(self, video, torch_dtype=None, device=None, pattern="B C T H W", min_value=-1, max_value=1)
| 68 | |
| 69 | |
| 70 | def preprocess_video(self, video, torch_dtype=None, device=None, pattern="B C T H W", min_value=-1, max_value=1): |
| 71 | # Transform a list of PIL.Image to torch.Tensor |
| 72 | video = [self.preprocess_image(image, torch_dtype=torch_dtype, device=device, min_value=min_value, max_value=max_value) for image in video] |
| 73 | video = torch.stack(video, dim=pattern.index("T") // 2) |
| 74 | return video |
| 75 | |
| 76 | |
| 77 | def vae_output_to_image(self, vae_output, pattern="B C H W", min_value=-1, max_value=1): |
no test coverage detected