字节推1.58位量化FLUX模型 内存减少7.7倍，性能不减反增！
人工智能（AI）驱动的文本到图像(T2I)生成模型，如DALLE3、Adobe Firefly3等，展现出卓越的生成能力，在现实应用中潜力无限。然而，这些模型通常拥有数十亿的参数，对内存要求极高，这给在移动设备等资源受限的平台上部署带来了巨大挑战。为了解决这些难题，ByteDance和POSTECH的研究人员探索了对T2I模型进行极低位量化的技术。在众多先进模型中，FLUX.1-dev因其公开可用性和出色的性能成为研究目标。研究人员通过一种名为1.58位量化的方法，对FLUX模型中的视觉转换器权重进行压缩，使其仅采
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