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 = """ """ with gr.Blocks(css=css) as demo: gr.HTML( """

Finetuned Diffusion

Demo for multiple fine-tuned Stable Diffusion models, trained on different styles:
Arcane, Archer, Elden Ring, Spiderverse, Modern Disney, Waifu, Pokemon, Fuyuko Waifu, Pony, Hergé (Tintin), Robo, Cyberpunk Anime

""" ) 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. ❤️
Space by: [![Twitter Follow](https://img.shields.io/twitter/follow/hahahahohohe?label=%40anzorq&style=social)](https://twitter.com/hahahahohohe) ![visitors](https://visitor-badge.glitch.me/badge?page_id=anzorq.finetuned_diffusion) ''') demo.queue() demo.launch()