DALLE2-pytorch

The main novelty seems to be an extra layer of indirection with the prior network (whether it is an autoregressive transformer or a diffusion network), which predicts an image embedding based on the text embedding from CLIP. Specifically, this repository will only build out the diffusion prior network, as it is the best performing variant (but which incidentally involves a causal transformer as the denoising network 😂)

This model is SOTA for text-to-image for now.

It may also explore an extension of using latent diffusion in the decoder from Rombach et al.

Please join if you are interested in helping out with the replication

Do let me know if anyone is interested in a Jax version https://github.com/lucidrains/DALLE2-pytorch/discussions/8

Install

$ pip install dalle2-pytorch

Usage (work in progress)

template

$ dream 'sharing a sunset at the summit of mount everest with my dog'

Once built, images will be saved to the same directory the command is invoked

Training (work in progress, will offer both in code and as command-line)

template

Todo

Citations

@misc{ramesh2022,
    title   = {Hierarchical Text-Conditional Image Generation with CLIP Latents}, 
    author  = {Aditya Ramesh et al},
    year    = {2022}
}

@misc{crowson2022,
    author  = {Katherine Crowson},
    url     = {https://twitter.com/rivershavewings}
}

@misc{rombach2021highresolution,
    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}
}

@inproceedings{Liu2022ACF,
    title   = {A ConvNet for the 2020s},
    author  = {Zhuang Liu and Hanzi Mao and Chaozheng Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
    year    = {2022}
}

@misc{zhang2019root,
    title   = {Root Mean Square Layer Normalization},
    author  = {Biao Zhang and Rico Sennrich},
    year    = {2019},
    eprint  = {1910.07467},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG}
}