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mod.py

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+import spaces
+import gradio as gr
+import torch
+from PIL import Image
+from pathlib import Path
+import gc
+import subprocess
+from env import num_cns, model_trigger
+
+
+subprocess.run('pip install flash-attn --no-build-isolation', env={'FLASH_ATTENTION_SKIP_CUDA_BUILD': "TRUE"}, shell=True)
+subprocess.run('pip cache purge', shell=True)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+torch.set_grad_enabled(False)
+
+
+control_images = [None] * num_cns
+control_modes = [-1] * num_cns
+control_scales = [0] * num_cns
+
+
+def is_repo_name(s):
+    import re
+    return re.fullmatch(r'^[^/,\s\"\']+/[^/,\s\"\']+$', s)
+
+
+def is_repo_exists(repo_id):
+    from huggingface_hub import HfApi
+    api = HfApi()
+    try:
+        if api.repo_exists(repo_id=repo_id): return True
+        else: return False
+    except Exception as e:
+        print(f"Error: Failed to connect {repo_id}.")
+        print(e)
+        return True # for safe
+
+
+from translatepy import Translator
+translator = Translator()
+def translate_to_en(input: str):
+    try:
+        output = str(translator.translate(input, 'English'))
+    except Exception as e:
+        output = input
+        print(e)
+    return output
+
+
+def clear_cache():
+    try:
+        torch.cuda.empty_cache()
+        #torch.cuda.reset_max_memory_allocated()
+        #torch.cuda.reset_peak_memory_stats()
+        gc.collect()
+    except Exception as e:
+        print(e)
+        raise Exception(f"Cache clearing error: {e}") from e
+
+
+def get_repo_safetensors(repo_id: str):
+    from huggingface_hub import HfApi
+    api = HfApi()
+    try:
+        if not is_repo_name(repo_id) or not is_repo_exists(repo_id): return gr.update(value="", choices=[])
+        files = api.list_repo_files(repo_id=repo_id)
+    except Exception as e:
+        print(f"Error: Failed to get {repo_id}'s info.")
+        print(e)
+        gr.Warning(f"Error: Failed to get {repo_id}'s info.")
+        return gr.update(choices=[])
+    files = [f for f in files if f.endswith(".safetensors")]
+    if len(files) == 0: return gr.update(value="", choices=[])
+    else: return gr.update(value=files[0], choices=files)
+
+
+def expand2square(pil_img: Image.Image, background_color: tuple=(0, 0, 0)):
+    width, height = pil_img.size
+    if width == height:
+        return pil_img
+    elif width > height:
+        result = Image.new(pil_img.mode, (width, width), background_color)
+        result.paste(pil_img, (0, (width - height) // 2))
+        return result
+    else:
+        result = Image.new(pil_img.mode, (height, height), background_color)
+        result.paste(pil_img, ((height - width) // 2, 0))
+        return result
+
+
+# https://huggingface.co/spaces/DamarJati/FLUX.1-DEV-Canny/blob/main/app.py
+def resize_image(image, target_width, target_height, crop=True):
+    from image_datasets.canny_dataset import c_crop
+    if crop:
+        image = c_crop(image)  # Crop the image to square
+        original_width, original_height = image.size
+
+        # Resize to match the target size without stretching
+        scale = max(target_width / original_width, target_height / original_height)
+        resized_width = int(scale * original_width)
+        resized_height = int(scale * original_height)
+
+        image = image.resize((resized_width, resized_height), Image.LANCZOS)
+        
+        # Center crop to match the target dimensions
+        left = (resized_width - target_width) // 2
+        top = (resized_height - target_height) // 2
+        image = image.crop((left, top, left + target_width, top + target_height))
+    else:
+        image = image.resize((target_width, target_height), Image.LANCZOS)
+    
+    return image
+
+
+# https://huggingface.co/spaces/jiuface/FLUX.1-dev-Controlnet-Union/blob/main/app.py
+# https://huggingface.co/InstantX/FLUX.1-dev-Controlnet-Union
+controlnet_union_modes = {
+    "None": -1,
+    #"scribble_hed": 0,
+    "canny": 0, # supported
+    "mlsd": 0, #supported
+    "tile": 1, #supported
+    "depth_midas": 2, # supported
+    "blur": 3, # supported
+    "openpose": 4,  # supported
+    "gray": 5,  # supported
+    "low_quality": 6,  # supported
+}
+
+
+# https://github.com/pytorch/pytorch/issues/123834
+def get_control_params():
+    from diffusers.utils import load_image
+    modes = []
+    images = []
+    scales = []
+    for i, mode in enumerate(control_modes):
+        if mode == -1 or control_images[i] is None: continue
+        modes.append(control_modes[i])
+        images.append(load_image(control_images[i]))
+        scales.append(control_scales[i])
+    return modes, images, scales
+
+
+from preprocessor import Preprocessor
+def preprocess_image(image: Image.Image, control_mode: str, height: int, width: int,
+                     preprocess_resolution: int):
+    if control_mode == "None": return image
+    image_resolution = max(width, height)
+    image_before = resize_image(expand2square(image.convert("RGB")), image_resolution, image_resolution, False)
+    # generated control_
+    print("start to generate control image")
+    preprocessor = Preprocessor()
+    if control_mode == "depth_midas":
+        preprocessor.load("Midas")
+        control_image = preprocessor(
+            image=image_before,
+            image_resolution=image_resolution,
+            detect_resolution=preprocess_resolution,
+        )
+    if control_mode == "openpose":
+        preprocessor.load("Openpose")
+        control_image = preprocessor(
+            image=image_before,
+            hand_and_face=True,
+            image_resolution=image_resolution,
+            detect_resolution=preprocess_resolution,
+        )
+    if control_mode == "canny":
+        preprocessor.load("Canny")
+        control_image = preprocessor(
+            image=image_before,
+            image_resolution=image_resolution,
+            detect_resolution=preprocess_resolution,
+        )
+
+    if control_mode == "mlsd":
+        preprocessor.load("MLSD")
+        control_image = preprocessor(
+            image=image_before,
+            image_resolution=image_resolution,
+            detect_resolution=preprocess_resolution,
+        )
+
+    if control_mode == "scribble_hed":
+        preprocessor.load("HED")
+        control_image = preprocessor(
+            image=image_before,
+            image_resolution=image_resolution,
+            detect_resolution=preprocess_resolution,
+        )
+    
+    if control_mode == "low_quality" or control_mode == "gray" or control_mode == "blur" or control_mode == "tile":
+        control_image = image_before
+        image_width = 768
+        image_height = 768
+    else:
+        # make sure control image size is same as resized_image
+        image_width, image_height = control_image.size
+    
+    image_after = resize_image(control_image, width, height, False)
+    ref_width, ref_height = image.size
+    print(f"generate control image success: {ref_width}x{ref_height} => {image_width}x{image_height}")
+    return image_after
+
+
+def get_control_union_mode():
+    return list(controlnet_union_modes.keys())
+
+
+def set_control_union_mode(i: int, mode: str, scale: str):
+    global control_modes
+    global control_scales
+    control_modes[i] = controlnet_union_modes.get(mode, 0)
+    control_scales[i] = scale
+    if mode != "None": return True
+    else: return gr.update(visible=True)
+
+
+def set_control_union_image(i: int, mode: str, image: Image.Image | None, height: int, width: int, preprocess_resolution: int):
+    global control_images
+    if image is None: return None
+    control_images[i] = preprocess_image(image, mode, height, width, preprocess_resolution)
+    return control_images[i]
+
+
+def preprocess_i2i_image(image_path: str, is_preprocess: bool, height: int, width: int):
+    try:
+        if not is_preprocess: return image_path
+        image_resolution = max(width, height) 
+        image = Image.open(image_path)
+        image_resized = resize_image(expand2square(image.convert("RGB")), image_resolution, image_resolution, False)
+        image_resized.save(image_path)
+    except Exception as e:
+        raise gr.Error(f"Error: {e}")
+    return image_path
+
+
+def compose_lora_json(lorajson: list[dict], i: int, name: str, scale: float, filename: str, trigger: str):
+    lorajson[i]["name"] = str(name) if name != "None" else ""
+    lorajson[i]["scale"] = float(scale)
+    lorajson[i]["filename"] = str(filename)
+    lorajson[i]["trigger"] = str(trigger)
+    return lorajson
+
+
+def is_valid_lora(lorajson: list[dict]):
+    valid = False
+    for d in lorajson:
+        if "name" in d.keys() and d["name"] and d["name"] != "None": valid = True
+    return valid
+
+
+def get_trigger_word(lorajson: list[dict]):
+    trigger = ""
+    for d in lorajson:
+        if "name" in d.keys() and d["name"] and d["name"] != "None" and d["trigger"]:
+            trigger += ", " + d["trigger"]
+    return trigger
+
+
+def get_model_trigger(model_name: str):
+    trigger = ""
+    if model_name in model_trigger.keys(): trigger += ", " + model_trigger[model_name]
+    return trigger
+
+
+# https://huggingface.co/docs/diffusers/v0.23.1/en/api/loaders#diffusers.loaders.LoraLoaderMixin.fuse_lora
+# https://github.com/huggingface/diffusers/issues/4919
+def fuse_loras(pipe, lorajson: list[dict]):
+    try:
+        if not lorajson or not isinstance(lorajson, list): return pipe, [], []
+        a_list = []
+        w_list = []
+        for d in lorajson:
+            if not d or not isinstance(d, dict) or not d["name"] or d["name"] == "None": continue
+            k = d["name"]
+            if is_repo_name(k) and is_repo_exists(k):
+                a_name = Path(k).stem
+                pipe.load_lora_weights(k, weight_name=d["filename"], adapter_name = a_name, low_cpu_mem_usage=True)
+            elif not Path(k).exists():
+                print(f"LoRA not found: {k}")
+                continue
+            else:
+                w_name = Path(k).name
+                a_name = Path(k).stem
+                pipe.load_lora_weights(k, weight_name = w_name, adapter_name = a_name, low_cpu_mem_usage=True)
+            a_list.append(a_name)
+            w_list.append(d["scale"])
+        if not a_list: return pipe, [], []
+        #pipe.set_adapters(a_list, adapter_weights=w_list)
+        #pipe.fuse_lora(adapter_names=a_list, lora_scale=1.0)
+        #pipe.unload_lora_weights()
+        return pipe, a_list, w_list
+    except Exception as e:
+        print(f"External LoRA Error: {e}")
+        raise Exception(f"External LoRA Error: {e}") from e
+
+
+def description_ui():
+    gr.Markdown(
+        """
+- Mod of [multimodalart/flux-lora-the-explorer](https://huggingface.co/spaces/multimodalart/flux-lora-the-explorer),
+ [multimodalart/flux-lora-lab](https://huggingface.co/spaces/multimodalart/flux-lora-lab),
+ [jiuface/FLUX.1-dev-Controlnet-Union](https://huggingface.co/spaces/jiuface/FLUX.1-dev-Controlnet-Union),
+ [DamarJati/FLUX.1-DEV-Canny](https://huggingface.co/spaces/DamarJati/FLUX.1-DEV-Canny),
+ [gokaygokay/FLUX-Prompt-Generator](https://huggingface.co/spaces/gokaygokay/FLUX-Prompt-Generator).
