from diffusers import StableDiffusionPipeline import gradio as gr import torch models = [ "nitrosocke/Arcane-Diffusion", "nitrosocke/archer-diffusion", "nitrosocke/elden-ring-diffusion", "nitrosocke/spider-verse-diffusion" ] prompt_prefixes = { models[0]: "arcane style ", models[1]: "archer style ", models[2]: "elden ring style ", models[3]: "spiderverse style " } 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 on_model_change(model): 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") def inference(prompt, guidance, steps): 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 with gr.Blocks() as demo: gr.HTML( """
Demo for multiple fine-tuned Stable Diffusion models, trained on different styles: Arcane, Archer, Elden Ring, Spiderverse.
Model by @nitrosocke ❤️