diff --git a/sd3_train.py b/sd3_train.py
index 6336b4cf9..d4ab13a34 100644
--- a/sd3_train.py
+++ b/sd3_train.py
@@ -321,7 +321,7 @@ def train(args):
         accelerator.wait_for_everyone()
 
         # now we can delete Text Encoders to free memory
-        if args.use_t5xxl_cache_only:
+        if not args.use_t5xxl_cache_only:
             clip_l = None
             clip_g = None
         t5xxl = None
@@ -330,6 +330,7 @@ def train(args):
 
     # load VAE for caching latents
     if sd3_state_dict is None:
+        logger.info(f"load state dict for MMDiT and VAE from {args.pretrained_model_name_or_path}")
         sd3_state_dict = utils.load_safetensors(
             args.pretrained_model_name_or_path, "cpu", args.disable_mmap_load_safetensors, model_dtype
         )
@@ -360,11 +361,6 @@ def train(args):
     #     attn_mode == "torch"
     # ), f"attn_mode {attn_mode} is not supported yet. Please use `--sdpa` instead of `--xformers`. / attn_mode {attn_mode} はサポートされていません。`--xformers`の代わりに`--sdpa`を使ってください。"
 
-    # SD3 state dict may contain multiple models, so we need to load it and extract one by one. annoying.
-    logger.info(f"Loading SD3 models from {args.pretrained_model_name_or_path}")
-    device_to_load = accelerator.device if args.lowram else "cpu"
-    sd3_state_dict = utils.load_safetensors(args.pretrained_model_name_or_path, device_to_load, args.disable_mmap_load_safetensors)
-
     if args.gradient_checkpointing:
         mmdit.enable_gradient_checkpointing()
 
@@ -555,7 +551,7 @@ def train(args):
     #     clip_l.text_model.encoder.layers[-1].requires_grad_(False)
     #     clip_l.text_model.final_layer_norm.requires_grad_(False)
 
-    # TextEncoderの出力をキャッシュするときには、すでに出力を取得済みなのでCPUへ移動する
+    # move Text Encoders to GPU if not caching outputs
     if not args.cache_text_encoder_outputs:
         # make sure Text Encoders are on GPU
         # TODO support CPU for text encoders
@@ -817,6 +813,13 @@ def optimizer_hook(parameter: torch.Tensor):
         # log empty object to commit the sample images to wandb
         accelerator.log({}, step=0)
 
+    # show model device and dtype
+    logger.info(f"mmdit device: {mmdit.device}, dtype: {mmdit.dtype}" if mmdit else "mmdit is None")
+    logger.info(f"clip_l device: {clip_l.device}, dtype: {clip_l.dtype}" if clip_l else "clip_l is None")
+    logger.info(f"clip_g device: {clip_g.device}, dtype: {clip_g.dtype}" if clip_g else "clip_g is None")
+    logger.info(f"t5xxl device: {t5xxl.device}, dtype: {t5xxl.dtype}" if t5xxl else "t5xxl is None")
+    logger.info(f"vae device: {vae.device}, dtype: {vae.dtype}" if vae is not None else "vae is None")
+
     loss_recorder = train_util.LossRecorder()
     epoch = 0  # avoid error when max_train_steps is 0
     for epoch in range(num_train_epochs):
@@ -1055,10 +1058,10 @@ def optimizer_hook(parameter: torch.Tensor):
             save_dtype,
             epoch,
             global_step,
-            accelerator.unwrap_model(clip_l) if train_clip else None,
-            accelerator.unwrap_model(clip_g) if train_clip else None,
-            accelerator.unwrap_model(t5xxl) if train_t5xxl else None,
-            accelerator.unwrap_model(mmdit) if train_mmdit else None,
+            clip_l if train_clip else None,
+            clip_g if train_clip else None,
+            t5xxl if train_t5xxl else None,
+            mmdit if train_mmdit else None,
             vae,
         )
         logger.info("model saved.")
@@ -1153,6 +1156,16 @@ def setup_parser() -> argparse.ArgumentParser:
         action="store_true",
         help="[Deprecated] use 'skip_cache_check' instead / 代わりに 'skip_cache_check' を使用してください",
     )
+    parser.add_argument(
+        "--blocks_to_swap",
+        type=int,
+        default=None,
+        help="[EXPERIMENTAL] "
+        "Sets the number of blocks (~640MB) to swap during the forward and backward passes."
+        "Increasing this number lowers the overall VRAM used during training at the expense of training speed (s/it)."
+        " / 順伝播および逆伝播中にスワップするブロック（約640MB）の数を設定します。"
+        "この数を増やすと、トレーニング中のVRAM使用量が減りますが、トレーニング速度（s/it）も低下します。",
+    )
     parser.add_argument(
         "--num_last_block_to_freeze",
         type=int,