Delete modutils.py

modutils.py

@@ -1,1290 +0,0 @@
-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
-