from diffusers import DiffusionPipeline, DDIMScheduler from PIL import Image import imageio import torch import gradio as gr 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_inpaint( dict:str, model_path:str, prompt:str, negative_prompt:str, guidance_scale:int, num_inference_step:int, ): image = dict["image"].convert("RGB").resize((512, 512)) mask_image = dict["mask"].convert("RGB").resize((512, 512)) pipe = DiffusionPipeline.from_pretrained( model_path, revision="fp16", 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, mask_image=mask_image, negative_prompt = negative_prompt, num_inference_steps = num_inference_step, guidance_scale = guidance_scale, ).images return output[0] def stable_diffusion_inpaint_app(): with gr.Blocks(): with gr.Row(): with gr.Column(): inpaint_image_file = gr.Image( source='upload', tool='sketch', elem_id="image_upload", type="pil", label="Upload" ) inpaint_model_id = gr.Dropdown( choices=stable_inpiant_model_list, value=stable_inpiant_model_list[0], label='Inpaint Model Id' ) inpaint_prompt = gr.Textbox( lines=1, value=stable_prompt_list[0], label='Prompt' ) inpaint_negative_prompt = gr.Textbox( lines=1, value=stable_negative_prompt_list[0], label='Negative Prompt' ) with gr.Accordion("Advanced Options", open=False): inpaint_guidance_scale = gr.Slider( minimum=0.1, maximum=15, step=0.1, value=7.5, label='Guidance Scale' ) inpaint_num_inference_step = gr.Slider( minimum=1, maximum=100, step=1, value=50, label='Num Inference Step' ) inpaint_predict = gr.Button(value='Generator') with gr.Column(): output_image = gr.Gallery(label="Outputs") inpaint_predict.click( fn=stable_diffusion_inpaint, inputs=[ inpaint_image_file, inpaint_model_id, inpaint_prompt, inpaint_negative_prompt, inpaint_guidance_scale, inpaint_num_inference_step, ], outputs=output_image )