Update multit2i.py

multit2i.py

@@ -1,502 +1,502 @@
-import gradio as gr
-import asyncio
-from threading import RLock
-from pathlib import Path
-from huggingface_hub import InferenceClient
-import os
-
-
-HF_TOKEN = os.environ.get("HF_TOKEN") if os.environ.get("HF_TOKEN") else None # If private or gated models aren't used, ENV setting is unnecessary.
-server_timeout = 600
-inference_timeout = 300
-
-
-lock = RLock()
-loaded_models = {}
-model_info_dict = {}
-
-
-def to_list(s):
-    return [x.strip() for x in s.split(",")]
-
-
-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)
-
-
-def is_repo_name(s):
-    import re
-    return re.fullmatch(r'^[^/]+?/[^/]+?$', s)
-
-
-def get_status(model_name: str):
-    from huggingface_hub import InferenceClient
-    client = InferenceClient(token=HF_TOKEN, timeout=10)
-    return client.get_model_status(model_name)
-
-
-def is_loadable(model_name: str, force_gpu: bool = False):
-    try:
-        status = get_status(model_name)
-    except Exception as e:
-        print(e)
-        print(f"Couldn't load {model_name}.")
-        return False
-    gpu_state = isinstance(status.compute_type, dict) and "gpu" in status.compute_type.keys()
-    if status is None or status.state not in ["Loadable", "Loaded"] or (force_gpu and not gpu_state):
-        print(f"Couldn't load {model_name}. Model state:'{status.state}', GPU:{gpu_state}")
-    return status is not None and status.state in ["Loadable", "Loaded"] and (not force_gpu or gpu_state)
-
-
-def find_model_list(author: str="", tags: list[str]=[], not_tag="", sort: str="last_modified", limit: int=30, force_gpu=False, check_status=False):
-    from huggingface_hub import HfApi
-    api = HfApi(token=HF_TOKEN)
-    default_tags = ["diffusers"]
-    if not sort: sort = "last_modified"
-    limit = limit * 20 if check_status and force_gpu else limit * 5
-    models = []
-    try:
-        model_infos = api.list_models(author=author, #task="text-to-image",
-                                       tags=list_uniq(default_tags + tags), cardData=True, sort=sort, limit=limit)
-    except Exception as e:
-        print(f"Error: Failed to list models.")
-        print(e)
-        return models
-    for model in model_infos:
-        if not model.private and not model.gated or HF_TOKEN is not None:
-           loadable = is_loadable(model.id, force_gpu) if check_status else True
-           if not_tag and not_tag in model.tags or not loadable or "not-for-all-audiences" in model.tags: continue
-           models.append(model.id)
-           if len(models) == limit: break
-    return models
-
-
-def get_t2i_model_info_dict(repo_id: str):
-    from huggingface_hub import HfApi
-    api = HfApi(token=HF_TOKEN)
-    info = {"md": "None"}
-    try:
-        if not is_repo_name(repo_id) or not api.repo_exists(repo_id=repo_id): return info
-        model = api.model_info(repo_id=repo_id, token=HF_TOKEN)
-    except Exception as e:
-        print(f"Error: Failed to get {repo_id}'s info.")
-        print(e)
-        return info
-    if model.private or model.gated and HF_TOKEN is None: return info
-    try:
-        tags = model.tags
-    except Exception as e:
-        print(e)
-        return info
-    if not 'diffusers' in model.tags: return info
-    if 'diffusers:FluxPipeline' in tags: info["ver"] = "FLUX.1"
-    elif 'diffusers:StableDiffusionXLPipeline' in tags: info["ver"] = "SDXL"
-    elif 'diffusers:StableDiffusionPipeline' in tags: info["ver"] = "SD1.5"
-    elif 'diffusers:StableDiffusion3Pipeline' in tags: info["ver"] = "SD3"
-    else: info["ver"] = "Other"
-    info["url"] = f"https://huggingface.co/{repo_id}/"
-    info["tags"] = model.card_data.tags if model.card_data and model.card_data.tags else []
-    info["downloads"] = model.downloads
-    info["likes"] = model.likes
-    info["last_modified"] = model.last_modified.strftime("lastmod: %Y-%m-%d")
-    un_tags = ['text-to-image', 'stable-diffusion', 'stable-diffusion-api', 'safetensors', 'stable-diffusion-xl']
-    descs = [info["ver"]] + list_sub(info["tags"], un_tags) + [f'DLs: {info["downloads"]}'] + [f'❤: {info["likes"]}'] + [info["last_modified"]]
-    info["md"] = f'Model Info: {", ".join(descs)} [Model Repo]({info["url"]})'
-    return info
-
-
-def rename_image(image_path: str | None, model_name: str, save_path: str | None = None):
-    import shutil
-    from datetime import datetime, timezone, timedelta
-    if image_path is None: return None
-    dt_now = datetime.now(timezone(timedelta(hours=9)))
-    filename = f"{model_name.split('/')[-1]}_{dt_now.strftime('%Y%m%d_%H%M%S')}.png"
-    try:
-        if Path(image_path).exists():
-            png_path = "image.png"
-            if str(Path(image_path).resolve()) != str(Path(png_path).resolve()): shutil.copy(image_path, png_path)
-            if save_path is not None:
-                new_path = str(Path(png_path).resolve().rename(Path(save_path).resolve()))
-            else:
-                new_path = str(Path(png_path).resolve().rename(Path(filename).resolve()))
-            return new_path
-        else:
-            return None
-    except Exception as e:
-        print(e)
-        return None
-
-
-def save_gallery(image_path: str | None, images: list[tuple] | None):
-    if images is None: images = []
-    files = [i[0] for i in images]
-    if image_path is None: return images, files
-    files.insert(0, str(image_path))
-    images.insert(0, (str(image_path), Path(image_path).stem))
-    return images, files
-
-
-# https://github.com/gradio-app/gradio/blob/main/gradio/external.py
-# https://huggingface.co/docs/huggingface_hub/package_reference/inference_client
-from typing import Literal
-def load_from_model(model_name: str, hf_token: str | Literal[False] | None = None):
-    import httpx
-    import huggingface_hub
-    from gradio.exceptions import ModelNotFoundError, TooManyRequestsError
-    model_url = f"https://huggingface.co/{model_name}"
-    api_url = f"https://api-inference.huggingface.co/models/{model_name}"
-    print(f"Fetching model from: {model_url}")
-
-    headers = ({} if hf_token in [False, None] else {"Authorization": f"Bearer {hf_token}"})
-    response = httpx.request("GET", api_url, headers=headers)
-    if response.status_code != 200:
-        raise ModelNotFoundError(
-            f"Could not find model: {model_name}. If it is a private or gated model, please provide your Hugging Face access token (https://huggingface.co/settings/tokens) as the argument for the `hf_token` parameter."
