Upload 23 files

app.py

@@ -8,7 +8,10 @@ from dc import (infer, _infer, pass_result, get_diffusers_model_list, get_sample
     preset_quality, preset_styles, process_style_prompt, get_all_lora_tupled_list, update_loras, apply_lora_prompt,
     download_my_lora, search_civitai_lora, update_civitai_selection, select_civitai_lora, search_civitai_lora_json,
     get_t2i_model_info, get_civitai_tag, CIVITAI_SORT, CIVITAI_PERIOD, CIVITAI_BASEMODEL,
-    SCHEDULE_TYPE_OPTIONS, SCHEDULE_PREDICTION_TYPE_OPTIONS)
+    SCHEDULE_TYPE_OPTIONS, SCHEDULE_PREDICTION_TYPE_OPTIONS, preprocessor_tab, SDXL_TASK, TASK_MODEL_LIST,
+    PROMPT_W_OPTIONS, POST_PROCESSING_SAMPLER, IP_ADAPTERS_SD, IP_ADAPTERS_SDXL, DIFFUSERS_CONTROLNET_MODEL,
+    TASK_AND_PREPROCESSORS, update_task_options, change_preprocessor_choices, get_ti_choices,
+    update_textual_inversion, set_textual_inversion_prompt, create_mask_now)
 # Translator
 from llmdolphin import (dolphin_respond_auto, dolphin_parse_simple,
     get_llm_formats, get_dolphin_model_format, get_dolphin_models,
@@ -34,7 +37,8 @@ def description_ui():
 
 
 MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1216
+MAX_IMAGE_SIZE = 4096
+MIN_IMAGE_SIZE = 256
 
 css = """
 #container { margin: 0 auto; !important; }
@@ -60,54 +64,62 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
                 auto_trans = gr.Checkbox(label="Auto translate to English", value=False, scale=2)
 
             result = gr.Image(label="Result", elem_id="result", format="png", type="filepath", show_label=False, interactive=False,
-                            show_download_button=True, show_share_button=False, container=True)
+                              show_download_button=True, show_share_button=False, container=True)
+
             with gr.Accordion("History", open=False):
+                history_files = gr.Files(interactive=False, visible=False)
                 history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", format="png", interactive=False, show_share_button=False,
                 show_download_button=True)
-                history_files = gr.Files(interactive=False, visible=False)
                 history_clear_button = gr.Button(value="Clear History", variant="secondary")
                 history_clear_button.click(lambda: ([], []), None, [history_gallery, history_files], queue=False, show_api=False)
 
-            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)
-                    gpu_duration = gr.Number(minimum=5, maximum=240, value=59, show_label=False, container=False, info="GPU time duration (seconds)")
-
-                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)
-                    pag_scale = gr.Slider(minimum=0.0, maximum=10.0, step=0.1, value=0.0, label="PAG Scale")
-                    clip_skip = gr.Checkbox(value=True, label="Layer 2 Clip Skip")
-                    free_u = gr.Checkbox(value=False, label="FreeU")
-
-                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_classes="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")
-                    schedule_type = gr.Dropdown(label="Schedule type", choices=SCHEDULE_TYPE_OPTIONS, value=SCHEDULE_TYPE_OPTIONS[0])
-                    schedule_prediction_type = gr.Dropdown(label="Discrete Sampling Type", choices=SCHEDULE_PREDICTION_TYPE_OPTIONS, value=SCHEDULE_PREDICTION_TYPE_OPTIONS[0])
-                    vae_model = gr.Dropdown(label="VAE Model", choices=get_vaes(), value=get_vaes()[0])
-
-                with gr.Accordion("LoRA", open=True, visible=True):
-                    def lora_dropdown(label):
-                        return gr.Dropdown(label=label, choices=get_all_lora_tupled_list(), value="", allow_custom_value=True, elem_classes="lora", min_width=320)
+            with gr.Accordion("Advanced Settings", open=True):
+                task = gr.Dropdown(label="Task", choices=SDXL_TASK, value=TASK_MODEL_LIST[0])
+                with gr.Tab("Model & Prompt"):
+                    with gr.Row():
+                        negative_prompt = gr.Text(label="Negative prompt", lines=1, max_lines=6, placeholder="Enter a negative prompt", show_copy_button=True,
+                                                value="(low quality, worst quality:1.2), very displeasing, watermark, signature, ugly")
+                    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_classes="info")
+                        with gr.Column(scale=1):
+                            model_detail = gr.Checkbox(label="Show detail of model in list", value=False)
+                    with gr.Row():
+                        quality_selector = gr.Radio(label="Quality Tag Presets", interactive=True, choices=list(preset_quality.keys()), value="None", scale=3)
+                        style_selector = gr.Radio(label="Style Presets", interactive=True, choices=list(preset_styles.keys()), value="None", scale=3)
+                        recom_prompt = gr.Checkbox(label="Recommended prompt", value=True, scale=1)
 
-                    def lora_scale_slider(label):
-                        return gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label=label)
+                with gr.Tab("Generation Settings"):
+                    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)
+                        gpu_duration = gr.Slider(label="GPU time duration (seconds)", minimum=5, maximum=240, value=59)
+                    with gr.Row():
+                        width = gr.Slider(label="Width", minimum=MIN_IMAGE_SIZE, maximum=MAX_IMAGE_SIZE, step=32, value=1024) # 832
+                        height = gr.Slider(label="Height", minimum=MIN_IMAGE_SIZE, 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)
+                        guidance_rescale = gr.Slider(label="CFG rescale", value=0., step=0.01, minimum=0., maximum=1.5)
+                    with gr.Row():
+                        num_inference_steps = gr.Slider(label="Number of inference steps", minimum=1, maximum=100, step=1, value=28)
+                        pag_scale = gr.Slider(minimum=0.0, maximum=10.0, step=0.1, value=0.0, label="PAG Scale")
+                        clip_skip = gr.Checkbox(value=True, label="Layer 2 Clip Skip")
+                        free_u = gr.Checkbox(value=False, label="FreeU")
+                    with gr.Row():
+                        sampler = gr.Dropdown(label="Sampler", choices=get_samplers(), value="Euler")
+                        schedule_type = gr.Dropdown(label="Schedule type", choices=SCHEDULE_TYPE_OPTIONS, value=SCHEDULE_TYPE_OPTIONS[0])
+                        schedule_prediction_type = gr.Dropdown(label="Discrete Sampling Type", choices=SCHEDULE_PREDICTION_TYPE_OPTIONS, value=SCHEDULE_PREDICTION_TYPE_OPTIONS[0])
+                        vae_model = gr.Dropdown(label="VAE Model", choices=get_vaes(), value=get_vaes()[0])
+                        prompt_syntax = gr.Dropdown(label="Prompt Syntax", choices=PROMPT_W_OPTIONS, value=PROMPT_W_OPTIONS[1][1])
+
+                with gr.Tab("LoRA"):
+                    def lora_dropdown(label, visible=True):
+                        return gr.Dropdown(label=label, choices=get_all_lora_tupled_list(), value="", allow_custom_value=True, elem_classes="lora", min_width=320, visible=visible)
+
+                    def lora_scale_slider(label, visible=True):
+                        return gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label=label, visible=visible)
                     
                     def lora_textbox():
                         return gr.Textbox(label="", info="Example of prompt:", value="", show_copy_button=True, interactive=False, visible=False)
@@ -153,6 +165,22 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
                                 lora5_info = lora_textbox()
                                 lora5_copy = gr.Button(value="Copy example to prompt", visible=False)
                             lora5_md = gr.Markdown(value="", visible=False)
+                        with gr.Column():
+                            with gr.Row():
+                                lora6 = lora_dropdown("LoRA 6", visible=False)
+                                lora6_wt = lora_scale_slider("LoRA 6: weight", visible=False)
+                            with gr.Row():
+                                lora6_info = lora_textbox()
+                                lora6_copy = gr.Button(value="Copy example to prompt", visible=False)
+                            lora6_md = gr.Markdown(value="", visible=False)
+                        with gr.Column():
+                            with gr.Row():
+                                lora7 = lora_dropdown("LoRA 7", visible=False)
+                                lora7_wt = lora_scale_slider("LoRA 7: weight", visible=False)
+                            with gr.Row():
+                                lora7_info = lora_textbox()
+                                lora7_copy = gr.Button(value="Copy example to prompt", visible=False)
+                            lora7_md = gr.Markdown(value="", visible=False)
                     with gr.Accordion("From URL", open=True, visible=True):
                         with gr.Row():
                             lora_search_civitai_basemodel = gr.CheckboxGroup(label="Search LoRA for", choices=CIVITAI_BASEMODEL, value=["Pony", "Illustrious", "SDXL 1.0"])
@@ -171,13 +199,143 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
                         lora_search_civitai_result = gr.Dropdown(label="Search Results", choices=[("", "")], value="", allow_custom_value=True, visible=False)
                         lora_download_url = gr.Textbox(label="LoRA's download URL", placeholder="https://civitai.com/api/download/models/28907", info="It has to be .safetensors files, and you can also download them from Hugging Face.", lines=1)
                         lora_download = gr.Button("Get and set LoRA and apply to prompt")
-
-                with gr.Row():
-                    quality_selector = gr.Radio(label="Quality Tag Presets", interactive=True, choices=list(preset_quality.keys()), value="None", scale=3)
-                    style_selector = gr.Radio(label="Style Presets", interactive=True, choices=list(preset_styles.keys()), value="None", scale=3)
-                    recom_prompt = gr.Checkbox(label="Recommended prompt", value=True, scale=1)
                 
