sh sample/t2v.sh error, about latte HOT 3 CLOSED

This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented
for all models (such as downloading or saving).

Parameters:
    in_channels (int, *optional*, defaults to 3): Number of channels in the input image.
    out_channels (int,  *optional*, defaults to 3): Number of channels in the output.
    down_block_types (`Tuple[str]`, *optional*, defaults to `("DownEncoderBlock2D",)`):
        Tuple of downsample block types.
    up_block_types (`Tuple[str]`, *optional*, defaults to `("UpDecoderBlock2D",)`):
        Tuple of upsample block types.
    block_out_channels (`Tuple[int]`, *optional*, defaults to `(64,)`):
        Tuple of block output channels.
    act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use.
    latent_channels (`int`, *optional*, defaults to 4): Number of channels in the latent space.
    sample_size (`int`, *optional*, defaults to `32`): Sample input size.
    scaling_factor (`float`, *optional*, defaults to 0.18215):
        The component-wise standard deviation of the trained latent space computed using the first batch of the
        training set. This is used to scale the latent space to have unit variance when training the diffusion
        model. The latents are scaled with the formula `z = z * scaling_factor` before being passed to the
        diffusion model. When decoding, the latents are scaled back to the original scale with the formula: `z = 1
        / scaling_factor * z`. For more details, refer to sections 4.3.2 and D.1 of the [High-Resolution Image
        Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752) paper.
    force_upcast (`bool`, *optional*, default to `True`):
        If enabled it will force the VAE to run in float32 for high image resolution pipelines, such as SD-XL. VAE
        can be fine-tuned / trained to a lower range without loosing too much precision in which case
        `force_upcast` can be set to `False` - see: https://huggingface.co/madebyollin/sdxl-vae-fp16-fix
"""

_supports_gradient_checkpointing = True

@register_to_config
def __init__(
    self,
    in_channels: int = 3,
    out_channels: int = 3,
    down_block_types: Tuple[str] = ("DownEncoderBlock2D","DownEncoderBlock2D","DownEncoderBlock2D","DownEncoderBlock2D"),
    up_block_types: Tuple[str] = ("UpDecoderBlock2D","UpDecoderBlock2D","UpDecoderBlock2D","UpDecoderBlock2D"),
    block_out_channels: Tuple[int] = (128,256,512,512),
    layers_per_block: int = 2,
    act_fn: str = "silu",
    latent_channels: int = 4,
    norm_num_groups: int = 32,
    sample_size: int = 32,
    scaling_factor: float = 0.18215,
    force_upcast: float = True,
):