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| model: | |
| base_learning_rate: 1.0e-4 | |
| target: sgm.models.diffusion.DiffusionEngine | |
| params: | |
| scale_factor: 0.13025 | |
| disable_first_stage_autocast: True | |
| log_keys: | |
| - txt | |
| scheduler_config: | |
| target: sgm.lr_scheduler.LambdaLinearScheduler | |
| params: | |
| warm_up_steps: [10000] | |
| cycle_lengths: [10000000000000] | |
| f_start: [1.e-6] | |
| f_max: [1.] | |
| f_min: [1.] | |
| denoiser_config: | |
| target: sgm.modules.diffusionmodules.denoiser.DiscreteDenoiser | |
| params: | |
| num_idx: 1000 | |
| scaling_config: | |
| target: sgm.modules.diffusionmodules.denoiser_scaling.EpsScaling | |
| discretization_config: | |
| target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization | |
| network_config: | |
| target: sgm.modules.diffusionmodules.openaimodel.UNetModel | |
| params: | |
| use_checkpoint: True | |
| in_channels: 4 | |
| out_channels: 4 | |
| model_channels: 320 | |
| attention_resolutions: [1, 2, 4] | |
| num_res_blocks: 2 | |
| channel_mult: [1, 2, 4, 4] | |
| num_head_channels: 64 | |
| num_classes: sequential | |
| adm_in_channels: 1792 | |
| num_heads: 1 | |
| transformer_depth: 1 | |
| context_dim: 768 | |
| spatial_transformer_attn_type: softmax-xformers | |
| conditioner_config: | |
| target: sgm.modules.GeneralConditioner | |
| params: | |
| emb_models: | |
| - is_trainable: True | |
| input_key: txt | |
| ucg_rate: 0.1 | |
| legacy_ucg_value: "" | |
| target: sgm.modules.encoders.modules.FrozenCLIPEmbedder | |
| params: | |
| always_return_pooled: True | |
| - is_trainable: False | |
| ucg_rate: 0.1 | |
| input_key: original_size_as_tuple | |
| target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND | |
| params: | |
| outdim: 256 | |
| - is_trainable: False | |
| input_key: crop_coords_top_left | |
| ucg_rate: 0.1 | |
| target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND | |
| params: | |
| outdim: 256 | |
| first_stage_config: | |
| target: sgm.models.autoencoder.AutoencoderKL | |
| params: | |
| ckpt_path: CKPT_PATH | |
| embed_dim: 4 | |
| monitor: val/rec_loss | |
| ddconfig: | |
| attn_type: vanilla-xformers | |
| double_z: true | |
| z_channels: 4 | |
| 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 | |
| lossconfig: | |
| target: torch.nn.Identity | |
| loss_fn_config: | |
| target: sgm.modules.diffusionmodules.loss.StandardDiffusionLoss | |
| params: | |
| loss_weighting_config: | |
| target: sgm.modules.diffusionmodules.loss_weighting.EpsWeighting | |
| sigma_sampler_config: | |
| target: sgm.modules.diffusionmodules.sigma_sampling.DiscreteSampling | |
| params: | |
| num_idx: 1000 | |
| discretization_config: | |
| target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization | |
| sampler_config: | |
| target: sgm.modules.diffusionmodules.sampling.EulerEDMSampler | |
| params: | |
| num_steps: 50 | |
| discretization_config: | |
| target: sgm.modules.diffusionmodules.discretizer.LegacyDDPMDiscretization | |
| guider_config: | |
| target: sgm.modules.diffusionmodules.guiders.VanillaCFG | |
| params: | |
| scale: 7.5 | |
| data: | |
| target: sgm.data.dataset.StableDataModuleFromConfig | |
| params: | |
| train: | |
| datapipeline: | |
| urls: | |
| # USER: adapt this path the root of your custom dataset | |
| - DATA_PATH | |
| pipeline_config: | |
| shardshuffle: 10000 | |
| sample_shuffle: 10000 | |
| decoders: | |
| - pil | |
| postprocessors: | |
| - target: sdata.mappers.TorchVisionImageTransforms | |
| params: | |
| key: jpg # USER: you might wanna adapt this for your custom dataset | |
| transforms: | |
| - target: torchvision.transforms.Resize | |
| params: | |
| size: 256 | |
| interpolation: 3 | |
| - target: torchvision.transforms.ToTensor | |
| - target: sdata.mappers.Rescaler | |
| # USER: you might wanna use non-default parameters due to your custom dataset | |
| - target: sdata.mappers.AddOriginalImageSizeAsTupleAndCropToSquare | |
| # USER: you might wanna use non-default parameters due to your custom dataset | |
| loader: | |
| batch_size: 64 | |
| num_workers: 6 | |
| 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 | |
| enable_autocast: False | |
| batch_frequency: 1000 | |
| max_images: 8 | |
| increase_log_steps: True | |
| log_first_step: False | |
| log_images_kwargs: | |
| use_ema_scope: False | |
| N: 8 | |
| n_rows: 2 | |
| trainer: | |
| devices: 0, | |
| benchmark: True | |
| num_sanity_val_steps: 0 | |
| accumulate_grad_batches: 1 | |
| max_epochs: 1000 |