from diffusers import DiffusionPipeline 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 = DiffusionPipeline.from_pretrained("runwayml/stable-diffusion-inpainting", 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(source_img, 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") images_list = pipe([prompt] * 1, image=src, mask_image=mask, strength=0.75) images = [] safe_image = Image.open(r"unsafe.png") for i, image in enumerate(images_list["images"]): 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.
Warning: Slow process... ~5/10 min inference time. NSFW filter enabled.
Please use 512*512 square image as input to avoid memory error !" gr.Interface(fn=predict, inputs=[source_img, "text"], outputs=gallery, css=custom_css, title=title, description=description, allow_flagging="manual").launch(enable_queue=True)