Upload 6 files

app.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
@@ -42,15 +43,18 @@ 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"))
 
@@ -61,7 +65,7 @@ from modutils import (list_uniq, download_private_repo, get_model_id_list, get_t
     update_civitai_selection, get_civitai_tag, CIVITAI_SORT, CIVITAI_PERIOD, CIVITAI_BASEMODEL,
     set_textual_inversion_prompt, get_model_pipeline, change_interface_mode, get_t2i_model_info,
     get_tupled_model_list, save_gallery_images, save_gallery_history, set_optimization, set_sampler_settings,
-    set_quick_presets, process_style_prompt, optimization_list, save_images, download_things,
+    set_quick_presets, process_style_prompt, optimization_list, save_images, download_things, valid_model_name,
     preset_styles, preset_quality, preset_sampler_setting, translate_to_en, EXAMPLES_GUI, RESOURCES)
 from env import (HF_TOKEN, CIVITAI_API_KEY, HF_LORA_ESSENTIAL_PRIVATE_REPO, HF_VAE_PRIVATE_REPO,
     HF_SDXL_EMBEDS_NEGATIVE_PRIVATE_REPO, HF_SDXL_EMBEDS_POSITIVE_PRIVATE_REPO,
@@ -116,21 +120,25 @@ 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)
 
 #######################
 # GUI
 #######################
-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")
 ## BEGIN MOD
-from stablepy import logger
 #logger.setLevel(logging.CRITICAL)
 logger.setLevel(logging.DEBUG)
 
@@ -173,12 +181,14 @@ class GuiSD:
             ] + [model_name]
         print(self.inventory)
 
-    def load_new_model(self, model_name, vae_model, task, progress=gr.Progress(track_tqdm=True)):
-
-        self.update_storage_models()
+    def load_new_model(self, model_name, vae_model, task, controlnet_model, progress=gr.Progress(track_tqdm=True)):
 
         # download link model > model_name
 
+        model_name = valid_model_name(model_name) # MOD
+
+        self.update_storage_models()
+
         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
@@ -230,17 +240,19 @@ 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(
@@ -249,6 +261,7 @@ 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()
@@ -285,6 +298,10 @@ class GuiSD:
         lora_scale4,
         lora5,
         lora_scale5,
+        lora6,
+        lora_scale6,
+        lora7,
+        lora_scale7,
         sampler,
         schedule_type,
         schedule_prediction_type,
@@ -305,6 +322,8 @@ 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,
@@ -321,6 +340,9 @@ 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,
@@ -362,6 +384,7 @@ class GuiSD:
         mask_blur_b,
         mask_padding_b,
         retain_task_cache_gui,
+        guidance_rescale,
         image_ip1,
         mask_ip1,
         model_ip1,
@@ -378,7 +401,7 @@ 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 = ""
 
@@ -386,9 +409,9 @@ class GuiSD:
         loras_list = [s if s else "None" for s in loras_list]
         global lora_model_list
         lora_model_list = get_lora_model_list()
-        lora1, lora_scale1, lora2, lora_scale2, lora3, lora_scale3, lora4, lora_scale4, lora5, lora_scale5 = \
+        lora1, lora_scale1, lora2, lora_scale2, lora3, lora_scale3, lora4, lora_scale4, lora5, lora_scale5, lora6, lora_scale6, lora7, lora_scale7 = \
                 set_prompt_loras(prompt, syntax_weights, model_name, lora1, lora_scale1, lora2, lora_scale2, lora3,
-                                  lora_scale3, lora4, lora_scale4, lora5, lora_scale5)
+                                  lora_scale3, lora4, lora_scale4, lora5, lora_scale5, lora6, lora_scale6, lora7, lora_scale7)
 ## END MOD
 
         print("Config model:", model_name, vae_model, loras_list)
@@ -490,6 +513,8 @@ 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,
@@ -500,6 +525,10 @@ 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
@@ -543,6 +572,8 @@ 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,
@@ -550,8 +581,12 @@ 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")
 
