| from diffusers import StableDiffusionImg2ImgPipeline | |
| import torch | |
| import gradio as gr | |
| def get_model(): | |
| pipe = StableDiffusionImg2ImgPipeline.from_pretrained("stabilityai/sdxl-turbo", variant="fp16") | |
| pipe.to("cpu") | |
| pipe.enable_model_cpu_offload() | |
| return pipe | |
| model = get_model() | |
| def return_image(text,inf_steps,gScale): | |
| image = model(prompt=text, num_inference_steps=inf_steps, guidance_scale=gScale).images[0] | |
| return image | |
| piece = gr.Interface( | |
| return_image, | |
| [ | |
| gr.Textbox(label = "Enter a prompt to describe the image you want to be generated!",placeholder = "A cinematic shot of a baby racoon wearing an intricate italian priest robe"), | |
| gr.Slider(minimum = 1, maximum = 50, step = 1,label = "number of inference steps"), | |
| gr.Slider(minimum = 0, maximum = 1, step = 0.1, label = "guidance scale"), | |
| ], | |
| outputs = [gr.Image(label = "Image Output")], | |
| title = "Stable Diffusion XL Turbo Model Demo" | |
| ) | |
| piece.launch() |