-                with gr.Accordion("Translation Settings", open=False):
+                with gr.Tab("ControlNet / Img2img / Inpaint"):
+                    with gr.Row():
+                        #image_control = gr.Image(label="Image ControlNet / Inpaint / Img2img", type="filepath", height=384, sources=["upload", "clipboard", "webcam"], show_share_button=False)
+                        image_control = gr.ImageEditor(label="Image ControlNet / Inpaint / Img2img", type="filepath", sources=["upload", "clipboard", "webcam"], image_mode='RGB',
+                                                       show_share_button=False, show_fullscreen_button=False, layers=False, canvas_size=(384, 384), width=384, height=512,
+                                                       brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed", default_size=32), eraser=gr.Eraser(default_size="32"))
+                        image_mask = gr.Image(label="Image Mask", type="filepath", height=384, sources=["upload", "clipboard"], show_share_button=False)
+                    with gr.Row():
+                        strength = gr.Slider(minimum=0.01, maximum=1.0, step=0.01, value=0.55, label="Strength",
+                                            info="This option adjusts the level of changes for img2img and inpainting.")
+                        image_resolution = gr.Slider(minimum=64, maximum=2048, step=64, value=1024, label="Image Resolution",
+                                                    info="The maximum proportional size of the generated image based on the uploaded image.")
+                    with gr.Row():
+                        controlnet_model = gr.Dropdown(label="ControlNet model", choices=DIFFUSERS_CONTROLNET_MODEL, value=DIFFUSERS_CONTROLNET_MODEL[0])
+                        control_net_output_scaling = gr.Slider(minimum=0, maximum=5.0, step=0.1, value=1, label="ControlNet Output Scaling in UNet")
+                        control_net_start_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, value=0, label="ControlNet Start Threshold (%)")
+                        control_net_stop_threshold = gr.Slider(minimum=0, maximum=1, step=0.01, value=1, label="ControlNet Stop Threshold (%)")
+                    with gr.Row():
+                        preprocessor_name = gr.Dropdown(label="Preprocessor Name", choices=TASK_AND_PREPROCESSORS["canny"])
+                        preprocess_resolution = gr.Slider(minimum=64, maximum=2048, step=64, value=512, label="Preprocessor Resolution")
+                        low_threshold = gr.Slider(minimum=1, maximum=255, step=1, value=100, label="'CANNY' low threshold")
+                        high_threshold = gr.Slider(minimum=1, maximum=255, step=1, value=200, label="'CANNY' high threshold")
+                    with gr.Row():
+                        value_threshold = gr.Slider(minimum=1, maximum=2.0, step=0.01, value=0.1, label="'MLSD' Hough value threshold")
+                        distance_threshold = gr.Slider(minimum=1, maximum=20.0, step=0.01, value=0.1, label="'MLSD' Hough distance threshold")
+                        recolor_gamma_correction = gr.Number(minimum=0., maximum=25., value=1., step=0.001, label="'RECOLOR' gamma correction")
+                        tile_blur_sigma = gr.Number(minimum=0, maximum=100, value=9, step=1, label="'TILE' blur sigma")
+
+                with gr.Tab("IP-Adapter"):
+                    IP_MODELS = sorted(list(set(IP_ADAPTERS_SD + IP_ADAPTERS_SDXL)))
+                    MODE_IP_OPTIONS = ["original", "style", "layout", "style+layout"]
+                    with gr.Accordion("IP-Adapter 1", open=True, visible=True):
+                        with gr.Row():
+                            #image_ip1 = gr.Image(label="IP Image", type="filepath", height=384, sources=["upload", "clipboard"], show_share_button=False)
+                            image_ip1 = gr.ImageEditor(label="IP Image", type="filepath", sources=["upload", "clipboard", "webcam"], image_mode='RGB',
+                                                       show_share_button=False, show_fullscreen_button=False, layers=False, canvas_size=(384, 384), width=384, height=512,
+                                                       brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed", default_size=32), eraser=gr.Eraser(default_size="32"))
+                            mask_ip1 = gr.Image(label="IP Mask (optional)", type="filepath", height=384, sources=["upload", "clipboard"], show_share_button=False)
+                        with gr.Row():
+                            model_ip1 = gr.Dropdown(value="plus_face", label="Model", choices=IP_MODELS)
+                            mode_ip1 = gr.Dropdown(value="original", label="Mode", choices=MODE_IP_OPTIONS)
+                            scale_ip1 = gr.Slider(minimum=0., maximum=2., step=0.01, value=0.7, label="Scale")
+                    with gr.Accordion("IP-Adapter 2", open=True, visible=True):
+                        with gr.Row():
+                            #image_ip2 = gr.Image(label="IP Image", type="filepath", height=384, sources=["upload", "clipboard"], show_share_button=False)
+                            image_ip2 = gr.ImageEditor(label="IP Image", type="filepath", sources=["upload", "clipboard", "webcam"], image_mode='RGB',
+                                                       show_share_button=False, show_fullscreen_button=False, layers=False, canvas_size=(384, 384), width=384, height=512,
+                                                       brush=gr.Brush(colors=["#FFFFFF"], color_mode="fixed", default_size=32), eraser=gr.Eraser(default_size="32"))
+                            mask_ip2 = gr.Image(label="IP Mask (optional)", type="filepath", height=384, sources=["upload", "clipboard"], show_share_button=False)
+                        with gr.Row():
+                            model_ip2 = gr.Dropdown(value="base", label="Model", choices=IP_MODELS)
+                            mode_ip2 = gr.Dropdown(value="style", label="Mode", choices=MODE_IP_OPTIONS)
+                            scale_ip2 = gr.Slider(minimum=0., maximum=2., step=0.01, value=0.7, label="Scale")
+
+                with gr.Tab("Inpaint Mask Maker"):
+                    with gr.Row():
+                        with gr.Column():
+                            image_base = gr.ImageEditor(sources=["upload", "clipboard", "webcam"],
+                                brush=gr.Brush(default_size="32", color_mode="fixed", colors=["rgba(0, 0, 0, 1)", "rgba(0, 0, 0, 0.1)", "rgba(255, 255, 255, 0.1)"]),
+                                eraser=gr.Eraser(default_size="32"), show_share_button=False, show_fullscreen_button=False,
+                                canvas_size=(384, 384), width=384, height=512)
+                            invert_mask = gr.Checkbox(value=False, label="Invert mask")
+                            cm_btn = gr.Button("Create mask")
+                        with gr.Column():
+                            img_source = gr.Image(interactive=False, height=384, show_share_button=False)
+                            img_result = gr.Image(label="Mask image", show_label=True, interactive=False, height=384, show_share_button=False)
+                            cm_btn_send = gr.Button("Send to ControlNet / Img2img / Inpaint")
+                            cm_btn_send_ip1 = gr.Button("Send to IP-Adapter 1")
+                            cm_btn_send_ip2 = gr.Button("Send to IP-Adapter 2")
+                        cm_btn.click(create_mask_now, [image_base, invert_mask], [img_source, img_result], show_api=False)
+                        def send_img(img_source, img_result):
+                            return img_source, img_result
+                        cm_btn_send.click(send_img, [img_source, img_result], [image_control, image_mask], queue=False, show_api=False)
+                        cm_btn_send_ip1.click(send_img, [img_source, img_result], [image_ip1, mask_ip1], queue=False, show_api=False)
+                        cm_btn_send_ip2.click(send_img, [img_source, img_result], [image_ip2, mask_ip2], queue=False, show_api=False)
+
+                with gr.Tab("Hires fix"):
+                    with gr.Row():
+                        upscaler_model_path = gr.Dropdown(label="Upscaler", choices=UPSCALER_KEYS, value=UPSCALER_KEYS[0])
+                        upscaler_increases_size = gr.Slider(minimum=1.1, maximum=4., step=0.1, value=1.2, label="Upscale by")
+                        esrgan_tile = gr.Slider(minimum=0, value=0, maximum=500, step=1, label="ESRGAN Tile")
+                        esrgan_tile_overlap = gr.Slider(minimum=1, maximum=200, step=1, value=8, label="ESRGAN Tile Overlap")
+                    with gr.Row():
+                        hires_steps = gr.Slider(minimum=0, value=30, maximum=100, step=1, label="Hires Steps")
+                        hires_denoising_strength = gr.Slider(minimum=0.1, maximum=1.0, step=0.01, value=0.55, label="Hires Denoising Strength")
+                        hires_sampler = gr.Dropdown(label="Hires Sampler", choices=POST_PROCESSING_SAMPLER, value=POST_PROCESSING_SAMPLER[0])
+                        hires_schedule_list = ["Use same schedule type"] + SCHEDULE_TYPE_OPTIONS
+                        hires_schedule_type = gr.Dropdown(label="Hires Schedule type", choices=hires_schedule_list, value=hires_schedule_list[0])
+                        hires_guidance_scale = gr.Slider(minimum=-1., maximum=30., step=0.5, value=-1., label="Hires CFG", info="If the value is -1, the main CFG will be used")
+                    with gr.Row():
+                        hires_prompt = gr.Textbox(label="Hires Prompt", placeholder="Main prompt will be use", lines=3)
+                        hires_negative_prompt = gr.Textbox(label="Hires Negative Prompt", placeholder="Main negative prompt will be use", lines=3)
+
+                with gr.Tab("Detailfix"):
+                    with gr.Row():
+                        # Adetailer Inpaint Only
+                        adetailer_inpaint_only = gr.Checkbox(label="Inpaint only", value=True)
+                        # Adetailer Verbose
+                        adetailer_verbose = gr.Checkbox(label="Verbose", value=False)
+                        # Adetailer Sampler
+                        adetailer_sampler = gr.Dropdown(label="Adetailer sampler:", choices=POST_PROCESSING_SAMPLER, value=POST_PROCESSING_SAMPLER[0])
+                    with gr.Row():
+                        with gr.Accordion("Detailfix A", open=True, visible=True):
+                            # Adetailer A
+                            adetailer_active_a = gr.Checkbox(label="Enable Adetailer A", value=False)
+                            prompt_ad_a = gr.Textbox(label="Main prompt", placeholder="Main prompt will be use", lines=3)
+                            negative_prompt_ad_a = gr.Textbox(label="Negative prompt", placeholder="Main negative prompt will be use", lines=3)
+                            with gr.Row():
+                                strength_ad_a = gr.Number(label="Strength:", value=0.35, step=0.01, minimum=0.01, maximum=1.0)
+                                face_detector_ad_a = gr.Checkbox(label="Face detector", value=False)
+                                person_detector_ad_a = gr.Checkbox(label="Person detector", value=True)
+                                hand_detector_ad_a = gr.Checkbox(label="Hand detector", value=False)
+                            with gr.Row():
+                                mask_dilation_a = gr.Number(label="Mask dilation:", value=4, minimum=1)
+                                mask_blur_a = gr.Number(label="Mask blur:", value=4, minimum=1)
+                                mask_padding_a = gr.Number(label="Mask padding:", value=32, minimum=1)
+                        with gr.Accordion("Detailfix B", open=True, visible=True):
+                            # Adetailer B
+                            adetailer_active_b = gr.Checkbox(label="Enable Adetailer B", value=False)
+                            prompt_ad_b = gr.Textbox(label="Main prompt", placeholder="Main prompt will be use", lines=3)
+                            negative_prompt_ad_b = gr.Textbox(label="Negative prompt", placeholder="Main negative prompt will be use", lines=3)
+                            with gr.Row():
+                                strength_ad_b = gr.Number(label="Strength:", value=0.35, step=0.01, minimum=0.01, maximum=1.0)
+                                face_detector_ad_b = gr.Checkbox(label="Face detector", value=False)
+                                person_detector_ad_b = gr.Checkbox(label="Person detector", value=True)
+                                hand_detector_ad_b = gr.Checkbox(label="Hand detector", value=False)
+                            with gr.Row():
+                                mask_dilation_b = gr.Number(label="Mask dilation:", value=4, minimum=1)
+                                mask_blur_b = gr.Number(label="Mask blur:", value=4, minimum=1)
+                                mask_padding_b = gr.Number(label="Mask padding:", value=32, minimum=1)
+                                    
+                with gr.Tab("Textual inversion"):
+                    active_textual_inversion = gr.Checkbox(value=False, label="Active Textual Inversion in prompt")
+                    use_textual_inversion = gr.CheckboxGroup(choices=get_ti_choices(model_name.value) if active_textual_inversion.value else [], value=None, label="Use Textual Invertion in prompt")
+
+                with gr.Tab("Translation Settings"):
                     chatbot = gr.Chatbot(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")
@@ -202,14 +360,34 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
             cache_examples=False,
         )
 
+    model_name.change(update_task_options, [model_name, task], [task], queue=False, show_api=False)
+    task.change(change_preprocessor_choices, [task], [preprocessor_name], queue=False, show_api=False)
+    active_textual_inversion.change(update_textual_inversion, [active_textual_inversion, model_name], [use_textual_inversion], queue=False, show_api=False)
+    model_name.change(update_textual_inversion, [active_textual_inversion, model_name], [use_textual_inversion], queue=False, show_api=False)
+    use_textual_inversion.change(set_textual_inversion_prompt, [use_textual_inversion, prompt, negative_prompt, prompt_syntax], [prompt, negative_prompt])
+
     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, auto_trans, schedule_type, schedule_prediction_type,
-                clip_skip, pag_scale, free_u, gpu_duration, recom_prompt],
+                lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt,
+                lora5, lora5_wt, lora6, lora6_wt, lora7, lora7_wt, task, prompt_syntax,
+                sampler, vae_model, schedule_type, schedule_prediction_type,
+                clip_skip, pag_scale, free_u, guidance_rescale,
+                image_control, image_mask, strength, image_resolution,
+                controlnet_model, control_net_output_scaling, control_net_start_threshold, control_net_stop_threshold,
+                preprocessor_name, preprocess_resolution, low_threshold, high_threshold,
+                value_threshold, distance_threshold, recolor_gamma_correction, tile_blur_sigma,
+                image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1,
+                image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2,
+                upscaler_model_path, upscaler_increases_size, esrgan_tile, esrgan_tile_overlap, hires_steps, hires_denoising_strength,
+                hires_sampler, hires_schedule_type, hires_guidance_scale, hires_prompt, hires_negative_prompt,
+                adetailer_inpaint_only, adetailer_verbose, adetailer_sampler, adetailer_active_a,
+                prompt_ad_a, negative_prompt_ad_a, strength_ad_a, face_detector_ad_a, person_detector_ad_a, hand_detector_ad_a,
+                mask_dilation_a, mask_blur_a, mask_padding_a, adetailer_active_b, prompt_ad_b, negative_prompt_ad_b, strength_ad_b,
+                face_detector_ad_b, person_detector_ad_b, hand_detector_ad_b, mask_dilation_b, mask_blur_b, mask_padding_b,
+                active_textual_inversion, gpu_duration, auto_trans, recom_prompt],
         outputs=[result],
         queue=True,
         show_progress="full",
@@ -221,9 +399,23 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
         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, auto_trans, schedule_type, schedule_prediction_type,
-                clip_skip, pag_scale, free_u, gpu_duration, recom_prompt],
+                lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt,
+                lora5, lora5_wt, lora6, lora6_wt, lora7, lora7_wt, task, prompt_syntax,
+                sampler, vae_model, schedule_type, schedule_prediction_type,
+                clip_skip, pag_scale, free_u, guidance_rescale,
+                image_control, image_mask, strength, image_resolution,
+                controlnet_model, control_net_output_scaling, control_net_start_threshold, control_net_stop_threshold,
+                preprocessor_name, preprocess_resolution, low_threshold, high_threshold,
+                value_threshold, distance_threshold, recolor_gamma_correction, tile_blur_sigma,
+                image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1,
+                image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2,
+                upscaler_model_path, upscaler_increases_size, esrgan_tile, esrgan_tile_overlap, hires_steps, hires_denoising_strength,
+                hires_sampler, hires_schedule_type, hires_guidance_scale, hires_prompt, hires_negative_prompt,
+                adetailer_inpaint_only, adetailer_verbose, adetailer_sampler, adetailer_active_a,
+                prompt_ad_a, negative_prompt_ad_a, strength_ad_a, face_detector_ad_a, person_detector_ad_a, hand_detector_ad_a,
+                mask_dilation_a, mask_blur_a, mask_padding_a, adetailer_active_b, prompt_ad_b, negative_prompt_ad_b, strength_ad_b,
+                face_detector_ad_b, person_detector_ad_b, hand_detector_ad_b, mask_dilation_b, mask_blur_b, mask_padding_b,
+                active_textual_inversion, gpu_duration, auto_trans, recom_prompt],
         outputs=[result],
         queue=False,
         show_api=True,
@@ -245,9 +437,23 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
         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, auto_trans, schedule_type, schedule_prediction_type,
-                clip_skip, pag_scale, free_u, gpu_duration, recom_prompt],
+                lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt,
+                lora5, lora5_wt, lora6, lora6_wt, lora7, lora7_wt, task, prompt_syntax,
+                sampler, vae_model, schedule_type, schedule_prediction_type,
+                clip_skip, pag_scale, free_u, guidance_rescale,
+                image_control, image_mask, strength, image_resolution,
+                controlnet_model, control_net_output_scaling, control_net_start_threshold, control_net_stop_threshold,
+                preprocessor_name, preprocess_resolution, low_threshold, high_threshold,
+                value_threshold, distance_threshold, recolor_gamma_correction, tile_blur_sigma,
+                image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1,
+                image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2,
+                upscaler_model_path, upscaler_increases_size, esrgan_tile, esrgan_tile_overlap, hires_steps, hires_denoising_strength,
+                hires_sampler, hires_schedule_type, hires_guidance_scale, hires_prompt, hires_negative_prompt,
+                adetailer_inpaint_only, adetailer_verbose, adetailer_sampler, adetailer_active_a,
+                prompt_ad_a, negative_prompt_ad_a, strength_ad_a, face_detector_ad_a, person_detector_ad_a, hand_detector_ad_a,
+                mask_dilation_a, mask_blur_a, mask_padding_a, adetailer_active_b, prompt_ad_b, negative_prompt_ad_b, strength_ad_b,
+                face_detector_ad_b, person_detector_ad_b, hand_detector_ad_b, mask_dilation_b, mask_blur_b, mask_padding_b,
+                active_textual_inversion, gpu_duration, auto_trans, recom_prompt],
         outputs=[result],
         queue=True,
         show_progress="full",
@@ -259,12 +465,13 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
 
     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],
+                  lora4.change, lora4_wt.change, lora5.change, lora5_wt.change, lora6.change, lora6_wt.change, lora7.change, lora7_wt.change, prompt_syntax.change],
         fn=update_loras,
-        inputs=[prompt, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt],
+        inputs=[prompt, prompt_syntax, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt, lora6, lora6_wt, lora7, lora7_wt],
         outputs=[prompt, lora1, lora1_wt, lora1_info, lora1_copy, lora1_md,
                  lora2, lora2_wt, lora2_info, lora2_copy, lora2_md, lora3, lora3_wt, lora3_info, lora3_copy, lora3_md, 
-                 lora4, lora4_wt, lora4_info, lora4_copy, lora4_md, lora5, lora5_wt, lora5_info, lora5_copy, lora5_md],
+                 lora4, lora4_wt, lora4_info, lora4_copy, lora4_md, lora5, lora5_wt, lora5_info, lora5_copy, lora5_md,
+                 lora6, lora6_wt, lora6_info, lora6_copy, lora6_md, lora7, lora7_wt, lora7_info, lora7_copy, lora7_md],
         queue=False,
         trigger_mode="once",
         show_api=False,
@@ -274,6 +481,8 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
     lora3_copy.click(apply_lora_prompt, [prompt, lora3_info], [prompt], queue=False, show_api=False)
     lora4_copy.click(apply_lora_prompt, [prompt, lora4_info], [prompt], queue=False, show_api=False)
     lora5_copy.click(apply_lora_prompt, [prompt, lora5_info], [prompt], queue=False, show_api=False)
+    lora6_copy.click(apply_lora_prompt, [prompt, lora6_info], [prompt], queue=False, show_api=False)
+    lora7_copy.click(apply_lora_prompt, [prompt, lora7_info], [prompt], queue=False, show_api=False)
 
     gr.on(
         triggers=[lora_search_civitai_submit.click, lora_search_civitai_query.submit],
@@ -289,8 +498,8 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
     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],
+        inputs=[lora_download_url, lora1, lora2, lora3, lora4, lora5, lora6, lora7],
+        outputs=[lora1, lora2, lora3, lora4, lora5, lora6, lora7],
         scroll_to_output=True,
         queue=True,
         show_api=False,
@@ -404,7 +613,7 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
         ).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)
+                  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)
@@ -441,6 +650,9 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
                     outputs=[result_up_tab],
                 )
 
