  We present an end-to-end Transformer based Latent Diffusion model for image
synthesis. On the ImageNet class conditioned generation task we show that a
Transformer based Latent Diffusion model achieves a 14.1FID which is comparable
to the 13.1FID score of a UNet based architecture. In addition to showing the
application of Transformer models for Diffusion based image synthesis this
simplification in architecture allows easy fusion and modeling of text and
image data. The multi-head attention mechanism of Transformers enables
simplified interaction between the image and text features which removes the
requirement for crossattention mechanism in UNet based Diffusion models.
