diff --git a/comfy/sd.py b/comfy/sd.py
index 0c5a6dc6e..3fba9421c 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -829,7 +829,7 @@ 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:
-            print("Single LLAMA3_8 for HiDreams")
+            logging.info("Single LLAMA3_8 for HiDreams")
             clip_target.clip = comfy.text_encoders.hidream.hidream_clip(False, **llama_detect(clip_data), clip_g=False, t5=False)
             clip_target.tokenizer = comfy.text_encoders.hidream.HiDreamTokenizer
         else:
diff --git a/comfy/text_encoders/hidream.py b/comfy/text_encoders/hidream.py
index cce1e3cce..417f6fb2b 100644
--- a/comfy/text_encoders/hidream.py
+++ b/comfy/text_encoders/hidream.py
@@ -98,13 +98,13 @@ class HiDreamTEModel(torch.nn.Module):
 
         if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0:
             if self.clip_l is not None:
-                print("Encoding clip_l token weights")
+                logging.info("Encoding clip_l token weights")
                 lg_out, l_pooled = self.clip_l.encode_token_weights(token_weight_pairs_l)
             else:
                 l_pooled = torch.zeros((1, 768), device=comfy.model_management.intermediate_device())
 
             if self.clip_g is not None:
-                print("Encoding clip_g token weights")
+                logging.info("Encoding clip_g token weights")
                 g_out, g_pooled = self.clip_g.encode_token_weights(token_weight_pairs_g)
             else:
                 g_pooled = torch.zeros((1, 1280), device=comfy.model_management.intermediate_device())
@@ -113,27 +113,27 @@ class HiDreamTEModel(torch.nn.Module):
                 pooled = torch.cat((l_pooled, g_pooled), dim=-1)            
 
         if self.t5xxl is not None:
-            print("Encoding t5 token weights")
+            logging.info("Encoding t5 token weights")
             t5_output = self.t5xxl.encode_token_weights(token_weight_pairs_t5)
             t5_out, t5_pooled = t5_output[:2]
 
         if self.llama is not None:
-            print("Encoding llama token weights")
+            logging.info("Encoding llama token weights")
             ll_output = self.llama.encode_token_weights(token_weight_pairs_llama)
             ll_out, ll_pooled = ll_output[:2]
             ll_out = ll_out[:, 1:]
 
         if t5_out is None:
-            print("Loading t5_out from disk")
+            logging.info("Loading t5_out from disk")
             t5_path = folder_paths.get_full_path_or_raise("hidream_empty_latents", "t5_out.pt")
             t5_out = torch.load(t5_path, map_location=comfy.model_management.intermediate_device())
 
         if ll_out is None:
-            print("No llama encoder found, filling with zeroes")
+            logging.info("No llama encoder found, filling with zeroes")
             ll_out = torch.zeros((1, 32, 1, 4096), device=comfy.model_management.intermediate_device())
 
         if pooled is None:            
-            print("Loading pooled from disk")
+            logging.info("Loading pooled from disk")
             pooled_path = folder_paths.get_full_path_or_raise("hidream_empty_latents", "pooled.pt")
             pooled = torch.load(pooled_path, map_location=comfy.model_management.intermediate_device())