import gradio as gr #import torch #from torch import autocast // only for GPU from PIL import Image import numpy as np from io import BytesIO import os MY_SECRET_TOKEN=os.environ.get('HF_TOKEN_SD') #from diffusers import StableDiffusionPipeline from diffusers import StableDiffusionImg2ImgPipeline print("hello sylvain") YOUR_TOKEN=MY_SECRET_TOKEN device="cpu" #prompt_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=YOUR_TOKEN) #prompt_pipe.to(device) img_pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=YOUR_TOKEN) img_pipe.to(device) source_img = gr.Image(source="upload", type="filepath", label="init_img | 512*512 px") gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[2], height="auto") def resize(value,img): #baseheight = value 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) img = img.resize((value,value), Image.Resampling.LANCZOS) return img def infer(source_img, prompt): source_image = resize(512, source_img) source_image.save('source.png') images_list = img_pipe([prompt] * 2, init_image=source_image, 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 print("Great sylvain ! Everything is working fine !") title="Img2Img Stable Diffusion CPU" description="Img2Img Stable Diffusion example using CPU and HF token.
Warning: Slow process... ~5/10 min inference time. NSFW filter enabled." gr.Interface(fn=infer, inputs=[source_img, "text"], outputs=gallery,title=title,description=description).queue(max_size=100, allow_flagging="manual").launch(enable_queue=True)