diff --git a/README.md b/README.md
index 6b5d39c..f937246 100644
--- a/README.md
+++ b/README.md
@@ -76,6 +76,18 @@ For training synthetic scenes such as `bouncingballs`, run
 python train.py -s data/dnerf/bouncingballs --port 6017 --expname "dnerf/bouncingballs" --configs arguments/dnerf/bouncingballs.py 
 ``` 
 You can customize your training config through the config files.
+
+# Checkpoint
+Also, you can training your model with checkpoint.
+```python
+python train.py -s data/dnerf/bouncingballs --port 6017 --expname "dnerf/bouncingballs" --configs arguments/dnerf/bouncingballs.py --checkpoint_iterations 200 # change it.
+```
+Then load checkpoint with:
+```python
+python train.py -s data/dnerf/bouncingballs --port 6017 --expname "dnerf/bouncingballs" --configs arguments/dnerf/bouncingballs.py --start_checkpoint "output/dnerf/bouncingballs/chkpnt_coarse_200.pth"
+# finestage: --start_checkpoint "output/dnerf/bouncingballs/chkpnt_fine_200.pth"
+```
+
 ## Rendering
 Run the following script to render the images.  
 
diff --git a/submodules/depth-diff-gaussian-rasterization b/submodules/depth-diff-gaussian-rasterization
index 2eb32ea..e495066 160000
--- a/submodules/depth-diff-gaussian-rasterization
+++ b/submodules/depth-diff-gaussian-rasterization
@@ -1 +1 @@
-Subproject commit 2eb32ea251d3b339dab3af8b6fd78d7dec3caf8e
+Subproject commit e49506654e8e11ed8a62d22bcb693e943fdecacf
diff --git a/train.py b/train.py
index d2c0384..13f9e51 100644
--- a/train.py
+++ b/train.py
@@ -45,9 +45,15 @@ def scene_reconstruction(dataset, opt, hyper, pipe, testing_iterations, saving_i
 
     gaussians.training_setup(opt)
     if checkpoint:
-        breakpoint()
-        (model_params, first_iter) = torch.load(checkpoint)
-        gaussians.restore(model_params, opt)
+        # breakpoint()
+        if stage == "coarse" and stage not in checkpoint:
+            print("start from fine stage, skip coarse stage.")
+            # process is in the coarse stage, but start from fine stage
+            return
+        if stage in checkpoint: 
+            (model_params, first_iter) = torch.load(checkpoint)
+            gaussians.restore(model_params, opt)
+
 
     bg_color = [1, 1, 1] if dataset.white_background else [0, 0, 0]
     background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
@@ -274,7 +280,7 @@ def scene_reconstruction(dataset, opt, hyper, pipe, testing_iterations, saving_i
 
             if (iteration in checkpoint_iterations):
                 print("\n[ITER {}] Saving Checkpoint".format(iteration))
-                torch.save((gaussians.capture(), iteration), scene.model_path + "/chkpnt" + str(iteration) + ".pth")
+                torch.save((gaussians.capture(), iteration), scene.model_path + "/chkpnt" +f"_{stage}_" + str(iteration) + ".pth")
 def training(dataset, hyper, opt, pipe, testing_iterations, saving_iterations, checkpoint_iterations, checkpoint, debug_from, expname):
     # first_iter = 0
     tb_writer = prepare_output_and_logger(expname)