+"""
+    )
+
+
+from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM
+def load_prompt_enhancer():
+    try:
+        model_checkpoint = "gokaygokay/Flux-Prompt-Enhance"
+        tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+        model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint).eval().to(device=device)
+        enhancer_flux = pipeline('text2text-generation', model=model, tokenizer=tokenizer, repetition_penalty=1.5, device=device)
+    except Exception as e:
+        print(e)
+        enhancer_flux = None
+    return enhancer_flux
+
+
+enhancer_flux = load_prompt_enhancer()
+
+
+@spaces.GPU(duration=30)
+def enhance_prompt(input_prompt):
+    result = enhancer_flux("enhance prompt: " + translate_to_en(input_prompt), max_length = 256)
+    enhanced_text = result[0]['generated_text']
+    return enhanced_text
+
+
+def save_image(image, savefile, modelname, prompt, height, width, steps, cfg, seed):
+    import uuid
+    from PIL import PngImagePlugin
+    import json
+    try:
+        if savefile is None: savefile = f"{modelname.split('/')[-1]}_{str(uuid.uuid4())}.png"
+        metadata = {"prompt": prompt, "Model": {"Model": modelname.split("/")[-1]}}
+        metadata["num_inference_steps"] = steps
+        metadata["guidance_scale"] = cfg
+        metadata["seed"] = seed
+        metadata["resolution"] = f"{width} x {height}"
+        metadata_str = json.dumps(metadata)
+        info = PngImagePlugin.PngInfo()
+        info.add_text("metadata", metadata_str)
+        image.save(savefile, "PNG", pnginfo=info)
+        return str(Path(savefile).resolve())
+    except Exception as e:
+        print(f"Failed to save image file: {e}")
+        raise Exception(f"Failed to save image file:") from e
+
+
+load_prompt_enhancer.zerogpu = True
+fuse_loras.zerogpu = True
+preprocess_image.zerogpu = True
+get_control_params.zerogpu = True
+clear_cache.zerogpu = True

modutils.py

@@ -0,0 +1,1290 @@
+import spaces
+import json
+import gradio as gr
+from huggingface_hub import HfApi
+import os
+from pathlib import Path
+from PIL import Image
+
+
+from env import (HF_LORA_PRIVATE_REPOS1, HF_LORA_PRIVATE_REPOS2,
+    HF_MODEL_USER_EX, HF_MODEL_USER_LIKES, DIFFUSERS_FORMAT_LORAS,
+    directory_loras, hf_read_token, HF_TOKEN, CIVITAI_API_KEY)
+
+
+MODEL_TYPE_DICT = {
+    "diffusers:StableDiffusionPipeline": "SD 1.5",
+    "diffusers:StableDiffusionXLPipeline": "SDXL",
+    "diffusers:FluxPipeline": "FLUX",
+}
+
+
+def get_user_agent():
+    return 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:127.0) Gecko/20100101 Firefox/127.0'
+
+
+def to_list(s):
+    return [x.strip() for x in s.split(",") if not s == ""]
+
+
+def list_uniq(l):
+        return sorted(set(l), key=l.index)
+
+
+def list_sub(a, b):
+    return [e for e in a if e not in b]
+
+
+def is_repo_name(s):
+    import re
+    return re.fullmatch(r'^[^/]+?/[^/]+?$', s)
+
+
+from translatepy import Translator
+translator = Translator()
+def translate_to_en(input: str):
+    try:
+        output = str(translator.translate(input, 'English'))
+    except Exception as e:
+        output = input
+        print(e)
+    return output
+
+
+def get_local_model_list(dir_path):
+    model_list = []
+    valid_extensions = ('.ckpt', '.pt', '.pth', '.safetensors', '.bin')
+    for file in Path(dir_path).glob("*"):
+        if file.suffix in valid_extensions:
+            file_path = str(Path(f"{dir_path}/{file.name}"))
+            model_list.append(file_path)
+    return model_list
+
+
+def download_things(directory, url, hf_token="", civitai_api_key=""):
+    url = url.strip()
+    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}"'
+        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 {url.split('/')[-1]}")
+        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 {url.split('/')[-1]}")
+    elif "civitai.com" in url:
+        if "?" in url:
+            url = url.split("?")[0]
+        if civitai_api_key:
+            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:
+            print("\033[91mYou need an API key to download Civitai models.\033[0m")
+    else:
+        os.system(f"aria2c --console-log-level=error --summary-interval=10 -c -x 16 -k 1M -s 16 -d {directory} {url}")
+
+
+def escape_lora_basename(basename: str):
+    return basename.replace(".", "_").replace(" ", "_").replace(",", "")
+
+
+def to_lora_key(path: str):
+    return escape_lora_basename(Path(path).stem)
+
+
+def to_lora_path(key: str):
+    if Path(key).is_file(): return key
+    path = Path(f"{directory_loras}/{escape_lora_basename(key)}.safetensors")
+    return str(path)
+
+
+def safe_float(input):
+    output = 1.0
+    try:
+        output = float(input)
+    except Exception:
+        output = 1.0
+    return output
+
+
+def save_images(images: list[Image.Image], metadatas: list[str]):
+    from PIL import PngImagePlugin
+    import uuid
+    try:
+        output_images = []
+        for image, metadata in zip(images, metadatas):
+            info = PngImagePlugin.PngInfo()
+            info.add_text("parameters", metadata)
+            savefile = f"{str(uuid.uuid4())}.png"
+            image.save(savefile, "PNG", pnginfo=info)
+            output_images.append(str(Path(savefile).resolve()))
+        return output_images
+    except Exception as e:
+        print(f"Failed to save image file: {e}")
+        raise Exception(f"Failed to save image file:") from e
+
+
+def save_gallery_images(images, progress=gr.Progress(track_tqdm=True)):
+    from datetime import datetime, timezone, timedelta
+    progress(0, desc="Updating gallery...")
