": [28, 7., 'Euler a', False, 'None', 1.],
+    "Default": [28, 7., 'Euler a', False, 'None', 1.],
+    "SPO": [28, 7., 'Euler a', True, 'loras/spo_sdxl_10ep_4k-data_lora_diffusers.safetensors', 1.],
+    "DPO": [28, 7., 'Euler a', True, 'loras/sdxl-DPO-LoRA.safetensors', 1.],
+    "DPO Turbo": [8, 2.5, 'LCM', True, 'loras/sd_xl_dpo_turbo_lora_v1-128dim.safetensors', 1.],
+    "SDXL Turbo": [8, 2.5, 'LCM', True, 'loras/sd_xl_turbo_lora_v1.safetensors', 1.],
+    "Hyper-SDXL 12step": [12, 5., 'TCD', True, 'loras/Hyper-SDXL-12steps-CFG-lora.safetensors', 1.],
+    "Hyper-SDXL 8step": [8, 5., 'TCD', True, 'loras/Hyper-SDXL-8steps-CFG-lora.safetensors', 1.],
+    "Hyper-SDXL 4step": [4, 0, 'TCD', True, 'loras/Hyper-SDXL-4steps-lora.safetensors', 1.],
+    "Hyper-SDXL 2step": [2, 0, 'TCD', True, 'loras/Hyper-SDXL-2steps-lora.safetensors', 1.],
+    "Hyper-SDXL 1step": [1, 0, 'TCD', True, 'loras/Hyper-SDXL-1steps-lora.safetensors', 1.],
+    "PCM 16step": [16, 4., 'Euler a trailing', True, 'loras/pcm_sdxl_normalcfg_16step_converted.safetensors', 1.],
+    "PCM 8step": [8, 4., 'Euler a trailing', True, 'loras/pcm_sdxl_normalcfg_8step_converted.safetensors', 1.],
+    "PCM 4step": [4, 2., 'Euler a trailing', True, 'loras/pcm_sdxl_smallcfg_4step_converted.safetensors', 1.],
+    "PCM 2step": [2, 1., 'Euler a trailing', True, 'loras/pcm_sdxl_smallcfg_2step_converted.safetensors', 1.],
+}
+
+
+def set_optimization(opt, steps_gui, cfg_gui, sampler_gui, clip_skip_gui, lora_gui, lora_scale_gui):
+    if not opt in list(optimization_list.keys()): opt = "None"
+    def_steps_gui = 28
+    def_cfg_gui = 7.
+    steps = optimization_list.get(opt, "None")[0]
+    cfg = optimization_list.get(opt, "None")[1]
+    sampler = optimization_list.get(opt, "None")[2]
+    clip_skip = optimization_list.get(opt, "None")[3]
+    lora = optimization_list.get(opt, "None")[4]
+    lora_scale = optimization_list.get(opt, "None")[5]
+    if opt == "None":
+        steps = max(steps_gui, def_steps_gui)
+        cfg = max(cfg_gui, def_cfg_gui)
+        clip_skip = clip_skip_gui
+    elif opt == "SPO" or opt == "DPO":
+        steps = max(steps_gui, def_steps_gui)
+        cfg = max(cfg_gui, def_cfg_gui)
+
+    return gr.update(value=steps), gr.update(value=cfg), gr.update(value=sampler),\
+          gr.update(value=clip_skip), gr.update(value=lora), gr.update(value=lora_scale),
+
+
+# [sampler_gui, steps_gui, cfg_gui, clip_skip_gui, img_width_gui, img_height_gui, optimization_gui]
+preset_sampler_setting = {
+    "None": ["Euler a", 28, 7., True, 1024, 1024, "None"],
+    "Anime 3:4 Fast": ["LCM", 8, 2.5, True, 896, 1152, "DPO Turbo"],
+    "Anime 3:4 Standard": ["Euler a", 28, 7., True, 896, 1152, "None"],
+    "Anime 3:4 Heavy": ["Euler a", 40, 7., True, 896, 1152, "None"],
+    "Anime 1:1 Fast": ["LCM", 8, 2.5, True, 1024, 1024, "DPO Turbo"],
+    "Anime 1:1 Standard": ["Euler a", 28, 7., True, 1024, 1024, "None"],
+    "Anime 1:1 Heavy": ["Euler a", 40, 7., True, 1024, 1024, "None"],
+    "Photo 3:4 Fast": ["LCM", 8, 2.5, False, 896, 1152, "DPO Turbo"],
+    "Photo 3:4 Standard": ["DPM++ 2M Karras", 28, 7., False, 896, 1152, "None"],
+    "Photo 3:4 Heavy": ["DPM++ 2M Karras", 40, 7., False, 896, 1152, "None"],
+    "Photo 1:1 Fast": ["LCM", 8, 2.5, False, 1024, 1024, "DPO Turbo"],
+    "Photo 1:1 Standard": ["DPM++ 2M Karras", 28, 7., False, 1024, 1024, "None"],
+    "Photo 1:1 Heavy": ["DPM++ 2M Karras", 40, 7., False, 1024, 1024, "None"],
+}
+
+
+def set_sampler_settings(sampler_setting):
+    if not sampler_setting in list(preset_sampler_setting.keys()) or sampler_setting == "None":
+        return gr.update(value="Euler a"), gr.update(value=28), gr.update(value=7.), gr.update(value=True),\
+              gr.update(value=1024), gr.update(value=1024), gr.update(value="None")
+    v = preset_sampler_setting.get(sampler_setting, ["Euler a", 28, 7., True, 1024, 1024])
+    # sampler, steps, cfg, clip_skip, width, height, optimization
+    return gr.update(value=v[0]), gr.update(value=v[1]), gr.update(value=v[2]), gr.update(value=v[3]),\
+          gr.update(value=v[4]), gr.update(value=v[5]), gr.update(value=v[6])
+
+
+preset_styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
+preset_quality = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in quality_prompt_list}
+
+
+def process_style_prompt(prompt: str, neg_prompt: str, styles_key: str = "None", quality_key: str = "None", type: str = "Auto"):
+    def to_list(s):
+        return [x.strip() for x in s.split(",") if not s == ""]
+    
+    def list_sub(a, b):
+        return [e for e in a if e not in b]
+    
+    def list_uniq(l):
+        return sorted(set(l), key=l.index)
+
+    animagine_ps = to_list("anime artwork, anime style, vibrant, studio anime, highly detailed, masterpiece, best quality, very aesthetic, absurdres")
+    animagine_nps = to_list("lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]")
+    pony_ps = to_list("source_anime, score_9, score_8_up, score_7_up, masterpiece, best quality, very aesthetic, absurdres")
+    pony_nps = to_list("source_pony, source_furry, source_cartoon, score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends")
+    prompts = to_list(prompt)
+    neg_prompts = to_list(neg_prompt)
+
+    all_styles_ps = []
+    all_styles_nps = []
+    for d in style_list:
+        all_styles_ps.extend(to_list(str(d.get("prompt", ""))))
+        all_styles_nps.extend(to_list(str(d.get("negative_prompt", ""))))
+
+    all_quality_ps = []
+    all_quality_nps = []
+    for d in quality_prompt_list:
+        all_quality_ps.extend(to_list(str(d.get("prompt", ""))))
+        all_quality_nps.extend(to_list(str(d.get("negative_prompt", ""))))
+
+    quality_ps = to_list(preset_quality[quality_key][0])
