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| from diffusers import StableDiffusionPipeline | |
| from diffusers import StableDiffusionImg2ImgPipeline | |
| import gradio as gr | |
| import torch | |
| from PIL import Image | |
| import utils | |
| is_colab = utils.is_google_colab() | |
| max_width = 832 | |
| max_height = 832 | |
| class Model: | |
| def __init__(self, name, path, prefix): | |
| self.name = name | |
| self.path = path | |
| self.prefix = prefix | |
| models = [ | |
| Model("Arcane", "nitrosocke/Arcane-Diffusion", "arcane style "), | |
| Model("Archer", "nitrosocke/archer-diffusion", "archer style "), | |
| Model("Elden Ring", "nitrosocke/elden-ring-diffusion", "elden ring style "), | |
| Model("Spider-Verse", "nitrosocke/spider-verse-diffusion", "spiderverse style "), | |
| Model("Modern Disney", "nitrosocke/modern-disney-diffusion", "modern disney style "), | |
| Model("Classic Disney", "nitrosocke/classic-anim-diffusion", ""), | |
| Model("Waifu", "hakurei/waifu-diffusion", ""), | |
| Model("Pokémon", "lambdalabs/sd-pokemon-diffusers", ""), | |
| Model("Fuyuko Waifu", "yuk/fuyuko-waifu-diffusion", ""), | |
| Model("Pony Diffusion", "AstraliteHeart/pony-diffusion", ""), | |
| Model("Robo Diffusion", "nousr/robo-diffusion", ""), | |
| Model("Cyberpunk Anime", "DGSpitzer/Cyberpunk-Anime-Diffusion", "dgs illustration style "), | |
| Model("Hergé Style", "sd-dreambooth-library/herge-style", "herge_style "), | |
| ] | |
| current_model = models[0] | |
| pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16) | |
| if torch.cuda.is_available(): | |
| pipe = pipe.to("cuda") | |
| device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶" | |
| def inference(model_name, prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt=""): | |
| generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None | |
| if img is not None: | |
| return img_to_img(model_name, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator) | |
| else: | |
| return txt_to_img(model_name, prompt, neg_prompt, guidance, steps, width, height, generator) | |
| def txt_to_img(model_name, prompt, neg_prompt, guidance, steps, width, height, generator=None): | |
| global current_model | |
| global pipe | |
| if model_name != current_model.name: | |
| for model in models: | |
| if model.name == model_name: | |
| current_model = model | |
| pipe = StableDiffusionPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16) | |
| if torch.cuda.is_available(): | |
| pipe = pipe.to("cuda") | |
| prompt = current_model.prefix + prompt | |
| results = pipe( | |
| prompt, | |
| negative_prompt=neg_prompt, | |
| num_inference_steps=int(steps), | |
| guidance_scale=guidance, | |
| width=width, | |
| height=height, | |
| generator=generator) | |
| image = results.images[0] if not results.nsfw_content_detected[0] else Image.open("nsfw.png") | |
| return image | |
| def img_to_img(model, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator): | |
| global current_model | |
| global pipe | |
| if model_name != current_model.name: | |
| for model in models: | |
| if model.name == model_name: | |
| current_model = model | |
| pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model.path, torch_dtype=torch.float16) | |
| if torch.cuda.is_available(): | |
| pipe = pipe.to("cuda") | |
| prompt = current_model.prefix + prompt | |
| ratio = min(max_height / img.height, max_width / img.width) | |
| img = img.resize((int(img.width * ratio), int(img.height * ratio))) | |
| results = pipe( | |
| prompt, | |
| negative_prompt=neg_prompt, | |
| init_image=img, | |
| num_inference_steps=int(steps), | |
| strength=strength, | |
| guidance_scale=guidance, | |
| width=width, | |
| height=height, | |
| generator=generator) | |
| image = results.images[0] if not results.nsfw_content_detected[0] else Image.open("nsfw.png") | |
| return image | |
| css = """ | |
| <style> | |
| .finetuned-diffusion-div { | |
| text-align: center; | |
| max-width: 700px; | |
| margin: 0 auto; | |
| } | |
| .finetuned-diffusion-div div { | |
| display: inline-flex; | |
| align-items: center; | |
| gap: 0.8rem; | |
| font-size: 1.75rem; | |
| } | |
| .finetuned-diffusion-div div h1 { | |
| font-weight: 900; | |
| margin-bottom: 7px; | |
| } | |
| .finetuned-diffusion-div p { | |
| margin-bottom: 10px; | |
| font-size: 94%; | |
| } | |
| .finetuned-diffusion-div p a { | |
| text-decoration: underline; | |
| } | |
| </style> | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| gr.HTML( | |
| f""" | |
| <div class="finetuned-diffusion-div"> | |
| <div> | |
| <h1>Finetuned Diffusion</h1> | |
| </div> | |
| <p> | |
| Demo for multiple fine-tuned Stable Diffusion models, trained on different styles: <br> | |
| <a href="https://huggingface.co/nitrosocke/Arcane-Diffusion">Arcane</a>, <a href="https://huggingface.co/nitrosocke/archer-diffusion">Archer</a>, <a href="https://huggingface.co/nitrosocke/elden-ring-diffusion">Elden Ring</a>, <a href="https://huggingface.co/nitrosocke/spider-verse-diffusion">Spiderverse</a>, <a href="https://huggingface.co/nitrosocke/modern-disney-diffusion">Modern Disney</a>, <a href="https://huggingface.co/hakurei/waifu-diffusion">Waifu</a>, <a href="https://huggingface.co/lambdalabs/sd-pokemon-diffusers">Pokemon</a>, <a href="https://huggingface.co/yuk/fuyuko-waifu-diffusion">Fuyuko Waifu</a>, <a href="https://huggingface.co/AstraliteHeart/pony-diffusion">Pony</a>, <a href="https://huggingface.co/sd-dreambooth-library/herge-style">Hergé (Tintin)</a>, <a href="https://huggingface.co/nousr/robo-diffusion">Robo</a>, <a href="https://huggingface.co/DGSpitzer/Cyberpunk-Anime-Diffusion">Cyberpunk Anime</a> | |
| </p> <br> | |
| <p> | |
| Running on <b>{device}</b> | |
| </p> | |
| </div> | |
| """ | |
| ) | |
| # gr.Markdown(f"Running on: {device}", elem_id="markdown_device") | |
| with gr.Row(): | |
| with gr.Column(): | |
| model_name = gr.Dropdown(label="Model", choices=[m.name for m in models], value=models[0].name) | |
| prompt = gr.Textbox(label="Prompt", placeholder="Style prefix is applied automatically") | |
| run = gr.Button(value="Run") | |
| with gr.Tab("Options"): | |
| neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image") | |
| guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15) | |
| steps = gr.Slider(label="Steps", value=50, maximum=100, minimum=2, step=1) | |
| width = gr.Slider(label="Width", value=512, maximum=max_width, minimum=64, step=8) | |
| height = gr.Slider(label="Height", value=512, maximum=max_height, minimum=64, step=8) | |
| seed = gr.Slider(0, 2147483647, label='Seed (0 = random)', value=0, step=1) | |
| with gr.Tab("Image to image"): | |
| image = gr.Image(label="Image", height=256, tool="editor", type="pil") | |
| strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5) | |
| with gr.Column(): | |
| image_out = gr.Image(height=512) | |
| inputs = [model_name, prompt, guidance, steps, width, height, seed, image, strength, neg_prompt] | |
| prompt.submit(inference, inputs=inputs, outputs=image_out) | |
| run.click(inference, inputs=inputs, outputs=image_out) | |
| gr.Examples([ | |
| [models[0].name, "jason bateman disassembling the demon core", 7.5, 50], | |
| [models[3].name, "portrait of dwayne johnson", 7.0, 75], | |
| [models[4].name, "portrait of a beautiful alyx vance half life", 10, 50], | |
| [models[5].name, "Aloy from Horizon: Zero Dawn, half body portrait, smooth, detailed armor, beautiful face, illustration", 7.0, 45], | |
| [models[4].name, "fantasy portrait painting, digital art", 4.0, 30], | |
| ], [model_name, prompt, guidance, steps, seed], image_out, inference, cache_examples=not is_colab and torch.cuda.is_available()) | |
| gr.Markdown(''' | |
| Models by [@nitrosocke](https://huggingface.co/nitrosocke), [@Helixngc7293](https://twitter.com/DGSpitzer) and others. ❤️<br> | |
| Space by: [](https://twitter.com/hahahahohohe) | |
|  | |
| ''') | |
| if not is_colab: | |
| demo.queue() | |
| demo.launch(debug=is_colab) |