| 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 | |