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