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README.md

@@ -1,12 +1,14 @@
----
-title: Votepurchase Crash
-emoji: 🐨
-colorFrom: pink
-colorTo: indigo
-sdk: gradio
-sdk_version: 4.42.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+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

@@ -0,0 +1,373 @@
+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; !important; }
+#col-container { margin: 0 auto; !important; }
+#result { display: inline-block; max-width: 520px; max-height: 520px; width: 520px; height: 520px; align: center; margin: 0px auto; !important; }
+.lora { display: inline-block; min-width: 480px; !important; }
+#model-info { text-align: center; !important; }
+"""
+
+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"):
+            with gr.Row():
+                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", elem_id="result", show_label=False, interactive=False,
+                              show_download_button=True, show_share_button=False, container=True)
+                
+            with gr.Accordion("Advanced Settings", open=False):
+                with gr.Row():
+                    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")
+
+                with gr.Row():
+                    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
+                    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.Row():
+                    with gr.Column(scale=4):
+                        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, min_width=320)
+                        model_info = gr.Markdown(elem_id="model-info")
+                    with gr.Column(scale=1):
+                        model_detail = gr.Checkbox(label="Show detail of model in list", value=False)
+
+                with gr.Row():
+                    sampler = gr.Dropdown(label="Sampler", choices=get_samplers(), value="Euler a")
+                    vae_model = gr.Dropdown(label="VAE Model", choices=get_vaes(), value=get_vaes()[0])
+
+                with gr.Accordion("LoRA", open=True, visible=True):
+                    with gr.Row():
+                        with gr.Column():
+                            with gr.Row():
+                                lora1 = gr.Dropdown(label="LoRA 1", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True, elem_classes="lora", min_width=320)
+                                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.Column():
+                            with gr.Row():
+                                lora2 = gr.Dropdown(label="LoRA 2", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True, elem_classes="lora", min_width=320)
+                                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.Column():
+                            with gr.Row():
+                                lora3 = gr.Dropdown(label="LoRA 3", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True, elem_classes="lora", min_width=320)
+                                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.Column():
+                            with gr.Row():
+                                lora4 = gr.Dropdown(label="LoRA 4", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True, elem_classes="lora", min_width=320)
+                                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.Column():
+                            with gr.Row():
+                                lora5 = gr.Dropdown(label="LoRA 5", choices=get_all_lora_tupled_list(), value="", allow_custom_value=True, elem_classes="lora", min_width=320)
+                                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")
+                        with gr.Row():
+                            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")
+
+                with gr.Row():
+                    recom_prompt = gr.Checkbox(label="Recommended prompt", value=True)
+                    quality_selector = gr.Radio(label="Quality Tag Presets", interactive=True, choices=list(preset_quality.keys()), value="None")
+                    style_selector = gr.Radio(label="Style Presets", interactive=True, choices=list(preset_styles.keys()), value="None")
+                
+                with gr.Accordion("Translation Settings", open=False):
+                    chatbot = gr.Chatbot(likeable=False, render_markdown=False, visible=False) # component for auto-translation
+                    chat_model = gr.Dropdown(choices=get_dolphin_models(), value=get_dolphin_models()[0][1], allow_custom_value=True, label="Model")
+                    chat_model_info = gr.Markdown(value=get_dolphin_model_info(get_dolphin_models()[0][1]), label="Model info")
+                    chat_format = gr.Dropdown(choices=get_llm_formats(), value=get_dolphin_model_format(get_dolphin_models()[0][1]), label="Message format")
+                    with gr.Row():
+                        chat_tokens = gr.Slider(minimum=1, maximum=4096, value=512, step=1, label="Max tokens")
+                        chat_temperature = gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature")
+                        chat_topp = gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p")
+                        chat_topk = gr.Slider(minimum=0, maximum=100, value=40, step=1, label="Top-k")
+                        chat_rp = gr.Slider(minimum=0.0, maximum=2.0, value=1.1, step=0.1, label="Repetition penalty")
+                    chat_sysmsg = gr.Textbox(value=get_dolphin_sysprompt(), label="System message")
+
+            examples = gr.Examples(
+                examples = [
+                    ["souryuu asuka langley, 1girl, neon genesis evangelion, plugsuit, pilot suit, red bodysuit, sitting, crossing legs, black eye patch, cat hat, throne, symmetrical, looking down, from bottom, looking at viewer, outdoors"],
+                    ["sailor moon, magical girl transformation, sparkles and ribbons, soft pastel colors, crescent moon motif, starry night sky background, shoujo manga style"],
+                    ["kafuu chino, 1girl, solo"],
+                    ["1girl"],
+                    ["beautiful sunset"],
+                ],
+                inputs=[prompt],
+            )
+
+    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 = []
+