-        )
-    p = response.json().get("pipeline_tag")
-    if p != "text-to-image": raise ModelNotFoundError(f"This model isn't for text-to-image or unsupported: {model_name}.")
-    headers["X-Wait-For-Model"] = "true"
-    client = huggingface_hub.InferenceClient(model=model_name, headers=headers,
-                                              token=hf_token, timeout=server_timeout)
-    inputs = gr.components.Textbox(label="Input")
-    outputs = gr.components.Image(label="Output")
-    fn = client.text_to_image
-
-    def query_huggingface_inference_endpoints(*data, **kwargs):
-        try:
-            data = fn(*data, **kwargs)  # type: ignore
-        except huggingface_hub.utils.HfHubHTTPError as e:
-            if "429" in str(e):
-                raise TooManyRequestsError() from e
-        except Exception as e:
-            raise Exception() from e
-        return data
-
-    interface_info = {
-        "fn": query_huggingface_inference_endpoints,
-        "inputs": inputs,
-        "outputs": outputs,
-        "title": model_name,
-    }
-    return gr.Interface(**interface_info)
-
-
-def load_model(model_name: str):
-    global loaded_models
-    global model_info_dict
-    if model_name in loaded_models.keys(): return loaded_models[model_name]
-    try:
-        loaded_models[model_name] = load_from_model(model_name, hf_token=HF_TOKEN)
-        print(f"Loaded: {model_name}")
-    except Exception as e:
-        if model_name in loaded_models.keys(): del loaded_models[model_name]
-        print(f"Failed to load: {model_name}")
-        print(e)
-        return None
-    try:
-        model_info_dict[model_name] = get_t2i_model_info_dict(model_name)
-        print(f"Assigned: {model_name}")
-    except Exception as e:
-        if model_name in model_info_dict.keys(): del model_info_dict[model_name]
-        print(f"Failed to assigned: {model_name}")
-        print(e)
-    return loaded_models[model_name]
-
-
-def load_model_api(model_name: str):
-    global loaded_models
-    global model_info_dict
-    if model_name in loaded_models.keys(): return loaded_models[model_name]
-    try:
-        client = InferenceClient(timeout=5)
-        status = client.get_model_status(model_name, token=HF_TOKEN)
-        if status is None or status.framework != "diffusers" or status.state not in ["Loadable", "Loaded"]:
-            print(f"Failed to load by API: {model_name}")
-            return None
-        else:
-            loaded_models[model_name] = InferenceClient(model_name, token=HF_TOKEN, timeout=server_timeout)
-            print(f"Loaded by API: {model_name}")
-    except Exception as e:
-        if model_name in loaded_models.keys(): del loaded_models[model_name]
-        print(f"Failed to load by API: {model_name}")
-        print(e)
-        return None
-    try:
-        model_info_dict[model_name] = get_t2i_model_info_dict(model_name)
-        print(f"Assigned by API: {model_name}")
-    except Exception as e:
-        if model_name in model_info_dict.keys(): del model_info_dict[model_name]
-        print(f"Failed to assigned by API: {model_name}")
-        print(e)
-    return loaded_models[model_name]
-
-
-def load_models(models: list):
-    for model in models:
-        load_model(model)
-
-
-positive_prefix = {
-    "Pony": to_list("score_9, score_8_up, score_7_up"),
-    "Pony Anime": to_list("source_anime, anime, score_9, score_8_up, score_7_up"),
-}
-positive_suffix = {
-    "Common": to_list("highly detailed, masterpiece, best quality, very aesthetic, absurdres"),
-    "Anime": to_list("anime artwork, anime style, studio anime, highly detailed"),
-}
-negative_prefix = {
-    "Pony": to_list("score_6, score_5, score_4"),
-    "Pony Anime": to_list("score_6, score_5, score_4, source_pony, source_furry, source_cartoon"),
-    "Pony Real": to_list("score_6, score_5, score_4, source_anime, source_pony, source_furry, source_cartoon"),
-}
-negative_suffix = {
-    "Common": to_list("lowres, (bad), bad hands, bad feet, 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 Anime": to_list("busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends"),
-    "Pony Real": to_list("ugly, airbrushed, simple background, cgi, cartoon, anime"),
-}
-positive_all = negative_all = []
-for k, v in (positive_prefix | positive_suffix).items():
-    positive_all = positive_all + v + [s.replace("_", " ") for s in v]
-positive_all = list_uniq(positive_all)
-for k, v in (negative_prefix | negative_suffix).items():
-    negative_all = negative_all + v + [s.replace("_", " ") for s in v]
-positive_all = list_uniq(positive_all)
-
-
-def recom_prompt(prompt: str = "", neg_prompt: str = "", pos_pre: list = [], pos_suf: list = [], neg_pre: list = [], neg_suf: list = []):
-    def flatten(src):
-        return [item for row in src for item in row]
-    prompts = to_list(prompt)
-    neg_prompts = to_list(neg_prompt)
-    prompts = list_sub(prompts, positive_all)
-    neg_prompts = list_sub(neg_prompts, negative_all)
-    last_empty_p = [""] if not prompts and type != "None" else []
-    last_empty_np = [""] if not neg_prompts and type != "None" else []
-    prefix_ps = flatten([positive_prefix.get(s, []) for s in pos_pre])
-    suffix_ps = flatten([positive_suffix.get(s, []) for s in pos_suf])
-    prefix_nps = flatten([negative_prefix.get(s, []) for s in neg_pre])
-    suffix_nps = flatten([negative_suffix.get(s, []) for s in neg_suf])
-    prompt = ", ".join(list_uniq(prefix_ps + prompts + suffix_ps) + last_empty_p)
-    neg_prompt = ", ".join(list_uniq(prefix_nps + neg_prompts + suffix_nps) + last_empty_np)
-    return prompt, neg_prompt
-
-
-recom_prompt_type = {
-    "None": ([], [], [], []),
-    "Auto": ([], [], [], []),
-    "Common": ([], ["Common"], [], ["Common"]),
-    "Animagine": ([], ["Common", "Anime"], [], ["Common"]),
-    "Pony": (["Pony"], ["Common"], ["Pony"], ["Common"]),
-    "Pony Anime": (["Pony", "Pony Anime"], ["Common", "Anime"], ["Pony", "Pony Anime"], ["Common", "Pony Anime"]),
-    "Pony Real": (["Pony"], ["Common"], ["Pony", "Pony Real"], ["Common", "Pony Real"]),
-}
-
-
-enable_auto_recom_prompt = False
-def insert_recom_prompt(prompt: str = "", neg_prompt: str = "", type: str = "None"):
-    global enable_auto_recom_prompt
-    if type == "Auto":  enable_auto_recom_prompt = True
-    else: enable_auto_recom_prompt = False
-    pos_pre, pos_suf, neg_pre, neg_suf = recom_prompt_type.get(type, ([], [], [], []))
-    return recom_prompt(prompt, neg_prompt, pos_pre, pos_suf, neg_pre, neg_suf)
-
-
-def set_recom_prompt_preset(type: str = "None"):
-    pos_pre, pos_suf, neg_pre, neg_suf = recom_prompt_type.get(type, ([], [], [], []))
-    return pos_pre, pos_suf, neg_pre, neg_suf
-
-
-def get_recom_prompt_type():
-    type = list(recom_prompt_type.keys())
-    type.remove("Auto")
-    return type
-
-
-def get_positive_prefix():
-    return list(positive_prefix.keys())
-
-
-def get_positive_suffix():
-    return list(positive_suffix.keys())
-
-
-def get_negative_prefix():
-    return list(negative_prefix.keys())
-
-
-def get_negative_suffix():
-    return list(negative_suffix.keys())
-
-
-def get_tag_type(pos_pre: list = [], pos_suf: list = [], neg_pre: list = [], neg_suf: list = []):
-    tag_type = "danbooru"
-    words = pos_pre + pos_suf + neg_pre + neg_suf
-    for word in words:
-        if "Pony" in word:
-            tag_type = "e621"
-            break
-    return tag_type
-
-
-def get_model_info_md(model_name: str):
-    if model_name in model_info_dict.keys(): return model_info_dict[model_name].get("md", "")
-
-
-def change_model(model_name: str):
-    load_model_api(model_name)
-    return get_model_info_md(model_name)
-
-
-def warm_model(model_name: str):
-    model = load_model_api(model_name)
-    if model:
-        try:
-            print(f"Warming model: {model_name}")
-            infer_body(model, " ")
-        except Exception as e:
-            print(e)
-
-
-# https://huggingface.co/docs/api-inference/detailed_parameters
-# https://huggingface.co/docs/huggingface_hub/package_reference/inference_client
-def infer_body(client: InferenceClient | gr.Interface | object, model_str: str, prompt: str, neg_prompt: str = "",
-               height: int = 0, width: int = 0, steps: int = 0, cfg: int = 0, seed: int = -1):
-    png_path = "image.png"
-    kwargs = {}
-    if height > 0: kwargs["height"] = height
-    if width > 0: kwargs["width"] = width
-    if steps > 0: kwargs["num_inference_steps"] = steps
-    if cfg > 0: cfg = kwargs["guidance_scale"] = cfg
-    if seed == -1: kwargs["seed"] = randomize_seed()
-    else: kwargs["seed"] = seed
-    try:
-        if isinstance(client, InferenceClient):
-            image = client.text_to_image(prompt=prompt, negative_prompt=neg_prompt, **kwargs, token=HF_TOKEN)
-        elif isinstance(client, gr.Interface):
-            image = client.fn(prompt=prompt, negative_prompt=neg_prompt, **kwargs, token=HF_TOKEN)
-        else: return None
-        if isinstance(image, tuple): return None
-        return save_image(image, png_path, model_str, prompt, neg_prompt, height, width, steps, cfg, seed)
-    except Exception as e:
-        print(e)
-        raise Exception() from e
-
-
-async def infer(model_name: str, prompt: str, neg_prompt: str ="", height: int = 0, width: int = 0,
-               steps: int = 0, cfg: int = 0, seed: int = -1,
