diff --git a/grl/generative_models/diffusion_model/diffusion_model.py b/grl/generative_models/diffusion_model/diffusion_model.py
index 093f7e8c..f2aee116 100644
--- a/grl/generative_models/diffusion_model/diffusion_model.py
+++ b/grl/generative_models/diffusion_model/diffusion_model.py
@@ -181,9 +181,10 @@ def sample_forward_process(
                 assert False, "Invalid batch size"
 
         if x_0 is not None and condition is not None:
-            assert (
-                x_0.shape[0] == condition.shape[0]
-            ), "The batch size of x_0 and condition must be the same"
+            if type(x_0) == type(condition):
+                assert (
+                    x_0.shape[0] == condition.shape[0]
+                ), "The batch size of x_0 and condition must be the same"
             data_batch_size = x_0.shape[0]
         elif x_0 is not None:
             data_batch_size = x_0.shape[0]
@@ -225,10 +226,20 @@ def sample_forward_process(
             # x.shape = (B*N, D)
 
         if condition is not None:
-            condition = torch.repeat_interleave(
-                condition, torch.prod(extra_batch_size), dim=0
-            )
-            # condition.shape = (B*N, D)
+            if isinstance(condition, torch.Tensor):
+                condition = torch.repeat_interleave(
+                    condition, torch.prod(extra_batch_size), dim=0
+                )
+                # condition.shape = (B*N, D)
+            elif isinstance(condition, treetensor.torch.Tensor):
+                for key in condition.keys():
+                    condition[key] = torch.repeat_interleave(
+                        condition[key], torch.prod(extra_batch_size), dim=0
+                    )
+                    # condition.shape = (B*N, D)
+            else:
+                raise NotImplementedError("Not implemented")
+
 
         if isinstance(solver, DPMSolver):
             # Note: DPMSolver does not support t_span argument assignment