扩展GAN用于文本到图像合成
Scaling up GANs for Text-to-Image Synthesis
AI总结：
本文提出GigaGAN架构，证明GAN可扩展用于文本到图像合成，推理速度快（0.13秒生成512px图像），支持高分辨率生成和潜在空间编辑。
摘要：
文本到图像合成的最新成功席卷了世界，并激发了公众的想象力。从技术角度来看，它也标志着生成图像模型设计首选架构的巨大转变。GAN曾经是事实上的选择，如StyleGAN等技术。随着DALL-E 2的出现，自回归和扩散模型一夜之间成为大规模生成模型的新标准。这种快速转变引发了一个根本性问题：我们能否扩展GAN以受益于像LAION这样的大规模数据集？我们发现，天真地增加StyleGAN架构的容量很快就会变得不稳定。我们引入了GigaGAN，一种远远超出这一限制的新GAN架构，证明了GAN作为文本到图像合成的可行选择。GigaGAN提供了三个主要优势。首先，它在推理时速度快了几个数量级，仅需0.13秒即可合成512像素的图像。其次，它可以合成高分辨率图像，例如在3.66秒内生成16兆像素的图像。最后，GigaGAN支持各种潜在空间编辑应用，如潜在插值、风格混合和向量算术运算。
英文摘要：
The recent success of text-to-image synthesis has taken the world by storm and captured the general public's imagination. From a technical standpoint, it also marked a drastic change in the favored architecture to design generative image models. GANs used to be the de facto choice, with techniques like StyleGAN. With DALL-E 2, auto-regressive and diffusion models became the new standard for large-scale generative models overnight. This rapid shift raises a fundamental question: can we scale up GANs to benefit from large datasets like LAION? We find that naÏvely increasing the capacity of the StyleGAN architecture quickly becomes unstable. We introduce GigaGAN, a new GAN architecture that far exceeds this limit, demonstrating GANs as a viable option for text-to-image synthesis. GigaGAN offers three major advantages. First, it is orders of magnitude faster at inference time, taking only 0.13 seconds to synthesize a 512px image. Second, it can synthesize high-resolution images, for example, 16-megapixel pixels in 3.66 seconds. Finally, GigaGAN supports various latent space editing applications such as latent interpolation, style mixing, and vector arithmetic operations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
