@@ -190,12 +190,12 @@ def decode(self, z: torch.FloatTensor, return_dict: bool = True) -> Union[Decode
         return DecoderOutput(sample=decoded)
 
     def blend_v(self, a, b, blend_extent):
-        for y in range(blend_extent):
+        for y in range(min(a.shape[2], b.shape[2], blend_extent)):
             b[:, :, y, :] = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
         return b
 
     def blend_h(self, a, b, blend_extent):
-        for x in range(blend_extent):
+        for x in range(min(a.shape[3], b.shape[3], blend_extent)):
             b[:, :, :, x] = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
         return b
 
 
@@ -445,6 +445,12 @@ def test_stable_diffusion_vae_tiling(self):
 
         assert np.abs(output_2.images.flatten() - output_1.images.flatten()).max() < 5e-1
 
+        # test that tiled decode works with various shapes
+        shapes = [(1, 4, 73, 97), (1, 4, 97, 73), (1, 4, 49, 65), (1, 4, 65, 49)]
+        for shape in shapes:
+            zeros = torch.zeros(shape).to(device)
+            sd_pipe.vae.decode(zeros)
+
     def test_stable_diffusion_negative_prompt(self):
         device = "cpu"  # ensure determinism for the device-dependent torch.Generator
         components = self.get_dummy_components()