Photorealistic generation is undergoing a period that future scholars may well compare to the enlightenment era.

KAN oversaw the project and contributed valuable feedback. MindEye was developed using a training and validation set of Subject 1's data, with the test set (and other subjects' data) untouched until final PyTorch code for the MLP backbone and projector is depicted in Algorithm 1. Specifics on how we DALL-E 2. This makes our prior much faster at inference time. For simplicity we use bidirectional attention in our final model. To map to Stable Diffusion's V AE latent space we use a low-level pipeline with the same architecture as the high level pipeline. Recent works in low-level vision (super-resolution, denoising, deblurring, etc.) have observed that This performs worse than only applying the loss in latent space and also requires significantly more GPU memory.

We demonstrate through ablations that Mind-Eye's performance improvements over previous methods result from specialized