+    with gr.Tab("Preprocessor", render=True):
+        preprocessor_tab()
+
     gr.LoginButton()
     gr.DuplicateButton(value="Duplicate Space for private use (This demo does not work on CPU. Requires GPU Space)")
 

constants.py

@@ -17,7 +17,7 @@ DOWNLOAD_LORA = "https://huggingface.co/Leopain/color/resolve/main/Coloring_book
 
 LOAD_DIFFUSERS_FORMAT_MODEL = [
     'stabilityai/stable-diffusion-xl-base-1.0',
-    'Laxhar/noobai-XL-1.0',
+    'Laxhar/noobai-XL-1.1',
     'black-forest-labs/FLUX.1-dev',
     'John6666/blue-pencil-flux1-v021-fp8-flux',
     'John6666/wai-ani-flux-v10forfp8-fp8-flux',
@@ -31,6 +31,7 @@ LOAD_DIFFUSERS_FORMAT_MODEL = [
     'terminusresearch/FluxBooru-v0.3',
     'ostris/OpenFLUX.1',
     'shuttleai/shuttle-3-diffusion',
+    'Laxhar/noobai-XL-1.0',
     'John6666/noobai-xl-nai-xl-epsilonpred10version-sdxl',
     'Laxhar/noobai-XL-0.77',
     'John6666/noobai-xl-nai-xl-epsilonpred075version-sdxl',
@@ -40,9 +41,13 @@ LOAD_DIFFUSERS_FORMAT_MODEL = [
     'John6666/noobaiiter-xl-vpred-v075-sdxl',
     'John6666/ntr-mix-illustrious-xl-noob-xl-v40-sdxl',
     'John6666/ntr-mix-illustrious-xl-noob-xl-ntrmix35-sdxl',
+    'John6666/ntr-mix-illustrious-xl-noob-xl-v777-sdxl',
+    'John6666/ntr-mix-illustrious-xl-noob-xl-v777forlora-sdxl',
     'John6666/haruki-mix-illustrious-v10-sdxl',
     'John6666/noobreal-v10-sdxl',
     'John6666/complicated-noobai-merge-vprediction-sdxl',
+    'Laxhar/noobai-XL-Vpred-0.65s',
+    'Laxhar/noobai-XL-Vpred-0.65',
     'Laxhar/noobai-XL-Vpred-0.6',
     'John6666/noobai-xl-nai-xl-vpred05version-sdxl',
     'John6666/noobai-fusion2-vpred-itercomp-v1-sdxl',
@@ -56,6 +61,7 @@ LOAD_DIFFUSERS_FORMAT_MODEL = [
     'John6666/wai-nsfw-illustrious-v70-sdxl',
     'John6666/illustrious-pony-mix-v3-sdxl',
     'John6666/nova-anime-xl-illustriousv10-sdxl',
+    'John6666/nova-orange-xl-v30-sdxl',
     'John6666/silvermoon-mix03-illustrious-v10-sdxl',
     'eienmojiki/Anything-XL',
     'eienmojiki/Starry-XL-v5.2',
@@ -82,9 +88,8 @@ LOAD_DIFFUSERS_FORMAT_MODEL = [
     'John6666/prefect-pony-xl-v4-sdxl',
     'John6666/mala-anime-mix-nsfw-pony-xl-v5-sdxl',
     'John6666/wai-ani-nsfw-ponyxl-v10-sdxl',
-    'John6666/wai-ani-nsfw-ponyxl-v9-sdxl',
     'John6666/wai-real-mix-v11-sdxl',
-    'John6666/babes-by-stable-yogi-ponyv3-sdxl',
+    'John6666/wai-shuffle-pdxl-v2-sdxl',
     'John6666/wai-c-v6-sdxl',
     'John6666/iniverse-mix-xl-sfwnsfw-pony-guofeng-v43-sdxl',
     'John6666/sifw-annihilation-xl-v2-sdxl',
@@ -114,7 +119,7 @@ LOAD_DIFFUSERS_FORMAT_MODEL = [
     'digiplay/DarkSushi2.5D_v1',
     'digiplay/darkphoenix3D_v1.1',
     'digiplay/BeenYouLiteL11_diffusers',
-    'Yntec/RevAnimatedV2Rebirth',
+    'GraydientPlatformAPI/rev-animated2',
     'youknownothing/cyberrealistic_v50',
     'youknownothing/deliberate-v6',
     'GraydientPlatformAPI/deliberate-cyber3',
@@ -142,7 +147,7 @@ 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',
-    ]
+]
 
 CIVITAI_API_KEY = os.environ.get("CIVITAI_API_KEY")
 HF_TOKEN = os.environ.get("HF_READ_TOKEN")
@@ -155,79 +160,6 @@ DIRECTORY_EMBEDS = 'embedings'
 CACHE_HF = "/home/user/.cache/huggingface/hub/"
 STORAGE_ROOT = "/home/user/"
 
-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)",
-  ],
-  "lineart_anime": [
-    "Lineart",
-    "Lineart coarse",
-    "Lineart (anime)",
-    "None",
-    "None (anime)",
-  ],
-  "shuffle": [
-    "ContentShuffle",
-    "None",
-  ],
-  "canny": [
-    "Canny",
-    "None",
-  ],
-  "mlsd": [
-    "MLSD",
-    "None",
-  ],
-  "ip2p": [
-    "ip2p"
-  ],
-  "recolor": [
-    "Recolor luminance",
-    "Recolor intensity",
-    "None",
-  ],
-  "tile": [
-    "Mild Blur",
-    "Moderate Blur",
-    "Heavy Blur",
-    "None",
-  ],
-
-}
-
 TASK_STABLEPY = {
     'txt2img': 'txt2img',
     'img2img': 'img2img',
@@ -284,11 +216,74 @@ UPSCALER_DICT_GUI = {
 
 UPSCALER_KEYS = list(UPSCALER_DICT_GUI.keys())
 
+DIFFUSERS_CONTROLNET_MODEL = [
+    "Automatic",
+
+    "xinsir/controlnet-union-sdxl-1.0",
+    "xinsir/anime-painter",
+    "Eugeoter/noob-sdxl-controlnet-canny",
+    "Eugeoter/noob-sdxl-controlnet-lineart_anime",
+    "Eugeoter/noob-sdxl-controlnet-depth",
+    "Eugeoter/noob-sdxl-controlnet-normal",
+    "Eugeoter/noob-sdxl-controlnet-softedge_hed",
+    "Eugeoter/noob-sdxl-controlnet-scribble_pidinet",
+    "Eugeoter/noob-sdxl-controlnet-scribble_hed",
+    "Eugeoter/noob-sdxl-controlnet-manga_line",
+    "Eugeoter/noob-sdxl-controlnet-lineart_realistic",
+    "Eugeoter/noob-sdxl-controlnet-depth_midas-v1-1",
+    "dimitribarbot/controlnet-openpose-sdxl-1.0-safetensors",
+    "r3gm/controlnet-openpose-sdxl-1.0-fp16",
+    "r3gm/controlnet-canny-scribble-integrated-sdxl-v2-fp16",
+    "r3gm/controlnet-union-sdxl-1.0-fp16",
+    "r3gm/controlnet-lineart-anime-sdxl-fp16",
+    "r3gm/control_v1p_sdxl_qrcode_monster_fp16",
+    "r3gm/controlnet-tile-sdxl-1.0-fp16",
+    "r3gm/controlnet-recolor-sdxl-fp16",
+    "r3gm/controlnet-openpose-twins-sdxl-1.0-fp16",
+    "r3gm/controlnet-qr-pattern-sdxl-fp16",
+    "brad-twinkl/controlnet-union-sdxl-1.0-promax",
+    "Yakonrus/SDXL_Controlnet_Tile_Realistic_v2",
+    "TheMistoAI/MistoLine",
+    "briaai/BRIA-2.3-ControlNet-Recoloring",
+    "briaai/BRIA-2.3-ControlNet-Canny",
+
+    "lllyasviel/control_v11p_sd15_openpose",
+    "lllyasviel/control_v11p_sd15_canny",
+    "lllyasviel/control_v11p_sd15_mlsd",
+    "lllyasviel/control_v11p_sd15_scribble",
+    "lllyasviel/control_v11p_sd15_softedge",
+    "lllyasviel/control_v11p_sd15_seg",
+    "lllyasviel/control_v11f1p_sd15_depth",
+    "lllyasviel/control_v11p_sd15_normalbae",
+    "lllyasviel/control_v11p_sd15_lineart",
+    "lllyasviel/control_v11p_sd15s2_lineart_anime",
+    "lllyasviel/control_v11e_sd15_shuffle",
+    "lllyasviel/control_v11e_sd15_ip2p",
+    "lllyasviel/control_v11p_sd15_inpaint",
+    "monster-labs/control_v1p_sd15_qrcode_monster",
+    "lllyasviel/control_v11f1e_sd15_tile",
+    "latentcat/control_v1p_sd15_brightness",
+    "yuanqiuye/qrcode_controlnet_v3",
+
+    "Shakker-Labs/FLUX.1-dev-ControlNet-Union-Pro",
+    # "Shakker-Labs/FLUX.1-dev-ControlNet-Pose",
+    # "Shakker-Labs/FLUX.1-dev-ControlNet-Depth",
+    # "jasperai/Flux.1-dev-Controlnet-Upscaler",
+    # "jasperai/Flux.1-dev-Controlnet-Depth",
+    # "jasperai/Flux.1-dev-Controlnet-Surface-Normals",
+    # "XLabs-AI/flux-controlnet-canny-diffusers",
+    # "XLabs-AI/flux-controlnet-hed-diffusers",
+    # "XLabs-AI/flux-controlnet-depth-diffusers",
+    # "InstantX/FLUX.1-dev-Controlnet-Union",
+    # "InstantX/FLUX.1-dev-Controlnet-Canny",
+]
+
 PROMPT_W_OPTIONS = [
     ("Compel format: (word)weight", "Compel"),
     ("Classic format: (word:weight)", "Classic"),
     ("Classic-original format: (word:weight)", "Classic-original"),
     ("Classic-no_norm format: (word:weight)", "Classic-no_norm"),
+    ("Classic-sd_embed format: (word:weight)", "Classic-sd_embed"),
     ("Classic-ignore", "Classic-ignore"),
     ("None", "None"),
 ]
@@ -371,7 +366,7 @@ EXAMPLES_GUI = [
         1.0,  # cn scale
         0.0,  # cn start
         1.0,  # cn end
-        "Classic",
+        "Classic-no_norm",
         "Nearest",
         45,
         False,
@@ -384,7 +379,7 @@ EXAMPLES_GUI = [
         -1,
         "None",
         0.33,
-        "FlowMatchEuler",
+        "FlowMatch Euler",
         1152,
         896,
         "black-forest-labs/FLUX.1-dev",
@@ -408,7 +403,7 @@ EXAMPLES_GUI = [
         -1,
         "None",
         0.33,
-        "DPM++ 2M SDE Lu",
+        "DPM++ 2M SDE Ef",
         1024,
         1024,
         "John6666/epicrealism-xl-v10kiss2-sdxl",
@@ -491,7 +486,7 @@ EXAMPLES_GUI = [
         1.0,  # cn scale
         0.0,  # cn start
         0.9,  # cn end
-        "Compel",
+        "Classic-original",
         "Latent (antialiased)",
         46,
         False,

dc.py

@@ -5,9 +5,9 @@ from stablepy import (
     SCHEDULE_TYPE_OPTIONS,
     SCHEDULE_PREDICTION_TYPE_OPTIONS,
     check_scheduler_compatibility,
+    TASK_AND_PREPROCESSORS,
 )
 from constants import (
-    PREPROCESSOR_CONTROLNET,
     TASK_STABLEPY,
     TASK_MODEL_LIST,
     UPSCALER_DICT_GUI,
@@ -17,6 +17,7 @@ from constants import (
     SDXL_TASK,
     MODEL_TYPE_TASK,
     POST_PROCESSING_SAMPLER,
+    DIFFUSERS_CONTROLNET_MODEL,
 
 )
 from stablepy.diffusers_vanilla.style_prompt_config import STYLE_NAMES
@@ -42,29 +43,27 @@ from utils import (
     html_template_message,
     escape_html,
 )
+from image_processor import preprocessor_tab
 from datetime import datetime
 import gradio as gr
 import logging
 import diffusers
 import warnings
 from stablepy import logger
+from diffusers import FluxPipeline
 # import urllib.parse
 
 ImageFile.LOAD_TRUNCATED_IMAGES = True
+torch.backends.cuda.matmul.allow_tf32 = True
 # os.environ["PYTORCH_NO_CUDA_MEMORY_CACHING"] = "1"
 print(os.getenv("SPACES_ZERO_GPU"))
 
 ## BEGIN MOD
-import gradio as gr
-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.DEBUG)
 
 from env import (
@@ -120,8 +119,8 @@ vae_model_list = get_model_list(DIRECTORY_VAES)
 vae_model_list.insert(0, "BakedVAE")
 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)
+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):
@@ -130,6 +129,16 @@ def get_embed_list(pipeline_name):
 
 print('\033[33m🏁 Download and listing of valid models completed.\033[0m')
 
+flux_repo = "camenduru/FLUX.1-dev-diffusers"
+flux_pipe = FluxPipeline.from_pretrained(
+    flux_repo,
+    transformer=None,
+    torch_dtype=torch.bfloat16,
+).to("cuda")
+components = flux_pipe.components
+components.pop("transformer", None)
+delete_model(flux_repo)
+
 ## BEGIN MOD
 class GuiSD:
     def __init__(self, stream=True):
@@ -139,7 +148,7 @@ class GuiSD:
         self.last_load = datetime.now()
         self.inventory = []
 
