from diffusers import StableDiffusionPipeline from diffusers import StableDiffusionImg2ImgPipeline 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", "IfanSnek/JohnDiffusion", "nousr/robo-diffusion" ] 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]: "", } current_model = models[0] pipe = StableDiffusionPipeline.from_pretrained(current_model, 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, prompt, img, guidance, steps): if img is not None: return img_inference(model, prompt, img, guidance, steps) else: return text_inference(model, prompt, guidance, steps) def text_inference(model, prompt, guidance, steps): global current_model global pipe if model != current_model: current_model = model pipe = StableDiffusionPipeline.from_pretrained(current_model, torch_dtype=torch.float16) if torch.cuda.is_available(): pipe = pipe.to("cuda") prompt = prompt_prefixes[current_model] + prompt image = pipe(prompt, num_inference_steps=int(steps), guidance_scale=guidance, width=512, height=512).images[0] return image def img_inference(model, prompt, img, guidance, steps): global current_model global pipe if model != current_model: current_model = model pipe = StableDiffusionImg2ImgPipeline.from_pretrained(current_model, torch_dtype=torch.float16) if torch.cuda.is_available(): pipe = pipe.to("cuda") prompt = prompt_prefixes[current_model] + prompt img.resize((512, 512)) image = pipe( prompt, init_image=img, num_inference_steps=int(steps), strength=0.75, guidance_scale=guidance, width=512, height=512).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, John, Robo.

""" ) 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") img = gr.Image(label="img2img (optional)", type="pil", height=256, tool="editor") guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15) steps = gr.Slider(label="Steps", value=50, maximum=100, minimum=2) run = gr.Button(value="Run") gr.Markdown(f"Running on: {device}") with gr.Column(): image_out = gr.Image(height=512) run.click(inference, inputs=[model, prompt, img, guidance, steps], 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, text_inference, cache_examples=torch.cuda.is_available()) gr.HTML('''

Model by @nitrosocke ❤️

Space by Twitter Follow
''') demo.queue() demo.launch(debug=True)