@@ -579,7 +614,7 @@ 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(
                     [
@@ -614,37 +649,38 @@ 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
     )
@@ -658,7 +694,7 @@ 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
             )
@@ -715,6 +751,7 @@ 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()
@@ -740,7 +777,7 @@ with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, cs
     with gr.Column():
         with gr.Tab("Generation"):
             with gr.Row():
-                with gr.Column(scale=2):
+                with gr.Column(scale=1):
 
                     def update_task_options(model_name, task_name):
                         new_choices = MODEL_TYPE_TASK[get_model_type(model_name)]
@@ -781,7 +818,7 @@ with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, cs
                             generate_from_image_btn_gui = gr.Button(value="GENERATE TAGS FROM IMAGE")
                         prompt_gui = gr.Textbox(lines=6, placeholder="1girl, solo, ...", label="Prompt", show_copy_button=True)
                         with gr.Accordion("Negative prompt, etc.", open=False) as menu_negative:
-                            neg_prompt_gui = gr.Textbox(lines=3, placeholder="lowres, (bad), ...", label="Negative prompt", show_copy_button=True)
+                            neg_prompt_gui = gr.Textbox(lines=3, placeholder="Enter Neg prompt", label="Negative prompt", value="lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, worst quality, low quality, very displeasing, (bad)", show_copy_button=True)
                             translate_prompt_button = gr.Button(value="Translate prompt to English", size="sm", variant="secondary")
                             with gr.Row():
                                 insert_prompt_gui = gr.Radio(label="Insert reccomended positive / negative prompt", choices=["None", "Auto", "Animagine", "Pony"], value="Auto", interactive=True)                          
@@ -819,11 +856,14 @@ with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, cs
                         label="Generated images",
                         show_label=False,
                         elem_id="gallery",
-                        columns=[2],
-                        rows=[2],
+                        #columns=[2],
+                        columns=[1],
+                        #rows=[2],
+                        rows=[1],
                         object_fit="contain",
                         # height="auto",
                         interactive=False,
+                        #preview=False,
                         preview=False,
                         show_share_button=False,
                         show_download_button=True,
@@ -846,7 +886,7 @@ with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, cs
                         gpu_duration_gui = gr.Number(minimum=5, maximum=240, value=59, show_label=False, container=False, info="GPU time duration (seconds)")
                         with gr.Column():
                             verbose_info_gui = gr.Checkbox(value=False, container=False, label="Status info")
-                            load_lora_cpu_gui = gr.Checkbox(value=False, container=False, label="Load LoRAs on CPU (Save GPU time)")
+                            load_lora_cpu_gui = gr.Checkbox(value=False, container=False, label="Load LoRAs on CPU")
 
                 with gr.Column(scale=1):
                     with gr.Accordion("Generation settings", open=False, visible=True) as menu_gen:
@@ -855,6 +895,7 @@ with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, cs
                             img_height_gui = gr.Slider(minimum=64, maximum=4096, step=8, value=1024, label="Img Height")
                             steps_gui = gr.Slider(minimum=1, maximum=100, step=1, value=28, label="Steps")
                             cfg_gui = gr.Slider(minimum=0, maximum=30, step=0.5, value=7.0, label="CFG")
+                            guidance_rescale_gui = gr.Slider(label="CFG rescale:", value=0., step=0.01, minimum=0., maximum=1.5)
                         with gr.Row():
                             seed_gui = gr.Number(minimum=-1, maximum=2**32-1, value=-1, label="Seed")
                             pag_scale_gui = gr.Slider(minimum=0.0, maximum=10.0, step=0.1, value=0.0, label="PAG Scale")
@@ -864,118 +905,119 @@ with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, cs
                         with gr.Row():
                             sampler_gui = gr.Dropdown(label="Sampler", choices=scheduler_names, value="Euler")
                             schedule_type_gui = gr.Dropdown(label="Schedule type", choices=SCHEDULE_TYPE_OPTIONS, value=SCHEDULE_TYPE_OPTIONS[0])
+                            schedule_prediction_type_gui = gr.Dropdown(label="Discrete Sampling Type", choices=SCHEDULE_PREDICTION_TYPE_OPTIONS, value=SCHEDULE_PREDICTION_TYPE_OPTIONS[0])
                             vae_model_gui = gr.Dropdown(label="VAE Model", choices=vae_model_list, value=vae_model_list[0])
                             prompt_syntax_gui = gr.Dropdown(label="Prompt Syntax", choices=PROMPT_W_OPTIONS, value=PROMPT_W_OPTIONS[1][1])
 