+    dt_now = datetime.now(timezone(timedelta(hours=9)))
+    basename = dt_now.strftime('%Y%m%d_%H%M%S_')
+    i = 1
+    if not images: return images, gr.update(visible=False)
+    output_images = []
+    output_paths = []
+    for image in images:
+        filename = basename + str(i) + ".png"
+        i += 1
+        oldpath = Path(image[0])
+        newpath = oldpath
+        try:
+            if oldpath.exists():
+                newpath = oldpath.resolve().rename(Path(filename).resolve())
+        except Exception as e:
+           print(e)
+        finally: 
+            output_paths.append(str(newpath))
+            output_images.append((str(newpath), str(filename)))
+    progress(1, desc="Gallery updated.")
+    return gr.update(value=output_images), gr.update(value=output_paths, visible=True)
+
+
+def download_private_repo(repo_id, dir_path, is_replace):
+    from huggingface_hub import snapshot_download
+    if not hf_read_token: return
+    try:
+        snapshot_download(repo_id=repo_id, local_dir=dir_path, allow_patterns=['*.ckpt', '*.pt', '*.pth', '*.safetensors', '*.bin'], use_auth_token=hf_read_token)
+    except Exception as e:
+        print(f"Error: Failed to download {repo_id}.")
+        print(e)
+        return
+    if is_replace:
+        for file in Path(dir_path).glob("*"):
+            if file.exists() and "." in file.stem or " " in file.stem and file.suffix in ['.ckpt', '.pt', '.pth', '.safetensors', '.bin']:
+                newpath = Path(f'{file.parent.name}/{escape_lora_basename(file.stem)}{file.suffix}')
+                file.resolve().rename(newpath.resolve())
+
+
+private_model_path_repo_dict = {} # {"local filepath": "huggingface repo_id", ...}
+
+
+def get_private_model_list(repo_id, dir_path):
+    global private_model_path_repo_dict
+    api = HfApi()
+    if not hf_read_token: return []
+    try:
+        files = api.list_repo_files(repo_id, token=hf_read_token)
+    except Exception as e:
+        print(f"Error: Failed to list {repo_id}.")
+        print(e)
+        return []
+    model_list = []
+    for file in files:
+        path = Path(f"{dir_path}/{file}")
+        if path.suffix in ['.ckpt', '.pt', '.pth', '.safetensors', '.bin']:
+            model_list.append(str(path))
+    for model in model_list:
+        private_model_path_repo_dict[model] = repo_id
+    return model_list
+
+
+def download_private_file(repo_id, path, is_replace):
+    from huggingface_hub import hf_hub_download
+    file = Path(path)
+    newpath = Path(f'{file.parent.name}/{escape_lora_basename(file.stem)}{file.suffix}') if is_replace else file
+    if not hf_read_token or newpath.exists(): return
+    filename = file.name
+    dirname = file.parent.name
+    try:
+        hf_hub_download(repo_id=repo_id, filename=filename, local_dir=dirname, use_auth_token=hf_read_token)
+    except Exception as e:
+        print(f"Error: Failed to download {filename}.")
+        print(e)
+        return
+    if is_replace:
+        file.resolve().rename(newpath.resolve())
+
+
+def download_private_file_from_somewhere(path, is_replace):
+    if not path in private_model_path_repo_dict.keys(): return
+    repo_id = private_model_path_repo_dict.get(path, None)
+    download_private_file(repo_id, path, is_replace)
+
+
+model_id_list = []
+def get_model_id_list():
+    global model_id_list
+    if len(model_id_list) != 0: return model_id_list
+    api = HfApi()
+    model_ids = []
+    try:
+        models_likes = []
+        for author in HF_MODEL_USER_LIKES:
+            models_likes.extend(api.list_models(author=author, task="text-to-image", cardData=True, sort="likes"))
+        models_ex = []
+        for author in HF_MODEL_USER_EX:
+            models_ex = api.list_models(author=author, task="text-to-image", cardData=True, sort="last_modified")
+    except Exception as e:
+        print(f"Error: Failed to list {author}'s models.")
+        print(e)
+        return model_ids
+    for model in models_likes:
+        model_ids.append(model.id) if not model.private else ""
+    anime_models = []
+    real_models = []
+    anime_models_flux = []
+    real_models_flux = []
+    for model in models_ex:
+        if not model.private and not model.gated:
+            if "diffusers:FluxPipeline" in model.tags: anime_models_flux.append(model.id) if "anime" in model.tags else real_models_flux.append(model.id)
+            else: anime_models.append(model.id) if "anime" in model.tags else real_models.append(model.id)
+    model_ids.extend(anime_models)
+    model_ids.extend(real_models)
+    model_ids.extend(anime_models_flux)
+    model_ids.extend(real_models_flux)
+    model_id_list = model_ids.copy()
+    return model_ids
+
+
+model_id_list = get_model_id_list()
+
+
+def get_t2i_model_info(repo_id: str):
+    api = HfApi(token=HF_TOKEN)
+    try:
+        if not is_repo_name(repo_id): return ""
+        model = api.model_info(repo_id=repo_id, timeout=5.0)
+    except Exception as e:
+        print(f"Error: Failed to get {repo_id}'s info.")
+        print(e)
+        return ""
+    if model.private or model.gated: return ""
+    tags = model.tags
+    info = []
+    url = f"https://huggingface.co/{repo_id}/"
+    if not 'diffusers' in tags: return ""
+    for k, v in MODEL_TYPE_DICT.items():
+        if k in tags: info.append(v)
+    if model.card_data and model.card_data.tags:
+        info.extend(list_sub(model.card_data.tags, ['text-to-image', 'stable-diffusion', 'stable-diffusion-api', 'safetensors', 'stable-diffusion-xl']))
+    info.append(f"DLs: {model.downloads}")
+    info.append(f"likes: {model.likes}")
+    info.append(model.last_modified.strftime("lastmod: %Y-%m-%d"))
+    md = f"Model Info: {', '.join(info)}, [Model Repo]({url})"
+    return gr.update(value=md)
+
+
+def get_tupled_model_list(model_list):
+    if not model_list: return []
+    tupled_list = []
+    for repo_id in model_list:
+        api = HfApi()
+        try:
+            if not api.repo_exists(repo_id): continue
+            model = api.model_info(repo_id=repo_id)
+        except Exception as e:
+            print(e)
+            continue
+        if model.private or model.gated: continue
+        tags = model.tags
+        info = []
+        if not 'diffusers' in tags: continue
+        for k, v in MODEL_TYPE_DICT.items():
+            if k in tags: info.append(v)
+        if model.card_data and model.card_data.tags:
+            info.extend(list_sub(model.card_data.tags, ['text-to-image', 'stable-diffusion', 'stable-diffusion-api', 'safetensors', 'stable-diffusion-xl']))
+        if "pony" in info:
+            info.remove("pony")
+            name = f"{repo_id} (Pony🐴, {', '.join(info)})"
+        else:
+            name = f"{repo_id} ({', '.join(info)})"
+        tupled_list.append((name, repo_id))
+    return tupled_list
+
+
+private_lora_dict = {}
+try:
+    with open('lora_dict.json', encoding='utf-8') as f:
+        d = json.load(f)
+        for k, v in d.items():
+            private_lora_dict[escape_lora_basename(k)] = v
+except Exception as e:
+    print(e)
+loras_dict = {"None": ["", "", "", "", ""], "": ["", "", "", "", ""]} | private_lora_dict.copy()
+civitai_not_exists_list = []
+loras_url_to_path_dict = {} # {"URL to download": "local filepath", ...}
+civitai_lora_last_results = {}  # {"URL to download": {search results}, ...}
+all_lora_list = []
+
+
+private_lora_model_list = []
+def get_private_lora_model_lists():
+    global private_lora_model_list
+    if len(private_lora_model_list) != 0: return private_lora_model_list
+    models1 = []
+    models2 = []
+    for repo in HF_LORA_PRIVATE_REPOS1:
+        models1.extend(get_private_model_list(repo, directory_loras))
+    for repo in HF_LORA_PRIVATE_REPOS2:
+        models2.extend(get_private_model_list(repo, directory_loras))
+    models = list_uniq(models1 + sorted(models2))
+    private_lora_model_list = models.copy()
+    return models
+
+
+private_lora_model_list = get_private_lora_model_lists()
+
+
+def get_civitai_info(path):
+    global civitai_not_exists_list
+    import requests
+    from urllib3.util import Retry
+    from requests.adapters import HTTPAdapter
+    if path in set(civitai_not_exists_list): return ["", "", "", "", ""]
+    if not Path(path).exists(): return None
+    user_agent = get_user_agent()
+    headers = {'User-Agent': user_agent, 'content-type': 'application/json'}
+    base_url = 'https://civitai.com/api/v1/model-versions/by-hash/'
+    params = {}
+    session = requests.Session()
+    retries = Retry(total=5, backoff_factor=1, status_forcelist=[500, 502, 503, 504])
+    session.mount("https://", HTTPAdapter(max_retries=retries))
