diff --git a/codes/trainer/eval/music_diffusion_fid.py b/codes/trainer/eval/music_diffusion_fid.py
index ed2bf56b..428f3254 100644
--- a/codes/trainer/eval/music_diffusion_fid.py
+++ b/codes/trainer/eval/music_diffusion_fid.py
@@ -117,7 +117,10 @@ class MusicDiffusionFid(evaluator.Evaluator):
         mu2 = np.mean(proj2, axis=0)
         sigma1 = np.cov(proj1, rowvar=False)
         sigma2 = np.cov(proj2, rowvar=False)
-        return torch.tensor(calculate_frechet_distance(mu1, sigma1, mu2, sigma2))
+        try:
+            return torch.tensor(calculate_frechet_distance(mu1, sigma1, mu2, sigma2))
+        except:
+            return 0
 
     def perform_eval(self):
         save_path = osp.join(self.env['base_path'], "../", "audio_eval", str(self.env["step"]))
@@ -166,13 +169,13 @@ class MusicDiffusionFid(evaluator.Evaluator):
 
 
 if __name__ == '__main__':
-    diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_waveform_gen.yml', 'generator',
+    diffusion = load_model_from_config('X:\\dlas\\experiments\\train_music_diffusion_tfd.yml', 'generator',
                                        also_load_savepoint=False,
-                                       load_path='X:\\dlas\\experiments\\train_music_waveform_gen_reformed_mel\\models\\57500_generator_ema.pth'
+                                       load_path='X:\\dlas\\experiments\\train_music_diffusion_tfd\\models\\3000_generator_ema.pth'
                                        ).cuda()
     opt_eval = {'path': 'Y:\\split\\yt-music-eval', 'diffusion_steps': 500,
                 'conditioning_free': True, 'conditioning_free_k': 1,
-                'diffusion_schedule': 'linear', 'diffusion_type': 'spec_decode'}
+                'diffusion_schedule': 'linear', 'diffusion_type': 'from_codes'}
     env = {'rank': 0, 'base_path': 'D:\\tmp\\test_eval_music', 'step': 26, 'device': 'cuda', 'opt': {}}
     eval = MusicDiffusionFid(diffusion, opt_eval, env)
     print(eval.perform_eval())