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+***Update: Feb 21, 2022:*** We updated the weight initialization of the PyTorch version to the intended scale, leading to a substantial improvement in the model's learning speed!
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 This is an implementation of the [GANformer](https://arxiv.org/pdf/2103.01209.pdf) model, a novel and efficient type of transformer, explored for the task of image generation. The network employs a _bipartite structure_ that enables long-range interactions across the image, while maintaining computation of linearly efficiency, that can readily scale to high-resolution synthesis. 
 The model iteratively propagates information from a set of latent variables to the evolving visual features and vice versa, to support the refinement of each in light of the other and encourage the emergence of compositional representations of objects and scenes. 
 In contrast to the classic transformer architecture, it utilizes multiplicative integration that allows flexible region-based modulation, and can thus be seen as a generalization of the successful StyleGAN network.