Function unscale_lora_layers

diffusers/src/diffusers/utils/peft_utils.py:123–147

Removes the previously passed weight given to the LoRA layers of the model. Args: model (`torch.nn.Module`): The model to scale. weight (`float`, *optional*): The weight to be given to the LoRA layers. If no scale is passed the scale of the lora laye

(model, weight: Optional[float] = None)

def unscale_lora_layers(model, weight: Optional[float] = None):
    """
    Removes the previously passed weight given to the LoRA layers of the model.

    Args:
        model (`torch.nn.Module`):
            The model to scale.
        weight (`float`, *optional*):
            The weight to be given to the LoRA layers. If no scale is passed the scale of the lora layer will be
            re-initialized to the correct value. If 0.0 is passed, we will re-initialize the scale with the correct
            value.
    """
    from peft.tuners.tuners_utils import BaseTunerLayer

    if weight == 1.0:
        return

    for module in model.modules():
        if isinstance(module, BaseTunerLayer):
            if weight is not None and weight != 0:
                module.unscale_layer(weight)
            elif weight is not None and weight == 0:
                for adapter_name in module.active_adapters:
                    # if weight == 0 unscale should re-set the scale to the original value.
                    module.set_scale(adapter_name, 1.0)


def get_peft_kwargs(rank_dict, network_alpha_dict, peft_state_dict, is_unet=True):