-               save_path: str | None = None, timeout: float = inference_timeout):
-    model = load_model(model_name)
-    if not model: return None
-    task = asyncio.create_task(asyncio.to_thread(infer_body, model, model_name, prompt, neg_prompt,
-                                                 height, width, steps, cfg, seed))
-    await asyncio.sleep(0)
-    try:
-        result = await asyncio.wait_for(task, timeout=timeout)
-    except asyncio.TimeoutError as e:
-        print(e)
-        print(f"Task timed out: {model_name}")
-        if not task.done(): task.cancel()
-        result = None
-        raise Exception(f"Task timed out: {model_name}") from e
-    except Exception as e:
-        print(e)
-        if not task.done(): task.cancel()
-        result = None
-        raise Exception() from e
-    if task.done() and result is not None:
-        with lock:
-            image = rename_image(result, model_name, save_path)
-        return image
-    return None
-
-
-# https://github.com/aio-libs/pytest-aiohttp/issues/8 # also AsyncInferenceClient is buggy.
-def infer_fn(model_name: str, prompt: str, neg_prompt: str = "", height: int = 0, width: int = 0,
-             steps: int = 0, cfg: int = 0, seed: int = -1,
-             pos_pre: list = [], pos_suf: list = [], neg_pre: list = [], neg_suf: list = [], save_path: str | None = None):
-    if model_name == 'NA':
-        return None
-    try:
-        loop = asyncio.get_running_loop()
-    except Exception:
-        loop = asyncio.new_event_loop()
-    try:
-        prompt, neg_prompt = recom_prompt(prompt, neg_prompt, pos_pre, pos_suf, neg_pre, neg_suf)
-        result = loop.run_until_complete(infer(model_name, prompt, neg_prompt, height, width,
-                                               steps, cfg, seed, save_path, inference_timeout))
-    except (Exception, asyncio.CancelledError) as e:
-        print(e)
-        print(f"Task aborted: {model_name}, Error: {e}")
-        result = None
-        raise gr.Error(f"Task aborted: {model_name}, Error: {e}")
-    finally:
-        loop.close()
-    return result
-
-
-def infer_rand_fn(model_name_dummy: str, prompt: str, neg_prompt: str = "", height: int = 0, width: int = 0,
-             steps: int = 0, cfg: int = 0, seed: int = -1,
-             pos_pre: list = [], pos_suf: list = [], neg_pre: list = [], neg_suf: list = [], save_path: str | None = None):
-    import random
-    if model_name_dummy == 'NA':
-        return None
-    random.seed()
-    model_name = random.choice(list(loaded_models.keys()))
-    try:
-        loop = asyncio.get_running_loop()
-    except Exception:
-        loop = asyncio.new_event_loop()
-    try:
-        prompt, neg_prompt = recom_prompt(prompt, neg_prompt, pos_pre, pos_suf, neg_pre, neg_suf)
-        result = loop.run_until_complete(infer(model_name, prompt, neg_prompt, height, width,
-                                               steps, cfg, seed, save_path, inference_timeout))
-    except (Exception, asyncio.CancelledError) as e:
-        print(e)
-        print(f"Task aborted: {model_name}, Error: {e}")
-        result = None
-        raise gr.Error(f"Task aborted: {model_name}, Error: {e}")
-    finally:
-        loop.close()
-    return result
-
-
-def save_image(image, savefile, modelname, prompt, nprompt, height=0, width=0, steps=0, cfg=0, seed=-1):
-    from PIL import Image, PngImagePlugin
-    import json
-    try:
-        metadata = {"prompt": prompt, "negative_prompt": nprompt, "Model": {"Model": modelname.split("/")[-1]}}
-        if steps > 0: metadata["num_inference_steps"] = steps
-        if cfg > 0: metadata["guidance_scale"] = cfg
-        if seed != -1: metadata["seed"] = seed
-        if width > 0 and height > 0: metadata["resolution"] = f"{width} x {height}"
-        metadata_str = json.dumps(metadata)
-        info = PngImagePlugin.PngInfo()
-        info.add_text("metadata", metadata_str)
-        image.save(savefile, "PNG", pnginfo=info)
-        return str(Path(savefile).resolve())
-    except Exception as e:
-        print(f"Failed to save image file: {e}")
-        raise Exception(f"Failed to save image file:") from e
-
-
-def randomize_seed():
-    from random import seed, randint
-    MAX_SEED = 2**32-1
-    seed()
-    rseed = randint(0, MAX_SEED)
-    return rseed
-
-
-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
+import gradio as gr
+import asyncio
+from threading import RLock
+from pathlib import Path
+from huggingface_hub import InferenceClient
+import os
+
+
+HF_TOKEN = os.environ.get("HF_TOKEN") if os.environ.get("HF_TOKEN") else None # If private or gated models aren't used, ENV setting is unnecessary.