+    quality_nps = to_list(preset_quality[quality_key][1])
+    styles_ps = to_list(preset_styles[styles_key][0])
+    styles_nps = to_list(preset_styles[styles_key][1])
+
+    prompts = list_sub(prompts, animagine_ps + pony_ps + all_styles_ps + all_quality_ps)
+    neg_prompts = list_sub(neg_prompts, animagine_nps + pony_nps + all_styles_nps + all_quality_nps)
+
+    last_empty_p = [""] if not prompts and type != "None" and type != "Auto" and styles_key != "None" and quality_key != "None" else []
+    last_empty_np = [""] if not neg_prompts and type != "None" and type != "Auto" and styles_key != "None" and quality_key != "None" else []
+
+    if type == "Animagine":
+        prompts = prompts + animagine_ps
+        neg_prompts = neg_prompts + animagine_nps
+    elif type == "Pony":
+        prompts = prompts + pony_ps
+        neg_prompts = neg_prompts + pony_nps
+
+    prompts = prompts + styles_ps + quality_ps
+    neg_prompts = neg_prompts + styles_nps + quality_nps
+
+    prompt = ", ".join(list_uniq(prompts) + last_empty_p)
+    neg_prompt = ", ".join(list_uniq(neg_prompts) + last_empty_np)
+
+    return gr.update(value=prompt), gr.update(value=neg_prompt), gr.update(value=type) 
+
+
+def set_quick_presets(genre:str = "None", type:str = "Auto", speed:str = "None", aspect:str = "None"):
+    quality = "None"
+    style = "None"
+    sampler = "None"
+    opt = "None"
+
+    if genre == "Anime":
+        if type != "None" and type != "Auto": style = "Anime"
+        if aspect == "1:1":
+            if speed == "Heavy":
+                sampler = "Anime 1:1 Heavy"
+            elif speed == "Fast":
+                sampler = "Anime 1:1 Fast"
+            else:
+                sampler = "Anime 1:1 Standard"
+        elif aspect == "3:4":
+            if speed == "Heavy":
+                sampler = "Anime 3:4 Heavy"
+            elif speed == "Fast":
+                sampler = "Anime 3:4 Fast"
+            else:
+                sampler = "Anime 3:4 Standard"
+        if type == "Pony":
+            quality = "Pony Anime Common"
+        elif type == "Animagine":
+            quality = "Animagine Common"
+        else:
+            quality = "None"
+    elif genre == "Photo":
+        if type != "None" and type != "Auto": style = "Photographic"
+        if aspect == "1:1":
+            if speed == "Heavy":
+                sampler = "Photo 1:1 Heavy"
+            elif speed == "Fast":
+                sampler = "Photo 1:1 Fast"
+            else:
+                sampler = "Photo 1:1 Standard"
+        elif aspect == "3:4":
+            if speed == "Heavy":
+                sampler = "Photo 3:4 Heavy"
+            elif speed == "Fast":
+                sampler = "Photo 3:4 Fast"
+            else:
+                sampler = "Photo 3:4 Standard"
+        if type == "Pony":
+            quality = "Pony Common"
+        else:
+            quality = "None"
+
+    if speed == "Fast":
+        opt = "DPO Turbo"
+        if genre == "Anime" and type != "Pony" and type != "Auto": quality = "Animagine Light v3.1"
+
+    return gr.update(value=quality), gr.update(value=style), gr.update(value=sampler), gr.update(value=opt), gr.update(value=type)
+
+
+textual_inversion_dict = {}
+try:
+    with open('textual_inversion_dict.json', encoding='utf-8') as f:
+        textual_inversion_dict = json.load(f)
+except Exception:
+    pass
+textual_inversion_file_token_list = []
+
+
+def get_tupled_embed_list(embed_list):
+    global textual_inversion_file_list
+    tupled_list = []
+    for file in embed_list:
+        token = textual_inversion_dict.get(Path(file).name, [Path(file).stem.replace(",",""), False])[0]
+        tupled_list.append((token, file))
+        textual_inversion_file_token_list.append(token)
+    return tupled_list
+
+
+def set_textual_inversion_prompt(textual_inversion_gui, prompt_gui, neg_prompt_gui, prompt_syntax_gui):
+    ti_tags = list(textual_inversion_dict.values()) + textual_inversion_file_token_list
+    tags = prompt_gui.split(",") if prompt_gui else []
+    prompts = []
+    for tag in tags:
+        tag = str(tag).strip()
+        if tag and not tag in ti_tags:
+            prompts.append(tag)
+    ntags = neg_prompt_gui.split(",") if neg_prompt_gui else []
+    neg_prompts = []
+    for tag in ntags:
+        tag = str(tag).strip()
+        if tag and not tag in ti_tags:
+            neg_prompts.append(tag)
+    ti_prompts = []
+    ti_neg_prompts = []
+    for ti in textual_inversion_gui:
+        tokens = textual_inversion_dict.get(Path(ti).name, [Path(ti).stem.replace(",",""), False])
+        is_positive = tokens[1] == True or "positive" in Path(ti).parent.name
+        if is_positive: # positive prompt
+            ti_prompts.append(tokens[0])
+        else: # negative prompt (default)
+            ti_neg_prompts.append(tokens[0])
+    empty = [""]
+    prompt = ", ".join(prompts + ti_prompts + empty)
+    neg_prompt = ", ".join(neg_prompts + ti_neg_prompts + empty)
+    return gr.update(value=prompt), gr.update(value=neg_prompt),
+
+
+def get_model_pipeline(repo_id: str):
+    from huggingface_hub import HfApi
+    api = HfApi()
+    default = "StableDiffusionPipeline"
+    try:
+        if " " in repo_id or not api.repo_exists(repo_id): return default
+        model = api.model_info(repo_id=repo_id)
+    except Exception as e:
+        return default
+    if model.private or model.gated: return default
+    tags = model.tags
+    if not 'diffusers' in tags: return default
+    if 'diffusers:StableDiffusionXLPipeline' in tags:
+        return "StableDiffusionXLPipeline"
+    elif 'diffusers:StableDiffusionPipeline' in tags:
+        return "StableDiffusionPipeline"
+    else:
+        return default
+

