Method create_modules

wan/utils/lora_utils.py:191–247

(
            is_unet: bool,
            root_module: torch.nn.Module,
            target_replace_modules: List[torch.nn.Module],
        )

        # create module instances
        def create_modules(
            is_unet: bool,
            root_module: torch.nn.Module,
            target_replace_modules: List[torch.nn.Module],
        ) -> List[LoRAModule]:
            prefix = (
                self.LORA_PREFIX_TRANSFORMER
                if is_unet
                else self.LORA_PREFIX_TEXT_ENCODER
            )
            loras = []
            skipped = []
            for name, module in root_module.named_modules():
                if module.__class__.__name__ in target_replace_modules:
                    for child_name, child_module in module.named_modules():
                        is_linear = child_module.__class__.__name__ == "Linear" or child_module.__class__.__name__ == "LoRACompatibleLinear"
                        is_conv2d = child_module.__class__.__name__ == "Conv2d" or child_module.__class__.__name__ == "LoRACompatibleConv"
                        is_conv2d_1x1 = is_conv2d and child_module.kernel_size == (1, 1)
                        
                        if skip_name is not None and skip_name in child_name:
                            continue
                        
                        if target_name is not None:
                            target_name_in = False
                            if isinstance(target_name, str):
                                target_name_in = target_name in child_name
                            elif isinstance(target_name, list):
                                target_name_in = any([_target_name in child_name for _target_name in target_name])
                            if not target_name_in:
                                continue

                        if is_linear or is_conv2d:
                            lora_name = prefix + "." + name + "." + child_name
                            lora_name = lora_name.replace(".", "_")

                            dim = None
                            alpha = None

                            if is_linear or is_conv2d_1x1:
                                dim = self.lora_dim
                                alpha = self.alpha

                            if dim is None or dim == 0:
                                if is_linear or is_conv2d_1x1:
                                    skipped.append(lora_name)
                                continue

                            lora = module_class(
                                lora_name,
                                child_module,
                                self.multiplier,
                                dim,
                                alpha,
                                dropout=dropout,
                            )
                            loras.append(lora)
            return loras, skipped