configs/cc12m_uvit.yaml [46:100]:
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        initializer_range: 0.02
        norm_type: "rmsnorm"
        layer_norm_eps: 1e-6
        use_normformer: False
        use_encoder_layernorm: True
        use_bias: False
        hidden_dropout: 0.0
        attention_dropout: 0.0
        use_codebook_size_for_output: True

    gradient_checkpointing: True
    enable_xformers_memory_efficient_attention: True


dataset:
    type: "text2image"
    params:
        train_shards_path_or_url: 
            - "pipe:aws s3 cp s3://muse-datasets/conceptual-captions/CC12M/{00000..02484}.tar -"
            - "pipe:aws s3 cp s3://muse-datasets/conceptual-captions/CC3M/train/{00000..00663}.tar -"
        eval_shards_path_or_url: "pipe:aws s3 cp s3://muse-datasets/conceptual-captions/CC3M/valid/{00000..00003}.tar -"
        validation_prompts_file: "validation_prompts/cc_validation_prompts.txt"
        batch_size: ${training.batch_size}
        shuffle_buffer_size: 1000
        num_workers: 4
        resolution: 256
        pin_memory: True
        persistent_workers: True
    preprocessing:
        max_seq_length: 32
        resolution: 256
        center_crop: False
        random_flip: False


optimizer:
    name: fused_adamw
    params: # default adamw params
        learning_rate: 1e-4
        scale_lr: False # scale learning rate by total batch size
        beta1: 0.9
        beta2: 0.999
        weight_decay: 0.01
        epsilon: 1e-8


lr_scheduler:
    scheduler: "constant_with_warmup"
    params:
        learning_rate: ${optimizer.params.learning_rate}
        warmup_steps: 2000


training:
    gradient_accumulation_steps: 2
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configs/cc12m_uvit_paellavq_larger.yaml [46:100]:
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        initializer_range: 0.02
        norm_type: "rmsnorm"
        layer_norm_eps: 1e-6
        use_normformer: False
        use_encoder_layernorm: True
        use_bias: False
        hidden_dropout: 0.0
        attention_dropout: 0.0
        use_codebook_size_for_output: True

    gradient_checkpointing: True
    enable_xformers_memory_efficient_attention: True


dataset:
    type: "text2image"
    params:
        train_shards_path_or_url: 
            - "pipe:aws s3 cp s3://muse-datasets/conceptual-captions/CC12M/{00000..02484}.tar -"
            - "pipe:aws s3 cp s3://muse-datasets/conceptual-captions/CC3M/train/{00000..00663}.tar -"
        eval_shards_path_or_url: "pipe:aws s3 cp s3://muse-datasets/conceptual-captions/CC3M/valid/{00000..00003}.tar -"
        validation_prompts_file: "validation_prompts/cc_validation_prompts.txt"
        batch_size: ${training.batch_size}
        shuffle_buffer_size: 1000
        num_workers: 4
        resolution: 256
        pin_memory: True
        persistent_workers: True
    preprocessing:
        max_seq_length: 32
        resolution: 256
        center_crop: False
        random_flip: False


optimizer:
    name: fused_adamw
    params: # default adamw params
        learning_rate: 1e-4
        scale_lr: False # scale learning rate by total batch size
        beta1: 0.9
        beta2: 0.999
        weight_decay: 0.01
        epsilon: 1e-8


lr_scheduler:
    scheduler: "constant_with_warmup"
    params:
        learning_rate: ${optimizer.params.learning_rate}
        warmup_steps: 2000


training:
    gradient_accumulation_steps: 2
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