packages.txt

@@ -0,0 +1 @@
+git-lfs aria2 -y ffmpeg

pre-requirements.txt

@@ -0,0 +1 @@
+pip>=23.0.0

requirements.txt

@@ -0,0 +1,17 @@
+git+https://github.com/R3gm/stablepy.git@dev2
+torch==2.2.0
+gdown
+opencv-python
+pytorch-lightning
+torchvision
+accelerate
+transformers
+optimum[onnxruntime]
+spaces
+dartrs
+huggingface_hub
+httpx==0.13.3
+httpcore
+googletrans==4.0.0rc1
+timm
+rapidfuzz

tagger/fl2sd3longcap.py

@@ -0,0 +1,78 @@
+from transformers import AutoProcessor, AutoModelForCausalLM
+import spaces
+import re
+from PIL import Image 
+import torch
+
+import subprocess
+subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+fl_model = AutoModelForCausalLM.from_pretrained('gokaygokay/Florence-2-SD3-Captioner', trust_remote_code=True).to("cpu").eval()
+fl_processor = AutoProcessor.from_pretrained('gokaygokay/Florence-2-SD3-Captioner', trust_remote_code=True)
+
+
+def fl_modify_caption(caption: str) -> str:
+    """
+    Removes specific prefixes from captions if present, otherwise returns the original caption.
+    Args:
+        caption (str): A string containing a caption.
+    Returns:
+        str: The caption with the prefix removed if it was present, or the original caption.
+    """
+    # Define the prefixes to remove
+    prefix_substrings = [
+        ('captured from ', ''),
+        ('captured at ', '')
+    ]
+    
+    # Create a regex pattern to match any of the prefixes
+    pattern = '|'.join([re.escape(opening) for opening, _ in prefix_substrings])
+    replacers = {opening.lower(): replacer for opening, replacer in prefix_substrings}
+    
+    # Function to replace matched prefix with its corresponding replacement
+    def replace_fn(match):
+        return replacers[match.group(0).lower()]
+    
+    # Apply the regex to the caption
+    modified_caption = re.sub(pattern, replace_fn, caption, count=1, flags=re.IGNORECASE)
+    
+    # If the caption was modified, return the modified version; otherwise, return the original
+    return modified_caption if modified_caption != caption else caption
+
+
+@spaces.GPU(duration=30)
+def fl_run_example(image):
+    task_prompt = "<DESCRIPTION>"
+    prompt = task_prompt + "Describe this image in great detail."
+
+    # Ensure the image is in RGB mode
+    if image.mode != "RGB":
+        image = image.convert("RGB")
+
+    fl_model.to(device)
+    inputs = fl_processor(text=prompt, images=image, return_tensors="pt").to(device)
+    generated_ids = fl_model.generate(
+        input_ids=inputs["input_ids"],
+        pixel_values=inputs["pixel_values"],
+        max_new_tokens=1024,
+        num_beams=3
+    )
+    fl_model.to("cpu")
+    generated_text = fl_processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+    parsed_answer = fl_processor.post_process_generation(generated_text, task=task_prompt, image_size=(image.width, image.height))
+    return fl_modify_caption(parsed_answer["<DESCRIPTION>"])
+
+
+def predict_tags_fl2_sd3(image: Image.Image, input_tags: str, algo: list[str]):
+    def to_list(s):
+        return [x.strip() for x in s.split(",") if not s == ""]
+    
+    def list_uniq(l):
+        return sorted(set(l), key=l.index)
+    
+    if not "Use Florence-2-SD3-Long-Captioner" in algo:
+        return input_tags
+    tag_list = list_uniq(to_list(input_tags) + to_list(fl_run_example(image) + ", "))
+    tag_list.remove("")
+    return ", ".join(tag_list)

tagger/output.py

@@ -0,0 +1,16 @@
+from dataclasses import dataclass
+
+
+@dataclass
+class UpsamplingOutput:
+    upsampled_tags: str
+
+    copyright_tags: str
+    character_tags: str
+    general_tags: str
+    rating_tag: str
+    aspect_ratio_tag: str
+    length_tag: str
+    identity_tag: str
+
+    elapsed_time: float = 0.0

