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import imageio
import numpy as np
from PIL import Image
from diffusers import AutoPipelineForInpainting
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device for I2I: {device}")
# Load the inpainting pipeline
def resize_image(image, height, width):
"""Resize image tensor to the desired height and width."""
return torch.nn.functional.interpolate(image, size=(height, width), mode='nearest')
def dummy(img):
"""Save the composite image and generate a mask from the alpha channel."""
imageio.imwrite("output_image.png", img["composite"])
# Extract alpha channel from the first layer to create the mask
alpha_channel = img["layers"][0][:, :, 3]
mask = np.where(alpha_channel == 0, 0, 255).astype(np.uint8)
return img["background"], mask
def I2I(prompt, image, width=1024, height=1024, guidance_scale=8.0, num_inference_steps=20, strength=0.99):
pipe = AutoPipelineForInpainting.from_pretrained(
"diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
torch_dtype=torch.float16, variant="fp16").to(device)
img_url, mask = dummy(image)
# Resize image and mask to the target dimensions (height x width)
img_url = Image.fromarray(img_url, mode="RGB").resize((width, height))
mask_url = Image.fromarray(mask,mode="L").resize((width, height))
# Make sure both image and mask are converted into correct tensors
generator = torch.Generator(device=device).manual_seed(0)
# Generate the inpainted image
output = pipe(
prompt=prompt,
image=img_url,
mask_image=mask_url,
guidance_scale=guidance_scale,
num_inference_steps=num_inference_steps, # steps between 15 and 30 work well for us
strength=strength, # make sure to use `strength` below 1.0
generator=generator,
)
return output.images[0]
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