fix: clone upsampler conditioning

Author: flavioschneider

audio_diffusion_pytorch/model.py

@@ -78,6 +78,7 @@ def random_reupsample(self, x: Tensor) -> Tensor:
         batch_size, factors = x.shape[0], self.factors
         # Pick random factor for each batch element
         factor_batch_idx = torch.randint(0, len(factors), (batch_size,))
+        x = x.clone()
 
         for i, factor in enumerate(factors):
             # Pick random items with current factor, skip if 0

setup.py

@@ -3,7 +3,7 @@
 setup(
     name="audio-diffusion-pytorch",
     packages=find_packages(exclude=[]),
-    version="0.0.49",
+    version="0.0.50",
     license="MIT",
     description="Audio Diffusion - PyTorch",
     long_description_content_type="text/markdown",