Update utils.py

.gitignore

@@ -1,207 +0,0 @@
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-__pycache__/
-*.py[codz]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
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-wheels/
-share/python-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
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-
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-pip-log.txt
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-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.nox/
-.coverage
-.coverage.*
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-nosetests.xml
-coverage.xml
-*.cover
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-.hypothesis/
-.pytest_cache/
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-
-# Translations
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-
-# Django stuff:
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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

@@ -1,21 +1,17 @@
 import spaces
 import os
-from argparse import ArgumentParser
 from stablepy import (
     Model_Diffusers,
     SCHEDULE_TYPE_OPTIONS,
     SCHEDULE_PREDICTION_TYPE_OPTIONS,
     check_scheduler_compatibility,
     TASK_AND_PREPROCESSORS,
-    FACE_RESTORATION_MODELS,
-    scheduler_names,
 )
 from constants import (
     DIRECTORY_MODELS,
     DIRECTORY_LORAS,
     DIRECTORY_VAES,
     DIRECTORY_EMBEDS,
-    DIRECTORY_UPSCALERS,
     DOWNLOAD_MODEL,
     DOWNLOAD_VAE,
     DOWNLOAD_LORA,
@@ -39,13 +35,15 @@ from constants import (
     EXAMPLES_GUI,
     RESOURCES,
     DIFFUSERS_CONTROLNET_MODEL,
-    IP_MODELS,
-    MODE_IP_OPTIONS,
-    CACHE_HF_ROOT,
 )
 from stablepy.diffusers_vanilla.style_prompt_config import STYLE_NAMES
 import torch
 import re
+from stablepy import (
+    scheduler_names,
+    IP_ADAPTERS_SD,
+    IP_ADAPTERS_SDXL,
+)
 import time
 from PIL import ImageFile
 from utils import (
@@ -62,7 +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
@@ -73,35 +70,31 @@ import warnings
 from stablepy import logger
 from diffusers import FluxPipeline
 # import urllib.parse
-import subprocess
-
-IS_ZERO_GPU = bool(os.getenv("SPACES_ZERO_GPU"))
-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)
 
 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]
+directories = [DIRECTORY_MODELS, DIRECTORY_LORAS, DIRECTORY_VAES, DIRECTORY_EMBEDS]
 for directory in directories:
     os.makedirs(directory, exist_ok=True)
 
 # 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)
@@ -119,16 +112,15 @@ 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
+components.pop("transformer", None)
+delete_model(flux_repo)
 
 #######################
 # GUI
@@ -138,17 +130,7 @@ 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; }
@@ -158,12 +140,6 @@ 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
@@ -179,15 +155,6 @@ class GuiSD:
             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 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 = [
@@ -198,21 +165,14 @@ 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,
@@ -238,7 +198,10 @@ class GuiSD:
         yield f"Loading model: {model_name}"
 
         if vae_model == "BakedVAE":
-            vae_model = model_name
+            if not os.path.exists(model_name):
+                vae_model = model_name
+            else:
+                vae_model = None
         elif vae_model:
             vae_type = "SDXL" if "sdxl" in vae_model.lower() else "SD 1.5"
             if model_type != vae_type:
@@ -257,10 +220,10 @@ 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()
@@ -270,8 +233,7 @@ 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],
@@ -348,8 +310,8 @@ class GuiSD:
         syntax_weights,
         upscaler_model_path,
         upscaler_increases_size,
-        upscaler_tile_size,
-        upscaler_tile_overlap,
+        esrgan_tile,
+        esrgan_tile_overlap,
         hires_steps,
         hires_denoising_strength,
         hires_sampler,
@@ -413,9 +375,6 @@ class GuiSD:
         mode_ip2,
         scale_ip2,
         pag_scale,
-        face_restoration_model,
-        face_restoration_visibility,
-        face_restoration_weight,
     ):
         info_state = html_template_message("Navigating latent space...")
         yield info_state, gr.update(), gr.update()
@@ -425,7 +384,7 @@ 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]
 
@@ -454,20 +413,23 @@ class GuiSD:
         self.model.stream_config(concurrency=concurrency, latent_resize_by=1, vae_decoding=False)
 
         if task != "txt2img" and not image_control:
-            raise ValueError("Reference image is required. Please upload one in 'Image ControlNet/Inpaint/Img2img'.")
+            raise ValueError("No control image found: To use this function, you have to upload an image in 'Image ControlNet/Inpaint/Img2img'")
 
