diff --git a/ldm/modules/encoders/modules.py b/ldm/modules/encoders/modules.py
index 5b5f71cffd..32ac0de7a1 100644
--- a/ldm/modules/encoders/modules.py
+++ b/ldm/modules/encoders/modules.py
@@ -1,18 +1,16 @@
 import math
-import os.path
+from functools import partial
 from typing import Optional
 
+import clip
+import kornia
 import torch
 import torch.nn as nn
-from functools import partial
-import clip
-from einops import rearrange, repeat
+from einops import repeat
 from transformers import CLIPTokenizer, CLIPTextModel
-import kornia
-from ldm.invoke.devices import choose_torch_device
-from ldm.invoke.globals import Globals, global_cache_dir
-#from ldm.modules.textual_inversion_manager import TextualInversionManager
 
+from ldm.invoke.devices import choose_torch_device
+from ldm.invoke.globals import global_cache_dir
 from ldm.modules.x_transformer import (
     Encoder,
     TransformerWrapper,
@@ -664,12 +662,12 @@ class WeightedFrozenCLIPEmbedder(FrozenCLIPEmbedder):
             all_token_ids = all_token_ids[0:max_token_count_without_bos_eos_markers]
             per_token_weights = per_token_weights[0:max_token_count_without_bos_eos_markers]
 
-        # pad out to a 77-entry array: [eos_token, <prompt tokens>, eos_token, ..., eos_token]
+        # pad out to a 77-entry array: [bos_token, <prompt tokens>, eos_token, pad_token…]
         # (77 = self.max_length)
         all_token_ids = [self.tokenizer.bos_token_id] + all_token_ids + [self.tokenizer.eos_token_id]
         per_token_weights = [1.0] + per_token_weights + [1.0]
         pad_length = self.max_length - len(all_token_ids)
-        all_token_ids += [self.tokenizer.eos_token_id] * pad_length
+        all_token_ids += [self.tokenizer.pad_token_id] * pad_length
         per_token_weights += [1.0] * pad_length
 
         all_token_ids_tensor = torch.tensor(all_token_ids, dtype=torch.long).to(self.device)
diff --git a/ldm/modules/prompt_to_embeddings_converter.py b/ldm/modules/prompt_to_embeddings_converter.py
index ab989e4892..dea15d61b4 100644
--- a/ldm/modules/prompt_to_embeddings_converter.py
+++ b/ldm/modules/prompt_to_embeddings_converter.py
@@ -3,8 +3,9 @@ import math
 import torch
 from transformers import CLIPTokenizer, CLIPTextModel
 
-from ldm.modules.textual_inversion_manager import TextualInversionManager
 from ldm.invoke.devices import torch_dtype
+from ldm.modules.textual_inversion_manager import TextualInversionManager
+
 
 class WeightedPromptFragmentsToEmbeddingsConverter():
 
@@ -22,8 +23,8 @@ class WeightedPromptFragmentsToEmbeddingsConverter():
         return self.tokenizer.model_max_length
 
     def get_embeddings_for_weighted_prompt_fragments(self,
-                                                     text: list[str],
-                                                     fragment_weights: list[float],
+                                                     text: list[list[str]],
+                                                     fragment_weights: list[list[float]],
                                                      should_return_tokens: bool = False,
                                                      device='cpu'
                                                      ) -> torch.Tensor:
@@ -198,12 +199,12 @@ class WeightedPromptFragmentsToEmbeddingsConverter():
             all_token_ids = all_token_ids[0:max_token_count_without_bos_eos_markers]
             per_token_weights = per_token_weights[0:max_token_count_without_bos_eos_markers]
 
-        # pad out to a self.max_length-entry array: [eos_token, <prompt tokens>, eos_token, ..., eos_token]
+        # pad out to a self.max_length-entry array: [bos_token, <prompt tokens>, eos_token, pad_token…]
         # (typically self.max_length == 77)
         all_token_ids = [self.tokenizer.bos_token_id] + all_token_ids + [self.tokenizer.eos_token_id]
         per_token_weights = [1.0] + per_token_weights + [1.0]
         pad_length = self.max_length - len(all_token_ids)
-        all_token_ids += [self.tokenizer.eos_token_id] * pad_length
+        all_token_ids += [self.tokenizer.pad_token_id] * pad_length
         per_token_weights += [1.0] * pad_length
 
         all_token_ids_tensor = torch.tensor(all_token_ids, dtype=torch.long, device=device)