tagger/tagger.py

@@ -0,0 +1,556 @@
+from PIL import Image
+import torch
+import gradio as gr
+import spaces
+from transformers import (
+    AutoImageProcessor,
+    AutoModelForImageClassification,
+)
+from pathlib import Path
+
+
+WD_MODEL_NAMES = ["p1atdev/wd-swinv2-tagger-v3-hf"]
+WD_MODEL_NAME = WD_MODEL_NAMES[0]
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+default_device = device
+
+try:
+    wd_model = AutoModelForImageClassification.from_pretrained(WD_MODEL_NAME, trust_remote_code=True).to(default_device).eval()
+    wd_processor = AutoImageProcessor.from_pretrained(WD_MODEL_NAME, trust_remote_code=True)
+except Exception as e:
+    print(e)
+    wd_model = wd_processor = None
+
+def _people_tag(noun: str, minimum: int = 1, maximum: int = 5):
+    return (
+        [f"1{noun}"]
+        + [f"{num}{noun}s" for num in range(minimum + 1, maximum + 1)]
+        + [f"{maximum+1}+{noun}s"]
+    )
+
+
+PEOPLE_TAGS = (
+    _people_tag("girl") + _people_tag("boy") + _people_tag("other") + ["no humans"]
+)
+
+
+RATING_MAP = {
+    "sfw": "safe",
+    "general": "safe",
+    "sensitive": "sensitive",
+    "questionable": "nsfw",
+    "explicit": "explicit, nsfw",
+}
+DANBOORU_TO_E621_RATING_MAP = {
+    "sfw": "rating_safe",
+    "general": "rating_safe",
+    "safe": "rating_safe",
+    "sensitive": "rating_safe",
+    "nsfw": "rating_explicit",
+    "explicit, nsfw": "rating_explicit",
+    "explicit": "rating_explicit",
+    "rating:safe": "rating_safe",
+    "rating:general": "rating_safe",
+    "rating:sensitive": "rating_safe",
+    "rating:questionable, nsfw": "rating_explicit",
+    "rating:explicit, nsfw": "rating_explicit",
+}
+
+
+# https://github.com/toriato/stable-diffusion-webui-wd14-tagger/blob/a9eacb1eff904552d3012babfa28b57e1d3e295c/tagger/ui.py#L368
+kaomojis = [
+    "0_0",
+    "(o)_(o)",
+    "+_+",
+    "+_-",
+    "._.",
+    "<o>_<o>",
+    "<|>_<|>",
+    "=_=",
+    ">_<",
+    "3_3",
+    "6_9",
+    ">_o",
+    "@_@",
+    "^_^",
+    "o_o",
+    "u_u",
+    "x_x",
+    "|_|",
+    "||_||",
+]
+
+
+def replace_underline(x: str):
+    return x.strip().replace("_", " ") if x not in kaomojis else x.strip()
+
+
+def to_list(s):
+    return [x.strip() for x in s.split(",") if not s == ""]
+
+
+def list_sub(a, b):
+    return [e for e in a if e not in b]
+
+
+def list_uniq(l):
+    return sorted(set(l), key=l.index)
+
+
+def load_dict_from_csv(filename):
+    dict = {}
+    if not Path(filename).exists():
+        if Path('./tagger/', filename).exists(): filename = str(Path('./tagger/', filename))
+        else: return dict
+    try:
+        with open(filename, 'r', encoding="utf-8") as f:
+            lines = f.readlines()
+    except Exception:
+        print(f"Failed to open dictionary file: {filename}")
+        return dict
+    for line in lines:
+        parts = line.strip().split(',')
+        dict[parts[0]] = parts[1]
+    return dict
+
+
+anime_series_dict = load_dict_from_csv('character_series_dict.csv')
+
+
+def character_list_to_series_list(character_list):
+    output_series_tag = []
+    series_tag = ""
+    series_dict = anime_series_dict
+    for tag in character_list:
+        series_tag = series_dict.get(tag, "")
+        if tag.endswith(")"):
+            tags = tag.split("(")
+            character_tag = "(".join(tags[:-1])
+            if character_tag.endswith(" "):
+                character_tag = character_tag[:-1]
+            series_tag = tags[-1].replace(")", "")
+
+    if series_tag:
+        output_series_tag.append(series_tag)
+
+    return output_series_tag
+
+
+def select_random_character(series: str, character: str):
+    from random import seed, randrange
+    seed()
+    character_list = list(anime_series_dict.keys())
+    character = character_list[randrange(len(character_list) - 1)]
+    series = anime_series_dict.get(character.split(",")[0].strip(), "")
+    return series, character
+
+
+def danbooru_to_e621(dtag, e621_dict):
+    def d_to_e(match, e621_dict):
+        dtag = match.group(0)
+        etag = e621_dict.get(replace_underline(dtag), "")
+        if etag:
+            return etag
+        else:
+            return dtag
+    
+    import re
+    tag = re.sub(r'[\w ]+', lambda wrapper: d_to_e(wrapper, e621_dict), dtag, 2)
+    return tag
+
+
+danbooru_to_e621_dict = load_dict_from_csv('danbooru_e621.csv')
+
+
+def convert_danbooru_to_e621_prompt(input_prompt: str = "", prompt_type: str = "danbooru"):
+    if prompt_type == "danbooru": return input_prompt
+    tags = input_prompt.split(",") if input_prompt else []
+    people_tags: list[str] = []
+    other_tags: list[str] = []
+    rating_tags: list[str] = []
+
+    e621_dict = danbooru_to_e621_dict
+    for tag in tags:
+        tag = replace_underline(tag)
+        tag = danbooru_to_e621(tag, e621_dict)
+        if tag in PEOPLE_TAGS:        
+            people_tags.append(tag)
+        elif tag in DANBOORU_TO_E621_RATING_MAP.keys():
+            rating_tags.append(DANBOORU_TO_E621_RATING_MAP.get(tag.replace(" ",""), ""))            
+        else:
+            other_tags.append(tag)
+
+    rating_tags = sorted(set(rating_tags), key=rating_tags.index)
+    rating_tags = [rating_tags[0]] if rating_tags else []
+    rating_tags = ["explicit, nsfw"] if rating_tags and rating_tags[0] == "explicit" else rating_tags
+
+    output_prompt = ", ".join(people_tags + other_tags + rating_tags)
+    
+    return output_prompt
+
+
+def translate_prompt(prompt: str = ""):
+    def translate_to_english(prompt):
+        import httpcore
+        setattr(httpcore, 'SyncHTTPTransport', 'AsyncHTTPProxy')
+        from googletrans import Translator
+        translator = Translator()
+        try:
+            translated_prompt = translator.translate(prompt, src='auto', dest='en').text
+            return translated_prompt
+        except Exception as e:
+            print(e)
+            return prompt
+
+    def is_japanese(s):
+        import unicodedata
+        for ch in s:
+            name = unicodedata.name(ch, "") 
+            if "CJK UNIFIED" in name or "HIRAGANA" in name or "KATAKANA" in name:
+                return True
+        return False
+
+    def to_list(s):
+        return [x.strip() for x in s.split(",")]
+    
+    prompts = to_list(prompt)
+    outputs = []
