Upload 22 files

app.py

@@ -9,7 +9,9 @@ from dc import (infer, _infer, pass_result, get_diffusers_model_list, get_sample
     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, preprocessor_tab, SDXL_TASK, TASK_MODEL_LIST,
-    PROMPT_W_OPTIONS)
+    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,
@@ -63,6 +65,7 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
 
             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)
+
             with gr.Accordion("History", open=False):
                 history_gallery = gr.Gallery(label="History", columns=6, object_fit="contain", format="png", interactive=False, show_share_button=False,
                 show_download_button=True)
@@ -70,42 +73,48 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
                 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", show_copy_button=True,
-                                              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.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)
-                    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.Accordion("Advanced Settings", open=True):
+                task = gr.Radio(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)
 
-                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.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.Accordion("LoRA", open=True, visible=True):
+                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)
 
@@ -190,17 +199,136 @@ 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.Tab("ControlNet / Img2img / Inpaint"):
+                    with gr.Row():
+                        image_control = gr.Image(label="Image ControlNet / Inpaint / Img2img", type="filepath", height=384, sources=["upload", "clipboard", "webacam"], 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.Row():
+                        with gr.Accordion("IP-Adapter 1", open=True, visible=True):
+                            image_ip1 = gr.Image(label="IP Image", type="filepath", height=384, sources=["upload", "clipboard"], show_share_button=False)
+                            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):
+                            image_ip2 = gr.Image(label="IP Image", type="filepath", height=384, sources=["upload", "clipboard"], show_share_button=False)
+                            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.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.Tab("Inpaint Mask Maker"):
+                    with gr.Row():
+                        with gr.Column(scale=2):
+                            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(scale=1):
+                            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], queue=False, 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.Accordion("Other", open=True, visible=True):
-                    task = gr.Dropdown(label="Task", choices=SDXL_TASK, value=TASK_MODEL_LIST[0])
-                    prompt_syntax = gr.Dropdown(label="Prompt Syntax", choices=PROMPT_W_OPTIONS, value=PROMPT_W_OPTIONS[1][1])
-                
-                with gr.Accordion("Translation Settings", open=False):
+                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")
@@ -225,15 +353,34 @@ 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, lora6, lora6_wt, lora7, lora7_wt,
-                sampler, vae_model, auto_trans, schedule_type, schedule_prediction_type,
-                clip_skip, pag_scale, free_u, guidance_rescale, gpu_duration, recom_prompt],
+                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",
@@ -246,9 +393,22 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
         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, lora6, lora6_wt, lora7, lora7_wt,
-                sampler, vae_model, auto_trans, schedule_type, schedule_prediction_type,
-                clip_skip, pag_scale, free_u, guidance_rescale, gpu_duration, recom_prompt],
+                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,
@@ -271,9 +431,22 @@ with gr.Blocks(fill_width=True, elem_id="container", css=css, delete_cache=(60,
         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, lora6, lora6_wt, lora7, lora7_wt,
-                sampler, vae_model, auto_trans, schedule_type, schedule_prediction_type,
-                clip_skip, pag_scale, free_u, guidance_rescale, gpu_duration, recom_prompt],
+                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",
@@ -285,7 +458,7 @@ 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, lora6.change, lora6_wt.change, lora7.change, lora7_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, 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,
@@ -433,7 +606,7 @@ 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)

dc.py

@@ -119,8 +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):
@@ -148,7 +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
@@ -741,8 +741,8 @@ import random
 import json
 import shutil
 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,
-    get_local_model_list, get_private_lora_model_lists, get_valid_lora_name, get_state, set_state,
+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,
     get_all_lora_list, get_all_lora_tupled_list, update_lora_dict, download_lora, copy_lora, download_my_lora, set_prompt_loras,
@@ -753,45 +753,26 @@ from modutils import (safe_float, escape_lora_basename, to_lora_key, to_lora_pat
 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, lora6=None, lora6_wt=1.0, lora7=None, lora7_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, guidance_rescale=0., gpu_duration=59, recom_prompt=True, progress=gr.Progress(track_tqdm=True)):
+          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=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=None, mask_ip1=None, model_ip1="plus_face", mode_ip1="original", scale_ip1=0.7,
+          image_ip2=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
 