+    import hashlib
+    with open(path, 'rb') as file:
+        file_data = file.read()
+    hash_sha256 = hashlib.sha256(file_data).hexdigest()
+    url = base_url + hash_sha256
+    try:
+        r = session.get(url, params=params, headers=headers, stream=True, timeout=(3.0, 15))
+    except Exception as e:
+        print(e)
+        return ["", "", "", "", ""]
+    if not r.ok: return None
+    json = r.json()
+    if not 'baseModel' in json:
+        civitai_not_exists_list.append(path)
+        return ["", "", "", "", ""]
+    items = []
+    items.append(" / ".join(json['trainedWords']))
+    items.append(json['baseModel'])
+    items.append(json['model']['name'])
+    items.append(f"https://civitai.com/models/{json['modelId']}")
+    items.append(json['images'][0]['url'])
+    return items
+
+
+def get_lora_model_list():
+    loras = list_uniq(get_private_lora_model_lists() + get_local_model_list(directory_loras) + DIFFUSERS_FORMAT_LORAS)
+    loras.insert(0, "None")
+    loras.insert(0, "")
+    return loras
+
+
+def get_all_lora_list():
+    global all_lora_list
+    loras = get_lora_model_list()
+    all_lora_list = loras.copy()
+    return loras
+
+
+def get_all_lora_tupled_list():
+    global loras_dict
+    models = get_all_lora_list()
+    if not models: return []
+    tupled_list = []
+    for model in models:
+        #if not model: continue # to avoid GUI-related bug
+        basename = Path(model).stem
+        key = to_lora_key(model)
+        items = None
+        if key in loras_dict.keys():
+            items = loras_dict.get(key, None)
+        else:
+            items = get_civitai_info(model)
+            if items != None:
+                loras_dict[key] = items
+        name = basename
+        value = model
+        if items and items[2] != "":
+            if items[1] == "Pony":
+                name = f"{basename} (for {items[1]}🐴, {items[2]})"
+            else:
+                name = f"{basename} (for {items[1]}, {items[2]})"
+        tupled_list.append((name, value))
+    return tupled_list
+
+
+def update_lora_dict(path):
+    global loras_dict
+    key = escape_lora_basename(Path(path).stem)
+    if key in loras_dict.keys(): return
+    items = get_civitai_info(path)
+    if items == None: return
+    loras_dict[key] = items
+
+
+def download_lora(dl_urls: str):
+    global loras_url_to_path_dict
+    dl_path = ""
+    before = get_local_model_list(directory_loras)
+    urls = []
+    for url in [url.strip() for url in dl_urls.split(',')]:
+        local_path = f"{directory_loras}/{url.split('/')[-1]}"
+        if not Path(local_path).exists():
+            download_things(directory_loras, url, HF_TOKEN, CIVITAI_API_KEY)
+            urls.append(url)
+    after = get_local_model_list(directory_loras)
+    new_files = list_sub(after, before)
+    i = 0
+    for file in new_files:
+        path = Path(file)
+        if path.exists():
+            new_path = Path(f'{path.parent.name}/{escape_lora_basename(path.stem)}{path.suffix}')
+            path.resolve().rename(new_path.resolve())
+            loras_url_to_path_dict[urls[i]] = str(new_path)
+            update_lora_dict(str(new_path))
+            dl_path = str(new_path)
+        i += 1
+    return dl_path
+
+
+def copy_lora(path: str, new_path: str):
+    import shutil
+    if path == new_path: return new_path
+    cpath = Path(path)
+    npath = Path(new_path)
+    if cpath.exists():
+        try:
+            shutil.copy(str(cpath.resolve()), str(npath.resolve()))
+        except Exception as e:
+            print(e)
+            return None
+        update_lora_dict(str(npath))
+        return new_path
+    else:
+        return None
+
+
+def download_my_lora(dl_urls: str, lora1: str, lora2: str, lora3: str, lora4: str, lora5: str):
+    path = download_lora(dl_urls)
+    if path:
+        if not lora1 or lora1 == "None":
+            lora1 = path
+        elif not lora2 or lora2 == "None":
+            lora2 = path
+        elif not lora3 or lora3 == "None":
+            lora3 = path
+        elif not lora4 or lora4 == "None":
+            lora4 = path
+        elif not lora5 or lora5 == "None":
+            lora5 = path
+    choices = get_all_lora_tupled_list()
+    return gr.update(value=lora1, choices=choices), gr.update(value=lora2, choices=choices), gr.update(value=lora3, choices=choices),\
+        gr.update(value=lora4, choices=choices), gr.update(value=lora5, choices=choices)
+
+
+def get_valid_lora_name(query: str, model_name: str):
+    path = "None"
+    if not query or query == "None": return "None"
+    if to_lora_key(query) in loras_dict.keys(): return query
+    if query in loras_url_to_path_dict.keys():
+        path = loras_url_to_path_dict[query]
+    else:
+        path = to_lora_path(query.strip().split('/')[-1])
+    if Path(path).exists():
+        return path
+    elif "http" in query:
+        dl_file = download_lora(query)
+        if dl_file and Path(dl_file).exists(): return dl_file
+    else:
+        dl_file = find_similar_lora(query, model_name)
+        if dl_file and Path(dl_file).exists(): return dl_file
+    return "None"
+
+
+def get_valid_lora_path(query: str):
+    path = None
+    if not query or query == "None": return None
+    if to_lora_key(query) in loras_dict.keys(): return query
+    if Path(path).exists():
+        return path
+    else:
+        return None
+
+
+def get_valid_lora_wt(prompt: str, lora_path: str, lora_wt: float):
+    import re
+    wt = lora_wt
+    result = re.findall(f'<lora:{to_lora_key(lora_path)}:(.+?)>', prompt)
+    if not result: return wt
+    wt = safe_float(result[0][0])
+    return wt
+
+
+def set_prompt_loras(prompt, prompt_syntax, model_name, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt):
+    import re
+    if not "Classic" in str(prompt_syntax):  return lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt
+    lora1 = get_valid_lora_name(lora1, model_name)
+    lora2 = get_valid_lora_name(lora2, model_name)
+    lora3 = get_valid_lora_name(lora3, model_name)
+    lora4 = get_valid_lora_name(lora4, model_name)
+    lora5 = get_valid_lora_name(lora5, model_name)
+    if not "<lora" in prompt: return lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt
+    lora1_wt = get_valid_lora_wt(prompt, lora1, lora1_wt)
+    lora2_wt = get_valid_lora_wt(prompt, lora2, lora2_wt)
+    lora3_wt = get_valid_lora_wt(prompt, lora3, lora3_wt)
+    lora4_wt = get_valid_lora_wt(prompt, lora4, lora4_wt)
+    lora5_wt = get_valid_lora_wt(prompt, lora5, lora5_wt)
+    on1, label1, tag1, md1 = get_lora_info(lora1)
+    on2, label2, tag2, md2 = get_lora_info(lora2)
+    on3, label3, tag3, md3 = get_lora_info(lora3)
+    on4, label4, tag4, md4 = get_lora_info(lora4)
+    on5, label5, tag5, md5 = get_lora_info(lora5)
+    lora_paths = [lora1, lora2, lora3, lora4, lora5]
+    prompts = prompt.split(",") if prompt else []
+    for p in prompts:
+        p = str(p).strip()
+        if "<lora" in p:
+            result = re.findall(r'<lora:(.+?):(.+?)>', p)
+            if not result: continue
+            key = result[0][0]
+            wt = result[0][1]
+            path = to_lora_path(key)
+            if not key in loras_dict.keys() or not path:
+                path = get_valid_lora_name(path)
+                if not path or path == "None": continue
+            if path in lora_paths:
+                continue
+            elif not on1:
+                lora1 = path
+                lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                lora1_wt = safe_float(wt)
+                on1 = True
+            elif not on2:
+                lora2 = path
+                lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                lora2_wt = safe_float(wt)
+                on2 = True
+            elif not on3:
+                lora3 = path
+                lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                lora3_wt = safe_float(wt)
+                on3 = True
+            elif not on4:
+                lora4 = path
+                lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                lora4_wt = safe_float(wt)
+                on4, label4, tag4, md4 = get_lora_info(lora4)
+            elif not on5:
+                lora5 = path
+                lora_paths = [lora1, lora2, lora3, lora4, lora5]
+                lora5_wt = safe_float(wt)
+                on5 = True
+    return lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt
+
+
+def get_lora_info(lora_path: str):
+    is_valid = False
+    tag = ""
+    label = ""
+    md = "None"
+    if not lora_path or lora_path == "None":
+        print("LoRA file not found.")