-        if task in ["inpaint", "repaint"] and not image_mask:
-            raise ValueError("Mask image not found. Upload one in 'Image Mask' to proceed.")
+        if task == "inpaint" and not image_mask:
+            raise ValueError("No mask image found: Specify one in 'Image Mask'")
 
-        if "https://" not in str(UPSCALER_DICT_GUI[upscaler_model_path]):
+        if upscaler_model_path in UPSCALER_KEYS[:9]:
             upscaler_model = upscaler_model_path
         else:
+            directory_upscalers = 'upscalers'
+            os.makedirs(directory_upscalers, exist_ok=True)
+
             url_upscaler = UPSCALER_DICT_GUI[upscaler_model_path]
 
-            if not os.path.exists(f"./{DIRECTORY_UPSCALERS}/{url_upscaler.split('/')[-1]}"):
-                download_things(DIRECTORY_UPSCALERS, url_upscaler, HF_TOKEN)
+            if not os.path.exists(f"./upscalers/{url_upscaler.split('/')[-1]}"):
+                download_things(directory_upscalers, url_upscaler, HF_TOKEN)
 
-            upscaler_model = f"./{DIRECTORY_UPSCALERS}/{url_upscaler.split('/')[-1]}"
+            upscaler_model = f"./upscalers/{url_upscaler.split('/')[-1]}"
 
         logging.getLogger("ultralytics").setLevel(logging.INFO if adetailer_verbose else logging.ERROR)
 
@@ -523,19 +485,19 @@ 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"
@@ -569,8 +531,8 @@ class GuiSD:
             "t2i_adapter_conditioning_factor": float(t2i_adapter_conditioning_factor),
             "upscaler_model_path": upscaler_model,
             "upscaler_increases_size": upscaler_increases_size,
-            "upscaler_tile_size": upscaler_tile_size,
-            "upscaler_tile_overlap": upscaler_tile_overlap,
+            "esrgan_tile": esrgan_tile,
+            "esrgan_tile_overlap": esrgan_tile_overlap,
             "hires_steps": hires_steps,
             "hires_denoising_strength": hires_denoising_strength,
             "hires_prompt": hires_prompt,
@@ -585,19 +547,16 @@ class GuiSD:
             "ip_adapter_model": params_ip_model,
             "ip_adapter_mode": params_ip_mode,
             "ip_adapter_scale": params_ip_scale,
-            "face_restoration_model": face_restoration_model,
-            "face_restoration_visibility": face_restoration_visibility,
-            "face_restoration_weight": face_restoration_weight,
         }
 
         # 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()
@@ -627,7 +586,7 @@ 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)
                     ]
                 )
@@ -735,27 +694,22 @@ def sd_gen_generate_pipeline(*args):
 
 
 @spaces.GPU(duration=15)
-def process_upscale(image, upscaler_name, upscaler_size):
-    if image is None:
-        return None
+def esrgan_upscale(image, upscaler_name, upscaler_size):
+    if image is None: return None
 
     from stablepy.diffusers_vanilla.utils import save_pil_image_with_metadata
-    from stablepy import load_upscaler_model
+    from stablepy import UpscalerESRGAN
 
-    image = image.convert("RGB")
     exif_image = extract_exif_data(image)
 
-    name_upscaler = UPSCALER_DICT_GUI[upscaler_name]
-
-    if "https://" in str(name_upscaler):
-
-        if not os.path.exists(f"./{DIRECTORY_UPSCALERS}/{name_upscaler.split('/')[-1]}"):
-            download_things(DIRECTORY_UPSCALERS, name_upscaler, HF_TOKEN)
-
-        name_upscaler = f"./{DIRECTORY_UPSCALERS}/{name_upscaler.split('/')[-1]}"
+    url_upscaler = UPSCALER_DICT_GUI[upscaler_name]
+    directory_upscalers = 'upscalers'
+    os.makedirs(directory_upscalers, exist_ok=True)
+    if not os.path.exists(f"./upscalers/{url_upscaler.split('/')[-1]}"):
+        download_things(directory_upscalers, url_upscaler, HF_TOKEN)
 
-    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)
-    image_up = scaler_beta.upscale(image, upscaler_size, True)
+    scaler_beta = UpscalerESRGAN(0, 0)
+    image_up = scaler_beta.upscale(image, upscaler_size, f"./upscalers/{url_upscaler.split('/')[-1]}")
 
     image_path = save_pil_image_with_metadata(image_up, f'{os.getcwd()}/up_images', exif_image)
 
@@ -763,11 +717,11 @@ 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"):
@@ -816,7 +770,7 @@ 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")
@@ -852,22 +806,7 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
                             "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(),
                         }
-
-                        # 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)
@@ -881,36 +820,6 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
                                     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}")
-
-                            num_lora = 1
-                            for parsed_l, parsed_s in parsed_params:
-                                filtered_loras = [m for m in 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:
@@ -918,12 +827,9 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
                                     if key == "Sampler":
                                         if val not in scheduler_names:
                                             continue
-                                    if key in ["Schedule type", "Hires schedule type"]:
+                                    if key == "Schedule type":
                                         if val not in SCHEDULE_TYPE_OPTIONS:
-                                            continue
-                                    if key == "Hires sampler":
-                                        if val not in POST_PROCESSING_SAMPLER:
-                                            continue
+                                            val = "Automatic"
                                     elif key == "Clip skip":
                                         if "," in str(val):
                                             val = val.replace(",", "")
@@ -931,15 +837,15 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
                                             val = True
                                     if key == "prompt":
                                         if ">" in val and "<" in val:
-                                            val = re.sub(r'<[^>]+>', '', val)  # Delete html and loras
+                                            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", "Hires steps", "Image resolution"]:
+                                    if key in ["Steps", "width", "height", "Seed"]:
                                         val = int(val)
                                     if key == "FreeU":
                                         val = True
-                                    if key in ["CFG scale", "PAG", "Hires upscale", "Hires denoising strength", "Hires CFG", "Strength"]:
+                                    if key in ["CFG scale", "PAG"]:
                                         val = float(val)
                                     if key == "Model":
                                         filtered_models = [m for m in model_list if val in m]
@@ -947,12 +853,8 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
                                             val = filtered_models[0]
                                         else:
                                             val = name_model
-                                    if key == "Hires upscaler":
-                                        if val not in UPSCALER_KEYS:
-                                            continue
                                     if key == "Seed":
                                         continue
-
                                     valid_receptors[key] = gr.update(value=val)
                                     # print(val, type(val))
                                     # print(valid_receptors)
@@ -960,6 +862,24 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
                                     print(str(e))
                         return [value for value in valid_receptors.values()]
 
+                    set_params_gui.click(
+                        run_set_params_gui, [prompt_gui, model_name_gui], [
+                            prompt_gui,
+                            neg_prompt_gui,
+                            steps_gui,
+                            img_width_gui,
+                            img_height_gui,
+                            seed_gui,
+                            sampler_gui,
+                            cfg_gui,
+                            clip_skip_gui,
+                            model_name_gui,
+                            schedule_type_gui,
+                            pag_scale_gui,
+                            free_u_gui,
+                        ],
+                    )
+
                     def run_clear_prompt_gui():
                         return gr.update(value=""), gr.update(value="")
                     clear_prompt_gui.click(
@@ -972,7 +892,7 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
                         run_set_random_seed, [], seed_gui
                     )
 
-                num_images_gui = gr.Slider(minimum=1, maximum=(5 if IS_ZERO_GPU else 20), step=1, value=1, label="Images")
+                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])
 
@@ -980,8 +900,8 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
 
                     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_overlap_gui = gr.Slider(minimum=0, maximum=48, step=1, value=8, label="Upscaler Tile Overlap")
+                    esrgan_tile_gui = gr.Slider(minimum=0, value=0, maximum=500, step=1, label="ESRGAN Tile")
+                    esrgan_tile_overlap_gui = gr.Slider(minimum=1, maximum=200, step=1, value=8, label="ESRGAN 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")
                     hires_sampler_gui = gr.Dropdown(label="Hires Sampler", choices=POST_PROCESSING_SAMPLER, value=POST_PROCESSING_SAMPLER[0])
@@ -997,8 +917,7 @@ 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 = 2 if IS_ZERO_GPU else 8
-                        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")
@@ -1010,10 +929,10 @@ 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", visible=(not IS_ZERO_GPU))
-                    lora_scale_6_gui = lora_scale_slider("Lora Scale 6", visible=(not IS_ZERO_GPU))
-                    lora7_gui = lora_dropdown("Lora7", visible=(not IS_ZERO_GPU))
-                    lora_scale_7_gui = lora_scale_slider("Lora Scale 7", visible=(not IS_ZERO_GPU))
+                    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(
@@ -1022,7 +941,7 @@ 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(
@@ -1031,16 +950,11 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
                             [lora1_gui, lora2_gui, lora3_gui, lora4_gui, lora5_gui, lora6_gui, lora7_gui, new_lora_status]
                         )
 
