| import gradio as gr | |
| from diffusers import StableDiffusionPipeline | |
| import torch | |
| model_id = "runwayml/stable-diffusion-v1-5" | |
| pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float32, safety_checker=None) | |
| def infer(prompt): | |
| prompt = "a photo of an astronaut riding a horse on mars" | |
| image = pipe(prompt=prompt, guidance_scale=10.0, num_inference_steps=6, width=256, height=256).images[0] | |
| return image | |
| css=""" | |
| #col-container { | |
| margin: 0 auto; | |
| max-width: 720px; | |
| } | |
| """ | |
| with gr.Blocks(css=css) as demo: | |
| with gr.Column(elem_id="col-container"): | |
| with gr.Row(): | |
| prompt = gr.Text( | |
| label="Prompt", | |
| show_label=False, | |
| max_lines=1, | |
| placeholder="Enter your prompt", | |
| container=False, | |
| ) | |
| run_button = gr.Button("Run", scale=0) | |
| result = gr.Image(label="Result", show_label=False) | |
| run_button.click( | |
| fn = infer, | |
| inputs = [prompt], | |
| outputs = [result] | |
| ) | |
| demo.queue().launch() |