IntLoRA: Integral Low-rank Adaptation of Quantized Diffusion Models

Hang Guo, Yawei Li, Tao Dai, Shu-Tao Xia, Luca Benini

Keywords: Parameter efficient fine tuning, Network quantization, Diffusion model

TL;DR: This paper presents the integral low-rank adaptation which can adapt the quantized diffusion models by int-multiplication or bit-shiting.

Abstract: Fine-tuning large-scale text-to-image diffusion models for various downstream tasks has yielded impressive results. However, the heavy computational burdens of tuning large models prevent personal customization. Recent advances have attempted to employ parameter-efficient fine-tuning (PEFT) techniques to adapt the floating-point (FP) or quantized pre-trained weights. Nonetheless, the adaptation parameters in existing works are still restricted to FP arithmetic, hindering hardware-friendly acceleration. In this work, we propose IntLoRA, to further push the efficiency limits by using integer type (INT) low-rank parameters to adapt the quantized diffusion models. By working in the integer arithmetic, our IntLoRA offers three key advantages: (i) for fine-tuning, the pre-trained weights are quantized, reducing memory usage; (ii) for storage, both pre-trained and low-rank weights are in INT which consumes less disk space; (iii) for inference, IntLoRA weights can be naturally merged into quantized pre-trained weights through efficient integer multiplication or bit-shifting, eliminating additional post-training quantization. Extensive experiments demonstrate that IntLoRA can achieve performance on par with or even superior to the vanilla LoRA, accompanied by significant efficiency improvements.

Primary Area: applications to computer vision, audio, language, and other modalities

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Submission Number: 2140