Update constants.py

.gitignore

@@ -1,207 +0,0 @@
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-
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-MANIFEST
-
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-
-# Unit test / coverage reports
-htmlcov/
-.tox/
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-nosetests.xml
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-*.cover
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-
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README.md

@@ -4,7 +4,7 @@ emoji: 🧩🖼️
 colorFrom: red
 colorTo: pink
 sdk: gradio
-sdk_version: 5.44.1
+sdk_version: 4.31.3
 app_file: app.py
 pinned: true
 license: mit

app.py

@@ -1,6 +1,5 @@
 import spaces
 import os
-from argparse import ArgumentParser
 from stablepy import (
     Model_Diffusers,
     SCHEDULE_TYPE_OPTIONS,
@@ -8,7 +7,6 @@ from stablepy import (
     check_scheduler_compatibility,
     TASK_AND_PREPROCESSORS,
     FACE_RESTORATION_MODELS,
-    PROMPT_WEIGHT_OPTIONS_PRIORITY,
     scheduler_names,
 )
 from constants import (
@@ -42,8 +40,6 @@ from constants import (
     DIFFUSERS_CONTROLNET_MODEL,
     IP_MODELS,
     MODE_IP_OPTIONS,
-    CACHE_HF_ROOT,
-    CACHE_HF,
 )
 from stablepy.diffusers_vanilla.style_prompt_config import STYLE_NAMES
 import torch
@@ -64,7 +60,6 @@ from utils import (
     progress_step_bar,
     html_template_message,
     escape_html,
-    clear_hf_cache,
 )
 from image_processor import preprocessor_tab
 from datetime import datetime
@@ -77,18 +72,11 @@ from diffusers import FluxPipeline
 # import urllib.parse
 import subprocess
 
-IS_ZERO_GPU = bool(os.getenv("SPACES_ZERO_GPU"))
-HIDE_API = bool(os.getenv("HIDE_API"))
-if IS_ZERO_GPU:
-    subprocess.run("rm -rf /data-nvme/zerogpu-offload/*", env={}, shell=True)
-IS_GPU_MODE = True if IS_ZERO_GPU else (True if torch.cuda.is_available() else False)
-img_path = "./images/"
-allowed_path = os.path.abspath(img_path)
-delete_cache_time = (9600, 9600) if IS_ZERO_GPU else (86400, 86400)
-
+subprocess.run("rm -rf /data-nvme/zerogpu-offload/*", env={}, shell=True)
 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"))
 
 directories = [DIRECTORY_MODELS, DIRECTORY_LORAS, DIRECTORY_VAES, DIRECTORY_EMBEDS, DIRECTORY_UPSCALERS]
 for directory in directories:
@@ -96,15 +84,19 @@ for directory in directories:
 
 # Download stuffs
 for url in [url.strip() for url in DOWNLOAD_MODEL.split(',')]:
-    download_things(DIRECTORY_MODELS, url, HF_TOKEN, CIVITAI_API_KEY)
+    if not os.path.exists(f"./models/{url.split('/')[-1]}"):
+        download_things(DIRECTORY_MODELS, url, HF_TOKEN, CIVITAI_API_KEY)
 for url in [url.strip() for url in DOWNLOAD_VAE.split(',')]:
-    download_things(DIRECTORY_VAES, url, HF_TOKEN, CIVITAI_API_KEY)
+    if not os.path.exists(f"./vaes/{url.split('/')[-1]}"):
+        download_things(DIRECTORY_VAES, url, HF_TOKEN, CIVITAI_API_KEY)
 for url in [url.strip() for url in DOWNLOAD_LORA.split(',')]:
-    download_things(DIRECTORY_LORAS, url, HF_TOKEN, CIVITAI_API_KEY)
+    if not os.path.exists(f"./loras/{url.split('/')[-1]}"):
+        download_things(DIRECTORY_LORAS, url, HF_TOKEN, CIVITAI_API_KEY)
 
 # Download Embeddings
 for url_embed in DOWNLOAD_EMBEDS:
-    download_things(DIRECTORY_EMBEDS, url_embed, HF_TOKEN, CIVITAI_API_KEY)
+    if not os.path.exists(f"./embedings/{url_embed.split('/')[-1]}"):
+        download_things(DIRECTORY_EMBEDS, url_embed, HF_TOKEN, CIVITAI_API_KEY)
 
 # Build list models
 embed_list = get_model_list(DIRECTORY_EMBEDS)
@@ -122,16 +114,15 @@ vae_model_list.insert(0, "None")
 
 print('\033[33m🏁 Download and listing of valid models completed.\033[0m')
 
-components = None
-if IS_ZERO_GPU:
-    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
-    delete_model(flux_repo)
+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
+delete_model(flux_repo)
+# components = None
 
 #######################
 # GUI
@@ -141,17 +132,7 @@ diffusers.utils.logging.set_verbosity(40)
 warnings.filterwarnings(action="ignore", category=FutureWarning, module="diffusers")
 warnings.filterwarnings(action="ignore", category=UserWarning, module="diffusers")
 warnings.filterwarnings(action="ignore", category=FutureWarning, module="transformers")
-
-parser = ArgumentParser(description='DiffuseCraft: Create images from text prompts.', add_help=True)
-parser.add_argument("--share", action="store_true", dest="share_enabled", default=False, help="Enable sharing")
-parser.add_argument('--theme', type=str, default="NoCrypt/miku", help='Set the theme (default: NoCrypt/miku)')
-parser.add_argument("--ssr", action="store_true", help="Enable SSR (Server-Side Rendering)")
-parser.add_argument("--log-level", type=str, default="INFO", choices=["DEBUG", "INFO", "WARNING", "ERROR", "CRITICAL"], help="Set logging level (default: INFO)")
-args = parser.parse_args()
-
-logger.setLevel(
-    "INFO" if IS_ZERO_GPU else getattr(logging, args.log_level.upper())
-)
+logger.setLevel(logging.DEBUG)
 
 CSS = """
 .contain { display: flex; flex-direction: column; }
@@ -161,12 +142,6 @@ CSS = """
 """
 
 
-def lora_chk(lora_):
-    if isinstance(lora_, str) and lora_.strip() not in ["", "None"]:
-        return lora_
-    return None
-
-
 class GuiSD:
     def __init__(self, stream=True):
         self.model = None
@@ -175,22 +150,13 @@ class GuiSD:
         self.last_load = datetime.now()
         self.inventory = []
 
-    def update_storage_models(self, storage_floor_gb=30, 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
             removal_candidate = self.inventory.pop(0)
             delete_model(removal_candidate)
 
-        # Cleanup after 60 seconds of inactivity
-        lowPrioCleanup = max((datetime.now() - self.last_load).total_seconds(), 0) > 60
-        if lowPrioCleanup and (len(self.inventory) >= required_inventory_for_purge - 1) and not self.status_loading and get_used_storage_gb(CACHE_HF_ROOT) > (storage_floor_gb * 2):
-            print("Cleaning up Hugging Face cache...")
-            clear_hf_cache()
-            self.inventory = [
-                m for m in self.inventory if os.path.exists(m)
-            ]
-
     def update_inventory(self, model_name):
         if model_name not in single_file_model_list:
             self.inventory = [
@@ -201,21 +167,14 @@ class GuiSD:
     def load_new_model(self, model_name, vae_model, task, controlnet_model, progress=gr.Progress(track_tqdm=True)):
 
         # download link model > model_name
-        if model_name.startswith("http"):
-            yield f"Downloading model: {model_name}"
-            model_name = download_things(DIRECTORY_MODELS, model_name, HF_TOKEN, CIVITAI_API_KEY)
-            if not model_name:
-                raise ValueError("Error retrieving model information from URL")
 
-        if IS_ZERO_GPU:
-            self.update_storage_models()
+        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
 
         if not os.path.exists(model_name):
-            logger.debug(f"model_name={model_name}, vae_model={vae_model}, task={task}, controlnet_model={controlnet_model}")
             _ = download_diffuser_repo(
                 repo_name=model_name,
                 model_type=model_type,
@@ -260,10 +219,10 @@ class GuiSD:
                     type_model_precision=dtype_model,
                     retain_task_model_in_cache=False,
                     controlnet_model=controlnet_model,
-                    device="cpu" if IS_ZERO_GPU else None,
+                    device="cpu",
                     env_components=components,
                 )
-                self.model.advanced_params(image_preprocessor_cuda_active=IS_GPU_MODE)
+                self.model.advanced_params(image_preprocessor_cuda_active=True)
             else:
                 if self.model.base_model_id != model_name:
                     load_now_time = datetime.now()
@@ -273,8 +232,7 @@ class GuiSD:
                         print("Waiting for the previous model's time ops...")
                         time.sleep(9 - elapsed_time)
 
-                if IS_ZERO_GPU:
-                    self.model.device = torch.device("cpu")
+                self.model.device = torch.device("cpu")
                 self.model.load_pipe(
                     model_name,
                     task_name=TASK_STABLEPY[task],
@@ -377,7 +335,7 @@ class GuiSD:
         t2i_adapter_preprocessor,
         t2i_adapter_conditioning_scale,
         t2i_adapter_conditioning_factor,
-        enable_live_preview,
+        xformers_memory_efficient_attention,
         freeu,
         generator_in_cpu,
         adetailer_inpaint_only,
@@ -428,7 +386,7 @@ class GuiSD:
         vae_msg = f"VAE: {vae_model}" if vae_model else ""
         msg_lora = ""
 
-        logger.debug(f"Config model: {model_name}, {vae_model}, {loras_list}")
+        print("Config model:", model_name, vae_model, loras_list)
 
         task = TASK_STABLEPY[task]
 
@@ -526,26 +484,26 @@ class GuiSD:
             "distance_threshold": distance_threshold,
             "recolor_gamma_correction": float(recolor_gamma_correction),
             "tile_blur_sigma": int(tile_blur_sigma),
-            "lora_A": lora_chk(lora1),
+            "lora_A": lora1 if lora1 != "None" else None,
             "lora_scale_A": lora_scale1,
-            "lora_B": lora_chk(lora2),
+            "lora_B": lora2 if lora2 != "None" else None,
             "lora_scale_B": lora_scale2,
-            "lora_C": lora_chk(lora3),
+            "lora_C": lora3 if lora3 != "None" else None,
             "lora_scale_C": lora_scale3,
-            "lora_D": lora_chk(lora4),
+            "lora_D": lora4 if lora4 != "None" else None,
             "lora_scale_D": lora_scale4,
-            "lora_E": lora_chk(lora5),
+            "lora_E": lora5 if lora5 != "None" else None,
             "lora_scale_E": lora_scale5,
-            "lora_F": lora_chk(lora6),
+            "lora_F": lora6 if lora6 != "None" else None,
             "lora_scale_F": lora_scale6,
-            "lora_G": lora_chk(lora7),
+            "lora_G": lora7 if lora7 != "None" else None,
             "lora_scale_G": lora_scale7,
             "textual_inversion": embed_list if textual_inversion else [],
             "syntax_weights": syntax_weights,  # "Classic"
             "sampler": sampler,
             "schedule_type": schedule_type,
             "schedule_prediction_type": schedule_prediction_type,
-            "xformers_memory_efficient_attention": False,
+            "xformers_memory_efficient_attention": xformers_memory_efficient_attention,
             "gui_active": True,
             "loop_generation": loop_generation,
             "controlnet_conditioning_scale": float(controlnet_output_scaling_in_unet),
@@ -560,7 +518,7 @@ class GuiSD:
             "leave_progress_bar": leave_progress_bar,
             "disable_progress_bar": disable_progress_bar,
             "image_previews": image_previews,
-            "display_images": False,
+            "display_images": display_images,
             "save_generated_images": save_generated_images,
             "filename_pattern": filename_pattern,
             "image_storage_location": image_storage_location,
@@ -596,11 +554,11 @@ class GuiSD:
         # kwargs for diffusers pipeline
         if guidance_rescale:
             pipe_params["guidance_rescale"] = guidance_rescale
-        if IS_ZERO_GPU:
-            self.model.device = torch.device("cuda:0")
-            if hasattr(self.model.pipe, "transformer") and loras_list != ["None"] * self.model.num_loras:
-                self.model.pipe.transformer.to(self.model.device)
-                logger.debug("transformer to cuda")
+
+        self.model.device = torch.device("cuda:0")
+        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")
 
         actual_progress = 0
         info_images = gr.update()
@@ -630,20 +588,15 @@ class GuiSD:
 
                 download_links = "<br>".join(
                     [
-                        f'<a href="{path.replace("/images/", f"/gradio_api/file={allowed_path}/")}" download="{os.path.basename(path)}">Download Image {i + 1}</a>'
+                        f'<a href="{path.replace("/images/", "/file=/home/user/app/images/")}" download="{os.path.basename(path)}">Download Image {i + 1}</a>'
                         for i, path in enumerate(image_path)
                     ]
                 )
                 if save_generated_images:
                     info_images += f"<br>{download_links}"
 
-                if not display_images:
-                    img = gr.update()
                 info_state = "COMPLETE"
 
-            elif not enable_live_preview:
-                img = gr.update()
-
             yield info_state, img, info_images
 
 
@@ -744,8 +697,7 @@ def sd_gen_generate_pipeline(*args):
 
 @spaces.GPU(duration=15)
 def process_upscale(image, upscaler_name, upscaler_size):
-    if image is None:
-        return None
+    if image is None: return None
 
     from stablepy.diffusers_vanilla.utils import save_pil_image_with_metadata
     from stablepy import load_upscaler_model
@@ -762,7 +714,7 @@ def process_upscale(image, upscaler_name, upscaler_size):
 
         name_upscaler = f"./{DIRECTORY_UPSCALERS}/{name_upscaler.split('/')[-1]}"
 
-    scaler_beta = load_upscaler_model(model=name_upscaler, tile=(0 if IS_ZERO_GPU else 192), tile_overlap=8, device=("cuda" if IS_GPU_MODE else "cpu"), half=IS_GPU_MODE)
+    scaler_beta = load_upscaler_model(model=name_upscaler, tile=0, tile_overlap=8, device="cuda", half=True)
     image_up = scaler_beta.upscale(image, upscaler_size, True)
 
     image_path = save_pil_image_with_metadata(image_up, f'{os.getcwd()}/up_images', exif_image)
@@ -771,11 +723,11 @@ def process_upscale(image, upscaler_name, upscaler_size):
 
 
 # https://huggingface.co/spaces/BestWishYsh/ConsisID-preview-Space/discussions/1#674969a022b99c122af5d407
-# dynamic_gpu_duration.zerogpu = True
-# sd_gen_generate_pipeline.zerogpu = True
+dynamic_gpu_duration.zerogpu = True
+sd_gen_generate_pipeline.zerogpu = True
 sd_gen = GuiSD()
 
-with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as app:
+with gr.Blocks(theme="NoCrypt/miku", css=CSS) as app:
     gr.Markdown("# 🧩 DiffuseCraft")
     gr.Markdown(SUBTITLE_GUI)
     with gr.Tab("Generation"):
@@ -791,14 +743,10 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
 
                     return gr.update(value=task_name, choices=new_choices)
 
-                with gr.Accordion("Model and Task", open=True, visible=True):
-                    task_gui = gr.Dropdown(label="Task", choices=SDXL_TASK, value=TASK_MODEL_LIST[0])
-                    model_name_gui = gr.Dropdown(label="Model", choices=model_list, value=model_list[0], allow_custom_value=True)
+                task_gui = gr.Dropdown(label="Task", choices=SDXL_TASK, value=TASK_MODEL_LIST[0])
+                model_name_gui = gr.Dropdown(label="Model", choices=model_list, value=model_list[0], allow_custom_value=True)
                 prompt_gui = gr.Textbox(lines=5, placeholder="Enter prompt", label="Prompt")
-
-                with gr.Accordion("Negative prompt", open=False, visible=True):
-                    neg_prompt_gui = gr.Textbox(lines=3, placeholder="Enter Neg prompt", label="Negative prompt", value="bad anatomy, ((many hands, bad hands, missing fingers)), anatomical nonsense, ugly, deformed, bad proportions, bad shadow, extra limbs, missing limbs, floating limbs, disconnected limbs, malformed hands, poorly drawn, mutation, mutated hands and fingers, extra legs, interlocked fingers, extra arms, disfigured face, long neck, asymmetrical eyes, lowres, extra digit, fewer digits, cropped, jpeg artifacts, signature, watermark, username, blurry, duplicate, bad composition, text, worst quality, normal quality, low quality, very displeasing, monochrome, grayscale, black and white, desaturated, low contrast, muted tones, washed out, unfinished, incomplete, draft, logo, backlighting")
-                
+                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)")
                 with gr.Row(equal_height=False):
                     set_params_gui = gr.Button(value="↙️", variant="secondary", size="sm")
                     clear_prompt_gui = gr.Button(value="🗑️", variant="secondary", size="sm")
@@ -828,179 +776,137 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
 
                 actual_task_info = gr.HTML()
 
-                with gr.Row(equal_height=False, variant="default", visible=IS_ZERO_GPU):
+                with gr.Row(equal_height=False, variant="default"):
                     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")
 
             with gr.Column(scale=1):
-                with gr.Accordion("Generation settings", open=True, visible=True):
-                    steps_gui = gr.Slider(minimum=1, maximum=100, step=1, value=30, label="Steps")
-                    cfg_gui = gr.Slider(minimum=0, maximum=30, step=0.5, value=7., label="CFG")
-                    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])
-                    img_width_gui = gr.Slider(minimum=64, maximum=4096, step=8, value=832, label="Img Width")
-                    img_height_gui = gr.Slider(minimum=64, maximum=4096, step=8, value=1216, label="Img Height")
-                    seed_gui = gr.Number(minimum=-1, maximum=9999999999, value=-1, label="Seed")
-                    pag_scale_gui = gr.Slider(minimum=0.0, maximum=10.0, step=0.1, value=0.0, label="PAG Scale")
-                    with gr.Row():
-                        clip_skip_gui = gr.Checkbox(value=True, label="Layer 2 Clip Skip")
-                        free_u_gui = gr.Checkbox(value=True, label="FreeU")
-
-                    with gr.Row(equal_height=False):
-                        num_images_gui = gr.Slider(minimum=1, maximum=(16 if IS_ZERO_GPU else 20), step=1, value=1, label="Images")
-                        prompt_syntax_gui = gr.Dropdown(label="Prompt Syntax", choices=PROMPT_W_OPTIONS, value=PROMPT_W_OPTIONS[2][1])
-                        vae_model_gui = gr.Dropdown(label="VAE Model", choices=vae_model_list, value=vae_model_list[0])
-
-                
-                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),
-                        "Hires upscaler": gr.update(),
-                        "Hires upscale": gr.update(),
-                        "Hires steps": gr.update(),
-                        "Hires denoising strength": gr.update(),
-                        "Hires CFG": gr.update(),
-                        "Hires sampler": gr.update(),
-                        "Hires schedule type": gr.update(),
-                        "Image resolution": gr.update(value=1024),
-                        "Strength": gr.update(),
-                        "Prompt emphasis": gr.update(),
-                    }
-
-                    # Generate up to 7 LoRAs
-                    for i in range(1, 8):
-                        valid_receptors[f"Lora_{i}"] = gr.update()
-                        valid_receptors[f"Lora_scale_{i}"] = gr.update()
-
-                    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
-
-                    params_lora = []
-                    if ">" in parameters["prompt"] and "<" in parameters["prompt"]:
-                        params_lora = re.findall(r'<lora:[^>]+>', parameters["prompt"])
-                    if "Loras" in parameters:
-                        params_lora += re.findall(r'<lora:[^>]+>', parameters["Loras"])
-
-                    if params_lora:
-                        parsed_params = []
-                        for tag_l in params_lora:
-                            try:
-                                inner = tag_l.strip("<>")        # remove < >
-                                _, data_l = inner.split(":", 1)  # remove the "lora:" part
-                                parts_l = data_l.split(":")
-
-                                name_l = parts_l[0]
-                                weight_l = float(parts_l[1]) if len(parts_l) > 1 else 1.0  # default weight = 1.0
-
-                                parsed_params.append((name_l, weight_l))
-                            except Exception as e:
-                                print(f"Error parsing LoRA tag {tag_l}: {e}")
-
-                        new_lora_model_list = get_model_list(DIRECTORY_LORAS)
-                        new_lora_model_list.insert(0, "None")
-
-                        num_lora = 1
-                        for parsed_l, parsed_s in parsed_params:
-                            filtered_loras = [m for m in new_lora_model_list if parsed_l in m]
-                            if filtered_loras:
-                                parameters[f"Lora_{num_lora}"] = filtered_loras[0]
-                                parameters[f"Lora_scale_{num_lora}"] = parsed_s
-                                num_lora += 1
-
-                    # continue = discard new value
-                    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 in ["Schedule type", "Hires schedule type"]:
-                                    if val not in SCHEDULE_TYPE_OPTIONS:
-                                        continue
-                                if key == "Hires sampler":
-                                    if val not in POST_PROCESSING_SAMPLER:
-                                        continue
-                                if key == "Prompt emphasis":
-                                    if val not in PROMPT_WEIGHT_OPTIONS_PRIORITY:
-                                        continue
-                                elif key == "Clip skip":
-                                    if "," in str(val):
-                                        val = val.replace(",", "")
-                                    if int(val) >= 2:
+                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., label="CFG")
+                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])
+                img_width_gui = gr.Slider(minimum=64, maximum=4096, step=8, value=1024, label="Img Width")
+                img_height_gui = gr.Slider(minimum=64, maximum=4096, step=8, value=1024, label="Img Height")
+                seed_gui = gr.Number(minimum=-1, maximum=9999999999, value=-1, label="Seed")
+                pag_scale_gui = gr.Slider(minimum=0.0, maximum=10.0, step=0.1, value=0.0, label="PAG Scale")
+                with gr.Row():
+                    clip_skip_gui = gr.Checkbox(value=True, label="Layer 2 Clip Skip")
+                    free_u_gui = gr.Checkbox(value=False, label="FreeU")
+
+                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)  # Delete html and loras
-                                        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", "Hires steps", "Image resolution"]:
-                                    val = int(val)
-                                if key == "FreeU":
-                                    val = True
-                                if key in ["CFG scale", "PAG", "Hires upscale", "Hires denoising strength", "Hires CFG", "Strength"]:
-                                    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 == "Hires upscaler":
-                                    if val not in UPSCALER_KEYS:
+                                    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
-                                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()]
-
-                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]
-                )
+                                    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_set_random_seed():
-                    return -1
-                set_random_seed.click(
-                    run_set_random_seed, [], seed_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
+                    )
+
+                num_images_gui = gr.Slider(minimum=1, maximum=5, step=1, value=1, label="Images")
+                prompt_syntax_gui = gr.Dropdown(label="Prompt Syntax", choices=PROMPT_W_OPTIONS, value=PROMPT_W_OPTIONS[1][1])
+                vae_model_gui = gr.Dropdown(label="VAE Model", choices=vae_model_list, value=vae_model_list[0])
 
                 with gr.Accordion("Hires fix", open=False, visible=True):
 
