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RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths RAPHAEL: Text-to-Image Generation via Large Mixture of Diffusion Paths Zeyue Xue 2* , Guanglu Song 1* , Qiushan Guo 2 , Boxiao Liu 1 , Zhuofan Zong 1 , Yu Liu 1†‡ , Ping Luo 2‡ SenseTime 1 , The University of Hong Kong 2 * Indicates equal contribution. † Indicates project lead. ‡ Indicates corresponding authors. Work done during Zeyue's internship at SenseTime Research. Abstract Text-to-image generation has recently witnessed remarkable achievements. We introduce a text-conditional image diffusion model, termed RAPHAEL, to generate highly artistic images, which accurately portray the text prompts, encompassing multiple nouns, adjectives, and verbs. This is achieved by stacking tens of mixture-of-experts (MoEs) layers, i.e., space-MoE and time-MoE layers, enabling billions of diffusion paths (routes) from the network input to the output. Each path int...
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