Low-Rank Adaptation (LoRA) is extensively utilized in text-to-image models
for the accurate rendition of specific elements like distinct characters or
unique styles in generated images. Nonetheless, existing methods face
challenges in effectively composing multiple LoRAs, especially as the number of
LoRAs to be integrated grows, thus hindering the creation of complex imagery.
In this paper, we study multi-LoRA composition through a decoding-centric
perspective. We present two training-free methods: LoRA Switch, which
alternates between different LoRAs at each denoising step, and LoRA Composite,
which simultaneously incorporates all LoRAs to guide more cohesive image
synthesis. To evaluate the proposed approaches, we establish ComposLoRA, a new
comprehensive testbed as part of this research. It features a diverse range of
LoRA categories with 480 composition sets. Utilizing an evaluation framework
based on GPT-4V, our findings demonstrate a clear improvement in performance
with our methods over the prevalent baseline, particularly evident when
increasing the number of LoRAs in a composition.

通过解码为中心的视角，本研究提出了两种无需训练的方法：LoRA Switch 和 LoRA Composite，分别通过在去噪步骤中交替使用不同的 LoRA 和同时整合所有的 LoRA，以更好地指导图像合成，从而提高了综合性能，在组合中增加 LoRA 的数量时尤为明显。