-                with gr.Accordion("Face restoration", open=False, visible=True):
-
-                    face_rest_options = [None] + FACE_RESTORATION_MODELS
-
-                    face_restoration_model_gui = gr.Dropdown(label="Face restoration model", choices=face_rest_options, value=face_rest_options[0])
-                    face_restoration_visibility_gui = gr.Slider(minimum=0., maximum=1., step=0.001, value=1., label="Visibility")
-                    face_restoration_weight_gui = gr.Slider(minimum=0., maximum=1., step=0.001, value=.5, label="Weight", info="(0 = maximum effect, 1 = minimum effect)")
-
                 with gr.Accordion("IP-Adapter", open=False, visible=True):
 
+                    IP_MODELS = sorted(list(set(IP_ADAPTERS_SD + IP_ADAPTERS_SDXL)))
+                    MODE_IP_OPTIONS = ["original", "style", "layout", "style+layout"]
+
                     with gr.Accordion("IP-Adapter 1", open=False, visible=True):
                         image_ip1 = gr.Image(label="IP Image", type="filepath")
                         mask_ip1 = gr.Image(label="IP Mask", type="filepath")
@@ -1059,13 +973,13 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
                     image_mask_gui = gr.Image(label="Image Mask", type="filepath")
                     strength_gui = gr.Slider(
                         minimum=0.01, maximum=1.0, step=0.01, value=0.55, label="Strength",
-                        info="This option adjusts the level of changes for img2img, repaint and inpaint."
+                        info="This option adjusts the level of changes for img2img and inpainting."
                     )
                     image_resolution_gui = gr.Slider(
                         minimum=64, maximum=2048, step=64, value=1024, label="Image Resolution",
                         info="The maximum proportional size of the generated image based on the uploaded image."
                     )
-                    controlnet_model_gui = gr.Dropdown(label="ControlNet model", choices=DIFFUSERS_CONTROLNET_MODEL, value=DIFFUSERS_CONTROLNET_MODEL[0], allow_custom_value=True)
+                    controlnet_model_gui = gr.Dropdown(label="ControlNet model", choices=DIFFUSERS_CONTROLNET_MODEL, value=DIFFUSERS_CONTROLNET_MODEL[0])
                     control_net_output_scaling_gui = gr.Slider(minimum=0, maximum=5.0, step=0.1, value=1, label="ControlNet Output Scaling in UNet")
                     control_net_start_threshold_gui = gr.Slider(minimum=0, maximum=1, step=0.01, value=0, label="ControlNet Start Threshold (%)")
                     control_net_stop_threshold_gui = gr.Slider(minimum=0, maximum=1, step=0.01, value=1, label="ControlNet Stop Threshold (%)")
@@ -1087,8 +1001,8 @@ 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")
 
@@ -1123,7 +1037,7 @@ 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")
@@ -1173,62 +1087,20 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
                     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")
+                    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")
                     xformers_memory_efficient_attention_gui = gr.Checkbox(value=False, label="Xformers Memory Efficient Attention")
 
-                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,
-                        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,
-                    ],
-                )
-
         with gr.Accordion("Examples and help", open=False, visible=True):
             gr.Markdown(HELP_GUI)
             gr.Markdown(EXAMPLES_GUI_HELP)
@@ -1284,21 +1156,10 @@ 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)
@@ -1329,11 +1190,8 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
 
         with gr.Row():
             with gr.Column():
-
-                USCALER_TAB_KEYS = [name for name in UPSCALER_KEYS[9:]]
-
                 image_up_tab = gr.Image(label="Image", type="pil", sources=["upload"])
-                upscaler_tab = gr.Dropdown(label="Upscaler", choices=USCALER_TAB_KEYS, value=USCALER_TAB_KEYS[5])
+                upscaler_tab = gr.Dropdown(label="Upscaler", choices=UPSCALER_KEYS[9:], value=UPSCALER_KEYS[11])
                 upscaler_size_tab = gr.Slider(minimum=1., maximum=4., step=0.1, value=1.1, label="Upscale by")
                 generate_button_up_tab = gr.Button(value="START UPSCALE", variant="primary")
 