-    def update_storage_models(self, storage_floor_gb=32, required_inventory_for_purge=3):
+    def update_storage_models(self, storage_floor_gb=24, required_inventory_for_purge=3):
         while get_used_storage_gb() > storage_floor_gb:
             if len(self.inventory) < required_inventory_for_purge:
                 break
@@ -153,23 +162,12 @@ class GuiSD:
             ] + [model_name]
         print(self.inventory)
 
-    def infer_short(self, model, pipe_params, progress=gr.Progress(track_tqdm=True)):
-        #progress(0, desc="Start inference...")
-        images, seed, image_list, metadata = model(**pipe_params)
-        #progress(1, desc="Inference completed.")
-        if not isinstance(images, list): images = [images]
-        images = save_images(images, metadata)
-        img = []
-        for image in images:
-            img.append((image, None))
-        return img
+    def load_new_model(self, model_name, vae_model, task, controlnet_model, progress=gr.Progress(track_tqdm=True)):
 
-    def load_new_model(self, model_name, vae_model, task, progress=gr.Progress(track_tqdm=True)):
+        # download link model > model_name
 
         self.update_storage_models()
 
-        # download link model > model_name
-
         vae_model = vae_model if vae_model != "None" else None
         model_type = get_model_type(model_name)
         dtype_model = torch.bfloat16 if model_type == "FLUX" else torch.float16
@@ -221,17 +219,19 @@ class GuiSD:
                     vae_model=vae_model,
                     type_model_precision=dtype_model,
                     retain_task_model_in_cache=False,
+                    controlnet_model=controlnet_model,
                     device="cpu",
+                    env_components=components,
                 )
+                self.model.advanced_params(image_preprocessor_cuda_active=True)
             else:
-
                 if self.model.base_model_id != model_name:
                     load_now_time = datetime.now()
                     elapsed_time = max((load_now_time - self.last_load).total_seconds(), 0)
 
-                    if elapsed_time <= 8:
+                    if elapsed_time <= 9:
                         print("Waiting for the previous model's time ops...")
-                        time.sleep(8-elapsed_time)
+                        time.sleep(9 - elapsed_time)
 
                 self.model.device = torch.device("cpu")
                 self.model.load_pipe(
@@ -240,6 +240,7 @@ class GuiSD:
                     vae_model=vae_model,
                     type_model_precision=dtype_model,
                     retain_task_model_in_cache=False,
+                    controlnet_model=controlnet_model,
                 )
 
             end_time = time.time()
@@ -276,6 +277,10 @@ class GuiSD:
         lora_scale4,
         lora5,
         lora_scale5,
+        lora6,
+        lora_scale6,
+        lora7,
+        lora_scale7,
         sampler,
         schedule_type,
         schedule_prediction_type,
@@ -296,6 +301,8 @@ class GuiSD:
         high_threshold,
         value_threshold,
         distance_threshold,
+        recolor_gamma_correction,
+        tile_blur_sigma,
         controlnet_output_scaling_in_unet,
         controlnet_start_threshold,
         controlnet_stop_threshold,
@@ -312,6 +319,9 @@ class GuiSD:
         hires_negative_prompt,
         hires_before_adetailer,
         hires_after_adetailer,
+        hires_schedule_type,
+        hires_guidance_scale,
+        controlnet_model,
         loop_generation,
         leave_progress_bar,
         disable_progress_bar,
@@ -353,6 +363,7 @@ class GuiSD:
         mask_blur_b,
         mask_padding_b,
         retain_task_cache_gui,
+        guidance_rescale,
         image_ip1,
         mask_ip1,
         model_ip1,
@@ -369,7 +380,7 @@ class GuiSD:
         yield info_state, gr.update(), gr.update()
 
         vae_model = vae_model if vae_model != "None" else None
-        loras_list = [lora1, lora2, lora3, lora4, lora5]
+        loras_list = [lora1, lora2, lora3, lora4, lora5, lora6, lora7]
         vae_msg = f"VAE: {vae_model}" if vae_model else ""
         msg_lora = ""
 
@@ -478,6 +489,8 @@ class GuiSD:
             "high_threshold": high_threshold,
             "value_threshold": value_threshold,
             "distance_threshold": distance_threshold,
+            "recolor_gamma_correction": float(recolor_gamma_correction),
+            "tile_blur_sigma": int(tile_blur_sigma),
             "lora_A": lora1 if lora1 != "None" else None,
             "lora_scale_A": lora_scale1,
             "lora_B": lora2 if lora2 != "None" else None,
@@ -488,6 +501,10 @@ class GuiSD:
             "lora_scale_D": lora_scale4,
             "lora_E": lora5 if lora5 != "None" else None,
             "lora_scale_E": lora_scale5,
+            "lora_F": lora6 if lora6 != "None" else None,
+            "lora_scale_F": lora_scale6,
+            "lora_G": lora7 if lora7 != "None" else None,
+            "lora_scale_G": lora_scale7,
 ## BEGIN MOD
             "textual_inversion": get_embed_list(self.model.class_name) if textual_inversion else [],
 ## END MOD
@@ -531,6 +548,8 @@ class GuiSD:
             "hires_sampler": hires_sampler,
             "hires_before_adetailer": hires_before_adetailer,
             "hires_after_adetailer": hires_after_adetailer,
+            "hires_schedule_type": hires_schedule_type,
+            "hires_guidance_scale": hires_guidance_scale,
             "ip_adapter_image": params_ip_img,
             "ip_adapter_mask": params_ip_msk,
             "ip_adapter_model": params_ip_model,
@@ -538,13 +557,15 @@ class GuiSD:
             "ip_adapter_scale": params_ip_scale,
         }
 
+        # kwargs for diffusers pipeline
+        if guidance_rescale:
+            pipe_params["guidance_rescale"] = guidance_rescale
+
         self.model.device = torch.device("cuda:0")
-        if hasattr(self.model.pipe, "transformer") and loras_list != ["None"] * 5:
+        if hasattr(self.model.pipe, "transformer") and loras_list != ["None"] * self.model.num_loras:
             self.model.pipe.transformer.to(self.model.device)
             print("transformer to cuda")
 
-        #return self.infer_short(self.model, pipe_params), info_state
-
         actual_progress = 0
         info_images = gr.update()
         for img, [seed, image_path, metadata] in self.model(**pipe_params):
@@ -569,7 +590,7 @@ class GuiSD:
                 if msg_lora:
                     info_images += msg_lora
 
-                info_images = info_images + "<br>" + "GENERATION DATA:<br>" + escape_html(metadata[0]) + "<br>-------<br>"
+                info_images = info_images + "<br>" + "GENERATION DATA:<br>" + escape_html(metadata[-1]) + "<br>-------<br>"
 
                 download_links = "<br>".join(
                     [
@@ -604,37 +625,38 @@ def dummy_gpu():
 
 
 def sd_gen_generate_pipeline(*args):
-
     gpu_duration_arg = int(args[-1]) if args[-1] else 59
     verbose_arg = int(args[-2])
     load_lora_cpu = args[-3]
     generation_args = args[:-3]
     lora_list = [
         None if item == "None" or item == "" else item # MOD
-        for item in [args[7], args[9], args[11], args[13], args[15]]
+        for item in [args[7], args[9], args[11], args[13], args[15], args[17], args[19]]
     ]
-    lora_status = [None] * 5
+    lora_status = [None] * sd_gen.model.num_loras
 
     msg_load_lora = "Updating LoRAs in GPU..."
     if load_lora_cpu:
-        msg_load_lora = "Updating LoRAs in CPU (Slow but saves GPU usage)..."
+        msg_load_lora = "Updating LoRAs in CPU..."
 
-    if lora_list != sd_gen.model.lora_memory and lora_list != [None] * 5:
+    if lora_list != sd_gen.model.lora_memory and lora_list != [None] * sd_gen.model.num_loras:
         yield msg_load_lora, gr.update(), gr.update()
 
     # Load lora in CPU
     if load_lora_cpu:
-        lora_status = sd_gen.model.lora_merge(
+        lora_status = sd_gen.model.load_lora_on_the_fly(
             lora_A=lora_list[0], lora_scale_A=args[8],
             lora_B=lora_list[1], lora_scale_B=args[10],
             lora_C=lora_list[2], lora_scale_C=args[12],
             lora_D=lora_list[3], lora_scale_D=args[14],
             lora_E=lora_list[4], lora_scale_E=args[16],
+            lora_F=lora_list[5], lora_scale_F=args[18],
+            lora_G=lora_list[6], lora_scale_G=args[20],
         )
         print(lora_status)
 
-    sampler_name = args[17]
-    schedule_type_name = args[18]
+    sampler_name = args[21]
+    schedule_type_name = args[22]
     _, _, msg_sampler = check_scheduler_compatibility(
         sd_gen.model.class_name, sampler_name, schedule_type_name
     )
@@ -648,7 +670,7 @@ def sd_gen_generate_pipeline(*args):
             elif status is not None:
                 gr.Warning(f"Failed to load LoRA: {lora}")
 
-        if lora_status == [None] * 5 and sd_gen.model.lora_memory != [None] * 5 and load_lora_cpu:
+        if lora_status == [None] * sd_gen.model.num_loras and sd_gen.model.lora_memory != [None] * sd_gen.model.num_loras and load_lora_cpu:
             lora_cache_msg = ", ".join(
                 str(x) for x in sd_gen.model.lora_memory if x is not None
             )
@@ -664,7 +686,6 @@ def sd_gen_generate_pipeline(*args):
 
     # yield from sd_gen.generate_pipeline(*generation_args)
     yield from dynamic_gpu_duration(
-    #return dynamic_gpu_duration(
         sd_gen.generate_pipeline,
         gpu_duration_arg,
         *generation_args,
@@ -706,6 +727,7 @@ def esrgan_upscale(image, upscaler_name, upscaler_size):
     return image_path
 
 
+# https://huggingface.co/spaces/BestWishYsh/ConsisID-preview-Space/discussions/1#674969a022b99c122af5d407
 dynamic_gpu_duration.zerogpu = True
 sd_gen_generate_pipeline.zerogpu = True
 sd_gen = GuiSD()
@@ -718,30 +740,69 @@ import numpy as np
 import random
 import json
 import shutil
-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_state, set_state,
+from tagger.tagger import insert_model_recom_prompt
+from modutils import (safe_float, escape_lora_basename, to_lora_key, to_lora_path, valid_model_name, set_textual_inversion_prompt,
+    get_local_model_list, get_model_pipeline, get_private_lora_model_lists, get_valid_lora_name, get_state, set_state,
     get_valid_lora_path, get_valid_lora_wt, get_lora_info, CIVITAI_SORT, CIVITAI_PERIOD, CIVITAI_BASEMODEL,
-    normalize_prompt_list, get_civitai_info, search_lora_on_civitai, translate_to_en, get_t2i_model_info, get_civitai_tag, save_image_history)
+    normalize_prompt_list, get_civitai_info, search_lora_on_civitai, translate_to_en, get_t2i_model_info, get_civitai_tag, save_image_history,
+    get_all_lora_list, get_all_lora_tupled_list, update_lora_dict, download_lora, copy_lora, download_my_lora, set_prompt_loras,
+    apply_lora_prompt, update_loras, search_civitai_lora, search_civitai_lora_json, update_civitai_selection, select_civitai_lora)
 
 
 #@spaces.GPU
 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", vae=None, translate=False, schedule_type=SCHEDULE_TYPE_OPTIONS[0], schedule_prediction_type=SCHEDULE_PREDICTION_TYPE_OPTIONS[0],
-          clip_skip=True, pag_scale=0.0, free_u=False, gpu_duration=59, recom_prompt=True, progress=gr.Progress(track_tqdm=True)):
+          lora3=None, lora3_wt=1.0, lora4=None, lora4_wt=1.0, lora5=None, lora5_wt=1.0, lora6=None, lora6_wt=1.0, lora7=None, lora7_wt=1.0,
+          task=TASK_MODEL_LIST[0], prompt_syntax="Classic", sampler="Euler", vae=None, schedule_type=SCHEDULE_TYPE_OPTIONS[0], schedule_prediction_type=SCHEDULE_PREDICTION_TYPE_OPTIONS[0],
+          clip_skip=True, pag_scale=0.0, free_u=False, guidance_rescale=0., image_control_dict=None, image_mask=None, strength=0.35, image_resolution=1024,
+          controlnet_model=DIFFUSERS_CONTROLNET_MODEL[0], control_net_output_scaling=1.0, control_net_start_threshold=0., control_net_stop_threshold=1.,
+          preprocessor_name="Canny", preprocess_resolution=512, low_threshold=100, high_threshold=200,
+          value_threshold=0.1, distance_threshold=0.1, recolor_gamma_correction=1., tile_blur_sigma=9,
+          image_ip1_dict=None, mask_ip1=None, model_ip1="plus_face", mode_ip1="original", scale_ip1=0.7,
+          image_ip2_dict=None, mask_ip2=None, model_ip2="base", mode_ip2="style", scale_ip2=0.7,
+          upscaler_model_path=None, upscaler_increases_size=1.0, esrgan_tile=5, esrgan_tile_overlap=8, hires_steps=30, hires_denoising_strength=0.55,
+          hires_sampler="Use same sampler", hires_schedule_type="Use same schedule type", hires_guidance_scale=-1, hires_prompt="", hires_negative_prompt="",
+          adetailer_inpaint_only=True, adetailer_verbose=False, adetailer_sampler="Use same sampler", adetailer_active_a=False,
+          prompt_ad_a="", negative_prompt_ad_a="", strength_ad_a=0.35, face_detector_ad_a=True, person_detector_ad_a=True, hand_detector_ad_a=False,
+          mask_dilation_a=4, mask_blur_a=4, mask_padding_a=32, adetailer_active_b=False, prompt_ad_b="", negative_prompt_ad_b="", strength_ad_b=0.35,
+          face_detector_ad_b=True, person_detector_ad_b=True, hand_detector_ad_b=False, mask_dilation_b=4, mask_blur_b=4, mask_padding_b=32,
+          active_textual_inversion=False, gpu_duration=59, translate=False, recom_prompt=True, progress=gr.Progress(track_tqdm=True)):
     MAX_SEED = np.iinfo(np.int32).max
 
+    image_mask = image_control_dict['layers'][0] if isinstance(image_control_dict, dict) and not image_mask else image_mask
+    image_control = image_control_dict['background'] if isinstance(image_control_dict, dict) else None
+    mask_ip1 = image_ip1_dict['layers'][0] if isinstance(image_ip1_dict, dict) and not mask_ip1 else mask_ip1
+    image_ip1 = image_ip1_dict['background'] if isinstance(image_ip1_dict, dict) else None
+    mask_ip2 = image_ip2_dict['layers'][0] if isinstance(image_ip2_dict, dict) and not mask_ip1 else mask_ip1
+    image_ip2 = image_ip2_dict['background'] if isinstance(image_ip2_dict, dict) else None
+    style_prompt = None
+    style_json = None
+    hires_before_adetailer = False
+    hires_after_adetailer = True
+    loop_generation = 1
+    leave_progress_bar = True
+    disable_progress_bar = False
     image_previews = True
+    display_images = False
+    save_generated_images = False
+    filename_pattern = "model,seed"
+    image_storage_location = "./images"
+    retain_compel_previous_load = False
+    retain_detailfix_model_previous_load = False
+    retain_hires_model_previous_load = False
+    t2i_adapter_preprocessor = True
+    adapter_conditioning_scale = 1
+    adapter_conditioning_factor = 0.55
+    xformers_memory_efficient_attention = False
+    generator_in_cpu = False
+    retain_task_cache = True
     load_lora_cpu = False
     verbose_info = False
-    filename_pattern = "model,seed"
 
     images: list[tuple[PIL.Image.Image, str | None]] = []
     progress(0, desc="Preparing...")
 
     if randomize_seed: seed = random.randint(0, MAX_SEED)
-
     generator = torch.Generator().manual_seed(seed).seed()
 
     if translate:
@@ -750,31 +811,38 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
 
     prompt, negative_prompt = insert_model_recom_prompt(prompt, negative_prompt, model_name, recom_prompt)
     progress(0.5, desc="Preparing...")
-    lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt = \
-        set_prompt_loras(prompt, model_name, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt)
+    lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt, lora6, lora6_wt, lora7, lora7_wt = \
+        set_prompt_loras(prompt, prompt_syntax, model_name, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt, lora6, lora6_wt, lora7, lora7_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)
+    lora6 = get_valid_lora_path(lora6)
+    lora7 = get_valid_lora_path(lora7)
     progress(1, desc="Preparation completed. Starting inference...")
 
     progress(0, desc="Loading model...")
-    for _ in sd_gen.load_new_model(model_name, vae, TASK_MODEL_LIST[0]):
+    for _ in sd_gen.load_new_model(valid_model_name(model_name), vae, task, controlnet_model):
         pass
     progress(1, desc="Model loaded.")
     progress(0, desc="Starting Inference...")
     for info_state, stream_images, info_images in sd_gen_generate_pipeline(prompt, negative_prompt, 1, num_inference_steps,
         guidance_scale, clip_skip, generator, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt,
-        lora4, lora4_wt, lora5, lora5_wt, sampler, schedule_type, schedule_prediction_type,
-        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, image_previews, False, False, filename_pattern, "./images", False, False, False, True, 1, 0.55,
-        False, free_u, 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, pag_scale,
-        load_lora_cpu, verbose_info, gpu_duration
+        lora4, lora4_wt, lora5, lora5_wt, lora6, lora6_wt, lora7, lora7_wt, sampler, schedule_type, schedule_prediction_type,
+        height, width, model_name, vae, task, image_control, preprocessor_name, preprocess_resolution, image_resolution,
+        style_prompt, style_json, image_mask, strength, low_threshold, high_threshold, value_threshold, distance_threshold,
+        recolor_gamma_correction, tile_blur_sigma, control_net_output_scaling, control_net_start_threshold, control_net_stop_threshold,
+        active_textual_inversion, prompt_syntax, 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,
+        hires_schedule_type, hires_guidance_scale, controlnet_model, loop_generation, leave_progress_bar, disable_progress_bar, image_previews,
+        display_images, save_generated_images, filename_pattern, image_storage_location, retain_compel_previous_load, retain_detailfix_model_previous_load,
+        retain_hires_model_previous_load, t2i_adapter_preprocessor, adapter_conditioning_scale, adapter_conditioning_factor, xformers_memory_efficient_attention,
+        free_u, 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, guidance_rescale, image_ip1, mask_ip1, model_ip1, mode_ip1, scale_ip1,
+        image_ip2, mask_ip2, model_ip2, mode_ip2, scale_ip2, pag_scale, load_lora_cpu, verbose_info, gpu_duration
     ):
         images = stream_images if isinstance(stream_images, list) else images
     progress(1, desc="Inference completed.")
@@ -786,9 +854,21 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
 #@spaces.GPU
 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", vae=None, translate=False, schedule_type=SCHEDULE_TYPE_OPTIONS[0], schedule_prediction_type=SCHEDULE_PREDICTION_TYPE_OPTIONS[0],
-           clip_skip=True, pag_scale=0.0, free_u=False, gpu_duration=59, recom_prompt=True, progress=gr.Progress(track_tqdm=True)):
+           lora3=None, lora3_wt=1.0, lora4=None, lora4_wt=1.0, lora5=None, lora5_wt=1.0, lora6=None, lora6_wt=1.0, lora7=None, lora7_wt=1.0,
+           task=TASK_MODEL_LIST[0], prompt_syntax="Classic", sampler="Euler", vae=None, schedule_type=SCHEDULE_TYPE_OPTIONS[0], schedule_prediction_type=SCHEDULE_PREDICTION_TYPE_OPTIONS[0],
+           clip_skip=True, pag_scale=0.0, free_u=False, guidance_rescale=0., image_control_dict=None, image_mask=None, strength=0.35, image_resolution=1024,
+           controlnet_model=DIFFUSERS_CONTROLNET_MODEL[0], control_net_output_scaling=1.0, control_net_start_threshold=0., control_net_stop_threshold=1.,
+           preprocessor_name="Canny", preprocess_resolution=512, low_threshold=100, high_threshold=200,
+           value_threshold=0.1, distance_threshold=0.1, recolor_gamma_correction=1., tile_blur_sigma=9,
+           image_ip1_dict=None, mask_ip1=None, model_ip1="plus_face", mode_ip1="original", scale_ip1=0.7,
+           image_ip2_dict=None, mask_ip2=None, model_ip2="base", mode_ip2="style", scale_ip2=0.7,
+           upscaler_model_path=None, upscaler_increases_size=1.0, esrgan_tile=5, esrgan_tile_overlap=8, hires_steps=30, hires_denoising_strength=0.55,
+           hires_sampler="Use same sampler", hires_schedule_type="Use same schedule type", hires_guidance_scale=-1, hires_prompt="", hires_negative_prompt="",
+           adetailer_inpaint_only=True, adetailer_verbose=False, adetailer_sampler="Use same sampler", adetailer_active_a=False,
+           prompt_ad_a="", negative_prompt_ad_a="", strength_ad_a=0.35, face_detector_ad_a=True, person_detector_ad_a=True, hand_detector_ad_a=False,
+           mask_dilation_a=4, mask_blur_a=4, mask_padding_a=32, adetailer_active_b=False, prompt_ad_b="", negative_prompt_ad_b="", strength_ad_b=0.35,
+           face_detector_ad_b=True, person_detector_ad_b=True, hand_detector_ad_b=False, mask_dilation_b=4, mask_blur_b=4, mask_padding_b=32,
+           active_textual_inversion=False, gpu_duration=59, translate=False, recom_prompt=True, progress=gr.Progress(track_tqdm=True)):
     return gr.update()
 
 
@@ -808,6 +888,32 @@ def get_vaes():
     return vae_model_list
 
 
+def update_task_options(model_name, task_name):
+    new_choices = MODEL_TYPE_TASK[get_model_type(valid_model_name(model_name))]
+
+    if task_name not in new_choices:
+        task_name = "txt2img"
+
+    return gr.update(value=task_name, choices=new_choices)
+
+
+def change_preprocessor_choices(task):
+    task = TASK_STABLEPY[task]
+    if task in TASK_AND_PREPROCESSORS.keys():
+        choices_task = TASK_AND_PREPROCESSORS[task]
+    else:
+        choices_task = TASK_AND_PREPROCESSORS["canny"]
+    return gr.update(choices=choices_task, value=choices_task[0])
+
+
+def get_ti_choices(model_name: str):
+    return get_embed_list(get_model_pipeline(valid_model_name(model_name)))
+
+
+def update_textual_inversion(active_textual_inversion: bool, model_name: str):
+    return gr.update(choices=get_ti_choices(model_name) if active_textual_inversion else [])
+
+
 cached_diffusers_model_tupled_list = get_tupled_model_list(load_diffusers_format_model)
 def get_diffusers_model_list(state: dict = {}):
     show_diffusers_model_list_detail = get_state(state, "show_diffusers_model_list_detail")
@@ -831,337 +937,6 @@ def enable_diffusers_model_detail(is_enable: bool = False, model_name: str = "",
     return gr.update(value=is_enable), gr.update(value=new_value, choices=get_diffusers_model_list(state)), state
 
 
-def load_model_prompt_dict():
-    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()
-
-
-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", model_recom_prompt_enabled = True):
-    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
-
-
-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_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):
-    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, model_name, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt):
-    import re
-    lora1 = get_valid_lora_name(lora1, model_name)
-    lora2 = get_valid_lora_name(lora2, model_name)
-    lora3 = get_valid_lora_name(lora3, model_name)
-    lora4 = get_valid_lora_name(lora4, model_name)
-    lora5 = get_valid_lora_name(lora5, model_name)
-    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=[], sort=CIVITAI_SORT[0], period=CIVITAI_PERIOD[0], tag="", user="", gallery=[]):
-    global civitai_last_results, civitai_last_choices, civitai_last_gallery
-    civitai_last_choices = [("", "")]
-    civitai_last_gallery = []
-    civitai_last_results = {}
-    items = search_lora_on_civitai(query, base_model, 100, sort, period, tag, user)
-    if not items: return gr.update(choices=[("", "")], value="", visible=False),\
-          gr.update(value="", visible=False), gr.update(visible=True), gr.update(visible=True), gr.update(visible=True)
-    civitai_last_results = {}
-    choices = []
-    gallery = []
-    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))
-        gallery.append((item['img_url'], name))
-        civitai_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), gr.update(visible=True)
-    civitai_last_choices = choices
-    civitai_last_gallery = gallery
-    result = civitai_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), gr.update(value=gallery)
-
-
-def update_civitai_selection(evt: gr.SelectData):
-    try:
-        selected_index = evt.index
-        selected = civitai_last_choices[selected_index][1]
-        return gr.update(value=selected)
-    except Exception:
-        return 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_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",

image_processor.py

@@ -0,0 +1,130 @@
+import spaces
+import gradio as gr
+from stablepy import Preprocessor
+
+PREPROCESSOR_TASKS_LIST = [
+    "Canny",
+    "Openpose",
+    "DPT",
+    "Midas",
+    "ZoeDepth",
+    "DepthAnything",
+    "HED",
+    "PidiNet",
+    "TEED",
+    "Lineart",
+    "LineartAnime",
+    "Anyline",
+    "Lineart standard",
+    "SegFormer",
+    "UPerNet",
+    "ContentShuffle",
+    "Recolor",
+    "Blur",
+    "MLSD",
+    "NormalBae",
+]
+
+preprocessor = Preprocessor()
+
+
+def process_inputs(
+    image,
+    name,
+    resolution,
+    precessor_resolution,
+    low_threshold,
+    high_threshold,
+    value_threshod,
+    distance_threshold,
+    recolor_mode,
+    recolor_gamma_correction,
+    blur_k_size,
+    pre_openpose_extra,
+    hed_scribble,
+    pre_pidinet_safe,
+    pre_lineart_coarse,
+    use_cuda,
+):
+    if not image:
+        raise ValueError("To use this, simply upload an image.")
+
+    preprocessor.load(name, False)
+
+    params = dict(
+        image_resolution=resolution,
+        detect_resolution=precessor_resolution,
+        low_threshold=low_threshold,
+        high_threshold=high_threshold,
+        thr_v=value_threshod,
+        thr_d=distance_threshold,
+        mode=recolor_mode,
+        gamma_correction=recolor_gamma_correction,
+        blur_sigma=blur_k_size,
+        hand_and_face=pre_openpose_extra,
+        scribble=hed_scribble,
+        safe=pre_pidinet_safe,
+        coarse=pre_lineart_coarse,
+    )
+
+    if use_cuda:
+        @spaces.GPU(duration=15)
+        def wrapped_func():
+            preprocessor.to("cuda")
+            return preprocessor(image, **params)
+        return wrapped_func()
+
+    return preprocessor(image, **params)
+
+
+def preprocessor_tab():
+    with gr.Row():
+        with gr.Column():
+            pre_image = gr.Image(label="Image", type="pil", sources=["upload"])
+            pre_options = gr.Dropdown(label="Preprocessor", choices=PREPROCESSOR_TASKS_LIST, value=PREPROCESSOR_TASKS_LIST[0])
+            pre_img_resolution = gr.Slider(
+                minimum=64, maximum=4096, step=64, value=1024, label="Image Resolution",
+                info="The maximum proportional size of the generated image based on the uploaded image."
+            )
+            pre_start = gr.Button(value="PROCESS IMAGE", variant="primary")
+            with gr.Accordion("Advanced Settings", open=False):
+                with gr.Column():
+                    pre_processor_resolution = gr.Slider(minimum=64, maximum=2048, step=64, value=512, label="Preprocessor Resolution")
+                    pre_low_threshold = gr.Slider(minimum=1, maximum=255, step=1, value=100, label="'CANNY' low threshold")
+                    pre_high_threshold = gr.Slider(minimum=1, maximum=255, step=1, value=200, label="'CANNY' high threshold")
+                    pre_value_threshold = gr.Slider(minimum=1, maximum=2.0, step=0.01, value=0.1, label="'MLSD' Hough value threshold")
+                    pre_distance_threshold = gr.Slider(minimum=1, maximum=20.0, step=0.01, value=0.1, label="'MLSD' Hough distance threshold")
+                    pre_recolor_mode = gr.Dropdown(label="'RECOLOR' mode", choices=["luminance", "intensity"], value="luminance")
+                    pre_recolor_gamma_correction = gr.Number(minimum=0., maximum=25., value=1., step=0.001, label="'RECOLOR' gamma correction")
+                    pre_blur_k_size = gr.Number(minimum=0, maximum=100, value=9, step=1, label="'BLUR' sigma")
+                    pre_openpose_extra = gr.Checkbox(value=True, label="'OPENPOSE' face and hand")
+                    pre_hed_scribble = gr.Checkbox(value=False, label="'HED' scribble")
+                    pre_pidinet_safe = gr.Checkbox(value=False, label="'PIDINET' safe")
+                    pre_lineart_coarse = gr.Checkbox(value=False, label="'LINEART' coarse")
+                    pre_use_cuda = gr.Checkbox(value=False, label="Use CUDA")
+
+        with gr.Column():
+            pre_result = gr.Image(label="Result", type="pil", interactive=False, format="png")
+
+            pre_start.click(
+                fn=process_inputs,
+                inputs=[
+                    pre_image,
+                    pre_options,
+                    pre_img_resolution,
+                    pre_processor_resolution,
+                    pre_low_threshold,
+                    pre_high_threshold,
+                    pre_value_threshold,
+                    pre_distance_threshold,
+                    pre_recolor_mode,
+                    pre_recolor_gamma_correction,
+                    pre_blur_k_size,
+                    pre_openpose_extra,
+                    pre_hed_scribble,
+                    pre_pidinet_safe,
+                    pre_lineart_coarse,
+                    pre_use_cuda,
+                ],
+                outputs=[pre_result],
+            )

