diff --git a/README.md b/README.md
index 9418b9e..918360f 100644
--- a/README.md
+++ b/README.md
@@ -517,12 +517,11 @@ The required fields include the following six, with their explanations as follow
 
 All parameters related to training are stored in 'train_config/ace_plus_lora.yaml'. With the following default configuration, the memory usage for LoRA training is between 38GB and 40GB. 
 
-| Hyperparameter | Value     | Description|
-|::|:---:|
+| Hyperparameter | Value     | Description |
+| --- | --- | --- |
 | ATTN_BACKEND |   flash_attn / pytorch  |Set 'flash_attn' to use flash_attn2(Make sure you have installed flash-attn2 correctly). If the version of PyTorch is greater than 2.4.0, use 'pytorch' to utilize PyTorch's implementation.|
 | USE_GRAD_CHECKPOINT |   True / False  |Using gradient checkpointing can also significantly reduce GPU memory usage, but it may slow down the training speed. |
-| MAX_SEQ_LEN |   2048  | The MAX_SEQ_LEN refers to the sequence size limit for a single input image (calculated as H/16 * W/16). 
-    A larger value indicates a longer computation sequence and a higher training resolution. The default value I provided is 2048.|
+| MAX_SEQ_LEN |   2048  | The MAX_SEQ_LEN refers to the sequence size limit for a single input image (calculated as H/16 * W/16). A larger value indicates a longer computation sequence and a higher training resolution. The default value I provided is 2048.|
 
 To run the training code, execute the following command.
 
diff --git a/requirements.txt b/requirements.txt
index 8b13789..e69de29 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1 +0,0 @@
-
diff --git a/train_config/ace_plus_fft.yaml b/train_config/ace_plus_fft.yaml
index 5740324..b7f4b67 100644
--- a/train_config/ace_plus_fft.yaml
+++ b/train_config/ace_plus_fft.yaml
@@ -108,7 +108,10 @@ SOLVER:
       DEPTH: 19
       # DEPTH_SINGLE_BLOCKS DESCRIPTION: number of transformer blocks in the single stream block. TYPE: int default: 38
       DEPTH_SINGLE_BLOCKS: 38
+      # ATTN_BACKEND：setting 'flash_attn' to use flash_attn2, if the version of pytorch > 2.4.0, using 'pytorch' to use pytorch's implementation
       ATTN_BACKEND: flash_attn
+      # USE_GRAD_CHECKPOINT: setting gc to true can decrease the memory usage.
+      USE_GRAD_CHECKPOINT: True
 
     #
     FIRST_STAGE_MODEL:
diff --git a/train_config/ace_plus_fft_lora.yaml b/train_config/ace_plus_fft_lora.yaml
index 226717e..3e5fe5e 100644
--- a/train_config/ace_plus_fft_lora.yaml
+++ b/train_config/ace_plus_fft_lora.yaml
@@ -108,7 +108,10 @@ SOLVER:
       DEPTH: 19
       # DEPTH_SINGLE_BLOCKS DESCRIPTION: number of transformer blocks in the single stream block. TYPE: int default: 38
       DEPTH_SINGLE_BLOCKS: 38
+      # ATTN_BACKEND：setting 'flash_attn' to use flash_attn2, if the version of pytorch > 2.4.0, using 'pytorch' to use pytorch's implementation
       ATTN_BACKEND: flash_attn
+      # USE_GRAD_CHECKPOINT: setting gc to true can decrease the memory usage.
+      USE_GRAD_CHECKPOINT: True
 
     #
     FIRST_STAGE_MODEL:
diff --git a/train_config/ace_plus_lora.yaml b/train_config/ace_plus_lora.yaml
index dc261b2..4d19db2 100644
--- a/train_config/ace_plus_lora.yaml
+++ b/train_config/ace_plus_lora.yaml
@@ -108,7 +108,10 @@ SOLVER:
       DEPTH: 19
       # DEPTH_SINGLE_BLOCKS DESCRIPTION: number of transformer blocks in the single stream block. TYPE: int default: 38
       DEPTH_SINGLE_BLOCKS: 38
+      # ATTN_BACKEND：setting 'flash_attn' to use flash_attn2, if the version of pytorch > 2.4.0, using 'pytorch' to use pytorch's implementation
       ATTN_BACKEND: flash_attn
+      # USE_GRAD_CHECKPOINT: setting gc to true can decrease the memory usage.
+      USE_GRAD_CHECKPOINT: True
 
     #
     FIRST_STAGE_MODEL: