Function create_network

wan/utils/lora_utils.py:355–382

(
    multiplier: float,
    network_dim: Optional[int],
    network_alpha: Optional[float],
    text_encoder: Union[T5EncoderModel, List[T5EncoderModel]],
    transformer,
    neuron_dropout: Optional[float] = None,
    skip_name: str = None,
    target_name = None,
    **kwargs,
)

            torch.save(state_dict, file)

def create_network(
    multiplier: float,
    network_dim: Optional[int],
    network_alpha: Optional[float],
    text_encoder: Union[T5EncoderModel, List[T5EncoderModel]],
    transformer,
    neuron_dropout: Optional[float] = None,
    skip_name: str = None,
    target_name = None,
    **kwargs,
):
    if network_dim is None:
        network_dim = 4  # default
    if network_alpha is None:
        network_alpha = 1.0

    network = LoRANetwork(
        text_encoder,
        transformer,
        multiplier=multiplier,
        lora_dim=network_dim,
        alpha=network_alpha,
        dropout=neuron_dropout,
        skip_name=skip_name,
        target_name=target_name,
        varbose=True,
    )
    return network

def merge_lora(pipeline, lora_path, multiplier, device='cpu', dtype=torch.float32, state_dict=None, transformer_only=False, sub_transformer_name="transformer"):
    LORA_PREFIX_TRANSFORMER = "lora_unet"