diff --git a/comfy/text_encoders/hidream.py b/comfy/text_encoders/hidream.py
index 8e1abcfc1..4f0b145f1 100644
--- a/comfy/text_encoders/hidream.py
+++ b/comfy/text_encoders/hidream.py
@@ -5,6 +5,7 @@ from comfy import sdxl_clip
 import comfy.model_management
 import torch
 import logging
+import folder_paths
 
 
 class HiDreamTokenizer:
@@ -16,11 +17,11 @@ class HiDreamTokenizer:
 
     def tokenize_with_weights(self, text:str, return_word_ids=False, **kwargs):
         out = {}
-        out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids)
-        out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids)
-        t5xxl = self.t5xxl.tokenize_with_weights(text, return_word_ids)
+        out["g"] = self.clip_g.tokenize_with_weights(text, return_word_ids, **kwargs)
+        out["l"] = self.clip_l.tokenize_with_weights(text, return_word_ids, **kwargs)
+        t5xxl = self.t5xxl.tokenize_with_weights(text, return_word_ids, **kwargs)
         out["t5xxl"] = [t5xxl[0]]  # Use only first 128 tokens
-        out["llama"] = self.llama.tokenize_with_weights(text, return_word_ids)
+        out["llama"] = self.llama.tokenize_with_weights(text, return_word_ids, **kwargs)
         return out
 
     def untokenize(self, token_weight_pair):
@@ -91,6 +92,8 @@ class HiDreamTEModel(torch.nn.Module):
         token_weight_pairs_llama = token_weight_pairs["llama"]
         lg_out = None
         pooled = None
+        t5_out = None
+        ll_out = None
         extra = {}
 
         if len(token_weight_pairs_g) > 0 or len(token_weight_pairs_l) > 0:
@@ -104,8 +107,9 @@ class HiDreamTEModel(torch.nn.Module):
             else:
                 g_pooled = torch.zeros((1, 1280), device=comfy.model_management.intermediate_device())
 
-            pooled = torch.cat((l_pooled, g_pooled), dim=-1)
-
+            if self.clip_g is not None and self.clip_l is not None:
+                pooled = torch.cat((l_pooled, g_pooled), dim=-1)            
+                
         if self.t5xxl is not None:
             t5_output = self.t5xxl.encode_token_weights(token_weight_pairs_t5)
             t5_out, t5_pooled = t5_output[:2]
@@ -120,13 +124,15 @@ class HiDreamTEModel(torch.nn.Module):
             ll_out = None
 
         if t5_out is None:
-            t5_out = torch.zeros((1, 128, 4096), device=comfy.model_management.intermediate_device())
+            t5_path = folder_paths.get_full_path_or_raise("hidream_empty_latents", "t5_blank.pt")
+            t5_out = torch.load(t5_path, map_location=comfy.model_management.intermediate_device())
 
         if ll_out is None:
             ll_out = torch.zeros((1, 32, 1, 4096), device=comfy.model_management.intermediate_device())
 
         if pooled is None:
-            pooled = torch.zeros((1, 768 + 1280), device=comfy.model_management.intermediate_device())
+            pooled_path = folder_paths.get_full_path_or_raise("hidream_empty_latents", "pooled_blank.pt")
+            pooled = torch.load(pooled_path, map_location=comfy.model_management.intermediate_device())
 
         extra["conditioning_llama3"] = ll_out
         return t5_out, pooled, extra
diff --git a/folder_paths.py b/folder_paths.py
index 9a525e5a1..3f6bc03cd 100644
--- a/folder_paths.py
+++ b/folder_paths.py
@@ -4,7 +4,7 @@ import os
 import time
 import mimetypes
 import logging
-from typing import Literal
+from typing import Literal, List
 from collections.abc import Collection
 
 from comfy.cli_args import args
@@ -45,6 +45,7 @@ folder_names_and_paths["hypernetworks"] = ([os.path.join(models_dir, "hypernetwo
 folder_names_and_paths["photomaker"] = ([os.path.join(models_dir, "photomaker")], supported_pt_extensions)
 
 folder_names_and_paths["classifiers"] = ([os.path.join(models_dir, "classifiers")], {""})
+folder_names_and_paths["hidream_empty_latents"] = ([os.path.join(models_dir, "hidream_empty_latents")], supported_pt_extensions)
 
 output_directory = os.path.join(base_path, "output")
 temp_directory = os.path.join(base_path, "temp")
@@ -141,7 +142,7 @@ def get_directory_by_type(type_name: str) -> str | None:
         return get_input_directory()
     return None
 
-def filter_files_content_types(files: list[str], content_types: Literal["image", "video", "audio", "model"]) -> list[str]:
+def filter_files_content_types(files: list[str], content_types: List[Literal["image", "video", "audio", "model"]]) -> list[str]:
     """
     Example:
         files = os.listdir(folder_paths.get_input_directory())