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import sys
sys.path.append(".")
from PIL import Image
import torch
from torchvision.transforms import ToTensor, Compose, Resize, Normalize
from torch.nn import functional as F
from opensora.models.ae.videobase import CausalVAEModel
import argparse
import numpy as np
def preprocess(video_data: torch.Tensor, short_size: int = 128) -> torch.Tensor:
transform = Compose(
[
ToTensor(),
Normalize((0.5), (0.5)),
Resize(size=short_size),
]
)
outputs = transform(video_data)
outputs = outputs.unsqueeze(0).unsqueeze(2)
return outputs
def main(args: argparse.Namespace):
image_path = args.image_path
resolution = args.resolution
device = args.device
vqvae = CausalVAEModel.load_from_checkpoint(args.ckpt)
vqvae.eval()
vqvae = vqvae.to(device)
with torch.no_grad():
x_vae = preprocess(Image.open(image_path), resolution)
x_vae = x_vae.to(device)
latents = vqvae.encode(x_vae)
recon = vqvae.decode(latents.sample())
x = recon[0, :, 0, :, :]
x = x.squeeze()
x = x.detach().cpu().numpy()
x = np.clip(x, -1, 1)
x = (x + 1) / 2
x = (255*x).astype(np.uint8)
x = x.transpose(1,2,0)
image = Image.fromarray(x)
image.save(args.rec_path)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--image-path', type=str, default='')
parser.add_argument('--rec-path', type=str, default='')
parser.add_argument('--ckpt', type=str, default='')
parser.add_argument('--resolution', type=int, default=336)
parser.add_argument('--device', type=str, default='cuda')
args = parser.parse_args()
main(args)