diff --git a/extensions_built_in/diffusion_models/qwen_image/qwen_image.py b/extensions_built_in/diffusion_models/qwen_image/qwen_image.py
index 0163dc19..b257930a 100644
--- a/extensions_built_in/diffusion_models/qwen_image/qwen_image.py
+++ b/extensions_built_in/diffusion_models/qwen_image/qwen_image.py
@@ -251,34 +251,44 @@ class QwenImageModel(BaseModel):
         **kwargs
     ):
         batch_size, num_channels_latents, height, width = latent_model_input.shape
-        
+
+        # pack image tokens
         latent_model_input = latent_model_input.view(batch_size, num_channels_latents, height // 2, 2, width // 2, 2)
         latent_model_input = latent_model_input.permute(0, 2, 4, 1, 3, 5)
         latent_model_input = latent_model_input.reshape(batch_size, (height // 2) * (width // 2), num_channels_latents * 4)
-        
-        # make txt_seq_lens match the actual encoder_hidden_states length, and clamp the mask ---
-        seq_len = text_embeddings.text_embeds.shape[1]
-        prompt_embeds_mask = text_embeddings.attention_mask.to(self.device_torch, dtype=torch.int64)[:, :seq_len]
-        txt_seq_lens = [seq_len] * batch_size
 
-        img_shapes = [(1, height // 2, width  // 2)] * batch_size
+        # clamp text length to RoPE capacity for this image size
+        # img_shapes passed to the model
+        img_h2, img_w2 = height // 2, width // 2
+        img_shapes = [(1, img_h2, img_w2)] * batch_size
+
+        # QwenEmbedRope logic:
+        max_vid_index = max(img_h2 // 2, img_w2 // 2)
+
+        rope_cap = 1024 - max_vid_index  # available text positions in RoPE cache
+        seq_len_actual = text_embeddings.text_embeds.shape[1]
+        use_len = min(seq_len_actual, rope_cap)
+
+        enc_hs = text_embeddings.text_embeds[:, :use_len].to(self.device_torch, self.torch_dtype)
+        prompt_embeds_mask = text_embeddings.attention_mask.to(self.device_torch, dtype=torch.int64)[:, :use_len]
+        txt_seq_lens = [use_len] * batch_size
 
         noise_pred = self.transformer(
             hidden_states=latent_model_input.to(self.device_torch, self.torch_dtype),
             timestep=timestep / 1000,
             guidance=None,
-            encoder_hidden_states=text_embeddings.text_embeds.to(self.device_torch, self.torch_dtype),
+            encoder_hidden_states=enc_hs,
             encoder_hidden_states_mask=prompt_embeds_mask,
             img_shapes=img_shapes,
             txt_seq_lens=txt_seq_lens,
             return_dict=False,
             **kwargs,
         )[0]
-        
+
+        # unpack
         noise_pred = noise_pred.view(batch_size, height // 2, width // 2, num_channels_latents, 2, 2)
         noise_pred = noise_pred.permute(0, 3, 1, 4, 2, 5)
         noise_pred = noise_pred.reshape(batch_size, num_channels_latents, height, width)
-        
         return noise_pred
     
     def get_prompt_embeds(self, prompt: str) -> PromptEmbeds: