diff options
Diffstat (limited to 'main.py')
| -rw-r--r-- | main.py | 36 |
1 files changed, 9 insertions, 27 deletions
@@ -78,16 +78,11 @@ class UNet(nn.Module): DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") -DIFFU_STEPS = 20 -BETA = torch.linspace(1e-2, 3e-1, DIFFU_STEPS, device=DEVICE) +DIFFU_STEPS = 300 +BETA = torch.linspace(1e-4, 2e-2, DIFFU_STEPS, device=DEVICE) BETA = torch.cat((torch.tensor([0.], device=DEVICE), BETA)) ALPHA = 1 - BETA ALPHA_BAR = torch.cumprod(ALPHA, dim=0) -CUM_SQ_SUM = [] -for t in range(len(ALPHA)): - cum_sq_sum = sum([torch.prod(ALPHA[s+2:t+1]) * (1 - ALPHA[s+1])**2 for s in range(t)]) - CUM_SQ_SUM.append(cum_sq_sum) -CUM_SQ_SUM = torch.tensor(CUM_SQ_SUM, device=DEVICE) def q_xt_xt_1(xt_1, t): @@ -95,29 +90,17 @@ def q_xt_xt_1(xt_1, t): alpha = ALPHA[t_ind] mean = torch.sqrt(alpha) * xt_1 - std = 1 - alpha + std = torch.sqrt(1 - alpha) xt = torch.distributions.Normal(mean, std).sample() return xt -# def q_xt_x0(x0, t): -# t_ind = t.to(dtype=torch.long) if isinstance(t, torch.Tensor) else t - -# alpha = ALPHA[t_ind] -# alpha_bar = ALPHA_BAR[t_ind] -# alpha_bar_bar = ALPHA_BAR_BAR[t_ind - 1] -# mean = torch.sqrt(alpha_bar) * x0 -# std = alpha_bar_bar * (1 - alpha) -# return torch.distributions.Normal(mean, std).sample() - def q_xt_x0(x0, t): t_ind = t.to(dtype=torch.long) if isinstance(t, torch.Tensor) else t alpha_bar = ALPHA_BAR[t_ind] - cum_sq_sum = CUM_SQ_SUM[t_ind] - mean = torch.sqrt(alpha_bar) * x0 - std = torch.sqrt(cum_sq_sum) + std = torch.sqrt(1 - alpha_bar) return torch.distributions.Normal(mean, std).sample() @@ -156,8 +139,8 @@ class MNISTDiffusionDataset(Dataset): img, label = self.mnist_data[index] img = img.to(DEVICE) - # Normalize the image between -0.5 and 0.5. - img = img - 0.5 + # Normalize the image. + img = img * 2 - 1 # Add noise to the image. t = torch.randint(1, DIFFU_STEPS, (1,), device=DEVICE) @@ -204,9 +187,8 @@ if __name__ == '__main__': transform=transforms.ToTensor(), ) - img, label = mnist_data[1] - img = img.to(DEVICE) - 0.5 + img = img.to(DEVICE) * 2 - 1 nb_plots = 10 plots_id = [i for i in np.linspace(1, DIFFU_STEPS, nb_plots, dtype=int)] @@ -244,7 +226,7 @@ if __name__ == '__main__': model.load_state_dict(torch.load('model.pth')) # Define the optimizer. - optimizer = torch.optim.Adam(model.parameters(), lr=1e-4) + optimizer = torch.optim.Adam(model.parameters(), lr=2e-4) # Define the trainiself.encodevec = nn.Linear(10, 28*28)ng dataset. train_dataset = MNISTDiffusionDataset(train=True) @@ -252,7 +234,7 @@ if __name__ == '__main__': num_workers=4, persistent_workers=True) trainer = Trainer() criterion = loss - epochs = 1 + epochs = 10 # Train the model. trainer.train(model, train_loader, epochs, optimizer, criterion) |