diff --git a/src/diffusers/pipelines/photon/pipeline_photon.py b/src/diffusers/pipelines/photon/pipeline_photon.py
index 7673b89ca1..b30da7d627 100644
--- a/src/diffusers/pipelines/photon/pipeline_photon.py
+++ b/src/diffusers/pipelines/photon/pipeline_photon.py
@@ -234,8 +234,8 @@ class PhotonPipeline(
             The Photon transformer model to denoise the encoded image latents.
         scheduler ([`FlowMatchEulerDiscreteScheduler`]):
             A scheduler to be used in combination with `transformer` to denoise the encoded image latents.
-        text_encoder ([`T5EncoderModel`] or [`T5GemmaEncoder`]):
-            Text encoder model for encoding prompts. Supports T5EncoderModel or T5GemmaEncoder.
+        text_encoder ([`T5GemmaEncoder`]):
+            Text encoder model for encoding prompts.
         tokenizer ([`T5TokenizerFast` or `GemmaTokenizerFast`]):
             Tokenizer for the text encoder.
         vae ([`AutoencoderKL`] or [`AutoencoderDC`]):
@@ -251,9 +251,9 @@ class PhotonPipeline(
         self,
         transformer: PhotonTransformer2DModel,
         scheduler: FlowMatchEulerDiscreteScheduler,
-        text_encoder: Union[T5EncoderModel, T5GemmaEncoder],
+        text_encoder: Union[T5GemmaEncoder],
         tokenizer: Union[T5TokenizerFast, GemmaTokenizerFast, AutoTokenizer],
-        vae: Union[AutoencoderKL, AutoencoderDC],
+        vae: Optional[Union[AutoencoderKL, AutoencoderDC]] = None,
         default_sample_size: Optional[int] = DEFAULT_RESOLUTION,
     ):
         super().__init__()
@@ -277,8 +277,9 @@ class PhotonPipeline(
 
         self.register_to_config(default_sample_size=default_sample_size)
 
-        # Enhance VAE with universal properties for both AutoencoderKL and AutoencoderDC
-        self._enhance_vae_properties()
+        if vae is not None:
+            # Enhance VAE with universal properties for both AutoencoderKL and AutoencoderDC
+            self._enhance_vae_properties()
 
         self.image_processor = PixArtImageProcessor(vae_scale_factor=self.vae_scale_factor)