diff --git a/codes/models/diffusion/diffusion_dvae.py b/codes/models/diffusion/diffusion_dvae.py
index 3d11a873..09977a06 100644
--- a/codes/models/diffusion/diffusion_dvae.py
+++ b/codes/models/diffusion/diffusion_dvae.py
@@ -122,6 +122,7 @@ class DiffusionDVAE(nn.Module):
             linear(time_embed_dim, time_embed_dim),
         )
 
+        self.conditioning_enabled = conditioning_inputs_provided
         if conditioning_inputs_provided:
             self.contextual_embedder = AudioMiniEncoder(self.spectrogram_channels, time_embed_dim)
             self.query_gen = AudioMiniEncoder(decoder_channels[0], time_embed_dim)
@@ -249,6 +250,9 @@ class DiffusionDVAE(nn.Module):
         )
 
     def _decode_continouous(self, x, timesteps, embeddings, conditioning_inputs, num_conditioning_signals):
+        if self.conditioning_enabled:
+            assert conditioning_inputs is not None
+
         spec_hs = self.decoder(embeddings)[::-1]
         # Shape the spectrogram correctly. There is no guarantee it fits (though I probably should add an assertion here to make sure the resizing isn't too wacky.)
         spec_hs = [nn.functional.interpolate(sh, size=(x.shape[-1]//self.scale_steps**self.spectrogram_conditioning_levels[i],), mode='nearest') for i, sh in enumerate(spec_hs)]
@@ -257,7 +261,7 @@ class DiffusionDVAE(nn.Module):
         # Timestep embeddings and conditioning signals are combined using a small transformer.
         hs = []
         emb1 = self.time_embed(timestep_embedding(timesteps, self.model_channels))
-        if conditioning_inputs is not None:
+        if self.conditioning_enabled:
             mask = get_mask_from_lengths(num_conditioning_signals+1, conditioning_inputs.shape[1]+1)  # +1 to account for the timestep embeddings we'll add.
             emb2 = torch.stack([self.contextual_embedder(ci.squeeze(1)) for ci in list(torch.chunk(conditioning_inputs, conditioning_inputs.shape[1], dim=1))], dim=1)
             emb = torch.cat([emb1.unsqueeze(1), emb2], dim=1)
diff --git a/codes/trainer/ExtensibleTrainer.py b/codes/trainer/ExtensibleTrainer.py
index 1c92c0ce..edabeab0 100644
--- a/codes/trainer/ExtensibleTrainer.py
+++ b/codes/trainer/ExtensibleTrainer.py
@@ -117,7 +117,9 @@ class ExtensibleTrainer(BaseModel):
                     dnet = DistributedDataParallel(anet, delay_allreduce=True)
                 else:
                     from torch.nn.parallel.distributed import DistributedDataParallel
-                    dnet = DistributedDataParallel(anet, device_ids=[torch.cuda.current_device()], find_unused_parameters=True)
+                    # Do NOT be tempted to put find_unused_parameters=True here. It will not work in the current incarnation of this trainer.
+                    # Use all of your parameters in training, or delete them!
+                    dnet = DistributedDataParallel(anet, device_ids=[torch.cuda.current_device()])
             else:
                 dnet = DataParallel(anet, device_ids=opt['gpu_ids'])
             if self.is_train: