Update constants.py

.gitignore

@@ -1,207 +0,0 @@
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-*.py[codz]
-*$py.class
-
-# C extensions
-*.so
-
-# Distribution / packaging
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-.installed.cfg
-*.egg
-MANIFEST
-
-# PyInstaller
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-*.manifest
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-
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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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-
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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,
@@ -41,8 +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
@@ -63,7 +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
@@ -76,18 +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:
@@ -95,15 +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)
@@ -121,16 +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
@@ -140,17 +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; }
@@ -160,12 +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
@@ -174,22 +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 = [
@@ -200,21 +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,
@@ -259,10 +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()
@@ -272,8 +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],
@@ -427,7 +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]
 
@@ -525,19 +484,19 @@ 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"
@@ -595,11 +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()
@@ -629,7 +588,7 @@ 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)
                     ]
                 )
@@ -738,8 +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
@@ -756,7 +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)
@@ -765,11 +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"):
@@ -818,7 +776,7 @@ 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")
@@ -854,22 +812,7 @@ 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)
@@ -883,36 +826,6 @@ 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:
@@ -920,12 +833,9 @@ 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(",", "")
@@ -933,15 +843,15 @@ 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]
@@ -949,12 +859,8 @@ 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)
@@ -962,6 +868,24 @@ 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(
@@ -974,15 +898,15 @@ 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=(8 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[0][1])
+                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")
@@ -999,8 +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")
@@ -1012,10 +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", 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(
@@ -1024,7 +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(
@@ -1089,8 +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")
 
@@ -1125,7 +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")
@@ -1175,62 +1098,20 @@ 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)
@@ -1286,21 +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)
@@ -1362,7 +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=[
@@ -1491,16 +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

@@ -7,13 +7,10 @@ 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/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"
@@ -26,12 +23,12 @@ LOAD_DIFFUSERS_FORMAT_MODEL = [
     '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',
@@ -40,19 +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',
     'John6666/mistoon-anime-v10illustrious-sdxl',
-    'John6666/hassaku-xl-illustrious-v22-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',
@@ -63,7 +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/noobai-xl-nai-xl-vpred05version-sdxl',
     'John6666/noobai-fusion2-vpred-itercomp-v1-sdxl',
     'John6666/noobai-xl-nai-xl-vpredtestversion-sdxl',
@@ -74,34 +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/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-v10kiss2-sdxl',
+    'John6666/epicrealism-xl-v8kiss-sdxl',
     'misri/zavychromaxl_v80',
     'SG161222/RealVisXL_V4.0',
     'SG161222/RealVisXL_V5.0',
@@ -117,10 +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',
@@ -128,14 +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',
@@ -145,15 +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',
-    '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',
@@ -179,9 +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",
@@ -203,11 +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',
@@ -249,7 +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",
@@ -322,9 +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"
@@ -377,11 +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.
     """
@@ -505,7 +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,
@@ -600,7 +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>")