fix BDDMPipeline

src/diffusers/pipelines/pipeline_bddm.py

@@ -269,13 +269,14 @@ class BDDMPipeline(DiffusionPipeline):
         self.register_modules(diffwave=diffwave, noise_scheduler=noise_scheduler)
     
     @torch.no_grad()
-    def __call__(self, mel_spectrogram, generator):
+    def __call__(self, mel_spectrogram, generator, torch_device=None):
         if torch_device is None:
             torch_device = "cuda" if torch.cuda.is_available() else "cpu"
         
         self.diffwave.to(torch_device)
         
-        audio_length = mel_spectrogram.size(-1) * self.config.hop_len
+        mel_spectrogram = mel_spectrogram.to(torch_device)
+        audio_length = mel_spectrogram.size(-1) * 256
         audio_size = (1, 1, audio_length)
 
         # Sample gaussian noise to begin loop
@@ -285,9 +286,8 @@ class BDDMPipeline(DiffusionPipeline):
         num_prediction_steps = len(self.noise_scheduler)
         for t in tqdm.tqdm(reversed(range(num_prediction_steps)), total=num_prediction_steps):
             # 1. predict noise residual
-            with torch.no_grad():
-                t = (torch.tensor(timestep_values[t]) * torch.ones((1, 1))).to(torch_device)
-                residual = self.diffwave(audio, mel_spectrogram, t)
+            ts = (torch.tensor(timestep_values[t]) * torch.ones((1, 1))).to(torch_device)
+            residual = self.diffwave((audio, mel_spectrogram, ts))
 
             # 2. predict previous mean of audio x_t-1
             pred_prev_audio = self.noise_scheduler.step(residual, audio, t)