Papers › Exploring Sparse MoE in GANs for Text-conditioned Image Synthesis

Exploring Sparse MoE in GANs for Text-conditioned Image Synthesis

7 Sep 2023CVPR 2025 1arXiv:2309.03904archive 2025-07-28

Jiapeng Zhu, Ceyuan Yang, Kecheng Zheng, Yinghao Xu, Zifan Shi, Yujun Shen

Due to the difficulty in scaling up, generative adversarial networks (GANs) seem to be falling from grace on the task of text-conditioned image synthesis. Sparsely-activated mixture-of-experts (MoE) has recently been demonstrated as a valid solution to training large-scale models with limited computational resources. Inspired by such a philosophy, we present Aurora, a GAN-based text-to-image generator that employs a collection of experts to learn feature processing, together with a sparse router to help select the most suitable expert for each feature point. To faithfully decode the sampling stochasticity and the text condition to the final synthesis, our router adaptively makes its decision by taking into account the text-integrated global latent code. At 64x64 image resolution, our model trained on LAION2B-en and COYO-700M achieves 6.2 zero-shot FID on MS COCO. We release the code and checkpoints to facilitate the community for further development.

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router_z_loss_func zhujiapeng/aurora/models/text_generator.py official repository ran fingerprinted licence not identified · pointer only · 08d6b8ac35009b66 · report
run_mapping zhujiapeng/aurora/run_interpolate.py official repository ran licence not identified · pointer only · b7f4d2a919b82370 · report
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Image GenerationMixture-of-ExpertsPhilosophy

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