@@ -1341,7 +1199,7 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
                 result_up_tab = gr.Image(label="Result", type="pil", interactive=False, format="png")
 
                 generate_button_up_tab.click(
-                    fn=process_upscale,
+                    fn=esrgan_upscale,
                     inputs=[image_up_tab, upscaler_tab, upscaler_size_tab],
                     outputs=[result_up_tab],
                 )
@@ -1413,8 +1271,8 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
             prompt_syntax_gui,
             upscaler_model_path_gui,
             upscaler_increases_size_gui,
-            upscaler_tile_size_gui,
-            upscaler_tile_overlap_gui,
+            esrgan_tile_gui,
+            esrgan_tile_overlap_gui,
             hires_steps_gui,
             hires_denoising_strength_gui,
             hires_sampler_gui,
@@ -1478,9 +1336,6 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
             mode_ip2,
             scale_ip2,
             pag_scale_gui,
-            face_restoration_model_gui,
-            face_restoration_visibility_gui,
-            face_restoration_weight_gui,
             load_lora_cpu_gui,
             verbose_info_gui,
             gpu_duration_gui,
@@ -1490,12 +1345,10 @@ with gr.Blocks(theme=args.theme, css=CSS, fill_width=True, fill_height=False) as
         show_progress="minimal",
     )
 
-if __name__ == "__main__":
-    app.queue()
-    app.launch(
-        show_error=True,
-        share=args.share_enabled,
-        debug=True,
-        ssr_mode=args.ssr,
-        allowed_paths=[allowed_path],
-    )
+app.queue()
+
+app.launch(
+    show_error=True,
+    debug=True,
+    allowed_paths=["./images/"],
+)

constants.py

@@ -4,13 +4,8 @@ from stablepy import (
     scheduler_names,
     SD15_TASKS,
     SDXL_TASKS,
-    ALL_BUILTIN_UPSCALERS,
-    IP_ADAPTERS_SD,
-    IP_ADAPTERS_SDXL,
 )
 
-IS_ZERO_GPU = bool(os.getenv("SPACES_ZERO_GPU"))
-
 # - **Download Models**
 DOWNLOAD_MODEL = "https://huggingface.co/TechnoByte/MilkyWonderland/resolve/main/milkyWonderland_v40.safetensors"
 
