@@ -1740,9 +1740,12 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
                     prompt_embeds = prompt_embeds_cache[step]
                     text_ids = text_ids_cache[step]
                 else:
-                    num_repeat_elements = len(prompts)
-                    prompt_embeds = prompt_embeds.repeat(num_repeat_elements, 1, 1)
-                    text_ids = text_ids.repeat(num_repeat_elements, 1, 1)
+                    # With prior preservation, prompt_embeds/text_ids already contain [instance, class] entries,
+                    # while collate_fn orders batches as [inst1..instB, class1..classB]. Repeat each entry along
+                    # dim 0 to preserve that grouping instead of interleaving [inst, class, inst, class, ...].
+                    num_repeat_elements = len(prompts) // 2 if args.with_prior_preservation else len(prompts)
+                    prompt_embeds = prompt_embeds.repeat_interleave(num_repeat_elements, dim=0)
+                    text_ids = text_ids.repeat_interleave(num_repeat_elements, dim=0)
 
                 # Convert images to latent space
                 if args.cache_latents:
@@ -1809,10 +1812,11 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
                     # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
                     model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
                     target, target_prior = torch.chunk(target, 2, dim=0)
+                    weighting, weighting_prior = torch.chunk(weighting, 2, dim=0)
 
                     # Compute prior loss
                     prior_loss = torch.mean(
-                        (weighting.float() * (model_pred_prior.float() - target_prior.float()) ** 2).reshape(
+                        (weighting_prior.float() * (model_pred_prior.float() - target_prior.float()) ** 2).reshape(
                             target_prior.shape[0], -1
                         ),
                         1,
 
@@ -1680,9 +1680,12 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
                     prompt_embeds = prompt_embeds_cache[step]
                     text_ids = text_ids_cache[step]
                 else:
-                    num_repeat_elements = len(prompts)
-                    prompt_embeds = prompt_embeds.repeat(num_repeat_elements, 1, 1)
-                    text_ids = text_ids.repeat(num_repeat_elements, 1, 1)
+                    # With prior preservation, prompt_embeds/text_ids already contain [instance, class] entries,
+                    # while collate_fn orders batches as [inst1..instB, class1..classB]. Repeat each entry along
+                    # dim 0 to preserve that grouping instead of interleaving [inst, class, inst, class, ...].
+                    num_repeat_elements = len(prompts) // 2 if args.with_prior_preservation else len(prompts)
+                    prompt_embeds = prompt_embeds.repeat_interleave(num_repeat_elements, dim=0)
+                    text_ids = text_ids.repeat_interleave(num_repeat_elements, dim=0)
 
                 # Convert images to latent space
                 if args.cache_latents:
@@ -1752,10 +1755,11 @@ def get_sigmas(timesteps, n_dim=4, dtype=torch.float32):
                     # Chunk the noise and model_pred into two parts and compute the loss on each part separately.
                     model_pred, model_pred_prior = torch.chunk(model_pred, 2, dim=0)
                     target, target_prior = torch.chunk(target, 2, dim=0)
+                    weighting, weighting_prior = torch.chunk(weighting, 2, dim=0)
 
                     # Compute prior loss
                     prior_loss = torch.mean(
-                        (weighting.float() * (model_pred_prior.float() - target_prior.float()) ** 2).reshape(
+                        (weighting_prior.float() * (model_pred_prior.float() - target_prior.float()) ** 2).reshape(
                             target_prior.shape[0], -1
                         ),
                         1,