+    for p in prompts:
+        p = translate_to_english(p) if is_japanese(p) else p
+        outputs.append(p)
+
+    return ", ".join(outputs)
+
+
+def translate_prompt_to_ja(prompt: str = ""):
+    def translate_to_japanese(prompt):
+        import httpcore
+        setattr(httpcore, 'SyncHTTPTransport', 'AsyncHTTPProxy')
+        from googletrans import Translator
+        translator = Translator()
+        try:
+            translated_prompt = translator.translate(prompt, src='en', dest='ja').text
+            return translated_prompt
+        except Exception as e:
+            print(e)
+            return prompt
+
+    def is_japanese(s):
+        import unicodedata
+        for ch in s:
+            name = unicodedata.name(ch, "") 
+            if "CJK UNIFIED" in name or "HIRAGANA" in name or "KATAKANA" in name:
+                return True
+        return False
+
+    def to_list(s):
+        return [x.strip() for x in s.split(",")]
+    
+    prompts = to_list(prompt)
+    outputs = []
+    for p in prompts:
+        p = translate_to_japanese(p) if not is_japanese(p) else p
+        outputs.append(p)
+
+    return ", ".join(outputs)
+
+
+def tags_to_ja(itag, dict):
+    def t_to_j(match, dict):
+        tag = match.group(0)
+        ja = dict.get(replace_underline(tag), "")
+        if ja:
+            return ja
+        else:
+            return tag
+    
+    import re
+    tag = re.sub(r'[\w ]+', lambda wrapper: t_to_j(wrapper, dict), itag, 2)
+
+    return tag
+
+
+def convert_tags_to_ja(input_prompt: str = ""):
+    tags = input_prompt.split(",") if input_prompt else []
+    out_tags = []
+
+    tags_to_ja_dict = load_dict_from_csv('all_tags_ja_ext.csv')
+    dict = tags_to_ja_dict
+    for tag in tags:
+        tag = replace_underline(tag)
+        tag = tags_to_ja(tag, dict)
+        out_tags.append(tag)
+    
+    return ", ".join(out_tags)
+
+
+enable_auto_recom_prompt = True
+
+
+animagine_ps = to_list("masterpiece, best quality, very aesthetic, absurdres")
+animagine_nps = to_list("lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]")
+pony_ps = to_list("score_9, score_8_up, score_7_up, masterpiece, best quality, very aesthetic, absurdres")
+pony_nps = to_list("source_pony, score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends")
+other_ps = to_list("anime artwork, anime style, studio anime, highly detailed, cinematic photo, 35mm photograph, film, bokeh, professional, 4k, highly detailed")
+other_nps = to_list("photo, deformed, black and white, realism, disfigured, low contrast, drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly")
+default_ps = to_list("highly detailed, masterpiece, best quality, very aesthetic, absurdres")
+default_nps = to_list("score_6, score_5, score_4, lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]")
+def insert_recom_prompt(prompt: str = "", neg_prompt: str = "", type: str = "None"):
+    global enable_auto_recom_prompt
+    prompts = to_list(prompt)
+    neg_prompts = to_list(neg_prompt)
+
+    prompts = list_sub(prompts, animagine_ps + pony_ps)
+    neg_prompts = list_sub(neg_prompts, animagine_nps + pony_nps)
+
+    last_empty_p = [""] if not prompts and type != "None" else []
+    last_empty_np = [""] if not neg_prompts and type != "None" else []
+
+    if type == "Auto":
+        enable_auto_recom_prompt = True
+    else:
+        enable_auto_recom_prompt = False
+        if type == "Animagine":
+            prompts = prompts + animagine_ps
+            neg_prompts = neg_prompts + animagine_nps
+        elif type == "Pony":
+            prompts = prompts + pony_ps
+            neg_prompts = neg_prompts + pony_nps
+
+    prompt = ", ".join(list_uniq(prompts) + last_empty_p)
+    neg_prompt = ", ".join(list_uniq(neg_prompts) + last_empty_np)
+
+    return prompt, neg_prompt
+
+
+def load_model_prompt_dict():
+    import json
+    dict = {}
+    path = 'model_dict.json' if Path('model_dict.json').exists() else './tagger/model_dict.json'
+    try:
+        with open('model_dict.json', encoding='utf-8') as f:
+            dict = json.load(f)
+    except Exception:
+        pass
+    return dict
+
+
+model_prompt_dict = load_model_prompt_dict()
+
+
+def insert_model_recom_prompt(prompt: str = "", neg_prompt: str = "", model_name: str = "None"):
+    if not model_name or not enable_auto_recom_prompt: return prompt, neg_prompt
+    prompts = to_list(prompt)
+    neg_prompts = to_list(neg_prompt)
+    prompts = list_sub(prompts, animagine_ps + pony_ps + other_ps)
+    neg_prompts = list_sub(neg_prompts, animagine_nps + pony_nps + other_nps)
+    last_empty_p = [""] if not prompts and type != "None" else []
+    last_empty_np = [""] if not neg_prompts and type != "None" else []
+    ps = []
+    nps = []
+    if model_name in model_prompt_dict.keys(): 
+        ps = to_list(model_prompt_dict[model_name]["prompt"])
+        nps = to_list(model_prompt_dict[model_name]["negative_prompt"])
+    else:
+        ps = default_ps
+        nps = default_nps
+    prompts = prompts + ps
+    neg_prompts = neg_prompts + nps
+    prompt = ", ".join(list_uniq(prompts) + last_empty_p)
+    neg_prompt = ", ".join(list_uniq(neg_prompts) + last_empty_np)
+    return prompt, neg_prompt
+
+
+tag_group_dict = load_dict_from_csv('tag_group.csv')
+
+
+def remove_specific_prompt(input_prompt: str = "", keep_tags: str = "all"):
+    def is_dressed(tag):
+        import re
+        p = re.compile(r'dress|cloth|uniform|costume|vest|sweater|coat|shirt|jacket|blazer|apron|leotard|hood|sleeve|skirt|shorts|pant|loafer|ribbon|necktie|bow|collar|glove|sock|shoe|boots|wear|emblem')
+        return p.search(tag)
+
+    def is_background(tag):
+        import re
+        p = re.compile(r'background|outline|light|sky|build|day|screen|tree|city')
+        return p.search(tag)
+
+    un_tags = ['solo']
+    group_list = ['groups', 'body_parts', 'attire', 'posture', 'objects', 'creatures', 'locations', 'disambiguation_pages', 'commonly_misused_tags', 'phrases', 'verbs_and_gerunds', 'subjective', 'nudity', 'sex_objects', 'sex', 'sex_acts', 'image_composition', 'artistic_license', 'text', 'year_tags', 'metatags']
+    keep_group_dict = {
+        "body": ['groups', 'body_parts'],
+        "dress": ['groups', 'body_parts', 'attire'],
+        "all": group_list,
+    }
+