@@ -23,46 +18,37 @@ DOWNLOAD_LORA = "https://huggingface.co/Leopain/color/resolve/main/Coloring_book
 LOAD_DIFFUSERS_FORMAT_MODEL = [
     'stabilityai/stable-diffusion-xl-base-1.0',
     'Laxhar/noobai-XL-1.1',
-    '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',
     'terminusresearch/FluxBooru-v0.3',
-    'black-forest-labs/FLUX.1-schnell',
-    # 'ostris/OpenFLUX.1',
+    '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-xi-sdxl',
-    'John6666/ntr-mix-illustrious-xl-noob-xl-xii-sdxl',
-    'John6666/ntr-mix-illustrious-xl-noob-xl-xiii-sdxl',
-    'John6666/mistoon-anime-v10illustrious-sdxl',
-    'John6666/hassaku-xl-illustrious-v22-sdxl',
+    'John6666/ntr-mix-illustrious-xl-noob-xl-v40-sdxl',
+    'John6666/ntr-mix-illustrious-xl-noob-xl-ntrmix35-sdxl',
+    'John6666/ntr-mix-illustrious-xl-noob-xl-v777-sdxl',
+    'John6666/ntr-mix-illustrious-xl-noob-xl-v777forlora-sdxl',
     'John6666/haruki-mix-illustrious-v10-sdxl',
     'John6666/noobreal-v10-sdxl',
     'John6666/complicated-noobai-merge-vprediction-sdxl',
-    'Laxhar/noobai-XL-Vpred-0.9r',
-    'Laxhar/noobai-XL-Vpred-0.75s',
-    'Laxhar/noobai-XL-Vpred-0.75',
     'Laxhar/noobai-XL-Vpred-0.65s',
     '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/noobai-xl-nai-xl-vpred05version-sdxl',
     'John6666/noobai-fusion2-vpred-itercomp-v1-sdxl',
     'John6666/noobai-xl-nai-xl-vpredtestversion-sdxl',
@@ -72,35 +58,18 @@ LOAD_DIFFUSERS_FORMAT_MODEL = [
     'John6666/illustrious-pencil-xl-v200-sdxl',
     '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/prefect-illustrious-xl-v3-sdxl',
     'John6666/wai-nsfw-illustrious-v70-sdxl',
-    'John6666/wai-nsfw-illustrious-sdxl-v140-sdxl',
     'John6666/illustrious-pony-mix-v3-sdxl',
-    'John6666/nova-anime-xl-il-v90-sdxl',
-    'John6666/nova-anime-xl-il-v110-sdxl',
-    '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',
-    'John6666/nova-cartoon-xl-v40-sdxl',
+    'John6666/nova-anime-xl-illustriousv10-sdxl',
+    'John6666/nova-orange-xl-v30-sdxl',
     'John6666/silvermoon-mix03-illustrious-v10-sdxl',
     'eienmojiki/Anything-XL',
     'eienmojiki/Starry-XL-v5.2',
-    '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-v8kiss-sdxl',
     'misri/zavychromaxl_v80',
     'SG161222/RealVisXL_V4.0',
     'SG161222/RealVisXL_V5.0',
@@ -112,14 +81,11 @@ LOAD_DIFFUSERS_FORMAT_MODEL = [
     'John6666/ras-real-anime-screencap-v1-sdxl',
     'John6666/duchaiten-pony-xl-no-score-v60-sdxl',
     'John6666/mistoon-anime-ponyalpha-sdxl',
-    'John6666/mistoon-xl-copper-v20fast-sdxl',
     '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',
@@ -127,32 +93,24 @@ 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',
     'John6666/aimz-dream-real-pony-mix-v3-sdxl',
-    'John6666/prefectious-xl-nsfw-v10-sdxl',
-    'GraydientPlatformAPI/iniverseponyRealGuofeng49',
     'John6666/duchaiten-pony-real-v11fix-sdxl',
     'John6666/duchaiten-pony-real-v20-sdxl',
     'John6666/duchaiten-pony-xl-no-score-v70-sdxl',
     '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',
-    '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',
@@ -162,9 +120,7 @@ LOAD_DIFFUSERS_FORMAT_MODEL = [
     'digiplay/darkphoenix3D_v1.1',
     'digiplay/BeenYouLiteL11_diffusers',
     'GraydientPlatformAPI/rev-animated2',
-    'myxlmynx/cyberrealistic_classic40',
-    'GraydientPlatformAPI/cyberreal6',
-    'GraydientPlatformAPI/cyberreal5',
+    'youknownothing/cyberrealistic_v50',
     'youknownothing/deliberate-v6',
     'GraydientPlatformAPI/deliberate-cyber3',
     'GraydientPlatformAPI/picx-real',
@@ -178,9 +134,9 @@ 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",
@@ -200,13 +156,9 @@ DIRECTORY_MODELS = 'models'
 DIRECTORY_LORAS = 'loras'
 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',
@@ -232,23 +184,28 @@ TASK_STABLEPY = {
     'optical pattern ControlNet': 'pattern',
     'recolor ControlNet': 'recolor',
     'tile ControlNet': 'tile',
-    'repaint ControlNet': 'repaint',
 }
 
 TASK_MODEL_LIST = list(TASK_STABLEPY.keys())
 
 UPSCALER_DICT_GUI = {
     None: None,
-    **{bu: bu for bu in ALL_BUILTIN_UPSCALERS if bu not in ["HAT x4", "DAT x4", "DAT x3", "DAT x2", "SwinIR 4x"]},
-    # "RealESRGAN_x4plus": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
+    "Lanczos": "Lanczos",
+    "Nearest": "Nearest",
+    'Latent': 'Latent',
+    'Latent (antialiased)': 'Latent (antialiased)',
+    'Latent (bicubic)': 'Latent (bicubic)',
+    'Latent (bicubic antialiased)': 'Latent (bicubic antialiased)',
+    'Latent (nearest)': 'Latent (nearest)',
+    'Latent (nearest-exact)': 'Latent (nearest-exact)',
+    "RealESRGAN_x4plus": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
     "RealESRNet_x4plus": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/RealESRNet_x4plus.pth",
-    # "RealESRGAN_x4plus_anime_6B": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
-    # "RealESRGAN_x2plus": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
-    # "realesr-animevideov3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
-    # "realesr-general-x4v3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
-    # "realesr-general-wdn-x4v3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
+    "RealESRGAN_x4plus_anime_6B": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
+    "RealESRGAN_x2plus": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
+    "realesr-animevideov3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-animevideov3.pth",
+    "realesr-general-x4v3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth",
+    "realesr-general-wdn-x4v3": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth",
     "4x-UltraSharp": "https://huggingface.co/Shandypur/ESRGAN-4x-UltraSharp/resolve/main/4x-UltraSharp.pth",
-    "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",
@@ -262,7 +219,6 @@ UPSCALER_KEYS = list(UPSCALER_DICT_GUI.keys())
 DIFFUSERS_CONTROLNET_MODEL = [
     "Automatic",
 
