| model: | |
| base_learning_rate: 1.0e-4 | |
| target: sgm.models.diffusion.DiffusionEngine | |
| params: | |
| denoiser_config: | |
| target: sgm.modules.diffusionmodules.denoiser.Denoiser | |
| params: | |
| scaling_config: | |
| target: sgm.modules.diffusionmodules.denoiser_scaling.EDMScaling | |
| params: | |
| sigma_data: 1.0 | |
| network_config: | |
| target: sgm.modules.diffusionmodules.openaimodel.UNetModel | |
| params: | |
| in_channels: 1 | |
| out_channels: 1 | |
| model_channels: 32 | |
| attention_resolutions: [] | |
| num_res_blocks: 4 | |
| channel_mult: [1, 2, 2] | |
| num_head_channels: 32 | |
| first_stage_config: | |
| target: sgm.models.autoencoder.IdentityFirstStage | |
| loss_fn_config: | |
| target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss | |
| params: | |
| loss_weighting_config: | |
| target: sgm.modules.diffusionmodules.loss_weighting.EDMWeighting | |
| params: | |
| sigma_data: 1.0 | |
| sigma_sampler_config: | |
| target: sgm.modules.diffusionmodules.sigma_sampling.EDMSampling | |
| sampler_config: | |
| target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler | |
| params: | |
| num_steps: 50 | |
| discretization_config: | |
| target: sgm.modules.diffusionmodules.discretizer.EDMDiscretization | |
| data: | |
| target: sgm.data.mnist.MNISTLoader | |
| params: | |
| batch_size: 512 | |
| num_workers: 1 | |
| lightning: | |
| modelcheckpoint: | |
| params: | |
| every_n_train_steps: 5000 | |
| callbacks: | |
| metrics_over_trainsteps_checkpoint: | |
| params: | |
| every_n_train_steps: 25000 | |
| image_logger: | |
| target: main.ImageLogger | |
| params: | |
| disabled: False | |
| batch_frequency: 1000 | |
| max_images: 64 | |
| increase_log_steps: False | |
| log_first_step: False | |
| log_images_kwargs: | |
| use_ema_scope: False | |
| N: 64 | |
| n_rows: 8 | |
| trainer: | |
| devices: 0, | |
| benchmark: True | |
| num_sanity_val_steps: 0 | |
| accumulate_grad_batches: 1 | |
| max_epochs: 10 |