+        return is_valid, label, tag, md
+    path = Path(lora_path)
+    new_path = Path(f'{path.parent.name}/{escape_lora_basename(path.stem)}{path.suffix}')
+    if not to_lora_key(str(new_path)) in loras_dict.keys() and str(path) not in set(get_all_lora_list()):
+        print("LoRA file is not registered.")
+        return tag, label, tag, md
+    if not new_path.exists():
+        download_private_file_from_somewhere(str(path), True)
+    basename = new_path.stem
+    label = f'Name: {basename}'
+    items = loras_dict.get(basename, None)
+    if items == None:
+        items = get_civitai_info(str(new_path))
+        if items != None:
+            loras_dict[basename] = items
+    if items and items[2] != "":
+        tag = items[0]
+        label = f'Name: {basename}'
+        if items[1] == "Pony":
+            label = f'Name: {basename} (for Pony🐴)'
+        if items[4]:
+            md = f'<img src="{items[4]}" alt="thumbnail" width="150" height="240"><br>[LoRA Model URL]({items[3]})'
+        elif items[3]:
+            md = f'[LoRA Model URL]({items[3]})'
+    is_valid = True
+    return is_valid, label, tag, md
+
+
+def normalize_prompt_list(tags: list[str]):
+    prompts = []
+    for tag in tags:
+        tag = str(tag).strip()
+        if tag:
+            prompts.append(tag)
+    return prompts
+
+
+def apply_lora_prompt(prompt: str = "", lora_info: str = ""):
+    if lora_info == "None": return gr.update(value=prompt)
+    tags = prompt.split(",") if prompt else []
+    prompts = normalize_prompt_list(tags)
+
+    lora_tag = lora_info.replace("/",",")
+    lora_tags = lora_tag.split(",") if str(lora_info) != "None" else []
+    lora_prompts = normalize_prompt_list(lora_tags)
+ 
+    empty = [""]
+    prompt = ", ".join(list_uniq(prompts + lora_prompts) + empty)
+    return gr.update(value=prompt)
+
+
+def update_loras(prompt, prompt_syntax, lora1, lora1_wt, lora2, lora2_wt, lora3, lora3_wt, lora4, lora4_wt, lora5, lora5_wt):
+    import re
+    on1, label1, tag1, md1 = get_lora_info(lora1)
+    on2, label2, tag2, md2 = get_lora_info(lora2)
+    on3, label3, tag3, md3 = get_lora_info(lora3)
+    on4, label4, tag4, md4 = get_lora_info(lora4)
+    on5, label5, tag5, md5 = get_lora_info(lora5)
+    lora_paths = [lora1, lora2, lora3, lora4, lora5]
+
+    output_prompt = prompt
+    if "Classic" in str(prompt_syntax):
+        prompts = prompt.split(",") if prompt else []
+        output_prompts = []
+        for p in prompts:
+            p = str(p).strip()
+            if "<lora" in p:
+                result = re.findall(r'<lora:(.+?):(.+?)>', p)
+                if not result: continue
+                key = result[0][0]
+                wt = result[0][1]
+                path = to_lora_path(key)
+                if not key in loras_dict.keys() or not path: continue
+                if path in lora_paths:
+                    output_prompts.append(f"<lora:{to_lora_key(path)}:{safe_float(wt):.2f}>")
+            elif p:
+                output_prompts.append(p)
+        lora_prompts = []
+        if on1: lora_prompts.append(f"<lora:{to_lora_key(lora1)}:{lora1_wt:.2f}>")
+        if on2: lora_prompts.append(f"<lora:{to_lora_key(lora2)}:{lora2_wt:.2f}>")
+        if on3: lora_prompts.append(f"<lora:{to_lora_key(lora3)}:{lora3_wt:.2f}>")
+        if on4: lora_prompts.append(f"<lora:{to_lora_key(lora4)}:{lora4_wt:.2f}>")
+        if on5: lora_prompts.append(f"<lora:{to_lora_key(lora5)}:{lora5_wt:.2f}>")
+        output_prompt = ", ".join(list_uniq(output_prompts + lora_prompts + [""]))
+    choices = get_all_lora_tupled_list()
+
+    return gr.update(value=output_prompt), gr.update(value=lora1, choices=choices), gr.update(value=lora1_wt),\
+     gr.update(value=tag1, label=label1, visible=on1), gr.update(visible=on1), gr.update(value=md1, visible=on1),\
+     gr.update(value=lora2, choices=choices), gr.update(value=lora2_wt),\
+     gr.update(value=tag2, label=label2, visible=on2), gr.update(visible=on2), gr.update(value=md2, visible=on2),\
+     gr.update(value=lora3, choices=choices), gr.update(value=lora3_wt),\
+     gr.update(value=tag3, label=label3, visible=on3), gr.update(visible=on3), gr.update(value=md3, visible=on3),\
+     gr.update(value=lora4, choices=choices), gr.update(value=lora4_wt),\
+     gr.update(value=tag4, label=label4, visible=on4), gr.update(visible=on4), gr.update(value=md4, visible=on4),\
+     gr.update(value=lora5, choices=choices), gr.update(value=lora5_wt),\
+     gr.update(value=tag5, label=label5, visible=on5), gr.update(visible=on5), gr.update(value=md5, visible=on5)
+
+
+def get_my_lora(link_url):
+    from pathlib import Path
+    before = get_local_model_list(directory_loras)
+    for url in [url.strip() for url in link_url.split(',')]:
+        if not Path(f"{directory_loras}/{url.split('/')[-1]}").exists():
+            download_things(directory_loras, url, HF_TOKEN, CIVITAI_API_KEY)
+    after = get_local_model_list(directory_loras)
+    new_files = list_sub(after, before)
+    for file in new_files:
+        path = Path(file)
+        if path.exists():
+            new_path = Path(f'{path.parent.name}/{escape_lora_basename(path.stem)}{path.suffix}')
+            path.resolve().rename(new_path.resolve())
+            update_lora_dict(str(new_path))
+    new_lora_model_list = get_lora_model_list()
+    new_lora_tupled_list = get_all_lora_tupled_list()
+    
+    return gr.update(
+        choices=new_lora_tupled_list, value=new_lora_model_list[-1]
+    ), gr.update(
+        choices=new_lora_tupled_list
+    ), gr.update(
+        choices=new_lora_tupled_list
+    ), gr.update(
+        choices=new_lora_tupled_list
+    ), gr.update(
+        choices=new_lora_tupled_list
+    )
+
+
+def upload_file_lora(files, progress=gr.Progress(track_tqdm=True)):
+    progress(0, desc="Uploading...")
+    file_paths = [file.name for file in files]
+    progress(1, desc="Uploaded.")
+    return gr.update(value=file_paths, visible=True), gr.update(visible=True)
+
+
+def move_file_lora(filepaths):
+    import shutil
+    for file in filepaths:
+        path = Path(shutil.move(Path(file).resolve(), Path(f"./{directory_loras}").resolve()))
+        newpath = Path(f'{path.parent.name}/{escape_lora_basename(path.stem)}{path.suffix}')
+        path.resolve().rename(newpath.resolve())
+        update_lora_dict(str(newpath))
+
+    new_lora_model_list = get_lora_model_list()
+    new_lora_tupled_list = get_all_lora_tupled_list()
+    
+    return gr.update(
+        choices=new_lora_tupled_list, value=new_lora_model_list[-1]
+    ), gr.update(
+        choices=new_lora_tupled_list
+    ), gr.update(
+        choices=new_lora_tupled_list
+    ), gr.update(
+        choices=new_lora_tupled_list
+    ), gr.update(
+        choices=new_lora_tupled_list
+    )
+
+
+def get_civitai_info(path):
+    global civitai_not_exists_list, loras_url_to_path_dict
+    import requests
+    from requests.adapters import HTTPAdapter
+    from urllib3.util import Retry
+    default = ["", "", "", "", ""]
+    if path in set(civitai_not_exists_list): return default
+    if not Path(path).exists(): return None
+    user_agent = get_user_agent()
+    headers = {'User-Agent': user_agent, 'content-type': 'application/json'}
+    base_url = 'https://civitai.com/api/v1/model-versions/by-hash/'
+    params = {}
+    session = requests.Session()
+    retries = Retry(total=5, backoff_factor=1, status_forcelist=[500, 502, 503, 504])
+    session.mount("https://", HTTPAdapter(max_retries=retries))
+    import hashlib
+    with open(path, 'rb') as file:
+        file_data = file.read()
+    hash_sha256 = hashlib.sha256(file_data).hexdigest()
+    url = base_url + hash_sha256
+    try:
+        r = session.get(url, params=params, headers=headers, stream=True, timeout=(3.0, 15))
+    except Exception as e:
+        print(e)
+        return default
+    else:
+        if not r.ok: return None
+        json = r.json()
+        if 'baseModel' not in json:
+            civitai_not_exists_list.append(path)
+            return default
+        items = []
+        items.append(" / ".join(json['trainedWords']))                  # The words (prompts) used to trigger the model
+        items.append(json['baseModel'])                                 # Base model (SDXL1.0, Pony, ...)