-    task = TASK_MODEL_LIST[0]
-    image_control = None
-    preprocessor_name = "Canny"
-    preprocess_resolution = 512
-    image_resolution = 1024
     style_prompt = None
     style_json = None
-    image_mask = None
-    strength = 0.35
-    low_threshold = 100
-    high_threshold = 200
-    value_threshold = 0.1
-    distance_threshold = 0.1
-    recolor_gamma_correction = 1.
-    tile_blur_sigma = 9
-    control_net_output_scaling = 1.0
-    control_net_start_threshold = 0.
-    control_net_stop_threshold = 1.
-    active_textual_inversion = False
-    prompt_syntax = "Classic"
-    upscaler_model_path = None # UPSCALER_KEYS[0]
-    upscaler_increases_size = 1.0 # 1.2
-    esrgan_tile = 5
-    esrgan_tile_overlap = 8
-    hires_steps = 30
-    hires_denoising_strength = 0.55
-    hires_sampler = "Use same sampler" # POST_PROCESSING_SAMPLER[0]
-    hires_prompt = ""
-    hires_negative_prompt = ""
     hires_before_adetailer = False
     hires_after_adetailer = True
-    hires_schedule_list = ["Use same schedule type"] + SCHEDULE_TYPE_OPTIONS
-    hires_schedule_type = hires_schedule_list[0]
-    hires_guidance_scale = -1
-    controlnet_model = DIFFUSERS_CONTROLNET_MODEL[0]
     loop_generation = 1
     leave_progress_bar = True
     disable_progress_bar = False
@@ -808,40 +789,7 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
     adapter_conditioning_factor = 0.55
     xformers_memory_efficient_attention = False
     generator_in_cpu = False
-    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
     retain_task_cache = True
-    image_ip1 = None
-    mask_ip1 = None
-    model_ip1 = "plus_face"
-    mode_ip1 = "original"
-    scale_ip1 = 0.7
-    image_ip2 = None
-    mask_ip2 = None
-    model_ip2 = "base"
-    mode_ip2 = "style"
-    scale_ip2 = 0.7
     load_lora_cpu = False
     verbose_info = False
 
@@ -901,8 +849,8 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
 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, lora6=None, lora6_wt=1.0, lora7=None, lora7_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, guidance_rescale=0., gpu_duration=59, recom_prompt=True, progress=gr.Progress(track_tqdm=True)):
+           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., gpu_duration=59, translate=False, recom_prompt=True, progress=gr.Progress(track_tqdm=True)):
     return gr.update()
 
 
@@ -922,6 +870,32 @@ 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")

env.py

@@ -1,4 +1,5 @@
 import os
+from constants import LOAD_DIFFUSERS_FORMAT_MODEL as LOAD_DIFFUSERS_FORMAT_MODEL_DC
 