                     upscaler_model_path_gui = gr.Dropdown(label="Upscaler", choices=UPSCALER_KEYS, value=UPSCALER_KEYS[0])
                     upscaler_increases_size_gui = gr.Slider(minimum=1.1, maximum=4., step=0.1, value=1.2, label="Upscale by")
-                    upscaler_tile_size_gui = gr.Slider(minimum=0, maximum=512, step=16, value=(0 if IS_ZERO_GPU else 192), label="Upscaler Tile Size", info="0 = no tiling")
+                    upscaler_tile_size_gui = gr.Slider(minimum=0, maximum=512, step=16, value=0, label="Upscaler Tile Size", info="0 = no tiling")
                     upscaler_tile_overlap_gui = gr.Slider(minimum=0, maximum=48, step=1, value=8, label="Upscaler Tile Overlap")
                     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")
@@ -1017,8 +923,7 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
                         return gr.Dropdown(label=label, choices=lora_model_list, value="None", allow_custom_value=True, visible=visible)
 
                     def lora_scale_slider(label, visible=True):
-                        val_lora = 8 if IS_ZERO_GPU else 10
-                        return gr.Slider(minimum=-val_lora, maximum=val_lora, step=0.01, value=0.33, label=label, visible=visible)
+                        return gr.Slider(minimum=-2, maximum=2, step=0.01, value=0.33, label=label, visible=visible)
 
                     lora1_gui = lora_dropdown("Lora1")
                     lora_scale_1_gui = lora_scale_slider("Lora Scale 1")
@@ -1030,10 +935,10 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
                     lora_scale_4_gui = lora_scale_slider("Lora Scale 4")
                     lora5_gui = lora_dropdown("Lora5")
                     lora_scale_5_gui = lora_scale_slider("Lora Scale 5")
-                    lora6_gui = lora_dropdown("Lora6")
-                    lora_scale_6_gui = lora_scale_slider("Lora Scale 6")
-                    lora7_gui = lora_dropdown("Lora7")
-                    lora_scale_7_gui = lora_scale_slider("Lora Scale 7")
+                    lora6_gui = lora_dropdown("Lora6", visible=False)
+                    lora_scale_6_gui = lora_scale_slider("Lora Scale 6", visible=False)
+                    lora7_gui = lora_dropdown("Lora7", visible=False)
+                    lora_scale_7_gui = lora_scale_slider("Lora Scale 7", visible=False)
 
                     with gr.Accordion("From URL", open=False, visible=True):
                         text_lora = gr.Textbox(
@@ -1042,7 +947,7 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
                             lines=1,
                             info="It has to be .safetensors files, and you can also download them from Hugging Face.",
                         )
-                        romanize_text = gr.Checkbox(value=False, label="Transliterate name", visible=(not IS_ZERO_GPU))
+                        romanize_text = gr.Checkbox(value=False, label="Transliterate name", visible=False)
                         button_lora = gr.Button("Get and Refresh the LoRA Lists")
                         new_lora_status = gr.HTML()
                         button_lora.click(
@@ -1107,8 +1012,8 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
                     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")
-                    value_threshold_gui = gr.Slider(minimum=0.0, maximum=2.0, step=0.01, value=0.1, label="'MLSD' Hough value threshold")
-                    distance_threshold_gui = gr.Slider(minimum=0.0, maximum=20.0, step=0.01, value=0.1, label="'MLSD' Hough distance 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")
 
@@ -1143,7 +1048,7 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
                         gr.Info(f"{len(sd_gen.model.STYLE_NAMES)} styles loaded")
                         return gr.update(value=None, choices=sd_gen.model.STYLE_NAMES)
 
-                    style_button.click(load_json_style_file, [style_json_gui], [style_prompt_gui])
+                    style_button.click(load_json_style_file, [style_json_gui], [style_prompt_gui])                        
 
                 with gr.Accordion("Textual inversion", open=False, visible=False):
                     active_textual_inversion_gui = gr.Checkbox(value=False, label="Active Textual Inversion in prompt")
@@ -1189,67 +1094,23 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
                     schedule_prediction_type_gui = gr.Dropdown(label="Discrete Sampling Type", choices=SCHEDULE_PREDICTION_TYPE_OPTIONS, value=SCHEDULE_PREDICTION_TYPE_OPTIONS[0])
                     guidance_rescale_gui = gr.Number(label="CFG rescale:", value=0., step=0.01, minimum=0., maximum=1.5)
                     save_generated_images_gui = gr.Checkbox(value=True, label="Create a download link for the images")
-                    enable_live_preview_gui = gr.Checkbox(value=True, label="Enable live previews")
-                    display_images_gui = gr.Checkbox(value=True, label="Show final results")
                     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)
                     hires_before_adetailer_gui = gr.Checkbox(value=False, label="Hires Before Adetailer")
                     hires_after_adetailer_gui = gr.Checkbox(value=True, label="Hires After Adetailer")
                     generator_in_cpu_gui = gr.Checkbox(value=False, label="Generator in CPU")
-                    with gr.Column(visible=(not IS_ZERO_GPU)):
-                        image_storage_location_gui = gr.Textbox(value=img_path, label="Image Storage Location")
-                        disable_progress_bar_gui = gr.Checkbox(value=False, label="Disable Progress Bar")
-                        leave_progress_bar_gui = gr.Checkbox(value=True, label="Leave Progress Bar")
 
                 with gr.Accordion("More settings", open=False, visible=False):
                     loop_generation_gui = gr.Slider(minimum=1, value=1, label="Loop Generation")
                     retain_task_cache_gui = gr.Checkbox(value=False, label="Retain task model in cache")
-                    
-                    image_previews_gui = gr.Checkbox(value=True, label="Image Previews (alt)")
+                    leave_progress_bar_gui = gr.Checkbox(value=True, label="Leave Progress Bar")
+                    disable_progress_bar_gui = gr.Checkbox(value=False, label="Disable Progress Bar")
+                    display_images_gui = gr.Checkbox(value=False, label="Display Images")
+                    image_previews_gui = gr.Checkbox(value=True, label="Image Previews")
+                    image_storage_location_gui = gr.Textbox(value="./images", label="Image Storage Location")
                     retain_compel_previous_load_gui = gr.Checkbox(value=False, label="Retain Compel Previous Load")
                     retain_detailfix_model_previous_load_gui = gr.Checkbox(value=False, label="Retain Detailfix Model Previous Load")
                     retain_hires_model_previous_load_gui = gr.Checkbox(value=False, label="Retain Hires Model Previous Load")
-
-                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,
-                        upscaler_model_path_gui,
-                        upscaler_increases_size_gui,
-                        hires_steps_gui,
-                        hires_denoising_strength_gui,
-                        hires_guidance_scale_gui,
-                        hires_sampler_gui,
-                        hires_schedule_type_gui,
-                        image_resolution_gui,
-                        strength_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,
-                        lora6_gui,
-                        lora_scale_6_gui,
-                        lora7_gui,
-                        lora_scale_7_gui,
-                    ],
-                )
+                    xformers_memory_efficient_attention_gui = gr.Checkbox(value=False, label="Xformers Memory Efficient Attention")
 
         with gr.Accordion("Examples and help", open=False, visible=True):
             gr.Markdown(HELP_GUI)
@@ -1306,21 +1167,10 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
                             # "hsl(360, 120, 120)" # in fact any valid colorstring
                         ]
                     ),
-                    eraser=gr.Eraser(default_size="16"),
-                    render=True,
-                    visible=False,
-                    interactive=False,
+                    eraser=gr.Eraser(default_size="16")
                 )
-
-                show_canvas = gr.Button("SHOW INPAINT CANVAS")
-
-                def change_visibility_canvas():
-                    return gr.update(visible=True, interactive=True), gr.update(visible=False)
-                show_canvas.click(change_visibility_canvas, [], [image_base, show_canvas])
-
                 invert_mask = gr.Checkbox(value=False, label="Invert mask")
                 btn = gr.Button("Create mask")
-
             with gr.Column(scale=1):
                 img_source = gr.Image(interactive=False)
                 img_result = gr.Image(label="Mask image", show_label=True, interactive=False)
@@ -1382,7 +1232,6 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
         outputs=[load_model_gui],
         queue=True,
         show_progress="minimal",
-        api_name=(False if HIDE_API else None),
     ).success(
         fn=sd_gen_generate_pipeline,  # fn=sd_gen.generate_pipeline,
         inputs=[
@@ -1462,7 +1311,7 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
             t2i_adapter_preprocessor_gui,
             adapter_conditioning_scale_gui,
             adapter_conditioning_factor_gui,
-            enable_live_preview_gui,
+            xformers_memory_efficient_attention_gui,
             free_u_gui,
             generator_in_cpu_gui,
             adetailer_inpaint_only_gui,
@@ -1511,16 +1360,12 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
         outputs=[load_model_gui, result_images, actual_task_info],
         queue=True,
         show_progress="minimal",
-        # api_name=(False if HIDE_API else None),
     )
 
-if __name__ == "__main__":
-    app.queue()
-    app.launch(
-        show_error=True,
-        share=args.share_enabled,
-        debug=True,
-        ssr_mode=args.ssr,
-        allowed_paths=[allowed_path],
-        show_api=(not HIDE_API),
-    )
+app.queue()
+
+app.launch(
+    show_error=True,
+    debug=True,
+    allowed_paths=["./images/"],
+)

constants.py

@@ -7,34 +7,28 @@ from stablepy import (
     ALL_BUILTIN_UPSCALERS,
     IP_ADAPTERS_SD,
     IP_ADAPTERS_SDXL,
-    PROMPT_WEIGHT_OPTIONS_PRIORITY,
 )
 
-IS_ZERO_GPU = bool(os.getenv("SPACES_ZERO_GPU"))
-
 # - **Download Models**
-DOWNLOAD_MODEL = "https://huggingface.co/zuv0/test/resolve/main/milkyWonderland_v40.safetensors"
+DOWNLOAD_MODEL = "https://huggingface.co/TechnoByte/MilkyWonderland/resolve/main/milkyWonderland_v40.safetensors"
 
 # - **Download VAEs**
-DOWNLOAD_VAE = "https://huggingface.co/Anzhc/Anzhcs-VAEs/resolve/main/SDXL%20Anime%20VAE%20Dec-only%20B3.safetensors, https://huggingface.co/fp16-guy/anything_kl-f8-anime2_vae-ft-mse-840000-ema-pruned_blessed_clearvae_fp16_cleaned/resolve/main/vae-ft-mse-840000-ema-pruned_fp16.safetensors?download=true"
+DOWNLOAD_VAE = "https://huggingface.co/fp16-guy/anything_kl-f8-anime2_vae-ft-mse-840000-ema-pruned_blessed_clearvae_fp16_cleaned/resolve/main/vae-ft-mse-840000-ema-pruned_fp16.safetensors?download=true"
 
 # - **Download LoRAs**
 DOWNLOAD_LORA = "https://huggingface.co/Leopain/color/resolve/main/Coloring_book_-_LineArt.safetensors, https://civitai.com/api/download/models/135867, https://huggingface.co/Linaqruf/anime-detailer-xl-lora/resolve/main/anime-detailer-xl.safetensors?download=true, https://huggingface.co/Linaqruf/style-enhancer-xl-lora/resolve/main/style-enhancer-xl.safetensors?download=true, https://huggingface.co/ByteDance/Hyper-SD/resolve/main/Hyper-SD15-8steps-CFG-lora.safetensors?download=true, https://huggingface.co/ByteDance/Hyper-SD/resolve/main/Hyper-SDXL-8steps-CFG-lora.safetensors?download=true"
 
