TY  - JOUR
TI  - LoRA: Low-Rank Adaptation of Large Language Models
AU  - Hu, E. J.
AU  - Shen, Y.
AU  - Wallis, P.
AU  - Allen-Zhu, Z.
AU  - Li, Y.
AU  - Wang, S.
AU  - Wang, L.
AU  - Chen, W.
PY  - 2022
DA  - 2022///
JO  - ICLR 2022
AB  - Hu et al. (2021, published ICLR 2022) propose Low-Rank Adaptation, a parameter-efficient fine-tuning method that inserts trainable low-rank matrix pairs into the weight updates of a frozen pretrained model, dramatically reducing the compute and storage cost of task-specific adaptation. Although the original paper targets large language models, LoRA was rapidly adopted for diffusion models and is now the dominant mechanism for character and style fine-tuning in open-source image-generation pipelines. Character LoRAs and style LoRAs are in active use across animation pre-production, making this a foundational text for understanding how generative tools are customised in practice.
KW  - generative-ai
KW  - image-generation
KW  - character-animation
UR  - https://arxiv.org/abs/2106.09685
LA  - en
ER  -