llmdolphin.py

@@ -72,13 +72,179 @@ llm_models = {
     "Rocinante-12B-v2h-Q4_K_M.gguf": ["BeaverAI/Rocinante-12B-v2h-GGUF", MessagesFormatterType.MISTRAL],
     "Mistral-Nemo-12B-ArliAI-RPMax-v1.1.i1-Q4_K_M.gguf": ["mradermacher/Mistral-Nemo-12B-ArliAI-RPMax-v1.1-i1-GGUF", MessagesFormatterType.MISTRAL],
     "Pans_Gutenbergum_V0.1.Q4_K_M.gguf": ["mradermacher/Pans_Gutenbergum_V0.1-GGUF", MessagesFormatterType.MISTRAL],
+    "AbominationScience-12B-v4.i1-Q4_K_M.gguf": ["mradermacher/AbominationScience-12B-v4-i1-GGUF", MessagesFormatterType.MISTRAL],
     "ChronoStar-Unleashed-v0.1.i1-Q4_K_M.gguf": ["mradermacher/ChronoStar-Unleashed-v0.1-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Chatty-Harry_V3.0.i1-Q4_K_M.gguf": ["mradermacher/Chatty-Harry_V3.0-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Tora-12B.i1-Q4_K_M.gguf": ["mradermacher/Tora-12B-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "ChatML-Nemo-Pro-V2.i1-Q4_K_M.gguf": ["mradermacher/ChatML-Nemo-Pro-V2-i1-GGUF", MessagesFormatterType.MISTRAL],
     "Trinas_Nectar-8B-model_stock.i1-Q4_K_M.gguf": ["mradermacher/Trinas_Nectar-8B-model_stock-i1-GGUF", MessagesFormatterType.MISTRAL],
     "ChatWaifu_Magnum_V0.2.Q4_K_M.gguf": ["mradermacher/ChatWaifu_Magnum_V0.2-GGUF", MessagesFormatterType.MISTRAL],
     "ChatWaifu_12B_v2.0.Q5_K_M.gguf": ["mradermacher/ChatWaifu_12B_v2.0-GGUF", MessagesFormatterType.MISTRAL],
     "ChatWaifu_22B_v2.0_preview.Q4_K_S.gguf": ["mradermacher/ChatWaifu_22B_v2.0_preview-GGUF", MessagesFormatterType.MISTRAL],
     "ChatWaifu_v1.4.Q5_K_M.gguf": ["mradermacher/ChatWaifu_v1.4-GGUF", MessagesFormatterType.MISTRAL],
     "ChatWaifu_v1.3.1.Q4_K_M.gguf": ["mradermacher/ChatWaifu_v1.3.1-GGUF", MessagesFormatterType.MISTRAL],
+    "Lamarck-14B-v0.2-experimental.Q4_K_M.gguf": ["mradermacher/Lamarck-14B-v0.2-experimental-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Llama3.1-Reddit-Writer-8B.Q5_K_M.gguf": ["mradermacher/Llama3.1-Reddit-Writer-8B-GGUF", MessagesFormatterType.LLAMA_3],
+    "Franken-MistressMaid-10.5B-v2.i1-Q4_K_M.gguf": ["mradermacher/Franken-MistressMaid-10.5B-v2-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Mercury_In_Retrograde-ALT-8b-Model-Stock.i1-Q4_K_M.gguf": ["mradermacher/Mercury_In_Retrograde-ALT-8b-Model-Stock-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Virtuoso-Small.i1-Q4_K_M.gguf": ["mradermacher/Virtuoso-Small-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Tuldur-8B.Q4_K_M.gguf": ["mradermacher/Tuldur-8B-GGUF", MessagesFormatterType.LLAMA_3],
+    "Orbita-v0.1.i1-Q4_K_M.gguf": ["mradermacher/Orbita-v0.1-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Violet_Eris-BMO-12B.i1-Q4_K_M.gguf": ["mradermacher/Violet_Eris-BMO-12B-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Mistral-Darwin-7b-v0.1.i1-Q5_K_M.gguf": ["mradermacher/Mistral-Darwin-7b-v0.1-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "PrimaSumika-10.7B-128k.Q4_K_M.gguf": ["mradermacher/PrimaSumika-10.7B-128k-GGUF", MessagesFormatterType.MISTRAL],
+    "L3-Umbral-Mind-RP-v2-8B.i1-Q5_K_M.gguf": ["mradermacher/L3-Umbral-Mind-RP-v2-8B-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Arch-Function-7B.i1-Q5_K_M.gguf": ["mradermacher/Arch-Function-7B-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Llama-3-Nerdy-RP-8B.i1-Q5_K_M.gguf": ["mradermacher/Llama-3-Nerdy-RP-8B-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "magnum-twilight-12b.i1-Q4_K_M.gguf": ["mradermacher/magnum-twilight-12b-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Qwen2.5-Ultimate-14B-Instruct.i1-Q4_K_M.gguf": ["mradermacher/Qwen2.5-Ultimate-14B-Instruct-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "ContaLLM-Beauty-8B-Instruct.i1-Q5_K_M.gguf": ["mradermacher/ContaLLM-Beauty-8B-Instruct-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Eidolon-v3.1-14B-deconditioned.Q4_K_M.gguf": ["mradermacher/Eidolon-v3.1-14B-deconditioned-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "ZEUS-8B-V2L2.i1-Q5_K_M.gguf": ["mradermacher/ZEUS-8B-V2L2-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Rocinante-Prism_V2.0.Q4_K_M.gguf": ["mradermacher/Rocinante-Prism_V2.0-GGUF", MessagesFormatterType.MISTRAL],
+    "Rocinante-Prism_V2.1.Q4_K_M.gguf": ["mradermacher/Rocinante-Prism_V2.1-GGUF", MessagesFormatterType.MISTRAL],
+    "Virtuoso-Small-Q4_K_M.gguf": ["bartowski/Virtuoso-Small-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "ZEUS-8B-V2.i1-Q5_K_M.gguf": ["mradermacher/ZEUS-8B-V2-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Lamarck-14B-v0.1-experimental.i1-Q4_K_M.gguf": ["mradermacher/Lamarck-14B-v0.1-experimental-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "patricide-12B-Unslop-Mell.Q4_K_M.gguf": ["mradermacher/patricide-12B-Unslop-Mell-GGUF", MessagesFormatterType.MISTRAL],
+    "Eidolon-v3.1-14B.Q4_K_M.gguf": ["mradermacher/Eidolon-v3.1-14B-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Frigg-v1.4-8b-HIGH-FANTASY.Q5_K_M.gguf": ["mradermacher/Frigg-v1.4-8b-HIGH-FANTASY-GGUF", MessagesFormatterType.LLAMA_3],
+    "Thor-v1.4-8b-DARK-FICTION.i1-Q5_K_M.gguf": ["mradermacher/Thor-v1.4-8b-DARK-FICTION-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "QwenMosaic-7B.i1-Q5_K_M.gguf": ["mradermacher/QwenMosaic-7B-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Qwen2.5-7B-Spanish-0.2.i1-Q5_K_M.gguf": ["mradermacher/Qwen2.5-7B-Spanish-0.2-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "IceDrunkenCherryRP-7b-Q5_K_M.gguf": ["bartowski/IceDrunkenCherryRP-7b-GGUF", MessagesFormatterType.MISTRAL],
+    "SmolLumi-8B-Instruct.i1-Q5_K_M.gguf": ["mradermacher/SmolLumi-8B-Instruct-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Capt-Dark-Science-12B.i1-Q4_K_S.gguf": ["mradermacher/Capt-Dark-Science-12B-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Thor-v1.4-8b-DARK-FANTASY.i1-Q4_K_M.gguf": ["mradermacher/Thor-v1.4-8b-DARK-FANTASY-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Mayo.Q5_K_M.gguf": ["mradermacher/Mayo-GGUF", MessagesFormatterType.MISTRAL],
+    "Ella-9B.i1-Q4_K_M.gguf": ["mradermacher/Ella-9B-i1-GGUF", MessagesFormatterType.ALPACA],
+    "miscii-14b-1028-Q4_K_M.gguf": ["bartowski/miscii-14b-1028-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "SeaMarco-o1-7B-v1.Q4_K_M.gguf": ["mradermacher/SeaMarco-o1-7B-v1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "L3.1-RP-Hero-InBetween-8B.i1-Q5_K_M.gguf": ["mradermacher/L3.1-RP-Hero-InBetween-8B-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "SeQwence-14Bv4.Q4_K_M.gguf": ["mradermacher/SeQwence-14Bv4-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "QwenStock2-14B.Q4_K_M.gguf": ["mradermacher/QwenStock2-14B-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Dark-Science-12B.i1-Q4_K_S.gguf": ["mradermacher/Dark-Science-12B-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Liberated-Qwen1.5-14B.Q4_K_M.gguf": ["mradermacher/Liberated-Qwen1.5-14B-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Blur-7b-slerp-v1.4.Q5_K_M.gguf": ["mradermacher/Blur-7b-slerp-v1.4-GGUF", MessagesFormatterType.MISTRAL],
+    "Llama-3-linear-8B.Q5_K_M.gguf": ["mradermacher/Llama-3-linear-8B-GGUF", MessagesFormatterType.LLAMA_3],
+    "SAINEMO-reMIX.i1-Q4_K_M.gguf": ["mradermacher/SAINEMO-reMIX-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Blur-7b-slerp-v1.44.Q5_K_M.gguf": ["mradermacher/Blur-7b-slerp-v1.44-GGUF", MessagesFormatterType.MISTRAL],
+    "QwenStock1-14B.Q4_K_S.gguf": ["mradermacher/QwenStock1-14B-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Matryoshka-8B-LINEAR.i1-Q4_K_S.gguf": ["mradermacher/Matryoshka-8B-LINEAR-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "8b-Base-mixed-1.Q5_K_M.gguf": ["mradermacher/8b-Base-mixed-1-GGUF", MessagesFormatterType.LLAMA_3],
+    "MT-Gen3-IMM-gemma-2-9B.Q4_K_M.gguf": ["mradermacher/MT-Gen3-IMM-gemma-2-9B-GGUF", MessagesFormatterType.ALPACA],
+    "MT-Gen3-IF-gemma-2-MT4g2S5-9B.Q4_K_M.gguf": ["mradermacher/MT-Gen3-IF-gemma-2-MT4g2S5-9B-GGUF", MessagesFormatterType.ALPACA],
+    "Nemo-DPO-v11.Q4_K_M.gguf": ["mradermacher/Nemo-DPO-v11-GGUF", MessagesFormatterType.MISTRAL],
+    "SeQwence-14B-EvolMerge.i1-Q4_K_M.gguf": ["mradermacher/SeQwence-14B-EvolMerge-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Q2.5-14B-Evalternagar.Q4_K_M.gguf": ["mradermacher/Q2.5-14B-Evalternagar-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "RP-SAINEMO.i1-Q4_K_M.gguf": ["mradermacher/RP-SAINEMO-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Freyja-v4.95-New-writer7-7b-NON-FICTION.i1-Q4_K_M.gguf": ["mradermacher/Freyja-v4.95-New-writer7-7b-NON-FICTION-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "WIP-Acacia-8B-Model_Stock.Q5_K_M.gguf": ["mradermacher/WIP-Acacia-8B-Model_Stock-GGUF", MessagesFormatterType.LLAMA_3],
+    "Freyja-v4.95-Sao10K-7b-NON-FICTION.i1-Q4_K_M.gguf": ["mradermacher/Freyja-v4.95-Sao10K-7b-NON-FICTION-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "MT-Merge2-MUB-gemma-2-9B.Q4_K_M.gguf": ["mradermacher/MT-Merge2-MUB-gemma-2-9B-GGUF", MessagesFormatterType.ALPACA],
+    "llama3.1-8b-instruct-political-subreddits.i1-Q5_K_M.gguf": ["mradermacher/llama3.1-8b-instruct-political-subreddits-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Freyja-v4.95-Undi95-7b-NON-FICTION.i1-Q5_K_M.gguf": ["mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Freyja-v4.95-mix-7b-NON-FICTION.i1-Q4_K_M.gguf": ["mradermacher/Freyja-v4.95-mix-7b-NON-FICTION-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Condensed_Milk-8B-Model_Stock.Q4_K_S.gguf": ["mradermacher/Condensed_Milk-8B-Model_Stock-GGUF", MessagesFormatterType.LLAMA_3],
+    "Freyja-v4.95-maldv-7b-NON-FICTION.i1-Q4_K_S.gguf": ["mradermacher/Freyja-v4.95-maldv-7b-NON-FICTION-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Freyja-v4.95-New-writer-7b-NON-FICTION.i1-Q4_K_M.gguf": ["mradermacher/Freyja-v4.95-New-writer-7b-NON-FICTION-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Freyja-v4.95-Dark-Planet-7b-NON-FICTION.Q5_K_M.gguf": ["mradermacher/Freyja-v4.95-Dark-Planet-7b-NON-FICTION-GGUF", MessagesFormatterType.LLAMA_3],
+    "AgoraMix-14B-stock-v0.1.i1-Q4_K_M.gguf": ["mradermacher/AgoraMix-14B-stock-v0.1-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Keiana-L3-Test6.2-8B-18.Q5_K_M.gguf": ["mradermacher/Keiana-L3-Test6.2-8B-18-GGUF", MessagesFormatterType.LLAMA_3],
+    "MFANNv0.25.i1-Q5_K_M.gguf": ["mradermacher/MFANNv0.25-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Qwestion-14B.i1-Q4_K_M.gguf": ["mradermacher/Qwestion-14B-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "SeQwence-14B-EvolMergev1.i1-Q4_K_M.gguf": ["mradermacher/SeQwence-14B-EvolMergev1-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Frigg-v1.35-8b-HIGH-FANTASY-1024k.i1-Q5_K_M.gguf": ["mradermacher/Frigg-v1.35-8b-HIGH-FANTASY-1024k-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Odin-v1.0-8b-FICTION-1024k.i1-Q4_K_M.gguf": ["mradermacher/Odin-v1.0-8b-FICTION-1024k-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Marco-01-slerp6-7B.Q5_K_M.gguf": ["mradermacher/Marco-01-slerp6-7B-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Alita99-8B-LINEAR.Q5_K_M.gguf": ["mradermacher/Alita99-8B-LINEAR-GGUF", MessagesFormatterType.LLAMA_3],
+    "Thor-v1.35-8b-DARK-FANTASY-1024k.Q5_K_M.gguf": ["mradermacher/Thor-v1.35-8b-DARK-FANTASY-1024k-GGUF", MessagesFormatterType.LLAMA_3],
+    "Thor-v1.3a-8b-FANTASY-1024k.Q5_K_M.gguf": ["mradermacher/Thor-v1.3a-8b-FANTASY-1024k-GGUF", MessagesFormatterType.LLAMA_3],
