diff --git a/comfy/ldm/lightricks/model.py b/comfy/ldm/lightricks/model.py
index bfbc08357..85b47351e 100644
--- a/comfy/ldm/lightricks/model.py
+++ b/comfy/ldm/lightricks/model.py
@@ -858,7 +858,7 @@ class LTXBaseModel(torch.nn.Module, ABC):
         return attention_mask
 
     def forward(
-        self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, denoise_mask=None, **kwargs
+        self, x, timestep, context, attention_mask=None, frame_rate=25, transformer_options={}, keyframe_idxs=None, denoise_mask=None, **kwargs
     ):
         """
         Forward pass for LTX models.
@@ -867,7 +867,7 @@ class LTXBaseModel(torch.nn.Module, ABC):
             x: Input tensor
             timestep: Timestep tensor
             context: Context tensor (e.g., text embeddings)
-            attention_mask: Attention mask tensor
+            attention_mask: Attention mask tensor (optional)
             frame_rate: Frame rate for temporal processing
             transformer_options: Additional options for transformer blocks
             keyframe_idxs: Keyframe indices for temporal processing
@@ -885,7 +885,7 @@ class LTXBaseModel(torch.nn.Module, ABC):
         ).execute(x, timestep, context, attention_mask, frame_rate, transformer_options, keyframe_idxs, denoise_mask=denoise_mask, **kwargs)
 
     def _forward(
-        self, x, timestep, context, attention_mask, frame_rate=25, transformer_options={}, keyframe_idxs=None, denoise_mask=None, **kwargs
+        self, x, timestep, context, attention_mask=None, frame_rate=25, transformer_options={}, keyframe_idxs=None, denoise_mask=None, **kwargs
     ):
         """
         Internal forward pass for LTX models.
diff --git a/comfy/sd.py b/comfy/sd.py
index f331feefb..93f1d6337 100644
--- a/comfy/sd.py
+++ b/comfy/sd.py
@@ -424,6 +424,12 @@ class CLIP:
         return self.patcher.get_key_patches()
 
     def generate(self, tokens, do_sample=True, max_length=256, temperature=1.0, top_k=50, top_p=0.95, min_p=0.0, repetition_penalty=1.0, seed=None, presence_penalty=0.0):
+        if not hasattr(self.cond_stage_model, 'generate'):
+            raise RuntimeError(
+                f"The loaded model ({type(self.cond_stage_model).__name__}) does not support text generation. "
+                "The TextGenerate node requires a language model (LLM) such as Qwen, LLaMA, or Gemma, "
+                "not a CLIP text encoder. Please load the correct model type."
+            )
         self.cond_stage_model.reset_clip_options()
 
         self.load_model(tokens)