def merge_loras(model, lora_loader):
    import copy
    model_transformer_blocks_bak = copy.deepcopy(model.transformer_blocks)
    paths, weights, scale = lora_loader.paths, lora_loader.weights, lora_loader.scale
    for i, path in enumerate(paths):
        print(f"[I] Loading LoRA: {path}, weight {weights[i]}")
        if isinstance(lora_loader, SDLoraLoader):
            state_dict, network_alphas = lora_loader.lora_state_dict(path, unet_config=model.config)
            lora_loader.load_lora_into_unet(state_dict, network_alphas=network_alphas, unet=model, adapter_name=path)
        elif isinstance(lora_loader, FLUXLoraLoader):
            state_dict, network_alphas = lora_loader.lora_state_dict(path, return_alphas=True)
            # lora_loader.load_lora_into_transformer(state_dict, network_alphas=network_alphas, transformer=model, adapter_name=path)
            lora_loader.load_lora_into_transformer(state_dict, network_alphas=network_alphas, transformer=model, adapter_name=None)
        else:
            raise ValueError(f"Unsupported LoRA loader: {lora_loader}")
    model.set_adapters(paths, weights=weights)
    # NOTE: fuse_lora an experimental API in Diffusers
    model.fuse_lora(adapter_names=paths, lora_scale=scale)
    model.unload_lora()
    return model

 import copy
 model_transformer_blocks_bak = copy.deepcopy(model.transformer_blocks)

model.transformer_blocks

model_transformer_blocks_bak