-                    with gr.Row(equal_height=False):
-
-                        def run_set_params_gui(base_prompt, name_model):
-                            valid_receptors = {  # default values
-                                "prompt": gr.update(value=base_prompt),
-                                "neg_prompt": gr.update(value=""),
-                                "Steps": gr.update(value=30),
-                                "width": gr.update(value=1024),
-                                "height": gr.update(value=1024),
-                                "Seed": gr.update(value=-1),
-                                "Sampler": gr.update(value="Euler"),
-                                "CFG scale": gr.update(value=7.),  # cfg
-                                "Clip skip": gr.update(value=True),
-                                "Model": gr.update(value=name_model),
-                                "Schedule type": gr.update(value="Automatic"),
-                                "PAG": gr.update(value=.0),
-                                "FreeU": gr.update(value=False),
-                            }
-                            valid_keys = list(valid_receptors.keys())
-
-                            parameters = extract_parameters(base_prompt)
-                            # print(parameters)
-
-                            if "Sampler" in parameters:
-                                value_sampler = parameters["Sampler"]
-                                for s_type in SCHEDULE_TYPE_OPTIONS:
-                                    if s_type in value_sampler:
-                                        value_sampler = value_sampler.replace(s_type, "").strip()
-                                        parameters["Sampler"] = value_sampler
-                                        parameters["Schedule type"] = s_type
-
-                            for key, val in parameters.items():
-                                # print(val)
-                                if key in valid_keys:
-                                    try:
-                                        if key == "Sampler":
-                                            if val not in scheduler_names:
-                                                continue
-                                        if key == "Schedule type":
-                                            if val not in SCHEDULE_TYPE_OPTIONS:
-                                                val = "Automatic"
-                                        elif key == "Clip skip":
-                                            if "," in str(val):
-                                                val = val.replace(",", "")
-                                            if int(val) >= 2:
+                        with gr.Row(equal_height=False):
+
+                            def run_set_params_gui(base_prompt, name_model):
+                                valid_receptors = {  # default values
+                                    "prompt": gr.update(value=base_prompt),
+                                    "neg_prompt": gr.update(value=""),
+                                    "Steps": gr.update(value=30),
+                                    "width": gr.update(value=1024),
+                                    "height": gr.update(value=1024),
+                                    "Seed": gr.update(value=-1),
+                                    "Sampler": gr.update(value="Euler"),
+                                    "CFG scale": gr.update(value=7.),  # cfg
+                                    "Clip skip": gr.update(value=True),
+                                    "Model": gr.update(value=name_model),
+                                    "Schedule type": gr.update(value="Automatic"),
+                                    "PAG": gr.update(value=.0),
+                                    "FreeU": gr.update(value=False),
+                                }
+                                valid_keys = list(valid_receptors.keys())
+
+                                parameters = extract_parameters(base_prompt)
+                                # print(parameters)
+
+                                if "Sampler" in parameters:
+                                    value_sampler = parameters["Sampler"]
+                                    for s_type in SCHEDULE_TYPE_OPTIONS:
+                                        if s_type in value_sampler:
+                                            value_sampler = value_sampler.replace(s_type, "").strip()
+                                            parameters["Sampler"] = value_sampler
+                                            parameters["Schedule type"] = s_type
+
+                                for key, val in parameters.items():
+                                    # print(val)
+                                    if key in valid_keys:
+                                        try:
+                                            if key == "Sampler":
+                                                if val not in scheduler_names:
+                                                    continue
+                                            if key == "Schedule type":
+                                                if val not in SCHEDULE_TYPE_OPTIONS:
+                                                    val = "Automatic"
+                                            elif key == "Clip skip":
+                                                if "," in str(val):
+                                                    val = val.replace(",", "")
+                                                if int(val) >= 2:
+                                                    val = True
+                                            if key == "prompt":
+                                                if ">" in val and "<" in val:
+                                                    val = re.sub(r'<[^>]+>', '', val)
+                                                    print("Removed LoRA written in the prompt")
+                                            if key in ["prompt", "neg_prompt"]:
+                                                val = re.sub(r'\s+', ' ', re.sub(r',+', ',', val)).strip()
+                                            if key in ["Steps", "width", "height", "Seed"]:
+                                                val = int(val)
+                                            if key == "FreeU":
                                                 val = True
-                                        if key == "prompt":
-                                            if ">" in val and "<" in val:
-                                                val = re.sub(r'<[^>]+>', '', val)
-                                                print("Removed LoRA written in the prompt")
-                                        if key in ["prompt", "neg_prompt"]:
-                                            val = re.sub(r'\s+', ' ', re.sub(r',+', ',', val)).strip()
-                                        if key in ["Steps", "width", "height", "Seed"]:
-                                            val = int(val)
-                                        if key == "FreeU":
-                                            val = True
-                                        if key in ["CFG scale", "PAG"]:
-                                            val = float(val)
-                                        if key == "Model":
-                                            filtered_models = [m for m in model_list if val in m]
-                                            if filtered_models:
-                                                val = filtered_models[0]
-                                            else:
-                                                val = name_model
-                                        if key == "Seed":
-                                            continue
-                                        valid_receptors[key] = gr.update(value=val)
-                                        # print(val, type(val))
-                                        # print(valid_receptors)
-                                    except Exception as e:
-                                        print(str(e))
-                            return [value for value in valid_receptors.values()]
-
-                        set_params_gui.click(
-                            run_set_params_gui, [prompt_gui, model_name_gui], [
-                                prompt_gui,
-                                neg_prompt_gui,
-                                steps_gui,
-                                img_width_gui,
-                                img_height_gui,
-                                seed_gui,
-                                sampler_gui,
-                                cfg_gui,
-                                clip_skip_gui,
-                                model_name_gui,
-                                schedule_type_gui,
-                                pag_scale_gui,
-                                free_u_gui,
-                            ],
-                        )
-
-                        def run_clear_prompt_gui():
-                            return gr.update(value=""), gr.update(value="")
-                        clear_prompt_gui.click(
-                            run_clear_prompt_gui, [], [prompt_gui, neg_prompt_gui]
-                        )
-
-                        def run_set_random_seed():
-                            return -1
-                        set_random_seed.click(
-                            run_set_random_seed, [], seed_gui
-                        )
+                                            if key in ["CFG scale", "PAG"]:
+                                                val = float(val)
+                                            if key == "Model":
+                                                filtered_models = [m for m in model_list if val in m]
+                                                if filtered_models:
+                                                    val = filtered_models[0]
+                                                else:
+                                                    val = name_model
+                                            if key == "Seed":
+                                                continue
+                                            valid_receptors[key] = gr.update(value=val)
+                                            # print(val, type(val))
+                                            # print(valid_receptors)
+                                        except Exception as e:
+                                            print(str(e))
+                                return [value for value in valid_receptors.values()]
+
+                            set_params_gui.click(
+                                run_set_params_gui, [prompt_gui, model_name_gui], [
+                                    prompt_gui,
+                                    neg_prompt_gui,
+                                    steps_gui,
+                                    img_width_gui,
+                                    img_height_gui,
+                                    seed_gui,
+                                    sampler_gui,
+                                    cfg_gui,
+                                    clip_skip_gui,
+                                    model_name_gui,
+                                    schedule_type_gui,
+                                    pag_scale_gui,
+                                    free_u_gui,
+                                ],
+                            )
+
+                            def run_clear_prompt_gui():
+                                return gr.update(value=""), gr.update(value="")
+                            clear_prompt_gui.click(
+                                run_clear_prompt_gui, [], [prompt_gui, neg_prompt_gui]
+                            )
+
+                            def run_set_random_seed():
+                                return -1
+                            set_random_seed.click(
+                                run_set_random_seed, [], seed_gui
+                            )
 
                     with gr.Accordion("LoRA", open=False, visible=True) as menu_lora:
-                        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)
+                        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):
-                            return gr.Slider(minimum=-2, maximum=2, step=0.01, value=1.00, label=label)
+                        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(label):
                             return gr.Textbox(label=label, info="Example of prompt:", value="None", show_copy_button=True, interactive=False, visible=False)
@@ -1021,6 +1063,22 @@ with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, cs
                                         lora5_info_gui = lora_textbox("LoRA5 prompts")
                                         lora5_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
                                     lora5_desc_gui = gr.Markdown(value="", visible=False)
+                            with gr.Column():
+                                lora6_gui = lora_dropdown("LoRA6", visible=False)
+                                lora_scale_6_gui = lora_scale_slider("LoRA Scale 6", visible=False)
+                                with gr.Row():
+                                    with gr.Group():
+                                        lora6_info_gui = lora_textbox("LoRA6 prompts")
+                                        lora6_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
+                                    lora6_desc_gui = gr.Markdown(value="", visible=False)
+                            with gr.Column():
+                                lora7_gui = lora_dropdown("LoRA7", visible=False)
+                                lora_scale_7_gui = lora_scale_slider("LoRA Scale 7", visible=False)
+                                with gr.Row():
+                                    with gr.Group():
+                                        lora7_info_gui = lora_textbox("LoRA7 prompts")
+                                        lora7_copy_gui = gr.Button(value="Copy example to prompt", visible=False)
+                                    lora7_desc_gui = gr.Markdown(value="", visible=False)
                         with gr.Accordion("From URL", open=True, visible=True):
                             with gr.Row():
                                 search_civitai_basemodel_lora = gr.CheckboxGroup(label="Search LoRA for", choices=CIVITAI_BASEMODEL, value=["Pony", "Illustrious", "SDXL 1.0"])
@@ -1037,7 +1095,7 @@ with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, cs
                             search_civitai_result_lora = gr.Dropdown(label="Search Results", choices=[("", "")], value="", allow_custom_value=True, visible=False)
                             with gr.Row():
                                 text_lora = 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, scale=4)
-                                romanize_text = gr.Checkbox(value=False, label="Transliterate name", scale=1)
+                                romanize_text = gr.Checkbox(value=False, label="Transliterate name", scale=1, visible=False)
                             button_lora = gr.Button("Get and Refresh the LoRA Lists")
                             new_lora_status = gr.HTML()
                         with gr.Accordion("From Local", open=True, visible=True):
@@ -1055,6 +1113,9 @@ with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, cs
                                 hires_steps_gui = gr.Slider(minimum=0, value=30, maximum=100, step=1, label="Hires Steps")
                                 hires_denoising_strength_gui = gr.Slider(minimum=0.1, maximum=1.0, step=0.01, value=0.55, label="Hires Denoising Strength")
                                 hires_sampler_gui = 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_gui = gr.Dropdown(label="Hires Schedule type", choices=hires_schedule_list, value=hires_schedule_list[0])
+                                hires_guidance_scale_gui = 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")
                             hires_prompt_gui = gr.Textbox(label="Hires Prompt", placeholder="Main prompt will be use", lines=3)
                             hires_negative_prompt_gui = gr.Textbox(label="Hires Negative Prompt", placeholder="Main negative prompt will be use", lines=3)
 