+server_timeout = 600
+inference_timeout = 300
+
+
+lock = RLock()
+loaded_models = {}
+model_info_dict = {}
+
+
+def to_list(s):
+    return [x.strip() for x in s.split(",")]
+
+
+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)
+
+
+def is_repo_name(s):
+    import re
+    return re.fullmatch(r'^[^/]+?/[^/]+?$', s)
+
+
+def get_status(model_name: str):
+    from huggingface_hub import InferenceClient
+    client = InferenceClient(token=HF_TOKEN, timeout=50)
+    return client.get_model_status(model_name)
+
+
+def is_loadable(model_name: str, force_gpu: bool = False):
+    try:
+        status = get_status(model_name)
+    except Exception as e:
+        print(e)
+        print(f"Couldn't load {model_name}.")
+        return False
+    gpu_state = isinstance(status.compute_type, dict) and "gpu" in status.compute_type.keys()
+    if status is None or status.state not in ["Loadable", "Loaded"] or (force_gpu and not gpu_state):
+        print(f"Couldn't load {model_name}. Model state:'{status.state}', GPU:{gpu_state}")
+    return status is not None and status.state in ["Loadable", "Loaded"] and (not force_gpu or gpu_state)
+
+
+def find_model_list(author: str="", tags: list[str]=[], not_tag="", sort: str="last_modified", limit: int=30, force_gpu=False, check_status=False):
+    from huggingface_hub import HfApi
+    api = HfApi(token=HF_TOKEN)
+    default_tags = ["diffusers"]
+    if not sort: sort = "last_modified"
+    limit = limit * 20 if check_status and force_gpu else limit * 5
+    models = []
+    try:
+        model_infos = api.list_models(author=author, #task="text-to-image",
+                                       tags=list_uniq(default_tags + tags), cardData=True, sort=sort, limit=limit)
+    except Exception as e:
+        print(f"Error: Failed to list models.")
+        print(e)
+        return models
+    for model in model_infos:
+        if not model.private and not model.gated or HF_TOKEN is not None:
+           loadable = is_loadable(model.id, force_gpu) if check_status else True
+           if not_tag and not_tag in model.tags or not loadable or "not-for-all-audiences" in model.tags: continue
+           models.append(model.id)
+           if len(models) == limit: break
+    return models
+
+
+def get_t2i_model_info_dict(repo_id: str):
+    from huggingface_hub import HfApi
+    api = HfApi(token=HF_TOKEN)
+    info = {"md": "None"}
+    try:
+        if not is_repo_name(repo_id) or not api.repo_exists(repo_id=repo_id): return info
+        model = api.model_info(repo_id=repo_id, token=HF_TOKEN)
+    except Exception as e:
+        print(f"Error: Failed to get {repo_id}'s info.")
+        print(e)
+        return info
+    if model.private or model.gated and HF_TOKEN is None: return info
+    try:
+        tags = model.tags
+    except Exception as e:
+        print(e)
+        return info
+    if not 'diffusers' in model.tags: return info
+    if 'diffusers:FluxPipeline' in tags: info["ver"] = "FLUX.1"
+    elif 'diffusers:StableDiffusionXLPipeline' in tags: info["ver"] = "SDXL"
+    elif 'diffusers:StableDiffusionPipeline' in tags: info["ver"] = "SD1.5"
+    elif 'diffusers:StableDiffusion3Pipeline' in tags: info["ver"] = "SD3"
+    else: info["ver"] = "Other"
+    info["url"] = f"https://huggingface.co/{repo_id}/"
+    info["tags"] = model.card_data.tags if model.card_data and model.card_data.tags else []
+    info["downloads"] = model.downloads
+    info["likes"] = model.likes
+    info["last_modified"] = model.last_modified.strftime("lastmod: %Y-%m-%d")
+    un_tags = ['text-to-image', 'stable-diffusion', 'stable-diffusion-api', 'safetensors', 'stable-diffusion-xl']
+    descs = [info["ver"]] + list_sub(info["tags"], un_tags) + [f'DLs: {info["downloads"]}'] + [f'❤: {info["likes"]}'] + [info["last_modified"]]
+    info["md"] = f'Model Info: {", ".join(descs)} [Model Repo]({info["url"]})'
+    return info
+
+
+def rename_image(image_path: str | None, model_name: str, save_path: str | None = None):
+    import shutil
+    from datetime import datetime, timezone, timedelta
+    if image_path is None: return None
+    dt_now = datetime.now(timezone(timedelta(hours=9)))
+    filename = f"{model_name.split('/')[-1]}_{dt_now.strftime('%Y%m%d_%H%M%S')}.png"
+    try:
+        if Path(image_path).exists():
+            png_path = "image.png"
+            if str(Path(image_path).resolve()) != str(Path(png_path).resolve()): shutil.copy(image_path, png_path)
+            if save_path is not None:
+                new_path = str(Path(png_path).resolve().rename(Path(save_path).resolve()))
+            else:
+                new_path = str(Path(png_path).resolve().rename(Path(filename).resolve()))
+            return new_path
+        else:
+            return None
+    except Exception as e:
+        print(e)
+        return None
+
+
+def save_gallery(image_path: str | None, images: list[tuple] | None):
+    if images is None: images = []
+    files = [i[0] for i in images]
+    if image_path is None: return images, files
+    files.insert(0, str(image_path))
+    images.insert(0, (str(image_path), Path(image_path).stem))
+    return images, files
+
+
+# https://github.com/gradio-app/gradio/blob/main/gradio/external.py
+# https://huggingface.co/docs/huggingface_hub/package_reference/inference_client
+from typing import Literal
+def load_from_model(model_name: str, hf_token: str | Literal[False] | None = None):
+    import httpx
+    import huggingface_hub
+    from gradio.exceptions import ModelNotFoundError, TooManyRequestsError
+    model_url = f"https://huggingface.co/{model_name}"
+    api_url = f"https://api-inference.huggingface.co/models/{model_name}"
+    print(f"Fetching model from: {model_url}")
+
+    headers = ({} if hf_token in [False, None] else {"Authorization": f"Bearer {hf_token}"})
+    response = httpx.request("GET", api_url, headers=headers)
+    if response.status_code != 200:
+        raise ModelNotFoundError(
+            f"Could not find model: {model_name}. If it is a private or gated model, please provide your Hugging Face access token (https://huggingface.co/settings/tokens) as the argument for the `hf_token` parameter."
+        )
+    p = response.json().get("pipeline_tag")
+    if p != "text-to-image": raise ModelNotFoundError(f"This model isn't for text-to-image or unsupported: {model_name}.")
+    headers["X-Wait-For-Model"] = "true"
+    client = huggingface_hub.InferenceClient(model=model_name, headers=headers,
+                                              token=hf_token, timeout=server_timeout)
+    inputs = gr.components.Textbox(label="Input")
+    outputs = gr.components.Image(label="Output")
+    fn = client.text_to_image
+
+    def query_huggingface_inference_endpoints(*data, **kwargs):
+        try:
+            data = fn(*data, **kwargs)  # type: ignore
+        except huggingface_hub.utils.HfHubHTTPError as e:
+            if "429" in str(e):
+                raise TooManyRequestsError() from e
+        except Exception as e:
+            raise Exception() from e
+        return data
+
+    interface_info = {
+        "fn": query_huggingface_inference_endpoints,
+        "inputs": inputs,
+        "outputs": outputs,
+        "title": model_name,
+    }
+    return gr.Interface(**interface_info)
+
+
+def load_model(model_name: str):
+    global loaded_models
+    global model_info_dict
+    if model_name in loaded_models.keys(): return loaded_models[model_name]
+    try:
+        loaded_models[model_name] = load_from_model(model_name, hf_token=HF_TOKEN)
+        print(f"Loaded: {model_name}")
+    except Exception as e:
+        if model_name in loaded_models.keys(): del loaded_models[model_name]
+        print(f"Failed to load: {model_name}")
+        print(e)
+        return None
+    try:
+        model_info_dict[model_name] = get_t2i_model_info_dict(model_name)
+        print(f"Assigned: {model_name}")
+    except Exception as e:
+        if model_name in model_info_dict.keys(): del model_info_dict[model_name]
+        print(f"Failed to assigned: {model_name}")
+        print(e)
+    return loaded_models[model_name]
+
+
+def load_model_api(model_name: str):
+    global loaded_models
+    global model_info_dict
+    if model_name in loaded_models.keys(): return loaded_models[model_name]
+    try:
+        client = InferenceClient(timeout=5)
+        status = client.get_model_status(model_name, token=HF_TOKEN)
+        if status is None or status.framework != "diffusers" or status.state not in ["Loadable", "Loaded"]:
+            print(f"Failed to load by API: {model_name}")
+            return None
+        else:
+            loaded_models[model_name] = InferenceClient(model_name, token=HF_TOKEN, timeout=server_timeout)
+            print(f"Loaded by API: {model_name}")
+    except Exception as e:
+        if model_name in loaded_models.keys(): del loaded_models[model_name]
+        print(f"Failed to load by API: {model_name}")
+        print(e)
+        return None
+    try:
+        model_info_dict[model_name] = get_t2i_model_info_dict(model_name)
+        print(f"Assigned by API: {model_name}")
+    except Exception as e:
+        if model_name in model_info_dict.keys(): del model_info_dict[model_name]
+        print(f"Failed to assigned by API: {model_name}")
+        print(e)
+    return loaded_models[model_name]
+
+
+def load_models(models: list):
+    for model in models:
+        load_model(model)
+
+
+positive_prefix = {
+    "Pony": to_list("score_9, score_8_up, score_7_up"),
+    "Pony Anime": to_list("source_anime, anime, score_9, score_8_up, score_7_up"),
+}
+positive_suffix = {
+    "Common": to_list("highly detailed, masterpiece, best quality, very aesthetic, absurdres"),
+    "Anime": to_list("anime artwork, anime style, studio anime, highly detailed"),
+}
+negative_prefix = {
+    "Pony": to_list("score_6, score_5, score_4"),
+    "Pony Anime": to_list("score_6, score_5, score_4, source_pony, source_furry, source_cartoon"),
+    "Pony Real": to_list("score_6, score_5, score_4, source_anime, source_pony, source_furry, source_cartoon"),
+}
+negative_suffix = {
+    "Common": to_list("lowres, (bad), bad hands, bad feet, 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 Anime": to_list("busty, ugly face, mutated hands, low res, blurry face, black and white, the simpsons, overwatch, apex legends"),
+    "Pony Real": to_list("ugly, airbrushed, simple background, cgi, cartoon, anime"),
+}
+positive_all = negative_all = []
+for k, v in (positive_prefix | positive_suffix).items():
+    positive_all = positive_all + v + [s.replace("_", " ") for s in v]
+positive_all = list_uniq(positive_all)
+for k, v in (negative_prefix | negative_suffix).items():
+    negative_all = negative_all + v + [s.replace("_", " ") for s in v]
+positive_all = list_uniq(positive_all)
+
+
+def recom_prompt(prompt: str = "", neg_prompt: str = "", pos_pre: list = [], pos_suf: list = [], neg_pre: list = [], neg_suf: list = []):
+    def flatten(src):
+        return [item for row in src for item in row]
+    prompts = to_list(prompt)
+    neg_prompts = to_list(neg_prompt)
+    prompts = list_sub(prompts, positive_all)
+    neg_prompts = list_sub(neg_prompts, negative_all)
+    last_empty_p = [""] if not prompts and type != "None" else []
+    last_empty_np = [""] if not neg_prompts and type != "None" else []
+    prefix_ps = flatten([positive_prefix.get(s, []) for s in pos_pre])
+    suffix_ps = flatten([positive_suffix.get(s, []) for s in pos_suf])
+    prefix_nps = flatten([negative_prefix.get(s, []) for s in neg_pre])
+    suffix_nps = flatten([negative_suffix.get(s, []) for s in neg_suf])
+    prompt = ", ".join(list_uniq(prefix_ps + prompts + suffix_ps) + last_empty_p)
+    neg_prompt = ", ".join(list_uniq(prefix_nps + neg_prompts + suffix_nps) + last_empty_np)
+    return prompt, neg_prompt
+
+
+recom_prompt_type = {
+    "None": ([], [], [], []),
+    "Auto": ([], [], [], []),
+    "Common": ([], ["Common"], [], ["Common"]),
+    "Animagine": ([], ["Common", "Anime"], [], ["Common"]),
+    "Pony": (["Pony"], ["Common"], ["Pony"], ["Common"]),
+    "Pony Anime": (["Pony", "Pony Anime"], ["Common", "Anime"], ["Pony", "Pony Anime"], ["Common", "Pony Anime"]),
+    "Pony Real": (["Pony"], ["Common"], ["Pony", "Pony Real"], ["Common", "Pony Real"]),
+}
+
+
+enable_auto_recom_prompt = False
+def insert_recom_prompt(prompt: str = "", neg_prompt: str = "", type: str = "None"):
+    global enable_auto_recom_prompt
+    if type == "Auto":  enable_auto_recom_prompt = True
+    else: enable_auto_recom_prompt = False
+    pos_pre, pos_suf, neg_pre, neg_suf = recom_prompt_type.get(type, ([], [], [], []))
+    return recom_prompt(prompt, neg_prompt, pos_pre, pos_suf, neg_pre, neg_suf)
+
+
+def set_recom_prompt_preset(type: str = "None"):
+    pos_pre, pos_suf, neg_pre, neg_suf = recom_prompt_type.get(type, ([], [], [], []))
+    return pos_pre, pos_suf, neg_pre, neg_suf
+
+
+def get_recom_prompt_type():
+    type = list(recom_prompt_type.keys())
+    type.remove("Auto")
+    return type
+
+
+def get_positive_prefix():
+    return list(positive_prefix.keys())
+
+
+def get_positive_suffix():
+    return list(positive_suffix.keys())
+
+
+def get_negative_prefix():
+    return list(negative_prefix.keys())
+
+
+def get_negative_suffix():
+    return list(negative_suffix.keys())
+
+
+def get_tag_type(pos_pre: list = [], pos_suf: list = [], neg_pre: list = [], neg_suf: list = []):
+    tag_type = "danbooru"
+    words = pos_pre + pos_suf + neg_pre + neg_suf
+    for word in words:
+        if "Pony" in word:
+            tag_type = "e621"
+            break
+    return tag_type
+
+
+def get_model_info_md(model_name: str):
+    if model_name in model_info_dict.keys(): return model_info_dict[model_name].get("md", "")
+
+
+def change_model(model_name: str):
+    load_model_api(model_name)
+    return get_model_info_md(model_name)
+
+
+def warm_model(model_name: str):
+    model = load_model_api(model_name)
+    if model:
+        try:
+            print(f"Warming model: {model_name}")
+            infer_body(model, " ")
+        except Exception as e:
+            print(e)
+
+
+# https://huggingface.co/docs/api-inference/detailed_parameters
+# https://huggingface.co/docs/huggingface_hub/package_reference/inference_client
+def infer_body(client: InferenceClient | gr.Interface | object, model_str: str, prompt: str, neg_prompt: str = "",
+               height: int = 0, width: int = 0, steps: int = 0, cfg: int = 0, seed: int = -1):
+    png_path = "image.png"
+    kwargs = {}
+    if height > 0: kwargs["height"] = height
+    if width > 0: kwargs["width"] = width
+    if steps > 0: kwargs["num_inference_steps"] = steps
+    if cfg > 0: cfg = kwargs["guidance_scale"] = cfg
+    if seed == -1: kwargs["seed"] = randomize_seed()
+    else: kwargs["seed"] = seed
+    try:
+        if isinstance(client, InferenceClient):
+            image = client.text_to_image(prompt=prompt, negative_prompt=neg_prompt, **kwargs, token=HF_TOKEN)
+        elif isinstance(client, gr.Interface):
+            image = client.fn(prompt=prompt, negative_prompt=neg_prompt, **kwargs, token=HF_TOKEN)
+        else: return None
+        if isinstance(image, tuple): return None
+        return save_image(image, png_path, model_str, prompt, neg_prompt, height, width, steps, cfg, seed)
+    except Exception as e:
