| activation: relu | |
| batch_size: 16 | |
| beta_max: 50.0 | |
| beta_min: 0.1 | |
| channels: 64 | |
| color_mode: rgb | |
| corrector: none | |
| dataset_name: mnist | |
| drop_prob: null | |
| embed_dim: 256 | |
| epochs: 100 | |
| eval_freq: 1 | |
| image_range: | |
| - 0 | |
| - 1 | |
| image_shape: | |
| - 64 | |
| - 64 | |
| - 3 | |
| image_size: 64 | |
| kernel_size: 3 | |
| likelihood_weighting: false | |
| lr: 0.0002 | |
| model_name: score | |
| n_steps_each: 1 | |
| noise_removal: true | |
| normalization: batch | |
| num_plot_img: 16 | |
| num_scales: 1000 | |
| patch_size: null | |
| predictor: euler_maruyama | |
| probability_flow: false | |
| reduce_mean: false | |
| sampling_method: pc | |
| save_freq: 10 | |
| score_backbone: NCSNv2 | |
| sde: simple | |
| seed: 1234 | |
| sigma: 25.0 | |
| sigma_max: 90.0 | |
| sigma_min: 0.01 | |
| snr: 0.17 | |
| upmode: upconv | |