@@ -1121,14 +1182,23 @@ with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, cs
                                     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."
                                 )
-                                preprocessor_name_gui = gr.Dropdown(label="Preprocessor Name", choices=PREPROCESSOR_CONTROLNET["canny"])
+                            with gr.Row():
+                                controlnet_model_gui = gr.Dropdown(label="ControlNet model", choices=DIFFUSERS_CONTROLNET_MODEL, value=DIFFUSERS_CONTROLNET_MODEL[0])
+                                control_net_output_scaling_gui = gr.Slider(minimum=0, maximum=5.0, step=0.1, value=1, label="ControlNet Output Scaling in UNet")
+                                control_net_start_threshold_gui = gr.Slider(minimum=0, maximum=1, step=0.01, value=0, label="ControlNet Start Threshold (%)")
+                                control_net_stop_threshold_gui = gr.Slider(minimum=0, maximum=1, step=0.01, value=1, label="ControlNet Stop Threshold (%)")
+                            with gr.Row():
+                                preprocessor_name_gui = gr.Dropdown(label="Preprocessor Name", choices=TASK_AND_PREPROCESSORS["canny"])
+                                preprocess_resolution_gui = gr.Slider(minimum=64, maximum=2048, step=64, value=512, label="Preprocessor Resolution")
+                                low_threshold_gui = gr.Slider(minimum=1, maximum=255, step=1, value=100, label="'CANNY' low threshold")
+                                high_threshold_gui = gr.Slider(minimum=1, maximum=255, step=1, value=200, label="'CANNY' high threshold")
 