+        items.append(json['model']['name'])                             # The name of the model version
+        items.append(f"https://civitai.com/models/{json['modelId']}")   # The repo url for the model
+        items.append(json['images'][0]['url'])                          # The url for a sample image
+        loras_url_to_path_dict[path] = json['downloadUrl']              # The download url to get the model file for this specific version
+        return items
+
+
+def search_lora_on_civitai(query: str, allow_model: list[str] = ["Pony", "SDXL 1.0"], limit: int = 100,
+                           sort: str = "Highest Rated", period: str = "AllTime", tag: str = ""):
+    import requests
+    from requests.adapters import HTTPAdapter
+    from urllib3.util import Retry
+    user_agent = get_user_agent()
+    headers = {'User-Agent': user_agent, 'content-type': 'application/json'}
+    base_url = 'https://civitai.com/api/v1/models'
+    params = {'types': ['LORA'], 'sort': sort, 'period': period, 'limit': limit, 'nsfw': 'true'}
+    if query: params["query"] = query
+    if tag: params["tag"] = tag
+    session = requests.Session()
+    retries = Retry(total=5, backoff_factor=1, status_forcelist=[500, 502, 503, 504])
+    session.mount("https://", HTTPAdapter(max_retries=retries))
+    try:
+        r = session.get(base_url, params=params, headers=headers, stream=True, timeout=(3.0, 30))
+    except Exception as e:
+        print(e)
+        return None
+    else:
+        if not r.ok: return None
+        json = r.json()
+        if 'items' not in json: return None
+        items = []
+        for j in json['items']:
+            for model in j['modelVersions']:
+                item = {}
+                if model['baseModel'] not in set(allow_model): continue
+                item['name'] = j['name']
+                item['creator'] = j['creator']['username']
+                item['tags'] = j['tags']
+                item['model_name'] = model['name']
+                item['base_model'] = model['baseModel']
+                item['dl_url'] = model['downloadUrl']
+                item['md'] = f'<img src="{model["images"][0]["url"]}" alt="thumbnail" width="150" height="240"><br>[LoRA Model URL](https://civitai.com/models/{j["id"]})'
+                items.append(item)
+        return items
+
+
+def search_civitai_lora(query, base_model, sort="Highest Rated", period="AllTime", tag=""):
+    global civitai_lora_last_results
+    items = search_lora_on_civitai(query, base_model, 100, sort, period, tag)
+    if not items: return gr.update(choices=[("", "")], value="", visible=False),\
+          gr.update(value="", visible=False), gr.update(visible=True), gr.update(visible=True)
+    civitai_lora_last_results = {}
+    choices = []
+    for item in items:
+        base_model_name = "Pony🐴" if item['base_model'] == "Pony" else item['base_model']
+        name = f"{item['name']} (for {base_model_name} / By: {item['creator']} / Tags: {', '.join(item['tags'])})"
+        value = item['dl_url']
+        choices.append((name, value))
+        civitai_lora_last_results[value] = item
+    if not choices: return gr.update(choices=[("", "")], value="", visible=False),\
+          gr.update(value="", visible=False), gr.update(visible=True), gr.update(visible=True)
+    result = civitai_lora_last_results.get(choices[0][1], "None")
+    md = result['md'] if result else ""
+    return gr.update(choices=choices, value=choices[0][1], visible=True), gr.update(value=md, visible=True),\
+          gr.update(visible=True), gr.update(visible=True)
+
+
+def select_civitai_lora(search_result):
+    if not "http" in search_result: return gr.update(value=""), gr.update(value="None", visible=True)
+    result = civitai_lora_last_results.get(search_result, "None")
+    md = result['md'] if result else ""
+    return gr.update(value=search_result), gr.update(value=md, visible=True)
+
+
+LORA_BASE_MODEL_DICT = {
+    "diffusers:StableDiffusionPipeline": ["SD 1.5"],
+    "diffusers:StableDiffusionXLPipeline": ["Pony", "SDXL 1.0"],
+    "diffusers:FluxPipeline": ["Flux.1 D", "Flux.1 S"],
+}
+
+
+def get_lora_base_model(model_name: str):
+    api = HfApi(token=HF_TOKEN)
+    default = ["Pony", "SDXL 1.0"]
+    try:
+        model = api.model_info(repo_id=model_name, timeout=5.0)
+        tags = model.tags
+        for tag in tags:
+            if tag in LORA_BASE_MODEL_DICT.keys(): return LORA_BASE_MODEL_DICT.get(tag, default)
+    except Exception:
+        return default
+    return default
+
+
+def find_similar_lora(q: str, model_name: str):
+    from rapidfuzz.process import extractOne
+    from rapidfuzz.utils import default_process
+    query = to_lora_key(q)
+    print(f"Finding <lora:{query}:...>...")
+    keys = list(private_lora_dict.keys())
+    values = [x[2] for x in list(private_lora_dict.values())]
+    s = default_process(query)
+    e1 = extractOne(s, keys + values, processor=default_process, score_cutoff=80.0)
+    key = ""
+    if e1:
+        e = e1[0]
+        if e in set(keys): key = e
+        elif e in set(values): key = keys[values.index(e)]
+    if key:
+        path = to_lora_path(key)
+        new_path = to_lora_path(query)
+        if not Path(path).exists():
+            if not Path(new_path).exists(): download_private_file_from_somewhere(path, True)
+            if Path(path).exists() and copy_lora(path, new_path): return new_path
+    print(f"Finding <lora:{query}:...> on Civitai...")
+    civitai_query = Path(query).stem if Path(query).is_file() else query
+    civitai_query = civitai_query.replace("_", " ").replace("-", " ")
+    base_model = get_lora_base_model(model_name)
+    items = search_lora_on_civitai(civitai_query, base_model, 1)
+    if items:
+        item = items[0]
+        path = download_lora(item['dl_url'])
+        new_path = query if Path(query).is_file() else to_lora_path(query)
+        if path and copy_lora(path, new_path): return new_path
+    return None
+
+
+def change_interface_mode(mode: str):
+    if mode == "Fast":
+        return gr.update(open=False), gr.update(visible=True), gr.update(open=False), gr.update(open=False),\
+        gr.update(visible=True), gr.update(open=False), gr.update(visible=True), gr.update(open=False),\
+        gr.update(visible=True), gr.update(value="Fast")
+    elif mode == "Simple": # t2i mode
+        return gr.update(open=True), gr.update(visible=True), gr.update(open=False), gr.update(open=False),\
+        gr.update(visible=True), gr.update(open=False), gr.update(visible=False), gr.update(open=True),\
+        gr.update(visible=False), gr.update(value="Standard")
+    elif mode == "LoRA": # t2i LoRA  mode
+        return gr.update(open=True), gr.update(visible=True), gr.update(open=True), gr.update(open=False),\
+        gr.update(visible=True), gr.update(open=True), gr.update(visible=True), gr.update(open=False),\
+        gr.update(visible=False), gr.update(value="Standard")
+    else: # Standard
+        return gr.update(open=False), gr.update(visible=True), gr.update(open=False), gr.update(open=False),\
+        gr.update(visible=True), gr.update(open=False), gr.update(visible=True), gr.update(open=False),\
+        gr.update(visible=True), gr.update(value="Standard")
+
+
+quality_prompt_list = [
+    {
+        "name": "None",
+        "prompt": "",
+        "negative_prompt": "lowres",
+    },
+    {
+        "name": "Animagine Common",
+        "prompt": "anime artwork, anime style, vibrant, studio anime, highly detailed, masterpiece, best quality, very aesthetic, absurdres",
+        "negative_prompt": "lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]",
+    },
+    {
+        "name": "Pony Anime Common",
+        "prompt": "source_anime, score_9, score_8_up, score_7_up, masterpiece, best quality, very aesthetic, absurdres",
+        "negative_prompt": "source_pony, source_furry, source_cartoon, score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends",
