Update README.md

README.md

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 - generative model
 ---
 
-UniDiffuser is a multi-modal diffusion model with a transformer-based backbone ([U-ViT](https://github.com/baofff/U-ViT)). UniDiffuser is able to perform image, text, text-to-image, image-to-text, and image-text pair generation by setting proper timesteps without additional overhead. 
+UniDiffuser is a unified diffusion framework to fit all distributions relevant to a set of multi-modal data in one transformer.
+UniDiffuser is able to perform image, text, text-to-image, image-to-text, and image-text pair generation by setting proper timesteps without additional overhead. 
 
 
 
-The main component of UniDiffuser is [U-ViT](https://github.com/baofff/U-ViT), which parameterizes the joint noise prediction network. Other components perform as encoders and decoders of different modalities, including a pretrained image autoencoder from [Stable Diffusion](https://github.com/CompVis/stable-diffusion), a pretrained [image ViT-B/32 CLIP encoder](https://github.com/openai/CLIP), a pretrained [text ViT-L CLIP encoder](https://huggingface.co/openai/clip-vit-large-patch14), and a [GPT-2](https://github.com/openai/gpt-2) text decoder finetuned by ourselves.
+Specifically, UniDiffuser employs a variation of transformer, called [U-ViT](https://github.com/baofff/U-ViT), which parameterizes the joint noise prediction network. Other components perform as encoders and decoders of different modalities, including a pretrained image autoencoder from [Stable Diffusion](https://github.com/CompVis/stable-diffusion), a pretrained [image ViT-B/32 CLIP encoder](https://github.com/openai/CLIP), a pretrained [text ViT-L CLIP encoder](https://huggingface.co/openai/clip-vit-large-patch14), and a [GPT-2](https://github.com/openai/gpt-2) text decoder finetuned by ourselves.
 
 
 We provide two versions of UniDiffuser: