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