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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] * 2, 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. <br />Warning: Slow process... ~5/10 min inference time. <b>NSFW filter enabled.</b><br />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) |