diff --git a/src/diffusers/models/transformers/transformer_flux.py b/src/diffusers/models/transformers/transformer_flux.py
index d772e89139..21bf42b57d 100644
--- a/src/diffusers/models/transformers/transformer_flux.py
+++ b/src/diffusers/models/transformers/transformer_flux.py
@@ -344,6 +344,10 @@ class FluxTransformer2DModel(
 
         self.gradient_checkpointing = False
 
+    @property
+    def is_chroma(self) -> bool:
+        return isinstance(self.time_text_embed, CombinedTimestepTextProjChromaEmbeddings)
+
     @property
     # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors
     def attn_processors(self) -> Dict[str, AttentionProcessor]:
@@ -500,7 +504,7 @@ class FluxTransformer2DModel(
                     "Passing `scale` via `joint_attention_kwargs` when not using the PEFT backend is ineffective."
                 )
 
-        is_chroma = isinstance(self.time_text_embed, CombinedTimestepTextProjChromaEmbeddings)
+        is_chroma = self.is_chroma
         hidden_states = self.x_embedder(hidden_states)
 
         timestep = timestep.to(hidden_states.dtype) * 1000
diff --git a/src/diffusers/pipelines/flux/pipeline_flux.py b/src/diffusers/pipelines/flux/pipeline_flux.py
index a7266c3a56..50c0c4cedc 100644
--- a/src/diffusers/pipelines/flux/pipeline_flux.py
+++ b/src/diffusers/pipelines/flux/pipeline_flux.py
@@ -191,6 +191,7 @@ class FluxPipeline(
         transformer: FluxTransformer2DModel,
         image_encoder: CLIPVisionModelWithProjection = None,
         feature_extractor: CLIPImageProcessor = None,
+        variant: str = "flux",
     ):
         super().__init__()
 
@@ -213,6 +214,17 @@ class FluxPipeline(
             self.tokenizer.model_max_length if hasattr(self, "tokenizer") and self.tokenizer is not None else 77
         )
         self.default_sample_size = 128
+        if variant not in {"flux", "chroma"}:
+            raise ValueError("`variant` must be `'flux' or `'chroma'`.")
+
+        self.variant = variant
+
+    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,
@@ -236,7 +248,7 @@ class FluxPipeline(
             padding="max_length",
             max_length=max_sequence_length,
             truncation=True,
-            return_length=False,
+            return_length=(self.variant == "chroma"),
             return_overflowing_tokens=False,
             return_tensors="pt",
         )
@@ -250,7 +262,15 @@ class FluxPipeline(
                 f" {max_sequence_length} tokens: {removed_text}"
             )
 
-        prompt_embeds = self.text_encoder_2(text_input_ids.to(device), output_hidden_states=False)[0]
+        prompt_embeds = self.text_encoder_2(
+            text_input_ids.to(device),
+            output_hidden_states=False,
+            attention_mask=(
+                self._get_chroma_attn_mask(text_inputs.length, max_sequence_length).to(device)
+                if self.variant == "chroma"
+                else None
+            ),
+        )[0]
 
         dtype = self.text_encoder_2.dtype
         prompt_embeds = prompt_embeds.to(dtype=dtype, device=device)