Refactor memory cleaning into a single function

library/train_util.py

@@ -20,7 +20,6 @@ from typing import (
     Union,
 )
 from accelerate import Accelerator, InitProcessGroupKwargs, DistributedDataParallelKwargs
-import gc
 import glob
 import math
 import os
@@ -67,6 +66,7 @@ import library.sai_model_spec as sai_model_spec
 
 # from library.attention_processors import FlashAttnProcessor
 # from library.hypernetwork import replace_attentions_for_hypernetwork
+from library.device_utils import clean_memory
 from library.original_unet import UNet2DConditionModel
 
 # Tokenizer: checkpointから読み込むのではなくあらかじめ提供されているものを使う
@@ -2278,8 +2278,7 @@ def cache_batch_latents(
                 info.latents_flipped = flipped_latent
 
     # FIXME this slows down caching a lot, specify this as an option
-    if torch.cuda.is_available():
-        torch.cuda.empty_cache()
+    clean_memory()
 
 
 def cache_batch_text_encoder_outputs(
@@ -4006,8 +4005,7 @@ def load_target_model(args, weight_dtype, accelerator, unet_use_linear_projectio
                 unet.to(accelerator.device)
                 vae.to(accelerator.device)
 
-            gc.collect()
-            torch.cuda.empty_cache()
+            clean_memory()
         accelerator.wait_for_everyone()
 
     return text_encoder, vae, unet, load_stable_diffusion_format
@@ -4816,7 +4814,7 @@ def sample_images_common(
 
     # clear pipeline and cache to reduce vram usage
     del pipeline
-    torch.cuda.empty_cache()
+    clean_memory()
 
     torch.set_rng_state(rng_state)
     if cuda_rng_state is not None: