diff --git a/modules/model/FluxModel.py b/modules/model/FluxModel.py
index ac7cf1b1..42e24824 100644
--- a/modules/model/FluxModel.py
+++ b/modules/model/FluxModel.py
@@ -224,8 +224,8 @@ def encode_text(
             tokenizer_output = self.tokenizer_2(
                 text,
                 # padding='max_length',
-                truncation=True,
-                max_length=4096,
+                truncation=False,
+                max_length=99999999,
                 return_tensors="pt",
             )
             tokens_2 = tokenizer_output.input_ids.to(self.text_encoder_2.device)
diff --git a/modules/model/util/clip_util.py b/modules/model/util/clip_util.py
index c6695ba9..e2f5c3ac 100644
--- a/modules/model/util/clip_util.py
+++ b/modules/model/util/clip_util.py
@@ -24,8 +24,8 @@ def encode_clip(
         return None, None
 
     chunks = [tokens[:, i:i + chunk_length] for i in range(0, tokens.shape[1], chunk_length)]
-    chunk_embeddings = [] if add_output else None
-    pooled_outputs = [] if add_pooled_output else None
+    chunk_embeddings = []
+    pooled_outputs = []
 
     for i, chunk in enumerate(chunks):
         if chunk.numel() == 0:
@@ -59,22 +59,27 @@ def encode_clip(
         if add_output:
             embedding = outputs.hidden_states[default_layer - layer_skip]
             chunk_embeddings.append(embedding)
-
         if add_pooled_output:
-            if hasattr(text_encoder_output, "text_embeds"):
-                pooled_outputs.append(text_encoder_output.text_embeds)
-            if hasattr(text_encoder_output, "pooler_output"):
-                pooled_outputs.append(text_encoder_output.pooler_output)
+            if hasattr(outputs, "text_embeds"):
+                pooled_outputs.append(outputs.text_embeds)
+            elif hasattr(outputs, "pooler_output"):
+                pooled_outputs.append(outputs.pooler_output)
 
-    if chunk_embeddings is not None and len(chunk_embeddings) > max_embeddings_multiples:
-        chunk_embeddings = chunk_embeddings[:max_embeddings_multiples]
-    if pooled_outputs is not None and len(pooled_outputs) > max_embeddings_multiples:
-        pooled_outputs = pooled_outputs[:max_embeddings_multiples]
-    text_encoder_output = torch.cat(chunk_embeddings, dim=1) if chunk_embeddings is not None else None
-    pooled_text_encoder_output = pooled_outputs[0] if pooled_outputs else None
+    if add_output:
+        if chunk_embeddings and len(chunk_embeddings) > max_embeddings_multiples:
+            chunk_embeddings = chunk_embeddings[:max_embeddings_multiples]
+        text_encoder_output = torch.cat(chunk_embeddings, dim=1)
+        if add_layer_norm:
+            final_layer_norm = text_encoder.text_model.final_layer_norm
+            text_encoder_output = final_layer_norm(text_encoder_output)
+    else:
+        text_encoder_output = None
 
-    if add_layer_norm and text_encoder_output is not None:
-        final_layer_norm = text_encoder.text_model.final_layer_norm
-        text_encoder_output = final_layer_norm(text_encoder_output)
+    if add_pooled_output:
+        if pooled_outputs and len(pooled_outputs) > max_embeddings_multiples:
+            pooled_outputs = pooled_outputs[:max_embeddings_multiples]
+        pooled_text_encoder_output = pooled_outputs[0] if pooled_outputs else None
+    else:
+        pooled_text_encoder_output = None
 
-    return text_encoder_output, pooled_text_encoder_output
+    return text_encoder_output, pooled_text_encoder_output
\ No newline at end of file