modutils.py

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