+    },
+    {
+        "name": "Pony Common",
+        "prompt": "source_anime, score_9, score_8_up, score_7_up",
+        "negative_prompt": "source_pony, source_furry, source_cartoon, score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends",
+    },
+    {
+        "name": "Animagine Standard v3.0",
+        "prompt": "masterpiece, best quality",
+        "negative_prompt": "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry, artist name",
+    },
+    {
+        "name": "Animagine Standard v3.1",
+        "prompt": "masterpiece, best quality, very aesthetic, absurdres",
+        "negative_prompt": "lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]",
+    },
+    {
+        "name": "Animagine Light v3.1",
+        "prompt": "(masterpiece), best quality, very aesthetic, perfect face",
+        "negative_prompt": "(low quality, worst quality:1.2), very displeasing, 3d, watermark, signature, ugly, poorly drawn",
+    },
+    {
+        "name": "Animagine Heavy v3.1",
+        "prompt": "(masterpiece), (best quality), (ultra-detailed), very aesthetic, illustration, disheveled hair, perfect composition, moist skin, intricate details",
+        "negative_prompt": "longbody, lowres, bad anatomy, bad hands, missing fingers, pubic hair, extra digit, fewer digits, cropped, worst quality, low quality, very displeasing",
+    },
+]
+
+
+style_list = [
+    {
+        "name": "None",
+        "prompt": "",
+        "negative_prompt": "",
+    },
+    {
+        "name": "Cinematic",
+        "prompt": "cinematic still, emotional, harmonious, vignette, highly detailed, high budget, bokeh, cinemascope, moody, epic, gorgeous, film grain, grainy",
+        "negative_prompt": "cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured",
+    },
+    {
+        "name": "Photographic",
+        "prompt": "cinematic photo, 35mm photograph, film, bokeh, professional, 4k, highly detailed",
+        "negative_prompt": "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly",
+    },
+    {
+        "name": "Anime",
+        "prompt": "anime artwork, anime style, vibrant, studio anime, highly detailed",
+        "negative_prompt": "photo, deformed, black and white, realism, disfigured, low contrast",
+    },
+    {
+        "name": "Manga",
+        "prompt": "manga style, vibrant, high-energy, detailed, iconic, Japanese comic style",
+        "negative_prompt": "ugly, deformed, noisy, blurry, low contrast, realism, photorealistic, Western comic style",
+    },
+    {
+        "name": "Digital Art",
+        "prompt": "concept art, digital artwork, illustrative, painterly, matte painting, highly detailed",
+        "negative_prompt": "photo, photorealistic, realism, ugly",
+    },
+    {
+        "name": "Pixel art",
+        "prompt": "pixel-art, low-res, blocky, pixel art style, 8-bit graphics",
+        "negative_prompt": "sloppy, messy, blurry, noisy, highly detailed, ultra textured, photo, realistic",
+    },
+    {
+        "name": "Fantasy art",
+        "prompt": "ethereal fantasy concept art, magnificent, celestial, ethereal, painterly, epic, majestic, magical, fantasy art, cover art, dreamy",
+        "negative_prompt": "photographic, realistic, realism, 35mm film, dslr, cropped, frame, text, deformed, glitch, noise, noisy, off-center, deformed, cross-eyed, closed eyes, bad anatomy, ugly, disfigured, sloppy, duplicate, mutated, black and white",
+    },
+    {
+        "name": "Neonpunk",
+        "prompt": "neonpunk style, cyberpunk, vaporwave, neon, vibes, vibrant, stunningly beautiful, crisp, detailed, sleek, ultramodern, magenta highlights, dark purple shadows, high contrast, cinematic, ultra detailed, intricate, professional",
+        "negative_prompt": "painting, drawing, illustration, glitch, deformed, mutated, cross-eyed, ugly, disfigured",
+    },
+    {
+        "name": "3D Model",
+        "prompt": "professional 3d model, octane render, highly detailed, volumetric, dramatic lighting",
+        "negative_prompt": "ugly, deformed, noisy, low poly, blurry, painting",
+    },
+]
+
+
+optimization_list = {
+    "None": [28, 7., 'Euler a', False, 'None', 1.],
+    "Default": [28, 7., 'Euler a', False, 'None', 1.],
+    "SPO": [28, 7., 'Euler a', True, 'loras/spo_sdxl_10ep_4k-data_lora_diffusers.safetensors', 1.],
+    "DPO": [28, 7., 'Euler a', True, 'loras/sdxl-DPO-LoRA.safetensors', 1.],
+    "DPO Turbo": [8, 2.5, 'LCM', True, 'loras/sd_xl_dpo_turbo_lora_v1-128dim.safetensors', 1.],
+    "SDXL Turbo": [8, 2.5, 'LCM', True, 'loras/sd_xl_turbo_lora_v1.safetensors', 1.],
+    "Hyper-SDXL 12step": [12, 5., 'TCD', True, 'loras/Hyper-SDXL-12steps-CFG-lora.safetensors', 1.],
+    "Hyper-SDXL 8step": [8, 5., 'TCD', True, 'loras/Hyper-SDXL-8steps-CFG-lora.safetensors', 1.],
+    "Hyper-SDXL 4step": [4, 0, 'TCD', True, 'loras/Hyper-SDXL-4steps-lora.safetensors', 1.],
+    "Hyper-SDXL 2step": [2, 0, 'TCD', True, 'loras/Hyper-SDXL-2steps-lora.safetensors', 1.],
+    "Hyper-SDXL 1step": [1, 0, 'TCD', True, 'loras/Hyper-SDXL-1steps-lora.safetensors', 1.],
+    "PCM 16step": [16, 4., 'Euler a trailing', True, 'loras/pcm_sdxl_normalcfg_16step_converted.safetensors', 1.],
+    "PCM 8step": [8, 4., 'Euler a trailing', True, 'loras/pcm_sdxl_normalcfg_8step_converted.safetensors', 1.],
+    "PCM 4step": [4, 2., 'Euler a trailing', True, 'loras/pcm_sdxl_smallcfg_4step_converted.safetensors', 1.],
+    "PCM 2step": [2, 1., 'Euler a trailing', True, 'loras/pcm_sdxl_smallcfg_2step_converted.safetensors', 1.],
+}
+
+
+def set_optimization(opt, steps_gui, cfg_gui, sampler_gui, clip_skip_gui, lora_gui, lora_scale_gui):
+    if not opt in list(optimization_list.keys()): opt = "None"
+    def_steps_gui = 28
+    def_cfg_gui = 7.
+    steps = optimization_list.get(opt, "None")[0]
+    cfg = optimization_list.get(opt, "None")[1]
+    sampler = optimization_list.get(opt, "None")[2]
+    clip_skip = optimization_list.get(opt, "None")[3]
+    lora = optimization_list.get(opt, "None")[4]
+    lora_scale = optimization_list.get(opt, "None")[5]
+    if opt == "None":
+        steps = max(steps_gui, def_steps_gui)
+        cfg = max(cfg_gui, def_cfg_gui)
+        clip_skip = clip_skip_gui
+    elif opt == "SPO" or opt == "DPO":
+        steps = max(steps_gui, def_steps_gui)
+        cfg = max(cfg_gui, def_cfg_gui)
+
+    return gr.update(value=steps), gr.update(value=cfg), gr.update(value=sampler),\
+          gr.update(value=clip_skip), gr.update(value=lora), gr.update(value=lora_scale),
+
+
+# [sampler_gui, steps_gui, cfg_gui, clip_skip_gui, img_width_gui, img_height_gui, optimization_gui]
+preset_sampler_setting = {
+    "None": ["Euler a", 28, 7., True, 1024, 1024, "None"],
+    "Anime 3:4 Fast": ["LCM", 8, 2.5, True, 896, 1152, "DPO Turbo"],
+    "Anime 3:4 Standard": ["Euler a", 28, 7., True, 896, 1152, "None"],
+    "Anime 3:4 Heavy": ["Euler a", 40, 7., True, 896, 1152, "None"],
+    "Anime 1:1 Fast": ["LCM", 8, 2.5, True, 1024, 1024, "DPO Turbo"],
+    "Anime 1:1 Standard": ["Euler a", 28, 7., True, 1024, 1024, "None"],
+    "Anime 1:1 Heavy": ["Euler a", 40, 7., True, 1024, 1024, "None"],
+    "Photo 3:4 Fast": ["LCM", 8, 2.5, False, 896, 1152, "DPO Turbo"],
+    "Photo 3:4 Standard": ["DPM++ 2M Karras", 28, 7., False, 896, 1152, "None"],
+    "Photo 3:4 Heavy": ["DPM++ 2M Karras", 40, 7., False, 896, 1152, "None"],
+    "Photo 1:1 Fast": ["LCM", 8, 2.5, False, 1024, 1024, "DPO Turbo"],
+    "Photo 1:1 Standard": ["DPM++ 2M Karras", 28, 7., False, 1024, 1024, "None"],
+    "Photo 1:1 Heavy": ["DPM++ 2M Karras", 40, 7., False, 1024, 1024, "None"],
+}
+
+
+def set_sampler_settings(sampler_setting):
+    if not sampler_setting in list(preset_sampler_setting.keys()) or sampler_setting == "None":
+        return gr.update(value="Euler a"), gr.update(value=28), gr.update(value=7.), gr.update(value=True),\
+              gr.update(value=1024), gr.update(value=1024), gr.update(value="None")
