The tweet is wrong and this is important. There’s a difference between DALL-E and DALL-E 2. This phenomenon is intuitive if you know how diffusion models work (

The tweet is wrong and this is important. There’s a difference between DALL-E and DALL-E 2. This phenomenon is intuitive if you know how diffusion models work (DALL-E 2) 1. Tokenized text embeddings can map to similar points in latent space. The text encoder is autoregressive so this won’t work for all random sequence of tokens but it can work for the right ones. I wonder if anyone has tried reverse decoding the embeddings of interest to see if they cluster around known words that are relevant. 2. Diffusion models are trained by pushing off manifold points onto the manifold so to speak. It is not surprising that off manifold points map onto known concepts during the reversal process.