Super-squash branch 'main' using huggingface_hub

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README.md

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+---
+title: Free Multi Models Text-to-Image Demo
+emoji: 🌐🌊
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 4.39.0
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+from multit2i import (
+    load_models,
+    find_model_list,
+    infer_multi,
+    save_gallery_images,
+    change_model,
+    get_model_info_md,
+    loaded_models,
+)
+
+
+models = [
+    'cagliostrolab/animagine-xl-3.1',
+    'votepurchase/ponyDiffusionV6XL',
+    'yodayo-ai/kivotos-xl-2.0',
+    'yodayo-ai/holodayo-xl-2.1',
+    'stabilityai/stable-diffusion-xl-base-1.0',
+    'eienmojiki/Anything-XL',
+    'eienmojiki/Starry-XL-v5.2',
+    'digiplay/majicMIX_sombre_v2',
+    'digiplay/majicMIX_realistic_v7',
+    'digiplay/DreamShaper_8',
+    'digiplay/BeautifulArt_v1',
+    'digiplay/DarkSushi2.5D_v1',
+    'digiplay/darkphoenix3D_v1.1',
+    'digiplay/BeenYouLiteL11_diffusers',
+    'votepurchase/counterfeitV30_v30',
+    'Meina/MeinaMix_V11',
+    'Meina/MeinaUnreal_V5',
+    'Meina/MeinaPastel_V7',
+    'KBlueLeaf/Kohaku-XL-Epsilon-rev2',
+    'KBlueLeaf/Kohaku-XL-Epsilon-rev3',
+    'kayfahaarukku/UrangDiffusion-1.1',
+    'Raelina/Rae-Diffusion-XL-V2',
+    'Raelina/Raemu-XL-V4',
+]
+
+
+# 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
+
+
+load_models(models, 10)
+#load_models(models, 20) # Fetching 20 models at the same time. default: 5
+
+
+css = """"""
+
+with gr.Blocks(theme="NoCrypt/miku@>=1.2.2", css=css) as demo:
+    with gr.Column(): 
+        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]))
+        image_num = gr.Slider(label="Number of Images", minimum=1, maximum=8, value=1, step=1)
+        recom_prompt = gr.Checkbox(label="Recommended Prompt", value=True)
+        prompt = gr.Text(label="Prompt", lines=1, max_lines=8, placeholder="1girl, solo, ...")
+        run_button = gr.Button("Generate Image")
+        results = gr.Gallery(label="Gallery", interactive=False, show_download_button=True, show_share_button=False,
+                              container=True, format="png", object_fit="contain")
+        image_files = gr.Files(label="Download", interactive=False)
+        clear_results = gr.Button("Clear Gallery and Download")
+    gr.Markdown(
+        f"""This demo was created in reference to the following demos.
+- [Nymbo/Flood](https://huggingface.co/spaces/Nymbo/Flood).
+- [Yntec/ToyWorldXL](https://huggingface.co/spaces/Yntec/ToyWorldXL).
+<br>The first startup takes a mind-boggling amount of time, but not so much after the second.
+This is due to the time it takes for Gradio to generate an example image to cache.
+            """
+    )
+    gr.DuplicateButton(value="Duplicate Space")
+
+    model_name.change(change_model, [model_name], [model_info], queue=False, show_api=False)
+    gr.on(
+        triggers=[run_button.click, prompt.submit],
+        fn=infer_multi,
+        inputs=[prompt, model_name, recom_prompt, image_num, results],
+        outputs=[results],
+        queue=True,
+        show_progress="full",
+        show_api=True,
+    ).success(save_gallery_images, [results], [results, image_files], queue=False, show_api=False)
+    clear_results.click(lambda: (None, None), None, [results, image_files], queue=False, show_api=False)
+
+demo.queue()
+demo.launch()

multit2i.py

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+import gradio as gr
+import asyncio
+from pathlib import Path
+
+
+loaded_models = {}
+model_info_dict = {}
+
+
+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 find_model_list(author: str="", tags: list[str]=[], not_tag="", sort: str="last_modified", limit: int=30):
+    from huggingface_hub import HfApi
+    api = HfApi()
+    default_tags = ["diffusers"]
+    if not sort: sort = "last_modified"
+    models = []
+    try:
+        model_infos = api.list_models(author=author, task="text-to-image", pipeline_tag="text-to-image",
+                                       tags=list_uniq(default_tags + tags), cardData=True, sort=sort, limit=limit * 5)
+    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:
+           if not_tag and not_tag 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()
+    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)
+    except Exception as e:
+        print(f"Error: Failed to get {repo_id}'s info.")
+        print(e)
+        return info
+    if model.private or model.gated: return info
+    try:
+        tags = model.tags
+    except Exception:
+        return info
+    if not 'diffusers' in model.tags: return info
+    if '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}/"
+    if model.card_data and model.card_data.tags:
+        info["tags"] = model.card_data.tags
+    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 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
+    output_images = []
+    output_paths = []
+    for image in images:
+        filename = f'{image[1]}_{basename}{str(i)}.png'
+        i += 1
+        oldpath = Path(image[0])
+        newpath = oldpath
+        try:
+            if oldpath.stem == "image" and oldpath.exists():
+                newpath = oldpath.resolve().rename(Path(filename).resolve())
+        except Exception as e:
+           print(e)
+           pass
+        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)
+
+
+def load_model(model_name: str):
+    global loaded_models
+    global model_info_dict
+    if model_name in loaded_models.keys(): return model_name
+    try:
+        loaded_models[model_name] = gr.load(f'models/{model_name}')
+        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 ""
+    try:
+        model_info_dict[model_name] = get_t2i_model_info_dict(model_name)
+    except Exception as e:
+        if model_name in model_info_dict.keys(): del model_info_dict[model_name]
+        print(e)
+    return model_name
+
+
+async def async_load_models(models: list, limit: int=5):
+    sem = asyncio.Semaphore(limit)
+    async def async_load_model(model: str):
+        async with sem:
+           try:
+               return load_model(model)
+           except Exception as e:
+               print(e)
+    tasks = [asyncio.create_task(async_load_model(model)) for model in models]
+    return await asyncio.wait(tasks)
+
+
+def load_models(models: list, limit: int=5):
+    loop = asyncio.get_event_loop()
+    try:
+        loop.run_until_complete(async_load_models(models, limit))
+    except Exception as e:
+        print(e)
+        pass
+    loop.close()
+
+
+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(model_name)
+    return get_model_info_md(model_name)
+
+
+def infer(prompt: str, model_name: str, recom_prompt: bool, progress=gr.Progress(track_tqdm=True)):
+    from PIL import Image
+    import random
+    seed = ""
+    rand = random.randint(1, 500)
+    for i in range(rand):
+        seed += " "
+    rprompt = ", highly detailed, masterpiece, best quality, very aesthetic, absurdres, " if recom_prompt else ""
+    caption = model_name.split("/")[-1]
+    try:
+        model = load_model(model_name)
+        if not model: return (None, None)
+        image_path = model(prompt + rprompt + seed)
+        image = Image.open(image_path).convert('RGB')
+    except Exception as e:
+        print(e)
+        return (None, None)
+    return (image, caption)
+
+
+def infer_multi(prompt: str, model_name: str, recom_prompt: bool, image_num: float, results: list, progress=gr.Progress(track_tqdm=True)):
+    image_num = int(image_num)
+    images = results if results else []
+    for i in range(image_num):
+        images.append(infer(prompt, model_name, recom_prompt))
+        yield images

requirements.txt

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+huggingface_hub