repositories/generative-models/configs/example_training/txt2img-clipl.yaml

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

 weighting_config:
 target: sgm.modules.diffusionmodules.denoiser_weighting.EpsWeighting
 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
 use_fp16: 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
 use_spatial_transformer: true
 transformer_depth: 1
 context_dim: 768
 spatial_transformer_attn_type: softmax-xformers

 conditioner_config:
 target: sgm.modules.GeneralConditioner
 params:
 emb_models:
 # crossattn cond
 - is_trainable: True
 input_key: txt
 ucg_rate: 0.1
 target: sgm.modules.encoders.modules.FrozenCLIPEmbedder
 params:
 always_return_pooled: True
 # vector cond
 - is_trainable: False
 ucg_rate: 0.1
 input_key: original_size_as_tuple
 target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
 params:
 outdim: 256 # multiplied by two
 # vector cond
 - is_trainable: False
 input_key: crop_coords_top_left
 ucg_rate: 0.1
 target: sgm.modules.encoders.modules.ConcatTimestepEmbedderND
 params:
 outdim: 256 # multiplied by two

 first_stage_config:
 target: sgm.models.autoencoder.AutoencoderKLInferenceWrapper
 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:
 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