| class Config: | |
| # Dataset | |
| dataset_path = "./data/tiny-imagenet-200" | |
| image_size = 64 | |
| num_workers = 4 | |
| # Model | |
| in_channels = 3 | |
| base_channels = 64 | |
| time_emb_dim = 256 | |
| # Training | |
| batch_size = 32 | |
| epochs = 100 | |
| lr = 1e-4 # Increased back up since we simplified the loss | |
| beta_start = 1e-4 | |
| beta_end = 0.02 | |
| T = 500 # Reduced from 1000 for faster training | |
| # Frequency-aware | |
| patch_size = 16 | |
| # Regularization | |
| tv_weight = 0.01 # Reduced from 0.1 | |
| # Logging | |
| log_dir = "./logs" | |
| sample_every = 5 # More frequent sampling to monitor progress |