from diffusers import StableDiffusionPipeline import gradio as gr import torch device = "GPU 🔥" if torch.cuda.is_available() else "CPU 🥶" pipe = StableDiffusionPipeline.from_pretrained("nitrosocke/Arcane-Diffusion", torch_dtype=torch.float16) if torch.cuda.is_available(): pipe = pipe.to("cuda") def inference(prompt, guidance, steps): prompt = prompt + ", arcane style" 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( """

Arcane Diffusion

Demo for a fine-tuned Stable Diffusion model trained on images from the TV Show Arcane.

""" ) with gr.Row(): with gr.Column(): prompt = gr.Textbox(label="prompt", placeholder="' , arcane style' is appended automatically") 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=[prompt, guidance, steps], outputs=image_out) gr.Examples([ ["jason bateman disassembling the demon core", 7.5, 50], ["portrait of dwayne johnson", 7.0, 75], ["portrait of a beautiful alyx vance half life, volume lighting, concept art, by greg rutkowski!!, colorful, xray melting colors!!", 7, 50], ["Aloy from Horizon: Zero Dawn, half body portrait, videogame cover art, highly detailed, digital painting, artstation, concept art, smooth, detailed armor, sharp focus, beautiful face, illustration, art by Artgerm and greg rutkowski and alphonse mucha", 7, 50], ["fantasy portrait painting, digital art", 4, 30], ], [prompt, guidance, steps], image_out, inference, cache_examples=torch.cuda.is_available()) gr.HTML('''

Model by @nitrosocke ❤️

Space by Twitter Follow
''') demo.queue() demo.launch()