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app (3).py

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+import gradio as gr
+from model import models
+from multit2i import (load_models, infer_fn, infer_rand_fn, save_gallery,
+    change_model, warm_model, get_model_info_md, loaded_models,
+    get_positive_prefix, get_positive_suffix, get_negative_prefix, get_negative_suffix,
+    get_recom_prompt_type, set_recom_prompt_preset, get_tag_type, randomize_seed, translate_to_en)
+
+
+max_images = 8
+MAX_SEED = 2**32-1
+load_models(models)
+
+css = """
+.model_info { text-align: center; }
+.output { width=112px; height=112px; max_width=112px; max_height=112px; !important; }
+.gallery { min_width=512px; min_height=512px; max_height=1024px; !important; }
+"""
+
+with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", fill_width=True, css=css) as demo:
+    with gr.Tab("Image Generator"):
+        with gr.Row():
+            with gr.Column(scale=10): 
+                with gr.Group():
+                    prompt = gr.Text(label="Prompt", lines=2, max_lines=8, placeholder="1girl, solo, ...", show_copy_button=True)
+                    with gr.Accordion("Advanced options", open=False):
+                        neg_prompt = gr.Text(label="Negative Prompt", lines=1, max_lines=8, placeholder="")                      
+                        with gr.Row():
+                            width = gr.Slider(label="Width", info="If 0, the default value is used.", maximum=2048, step=32, value=0)
+                            height = gr.Slider(label="Height", info="If 0, the default value is used.", maximum=2048, step=32, value=0)
+                            steps = gr.Slider(label="Number of inference steps", info="If 0, the default value is used.", maximum=100, step=1, value=0)
+                        with gr.Row():
+                            cfg = gr.Slider(label="Guidance scale", info="If 0, the default value is used.", maximum=30.0, step=0.1, value=0)
+                            seed = gr.Slider(label="Seed", info="Randomize Seed if -1.", minimum=-1, maximum=MAX_SEED, step=1, value=-1)
+                            seed_rand = gr.Button("Randomize Seed 🎲", size="sm", variant="secondary")
+                        recom_prompt_preset = gr.Radio(label="Set Presets", choices=get_recom_prompt_type(), value="Common")
+                        with gr.Row():
+                            positive_prefix = gr.CheckboxGroup(label="Use Positive Prefix", choices=get_positive_prefix(), value=[])
+                            positive_suffix = gr.CheckboxGroup(label="Use Positive Suffix", choices=get_positive_suffix(), value=["Common"])
+                            negative_prefix = gr.CheckboxGroup(label="Use Negative Prefix", choices=get_negative_prefix(), value=[])
+                            negative_suffix = gr.CheckboxGroup(label="Use Negative Suffix", choices=get_negative_suffix(), value=["Common"])
+                    with gr.Row():
+                        image_num = gr.Slider(label="Number of images", minimum=1, maximum=max_images, value=1, step=1, interactive=True, scale=2)
+                        trans_prompt = gr.Button(value="Translate 📝", variant="secondary", size="sm", scale=2)
+                        clear_prompt = gr.Button(value="Clear 🗑️", variant="secondary", size="sm", scale=1)
+                with gr.Row():
+                    run_button = gr.Button("Generate Image", variant="primary", scale=8)
+                    random_button = gr.Button("Random Model 🎲", variant="secondary", scale=3)
+                    stop_button = gr.Button('Stop', interactive=False, variant="stop", scale=1)
+                 
+                with gr.Group():
+                    model_name = gr.Dropdown(label="Select Model", choices=list(loaded_models.keys()), value=list(loaded_models.keys())[0], allow_custom_value=True)
+                    model_info = gr.Markdown(value=get_model_info_md(list(loaded_models.keys())[0]), elem_classes="model_info")
+            with gr.Column(scale=10): 
+                with gr.Group():
+                    with gr.Row():
+                        output = [gr.Image(label='', elem_classes="output", type="filepath", format="png",
+                                show_download_button=True, show_share_button=False, show_label=False,
+                                interactive=False, min_width=80, visible=True, width=112, height=112) for _ in range(max_images)]
+                with gr.Group():
+                    results = gr.Gallery(label="Gallery", elem_classes="gallery", interactive=False, show_download_button=True, show_share_button=False,
+                                        container=True, format="png", object_fit="cover", columns=2, rows=2)
+                    image_files = gr.Files(label="Download", interactive=False)
+                    clear_results = gr.Button("Clear Gallery / Download 🗑️", variant="secondary")