+    def is_necessary(tag, keep_tags, group_dict):
+        if keep_tags == "all":
+            return True
+        elif tag in un_tags or group_dict.get(tag, "") in explicit_group:
+            return False
+        elif keep_tags == "body" and is_dressed(tag):
+            return False
+        elif is_background(tag):
+            return False
+        else:
+            return True
+    
+    if keep_tags == "all": return input_prompt
+    keep_group = keep_group_dict.get(keep_tags, keep_group_dict["body"])
+    explicit_group = list(set(group_list) ^ set(keep_group))
+
+    tags = input_prompt.split(",") if input_prompt else []
+    people_tags: list[str] = []
+    other_tags: list[str] = []
+
+    group_dict = tag_group_dict
+    for tag in tags:
+        tag = replace_underline(tag)
+        if tag in PEOPLE_TAGS:
+            people_tags.append(tag)
+        elif is_necessary(tag, keep_tags, group_dict):
+            other_tags.append(tag)
+
+    output_prompt = ", ".join(people_tags + other_tags)
+    
+    return output_prompt
+
+
+def sort_taglist(tags: list[str]):
+    if not tags: return []
+    character_tags: list[str] = []
+    series_tags: list[str] = []
+    people_tags: list[str] = []
+    group_list = ['groups', 'body_parts', 'attire', 'posture', 'objects', 'creatures', 'locations', 'disambiguation_pages', 'commonly_misused_tags', 'phrases', 'verbs_and_gerunds', 'subjective', 'nudity', 'sex_objects', 'sex', 'sex_acts', 'image_composition', 'artistic_license', 'text', 'year_tags', 'metatags']
+    group_tags = {}
+    other_tags: list[str] = []
+    rating_tags: list[str] = []
+
+    group_dict = tag_group_dict
+    group_set = set(group_dict.keys())
+    character_set = set(anime_series_dict.keys())
+    series_set = set(anime_series_dict.values())
+    rating_set = set(DANBOORU_TO_E621_RATING_MAP.keys()) | set(DANBOORU_TO_E621_RATING_MAP.values())
+
+    for tag in tags:
+        tag = replace_underline(tag)
+        if tag in PEOPLE_TAGS:
+            people_tags.append(tag)
+        elif tag in rating_set:
+            rating_tags.append(tag)
+        elif tag in group_set:
+            elem = group_dict[tag]
+            group_tags[elem] = group_tags[elem] + [tag] if elem in group_tags else [tag]
+        elif tag in character_set:
+            character_tags.append(tag)
+        elif tag in series_set:
+            series_tags.append(tag)
+        else:
+            other_tags.append(tag)
+
+    output_group_tags: list[str] = []
+    for k in group_list:
+        output_group_tags.extend(group_tags.get(k, []))
+
+    rating_tags = [rating_tags[0]] if rating_tags else []
+    rating_tags = ["explicit, nsfw"] if rating_tags and rating_tags[0] == "explicit" else rating_tags
+
+    output_tags = character_tags + series_tags + people_tags + output_group_tags + other_tags + rating_tags
+    
+    return output_tags
+
+
+def sort_tags(tags: str):
+    if not tags: return ""
+    taglist: list[str] = []
+    for tag in tags.split(","):
+        taglist.append(tag.strip())
+    taglist = list(filter(lambda x: x != "", taglist))
+    return ", ".join(sort_taglist(taglist))
+
+
+def postprocess_results(results: dict[str, float], general_threshold: float, character_threshold: float):
+    results = {
+        k: v for k, v in sorted(results.items(), key=lambda item: item[1], reverse=True)
+    }
+
+    rating = {}
+    character = {}
+    general = {}
+
+    for k, v in results.items():
+        if k.startswith("rating:"):
+            rating[k.replace("rating:", "")] = v
+            continue
+        elif k.startswith("character:"):
+            character[k.replace("character:", "")] = v
+            continue
+
+        general[k] = v
+
+    character = {k: v for k, v in character.items() if v >= character_threshold}
+    general = {k: v for k, v in general.items() if v >= general_threshold}
+
+    return rating, character, general
+
+
+def gen_prompt(rating: list[str], character: list[str], general: list[str]):
+    people_tags: list[str] = []
+    other_tags: list[str] = []
+    rating_tag = RATING_MAP[rating[0]]
+
+    for tag in general:
+        if tag in PEOPLE_TAGS:
+            people_tags.append(tag)
+        else:
+            other_tags.append(tag)
+
+    all_tags = people_tags + other_tags
+
+    return ", ".join(all_tags)
+
+
+@spaces.GPU(duration=30)
+def predict_tags(image: Image.Image, general_threshold: float = 0.3, character_threshold: float = 0.8):
+    inputs = wd_processor.preprocess(image, return_tensors="pt")
+
+    outputs = wd_model(**inputs.to(wd_model.device, wd_model.dtype))
+    logits = torch.sigmoid(outputs.logits[0])  # take the first logits
+
+    # get probabilities
+    if device != default_device: wd_model.to(device=device)
+    results = {
+        wd_model.config.id2label[i]: float(logit.float()) for i, logit in enumerate(logits)
+    }
+    if device != default_device: wd_model.to(device=default_device)
+    # rating, character, general
+    rating, character, general = postprocess_results(
+        results, general_threshold, character_threshold
+    )
+    prompt = gen_prompt(
+        list(rating.keys()), list(character.keys()), list(general.keys())
+    )
+    output_series_tag = ""
+    output_series_list = character_list_to_series_list(character.keys())
+    if output_series_list:
+        output_series_tag = output_series_list[0]
+    else:
+        output_series_tag = ""
+    return output_series_tag, ", ".join(character.keys()), prompt, gr.update(interactive=True)
+
+
+def predict_tags_wd(image: Image.Image, input_tags: str, algo: list[str], general_threshold: float = 0.3,
+                     character_threshold: float = 0.8, input_series: str = "", input_character: str = ""):
+    if not "Use WD Tagger" in algo and len(algo) != 0:
+        return input_series, input_character, input_tags, gr.update(interactive=True)
+    return predict_tags(image, general_threshold, character_threshold)
+
+
+def compose_prompt_to_copy(character: str, series: str, general: str):
+    characters = character.split(",") if character else []
+    serieses = series.split(",") if series else []
+    generals = general.split(",") if general else []
+    tags = characters + serieses + generals
+    cprompt = ",".join(tags) if tags else ""
+    return cprompt