-    "brad-twinkl/controlnet-union-sdxl-1.0-promax",
     "xinsir/controlnet-union-sdxl-1.0",
     "xinsir/anime-painter",
     "Eugeoter/noob-sdxl-controlnet-canny",
@@ -285,6 +241,7 @@ DIFFUSERS_CONTROLNET_MODEL = [
     "r3gm/controlnet-recolor-sdxl-fp16",
     "r3gm/controlnet-openpose-twins-sdxl-1.0-fp16",
     "r3gm/controlnet-qr-pattern-sdxl-fp16",
+    "brad-twinkl/controlnet-union-sdxl-1.0-promax",
     "Yakonrus/SDXL_Controlnet_Tile_Realistic_v2",
     "TheMistoAI/MistoLine",
     "briaai/BRIA-2.3-ControlNet-Recoloring",
@@ -363,30 +320,14 @@ POST_PROCESSING_SAMPLER = ["Use same sampler"] + [
     name_s for name_s in scheduler_names if "Auto-Loader" not in name_s
 ]
 
-IP_MODELS = []
-ALL_IPA = sorted(set(IP_ADAPTERS_SD + IP_ADAPTERS_SDXL))
-
-for origin_name in ALL_IPA:
-    suffixes = []
-    if origin_name in IP_ADAPTERS_SD:
-        suffixes.append("sd1.5")
-    if origin_name in IP_ADAPTERS_SDXL:
-        suffixes.append("sdxl")
-    ref_name = f"{origin_name} ({'/'.join(suffixes)})"
-    IP_MODELS.append((ref_name, origin_name))
-
-MODE_IP_OPTIONS = ["original", "style", "layout", "style+layout"]
-
 SUBTITLE_GUI = (
     "### This demo uses [diffusers](https://github.com/huggingface/diffusers)"
     " 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.
     """
@@ -399,9 +340,7 @@ EXAMPLES_GUI_HELP = (
     3. ControlNet Canny SDXL
     4. Optical pattern (Optical illusion) SDXL
     5. Convert an image to a coloring drawing
-    6. V prediction model inference
-    7. V prediction model sd_embed variant inference
-    8. ControlNet OpenPose SD 1.5 and Latent upscale
+    6. ControlNet OpenPose SD 1.5 and Latent upscale
 
     - Different tasks can be performed, such as img2img or using the IP adapter, to preserve a person's appearance or a specific style based on an image.
     """
@@ -510,7 +449,7 @@ 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,
@@ -528,54 +467,6 @@ EXAMPLES_GUI = [
         35,
         False,
     ],
-    [
-        "[mochizuki_shiina], [syuri22], newest, reimu, solo, outdoors, water, flower, lantern",
-        "worst quality, normal quality, old, sketch,",
-        28,
-        7.0,
-        -1,
-        "None",
-        0.33,
-        "DPM 3M Ef",
-        1600,
-        1024,
-        "Laxhar/noobai-XL-Vpred-1.0",
-        "txt2img",
-        "color_image.png",  # img conttol
-        1024,  # img resolution
-        0.35,  # strength
-        1.0,  # cn scale
-        0.0,  # cn start
-        1.0,  # cn end
-        "Classic",
-        None,
-        30,
-        False,
-    ],
-    [
-        "[mochizuki_shiina], [syuri22], newest, multiple girls, 2girls, earrings, jewelry, gloves, purple eyes, black hair, looking at viewer, nail polish, hat, smile, open mouth, fingerless gloves, sleeveless, :d, upper body, blue eyes, closed mouth, black gloves, hands up, long hair, shirt, bare shoulders, white headwear, blush, black headwear, blue nails, upper teeth only, short hair, white gloves, white shirt, teeth, rabbit hat, star earrings, purple nails, pink hair, detached sleeves, fingernails, fake animal ears, animal hat, sleeves past wrists, black shirt, medium hair, fur trim, sleeveless shirt, turtleneck, long sleeves, rabbit ears, star \\(symbol\\)",
-        "worst quality, normal quality, old, sketch,",
-        28,
-        7.0,
-        -1,
-        "None",
-        0.33,
-        "DPM 3M Ef",
-        1600,
-        1024,
-        "Laxhar/noobai-XL-Vpred-1.0",
-        "txt2img",
-        "color_image.png",  # img conttol
-        1024,  # img resolution
-        0.35,  # strength
-        1.0,  # cn scale
-        0.0,  # cn start
-        1.0,  # cn end
-        "Classic-sd_embed",
-        None,
-        30,
-        False,
-    ],
     [
         "1girl,face,curly hair,red hair,white background,",
         "(worst quality:2),(low quality:2),(normal quality:2),lowres,watermark,",
@@ -605,7 +496,6 @@ 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==0.6.2
-torch==2.5.1
-diffusers
+git+https://github.com/R3gm/stablepy.git@a9fe2dc  # -b refactor_sampler_fix
+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

