diff --git a/codes/models/audio/music/transformer_diffusion13.py b/codes/models/audio/music/transformer_diffusion13.py
index 3fdcdce9..05ff8fe4 100644
--- a/codes/models/audio/music/transformer_diffusion13.py
+++ b/codes/models/audio/music/transformer_diffusion13.py
@@ -178,12 +178,12 @@ class TransformerDiffusion(nn.Module):
         """
         resolution = randrange(0, self.resolution_steps)
         resolution_scale = 2 ** resolution
-        s = F.interpolate(x, scale_factor=1/resolution_scale, mode='linear', align_corners=True)
+        s = F.interpolate(x, scale_factor=1/resolution_scale, mode='nearest')
         s_diff = s.shape[-1] - self.max_window
         if s_diff > 1:
             start = randrange(0, s_diff)
             s = s[:,:,start:start+self.max_window]
-        s_prior = F.interpolate(s, scale_factor=.25, mode='linear', align_corners=True)
+        s_prior = F.interpolate(s, scale_factor=.25, mode='nearest')
         s_prior = F.interpolate(s_prior, size=(s.shape[-1],), mode='linear', align_corners=True)
         self.preprocessed = (s_prior, torch.tensor([resolution] * x.shape[0], dtype=torch.long, device=x.device))
         return s
diff --git a/codes/trainer/eval/music_diffusion_fid.py b/codes/trainer/eval/music_diffusion_fid.py
index 50c03dc1..8b8c10fa 100644
--- a/codes/trainer/eval/music_diffusion_fid.py
+++ b/codes/trainer/eval/music_diffusion_fid.py
@@ -232,7 +232,7 @@ class MusicDiffusionFid(evaluator.Evaluator):
         mel_norm = normalize_torch_mel(mel)
         #mel_norm = mel_norm[:,:,:448*4]  # restricts first stage to optimal training window.
         conditioning = mel_norm[:,:,:1200]
-        downsampled = F.interpolate(mel_norm, scale_factor=1/16, mode='linear', align_corners=True)
+        downsampled = F.interpolate(mel_norm, scale_factor=1/16, mode='nearest')
         stage1_shape = (1, 256, downsampled.shape[-1]*4)
         sampler = self.diffuser.ddim_sample_loop if self.ddim else self.diffuser.p_sample_loop
         # Chain super-sampling using 2 stages.