+        with gr.Column():
+            examples = gr.Examples(
+                examples = [
+                    ["souryuu asuka langley, 1girl, neon genesis evangelion, plugsuit, pilot suit, red bodysuit, sitting, crossing legs, black eye patch, cat hat, throne, symmetrical, looking down, from bottom, looking at viewer, outdoors"],
+                    ["sailor moon, magical girl transformation, sparkles and ribbons, soft pastel colors, crescent moon motif, starry night sky background, shoujo manga style"],
+                    ["kafuu chino, 1girl, solo"],
+                    ["1girl"],
+                    ["beautiful sunset"],
+                ],
+                inputs=[prompt],
+                cache_examples=False,
+            )
+    with gr.Tab("PNG Info"):
+        def extract_exif_data(image):
+            if image is None: return ""
+            try:
+                metadata_keys = ['parameters', 'metadata', 'prompt', 'Comment']
+                for key in metadata_keys:
+                    if key in image.info:
+                        return image.info[key]
+                return str(image.info)
+            except Exception as e:
+                return f"Error extracting metadata: {str(e)}"
+        with gr.Row():
+            with gr.Column():
+                image_metadata = gr.Image(label="Image with metadata", type="pil", sources=["upload"])
+            with gr.Column():
+                result_metadata = gr.Textbox(label="Metadata", show_label=True, show_copy_button=True, interactive=False, container=True, max_lines=99)
+
+                image_metadata.change(
+                    fn=extract_exif_data,
+                    inputs=[image_metadata],
+                    outputs=[result_metadata],
+                )
+    gr.Markdown(
+        f"""This demo was created in reference to the following demos.<br>
+[Nymbo/Flood](https://huggingface.co/spaces/Nymbo/Flood), 
+[Yntec/ToyWorldXL](https://huggingface.co/spaces/Yntec/ToyWorldXL), 
+[Yntec/Diffusion80XX](https://huggingface.co/spaces/Yntec/Diffusion80XX).
+            """
+    )
+    gr.DuplicateButton(value="Duplicate Space")
+    gr.Markdown(f"Just a few edits to *model.py* are all it takes to complete your own collection.")
+
+    gr.on(triggers=[run_button.click, prompt.submit, random_button.click], fn=lambda: gr.update(interactive=True), inputs=None, outputs=stop_button, show_api=False)
+    model_name.change(change_model, [model_name], [model_info], queue=True, show_api=True)\
+    .success(warm_model, [model_name], None, queue=True, show_api=True)
+    for i, o in enumerate(output):
+        img_i = gr.Number(i, visible=False)
+        image_num.change(lambda i, n: gr.update(visible = (i < n)), [img_i, image_num], o, show_api=True)
+        gen_event = gr.on(triggers=[run_button.click, prompt.submit],
+         fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4: infer_fn(m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4) if (i < n) else None,
+         inputs=[img_i, image_num, model_name, prompt, neg_prompt, height, width, steps, cfg, seed,
+                  positive_prefix, positive_suffix, negative_prefix, negative_suffix],
+         outputs=[o], queue=True, show_api=False) # Be sure to delete ", queue=False" when activating the stop button
+        gen_event2 = gr.on(triggers=[random_button.click],
+         fn=lambda i, n, m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4: infer_rand_fn(m, t1, t2, n1, n2, n3, n4, n5, l1, l2, l3, l4) if (i < n) else None,
+         inputs=[img_i, image_num, model_name, prompt, neg_prompt, height, width, steps, cfg, seed,
+                  positive_prefix, positive_suffix, negative_prefix, negative_suffix],
+         outputs=[o], queue=True, show_api=False) # Be sure to delete ", queue=False" when activating the stop button
+        o.change(save_gallery, [o, results], [results, image_files], show_api=False)
+        stop_button.click(lambda: gr.update(interactive=False), None, stop_button, cancels=[gen_event, gen_event2], show_api=False)
+
+    clear_prompt.click(lambda: (None, None), None, [prompt, neg_prompt], queue=True, show_api=True)
+    clear_results.click(lambda: (None, None), None, [results, image_files], queue=True, show_api=True)
+    recom_prompt_preset.change(set_recom_prompt_preset, [recom_prompt_preset],
+     [positive_prefix, positive_suffix, negative_prefix, negative_suffix], queue=True, show_api=True)
+    seed_rand.click(randomize_seed, None, [seed], queue=True, show_api=True)
+    trans_prompt.click(translate_to_en, [prompt], [prompt], queue=True, show_api=True)\
+    .then(translate_to_en, [neg_prompt], [neg_prompt], queue=True, show_api=True)
+
+
+
+
+demo.queue(default_concurrency_limit=240, max_size=240)
+demo.launch(max_threads=400, ssr_mode=True)
+# https://github.com/gradio-app/gradio/issues/6339
+
+demo.queue(concurrency_count=50)
+demo.launch()

model (2).py

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+from multit2i import find_model_list
+
+models = [
+    'digiplay/m3u',
+    'digiplay/FishMix_v1.1',
+    'digiplay/YabaLMixAnimeRealistic_V1.0_VAE',
+    'digiplay/unstableDiffusersYamerMIX_v3',
+    'digiplay/cosfMix_v1',
+    'digiplay/rRealism_v1.0_riiwa',
+    'vcolamatteo/pokemon-text_to_image_lora_1',
+    'stabilityai/stable-diffusion-2',
+    'KBlueLeaf/kohaku-xl-beta5',
+    'calcuis/illustrious',
+    'stabilityai/stable-diffusion-3.5-large-turbo',
+    'stabilityai/stable-diffusion-3.5-large',
+    'black-forest-labs/FLUX.1-dev',
+    'votepurchase/kivotos-xl-2.',
+    'strangerzonehf/Flux-Isometric-3D-LoRA',
+    'prithivMLmods/SD3.5-Large-Anime-LoRA',
+    'Linaqruf/animagine-xl-2.0',
+    'ckpt/kivotos-xl-2.0',
+    'yodayo-ai/clandestine-xl-1.0',
+    'yodayo-ai/kivotos-xl-2.0',
+    'yodayo-ai/holodayo-xl-2.1',
+    'cagliostrolab/animagine-xl-3.1',
+    'votepurchase/ponyDiffusionV6XL',
+    'eienmojiki/Anything-XL',
+    'eienmojiki/Starry-XL-v5.2',
+    "digiplay/MilkyWonderland_v1",
+    'digiplay/majicMIX_sombre_v2',
+    'digiplay/majicMIX_realistic_v7',
+    'digiplay/majicMIX_realistic_v6',
+    'digiplay/2K',
+    'digiplay/2K-VAE',
+    'digiplay/ya3_VAE',
+    'digiplay/ya3p_VAE',
+    'digiplay/pan04',
+    'digiplay/AM-mix1',
+    'digiplay/MRMD_0505',
+    'Yntec/NostalgicLife',
+    'Yntec/Genuine',
+    'Yntec/Abased',
+    'Yntec/CuteFurry',
+    'Yntec/GOLDFish',
+    'Yntec/Isabelia',
+    'Yntec/incha_re_zoro',
+    'Yntec/InsaneM3U',    
+    'votepurchase/counterfeitV30_v30',
+    'Meina/MeinaMix_V11',
+    'KBlueLeaf/Kohaku-XL-Epsilon-rev3',
+    'KBlueLeaf/Kohaku-XL-Zeta',
+    'kayfahaarukku/UrangDiffusion-1.4',
+    'Eugeoter/artiwaifu-diffusion-2.0',
+    'Raelina/Rae-Diffusion-XL-V2',
+    'Raelina/Raemu-XL-V4',    
+    'FluffyKaeloky/Midnight-Miqu-103B-v1.5',   
+    'Sombressoul/Yi-34B-200K-AWQ',  
+    'Jonjew/NSFWMaster',
+    'xey/sldr_flux_nsfw_v2-studio',
+]
+
+#models = find_model_list("Disty0", [], "", "last_modified", 100)
+
+
+# Examples:
+#models = ['yodayo-ai/kivotos-xl-2.0', 'yodayo-ai/holodayo-xl-2.1'] # specific models
+#models = find_model_list("John6666", [], "", "last_modified", 20) # John6666's latest 20 models
+#models = find_model_list("John6666", ["anime"], "", "last_modified", 20) # John6666's latest 20 models with 'anime' tag
+#models = find_model_list("John6666", [], "anime", "last_modified", 20) # John6666's latest 20 models without 'anime' tag
+#models = find_model_list("", [], "", "last_modified", 20) # latest 20 text-to-image models of huggingface
+#models = find_model_list("", [], "", "downloads", 20) # monthly most downloaded 20 text-to-image models of huggingface

multit2i (2).py

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+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=150)
+    return client.get_model_status(model_name)
+
+
+def is_loadable(model_name: str, force_gpu: bool = True):
+    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

requirements (3).txt

@@ -0,0 +1,3 @@
+huggingface_hub
+translatepy
+torch