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| from diffusers import StableDiffusionPipeline | |
| from diffusers import StableDiffusionImg2ImgPipeline | |
| from diffusers import AutoencoderKL, UNet2DConditionModel | |
| import gradio as gr | |
| import torch | |
| models = [ | |
| "nitrosocke/Arcane-Diffusion", | |
| "nitrosocke/archer-diffusion", | |
| "nitrosocke/elden-ring-diffusion", | |
| "nitrosocke/spider-verse-diffusion", | |
| "nitrosocke/modern-disney-diffusion", | |
| "hakurei/waifu-diffusion", | |
| "lambdalabs/sd-pokemon-diffusers", | |
| "yuk/fuyuko-waifu-diffusion", | |
| "AstraliteHeart/pony-diffusion", | |
| "nousr/robo-diffusion", | |
| "DGSpitzer/Cyberpunk-Anime-Diffusion", | |
| "sd-dreambooth-library/herge-style" | |
| ] | |
| prompt_prefixes = { | |
| models[0]: "arcane style ", | |
| models[1]: "archer style ", | |
| models[2]: "elden ring style ", | |
| models[3]: "spiderverse style ", | |
| models[4]: "modern disney style ", | |
| models[5]: "", | |
| models[6]: "", | |
| models[7]: "", | |
| models[8]: "", | |
| models[9]: "", | |
| models[10]: "dgs illustration style ", | |
| models[11]: "herge_style ", | |
| } | |
| current_model = models[0] | |
| pipes = [] | |
| vae = AutoencoderKL.from_pretrained(current_model, subfolder="vae", torch_dtype=torch.float16) | |
| for model in models: | |
| unet = UNet2DConditionModel.from_pretrained(model, subfolder="unet", torch_dtype=torch.float16) | |
| pipe = StableDiffusionPipeline.from_pretrained(model, unet=unet, vae=vae, torch_dtype=torch.float16) | |
| pipe_i2i = StableDiffusionImg2ImgPipeline.from_pretrained(model, unet=unet, vae=vae, torch_dtype=torch.float16) | |
| pipes.append({"name":model, "pipeline":pipe, "pipeline_i2i":pipe_i2i}) | |
| device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶" | |
| def inference(model, img, strength, prompt, neg_prompt, guidance, steps, width, height, seed): | |
| generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None | |
| if img is not None: | |
| return txt_to_img(model, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator) | |
| else: | |
| return img_to_img(model, prompt, neg_prompt, guidance, steps, width, height, generator) | |
| def img_to_img(model, prompt, neg_prompt, guidance, steps, width, height, generator=None): | |
| global current_model | |
| global pipe | |
| if model != current_model: | |
| current_model = model | |
| pipe = pipe.to("cpu") | |
| for pipe_dict in pipes: | |
| if(pipe_dict["name"] == current_model): | |
| pipe = pipe_dict["pipeline"] | |
| if torch.cuda.is_available(): | |
| pipe = pipe.to("cuda") | |
| prompt = prompt_prefixes[current_model] + prompt | |
| image = pipe( | |
| prompt, | |
| negative_prompt=neg_prompt, | |
| num_inference_steps=int(steps), | |
| guidance_scale=guidance, | |
| width=width, | |
| height=height, | |
| generator=generator).images[0] | |
| return image | |
| def txt_to_img(model, prompt, neg_prompt, img, strength, guidance, steps, width, height, generator): | |
| global current_model | |
| global pipe | |
| if model != current_model: | |
| current_model = model | |
| pipe = pipe.to("cpu") | |
| for pipe_dict in pipes: | |
| if(pipe_dict["name"] == current_model): | |
| pipe = pipe_dict["pipeline_i2i"] | |
| if torch.cuda.is_available(): | |
| pipe = pipe.to("cuda") | |
| prompt = prompt_prefixes[current_model] + prompt | |
| ratio = min(height / img.height, width / img.width) | |
| img = img.resize((int(img.width * ratio), int(img.height * ratio))) | |
| image = 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).images[0] | |
| 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( | |
| """ | |
| <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> | |
| </div> | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| model = gr.Dropdown(label="Model", choices=models, value=models[0]) | |
| prompt = gr.Textbox(label="Prompt", placeholder="Style prefix is applied automatically") | |
| 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) | |
| width = gr.Slider(label="Width", value=512, maximum=1024, minimum=64) | |
| height = gr.Slider(label="Height", value=512, maximum=1024, minimum=64) | |
| 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) | |
| run = gr.Button(value="Run") | |
| gr.Markdown(f"Running on: {device}") | |
| inputs = [model, image, strength, prompt, neg_prompt, guidance, steps, width, height, seed] | |
| prompt.submit(inference, inputs=inputs, outputs=image_out) | |
| run.click(inference, inputs=inputs, outputs=image_out) | |
| gr.Examples([ | |
| [models[0], "jason bateman disassembling the demon core", 7.5, 50], | |
| [models[3], "portrait of dwayne johnson", 7.0, 75], | |
| [models[4], "portrait of a beautiful alyx vance half life", 10, 50], | |
| [models[5], "Aloy from Horizon: Zero Dawn, half body portrait, smooth, detailed armor, beautiful face, illustration", 7, 45], | |
| [models[4], "fantasy portrait painting, digital art", 4, 30], | |
| ], [model, prompt, guidance, steps], image_out, img_to_img, cache_examples=False) | |
| gr.Markdown(''' | |
| Models by [@nitrosocke](https://huggingface.co/nitrosocke), [@Helixngc7293](https://twitter.com/DGSpitzer) and others. ❤️<br> | |
| Space by: [](https://twitter.com/hahahahohohe) | |
|  | |
| ''') | |
| demo.queue() | |
| demo.launch() |