refactor: remove unused prepare_split_model method from FluxNetworkTr…

…ainer

flux_train_network.py

@@ -127,45 +127,6 @@ def load_target_model(self, args, weight_dtype, accelerator):
 
         return flux_utils.MODEL_VERSION_FLUX_V1, [clip_l, t5xxl], ae, model
 
-    """
-    def prepare_split_model(self, model, weight_dtype, accelerator):
-        from accelerate import init_empty_weights
-
-        logger.info("prepare split model")
-        with init_empty_weights():
-            flux_upper = flux_models.FluxUpper(model.params)
-            flux_lower = flux_models.FluxLower(model.params)
-        sd = model.state_dict()
-
-        # lower (trainable)
-        logger.info("load state dict for lower")
-        flux_lower.load_state_dict(sd, strict=False, assign=True)
-        flux_lower.to(dtype=weight_dtype)
-
-        # upper (frozen)
-        logger.info("load state dict for upper")
-        flux_upper.load_state_dict(sd, strict=False, assign=True)
-
-        logger.info("prepare upper model")
-        target_dtype = torch.float8_e4m3fn if args.fp8_base else weight_dtype
-        flux_upper.to(accelerator.device, dtype=target_dtype)
-        flux_upper.eval()
-
-        if args.fp8_base:
-            # this is required to run on fp8
-            flux_upper = accelerator.prepare(flux_upper)
-
-        flux_upper.to("cpu")
-
-        self.flux_upper = flux_upper
-        del model  # we don't need model anymore
-        clean_memory_on_device(accelerator.device)
-
-        logger.info("split model prepared")
-
-        return flux_lower
-    """
-
     def get_tokenize_strategy(self, args):
         _, is_schnell, _, _ = flux_utils.analyze_checkpoint_state(args.pretrained_model_name_or_path)