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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"
#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. <br />Warning: Slow process... ~5/10 min inference time. <b>NSFW filter enabled.</b>"
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) |