                                 def change_preprocessor_choices(task):
                                     task = TASK_STABLEPY[task]
-                                    if task in PREPROCESSOR_CONTROLNET.keys():
-                                        choices_task = PREPROCESSOR_CONTROLNET[task]
+                                    if task in TASK_AND_PREPROCESSORS.keys():
+                                        choices_task = TASK_AND_PREPROCESSORS[task]
                                     else:
-                                        choices_task = PREPROCESSOR_CONTROLNET["canny"]
+                                        choices_task = TASK_AND_PREPROCESSORS["canny"]
                                     return gr.update(choices=choices_task, value=choices_task[0])
 
                                 task_gui.change(
@@ -1136,16 +1206,12 @@ with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, cs
                                     [task_gui],
                                     [preprocessor_name_gui],
                                 )
+
                             with gr.Row():
-                                preprocess_resolution_gui = gr.Slider(minimum=64, maximum=2048, step=64, value=512, label="Preprocess Resolution")
-                                low_threshold_gui = gr.Slider(minimum=1, maximum=255, step=1, value=100, label="Canny low threshold")
-                                high_threshold_gui = gr.Slider(minimum=1, maximum=255, step=1, value=200, label="Canny high threshold")
-                                value_threshold_gui = gr.Slider(minimum=1, maximum=2.0, step=0.01, value=0.1, label="Hough value threshold (MLSD)")
-                            with gr.Row():
-                                distance_threshold_gui = gr.Slider(minimum=1, maximum=20.0, step=0.01, value=0.1, label="Hough distance threshold (MLSD)")
-                                control_net_output_scaling_gui = gr.Slider(minimum=0, maximum=5.0, step=0.1, value=1, label="ControlNet Output Scaling in UNet")
-                                control_net_start_threshold_gui = gr.Slider(minimum=0, maximum=1, step=0.01, value=0, label="ControlNet Start Threshold (%)")
-                                control_net_stop_threshold_gui = gr.Slider(minimum=0, maximum=1, step=0.01, value=1, label="ControlNet Stop Threshold (%)")
+                                value_threshold_gui = gr.Slider(minimum=1, maximum=2.0, step=0.01, value=0.1, label="'MLSD' Hough value threshold")
+                                distance_threshold_gui = gr.Slider(minimum=1, maximum=20.0, step=0.01, value=0.1, label="'MLSD' Hough distance threshold")
+                                recolor_gamma_correction_gui = gr.Number(minimum=0., maximum=25., value=1., step=0.001, label="'RECOLOR' gamma correction")
+                                tile_blur_sigma_gui = gr.Number(minimum=0, maximum=100, value=9, step=1, label="'TILE' blur sigma")
 
                         with gr.Accordion("IP-Adapter", open=False, visible=True) as menu_ipa:
 
@@ -1204,7 +1270,6 @@ with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, cs
                             style_button.click(load_json_style_file, [style_json_gui], [style_prompt_gui])
 
                         with gr.Accordion("Other settings", open=False, visible=True) as menu_other:
-                            schedule_prediction_type_gui = gr.Dropdown(label="Discrete Sampling Type", choices=SCHEDULE_PREDICTION_TYPE_OPTIONS, value=SCHEDULE_PREDICTION_TYPE_OPTIONS[0])
                             with gr.Row():
                                 save_generated_images_gui = gr.Checkbox(value=False, label="Save Generated Images")
                                 filename_pattern_gui = gr.Textbox(label="Filename pattern", value="model,seed", placeholder="model,seed,sampler,schedule_type,img_width,img_height,guidance_scale,num_steps,vae,prompt_section,neg_prompt_section", lines=1)
@@ -1289,15 +1354,15 @@ with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, cs
                         # enable crop (or disable it)
                         # transforms=["crop"],
                         brush=gr.Brush(
-                        default_size="16", # or leave it as 'auto'
-                        color_mode="fixed", # 'fixed' hides the user swatches and colorpicker, 'defaults' shows it
-                        # default_color="black", # html names are supported
-                        colors=[
-                            "rgba(0, 0, 0, 1)", # rgb(a)
-                            "rgba(0, 0, 0, 0.1)",
-                            "rgba(255, 255, 255, 0.1)",
-                            # "hsl(360, 120, 120)" # in fact any valid colorstring
-                        ]
+                            default_size="16",  # or leave it as 'auto'
+                            color_mode="fixed",  # 'fixed' hides the user swatches and colorpicker, 'defaults' shows it
+                            # default_color="black", # html names are supported
+                            colors=[
+                                "rgba(0, 0, 0, 1)",  # rgb(a)
+                                "rgba(0, 0, 0, 0.1)",
+                                "rgba(255, 255, 255, 0.1)",
+                                # "hsl(360, 120, 120)" # in fact any valid colorstring
+                            ]
                         ),
                         eraser=gr.Eraser(default_size="16")
                     )
@@ -1345,6 +1410,9 @@ with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, cs
                         outputs=[result_up_tab],
                     )
 
+        with gr.Tab("Preprocessor", render=True):
+            preprocessor_tab()
+
 ## BEGIN MOD
         interface_mode_gui.change(
             change_interface_mode,
@@ -1379,15 +1447,19 @@ with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, cs
         gr.on(
             triggers=[lora1_gui.change, lora_scale_1_gui.change, lora2_gui.change, lora_scale_2_gui.change,
                        lora3_gui.change, lora_scale_3_gui.change, lora4_gui.change, lora_scale_4_gui.change,
-                       lora5_gui.change, lora_scale_5_gui.change, prompt_syntax_gui.change],
+                       lora5_gui.change, lora_scale_5_gui.change, lora6_gui.change, lora_scale_6_gui.change,
+                       lora7_gui.change, lora_scale_7_gui.change, prompt_syntax_gui.change],
             fn=update_loras,
             inputs=[prompt_gui, prompt_syntax_gui, lora1_gui, lora_scale_1_gui, lora2_gui, lora_scale_2_gui,
-                     lora3_gui, lora_scale_3_gui, lora4_gui, lora_scale_4_gui, lora5_gui, lora_scale_5_gui],
+                     lora3_gui, lora_scale_3_gui, lora4_gui, lora_scale_4_gui, lora5_gui, lora_scale_5_gui,
+                     lora6_gui, lora_scale_6_gui, lora7_gui, lora_scale_7_gui],
             outputs=[prompt_gui, lora1_gui, lora_scale_1_gui, lora1_info_gui, lora1_copy_gui, lora1_desc_gui,
                     lora2_gui, lora_scale_2_gui, lora2_info_gui, lora2_copy_gui, lora2_desc_gui,
                     lora3_gui, lora_scale_3_gui, lora3_info_gui, lora3_copy_gui, lora3_desc_gui, 
                     lora4_gui, lora_scale_4_gui, lora4_info_gui, lora4_copy_gui, lora4_desc_gui,
-                    lora5_gui, lora_scale_5_gui, lora5_info_gui, lora5_copy_gui, lora5_desc_gui],
+                    lora5_gui, lora_scale_5_gui, lora5_info_gui, lora5_copy_gui, lora5_desc_gui,
+                    lora6_gui, lora_scale_6_gui, lora6_info_gui, lora6_copy_gui, lora6_desc_gui,
+                    lora7_gui, lora_scale_7_gui, lora7_info_gui, lora7_copy_gui, lora7_desc_gui],
             queue=False,
             trigger_mode="once",
         )
@@ -1396,6 +1468,8 @@ with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, cs
         lora3_copy_gui.click(apply_lora_prompt, [prompt_gui, lora3_info_gui], [prompt_gui], queue=False)
         lora4_copy_gui.click(apply_lora_prompt, [prompt_gui, lora4_info_gui], [prompt_gui], queue=False)
         lora5_copy_gui.click(apply_lora_prompt, [prompt_gui, lora5_info_gui], [prompt_gui], queue=False)
+        lora6_copy_gui.click(apply_lora_prompt, [prompt_gui, lora6_info_gui], [prompt_gui], queue=False)
+        lora7_copy_gui.click(apply_lora_prompt, [prompt_gui, lora7_info_gui], [prompt_gui], queue=False)
         gr.on(
             triggers=[search_civitai_button_lora.click, search_civitai_query_lora.submit],
             fn=search_civitai_lora,
@@ -1407,9 +1481,9 @@ with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, cs
         )
         search_civitai_result_lora.change(select_civitai_lora, [search_civitai_result_lora], [text_lora, search_civitai_desc_lora], queue=False, scroll_to_output=True)
         search_civitai_gallery_lora.select(update_civitai_selection, None, [search_civitai_result_lora], queue=False, show_api=False)
-        button_lora.click(get_my_lora, [text_lora, romanize_text], [lora1_gui, lora2_gui, lora3_gui, lora4_gui, lora5_gui, new_lora_status], scroll_to_output=True)
+        button_lora.click(get_my_lora, [text_lora, romanize_text], [lora1_gui, lora2_gui, lora3_gui, lora4_gui, lora5_gui, lora6_gui, lora7_gui, new_lora_status], scroll_to_output=True)
         upload_button_lora.upload(upload_file_lora, [upload_button_lora], [file_output_lora, upload_button_lora]).success(
-            move_file_lora, [file_output_lora], [lora1_gui, lora2_gui, lora3_gui, lora4_gui, lora5_gui], scroll_to_output=True)
+            move_file_lora, [file_output_lora], [lora1_gui, lora2_gui, lora3_gui, lora4_gui, lora5_gui, lora6_gui, lora7_gui], scroll_to_output=True)
 