 CIVITAI_API_KEY = os.environ.get("CIVITAI_API_KEY")
 HF_TOKEN = os.environ.get("HF_TOKEN")
@@ -38,98 +39,6 @@ LOAD_DIFFUSERS_FORMAT_MODEL = [
     'votepurchase/RealVisXL_V4.0',
     'votepurchase/juggernautXL_hyper_8step_sfw',
     'votepurchase/ponyRealism_v21MainVAE',
-    'stabilityai/stable-diffusion-xl-base-1.0',
-    'black-forest-labs/FLUX.1-dev',
-    'John6666/blue-pencil-flux1-v021-fp8-flux',
-    'John6666/wai-ani-flux-v10forfp8-fp8-flux',
-    'John6666/xe-anime-flux-v04-fp8-flux',
-    'John6666/lyh-anime-flux-v2a1-fp8-flux',
-    'John6666/carnival-unchained-v10-fp8-flux',
-    'Freepik/flux.1-lite-8B-alpha',
-    'ostris/OpenFLUX.1',
-    'John6666/noobai-xl-nai-xl-epsilonpred10version-sdxl',
-    'John6666/noobai-xl-nai-xl-epsilonpred075version-sdxl',
-    'John6666/noobai-xl-nai-xl-epsilonpred05version-sdxl',
-    'John6666/noobai-cyberfix-v10-sdxl',
-    'John6666/noobaiiter-xl-vpred-v075-sdxl',
-    'John6666/complicated-noobai-merge-vprediction-sdxl',
-    'John6666/noobai-fusion2-vpred-itercomp-v1-sdxl',
-    'John6666/noobai-xl-nai-xl-vpredtestversion-sdxl',
-    'John6666/chadmix-noobai075-illustrious01-v10-sdxl',
-    'OnomaAIResearch/Illustrious-xl-early-release-v0',
-    'John6666/obsession-illustriousxl-v21-sdxl',
-    'eienmojiki/Anything-XL',
-    'eienmojiki/Starry-XL-v5.2',
-    'John6666/meinaxl-v2-sdxl',
-    'John6666/epicrealism-xl-v10kiss2-sdxl',
-    'John6666/epicrealism-xl-v8kiss-sdxl',
-    'misri/zavychromaxl_v80',
-    'SG161222/RealVisXL_V4.0',
-    'SG161222/RealVisXL_V5.0',
-    'misri/newrealityxlAllInOne_Newreality40',
-    'gsdf/CounterfeitXL',
-    'John6666/silvermoon-mix-01xl-v11-sdxl',
-    'WhiteAiZ/autismmixSDXL_autismmixConfetti_diffusers',
-    'kitty7779/ponyDiffusionV6XL',
-    'GraydientPlatformAPI/aniverse-pony',
-    'John6666/ras-real-anime-screencap-v1-sdxl',
-    'John6666/duchaiten-pony-xl-no-score-v60-sdxl',
-    'John6666/mistoon-anime-ponyalpha-sdxl',
-    'John6666/3x3x3mixxl-v2-sdxl',
-    'John6666/3x3x3mixxl-3dv01-sdxl',
-    'John6666/ebara-mfcg-pony-mix-v12-sdxl',
-    'John6666/t-ponynai3-v51-sdxl',
-    'John6666/t-ponynai3-v65-sdxl',
-    'John6666/prefect-pony-xl-v3-sdxl',
-    'John6666/mala-anime-mix-nsfw-pony-xl-v5-sdxl',
-    'John6666/wai-real-mix-v11-sdxl',
-    'John6666/wai-c-v6-sdxl',
-    'John6666/iniverse-mix-xl-sfwnsfw-pony-guofeng-v43-sdxl',
-    'John6666/sifw-annihilation-xl-v2-sdxl',
-    'John6666/photo-realistic-pony-v5-sdxl',
-    'John6666/pony-realism-v21main-sdxl',
-    'John6666/pony-realism-v22main-sdxl',
-    'John6666/cyberrealistic-pony-v63-sdxl',
-    'John6666/cyberrealistic-pony-v64-sdxl',
-    'John6666/cyberrealistic-pony-v65-sdxl',
-    'GraydientPlatformAPI/realcartoon-pony-diffusion',
-    'John6666/nova-anime-xl-pony-v5-sdxl',
-    'John6666/autismmix-sdxl-autismmix-pony-sdxl',
-    'John6666/aimz-dream-real-pony-mix-v3-sdxl',
-    'John6666/duchaiten-pony-real-v11fix-sdxl',
-    'John6666/duchaiten-pony-real-v20-sdxl',
-    'John6666/duchaiten-pony-xl-no-score-v70-sdxl',
-    'Eugeoter/artiwaifu-diffusion-2.0',
-    'comin/IterComp',
-    'KBlueLeaf/Kohaku-XL-Zeta',
-    'cagliostrolab/animagine-xl-3.1',
-    'yodayo-ai/kivotos-xl-2.0',
-    'yodayo-ai/holodayo-xl-2.1',
-    'yodayo-ai/clandestine-xl-1.0',
-    'digiplay/majicMIX_sombre_v2',
-    'digiplay/majicMIX_realistic_v6',
-    'digiplay/majicMIX_realistic_v7',
-    'digiplay/DreamShaper_8',
-    'digiplay/BeautifulArt_v1',
-    'digiplay/DarkSushi2.5D_v1',
-    'digiplay/darkphoenix3D_v1.1',
-    'digiplay/BeenYouLiteL11_diffusers',
-    'Yntec/RevAnimatedV2Rebirth',
-    'youknownothing/cyberrealistic_v50',
-    'youknownothing/deliberate-v6',
-    'GraydientPlatformAPI/deliberate-cyber3',
-    'GraydientPlatformAPI/picx-real',
-    'GraydientPlatformAPI/perfectworld6',
-    'emilianJR/epiCRealism',
-    'votepurchase/counterfeitV30_v30',
-    'votepurchase/ChilloutMix',
-    'Meina/MeinaMix_V11',
-    'Meina/MeinaUnreal_V5',
-    'Meina/MeinaPastel_V7',
-    'GraydientPlatformAPI/realcartoon3d-17',
-    'GraydientPlatformAPI/realcartoon-pixar11',
-    'GraydientPlatformAPI/realcartoon-real17',
-    'nitrosocke/Ghibli-Diffusion',
     'KBlueLeaf/Kohaku-XL-Epsilon-rev2',
     'KBlueLeaf/Kohaku-XL-Epsilon-rev3',
     'KBlueLeaf/Kohaku-XL-Zeta',
@@ -138,6 +47,7 @@ LOAD_DIFFUSERS_FORMAT_MODEL = [
     'Eugeoter/artiwaifu-diffusion-2.0',
     'comin/IterComp',
     'OnomaAIResearch/Illustrious-xl-early-release-v0',
+    'Laxhar/noobai-XL-1.0',
     'Raelina/Rae-Diffusion-XL-V2',
     'Raelina/Raemu-XL-V4',
     'Raelina/Raehoshi-illust-XL',
@@ -150,17 +60,18 @@ LOAD_DIFFUSERS_FORMAT_MODEL = [
     "multimodalart/FLUX.1-dev2pro-full",
     "Raelina/Raemu-Flux",
 ]
+LOAD_DIFFUSERS_FORMAT_MODEL = LOAD_DIFFUSERS_FORMAT_MODEL + LOAD_DIFFUSERS_FORMAT_MODEL_DC
 
 DIFFUSERS_FORMAT_LORAS = [
     "nerijs/animation2k-flux",
     "XLabs-AI/flux-RealismLora",
+    "Shakker-Labs/FLUX.1-dev-LoRA-Logo-Design",
 ]
 
 # List all Models for specified user
 HF_MODEL_USER_LIKES = ["votepurchase"] # sorted by number of likes
 HF_MODEL_USER_EX = ["John6666"] # sorted by a special rule
 
-
 # - **Download Models**
 DOWNLOAD_MODEL_LIST = [
 ]