| # Previous-study DINO baseline: full-field diffusion, not residual diffusion. | |
| config_type: diffusion | |
| model_params: | |
| model_type: elucidated | |
| base_dim: 128 | |
| dim_mults: [1, 2, 3, 5, 8, 12] | |
| channels: 3 | |
| self_condition: true | |
| learned_variance: false | |
| learned_sinusoidal_cond: false | |
| random_fourier_features: false | |
| sinusoidal_pos_emb_theta: 10000 | |
| learned_sinusoidal_dim: 16 | |
| dropout: 0.0 | |
| attn_heads: 8 | |
| attn_dim_head: 64 | |
| flash_attn: true | |
| image_size: 128 | |
| num_sample_steps: 32 | |
| sigma_min: 0.002 | |
| sigma_max: 80 | |
| sigma_data: 0.5 | |
| rho: 7 | |
| P_mean: -1.2 | |
| P_std: 1.2 | |
| S_churn: 80 | |
| S_tmin: 0.05 | |
| S_tmax: 50 | |
| S_noise: 1.003 | |
| helmholtz_projection: false | |
| project_velocity: false | |
| project_B: false | |
| normalization_params: | |
| type: minmax | |
| feature_range: [-1, 1] | |
| inputs_stats_file: ${MODEL_ROOT}/normalization/diffusion_inputs_stats.npz | |
| targets_stats_file: ${MODEL_ROOT}/normalization/diffusion_targets_stats.npz | |
| dataset_params: | |
| train_path: ${FEATURE_ROOT}/train | |
| val_path: ${FEATURE_ROOT}/val | |
| test_path: ${FEATURE_ROOT}/test | |
| prediction_mode: direct | |
| residual_target: false | |
| dataloader_params: | |
| train: {batch_size: 64, num_workers: 1, shuffle: true, pin_memory: true} | |
| # Backward-compatible key consumed by the shared diffusion dataloader. | |
| train_loader_params: | |
| batch_size: 64 | |
| shuffle: true | |
| num_workers: 1 | |
| pin_memory: true | |
| optimizer_params: | |
| optimizer_type: adamw | |
| lr: 1.0e-4 | |
| weight_decay: 1.0e-4 | |
| betas: [0.9, 0.999] | |
| use_scheduler: true | |
| scheduler_type: cosine | |
| T_max: 1000 | |
| eta_min: 5.0e-5 | |
| train_params: | |
| recipe: previous_dino | |
| epochs: 101 | |
| checkpoint_path: ${OUTPUT_ROOT}/checkpoints/dino_re1000.pt | |
| load_checkpoint: "" | |
| validation_interval: 10 | |
| checkpoint_metric: denorm_rel_l2 | |
| clip_grad: true | |
| clip_grad_max_norm: 1.0 | |