+    "LemonP_ALT-8B-Model_Stock.Q5_K_M.gguf": ["mradermacher/LemonP_ALT-8B-Model_Stock-GGUF", MessagesFormatterType.LLAMA_3],
+    "Loki-v2.75-8b-EROTICA-1024k.Q5_K_M.gguf": ["mradermacher/Loki-v2.75-8b-EROTICA-1024k-GGUF", MessagesFormatterType.LLAMA_3],
+    "marco-o1-uncensored.Q5_K_M.gguf": ["mradermacher/marco-o1-uncensored-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "SeQwence-14Bv1.i1-Q4_K_M.gguf": ["mradermacher/SeQwence-14Bv1-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "MT5-Gen2-GP-gemma-2-MT1RGDv0.1-9B.Q4_K_M.gguf": ["mradermacher/MT5-Gen2-GP-gemma-2-MT1RGDv0.1-9B-GGUF", MessagesFormatterType.ALPACA],
+    "MT5-Gen2-IF-gemma-2-MT1RAv0.1-9B.Q4_K_M.gguf": ["mradermacher/MT5-Gen2-IF-gemma-2-MT1RAv0.1-9B-GGUF", MessagesFormatterType.ALPACA],
+    "SeQwence-14Bv2.i1-Q4_K_M.gguf": ["mradermacher/SeQwence-14Bv2-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "ChaiML-Nemo-DPO-V8.i1-Q4_K_M.gguf": ["mradermacher/ChaiML-Nemo-DPO-V8-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "MT4-Gen2-MAMU-gemma-2-9B.Q4_K_M.gguf": ["mradermacher/MT4-Gen2-MAMU-gemma-2-9B-GGUF", MessagesFormatterType.ALPACA],
+    "MT4-Gen2-IF-gemma-2-MT5MT1-9B.Q4_K_M.gguf": ["mradermacher/MT4-Gen2-IF-gemma-2-MT5MT1-9B-GGUF", MessagesFormatterType.ALPACA],
+    "Chronos-Prism_V1.0.i1-Q4_K_M.gguf": ["mradermacher/Chronos-Prism_V1.0-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Marco-01-slerp5-7B.Q5_K_M.gguf": ["mradermacher/Marco-01-slerp5-7B-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "RQwen-v0.1.Q4_K_M.gguf": ["mradermacher/RQwen-v0.1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Teleut-7b.i1-Q5_K_M.gguf": ["mradermacher/Teleut-7b-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "RP-Naughty-v1.0f-8b.i1-Q5_K_M.gguf": ["mradermacher/RP-Naughty-v1.0f-8b-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "MT4-Gen2-GBMAMU-gemma-2-9B.Q4_K_M.gguf": ["mradermacher/MT4-Gen2-GBMAMU-gemma-2-9B-GGUF", MessagesFormatterType.ALPACA],
+    "RP-Naughty-v1.1-8b.Q5_K_M.gguf": ["mradermacher/RP-Naughty-v1.1-8b-GGUF", MessagesFormatterType.LLAMA_3],
+    "RP-Naughty-v1.0e-8b.Q5_K_M.gguf": ["mradermacher/RP-Naughty-v1.0e-8b-GGUF", MessagesFormatterType.LLAMA_3],
+    "RP-Naughty-v1.1b-8b.Q5_K_M.gguf": ["mradermacher/RP-Naughty-v1.1b-8b-GGUF", MessagesFormatterType.LLAMA_3],
+    "RP-Naughty-v1.1a-8b.Q5_K_M.gguf": ["mradermacher/RP-Naughty-v1.1a-8b-GGUF", MessagesFormatterType.LLAMA_3],
+    "Marco-01-slerp4-7B.Q5_K_M.gguf": ["mradermacher/Marco-01-slerp4-7B-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "RP-Naughty-v1.0b-8b.Q5_K_M.gguf": ["mradermacher/RP-Naughty-v1.0b-8b-GGUF", MessagesFormatterType.LLAMA_3],
+    "RP-Naughty-v1.0c-8b.Q5_K_M.gguf": ["mradermacher/RP-Naughty-v1.0c-8b-GGUF", MessagesFormatterType.LLAMA_3],
+    "IceDrunkenCherryRP-7b.i1-Q5_K_M.gguf": ["mradermacher/IceDrunkenCherryRP-7b-i1-GGUF", MessagesFormatterType.ALPACA],
+    "Thor-v1.2-8b-1024k.i1-Q5_K_M.gguf": ["mradermacher/Thor-v1.2-8b-1024k-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "RP-Naughty-v1.0d-8b.Q5_K_M.gguf": ["mradermacher/RP-Naughty-v1.0d-8b-GGUF", MessagesFormatterType.LLAMA_3],
+    "Cakrawala-8B.i1-Q4_K_S.gguf": ["mradermacher/Cakrawala-8B-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Thor-v1.1e-8b-1024k.Q5_K_M.gguf": ["mradermacher/Thor-v1.1e-8b-1024k-GGUF", MessagesFormatterType.LLAMA_3],
+    "RP-Naughty-v1.0a-8b.Q5_K_M.gguf": ["mradermacher/RP-Naughty-v1.0a-8b-GGUF", MessagesFormatterType.LLAMA_3],
+    "Qwen2.5-14B-Mixed-Instruct.Q4_K_M.gguf": ["mradermacher/Qwen2.5-14B-Mixed-Instruct-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "WestKunai-Hermes-10.7b-test.Q4_K_M.gguf": ["mradermacher/WestKunai-Hermes-10.7b-test-GGUF", MessagesFormatterType.MISTRAL],
+    "Tulu-3.1-8B-SuperNova.i1-Q4_K_M.gguf": ["mradermacher/Tulu-3.1-8B-SuperNova-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Loki-v2.6-8b-1024k.i1-Q5_K_M.gguf": ["mradermacher/Loki-v2.6-8b-1024k-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Kosmos-8B-v1.i1-Q5_K_M.gguf": ["mradermacher/Kosmos-8B-v1-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Qwen2.5-7B-Instruct-DPO-v01.Q5_K_M.gguf": ["mradermacher/Qwen2.5-7B-Instruct-DPO-v01-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "HomerCreativeAnvita-Mix-Qw7B.i1-Q5_K_M.gguf": ["mradermacher/HomerCreativeAnvita-Mix-Qw7B-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "EVA-Tissint-v1.2-14B.i1-Q4_K_M.gguf": ["mradermacher/EVA-Tissint-v1.2-14B-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Llama-3.1-Tulu-3-8B-abliterated.i1-Q5_K_M.gguf": ["mradermacher/Llama-3.1-Tulu-3-8B-abliterated-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Mistral-Nemo-12B-ArliAI-RPMax-v1.2.i1-Q4_K_M.gguf": ["mradermacher/Mistral-Nemo-12B-ArliAI-RPMax-v1.2-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "EVA-Tissint-v1.2-14B.i1-Q4_K_M.gguf": ["mradermacher/EVA-Tissint-v1.2-14B-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Intelligence-7.i1-Q5_K_M.gguf": ["mradermacher/Intelligence-7-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Ice0.40-20.11-RP.i1-Q5_K_M.gguf": ["mradermacher/Ice0.40-20.11-RP-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Llama-3.1-8B-ArliAI-RPMax-v1.2.i1-Q5_K_M.gguf": ["mradermacher/Llama-3.1-8B-ArliAI-RPMax-v1.2-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "SzilviaB_DarkSlushNeuralDaredevil-8b-abliterated.i1-Q5_K_M.gguf": ["mradermacher/SzilviaB_DarkSlushNeuralDaredevil-8b-abliterated-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Qwen2.5-7B-HomerCreative-Mix.i1-Q5_K_M.gguf": ["mradermacher/Qwen2.5-7B-HomerCreative-Mix-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "MN-Instruct-2407-14.7B-BRAINSTORM-10x-FORM-3.i1-Q4_K_M.gguf": ["mradermacher/MN-Instruct-2407-14.7B-BRAINSTORM-10x-FORM-3-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "MN-Instruct-2407-13.35B-BRAINSTORM-5x-FORM-11.Q4_K_M.gguf": ["mradermacher/MN-Instruct-2407-13.35B-BRAINSTORM-5x-FORM-11-GGUF", MessagesFormatterType.MISTRAL],
+    "NeuralDarkDevil-8b-abliterated.i1-Q5_K_M.gguf": ["mradermacher/NeuralDarkDevil-8b-abliterated-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "DarkNeuralDaredevilUnholy-8b.i1-Q5_K_M.gguf": ["mradermacher/DarkNeuralDaredevilUnholy-8b-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "DarkAuraUnholy-Uncensored-OAS-8b.i1-Q5_K_M.gguf": ["mradermacher/DarkAuraUnholy-Uncensored-OAS-8b-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "DarkUnholyDareDevil-abliterated-8b.i1-Q5_K_M.gguf": ["mradermacher/DarkUnholyDareDevil-abliterated-8b-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "DarkDareDevilAura-abliterated-uncensored-OAS-8b.i1-Q5_K_M.gguf": ["mradermacher/DarkDareDevilAura-abliterated-uncensored-OAS-8b-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "DarkNeuralDareDevil-Eight-Orbs-Of-Power-8b.i1-Q5_K_M.gguf": ["mradermacher/DarkNeuralDareDevil-Eight-Orbs-Of-Power-8b-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Mistral-NeuralDPO-v0.4.Q5_K_M.gguf": ["mradermacher/Mistral-NeuralDPO-v0.4-GGUF", MessagesFormatterType.MISTRAL],
+    "Hermes-Instruct-7B-v0.2.i1-Q5_K_M.gguf": ["mradermacher/Hermes-Instruct-7B-v0.2-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "Llama-3.1-Tulu-3-8B-DPO-Q5_K_M.gguf": ["bartowski/Llama-3.1-Tulu-3-8B-DPO-GGUF", MessagesFormatterType.LLAMA_3],
+    "Platyboros-Instruct-7B.i1-Q5_K_M.gguf": ["mradermacher/Platyboros-Instruct-7B-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "hermes-llama3-roleplay-2000-v3.i1-Q5_K_M.gguf": ["mradermacher/hermes-llama3-roleplay-2000-v3-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Hermes-Instruct-7B-100K.i1-Q5_K_M.gguf": ["mradermacher/Hermes-Instruct-7B-100K-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "SeQwence-14B.i1-Q4_K_M.gguf": ["mradermacher/SeQwence-14B-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "Llama-3.1-Tulu-3-8B-Q5_K_M.gguf": ["bartowski/Llama-3.1-Tulu-3-8B-GGUF", MessagesFormatterType.LLAMA_3],
+    "Ministral-8B-Instruct-2410.Q5_K_M.gguf": ["mradermacher/Ministral-8B-Instruct-2410-GGUF", MessagesFormatterType.MISTRAL],
+    "Loki-v2.6-8b-1024k.Q4_K_M.gguf": ["mradermacher/Loki-v2.6-8b-1024k-GGUF", MessagesFormatterType.LLAMA_3],
+    "DarkUnholyPlanet-OAS-8b.i1-Q5_K_M.gguf": ["mradermacher/DarkUnholyPlanet-OAS-8b-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Qwen2.5-7B-HomerAnvita-NerdMix.i1-Q4_K_M.gguf": ["mradermacher/Qwen2.5-7B-HomerAnvita-NerdMix-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "DarkUnholyDareDevil-8b-abliterated.i1-Q4_K_M.gguf": ["mradermacher/DarkUnholyDareDevil-8b-abliterated-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "LLama3.1-Hawkish-Theia-Fireball-8B.i1-Q5_K_M.gguf": ["mradermacher/LLama3.1-Hawkish-Theia-Fireball-8B-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "MT3-Gen2-MU-gemma-2-GQv1-9B.Q4_K_M.gguf": ["mradermacher/MT3-Gen2-MU-gemma-2-GQv1-9B-GGUF", MessagesFormatterType.ALPACA],
+    "MT3-Gen2-GMM-gemma-2-9B.Q4_K_M.gguf": ["mradermacher/MT3-Gen2-GMM-gemma-2-9B-GGUF", MessagesFormatterType.ALPACA],
+    "Platyboros-Instruct-7B.Q5_K_M.gguf": ["mradermacher/Platyboros-Instruct-7B-GGUF", MessagesFormatterType.MISTRAL],
+    "Fuselage-8B.Q5_K_M.gguf": ["mradermacher/Fuselage-8B-GGUF", MessagesFormatterType.LLAMA_3],
+    "Kudzerk-8B.Q5_K_M.gguf": ["mradermacher/Kudzerk-8B-GGUF", MessagesFormatterType.LLAMA_3],
+    "Qwen2.5-Coder-7B-Instruct-abliterated-TIES-v2.0.i1-Q5_K_M.gguf": ["mradermacher/Qwen2.5-Coder-7B-Instruct-abliterated-TIES-v2.0-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "B-NIMITA-L3-8B-v0.02.Q5_K_M.gguf": ["mradermacher/B-NIMITA-L3-8B-v0.02-GGUF", MessagesFormatterType.LLAMA_3],
+    "L3.1-Aspire-Heart-Matrix-8B.Q5_K_M.gguf": ["mradermacher/L3.1-Aspire-Heart-Matrix-8B-GGUF", MessagesFormatterType.LLAMA_3],
+    "HomerSlerp1-7B.Q5_K_M.gguf": ["mradermacher/HomerSlerp1-7B-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "MN-Slush.i1-Q4_K_M.gguf": ["mradermacher/MN-Slush-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "HomerSlerp2-7B.i1-Q4_K_M.gguf": ["mradermacher/HomerSlerp2-7B-i1-GGUF", MessagesFormatterType.OPEN_CHAT],
+    "LLaMA-Mesh-Q5_K_M.gguf": ["bartowski/LLaMA-Mesh-GGUF", MessagesFormatterType.LLAMA_3],
+    "BgGPT-Gemma-2-9B-IT-v1.0.i1-Q4_K_M.gguf": ["mradermacher/BgGPT-Gemma-2-9B-IT-v1.0-i1-GGUF", MessagesFormatterType.ALPACA],
+    "Ice0.37-19.11-RP-orpo-1.i1-Q5_K_M.gguf": ["mradermacher/Ice0.37-19.11-RP-orpo-1-i1-GGUF", MessagesFormatterType.MISTRAL],
+    "CursedMatrix-8B-v9.i1-Q5_K_M.gguf": ["mradermacher/CursedMatrix-8B-v9-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "Cakrawala-8B.i1-Q5_K_M.gguf": ["mradermacher/Cakrawala-8B-i1-GGUF", MessagesFormatterType.LLAMA_3],
+    "JerseyDevil-14b.i1-Q4_K_M.gguf": ["mradermacher/JerseyDevil-14b-i1-GGUF", MessagesFormatterType.SOLAR],
+    "Llama-3.1-Jamet-8B-MK.I.i1-Q5_K_M.gguf": ["mradermacher/Llama-3.1-Jamet-8B-MK.I-i1-GGUF", MessagesFormatterType.LLAMA_3],
     "SeQwence-14B-v5.Q4_K_S.gguf": ["mradermacher/SeQwence-14B-v5-GGUF", MessagesFormatterType.OPEN_CHAT],
     "L3.1-8B-Dark-Planet-Slush.i1-Q4_K_M.gguf": ["mradermacher/L3.1-8B-Dark-Planet-Slush-i1-GGUF", MessagesFormatterType.LLAMA_3],
     "QwenSlerp12-7B.Q5_K_M.gguf": ["mradermacher/QwenSlerp12-7B-GGUF", MessagesFormatterType.OPEN_CHAT],
@@ -980,6 +1146,7 @@ llm_models = {
     "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],
+    "ChatWaifu_v1.0.i1-Q5_K_M.gguf": ["mradermacher/ChatWaifu_v1.0-i1-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],

modutils.py

@@ -172,7 +172,7 @@ class ModelInformation:
         self.download_url = json_data.get("downloadUrl", "")
         self.model_url = f"https://civitai.com/models/{self.model_id}?modelVersionId={self.model_version_id}"
         self.filename_url = next(
-            (v.get("name", "") for v in json_data.get("files", []) if str(self.model_version_id) in v.get("downloadUrl", "")), ""
+            (v.get("name", "") for v in reversed(json_data.get("files", [])) if str(self.model_version_id) in v.get("downloadUrl", "")), ""
         )
         self.filename_url = self.filename_url if self.filename_url else ""
         self.description = json_data.get("description", "")
@@ -302,6 +302,10 @@ def safe_float(input):
     return output
 
 
+def valid_model_name(model_name: str):
+    return model_name.split(" ")[0]
+
+
 def save_images(images: list[Image.Image], metadatas: list[str]):
     from PIL import PngImagePlugin
     import uuid
@@ -566,7 +570,8 @@ private_lora_model_list = get_private_lora_model_lists()
 
 def get_civitai_info(path):
     global civitai_not_exists_list
-    if path in set(civitai_not_exists_list): return ["", "", "", "", ""]
+    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'}
@@ -584,12 +589,12 @@ def get_civitai_info(path):
         r = session.get(url, params=params, headers=headers, stream=True, timeout=(3.0, 15))
     except Exception as e:
         print(e)
-        return ["", "", "", "", ""]
+        return default
     if not r.ok: return None
     json = r.json()
     if not 'baseModel' in json:
         civitai_not_exists_list.append(path)
-        return ["", "", "", "", ""]
+        return default
     items = []
     items.append(" / ".join(json['trainedWords']))
     items.append(json['baseModel'])
@@ -690,7 +695,7 @@ def copy_lora(path: str, new_path: str):
         return None
 
 
-def download_my_lora(dl_urls: str, lora1: str, lora2: str, lora3: str, lora4: str, lora5: str):
+def download_my_lora(dl_urls: str, lora1: str, lora2: str, lora3: str, lora4: str, lora5: str, lora6: str, lora7: str):
     path = download_lora(dl_urls)
     if path:
         if not lora1 or lora1 == "None":
@@ -703,9 +708,13 @@ def download_my_lora(dl_urls: str, lora1: str, lora2: str, lora3: str, lora4: st
             lora4 = path
         elif not lora5 or lora5 == "None":
             lora5 = path
+        #elif not lora6 or lora6 == "None":
+        #    lora6 = path
+        #elif not lora7 or lora7 == "None":
+        #    lora7 = 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)
+        gr.update(value=lora4, choices=choices), gr.update(value=lora5, choices=choices), gr.update(value=lora6, choices=choices), gr.update(value=lora7, choices=choices)
 
 
 def get_valid_lora_name(query: str, model_name: str):
@@ -745,25 +754,31 @@ def get_valid_lora_wt(prompt: str, lora_path: str, lora_wt: float):
     return wt
 
 
-def set_prompt_loras(prompt, prompt_syntax, model_name, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt):
-    if not "Classic" in str(prompt_syntax):  return lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt
+def set_prompt_loras(prompt, prompt_syntax, model_name, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt, lora6, lora6_wt, lora7, lora7_wt):
+    if not "Classic" in str(prompt_syntax):  return lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt, lora6, lora6_wt, lora7, lora7_wt
     lora1 = get_valid_lora_name(lora1, model_name)
     lora2 = get_valid_lora_name(lora2, model_name)
     lora3 = get_valid_lora_name(lora3, model_name)
     lora4 = get_valid_lora_name(lora4, model_name)
     lora5 = get_valid_lora_name(lora5, model_name)
-    if not "<lora" in prompt: return lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt
+    #lora6 = get_valid_lora_name(lora6, model_name)
+    #lora7 = get_valid_lora_name(lora7, model_name)
+    if not "<lora" in prompt: return lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt, lora6, lora6_wt, lora7, lora7_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)
+    #lora6_wt = get_valid_lora_wt(prompt, lora6, lora5_wt)
+    #lora7_wt = get_valid_lora_wt(prompt, lora7, 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]
+    #on6, label6, tag6, md6 = get_lora_info(lora6)
+    #on7, label7, tag7, md7 = get_lora_info(lora7)
+    lora_paths = [lora1, lora2, lora3, lora4, lora5, lora6, lora7]
     prompts = prompt.split(",") if prompt else []
     for p in prompts:
         p = str(p).strip()
@@ -780,30 +795,40 @@ def set_prompt_loras(prompt, prompt_syntax, model_name, lora1, lora1_wt, lora2,
                 continue
             elif not on1:
                 lora1 = path
-                lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                lora_paths = [lora1, lora2, lora3, lora4, lora5, lora6, lora7]
                 lora1_wt = safe_float(wt)
                 on1 = True
             elif not on2:
                 lora2 = path
-                lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                lora_paths = [lora1, lora2, lora3, lora4, lora5, lora6, lora7]
                 lora2_wt = safe_float(wt)
                 on2 = True
             elif not on3:
                 lora3 = path
-                lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                lora_paths = [lora1, lora2, lora3, lora4, lora5, lora6, lora7]
                 lora3_wt = safe_float(wt)
                 on3 = True
             elif not on4:
                 lora4 = path
-                lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                lora_paths = [lora1, lora2, lora3, lora4, lora5, lora6, lora7]
                 lora4_wt = safe_float(wt)
                 on4 = True
             elif not on5:
                 lora5 = path
-                lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                lora_paths = [lora1, lora2, lora3, lora4, lora5, lora6, lora7]
                 lora5_wt = safe_float(wt)
                 on5 = True
-    return lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt
+            #elif not on6:
+            #    lora6 = path
+            #    lora_paths = [lora1, lora2, lora3, lora4, lora5, lora6, lora7]
+            #    lora6_wt = safe_float(wt)
+            #    on6 = True
+            #elif not on7:
+            #    lora7 = path
+            #    lora_paths = [lora1, lora2, lora3, lora4, lora5, lora6, lora7]
+            #    lora7_wt = safe_float(wt)
+            #    on7 = True
+    return lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt, lora6, lora6_wt, lora7, lora7_wt
 
 
 def get_lora_info(lora_path: str):
@@ -864,13 +889,15 @@ def apply_lora_prompt(prompt: str = "", lora_info: str = ""):
     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):
+def update_loras(prompt, prompt_syntax, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt, lora6, lora6_wt, lora7, lora7_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]
+    on6, label6, tag6, md6 = get_lora_info(lora6)
+    on7, label7, tag7, md7 = get_lora_info(lora7)
+    lora_paths = [lora1, lora2, lora3, lora4, lora5, lora6, lora7]
 
     output_prompt = prompt
     if "Classic" in str(prompt_syntax):
@@ -895,6 +922,8 @@ def update_loras(prompt, prompt_syntax, lora1, lora1_wt, lora2, lora2_wt, lora3,
         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}>")
+        #if on6: lora_prompts.append(f"<lora:{to_lora_key(lora6)}:{lora6_wt:.2f}>")
+        #if on7: lora_prompts.append(f"<lora:{to_lora_key(lora7)}:{lora7_wt:.2f}>")
         output_prompt = ", ".join(list_uniq(output_prompts + lora_prompts + [""]))
     choices = get_all_lora_tupled_list()
 
@@ -907,7 +936,11 @@ def update_loras(prompt, prompt_syntax, lora1, lora1_wt, lora2, lora2_wt, lora3,
      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)
+     gr.update(value=tag5, label=label5, visible=on5), gr.update(visible=on5), gr.update(value=md5, visible=on5),\
+     gr.update(value=lora6, choices=choices), gr.update(value=lora6_wt),\
+     gr.update(value=tag6, label=label6, visible=on6), gr.update(visible=on6), gr.update(value=md6, visible=on6),\
+     gr.update(value=lora7, choices=choices), gr.update(value=lora7_wt),\
+     gr.update(value=tag7, label=label7, visible=on7), gr.update(visible=on7), gr.update(value=md7, visible=on7)
 
 
 def get_my_lora(link_url, romanize):
@@ -926,7 +959,6 @@ def get_my_lora(link_url, romanize):
             path.resolve().rename(new_path.resolve())
             update_lora_dict(str(new_path))
             l_path = str(new_path)
-    new_lora_model_list = get_lora_model_list()
     new_lora_tupled_list = get_all_lora_tupled_list()
     msg_lora = "Downloaded"
     if l_name:
@@ -943,6 +975,10 @@ def get_my_lora(link_url, romanize):
         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
     ), gr.update(
         value=msg_lora
     )
@@ -975,12 +1011,19 @@ def move_file_lora(filepaths):
         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
     )
 
 
-CIVITAI_SORT = ["Highest Rated", "Most Downloaded", "Newest"]
+CIVITAI_SORT = ["Highest Rated", "Most Downloaded", "Most Liked", "Most Discussed", "Most Collected", "Most Buzz", "Newest"]
 CIVITAI_PERIOD = ["AllTime", "Year", "Month", "Week", "Day"]
-CIVITAI_BASEMODEL = ["Pony", "Illustrious", "SDXL 1.0", "SD 1.5", "Flux.1 D", "Flux.1 S"]
+CIVITAI_BASEMODEL = ["Pony", "Illustrious", "SDXL 1.0", "SD 1.5", "Flux.1 D", "Flux.1 S"] # , "SD 3.5"
+CIVITAI_TYPE = ["Checkpoint", "TextualInversion", "Hypernetwork", "AestheticGradient", "LORA", "LoCon", "DoRA",
+                "Controlnet", "Upscaler", "MotionModule", "VAE", "Poses", "Wildcards", "Workflows", "Other"]
+CIVITAI_FILETYPE = ["Model", "VAE", "Config", "Training Data"]
 
 
 def get_civitai_info(path):
@@ -1025,6 +1068,7 @@ def search_lora_on_civitai(query: str, allow_model: list[str] = ["Pony", "SDXL 1
                            sort: str = "Highest Rated", period: str = "AllTime", tag: str = "", user: str = "", page: int = 1):
     user_agent = get_user_agent()
     headers = {'User-Agent': user_agent, 'content-type': 'application/json'}
+    if CIVITAI_API_KEY: headers['Authorization'] = f'Bearer {{{CIVITAI_API_KEY}}}'
     base_url = 'https://civitai.com/api/v1/models'
     params = {'types': ['LORA'], 'sort': sort, 'period': period, 'limit': limit, 'page': int(page), 'nsfw': 'true'}
     if query: params["query"] = query

requirements.txt

@@ -1,10 +1,8 @@
-spaces
+git+https://github.com/R3gm/stablepy.git@a9fe2dc  # -b refactor_sampler_fix
 accelerate
 diffusers
 invisible_watermark
 transformers
-xformers
-git+https://github.com/R3gm/stablepy.git@8edabb0  # -b refactor_sampler_fix
 torch==2.2.0
 numpy<2
 gdown
@@ -22,4 +20,5 @@ translatepy
 timm
 wrapt-timeout-decorator
 sentencepiece
-unidecode
+unidecode
+ultralytics==8.3.47

utils.py

@@ -62,7 +62,7 @@ class ModelInformation:
         self.download_url = json_data.get("downloadUrl", "")
         self.model_url = f"https://civitai.com/models/{self.model_id}?modelVersionId={self.model_version_id}"
         self.filename_url = next(
-            (v.get("name", "") for v in json_data.get("files", []) if str(self.model_version_id) in v.get("downloadUrl", "")), ""
+            (v.get("name", "") for v in reversed(json_data.get("files", [])) if str(self.model_version_id) in v.get("downloadUrl", "")), ""
         )
         self.filename_url = self.filename_url if self.filename_url else ""
         self.description = json_data.get("description", "")
@@ -274,6 +274,10 @@ def get_my_lora(link_url, romanize):
         choices=new_lora_model_list
     ), gr.update(
         choices=new_lora_model_list
+    ), gr.update(
+        choices=new_lora_model_list
+    ), gr.update(
+        choices=new_lora_model_list
     ), gr.update(
         value=msg_lora
     )