model: base_learning_rate: 1.0e-4 target: sgm.models.diffusion.DiffusionEngine params: use_ema: True 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 num_classes: sequential adm_in_channels: 128 conditioner_config: target: sgm.modules.GeneralConditioner params: emb_models: - is_trainable: True input_key: cls ucg_rate: 0.2 target: sgm.modules.encoders.modules.ClassEmbedder params: embed_dim: 128 n_classes: 10 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 guider_config: target: sgm.modules.diffusionmodules.guiders.VanillaCFG params: scale: 3.0 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: True 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: 20