diff --git a/hy3dgen/shapegen/models/vae.py b/hy3dgen/shapegen/models/vae.py
index b9b13bf..0a85785 100755
--- a/hy3dgen/shapegen/models/vae.py
+++ b/hy3dgen/shapegen/models/vae.py
@@ -636,13 +636,22 @@ class ShapeVAE(nn.Module):
         batch_logits = []
         batch_size = latents.shape[0]
         comfy_pbar = ProgressBar(xyz_samples.shape[0])
-        for start in tqdm(range(0, xyz_samples.shape[0], num_chunks),
-                          desc=f"MC Level {mc_level} Implicit Function:"):
-            if mc_algo == 'odc':
+        if mc_algo == 'odc':
                 imp_func = lambda xyz: torch.flatten(self.geo_decoder(repeat(xyz, "p c -> b p c", b=batch_size).to(latents.dtype), latents))
                 vertices, faces = odc.extract_mesh(imp_func, num_grid = octree_resolution, isolevel=mc_level, batch_size=num_chunks, min_coord=bbox_min, max_coord=bbox_max)
                 comfy_pbar.update(num_chunks)
-            else:
+        
+                vertices = vertices.detach().cpu().numpy()
+                faces = faces.detach().cpu().numpy()
+                outputs = [
+                        Latent2MeshOutput(
+                            mesh_v=vertices.astype(np.float32),
+                            mesh_f=np.ascontiguousarray(faces)
+                        )]
+                return outputs
+        else:
+            for start in tqdm(range(0, xyz_samples.shape[0], num_chunks),
+                            desc=f"MC Level {mc_level} Implicit Function:"):
                 queries = xyz_samples[start: start + num_chunks, :].to(device)
                 queries = queries.half()
                 batch_queries = repeat(queries, "p c -> b p c", b=batch_size)
@@ -654,16 +663,6 @@ class ShapeVAE(nn.Module):
                 batch_logits.append(logits)
                 comfy_pbar.update(num_chunks)
 
-        if mc_algo == 'odc':
-            vertices = vertices.detach().cpu().numpy()
-            faces = faces.detach().cpu().numpy()
-            outputs = [
-                    Latent2MeshOutput(
-                        mesh_v=vertices.astype(np.float32),
-                        mesh_f=np.ascontiguousarray(faces)
-                    )]
-            return outputs
-        else:
             grid_logits = torch.cat(batch_logits, dim=1)
             grid_logits = grid_logits.view((batch_size, grid_size[0], grid_size[1], grid_size[2])).float()