 LOAD_DIFFUSERS_FORMAT_MODEL = [
-    'TestOrganizationPleaseIgnore/potato_quality_anime_plzwork_sdxl',
-    'TestOrganizationPleaseIgnore/rinAnim8drawIllustriousXL_v20_sdxl',
-    'TestOrganizationPleaseIgnore/perfectrsbmixIllustrious_definitiveiota_sdxl',
     'stabilityai/stable-diffusion-xl-base-1.0',
     'Laxhar/noobai-XL-1.1',
     'Laxhar/noobai-XL-Vpred-1.0',
     'black-forest-labs/FLUX.1-dev',
-    'black-forest-labs/FLUX.1-Krea-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',
+    'John6666/iniverse-mix-xl-sfwnsfw-fluxdfp16nsfwv11-fp8-flux',
     'Freepik/flux.1-lite-8B-alpha',
     'shauray/FluxDev-HyperSD-merged',
     'mikeyandfriends/PixelWave_FLUX.1-dev_03',
@@ -43,21 +37,23 @@ LOAD_DIFFUSERS_FORMAT_MODEL = [
     # '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',
     'Laxhar/noobai-XL-0.6',
     'John6666/noobai-xl-nai-xl-epsilonpred05version-sdxl',
     'John6666/noobai-cyberfix-v10-sdxl',
     'John6666/noobaiiter-xl-vpred-v075-sdxl',
-    'John6666/ripplemix-noob-vpred10-illustrious01-v14-sdxl',
-    'John6666/sigmaih-15-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/ntr-mix-illustrious-xl-noob-xl-xi-sdxl',
     'John6666/ntr-mix-illustrious-xl-noob-xl-xii-sdxl',
     'John6666/ntr-mix-illustrious-xl-noob-xl-xiii-sdxl',
-    'martineux/nova-unreal10',
     'John6666/mistoon-anime-v10illustrious-sdxl',
-    'John6666/hassaku-xl-illustrious-v22-sdxl',
-    'John6666/hassaku-xl-illustrious-v31-sdxl',
+    'John6666/hassaku-xl-illustrious-v10-sdxl',
+    'John6666/hassaku-xl-illustrious-v10style-sdxl',
     'John6666/haruki-mix-illustrious-v10-sdxl',
     'John6666/noobreal-v10-sdxl',
     'John6666/complicated-noobai-merge-vprediction-sdxl',
@@ -68,8 +64,6 @@ LOAD_DIFFUSERS_FORMAT_MODEL = [
     'Laxhar/noobai-XL-Vpred-0.65',
     'Laxhar/noobai-XL-Vpred-0.6',
     'John6666/cat-tower-noobai-xl-checkpoint-v14vpred-sdxl',
-    'John6666/cat-tower-noobai-xl-checkpoint-v15vpred-sdxl',
-    'John6666/cat-tower-noobai-xl-checkpoint-v20-vpred-sdxl',
     'John6666/noobai-xl-nai-xl-vpred05version-sdxl',
     'John6666/noobai-fusion2-vpred-itercomp-v1-sdxl',
     'John6666/noobai-xl-nai-xl-vpredtestversion-sdxl',
@@ -80,46 +74,19 @@ LOAD_DIFFUSERS_FORMAT_MODEL = [
     'John6666/obsession-illustriousxl-v21-sdxl',
     'John6666/obsession-illustriousxl-v30-sdxl',
     'John6666/obsession-illustriousxl-v31-sdxl',
-    'John6666/one-obsession-13-sdxl',
-    'John6666/one-obsession-14-24d-sdxl',
-    'John6666/one-obsession-15-noobai-sdxl',
-    'John6666/one-obsession-v16-noobai-sdxl',
-    'John6666/one-obsession-17-red-sdxl',
-    'martineux/oneobs18',
-    'martineux/oneobsession19',
-    'John6666/cat-tower-noobai-xl-checkpoint-v14-epsilon-pred-sdxl',
-    'martineux/cattower-chenkin-xl',
-    'John6666/prefect-illustrious-xl-v3-sdxl',
-    'martineux/perfect4',
-    'martineux/prefectIllustriousXL_v5',
     'John6666/wai-nsfw-illustrious-v70-sdxl',
-    'John6666/wai-nsfw-illustrious-sdxl-v140-sdxl',
-    'martineux/waiIllustriousSDXL_v160',
     'John6666/illustrious-pony-mix-v3-sdxl',
-    'John6666/nova-anime-xl-il-v90-sdxl',
-    'John6666/nova-anime-xl-il-v110-sdxl',
-    'frankjoshua/novaAnimeXL_ilV140',
-    'John6666/nova-orange-xl-re-v10-sdxl',
-    'John6666/nova-orange-xl-v110-sdxl',
-    'John6666/nova-orange-xl-re-v20-sdxl',
-    'John6666/nova-unreal-xl-v60-sdxl',
-    'John6666/nova-unreal-xl-v70-sdxl',
-    'John6666/nova-unreal-xl-v80-sdxl',
-    'martineux/nova-unreal10',
-    'John6666/nova-cartoon-xl-v40-sdxl',
-    'martineux/novacartoon6',
-    'martineux/novareal8',
+    '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',
-    'votepurchase/plantMilkModelSuite_walnut',
     'John6666/meinaxl-v2-sdxl',
     'Eugeoter/artiwaifu-diffusion-2.0',
     'comin/IterComp',
-    'John6666/epicrealism-xl-v8kiss-sdxl',
-    'John6666/epicrealism-xl-v10kiss2-sdxl',
     'John6666/epicrealism-xl-vxiabeast-sdxl',
-    'John6666/epicrealism-xl-vxvii-crystal-clear-realism-sdxl',
+    'John6666/epicrealism-xl-v10kiss2-sdxl',
+    'John6666/epicrealism-xl-v8kiss-sdxl',
     'misri/zavychromaxl_v80',
     'SG161222/RealVisXL_V4.0',
     'SG161222/RealVisXL_V5.0',
@@ -135,10 +102,8 @@ LOAD_DIFFUSERS_FORMAT_MODEL = [
     'John6666/ebara-mfcg-pony-mix-v12-sdxl',
     'John6666/t-ponynai3-v51-sdxl',
     'John6666/t-ponynai3-v65-sdxl',
-    'John6666/t-ponynai3-v7-sdxl',
     'John6666/prefect-pony-xl-v3-sdxl',
     'John6666/prefect-pony-xl-v4-sdxl',
-    'John6666/prefect-pony-xl-v50-sdxl',
     'John6666/mala-anime-mix-nsfw-pony-xl-v5-sdxl',
     'John6666/wai-ani-nsfw-ponyxl-v10-sdxl',
     'John6666/wai-real-mix-v11-sdxl',
@@ -146,14 +111,13 @@ LOAD_DIFFUSERS_FORMAT_MODEL = [
     'John6666/wai-c-v6-sdxl',
     'John6666/iniverse-mix-xl-sfwnsfw-pony-guofeng-v43-sdxl',
     'John6666/sifw-annihilation-xl-v2-sdxl',
-    'John6666/sifw-annihilation-xl-v305illustrious-beta-sdxl',
     'John6666/photo-realistic-pony-v5-sdxl',
     'John6666/pony-realism-v21main-sdxl',
     'John6666/pony-realism-v22main-sdxl',
-    'John6666/pony-realism-v23-ultra-sdxl',
+    'John6666/cyberrealistic-pony-v63-sdxl',
+    'John6666/cyberrealistic-pony-v64-sdxl',
     'John6666/cyberrealistic-pony-v65-sdxl',
     'John6666/cyberrealistic-pony-v7-sdxl',
-    'John6666/cyberrealistic-pony-v127-alternative-sdxl',
     'GraydientPlatformAPI/realcartoon-pony-diffusion',
     'John6666/nova-anime-xl-pony-v5-sdxl',
     'John6666/autismmix-sdxl-autismmix-pony-sdxl',
@@ -163,31 +127,13 @@ LOAD_DIFFUSERS_FORMAT_MODEL = [
     'John6666/duchaiten-pony-real-v11fix-sdxl',
     'John6666/duchaiten-pony-real-v20-sdxl',
     'John6666/duchaiten-pony-xl-no-score-v70-sdxl',
+    'odyssey-labs/OdysseyXL-3.0',
+    'odyssey-labs/OdysseyXL-4.0',
     'KBlueLeaf/Kohaku-XL-Zeta',
     'cagliostrolab/animagine-xl-3.1',
-    'cagliostrolab/animagine-xl-4.0',
     'yodayo-ai/kivotos-xl-2.0',
     'yodayo-ai/holodayo-xl-2.1',
     'yodayo-ai/clandestine-xl-1.0',
-    'Raelina/Raehoshi-illust-XL-8',
-    'johnkillington/chenkinxmilfynoobai_v20-MLX',
-    'martineux/unholydesire5-xl',
-    'abacaxthebrave/Unholy_Desire_Mix_ILXL',
-    'martineux/diving5',
-    'martineux/diving7',
-    'martineux/mergestein-animuplus-xl',
-    'martineux/mergestein-uncannyr2-xl',
-    'martineux/steincustom_V12',
-    'martineux/miaomiao-realskin1p25-xl',
-    'martineux/miaov18',
-    'John6666/garage-mix-noob-vpred-eps-v10-vpred-sdxl',
-    'TestOrganizationPleaseIgnore/perfectrsbmixIllustrious_definitivelambda_sdxl',
-    'TestOrganizationPleaseIgnore/rinFlanimeIllustrious_v27_sdxl',
-    'TestOrganizationPleaseIgnore/rinAnimepopcute_v30_sdxl',
-    'TestOrganizationPleaseIgnore/potato_quality_anime_zzz_sdxl',
-    'https://huggingface.co/chemwolf/Karmix-XL-v0/resolve/main/Karmix-XL-v0.safetensors?download=true',
-    'https://civitai.com/api/download/models/128713?type=Model&format=SafeTensor&size=pruned&fp=fp16',
-    'https://civitai.com/models/30240?modelVersionId=125771',
     'digiplay/majicMIX_sombre_v2',
     'digiplay/majicMIX_realistic_v6',
     'digiplay/majicMIX_realistic_v7',
@@ -213,9 +159,9 @@ LOAD_DIFFUSERS_FORMAT_MODEL = [
     'GraydientPlatformAPI/realcartoon3d-17',
     'GraydientPlatformAPI/realcartoon-pixar11',
     'GraydientPlatformAPI/realcartoon-real17',
+    'nitrosocke/Ghibli-Diffusion',
 ]
 
-
 DIFFUSERS_FORMAT_LORAS = [
     "nerijs/animation2k-flux",
     "XLabs-AI/flux-RealismLora",
@@ -237,11 +183,8 @@ DIRECTORY_VAES = 'vaes'
 DIRECTORY_EMBEDS = 'embedings'
 DIRECTORY_UPSCALERS = 'upscalers'
 
+CACHE_HF = "/home/user/.cache/huggingface/hub/"
 STORAGE_ROOT = "/home/user/"
-CACHE_HF_ROOT = os.path.expanduser("~/.cache/huggingface")
-CACHE_HF = os.path.join(CACHE_HF_ROOT, "hub")
-if IS_ZERO_GPU:
-    os.environ["HF_HOME"] = CACHE_HF
 
 TASK_STABLEPY = {
     'txt2img': 'txt2img',
@@ -283,7 +226,6 @@ UPSCALER_DICT_GUI = {
     # "realesr-general-x4v3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
     # "realesr-general-wdn-x4v3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
     "4x-UltraSharp": "https://huggingface.co/Shandypur/ESRGAN-4x-UltraSharp/resolve/main/4x-UltraSharp.pth",
-    "Real-ESRGAN-Anime-finetuning": "https://huggingface.co/danhtran2mind/Real-ESRGAN-Anime-finetuning/resolve/main/Real-ESRGAN-Anime-finetuning.pth",
     "4x_foolhardy_Remacri": "https://huggingface.co/FacehugmanIII/4x_foolhardy_Remacri/resolve/main/4x_foolhardy_Remacri.pth",
     "Remacri4xExtraSmoother": "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/Remacri%204x%20ExtraSmoother.pth",
     "AnimeSharp4x": "https://huggingface.co/hollowstrawberry/upscalers-backup/resolve/main/ESRGAN/AnimeSharp%204x.pth",
@@ -356,9 +298,15 @@ DIFFUSERS_CONTROLNET_MODEL = [
     # "InstantX/FLUX.1-dev-Controlnet-Canny",
 ]
 
-PROMPT_W_OPTIONS = [(pwf, pwf) for pwf in PROMPT_WEIGHT_OPTIONS_PRIORITY]
-PROMPT_W_OPTIONS[0] = ("Classic format: (word:weight)", "Classic")
-PROMPT_W_OPTIONS[1] = ("Compel format: (word)weight", "Compel")
+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"),
+]
 
 WARNING_MSG_VAE = (
     "Use the right VAE for your model to maintain image quality. The wrong"
@@ -411,11 +359,9 @@ SUBTITLE_GUI = (
     " to perform different tasks in image generation."
 )
 
-msg_zero = "" if not IS_ZERO_GPU else "- The current space runs on a ZERO GPU which is assigned for approximately 60 seconds; Therefore, if you submit expensive tasks, the operation may be canceled upon reaching the maximum allowed time with 'GPU TASK ABORTED'."
-
 HELP_GUI = (
-    f"""### Help:
-    {msg_zero}
+    """### Help:
+    - The current space runs on a ZERO GPU which is assigned for approximately 60 seconds; Therefore, if you submit expensive tasks, the operation may be canceled upon reaching the maximum allowed time with 'GPU TASK ABORTED'.
     - Distorted or strange images often result from high prompt weights, so it's best to use low weights and scales, and consider using Classic variants like 'Classic-original'.
     - For better results with Pony Diffusion, try using sampler DPM++ 1s or DPM2 with Compel or Classic prompt weights.
     """
@@ -539,7 +485,7 @@ EXAMPLES_GUI = [
         20,
         4.0,
         -1,
-        ("loras/Coloring_book_-_LineArt.safetensors" if os.path.exists("loras/Coloring_book_-_LineArt.safetensors") else "None"),
+        "loras/Coloring_book_-_LineArt.safetensors",
         1.0,
         "DPM++ 2M SDE",
         1024,
@@ -634,7 +580,6 @@ EXAMPLES_GUI = [
 RESOURCES = (
     """### Resources
     - John6666's space has some great features you might find helpful [link](https://huggingface.co/spaces/John6666/DiffuseCraftMod).
-    - Try the image generator in Colab’s free tier, which provides free GPU [link](https://github.com/R3gm/SD_diffusers_interactive).
-    - `DiffuseCraft` in Colab:[link](https://github.com/R3gm/DiffuseCraft?tab=readme-ov-file#diffusecraft).
+    - You can also try the image generator in Colab’s free tier, which provides free GPU [link](https://github.com/R3gm/SD_diffusers_interactive).
     """
 )

image_processor.py

@@ -92,8 +92,8 @@ def preprocessor_tab():
                     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=0., maximum=2.0, step=0.01, value=0.1, label="'MLSD' Hough value threshold")
-                    pre_distance_threshold = gr.Slider(minimum=0., maximum=20.0, step=0.01, value=0.1, label="'MLSD' Hough distance 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")

packages.txt

@@ -1,3 +1,3 @@
 git-lfs
-aria2
+aria2 -y
 ffmpeg

pre-requirements.txt

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

requirements.txt

@@ -1,13 +1,5 @@
-stablepy==0.6.5
-torch==2.5.1
-diffusers
+stablepy==0.6.0
+torch==2.2.0
 gdown
 opencv-python
-unidecode
-pydantic==2.10.6
-huggingface_hub
-hf_transfer
-hf_xet
-spaces
-gradio==5.44.1
-matplotlib-inline
+unidecode

utils.py

@@ -1,714 +1,485 @@
-import os
-import re
-import gradio as gr
-from constants import (
-    DIFFUSERS_FORMAT_LORAS,
-    CIVITAI_API_KEY,
-    HF_TOKEN,
-    MODEL_TYPE_CLASS,
-    DIRECTORY_LORAS,
-    DIRECTORY_MODELS,
-    DIFFUSECRAFT_CHECKPOINT_NAME,
-    CACHE_HF_ROOT,
-    CACHE_HF,
-    STORAGE_ROOT,
-)
-from huggingface_hub import HfApi, get_hf_file_metadata, snapshot_download
-from diffusers import DiffusionPipeline
-from huggingface_hub import model_info as model_info_data
-from diffusers.pipelines.pipeline_loading_utils import variant_compatible_siblings
-from stablepy.diffusers_vanilla.utils import checkpoint_model_type
-from pathlib import PosixPath
-from unidecode import unidecode
-import urllib.parse
-import copy
-import requests
-from requests.adapters import HTTPAdapter
-from urllib3.util import Retry
-import shutil
-import subprocess
-import json
-import html as _html
-
-IS_ZERO_GPU = bool(os.getenv("SPACES_ZERO_GPU"))
-USER_AGENT = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:127.0) Gecko/20100101 Firefox/127.0'
-MODEL_ARCH = {
-    'stable-diffusion-xl-v1-base/lora': "Stable Diffusion XL (Illustrious, Pony, NoobAI)",
-    'stable-diffusion-v1/lora': "Stable Diffusion 1.5",
-    'flux-1-dev/lora': "Flux",
-}
-
-
-def read_safetensors_header_from_url(url: str):
-    """Read safetensors header from a remote Hugging Face file."""
-    meta = get_hf_file_metadata(url)
-
-    # Step 1: first 8 bytes → header length
-    resp = requests.get(meta.location, headers={"Range": "bytes=0-7"})
-    resp.raise_for_status()
-    header_len = int.from_bytes(resp.content, "little")
-
-    # Step 2: fetch full header JSON
-    end = 8 + header_len - 1
-    resp = requests.get(meta.location, headers={"Range": f"bytes=8-{end}"})
-    resp.raise_for_status()
-    header_json = resp.content.decode("utf-8")
-
-    return json.loads(header_json)
-
-
-def read_safetensors_header_from_file(path: str):
-    """Read safetensors header from a local file."""
-    with open(path, "rb") as f:
-        # Step 1: first 8 bytes → header length
-        header_len = int.from_bytes(f.read(8), "little")
-
-        # Step 2: read header JSON
-        header_json = f.read(header_len).decode("utf-8")
-
-    return json.loads(header_json)
-
-
-class LoraHeaderInformation:
-    """
-    Encapsulates parsed info from a LoRA JSON header and provides
-    a compact HTML summary via .to_html().
-    """
-
-    def __init__(self, json_data):
-        self.original_json = copy.deepcopy(json_data or {})
-
-        # Check if text encoder was trained
-        # guard for json_data being a mapping
-        try:
-            self.text_encoder_trained = any("text_model" in ln for ln in json_data)
-        except Exception:
-            self.text_encoder_trained = False
-
-        # Metadata (may be None)
-        metadata = (json_data or {}).get("__metadata__", None)
-        self.metadata = metadata
-
-        # Default values
-        self.architecture = "undefined"
-        self.prediction_type = "undefined"
-        self.base_model = "undefined"
-        self.author = "undefined"
-        self.title = "undefined"
-        self.common_tags_list = []
-
-        if metadata:
-            self.architecture = MODEL_ARCH.get(
-                metadata.get('modelspec.architecture', None),
-                "undefined"
-            )
-
-            self.prediction_type = metadata.get('modelspec.prediction_type', "undefined")
-            self.base_model = metadata.get('ss_sd_model_name', "undefined")
-            self.author = metadata.get('modelspec.author', "undefined")
-            self.title = metadata.get('modelspec.title', "undefined")
-
-            base_model_hash = metadata.get('ss_new_sd_model_hash', None)  # SHA256
-            # AUTOV1 ss_sd_model_hash
-            # https://civitai.com/api/v1/model-versions/by-hash/{base_model_hash}  # Info
-            if base_model_hash:
-                self.base_model += f" hash={base_model_hash}"
-
-            # Extract tags
-            try:
-                tags = metadata.get('ss_tag_frequency') if "ss_tag_frequency" in metadata else metadata.get('ss_datasets', "")
-                tags = json.loads(tags) if tags else ""
-
-                if isinstance(tags, list):
-                    tags = tags[0].get("tag_frequency", {})
-
-                if tags:
-                    self.common_tags_list = list(tags[list(tags.keys())[0]].keys())
-            except Exception:
-                self.common_tags_list = []
-
-    def to_dict(self):
-        """Return a plain dict summary of parsed fields."""
-        return {
-            "architecture": self.architecture,
-            "prediction_type": self.prediction_type,
-            "base_model": self.base_model,
-            "author": self.author,
-            "title": self.title,
-            "text_encoder_trained": bool(self.text_encoder_trained),
-            "common_tags": self.common_tags_list,
-        }
-
-    def to_html(self, limit_tags=20):
-        """
-        Return a compact HTML snippet (string) showing the parsed info
-        in a small font. Values are HTML-escaped.
-        """
-        # helper to escape
-        esc = _html.escape
-
-        rows = [
-            ("Title", esc(str(self.title))),
-            ("Author", esc(str(self.author))),
-            ("Architecture", esc(str(self.architecture))),
-            ("Base model", esc(str(self.base_model))),
-            ("Prediction type", esc(str(self.prediction_type))),
-            ("Text encoder trained", esc(str(self.text_encoder_trained))),
-            ("Reference tags", esc(str(", ".join(self.common_tags_list[:limit_tags])))),
-        ]
-
-        # small, compact table with inline styling (small font)
-        html_rows = "".join(
-            f"<tr><th style='text-align:left;padding:2px 6px;white-space:nowrap'>{k}</th>"
-            f"<td style='padding:2px 6px'>{v}</td></tr>"
-            for k, v in rows
-        )
-
-        html_snippet = (
-            "<div style='font-family:system-ui, -apple-system, \"Segoe UI\", Roboto, "
-            "Helvetica, Arial, \"Noto Sans\", sans-serif; font-size:12px; line-height:1.2; "
-            "'>"
-            f"<table style='border-collapse:collapse; font-size:12px;'>"
-            f"{html_rows}"
-            "</table>"
-            "</div>"
-        )
-
-        return html_snippet
-
-
-def request_json_data(url):
-    model_version_id = url.split('/')[-1]
-    if "?modelVersionId=" in model_version_id:
-        match = re.search(r'modelVersionId=(\d+)', url)
-        model_version_id = match.group(1)
-
-    endpoint_url = f"https://civitai.com/api/v1/model-versions/{model_version_id}"
-
-    params = {}
-    headers = {'User-Agent': USER_AGENT, 'content-type': 'application/json'}
-    session = requests.Session()
-    retries = Retry(total=5, backoff_factor=1, status_forcelist=[500, 502, 503, 504])
-    session.mount("https://", HTTPAdapter(max_retries=retries))
-
-    try:
-        result = session.get(endpoint_url, params=params, headers=headers, stream=True, timeout=(3.0, 15))
-        result.raise_for_status()
-        json_data = result.json()
-        return json_data if json_data else None
-    except Exception as e:
-        print(f"Error: {e}")
-        return None
-
-
-class ModelInformation:
-    def __init__(self, json_data):
-        self.model_version_id = json_data.get("id", "")
-        self.model_id = json_data.get("modelId", "")
-        self.download_url = json_data.get("downloadUrl", "")
-        self.model_url = f"https://civitai.com/models/{self.model_id}?modelVersionId={self.model_version_id}"
-        self.filename_url = next(
-            (v.get("name", "") for v in json_data.get("files", []) if str(self.model_version_id) in v.get("downloadUrl", "") and v.get("type", "Model") == "Model"), ""
-        )
-        self.filename_url = self.filename_url if self.filename_url else ""
-        self.description = json_data.get("description", "")
-        if self.description is None:
-            self.description = ""
-        self.model_name = json_data.get("model", {}).get("name", "")
-        self.model_type = json_data.get("model", {}).get("type", "")
-        self.nsfw = json_data.get("model", {}).get("nsfw", False)
-        self.poi = json_data.get("model", {}).get("poi", False)
-        self.images = [img.get("url", "") for img in json_data.get("images", [])]
-        self.example_prompt = json_data.get("trainedWords", [""])[0] if json_data.get("trainedWords") else ""
-        self.original_json = copy.deepcopy(json_data)
-
-
-def get_civit_params(url):
-    try:
-        json_data = request_json_data(url)
-        mdc = ModelInformation(json_data)
-        if mdc.download_url and mdc.filename_url:
-            return mdc.download_url, mdc.filename_url, mdc.model_url
-        else:
-            ValueError("Invalid Civitai model URL")
-    except Exception as e:
-        print(f"Error retrieving Civitai metadata: {e} — fallback to direct download")
-        return url, None, None
-
-
-def civ_redirect_down(url, dir_, civitai_api_key, romanize, alternative_name):
-    filename_base = filename = None
-
-    if alternative_name:
-        output_path = os.path.join(dir_, alternative_name)
-        if os.path.exists(output_path):
-            return output_path, alternative_name
-
-    # Follow the redirect to get the actual download URL
-    curl_command = (
-        f'curl -L -sI --connect-timeout 5 --max-time 5 '
-        f'-H "Content-Type: application/json" '
-        f'-H "Authorization: Bearer {civitai_api_key}" "{url}"'
-    )
-
-    headers = os.popen(curl_command).read()
-
-    # Look for the redirected "Location" URL
-    location_match = re.search(r'location: (.+)', headers, re.IGNORECASE)
-
-    if location_match:
-        redirect_url = location_match.group(1).strip()
-
-        # Extract the filename from the redirect URL's "Content-Disposition"
-        filename_match = re.search(r'filename%3D%22(.+?)%22', redirect_url)
-        if filename_match:
-            encoded_filename = filename_match.group(1)
-            # Decode the URL-encoded filename
-            decoded_filename = urllib.parse.unquote(encoded_filename)
-
-            filename = unidecode(decoded_filename) if romanize else decoded_filename
-            # print(f"Filename redirect: {filename}")
-
-    filename_base = alternative_name if alternative_name else filename
-    if not filename_base:
-        return None, None
-    elif os.path.exists(os.path.join(dir_, filename_base)):
-        return os.path.join(dir_, filename_base), filename_base
-
-    aria2_command = (
-        f'aria2c --console-log-level=error --summary-interval=10 -c -x 16 '
-        f'-k 1M -s 16 -d "{dir_}" -o "{filename_base}" "{redirect_url}"'
-    )
-    r_code = os.system(aria2_command)  # noqa
-
-    # if r_code != 0:
-    #     raise RuntimeError(f"Failed to download file: {filename_base}. Error code: {r_code}")
-
-    output_path = os.path.join(dir_, filename_base)
-    if not os.path.exists(output_path):
-        return None, filename_base
-
-    return output_path, filename_base
-
-
-def civ_api_down(url, dir_, civitai_api_key, civ_filename):
-    """
-    This method is susceptible to being blocked because it generates a lot of temp redirect links with aria2c.
-    If an API key limit is reached, generating a new API key and using it can fix the issue.
-    """
-    output_path = None
-
-    url_dl = url + f"?token={civitai_api_key}"
-    if not civ_filename:
-        aria2_command = f'aria2c -c -x 1 -s 1 -d "{dir_}" "{url_dl}"'
-        os.system(aria2_command)
-    else:
-        output_path = os.path.join(dir_, civ_filename)
-        if not os.path.exists(output_path):
-            aria2_command = (
-                f'aria2c --console-log-level=error --summary-interval=10 -c -x 16 '
-                f'-k 1M -s 16 -d "{dir_}" -o "{civ_filename}" "{url_dl}"'
-            )
-            os.system(aria2_command)
-
-    return output_path
-
-
-def drive_down(url, dir_):
-    import gdown
-
-    output_path = None
-
-    drive_id, _ = gdown.parse_url.parse_url(url, warning=False)
-    dir_files = os.listdir(dir_)
-
-    for dfile in dir_files:
-        if drive_id in dfile:
-            output_path = os.path.join(dir_, dfile)
-            break
-
-    if not output_path:
-        original_path = gdown.download(url, f"{dir_}/", fuzzy=True)
-
-        dir_name, base_name = os.path.split(original_path)
-        name, ext = base_name.rsplit(".", 1)
-        new_name = f"{name}_{drive_id}.{ext}"
-        output_path = os.path.join(dir_name, new_name)
-
-        os.rename(original_path, output_path)
-
-    return output_path
-
-
-def hf_down(url, dir_, hf_token, romanize):
-    url = url.replace("?download=true", "")
-    # url = urllib.parse.quote(url, safe=':/')  # fix encoding
-
-    filename = unidecode(url.split('/')[-1]) if romanize else url.split('/')[-1]
-    output_path = os.path.join(dir_, filename)
-
-    if os.path.exists(output_path):
-        return output_path
-
-    if "/blob/" in url:
-        url = url.replace("/blob/", "/resolve/")
-
-    if hf_token:
-        user_header = f'"Authorization: Bearer {hf_token}"'
-        os.system(f"aria2c --console-log-level=error --summary-interval=10 --header={user_header} -c -x 16 -k 1M -s 16 {url} -d {dir_}  -o {filename}")
-    else:
-        os.system(f"aria2c --optimize-concurrent-downloads --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 {url} -d {dir_}  -o {filename}")
-
-    return output_path
-
-
-def download_things(directory, url, hf_token="", civitai_api_key="", romanize=False):
-    url = url.strip()
-    downloaded_file_path = None
-
-    if "drive.google.com" in url:
-        downloaded_file_path = drive_down(url, directory)
-    elif "huggingface.co" in url:
-        downloaded_file_path = hf_down(url, directory, hf_token, romanize)
-    elif "civitai.com" in url:
-        if not civitai_api_key:
-            msg = "You need an API key to download Civitai models."
-            print(f"\033[91m{msg}\033[0m")
-            gr.Warning(msg)
-            return None
-
-        url, civ_filename, civ_page = get_civit_params(url)
-        if civ_page and not IS_ZERO_GPU:
-            print(f"\033[92mCivitai model: {civ_filename} [page: {civ_page}]\033[0m")
-
-        downloaded_file_path, civ_filename = civ_redirect_down(url, directory, civitai_api_key, romanize, civ_filename)
-
-        if not downloaded_file_path:
-            msg = (
-                "Download failed.\n"
-                "If this is due to an API limit, generating a new API key may resolve the issue.\n"
-                "Attempting to download using the old method..."
-            )
-            print(msg)
-            gr.Warning(msg)
-            downloaded_file_path = civ_api_down(url, directory, civitai_api_key, civ_filename)
-    else:
-        os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
-
-    return downloaded_file_path
-
-
-def get_model_list(directory_path):
-    model_list = []
-    valid_extensions = {'.ckpt', '.pt', '.pth', '.safetensors', '.bin'}
-
-    for filename in os.listdir(directory_path):
-        if os.path.splitext(filename)[1] in valid_extensions:
-            # name_without_extension = os.path.splitext(filename)[0]
-            file_path = os.path.join(directory_path, filename)
-            # model_list.append((name_without_extension, file_path))
-            model_list.append(file_path)
-            print('\033[34mFILE: ' + file_path + '\033[0m')
-    return model_list
-
-
-def extract_parameters(input_string):
-    parameters = {}
-    input_string = input_string.replace("\n", "")
-
-    if "Negative prompt:" not in input_string:
-        if "Steps:" in input_string:
-            input_string = input_string.replace("Steps:", "Negative prompt: Steps:")
-        else:
-            msg = "Generation data is invalid."
-            gr.Warning(msg)
-            print(msg)
-            parameters["prompt"] = input_string
-            return parameters
-
-    parm = input_string.split("Negative prompt:")
-    parameters["prompt"] = parm[0].strip()
-    if "Steps:" not in parm[1]:
-        parameters["neg_prompt"] = parm[1].strip()
-        return parameters
-    parm = parm[1].split("Steps:")
-    parameters["neg_prompt"] = parm[0].strip()
-    input_string = "Steps:" + parm[1]
-
-    # Extracting Steps
-    steps_match = re.search(r'Steps: (\d+)', input_string)
-    if steps_match:
-        parameters['Steps'] = int(steps_match.group(1))
-
-    # Extracting Size
-    size_match = re.search(r'Size: (\d+x\d+)', input_string)
-    if size_match:
-        parameters['Size'] = size_match.group(1)
-        width, height = map(int, parameters['Size'].split('x'))
-        parameters['width'] = width
-        parameters['height'] = height
-
-    # Extracting other parameters
-    other_parameters = re.findall(r'([^,:]+): (.*?)(?=, [^,:]+:|$)', input_string)
-    for param in other_parameters:
-        parameters[param[0].strip()] = param[1].strip('"')
-
-    return parameters
-
-
-def get_my_lora(link_url, romanize):
-    l_name = ""
-    for url in [url.strip() for url in link_url.split(',')]:
-        if not os.path.exists(f"./loras/{url.split('/')[-1]}"):
-            l_name = download_things(DIRECTORY_LORAS, url, HF_TOKEN, CIVITAI_API_KEY, romanize)
-    new_lora_model_list = get_model_list(DIRECTORY_LORAS)
-    new_lora_model_list.insert(0, "None")
-    new_lora_model_list = new_lora_model_list + DIFFUSERS_FORMAT_LORAS
-    msg_lora = "Downloaded"
-    if l_name:
-        msg_lora += f": <b>{l_name}</b>"
-        print(msg_lora)
-
-    try:
-        # Works with non-Civitai loras.
-        json_data = read_safetensors_header_from_file(l_name)
-        metadata_lora = LoraHeaderInformation(json_data)
-        msg_lora += "<br>" + metadata_lora.to_html()
-    except Exception:
-        pass
-
-    return gr.update(
-        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(
-        choices=new_lora_model_list
-    ), gr.update(
-        choices=new_lora_model_list
-    ), gr.update(
-        choices=new_lora_model_list
-    ), gr.update(
-        value=msg_lora
-    )
-
-
-def info_html(json_data, title, subtitle):
-    return f"""
-        <div style='padding: 0; border-radius: 10px;'>
-            <p style='margin: 0; font-weight: bold;'>{title}</p>
-            <details>
-                <summary>Details</summary>
-                <p style='margin: 0; font-weight: bold;'>{subtitle}</p>
-            </details>
-        </div>
-        """
-
-
-def get_model_type(repo_id: str):
-    api = HfApi(token=os.environ.get("HF_TOKEN"))  # if use private or gated model
-    default = "SD 1.5"
-    try:
-        if os.path.exists(repo_id):
-            tag, _, _, _ = checkpoint_model_type(repo_id)
-            return DIFFUSECRAFT_CHECKPOINT_NAME[tag]
-        else:
-            model = api.model_info(repo_id=repo_id, timeout=5.0)
-            tags = model.tags
-            for tag in tags:
-                if tag in MODEL_TYPE_CLASS.keys():
-                    return MODEL_TYPE_CLASS.get(tag, default)
-
-    except Exception:
-        return default
-    return default
-
-
-def restart_space(repo_id: str, factory_reboot: bool):
-    api = HfApi(token=os.environ.get("HF_TOKEN"))
-    try:
-        runtime = api.get_space_runtime(repo_id=repo_id)
-        if runtime.stage == "RUNNING":
-            api.restart_space(repo_id=repo_id, factory_reboot=factory_reboot)
-            print(f"Restarting space: {repo_id}")
-        else:
-            print(f"Space {repo_id} is in stage: {runtime.stage}")
-    except Exception as e:
-        print(e)
-
-
-def extract_exif_data(image):
-    if image is None:
-        return ""
-
-    try:
-        metadata_keys = ['parameters', 'metadata', 'prompt', 'Comment']
-
-        for key in metadata_keys:
-            if key in image.info:
-                return image.info[key]
-
-        return str(image.info)
-
-    except Exception as e:
-        return f"Error extracting metadata: {str(e)}"
-
-
-def create_mask_now(img, invert):
-    import numpy as np
-    import time
-
-    time.sleep(0.5)
-
-    transparent_image = img["layers"][0]
-
-    # Extract the alpha channel
-    alpha_channel = np.array(transparent_image)[:, :, 3]
-
-    # Create a binary mask by thresholding the alpha channel
-    binary_mask = alpha_channel > 1
-
-    if invert:
-        print("Invert")
-        # Invert the binary mask so that the drawn shape is white and the rest is black
-        binary_mask = np.invert(binary_mask)
-
-    # Convert the binary mask to a 3-channel RGB mask
-    rgb_mask = np.stack((binary_mask,) * 3, axis=-1)
-
-    # Convert the mask to uint8
-    rgb_mask = rgb_mask.astype(np.uint8) * 255
-
-    return img["background"], rgb_mask
-
-
-def download_diffuser_repo(repo_name: str, model_type: str, revision: str = "main", token=True):
-
-    variant = None
-    if token is True and not os.environ.get("HF_TOKEN"):
-        token = None
-
-    if model_type == "SDXL":
-        info = model_info_data(
-            repo_name,
-            token=token,
-            revision=revision,
-            timeout=5.0,
-        )
-
-        filenames = {sibling.rfilename for sibling in info.siblings}
-        model_filenames, variant_filenames = variant_compatible_siblings(
-            filenames, variant="fp16"
-        )
-
-        if len(variant_filenames):
-            variant = "fp16"
-
-    if model_type == "FLUX":
-        cached_folder = snapshot_download(
-            repo_id=repo_name,
-            allow_patterns="transformer/*"
-        )
-    else:
-        cached_folder = DiffusionPipeline.download(
-            pretrained_model_name=repo_name,
-            force_download=False,
-            token=token,
-            revision=revision,
-            # mirror="https://hf-mirror.com",
-            variant=variant,
-            use_safetensors=True,
-            trust_remote_code=False,
-            timeout=5.0,
-        )
-
-    if isinstance(cached_folder, PosixPath):
-        cached_folder = cached_folder.as_posix()
-
-    # Task model
-    # from huggingface_hub import hf_hub_download
-    # hf_hub_download(
-    #     task_model,
-    #     filename="diffusion_pytorch_model.safetensors",  # fix fp16 variant
-    # )
-
-    return cached_folder
-
-
-def get_folder_size_gb(folder_path):
-    result = subprocess.run(["du", "-s", folder_path], capture_output=True, text=True)
-
-    total_size_kb = int(result.stdout.split()[0])
-    total_size_gb = total_size_kb / (1024 ** 2)
-
-    return total_size_gb
-
-
-def get_used_storage_gb(path_storage=STORAGE_ROOT):
-    try:
-        used_gb = get_folder_size_gb(path_storage)
-        print(f"Used Storage: {used_gb:.2f} GB")
-    except Exception as e:
-        used_gb = 999
-        print(f"Error while retrieving the used storage: {e}.")
-
-    return used_gb
-
-
-def delete_model(removal_candidate):
-    print(f"Removing: {removal_candidate}")
-
-    if os.path.exists(removal_candidate):
-        os.remove(removal_candidate)
-    else:
-        diffusers_model = f"{CACHE_HF}{DIRECTORY_MODELS}--{removal_candidate.replace('/', '--')}"
-        if os.path.isdir(diffusers_model):
-            shutil.rmtree(diffusers_model)
-
-
-def clear_hf_cache():
-    """
-    Clears the entire Hugging Face cache at ~/.cache/huggingface.
-    Hugging Face will re-download models as needed later.
-    """
-    try:
-        if os.path.exists(CACHE_HF):
-            shutil.rmtree(CACHE_HF, ignore_errors=True)
-            print(f"Hugging Face cache cleared: {CACHE_HF}")
-        else:
-            print(f"No Hugging Face cache found at: {CACHE_HF}")
-    except Exception as e:
-        print(f"Error clearing Hugging Face cache: {e}")
-
-
-def progress_step_bar(step, total):
-    # Calculate the percentage for the progress bar width
-    percentage = min(100, ((step / total) * 100))
-
-    return f"""
-        <div style="position: relative; width: 100%; background-color: gray; border-radius: 5px; overflow: hidden;">
-            <div style="width: {percentage}%; height: 17px; background-color: #800080; transition: width 0.5s;"></div>
-            <div style="position: absolute; width: 100%; text-align: center; color: white; top: 0; line-height: 19px; font-size: 13px;">
-                {int(percentage)}%
-            </div>
-        </div>
-        """
-
-
-def html_template_message(msg):
-    return f"""
-        <div style="position: relative; width: 100%; background-color: gray; border-radius: 5px; overflow: hidden;">
-            <div style="width: 0%; height: 17px; background-color: #800080; transition: width 0.5s;"></div>
-            <div style="position: absolute; width: 100%; text-align: center; color: white; top: 0; line-height: 19px; font-size: 14px; font-weight: bold; text-shadow: 1px 1px 2px black;">
-                {msg}
-            </div>
-        </div>
-        """
-
-
-def escape_html(text):
-    """Escapes HTML special characters in the input text."""
-    return text.replace("<", "&lt;").replace(">", "&gt;").replace("\n", "<br>")
+import os
+import re
+import gradio as gr
+from constants import (
+    DIFFUSERS_FORMAT_LORAS,
+    CIVITAI_API_KEY,
+    HF_TOKEN,
+    MODEL_TYPE_CLASS,
+    DIRECTORY_LORAS,
+    DIRECTORY_MODELS,
+    DIFFUSECRAFT_CHECKPOINT_NAME,
+    CACHE_HF,
+    STORAGE_ROOT,
+)
+from huggingface_hub import HfApi
+from huggingface_hub import snapshot_download
+from diffusers import DiffusionPipeline
+from huggingface_hub import model_info as model_info_data
+from diffusers.pipelines.pipeline_loading_utils import variant_compatible_siblings
+from stablepy.diffusers_vanilla.utils import checkpoint_model_type
+from pathlib import PosixPath
+from unidecode import unidecode
+import urllib.parse
+import copy
+import requests
+from requests.adapters import HTTPAdapter
+from urllib3.util import Retry
+import shutil
+import subprocess
+
+USER_AGENT = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:127.0) Gecko/20100101 Firefox/127.0'
+
+
+def request_json_data(url):
+    model_version_id = url.split('/')[-1]
+    if "?modelVersionId=" in model_version_id:
+        match = re.search(r'modelVersionId=(\d+)', url)
+        model_version_id = match.group(1)
+
+    endpoint_url = f"https://civitai.com/api/v1/model-versions/{model_version_id}"
+
+    params = {}
+    headers = {'User-Agent': USER_AGENT, 'content-type': 'application/json'}
+    session = requests.Session()
+    retries = Retry(total=5, backoff_factor=1, status_forcelist=[500, 502, 503, 504])
+    session.mount("https://", HTTPAdapter(max_retries=retries))
+
+    try:
+        result = session.get(endpoint_url, params=params, headers=headers, stream=True, timeout=(3.0, 15))
+        result.raise_for_status()
+        json_data = result.json()
+        return json_data if json_data else None
+    except Exception as e:
+        print(f"Error: {e}")
+        return None
+
+
+class ModelInformation:
+    def __init__(self, json_data):
+        self.model_version_id = json_data.get("id", "")
+        self.model_id = json_data.get("modelId", "")
+        self.download_url = json_data.get("downloadUrl", "")
+        self.model_url = f"https://civitai.com/models/{self.model_id}?modelVersionId={self.model_version_id}"
+        self.filename_url = next(
+            (v.get("name", "") for v in json_data.get("files", []) if str(self.model_version_id) in v.get("downloadUrl", "") and v.get("type", "Model") == "Model"), ""
+        )
+        self.filename_url = self.filename_url if self.filename_url else ""
+        self.description = json_data.get("description", "")
+        if self.description is None: self.description = ""
+        self.model_name = json_data.get("model", {}).get("name", "")
+        self.model_type = json_data.get("model", {}).get("type", "")
+        self.nsfw = json_data.get("model", {}).get("nsfw", False)
+        self.poi = json_data.get("model", {}).get("poi", False)
+        self.images = [img.get("url", "") for img in json_data.get("images", [])]
+        self.example_prompt = json_data.get("trainedWords", [""])[0] if json_data.get("trainedWords") else ""