tagger/utils.py

@@ -0,0 +1,50 @@
+import gradio as gr
+from dartrs.v2 import AspectRatioTag, LengthTag, RatingTag, IdentityTag
+
+
+V2_ASPECT_RATIO_OPTIONS: list[AspectRatioTag] = [
+    "ultra_wide",
+    "wide",
+    "square",
+    "tall",
+    "ultra_tall",
+]
+V2_RATING_OPTIONS: list[RatingTag] = [
+    "sfw",
+    "general",
+    "sensitive",
+    "nsfw",
+    "questionable",
+    "explicit",
+]
+V2_LENGTH_OPTIONS: list[LengthTag] = [
+    "very_short",
+    "short",
+    "medium",
+    "long",
+    "very_long",
+]
+V2_IDENTITY_OPTIONS: list[IdentityTag] = [
+    "none",
+    "lax",
+    "strict",
+]
+
+
+# ref: https://qiita.com/tregu148/items/fccccbbc47d966dd2fc2
+def gradio_copy_text(_text: None):
+    gr.Info("Copied!")
+
+
+COPY_ACTION_JS = """\
+(inputs, _outputs) => {
+  // inputs is the string value of the input_text
+  if (inputs.trim() !== "") {
+    navigator.clipboard.writeText(inputs);
+  }
+}"""
+
+
+def gradio_copy_prompt(prompt: str):
+    gr.Info("Copied!")
+    return prompt

tagger/v2.py

@@ -0,0 +1,260 @@
+import time
+import torch
+from typing import Callable
+from pathlib import Path
+
+from dartrs.v2 import (
+    V2Model,
+    MixtralModel,
+    MistralModel,
+    compose_prompt,
+    LengthTag,
+    AspectRatioTag,
+    RatingTag,
+    IdentityTag,
+)
+from dartrs.dartrs import DartTokenizer
+from dartrs.utils import get_generation_config
+
+
+import gradio as gr
+from gradio.components import Component
+
+
+try:
+    from output import UpsamplingOutput
+except:
+    from .output import UpsamplingOutput
+
+
+V2_ALL_MODELS = {
+    "dart-v2-moe-sft": {
+        "repo": "p1atdev/dart-v2-moe-sft",
+        "type": "sft",
+        "class": MixtralModel,
+    },
+    "dart-v2-sft": {
+        "repo": "p1atdev/dart-v2-sft",
+        "type": "sft",
+        "class": MistralModel,
+    },
+}
+
+
+def prepare_models(model_config: dict):
+    model_name = model_config["repo"]
+    tokenizer = DartTokenizer.from_pretrained(model_name)
+    model = model_config["class"].from_pretrained(model_name)
+
+    return {
+        "tokenizer": tokenizer,
+        "model": model,
+    }
+
+
+def normalize_tags(tokenizer: DartTokenizer, tags: str):
+    """Just remove unk tokens."""
+    return ", ".join([tag for tag in tokenizer.tokenize(tags) if tag != "<|unk|>"])
+
+
+@torch.no_grad()
+def generate_tags(
+    model: V2Model,
+    tokenizer: DartTokenizer,
+    prompt: str,
+    ban_token_ids: list[int],
+):
+    output = model.generate(
+        get_generation_config(
+            prompt,
+            tokenizer=tokenizer,
+            temperature=1,
+            top_p=0.9,
+            top_k=100,
+            max_new_tokens=256,
+            ban_token_ids=ban_token_ids,
+        ),
+    )
+
+    return output
+
+
+def _people_tag(noun: str, minimum: int = 1, maximum: int = 5):
+    return (
+        [f"1{noun}"]
+        + [f"{num}{noun}s" for num in range(minimum + 1, maximum + 1)]
+        + [f"{maximum+1}+{noun}s"]
+    )
+
+
+PEOPLE_TAGS = (
+    _people_tag("girl") + _people_tag("boy") + _people_tag("other") + ["no humans"]
+)
+
+
+def gen_prompt_text(output: UpsamplingOutput):
+    # separate people tags (e.g. 1girl)
+    people_tags = []
+    other_general_tags = []
+    
+    for tag in output.general_tags.split(","):
+        tag = tag.strip()
+        if tag in PEOPLE_TAGS:
+            people_tags.append(tag)
+        else:
+            other_general_tags.append(tag)
+
+    return ", ".join(
+        [
+            part.strip()
+            for part in [
+                *people_tags,
+                output.character_tags,
+                output.copyright_tags,
+                *other_general_tags,
+                output.upsampled_tags,
+                output.rating_tag,
+            ]
+            if part.strip() != ""
+        ]
+    )
+
+
+def elapsed_time_format(elapsed_time: float) -> str:
+    return f"Elapsed: {elapsed_time:.2f} seconds"
+
+
+def parse_upsampling_output(
+    upsampler: Callable[..., UpsamplingOutput],
+):
+    def _parse_upsampling_output(*args) -> tuple[str, str, dict]:
+        output = upsampler(*args)
+
+        return (
+            gen_prompt_text(output),
+            elapsed_time_format(output.elapsed_time),
+            gr.update(interactive=True),
+            gr.update(interactive=True),
+        )
+
+    return _parse_upsampling_output
+
+
+class V2UI:
+    model_name: str | None = None
+    model: V2Model
+    tokenizer: DartTokenizer
+
+    input_components: list[Component] = []
+    generate_btn: gr.Button
+
+    def on_generate(
+        self,
+        model_name: str,
+        copyright_tags: str,
+        character_tags: str,
+        general_tags: str,
+        rating_tag: RatingTag,
+        aspect_ratio_tag: AspectRatioTag,
+        length_tag: LengthTag,
+        identity_tag: IdentityTag,
+        ban_tags: str,
+        *args,
+    ) -> UpsamplingOutput:
+        if self.model_name is None or self.model_name != model_name:
+            models = prepare_models(V2_ALL_MODELS[model_name])
+            self.model = models["model"]
+            self.tokenizer = models["tokenizer"]
+            self.model_name = model_name
+
+        # normalize tags
+        # copyright_tags = normalize_tags(self.tokenizer, copyright_tags)
+        # character_tags = normalize_tags(self.tokenizer, character_tags)
+        # general_tags = normalize_tags(self.tokenizer, general_tags)
+
+        ban_token_ids = self.tokenizer.encode(ban_tags.strip())
+
+        prompt = compose_prompt(
+            prompt=general_tags,
+            copyright=copyright_tags,
+            character=character_tags,
+            rating=rating_tag,
+            aspect_ratio=aspect_ratio_tag,
+            length=length_tag,
+            identity=identity_tag,
+        )
+
+        start = time.time()
+        upsampled_tags = generate_tags(
+            self.model,
+            self.tokenizer,
+            prompt,
+            ban_token_ids,
+        )
+        elapsed_time = time.time() - start
+
+        return UpsamplingOutput(
+            upsampled_tags=upsampled_tags,
+            copyright_tags=copyright_tags,
+            character_tags=character_tags,
+            general_tags=general_tags,
+            rating_tag=rating_tag,
+            aspect_ratio_tag=aspect_ratio_tag,
+            length_tag=length_tag,
+            identity_tag=identity_tag,
+            elapsed_time=elapsed_time,
+        )
+
+
+def parse_upsampling_output_simple(upsampler: UpsamplingOutput):
+    return gen_prompt_text(upsampler)
+
+
+v2 = V2UI()
+
+
+def v2_upsampling_prompt(model: str = "dart-v2-moe-sft", copyright: str = "", character: str = "",
+                          general_tags: str = "", rating: str = "nsfw", aspect_ratio: str = "square",
+                            length: str = "very_long", identity: str = "lax", ban_tags: str = "censored"):
+    raw_prompt = parse_upsampling_output_simple(v2.on_generate(model, copyright, character, general_tags,
+                                                                rating, aspect_ratio, length, identity, ban_tags))
+    return raw_prompt
+
+
+def load_dict_from_csv(filename):
+    dict = {}
+    if not Path(filename).exists():
+        if Path('./tagger/', filename).exists(): filename = str(Path('./tagger/', filename))
+        else: return dict
+    try:
+        with open(filename, 'r', encoding="utf-8") as f:
+            lines = f.readlines()
+    except Exception:
+        print(f"Failed to open dictionary file: {filename}")
+        return dict
+    for line in lines:
+        parts = line.strip().split(',')
+        dict[parts[0]] = parts[1]
+    return dict
+
+
+anime_series_dict = load_dict_from_csv('character_series_dict.csv')
+
+
+def select_random_character(series: str, character: str):
+    from random import seed, randrange
+    seed()
+    character_list = list(anime_series_dict.keys())
+    character = character_list[randrange(len(character_list) - 1)]
+    series = anime_series_dict.get(character.split(",")[0].strip(), "")
+    return series, character
+
+
+def v2_random_prompt(general_tags: str = "", copyright: str = "", character: str = "", rating: str = "nsfw",
+                      aspect_ratio: str = "square", length: str = "very_long", identity: str = "lax",
+                      ban_tags: str = "censored", model: str = "dart-v2-moe-sft"):
+    if copyright == "" and character == "":
+        copyright, character = select_random_character("", "")
+    raw_prompt = v2_upsampling_prompt(model, copyright, character, general_tags, rating,
+                                       aspect_ratio, length, identity, ban_tags)
+    return raw_prompt, copyright, character

textual_inversion_dict.json

@@ -0,0 +1,74 @@
+{
+  "bad_prompt_version2.pt": [
+    "bad_prompt",
+    false
+  ],
+  "EasyNegativeV2.safetensors": [
+    "EasyNegative",
+    false
+  ],
+  "bad-hands-5.pt": [
+    "bad_hand",
+    false
+  ],
+  "negativeXL_A.safetensors": [
+    "negativeXL_A",
+    false
+  ],
+  "negativeXL_B.safetensors": [
+    "negativeXL_B",
+    false
+  ],
+  "negativeXL_C.safetensors": [
+    "negativeXL_C",
+    false
+  ],
+  "negativeXL_D.safetensors": [
+    "negativeXL_D",
+    false
+  ],
+  "unaestheticXL2v10.safetensors": [
+    "2v10",
+    false
+  ],
+  "unaestheticXL_AYv1.safetensors": [
+    "_AYv1",
+    false
+  ],
+  "unaestheticXL_Alb2.safetensors": [
+    "_Alb2",
+    false
+  ],
+  "unaestheticXL_Jug6.safetensors": [
+    "_Jug6",
+    false
+  ],
+  "unaestheticXL_bp5.safetensors": [
+    "_bp5",
+    false
+  ],
+  "unaestheticXL_hk1.safetensors": [
+    "_hk1",
+    false
+  ],
+  "unaestheticXLv1.safetensors": [
+    "v1.0",
+    false
+  ],
+  "unaestheticXLv13.safetensors": [
+    "v1.3",
+    false
+  ],
+  "unaestheticXLv31.safetensors": [
+    "v3.1",
+    false
+  ],
+  "unaestheticXL_Sky3.1.safetensors": [
+    "_Sky3.1",
+    false
+  ],
+  "SimplePositiveXLv2.safetensors": [
+    "SIMPLEPOSITIVEXLV2",
+    true
+  ]
+}