diff --git a/comfy/sd.py b/comfy/sd.py
index a2725b8a9..8aba5d655 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -816,7 +816,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
                 clip_target.tokenizer = comfy.text_encoders.wan.WanT5Tokenizer
                 tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
             elif clip_type == CLIPType.HIDREAM:
-                clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**t5xxl_detect(clip_data), 
+                clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**t5xxl_detect(clip_data),
                                                                         clip_l=False, clip_g=False, t5=True, llama=False, dtype_llama=None, llama_scaled_fp8=None)
                 clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
             else: #CLIPType.MOCHI
@@ -836,7 +836,7 @@ def load_text_encoder_state_dicts(state_dicts=[], embedding_directory=None, clip
             clip_target.tokenizer = comfy.text_encoders.lumina2.LuminaTokenizer
             tokenizer_data["spiece_model"] = clip_data[0].get("spiece_model", None)
         elif te_model == TEModel.LLAMA3_8:
-            clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**llama_detect(clip_data), 
+            clip_target.clip = comfy.text_encoders.hidream.hidream_clip(**llama_detect(clip_data),
                                                                         clip_l=False, clip_g=False, t5=False, llama=True, dtype_t5=None, t5xxl_scaled_fp8=None)
             clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
         else:
diff --git a/comfy/text_encoders/hidream.py b/comfy/text_encoders/hidream.py
index 0adbad346..ca54eaa78 100644
--- a/comfy/text_encoders/hidream.py
+++ b/comfy/text_encoders/hidream.py
@@ -110,14 +110,14 @@ class HiDreamTEModel(torch.nn.Module):
             t5_output = self.t5xxl.encode_token_weights(token_weight_pairs_t5)
             t5_out, t5_pooled = t5_output[:2]
         else:
-            t5_out, t5_pooled = None, None
+            t5_out = None
 
         if self.llama is not None:
             ll_output = self.llama.encode_token_weights(token_weight_pairs_llama)
             ll_out, ll_pooled = ll_output[:2]
             ll_out = ll_out[:, 1:]
         else:
-            ll_out, ll_pooled = None, None
+            ll_out = None
 
         if t5_out is None:
             t5_out = torch.zeros((1, 1, 4096), device=comfy.model_management.intermediate_device())