+    v = preset_sampler_setting.get(sampler_setting, ["Euler a", 28, 7., True, 1024, 1024])
+    # sampler, steps, cfg, clip_skip, width, height, optimization
+    return gr.update(value=v[0]), gr.update(value=v[1]), gr.update(value=v[2]), gr.update(value=v[3]),\
+          gr.update(value=v[4]), gr.update(value=v[5]), gr.update(value=v[6])
+
+
+preset_styles = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in style_list}
+preset_quality = {k["name"]: (k["prompt"], k["negative_prompt"]) for k in quality_prompt_list}
+
+
+def process_style_prompt(prompt: str, neg_prompt: str, styles_key: str = "None", quality_key: str = "None", type: str = "Auto"):
+    def to_list(s):
+        return [x.strip() for x in s.split(",") if not s == ""]
+    
+    def list_sub(a, b):
+        return [e for e in a if e not in b]
+    
+    def list_uniq(l):
+        return sorted(set(l), key=l.index)
+
+    animagine_ps = to_list("anime artwork, anime style, vibrant, studio anime, highly detailed, masterpiece, best quality, very aesthetic, absurdres")
+    animagine_nps = to_list("lowres, (bad), text, error, fewer, extra, missing, worst quality, jpeg artifacts, low quality, watermark, unfinished, displeasing, oldest, early, chromatic aberration, signature, extra digits, artistic error, username, scan, [abstract]")
+    pony_ps = to_list("source_anime, score_9, score_8_up, score_7_up, masterpiece, best quality, very aesthetic, absurdres")
+    pony_nps = to_list("source_pony, source_furry, source_cartoon, score_6, score_5, score_4, busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends")
+    prompts = to_list(prompt)
+    neg_prompts = to_list(neg_prompt)
+
+    all_styles_ps = []
+    all_styles_nps = []
+    for d in style_list:
+        all_styles_ps.extend(to_list(str(d.get("prompt", ""))))
+        all_styles_nps.extend(to_list(str(d.get("negative_prompt", ""))))
+
+    all_quality_ps = []
+    all_quality_nps = []
+    for d in quality_prompt_list:
+        all_quality_ps.extend(to_list(str(d.get("prompt", ""))))
+        all_quality_nps.extend(to_list(str(d.get("negative_prompt", ""))))
+
+    quality_ps = to_list(preset_quality[quality_key][0])
+    quality_nps = to_list(preset_quality[quality_key][1])
+    styles_ps = to_list(preset_styles[styles_key][0])
+    styles_nps = to_list(preset_styles[styles_key][1])
+
+    prompts = list_sub(prompts, animagine_ps + pony_ps + all_styles_ps + all_quality_ps)
+    neg_prompts = list_sub(neg_prompts, animagine_nps + pony_nps + all_styles_nps + all_quality_nps)
+
+    last_empty_p = [""] if not prompts and type != "None" and type != "Auto" and styles_key != "None" and quality_key != "None" else []
+    last_empty_np = [""] if not neg_prompts and type != "None" and type != "Auto" and styles_key != "None" and quality_key != "None" else []
+
+    if type == "Animagine":
+        prompts = prompts + animagine_ps
+        neg_prompts = neg_prompts + animagine_nps
+    elif type == "Pony":
+        prompts = prompts + pony_ps
+        neg_prompts = neg_prompts + pony_nps
+
+    prompts = prompts + styles_ps + quality_ps
+    neg_prompts = neg_prompts + styles_nps + quality_nps
+
+    prompt = ", ".join(list_uniq(prompts) + last_empty_p)
+    neg_prompt = ", ".join(list_uniq(neg_prompts) + last_empty_np)
+
+    return gr.update(value=prompt), gr.update(value=neg_prompt), gr.update(value=type) 
+
+
+def set_quick_presets(genre:str = "None", type:str = "Auto", speed:str = "None", aspect:str = "None"):
+    quality = "None"
+    style = "None"
+    sampler = "None"
+    opt = "None"
+
+    if genre == "Anime":
+        if type != "None" and type != "Auto": style = "Anime"
+        if aspect == "1:1":
+            if speed == "Heavy":
+                sampler = "Anime 1:1 Heavy"
+            elif speed == "Fast":
+                sampler = "Anime 1:1 Fast"
+            else:
+                sampler = "Anime 1:1 Standard"
+        elif aspect == "3:4":
+            if speed == "Heavy":
+                sampler = "Anime 3:4 Heavy"
+            elif speed == "Fast":
+                sampler = "Anime 3:4 Fast"
+            else:
+                sampler = "Anime 3:4 Standard"
+        if type == "Pony":
+            quality = "Pony Anime Common"
+        elif type == "Animagine":
+            quality = "Animagine Common"
+        else:
+            quality = "None"
+    elif genre == "Photo":
+        if type != "None" and type != "Auto": style = "Photographic"
+        if aspect == "1:1":
+            if speed == "Heavy":
+                sampler = "Photo 1:1 Heavy"
+            elif speed == "Fast":
+                sampler = "Photo 1:1 Fast"
+            else:
+                sampler = "Photo 1:1 Standard"
+        elif aspect == "3:4":
+            if speed == "Heavy":
+                sampler = "Photo 3:4 Heavy"
+            elif speed == "Fast":
+                sampler = "Photo 3:4 Fast"
+            else:
+                sampler = "Photo 3:4 Standard"
+        if type == "Pony":
+            quality = "Pony Common"
+        else:
+            quality = "None"
+
+    if speed == "Fast":
+        opt = "DPO Turbo"
+        if genre == "Anime" and type != "Pony" and type != "Auto": quality = "Animagine Light v3.1"
+
+    return gr.update(value=quality), gr.update(value=style), gr.update(value=sampler), gr.update(value=opt), gr.update(value=type)
+
+
+textual_inversion_dict = {}
+try:
+    with open('textual_inversion_dict.json', encoding='utf-8') as f:
+        textual_inversion_dict = json.load(f)
+except Exception:
+    pass
+textual_inversion_file_token_list = []
+
+
+def get_tupled_embed_list(embed_list):
+    global textual_inversion_file_list
+    tupled_list = []
+    for file in embed_list:
+        token = textual_inversion_dict.get(Path(file).name, [Path(file).stem.replace(",",""), False])[0]
+        tupled_list.append((token, file))
+        textual_inversion_file_token_list.append(token)
+    return tupled_list
+
+
+def set_textual_inversion_prompt(textual_inversion_gui, prompt_gui, neg_prompt_gui, prompt_syntax_gui):
+    ti_tags = list(textual_inversion_dict.values()) + textual_inversion_file_token_list
+    tags = prompt_gui.split(",") if prompt_gui else []
+    prompts = []
+    for tag in tags:
+        tag = str(tag).strip()
+        if tag and not tag in ti_tags:
+            prompts.append(tag)
+    ntags = neg_prompt_gui.split(",") if neg_prompt_gui else []
+    neg_prompts = []
+    for tag in ntags:
+        tag = str(tag).strip()
+        if tag and not tag in ti_tags:
+            neg_prompts.append(tag)
+    ti_prompts = []
+    ti_neg_prompts = []
+    for ti in textual_inversion_gui:
+        tokens = textual_inversion_dict.get(Path(ti).name, [Path(ti).stem.replace(",",""), False])
+        is_positive = tokens[1] == True or "positive" in Path(ti).parent.name
+        if is_positive: # positive prompt
+            ti_prompts.append(tokens[0])
+        else: # negative prompt (default)
+            ti_neg_prompts.append(tokens[0])
+    empty = [""]
+    prompt = ", ".join(prompts + ti_prompts + empty)
+    neg_prompt = ", ".join(neg_prompts + ti_neg_prompts + empty)
+    return gr.update(value=prompt), gr.update(value=neg_prompt),
+
+
+def get_model_pipeline(repo_id: str):
+    from huggingface_hub import HfApi
+    api = HfApi(token=HF_TOKEN)
+    default = "StableDiffusionPipeline"
+    try:
+        if not is_repo_name(repo_id): return default
+        model = api.model_info(repo_id=repo_id, timeout=5.0)
+    except Exception:
+        return default
+    if model.private or model.gated: return default
+    tags = model.tags
+    if not 'diffusers' in tags: return default
+    if 'diffusers:FluxPipeline' in tags:
+        return "FluxPipeline"
+    if 'diffusers:StableDiffusionXLPipeline' in tags:
+        return "StableDiffusionXLPipeline"
+    elif 'diffusers:StableDiffusionPipeline' in tags:
+        return "StableDiffusionPipeline"
+    else:
+        return default
+