diff --git a/patches/external_pr/dataset.py b/patches/external_pr/dataset.py
index 56ea1af..bc5bed4 100644
--- a/patches/external_pr/dataset.py
+++ b/patches/external_pr/dataset.py
@@ -116,11 +116,11 @@ class PersonalizedBase(Dataset):
             npimage = (npimage / 127.5 - 1.0).astype(np.float32)
 
             torchdata = torch.from_numpy(npimage).permute(2, 0, 1).to(device=device, dtype=torch.float32)
-            latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
-            weight = torch.ones_like(latent_sample)
+
             with torch.autocast("cuda"):
                 latent_dist = model.encode_first_stage(torchdata.unsqueeze(dim=0))
-
+                latent_sample = model.get_first_stage_encoding(latent_dist).squeeze().to(devices.cpu)
+                weight = torch.ones_like(latent_sample)
             if latent_sampling_method == "once" or (
                     latent_sampling_method == "deterministic" and not isinstance(latent_dist,
                                                                                  DiagonalGaussianDistribution)):
@@ -209,6 +209,7 @@ class GroupedBatchSampler(Sampler):
         self.probs = [e % batch_size/self.n_rand_batches/batch_size if self.n_rand_batches > 0 else 0 for e in expected]
         self.batch_size = batch_size
 
+
     def __len__(self):
         return self.len
 
diff --git a/patches/external_pr/hypernetwork.py b/patches/external_pr/hypernetwork.py
index 8c5e892..291c6cf 100644
--- a/patches/external_pr/hypernetwork.py
+++ b/patches/external_pr/hypernetwork.py
@@ -550,7 +550,8 @@ Last saved image: {html.escape(last_saved_image)}<br/>
     finally:
         pbar.leave = False
         pbar.close()
-        hypernetwork.eval()
+        if hypernetwork is not None:
+            hypernetwork.eval()
         shared.parallel_processing_allowed = old_parallel_processing_allowed
         if hasattr(sd_hijack_checkpoint, 'remove'):
             sd_hijack_checkpoint.remove()
@@ -815,6 +816,7 @@ def internal_clean_training(hypernetwork_name, data_root, log_directory,
         shared.sd_model.first_stage_model.to(devices.cpu)
 
     weights = hypernetwork.weights(True)
+    optimizer_name = hypernetwork.optimizer_name
     if hypernetwork.optimizer_name == 'DAdaptAdamW':
         use_dadaptation = True
     optimizer = None
@@ -1070,6 +1072,9 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
     except Exception:
+        if pbar is not None:
+            pbar.set_description(traceback.format_exc())
+        shared.state.textinfo = traceback.format_exc()
         print(traceback.format_exc(), file=sys.stderr)
     finally:
         pbar.leave = False