+        self.original_json = copy.deepcopy(json_data)
+
+
+def retrieve_model_info(url):
+    json_data = request_json_data(url)
+    if not json_data:
+        return None
+    model_descriptor = ModelInformation(json_data)
+    return model_descriptor
+
+
+def download_things(directory, url, hf_token="", civitai_api_key="", romanize=False):
+    url = url.strip()
+    downloaded_file_path = None
+
+    if "drive.google.com" in url:
+        original_dir = os.getcwd()
+        os.chdir(directory)
+        os.system(f"gdown --fuzzy {url}")
+        os.chdir(original_dir)
+    elif "huggingface.co" in url:
+        url = url.replace("?download=true", "")
+        # url = urllib.parse.quote(url, safe=':/')  # fix encoding
+        if "/blob/" in url:
+            url = url.replace("/blob/", "/resolve/")
+        user_header = f'"Authorization: Bearer {hf_token}"'
+
+        filename = unidecode(url.split('/')[-1]) if romanize else url.split('/')[-1]
+
+        if hf_token:
+            os.system(f"aria2c --console-log-level=error --summary-interval=10 --header={user_header} -c -x 16 -k 1M -s 16 {url} -d {directory}  -o {filename}")
+        else:
+            os.system(f"aria2c --optimize-concurrent-downloads --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 {url} -d {directory}  -o {filename}")
+
+        downloaded_file_path = os.path.join(directory, filename)
+
+    elif "civitai.com" in url:
+
+        if not civitai_api_key:
+            print("\033[91mYou need an API key to download Civitai models.\033[0m")
+
+        model_profile = retrieve_model_info(url)
+        if (
+            model_profile is not None
+            and model_profile.download_url
+            and model_profile.filename_url
+        ):
+            url = model_profile.download_url
+            filename = unidecode(model_profile.filename_url) if romanize else model_profile.filename_url
+        else:
+            if "?" in url:
+                url = url.split("?")[0]
+            filename = ""
+
+        url_dl = url + f"?token={civitai_api_key}"
+        print(f"Filename: {filename}")
+
+        param_filename = ""
+        if filename:
+            param_filename = f"-o '{filename}'"
+
+        aria2_command = (
+            f'aria2c --console-log-level=error --summary-interval=10 -c -x 16 '
+            f'-k 1M -s 16 -d "{directory}" {param_filename} "{url_dl}"'
+        )
+        os.system(aria2_command)
+
+        if param_filename and os.path.exists(os.path.join(directory, filename)):
+            downloaded_file_path = os.path.join(directory, filename)
+
+        # # PLAN B
+        # # Follow the redirect to get the actual download URL
+        # curl_command = (
+        #     f'curl -L -sI --connect-timeout 5 --max-time 5 '
+        #     f'-H "Content-Type: application/json" '
+        #     f'-H "Authorization: Bearer {civitai_api_key}" "{url}"'
+        # )
+
+        # headers = os.popen(curl_command).read()
+
+        # # Look for the redirected "Location" URL
+        # location_match = re.search(r'location: (.+)', headers, re.IGNORECASE)
+
+        # if location_match:
+        #     redirect_url = location_match.group(1).strip()
+
+        #     # Extract the filename from the redirect URL's "Content-Disposition"
+        #     filename_match = re.search(r'filename%3D%22(.+?)%22', redirect_url)
+        #     if filename_match:
+        #         encoded_filename = filename_match.group(1)
+        #         # Decode the URL-encoded filename
+        #         decoded_filename = urllib.parse.unquote(encoded_filename)
+
+        #         filename = unidecode(decoded_filename) if romanize else decoded_filename
+        #         print(f"Filename: {filename}")
+
+        #         aria2_command = (
+        #             f'aria2c --console-log-level=error --summary-interval=10 -c -x 16 '
+        #             f'-k 1M -s 16 -d "{directory}" -o "{filename}" "{redirect_url}"'
+        #         )
+        #         return_code = os.system(aria2_command)
+
+        #         # if return_code != 0:
+        #         #     raise RuntimeError(f"Failed to download file: {filename}. Error code: {return_code}")
+        #         downloaded_file_path = os.path.join(directory, filename)
+        #         if not os.path.exists(downloaded_file_path):
+        #             downloaded_file_path = None
+
+        # if not downloaded_file_path:
+        #     # Old method
+        #     if "?" in url:
+        #         url = url.split("?")[0]
+        #     url = url + f"?token={civitai_api_key}"
+        #     os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
+
+    else:
+        os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
+
+    return downloaded_file_path
+
+
+def get_model_list(directory_path):
+    model_list = []
+    valid_extensions = {'.ckpt', '.pt', '.pth', '.safetensors', '.bin'}
+
+    for filename in os.listdir(directory_path):
+        if os.path.splitext(filename)[1] in valid_extensions:
+            # name_without_extension = os.path.splitext(filename)[0]
+            file_path = os.path.join(directory_path, filename)
+            # model_list.append((name_without_extension, file_path))
+            model_list.append(file_path)
+            print('\033[34mFILE: ' + file_path + '\033[0m')
+    return model_list
+
+
+def extract_parameters(input_string):
+    parameters = {}
+    input_string = input_string.replace("\n", "")
+
+    if "Negative prompt:" not in input_string:
+        if "Steps:" in input_string:
+            input_string = input_string.replace("Steps:", "Negative prompt: Steps:")
+        else:
+            print("Invalid metadata")
+            parameters["prompt"] = input_string
+            return parameters
+
+    parm = input_string.split("Negative prompt:")
+    parameters["prompt"] = parm[0].strip()
+    if "Steps:" not in parm[1]:
+        print("Steps not detected")
+        parameters["neg_prompt"] = parm[1].strip()
+        return parameters
+    parm = parm[1].split("Steps:")
+    parameters["neg_prompt"] = parm[0].strip()
+    input_string = "Steps:" + parm[1]
+
+    # Extracting Steps
+    steps_match = re.search(r'Steps: (\d+)', input_string)
+    if steps_match:
+        parameters['Steps'] = int(steps_match.group(1))
+
+    # Extracting Size
+    size_match = re.search(r'Size: (\d+x\d+)', input_string)
+    if size_match:
+        parameters['Size'] = size_match.group(1)
+        width, height = map(int, parameters['Size'].split('x'))
+        parameters['width'] = width
+        parameters['height'] = height
+
+    # Extracting other parameters
+    other_parameters = re.findall(r'([^,:]+): (.*?)(?=, [^,:]+:|$)', input_string)
+    for param in other_parameters:
+        parameters[param[0].strip()] = param[1].strip('"')
+
+    return parameters
+
+
+def get_my_lora(link_url, romanize):
+    l_name = ""
+    for url in [url.strip() for url in link_url.split(',')]:
+        if not os.path.exists(f"./loras/{url.split('/')[-1]}"):
+            l_name = download_things(DIRECTORY_LORAS, url, HF_TOKEN, CIVITAI_API_KEY, romanize)
+    new_lora_model_list = get_model_list(DIRECTORY_LORAS)
+    new_lora_model_list.insert(0, "None")
+    new_lora_model_list = new_lora_model_list + DIFFUSERS_FORMAT_LORAS
+    msg_lora = "Downloaded"
+    if l_name:
+        msg_lora += f": <b>{l_name}</b>"
+        print(msg_lora)
+
+    return gr.update(
+        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(
+        choices=new_lora_model_list
+    ), gr.update(
+        choices=new_lora_model_list
+    ), gr.update(
+        choices=new_lora_model_list
+    ), gr.update(
+        value=msg_lora
+    )
+
+
+def info_html(json_data, title, subtitle):
+    return f"""
+        <div style='padding: 0; border-radius: 10px;'>
+            <p style='margin: 0; font-weight: bold;'>{title}</p>
+            <details>
+                <summary>Details</summary>
+                <p style='margin: 0; font-weight: bold;'>{subtitle}</p>
+            </details>
+        </div>
+        """
+
+
+def get_model_type(repo_id: str):
+    api = HfApi(token=os.environ.get("HF_TOKEN"))  # if use private or gated model
+    default = "SD 1.5"
+    try:
+        if os.path.exists(repo_id):
+            tag, _, _, _ = checkpoint_model_type(repo_id)
+            return DIFFUSECRAFT_CHECKPOINT_NAME[tag]
+        else:
+            model = api.model_info(repo_id=repo_id, timeout=5.0)
+            tags = model.tags
+            for tag in tags:
+                if tag in MODEL_TYPE_CLASS.keys(): return MODEL_TYPE_CLASS.get(tag, default)
+
+    except Exception:
+        return default
+    return default
+
+
+def restart_space(repo_id: str, factory_reboot: bool):
+    api = HfApi(token=os.environ.get("HF_TOKEN"))
+    try:
+        runtime = api.get_space_runtime(repo_id=repo_id)
+        if runtime.stage == "RUNNING":
+            api.restart_space(repo_id=repo_id, factory_reboot=factory_reboot)
+            print(f"Restarting space: {repo_id}")
+        else:
+            print(f"Space {repo_id} is in stage: {runtime.stage}")
+    except Exception as e:
+        print(e)
+
+
+def extract_exif_data(image):
+    if image is None:
+        return ""
+
+    try:
+        metadata_keys = ['parameters', 'metadata', 'prompt', 'Comment']
+
+        for key in metadata_keys:
+            if key in image.info:
+                return image.info[key]
+
+        return str(image.info)
+
+    except Exception as e:
+        return f"Error extracting metadata: {str(e)}"
+
+
+def create_mask_now(img, invert):
+    import numpy as np
+    import time
+
+    time.sleep(0.5)
+
+    transparent_image = img["layers"][0]
+
+    # Extract the alpha channel
+    alpha_channel = np.array(transparent_image)[:, :, 3]
+
+    # Create a binary mask by thresholding the alpha channel
+    binary_mask = alpha_channel > 1
+
+    if invert:
+        print("Invert")
+        # Invert the binary mask so that the drawn shape is white and the rest is black
+        binary_mask = np.invert(binary_mask)
+
+    # Convert the binary mask to a 3-channel RGB mask
+    rgb_mask = np.stack((binary_mask,) * 3, axis=-1)
+
+    # Convert the mask to uint8
+    rgb_mask = rgb_mask.astype(np.uint8) * 255
+
+    return img["background"], rgb_mask
+
+
+def download_diffuser_repo(repo_name: str, model_type: str, revision: str = "main", token=True):
+
+    variant = None
+    if token is True and not os.environ.get("HF_TOKEN"):
+        token = None
+
+    if model_type == "SDXL":
+        info = model_info_data(
+            repo_name,
+            token=token,
+            revision=revision,
+            timeout=5.0,
+        )
+
+        filenames = {sibling.rfilename for sibling in info.siblings}
+        model_filenames, variant_filenames = variant_compatible_siblings(
+            filenames, variant="fp16"
+        )
+
+        if len(variant_filenames):
+            variant = "fp16"
+
+    if model_type == "FLUX":
+        cached_folder = snapshot_download(
+            repo_id=repo_name,
+            allow_patterns="transformer/*"
+        )
+    else:
+        cached_folder = DiffusionPipeline.download(
+            pretrained_model_name=repo_name,
+            force_download=False,
+            token=token,
+            revision=revision,
+            # mirror="https://hf-mirror.com",
+            variant=variant,
+            use_safetensors=True,
+            trust_remote_code=False,
+            timeout=5.0,
+        )
+
+    if isinstance(cached_folder, PosixPath):
+        cached_folder = cached_folder.as_posix()
+
+    # Task model
+    # from huggingface_hub import hf_hub_download
+    # hf_hub_download(
+    #     task_model,
+    #     filename="diffusion_pytorch_model.safetensors",  # fix fp16 variant
+    # )
+
+    return cached_folder
+
+
+def get_folder_size_gb(folder_path):
+    result = subprocess.run(["du", "-s", folder_path], capture_output=True, text=True)
+
+    total_size_kb = int(result.stdout.split()[0])
+    total_size_gb = total_size_kb / (1024 ** 2)
+
+    return total_size_gb
+
+
+def get_used_storage_gb():
+    try:
+        used_gb = get_folder_size_gb(STORAGE_ROOT)
+        print(f"Used Storage: {used_gb:.2f} GB")
+    except Exception as e:
+        used_gb = 999
+        print(f"Error while retrieving the used storage: {e}.")
+
+    return used_gb
+
+
+def delete_model(removal_candidate):
+    print(f"Removing: {removal_candidate}")
+
+    if os.path.exists(removal_candidate):
+        os.remove(removal_candidate)
+    else:
+        diffusers_model = f"{CACHE_HF}{DIRECTORY_MODELS}--{removal_candidate.replace('/', '--')}"
+        if os.path.isdir(diffusers_model):
+            shutil.rmtree(diffusers_model)
+
+
+def progress_step_bar(step, total):
+    # Calculate the percentage for the progress bar width
+    percentage = min(100, ((step / total) * 100))
+
+    return f"""
+        <div style="position: relative; width: 100%; background-color: gray; border-radius: 5px; overflow: hidden;">
+            <div style="width: {percentage}%; height: 17px; background-color: #800080; transition: width 0.5s;"></div>
+            <div style="position: absolute; width: 100%; text-align: center; color: white; top: 0; line-height: 19px; font-size: 13px;">
+                {int(percentage)}%
+            </div>
+        </div>
+        """
+
+
+def html_template_message(msg):
+    return f"""
+        <div style="position: relative; width: 100%; background-color: gray; border-radius: 5px; overflow: hidden;">
+            <div style="width: 0%; height: 17px; background-color: #800080; transition: width 0.5s;"></div>
+            <div style="position: absolute; width: 100%; text-align: center; color: white; top: 0; line-height: 19px; font-size: 14px; font-weight: bold; text-shadow: 1px 1px 2px black;">
+                {msg}
+            </div>
+        </div>
+        """
+
+
+def escape_html(text):
+    """Escapes HTML special characters in the input text."""
+    return text.replace("<", "&lt;").replace(">", "&gt;").replace("\n", "<br>")