| from diffusers import StableDiffusionInpaintPipeline | |
| import gradio as gr | |
| import numpy as np | |
| import imageio | |
| from PIL import Image | |
| from io import BytesIO | |
| import os | |
| MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD') | |
| print("hello sylvain") | |
| YOUR_TOKEN=MY_SECRET_TOKEN | |
| device="cpu" | |
| pipe = StableDiffusionInpaintPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=YOUR_TOKEN) | |
| pipe.to(device) | |
| source_img = gr.Image(source="upload", type="numpy", tool="sketch", elem_id="source_container"); | |
| gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[2], height="auto") | |
| 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 predict(prompt, source_img): | |
| 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") | |
| images_list = pipe([prompt] * 2, init_image=src, mask_image=mask, strength=0.75) | |
| images = [] | |
| safe_image = Image.open(r"unsafe.png") | |
| for i, image in enumerate(images_list["sample"]): | |
| if(images_list["nsfw_content_detected"][i]): | |
| images.append(safe_image) | |
| else: | |
| images.append(image) | |
| return images | |
| custom_css="style.css" | |
| title="InPainting Stable Diffusion CPU" | |
| description="Inpainting Stable Diffusion example using CPU and HF token. <br />Warning: Slow process... ~5/10 min inference time. <b>NSFW filter enabled.</b><br /><span style=\"color:'red';\">Please use 512*512 square image as input to avoid memory error </span>" | |
| gr.Interface(fn=predict, inputs=["text", source_img], outputs=gallery, css=custom_css, title=title, description=description).launch(enable_queue=True) |