DALL-E 2, generates more realistic and accurate images with 4x greater resolution, better caption matching and photorealism
Contrastive models like CLIP have been shown to learn robust representations of images that capture both semantics and style
two-stage model: a prior that generates a CLIP image Embedding given a text caption, and a “unCLIP” decoder that generates an image conditioned on the image Embedding
explicitly generating image representations improves image diversity with minimal loss in photorealism and caption similarity
decoder, which is conditioned on image representations, can also produce variations of an image that preserve both its semantics and style, while varying the non-essential details absent from the image representation
diffusion models for the decoder and experiment with both Autoregressive and diffusion models for the prior, finding that the latter are computationally more efficient and produce higher-quality samples