Implementation of TransGanFormer, an all-attention GAN that combines the finding from the recent GanFormer and TransGan paper

Related tags

Deep Learningdeep-learningtransformersartificial-intelligenceattention-mechanismgenerative-adversarial-networks
Overview

TransGanFormer (wip)

Implementation of TransGanFormer, an all-attention GAN that combines the finding from the recent GansFormer and TransGan paper. It will also contain a bunch of tricks I have picked up building transformers and GANs for the last year or so, including efficient linear attention and pixel level attention.

Install

$ pip install transganformer

Usage

$ transganformer --data ./path/to/data

Citations

@misc{jiang2021transgan,
    title   = {TransGAN: Two Transformers Can Make One Strong GAN}, 
    author  = {Yifan Jiang and Shiyu Chang and Zhangyang Wang},
    year    = {2021},
    eprint  = {2102.07074},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}
@misc{hudson2021generative,
    title   = {Generative Adversarial Transformers}, 
    author  = {Drew A. Hudson and C. Lawrence Zitnick},
    year    = {2021},
    eprint  = {2103.01209},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}
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Comments
  • kmeans_iters in attention

    kmeans_iters in attention

    Hello first at all thank you for taking the time and port all of theses models to pytorch, you are really helping out people like me who have tensorflow as a second language :D

    in line 689 of https://github.com/dorarad/gansformer/blob/main/training/network.py there is a loop over kmeans-iter in the transformerlayer, but i can t find this loop in your implementation of Attention, is it implicitly implemented with these einops - i am sadly not fluent with einops or tensorflow :/

    regards martin

    opened by martinpflaum 0
  • dual_contrast_loss argument - error while training

    dual_contrast_loss argument - error while training

    When trying to train a model, the dual_contrast_loss argument spits up an error on lightweight-gan.py. My guess is that it's not implemented yet.

    At first I thought it was a mismatch (should it be dual_contrastive_loss btw like in lightweight-gan?) but apparently needed to be commented out for training to start successfully.

    Very exciting by the way! Training seems to be super fast, about 28h for 150,000 iterations for a 256p model on my 2080 (although it's a very small dataset). I'll share some closer-to-final results once I have them.

    Best, Theodore.

    opened by TheodoreGalanos 2
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Phil Wang
Working with Attention. It's all we need.
Phil Wang
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