Refactor memory cleaning into a single function

sdxl_train.py

@@ -1,7 +1,6 @@
 # training with captions
 
 import argparse
-import gc
 import math
 import os
 from multiprocessing import Value
@@ -11,6 +10,7 @@ import toml
 from tqdm import tqdm
 import torch
 
+from library.device_utils import clean_memory
 from library.ipex_interop import init_ipex
 
 init_ipex()
@@ -252,9 +252,7 @@ def train(args):
         with torch.no_grad():
             train_dataset_group.cache_latents(vae, args.vae_batch_size, args.cache_latents_to_disk, accelerator.is_main_process)
         vae.to("cpu")
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
-        gc.collect()
+        clean_memory()
 
         accelerator.wait_for_everyone()
 
@@ -407,8 +405,7 @@ def train(args):
         # move Text Encoders for sampling images. Text Encoder doesn't work on CPU with fp16
         text_encoder1.to("cpu", dtype=torch.float32)
         text_encoder2.to("cpu", dtype=torch.float32)
-        if torch.cuda.is_available():
-            torch.cuda.empty_cache()
+        clean_memory()
     else:
         # make sure Text Encoders are on GPU
         text_encoder1.to(accelerator.device)