diff --git a/README.md b/README.md
index c07aa9b..c81dc92 100644
--- a/README.md
+++ b/README.md
@@ -136,7 +136,7 @@ Step 3: Training
 ```shell
 CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" python train_recammaster.py   --task train  --dataset_path recam_train_data   --output_path ./models/train   --dit_path "models/Wan-AI/Wan2.1-T2V-1.3B/diffusion_pytorch_model.safetensors"   --steps_per_epoch 8000   --max_epochs 100   --learning_rate 1e-4   --accumulate_grad_batches 1   --use_gradient_checkpointing  --dataloader_num_workers 4
 ```
-We do not explore the optimal set of hyper-parameters and train with a batch size of 1 on each GPU. You may achieve better model performance by adjusting hyper-parameters such as the learning rate and increasing the batch size.
+We do not explore the optimal set of hyper-parameters and train with a batch size of 1 on each GPU. You may achieve better model performance by adjusting hyper-parameters such as the learning rate ~and increasing the batch size~. We only support batch size=1, see more discussion here: [Wan2.1 finetuning script seems to only support bs = 1](https://github.com/modelscope/DiffSynth-Studio/issues/600)
 
 Step 4: Test the model
 
diff --git a/train_recammaster.py b/train_recammaster.py
index d687f21..ac60ef7 100644
--- a/train_recammaster.py
+++ b/train_recammaster.py
@@ -48,7 +48,9 @@ def crop_and_resize(self, image):
         )
         return image
 
-
+    def __len__(self):
+        return len(self.path)
+        
     def load_frames_using_imageio(self, file_path, max_num_frames, start_frame_id, interval, num_frames, frame_process):
         reader = imageio.get_reader(file_path)
         if reader.count_frames() < max_num_frames or reader.count_frames() - 1 < start_frame_id + (num_frames - 1) * interval:
@@ -115,8 +117,9 @@ def __getitem__(self, data_id):
                 else:
                     data = {"text": text, "video": video, "path": path}
                 break
-            except:
-                data_id += 1
+            except Exception as e:
+                print(f"ERROR WHEN LOADING: {e}")
+                self.__getitem__(data_id+1 if data_id+1 < len(self.path) else 0)
         return data
     
 
@@ -203,7 +206,6 @@ def get_relative_pose(self, cam_params):
         ret_poses = np.array(ret_poses, dtype=np.float32)
         return ret_poses
 
-
     def __getitem__(self, index):
         # Return: 
         # data['latents']: torch.Size([16, 21*2, 60, 104])
@@ -263,7 +265,7 @@ def __getitem__(self, index):
     
 
     def __len__(self):
-        return self.steps_per_epoch
+        return min(len(self.path), self.steps_per_epoch)