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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"
pipe = StableDiffusionImg2ImgPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=YOUR_TOKEN)
pipe.to(device)
source_img = gr.Image(image_mode="RGB",
source="upload",
type="filepath")
gallery = gr.Gallery(label="Generated images", show_label=False, elem_id="gallery").style(grid=[2], height="auto")
def resize(width,img):
basewidth = width
img = Image.open(img)
wpercent = (basewidth/float(img.size[0]))
hsize = int((float(img.size[1])*float(wpercent)))
img = img.resize((basewidth,hsize), Image.ANTIALIAS)
return img
def infer(prompt, init_image):
#init_image = resize(512,init_image)
#init_image = init_image.save("init_image.png")
#image = pipe(prompt, init_image=init_image)["sample"][0]
images_list = pipe([prompt] * 2, init_image=init_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="Stable Diffusion CPU"
description="Stable Diffusion example using CPU and HF token. <br />Warning: Slow process... ~5/10 min inference time. <b>NSFW filter enabled.</b>"
gr.Interface(fn=infer, inputs=["text", source_img], outputs=gallery,title=title,description=description).launch(enable_queue=True) |