cosmos_predict1/diffusion/training/utils/peft/lora_config.py

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def get_fa_ca_qv_lora_config(first_nblocks=28, rank=8, scale=1):
    """
    Get a LoRA configuration for the Self-Attention (FA) and Cross-Attention (CA) blocks in the model.
    This LoRA configuration is used to inject LoRA parameters into the model.

    Args:
        first_nblocks (int): The number of blocks to apply LoRA to.
        rank (int): The rank of the LoRA matrices.
    """
    blocks_regex = r"\b(" + "|".join([str(i) for i in range(first_nblocks)]) + r")\b"
    return dict(
        enabled=True,
        customization_type="LoRA",
        rank=rank,
        scale=scale,
        edits=[
            dict(
                blocks=blocks_regex,
                customization_type="LoRA",
                rank=rank,
                scale=scale,
                block_edit=[
                    "FA[to_q, to_v]",
                    "CA[to_q, to_v]",
                ],
            )
        ],
    )