@@ -9,7 +9,6 @@ from constants import (
     DIRECTORY_LORAS,
     DIRECTORY_MODELS,
     DIFFUSECRAFT_CHECKPOINT_NAME,
-    CACHE_HF_ROOT,
     CACHE_HF,
     STORAGE_ROOT,
 )
@@ -29,7 +28,6 @@ from urllib3.util import Retry
 import shutil
 import subprocess
 
-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'
 
 
@@ -64,12 +62,11 @@ class ModelInformation:
         self.download_url = json_data.get("downloadUrl", "")
         self.model_url = f"https://civitai.com/models/{self.model_id}?modelVersionId={self.model_version_id}"
         self.filename_url = next(
-            (v.get("name", "") for v in json_data.get("files", []) if str(self.model_version_id) in v.get("downloadUrl", "") and v.get("type", "Model") == "Model"), ""
+            (v.get("name", "") for v in reversed(json_data.get("files", [])) if str(self.model_version_id) in v.get("downloadUrl", "")), ""
         )
         self.filename_url = self.filename_url if self.filename_url else ""
         self.description = json_data.get("description", "")
-        if self.description is None:
-            self.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)
@@ -79,175 +76,118 @@ class ModelInformation:
         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)
+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
 
-    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)
+def download_things(directory, url, hf_token="", civitai_api_key="", romanize=False):
+    url = url.strip()
+    downloaded_file_path = None
 
-            filename = unidecode(decoded_filename) if romanize else decoded_filename
-            # print(f"Filename redirect: {filename}")
+    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_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
+        filename = unidecode(url.split('/')[-1]) if romanize else url.split('/')[-1]
 
-    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 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}")
 
-    # if r_code != 0:
-    #     raise RuntimeError(f"Failed to download file: {filename_base}. Error code: {r_code}")
+        downloaded_file_path = os.path.join(directory, filename)
 
-    output_path = os.path.join(dir_, filename_base)
-    if not os.path.exists(output_path):
-        return None, filename_base
+    elif "civitai.com" in url:
 
-    return output_path, filename_base
+        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}")
 
-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
+        param_filename = ""
+        if filename:
+            param_filename = f"-o '{filename}'"
 
-    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}"'
+        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)
-    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_)
+        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}")
 
-    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}")
 
@@ -276,15 +216,14 @@ def extract_parameters(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)
+            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:")
@@ -361,14 +300,13 @@ def get_model_type(repo_id: str):
     default = "SD 1.5"
     try:
         if os.path.exists(repo_id):
-            tag, _, _, _ = checkpoint_model_type(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)
+                if tag in MODEL_TYPE_CLASS.keys(): return MODEL_TYPE_CLASS.get(tag, default)
 
     except Exception:
         return default
@@ -495,9 +433,9 @@ def get_folder_size_gb(folder_path):
     return total_size_gb
 
 
-def get_used_storage_gb(path_storage=STORAGE_ROOT):
+def get_used_storage_gb():
     try:
-        used_gb = get_folder_size_gb(path_storage)
+        used_gb = get_folder_size_gb(STORAGE_ROOT)
         print(f"Used Storage: {used_gb:.2f} GB")
     except Exception as e:
         used_gb = 999
@@ -517,21 +455,6 @@ def delete_model(removal_candidate):
             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_ROOT):
-            shutil.rmtree(CACHE_HF_ROOT, ignore_errors=True)
-            print(f"Hugging Face cache cleared: {CACHE_HF_ROOT}")
-        else:
-            print(f"No Hugging Face cache found at: {CACHE_HF_ROOT}")
-    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))