Personalization has emerged as a prominent aspect within the field of generative AI, enabling the synthesis of individuals in diverse contexts and styles, while retaining high-fidelity to their identities. However, the process of personalization presents inherent challenges in terms of time and memory requirements. Fine-tuning each personalized model needs considerable GPU time investment, and storing a personalized model per subject can be demanding in terms of storage capacity. To overcome these challenges, we propose HyperDreamBooth-a hypernetwork capable of efficiently generating a small set of personalized weights from a single image of a person. By composing these weights into the diffusion model, coupled with fast finetuning, HyperDreamBooth can generate a person's face in various contexts and styles, with high subject details while also preserving the model's crucial knowledge of diverse styles and semantic modifications. Our method achieves personalization on faces in roughly 20 seconds, 25x faster than DreamBooth and 125x faster than Textual Inversion, using as few as one reference image, with the same quality and style diversity as DreamBooth. Also our method yields a model that is 10000x smaller than a normal DreamBooth model. Project page: https://hyperdreambooth.github.io

翻译：个性化已成为生成式AI领域的一个重要方面，能够在保持个体身份高保真度的同时，合成不同情境和风格下的个体形象。然而，个性化过程在时间和内存需求方面存在固有挑战。微调每个个性化模型需要大量的GPU时间投入，而为每个主体存储一个个性化模型也对存储容量提出了较高要求。为克服这些挑战，我们提出了HyperDreamBooth——一种能从单张人物图像高效生成一小套个性化权重的超网络。通过将这些权重组合到扩散模型中，并结合快速微调，HyperDreamBooth能够在保留模型对多样风格和语义修改的关键知识的同时，生成具有高主体细节的人物面部，且适用于各种情境和风格。我们的方法在仅需一张参考图像的情况下，约20秒内实现面部个性化，速度比DreamBooth快25倍，比Textual Inversion快125倍，同时保持与DreamBooth相同的质量和风格多样性。此外，我们的方法产生的模型比普通DreamBooth模型小10000倍。项目页面：https://hyperdreambooth.github.io