diff --git a/docs/source/en/api/pipelines/photon.md b/docs/source/en/api/pipelines/photon.md
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@@ -15,15 +15,7 @@
 # PhotonPipeline
 
 
-Photon is a text-to-image diffusion model using simplified MMDIT architecture with flow matching for efficient high-quality image generation. The model uses T5Gemma as the text encoder and supports either Flux VAE (AutoencoderKL) or DC-AE (AutoencoderDC) for latent compression.
-
-Key features:
-
-- **Simplified MMDIT architecture**: Uses a simplified MMDIT architecture for image generation where text tokens are not updated through the transformer blocks
-- **Flow Matching**: Employs flow matching with discrete scheduling for efficient sampling
-- **Flexible VAE Support**: Compatible with both Flux VAE (8x compression, 16 latent channels) and DC-AE (32x compression, 32 latent channels)
-- **T5Gemma Text Encoder**: Uses Google's T5Gemma-2B-2B-UL2 model for text encoding offering multiple language support
-- **Efficient Architecture**: ~1.3B parameters in the transformer, enabling fast inference while maintaining quality
+Photon generates high-quality images from text using a simplified MMDIT architecture where text tokens don't update through transformer blocks. It employs flow matching with discrete scheduling for efficient sampling and uses Google's T5Gemma-2B-2B-UL2 model for multi-language text encoding. The ~1.3B parameter transformer delivers fast inference without sacrificing quality. You can choose between Flux VAE (8x compression, 16 latent channels) for balanced quality and speed or DC-AE (32x compression, 32 latent channels) for latent compression and faster processing.
 
 ## Available models:
 We offer a range of **Photon models** featuring different **VAE configurations**, each optimized for generating images at various resolutions.