         use_textual_inversion_gui.change(set_textual_inversion_prompt, [use_textual_inversion_gui, prompt_gui, neg_prompt_gui, prompt_syntax_gui], [prompt_gui, neg_prompt_gui])
 
@@ -1454,7 +1528,8 @@ with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, cs
             inputs=[
                 model_name_gui,
                 vae_model_gui,
-                task_gui
+                task_gui,
+                controlnet_model_gui,
             ],
             outputs=[load_model_gui],
             queue=True,
@@ -1479,6 +1554,10 @@ with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, cs
                 lora_scale_4_gui,
                 lora5_gui,
                 lora_scale_5_gui,
+                lora6_gui,
+                lora_scale_6_gui,
+                lora7_gui,
+                lora_scale_7_gui,
                 sampler_gui,
                 schedule_type_gui,
                 schedule_prediction_type_gui,
@@ -1499,6 +1578,8 @@ with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, cs
                 high_threshold_gui,
                 value_threshold_gui,
                 distance_threshold_gui,
+                recolor_gamma_correction_gui,
+                tile_blur_sigma_gui,
                 control_net_output_scaling_gui,
                 control_net_start_threshold_gui,
                 control_net_stop_threshold_gui,
@@ -1515,6 +1596,9 @@ with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, cs
                 hires_negative_prompt_gui,
                 hires_before_adetailer_gui,
                 hires_after_adetailer_gui,
+                hires_schedule_type_gui,
+                hires_guidance_scale_gui,
+                controlnet_model_gui,
                 loop_generation_gui,
                 leave_progress_bar_gui,
                 disable_progress_bar_gui,
@@ -1556,6 +1640,7 @@ with gr.Blocks(theme='NoCrypt/miku@>=1.2.2', elem_id="main", fill_width=True, cs
                 mask_blur_b_gui,
                 mask_padding_b_gui,
                 retain_task_cache_gui,
+                guidance_rescale_gui,
                 image_ip1,
                 mask_ip1,
                 model_ip1,

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,

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],
+            )

modutils.py

@@ -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

@@ -1,4 +1,4 @@
-git+https://github.com/R3gm/stablepy.git@8edabb0  # -b refactor_sampler_fix
+git+https://github.com/R3gm/stablepy.git@a9fe2dc  # -b refactor_sampler_fix
 torch==2.2.0
 numpy<2
 gdown

utils.py

@@ -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
     )