Refactor memory cleaning into a single function

train_textual_inversion.py

@@ -1,5 +1,4 @@
 import argparse
-import gc
 import math
 import os
 from multiprocessing import Value
@@ -8,6 +7,7 @@ import toml
 from tqdm import tqdm
 import torch
 
+from library.device_utils import clean_memory
 from library.ipex_interop import init_ipex
 
 init_ipex()
@@ -363,9 +363,7 @@ class TextualInversionTrainer:
             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()