DALL-E 2 – Pytorch (wip)

Implementation of DALL-E 2, OpenAI’s updated text-to-image synthesis neural network, in Pytorch

The main novelty seems to be an extra layer of indirection with the prior network (whether it is a transformer or a diffusion network), which predicts an image embedding based on the text embedding from CLIP.

This is SOTA for text-to-image now, but probably not for long.

It may also explore an extension of using latent diffusion in the decoder


    title   = {Hierarchical Text-Conditional Image Generation with CLIP Latents}, 
    author  = {Aditya Ramesh et al},
    year    = {2022}

    author  = {Katherine Crowson},
    url     = {https://twitter.com/rivershavewings}

    title   = {High-Resolution Image Synthesis with Latent Diffusion Models}, 
    author  = {Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
    year    = {2021},
    eprint  = {2112.10752},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}


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