diff --git a/src/diffusers/pipelines/chroma/pipeline_chroma.py b/src/diffusers/pipelines/chroma/pipeline_chroma.py
index 88b435fb29..09883f54c7 100644
--- a/src/diffusers/pipelines/chroma/pipeline_chroma.py
+++ b/src/diffusers/pipelines/chroma/pipeline_chroma.py
@@ -198,13 +198,6 @@ class ChromaPipeline(
         self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor * 2)
         self.default_sample_size = 128
 
-    def _get_chroma_attn_mask(self, length: torch.Tensor, max_sequence_length: int) -> torch.Tensor:
-        attention_mask = torch.zeros((length.shape[0], max_sequence_length), dtype=torch.bool, device=length.device)
-        for i, n_tokens in enumerate(length):
-            n_tokens = torch.max(n_tokens + 1, max_sequence_length)
-            attention_mask[i, :n_tokens] = True
-        return attention_mask
-
     def _get_t5_prompt_embeds(
         self,
         prompt: Union[str, List[str]] = None,
@@ -234,12 +227,12 @@ class ChromaPipeline(
         text_input_ids = text_inputs.input_ids
         untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids
 
+        text_inputs.attention_mask[:, : text_inputs.length + 1] = 1.0
+        
         prompt_embeds = self.text_encoder(
             text_input_ids.to(device),
             output_hidden_states=False,
-            attention_mask=(
-                self._get_chroma_attn_mask(text_inputs.length, max_sequence_length).to(device)
-            ),
+            attention_mask=text_inputs.attention_mask.to(device),
         )[0]
 
         dtype = self.text_encoder.dtype