PixelFolder: An Efficient Progressive Pixel Synthesis Network for Image Generation

• URL: http://arxiv.org/abs/2204.00833v1
• Date: Sat, 2 Apr 2022 10:55:11 GMT
• Title: PixelFolder: An Efficient Progressive Pixel Synthesis Network for Image Generation
• Authors: Jing He, Yiyi Zhou, Qi Zhang, Yunhang Shen, Xiaoshuai Sun, Chao Chen, Rongrong Ji
• Abstract summary: Pixel is a promising research paradigm for image generation, which can well exploit pixel-wise prior knowledge for generation. In this paper, we propose a progressive pixel synthesis network towards efficient image generation, as Pixel. With much less expenditure, Pixel obtains new state-of-the-art (SOTA) performance on two benchmark datasets.
• Score: 88.55256389703082
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Pixel synthesis is a promising research paradigm for image generation, which can well exploit pixel-wise prior knowledge for generation. However, existing methods still suffer from excessive memory footprint and computation overhead. In this paper, we propose a progressive pixel synthesis network towards efficient image generation, coined as PixelFolder. Specifically, PixelFolder formulates image generation as a progressive pixel regression problem and synthesizes images by a multi-stage paradigm, which can greatly reduce the overhead caused by large tensor transformations. In addition, we introduce novel pixel folding operations to further improve model efficiency while maintaining pixel-wise prior knowledge for end-to-end regression. With these innovative designs, we greatly reduce the expenditure of pixel synthesis, e.g., reducing 90% computation and 57% parameters compared to the latest pixel synthesis method called CIPS. To validate our approach, we conduct extensive experiments on two benchmark datasets, namely FFHQ and LSUN Church. The experimental results show that with much less expenditure, PixelFolder obtains new state-of-the-art (SOTA) performance on two benchmark datasets, i.e., 3.77 FID and 2.45 FID on FFHQ and LSUN Church, respectively. Meanwhile, PixelFolder is also more efficient than the SOTA methods like StyleGAN2, reducing about 74% computation and 36% parameters, respectively. These results greatly validate the effectiveness of the proposed PixelFolder.

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