from diffusers import StableDiffusionImg2ImgPipeline, DDIMScheduler from PIL import Image import gradio as gr import torch stable_model_list = [ "runwayml/stable-diffusion-v1-5", "stabilityai/stable-diffusion-2", "stabilityai/stable-diffusion-2-base", "stabilityai/stable-diffusion-2-1", "stabilityai/stable-diffusion-2-1-base" ] stable_inpiant_model_list = [ "stabilityai/stable-diffusion-2-inpainting", "runwayml/stable-diffusion-inpainting" ] stable_prompt_list = [ "a photo of a man.", "a photo of a girl." ] stable_negative_prompt_list = [ "bad, ugly", "deformed" ] def stable_diffusion_img2img( image_path:str, model_path:str, prompt:str, negative_prompt:str, guidance_scale:int, num_inference_step:int, ): image = Image.open(image_path) pipe = StableDiffusionImg2ImgPipeline.from_pretrained( model_path, safety_checker=None, torch_dtype=torch.float16 ) pipe.to("cuda") pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config) pipe.enable_xformers_memory_efficient_attention() output = pipe( prompt = prompt, image = image, negative_prompt = negative_prompt, num_inference_steps = num_inference_step, guidance_scale = guidance_scale, ).images return output[0] def stable_diffusion_img2img_app(): with gr.Blocks(): with gr.Row(): with gr.Column(): image2image2_image_file = gr.Image( type='filepath', label='Image' ) image2image_model_path = gr.Dropdown( choices=stable_model_list, value=stable_model_list[0], label='Image-Image Model Id' ) image2image_prompt = gr.Textbox( lines=1, value=stable_prompt_list[0], label='Prompt' ) image2image_negative_prompt = gr.Textbox( lines=1, value=stable_negative_prompt_list[0], label='Negative Prompt' ) with gr.Accordion("Advanced Options", open=False): image2image_guidance_scale = gr.Slider( minimum=0.1, maximum=15, step=0.1, value=7.5, label='Guidance Scale' ) image2image_num_inference_step = gr.Slider( minimum=1, maximum=100, step=1, value=50, label='Num Inference Step' ) image2image_predict = gr.Button(value='Generator') with gr.Column(): output_image = gr.Image(label='Output') image2image_predict.click( fn=stable_diffusion_img2img, inputs=[ image2image2_image_file, image2image_model_path, image2image_prompt, image2image_negative_prompt, image2image_guidance_scale, image2image_num_inference_step, ], outputs=[output_image], )