configs/research_run_512.yaml [72:114]:
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dataset:
    type: "text2image"
    params:
        train_shards_path_or_url: "laion-aesthetic-475-max-1024-joined-with-stability-metadata-laicov2_shards"
        eval_shards_path_or_url: "pipe:aws s3 cp s3://muse-datasets/coco/2014/val/{00000..00010}.tar -"
        validation_prompts_file: "validation_prompts/dalle_mini_prompts.txt"
        batch_size: ${training.batch_size}
        shuffle_buffer_size: 1000
        num_workers: 4
        resolution: 512
        pin_memory: True
        persistent_workers: True
        use_filtered_dataset: True
        max_pnsfw: 0.45
        max_pwatermark: 0.5
        min_aesthetic_score: 4.5
    preprocessing:
        max_seq_length: 77
        resolution: 512
        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: 1
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configs/research_run_512_with_downsample.yaml [73:115]:
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dataset:
    type: "text2image"
    params:
        train_shards_path_or_url: "laion-aesthetic-475-max-1024-joined-with-stability-metadata-laicov2_shards"
        eval_shards_path_or_url: "pipe:aws s3 cp s3://muse-datasets/coco/2014/val/{00000..00010}.tar -"
        validation_prompts_file: "validation_prompts/dalle_mini_prompts.txt"
        batch_size: ${training.batch_size}
        shuffle_buffer_size: 1000
        num_workers: 4
        resolution: 512
        pin_memory: True
        persistent_workers: True
        use_filtered_dataset: True
        max_pnsfw: 0.45
        max_pwatermark: 0.5
        min_aesthetic_score: 4.5
    preprocessing:
        max_seq_length: 77
        resolution: 512
        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: 1
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