+        print(e)
+        raise Exception() from e
+
+
+async def infer(model_name: str, prompt: str, neg_prompt: str ="", height: int = 0, width: int = 0,
+               steps: int = 0, cfg: int = 0, seed: int = -1,
+               save_path: str | None = None, timeout: float = inference_timeout):
+    model = load_model(model_name)
+    if not model: return None
+    task = asyncio.create_task(asyncio.to_thread(infer_body, model, model_name, prompt, neg_prompt,
+                                                 height, width, steps, cfg, seed))
+    await asyncio.sleep(0)
+    try:
+        result = await asyncio.wait_for(task, timeout=timeout)
+    except asyncio.TimeoutError as e:
+        print(e)
+        print(f"Task timed out: {model_name}")
+        if not task.done(): task.cancel()
+        result = None
+        raise Exception(f"Task timed out: {model_name}") from e
+    except Exception as e:
+        print(e)
+        if not task.done(): task.cancel()
+        result = None
+        raise Exception() from e
+    if task.done() and result is not None:
+        with lock:
+            image = rename_image(result, model_name, save_path)
+        return image
+    return None
+
+
+# https://github.com/aio-libs/pytest-aiohttp/issues/8 # also AsyncInferenceClient is buggy.
+def infer_fn(model_name: str, prompt: str, neg_prompt: str = "", height: int = 0, width: int = 0,
+             steps: int = 0, cfg: int = 0, seed: int = -1,
+             pos_pre: list = [], pos_suf: list = [], neg_pre: list = [], neg_suf: list = [], save_path: str | None = None):
+    if model_name == 'NA':
+        return None
+    try:
+        loop = asyncio.get_running_loop()
+    except Exception:
+        loop = asyncio.new_event_loop()
+    try:
+        prompt, neg_prompt = recom_prompt(prompt, neg_prompt, pos_pre, pos_suf, neg_pre, neg_suf)
+        result = loop.run_until_complete(infer(model_name, prompt, neg_prompt, height, width,
+                                               steps, cfg, seed, save_path, inference_timeout))
+    except (Exception, asyncio.CancelledError) as e:
+        print(e)
+        print(f"Task aborted: {model_name}, Error: {e}")
+        result = None
+        raise gr.Error(f"Task aborted: {model_name}, Error: {e}")
+    finally:
+        loop.close()
+    return result
+
+
+def infer_rand_fn(model_name_dummy: str, prompt: str, neg_prompt: str = "", height: int = 0, width: int = 0,
+             steps: int = 0, cfg: int = 0, seed: int = -1,
+             pos_pre: list = [], pos_suf: list = [], neg_pre: list = [], neg_suf: list = [], save_path: str | None = None):
+    import random
+    if model_name_dummy == 'NA':
+        return None
+    random.seed()
+    model_name = random.choice(list(loaded_models.keys()))
+    try:
+        loop = asyncio.get_running_loop()
+    except Exception:
+        loop = asyncio.new_event_loop()
+    try:
+        prompt, neg_prompt = recom_prompt(prompt, neg_prompt, pos_pre, pos_suf, neg_pre, neg_suf)
+        result = loop.run_until_complete(infer(model_name, prompt, neg_prompt, height, width,
+                                               steps, cfg, seed, save_path, inference_timeout))
+    except (Exception, asyncio.CancelledError) as e:
+        print(e)
+        print(f"Task aborted: {model_name}, Error: {e}")
+        result = None
+        raise gr.Error(f"Task aborted: {model_name}, Error: {e}")
+    finally:
+        loop.close()
+    return result
+
+
+def save_image(image, savefile, modelname, prompt, nprompt, height=0, width=0, steps=0, cfg=0, seed=-1):
+    from PIL import Image, PngImagePlugin
+    import json
+    try:
+        metadata = {"prompt": prompt, "negative_prompt": nprompt, "Model": {"Model": modelname.split("/")[-1]}}
+        if steps > 0: metadata["num_inference_steps"] = steps
+        if cfg > 0: metadata["guidance_scale"] = cfg
+        if seed != -1: metadata["seed"] = seed
+        if width > 0 and height > 0: metadata["resolution"] = f"{width} x {height}"
+        metadata_str = json.dumps(metadata)
+        info = PngImagePlugin.PngInfo()
+        info.add_text("metadata", metadata_str)
+        image.save(savefile, "PNG", pnginfo=info)
+        return str(Path(savefile).resolve())
+    except Exception as e:
+        print(f"Failed to save image file: {e}")
+        raise Exception(f"Failed to save image file:") from e
+
+
+def randomize_seed():
+    from random import seed, randint
+    MAX_SEED = 2**32-1
+    seed()
+    rseed = randint(0, MAX_SEED)
+    return rseed
+
+
+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