model: base_learning_rate: 4.5e-6 target: sgm.models.autoencoder.AutoencodingEngine params: input_key: jpg monitor: val/loss/rec disc_start_iter: 0 encoder_config: target: sgm.modules.diffusionmodules.model.Encoder params: attn_type: vanilla-xformers double_z: true z_channels: 8 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: [1, 2, 4, 4] num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 decoder_config: target: sgm.modules.diffusionmodules.model.Decoder params: ${model.params.encoder_config.params} regularizer_config: target: sgm.modules.autoencoding.regularizers.DiagonalGaussianRegularizer loss_config: target: sgm.modules.autoencoding.losses.GeneralLPIPSWithDiscriminator params: perceptual_weight: 0.25 disc_start: 20001 disc_weight: 0.5 learn_logvar: True regularization_weights: kl_loss: 1.0 data: target: sgm.data.dataset.StableDataModuleFromConfig params: train: datapipeline: urls: - DATA-PATH pipeline_config: shardshuffle: 10000 sample_shuffle: 10000 decoders: - pil postprocessors: - target: sdata.mappers.TorchVisionImageTransforms params: key: jpg transforms: - target: torchvision.transforms.Resize params: size: 256 interpolation: 3 - target: torchvision.transforms.ToTensor - target: sdata.mappers.Rescaler - target: sdata.mappers.AddOriginalImageSizeAsTupleAndCropToSquare params: h_key: height w_key: width loader: batch_size: 8 num_workers: 4 lightning: strategy: target: pytorch_lightning.strategies.DDPStrategy params: find_unused_parameters: True modelcheckpoint: params: every_n_train_steps: 5000 callbacks: metrics_over_trainsteps_checkpoint: params: every_n_train_steps: 50000 image_logger: target: main.ImageLogger params: enable_autocast: False batch_frequency: 1000 max_images: 8 increase_log_steps: True trainer: devices: 0, limit_val_batches: 50 benchmark: True accumulate_grad_batches: 1 val_check_interval: 10000