Random Access

``` def sample(self, model, n): logging.info(f"Sampling {n} new images....") model.eval() with torch.no_grad(): x = torch.randn((n, 3, self.img_size, self.img_size)).to(self.device) for i in tqdm(reversed(range(1, self.noise_steps)), position=0): t = (torch.ones(n) * i).long().to(self.device) predicted_noise...

@ankan8145 `x = torch.randn((n, 3, self.img_size, self.img_size)).to(self.device)` is a random noise with `n=12`, `channels=3` and `self.img_size=64` (__probably__). Random sampling (__after training the DDPM__) is carried out by starting from pure...