字節推1.58位量化FLUX模型 內存減少7.7倍，性能不減反增！
人工智能（AI）驅動的文本到圖像(T2I)生成模型，如DALLE3、Adobe Firefly3等，展現出卓越的生成能力，在現實應用中潛力無限。然而，這些模型通常擁有數十億的參數，對內存要求極高，這給在移動設備等資源受限的平臺上部署帶來了巨大挑戰。爲了解決這些難題，ByteDance和POSTECH的研究人員探索了對T2I模型進行極低位量化的技術。在衆多先進模型中，FLUX.1-dev因其公開可用性和出色的性能成爲研究目標。研究人員通過一種名爲1.58位量化的方法，對FLUX模型中的視覺轉換器權重進行壓縮，使其僅採
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