| 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 resize(height,img): | |
| baseheight = height | |
| img = Image.open(img) | |
| hpercent = (baseheight/float(img.size[1])) | |
| wsize = int((float(img.size[0])*float(hpercent))) | |
| img = img.resize((wsize,baseheight), Image.Resampling.LANCZOS) | |
| return img | |
| def img_preprocces(source_img, prompt, negative_prompt): | |
| imageio.imwrite("data.png", source_img["image"]) | |
| imageio.imwrite("data_mask.png", source_img["mask"]) | |
| src = resize(512, "data.png") | |
| src.save("src.png") | |
| mask = resize(512, "data_mask.png") | |
| mask.save("mask.png") | |
| return src, mask | |
| def stable_diffusion_inpaint( | |
| image_path:str, | |
| model_path:str, | |
| prompt:str, | |
| negative_prompt:str, | |
| guidance_scale:int, | |
| num_inference_step:int, | |
| ): | |
| image, mask_image = img_preprocces(image_path, prompt, negative_prompt) | |
| 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.Tab('Inpaint'): | |
| inpaint_image_file = gr.Image( | |
| source="upload", | |
| type="numpy", | |
| tool="sketch", | |
| elem_id="source_container" | |
| ) | |
| 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') | |
| variables = { | |
| "image_path": inpaint_image_file, | |
| "model_path": inpaint_model_id, | |
| "prompt": inpaint_prompt, | |
| "negative_prompt": inpaint_negative_prompt, | |
| "guidance_scale": inpaint_guidance_scale, | |
| "num_inference_step": inpaint_num_inference_step, | |
| "predict": inpaint_predict | |
| } | |
| return variables | |