def jax_memory_efficient_attention(
    query, key, value, precision=jax.lax.Precision.HIGHEST, query_chunk_size: int = 1024, key_chunk_size: int = 4096
):
    r"""
    Flax Memory-efficient multi-head dot product attention. https://huggingface.co/papers/2112.05682v2
    https://github.com/AminRezaei0x443/memory-efficient-attention

    Args:
        query (`jnp.ndarray`): (batch..., query_length, head, query_key_depth_per_head)
        key (`jnp.ndarray`): (batch..., key_value_length, head, query_key_depth_per_head)
        value (`jnp.ndarray`): (batch..., key_value_length, head, value_depth_per_head)
        precision (`jax.lax.Precision`, *optional*, defaults to `jax.lax.Precision.HIGHEST`):
            numerical precision for computation
        query_chunk_size (`int`, *optional*, defaults to 1024):
            chunk size to divide query array value must divide query_length equally without remainder
        key_chunk_size (`int`, *optional*, defaults to 4096):
            chunk size to divide key and value array value must divide key_value_length equally without remainder

    Returns:
        (`jnp.ndarray`) with shape of (batch..., query_length, head, value_depth_per_head)
    """
    num_q, num_heads, q_features = query.shape[-3:]

    def chunk_scanner(chunk_idx, _):
        # julienne query array
        query_chunk = jax.lax.dynamic_slice(
            operand=query,
            start_indices=([0] * (query.ndim - 3)) + [chunk_idx, 0, 0],  # [...,q,h,d]
            slice_sizes=list(query.shape[:-3]) + [min(query_chunk_size, num_q), num_heads, q_features],  # [...,q,h,d]
        )

        return (
            chunk_idx + query_chunk_size,  # unused ignore it
            _query_chunk_attention(
                query=query_chunk, key=key, value=value, precision=precision, key_chunk_size=key_chunk_size
            ),
        )

    _, res = jax.lax.scan(
        f=chunk_scanner,
        init=0,
        xs=None,
        length=math.ceil(num_q / query_chunk_size),  # start counter  # stop counter
    )

    return jnp.concatenate(res, axis=-3)  # fuse the chunked result back

    Flax Memory-efficient multi-head dot product attention. https://huggingface.co/papers/2112.05682v2
    https://github.com/AminRezaei0x443/memory-efficient-attention

    Args:
        query (`jnp.ndarray`): (batch..., query_length, head, query_key_depth_per_head)
        key (`jnp.ndarray`): (batch..., key_value_length, head, query_key_depth_per_head)
        value (`jnp.ndarray`): (batch..., key_value_length, head, value_depth_per_head)
        precision (`jax.lax.Precision`, *optional*, defaults to `jax.lax.Precision.HIGHEST`):
            numerical precision for computation
        query_chunk_size (`int`, *optional*, defaults to 1024):
            chunk size to divide query array value must divide query_length equally without remainder
        key_chunk_size (`int`, *optional*, defaults to 4096):
            chunk size to divide key and value array value must divide key_value_length equally without remainder

    Returns:
        (`jnp.ndarray`) with shape of (batch..., query_length, head, value_depth_per_head)
    
src/diffusers/models/attention_flax.py