Conditional LoRA Parameter Generation

TL;DR

This work tackles the problem of generating high-performance neural network parameters conditioned on downstream tasks, focusing on LoRA-style weight updates. It introduces Cond P-Diff, a framework that uses a parameter autoencoder to learn compact latent representations of LoRA parameters and a conditional latent diffusion model to synthesize latent vectors conditioned on task information, followed by a decoder that yields task-specific LoRA weights. Empirical results in NLP and CV show Cond P-Diff can achieve competitive performance with generated parameters and reveal that the generated weights occupy a distinct, broader distribution than conventional optimization, indicating genuine generalization. The approach offers a promising direction for task-specific parameter generation and efficient adaptation of large models, though challenges remain in memory efficiency, conditioning robustness, and extending to larger architectures.

Abstract

Generative models have achieved remarkable success in image, video, and text domains. Inspired by this, researchers have explored utilizing generative models to generate neural network parameters. However, these efforts have been limited by the parameter size and the practicality of generating high-performance parameters. In this paper, we propose COND P-DIFF, a novel approach that demonstrates the feasibility of controllable high-performance parameter generation, particularly for LoRA (Low-Rank Adaptation) weights, during the fine-tuning process. Specifically, we employ an autoencoder to extract efficient latent representations for parameters. We then train a conditional latent diffusion model to synthesize high-performing model parameters from random noise based on specific task conditions. Experimental results in both computer vision and natural language processing domains consistently demonstrate that COND P-DIFF can generate high-performance parameters conditioned on the given task. Moreover, we observe that the parameter distribution generated by COND P-DIFF exhibits differences compared to the distribution obtained through normal optimization methods, indicating a certain level of generalization capability. Our work paves the way for further exploration of condition-driven parameter generation, offering a promising direction for task-specific adaptation of neural networks.

Figures (7)

• Figure 1: High-performance LoRA parameters generation process by Cond P-Diff in vision and language domains.
• Figure 2: The framework of Cond P-Diff. The autoencoder is employed to extract the latent representation of LoRA parameters and reduce memory consumption. The conditional parameter diffusion model aims to synthesize high-performance parameters based on specific task conditions.
• Figure 3: (a) visualize the images generated by Cond P-Diff synthetic parameters in style transfer tasks. (b) shows the t-SNE of LoRA parameters of the original models, Cond P-Diff models on three datasets COLA, QNLI, and STSB. (SST2-Ori. means original parameters and SST-Gen. means generated parameters) (c) displays the accuracy and similarity of fine-tuned performance and parameters generated by Cond P-Diff.
• Figure 4: (a) visualizes images generated by interpolated parameters between Style-1 and Style-2. As $\lambda$ increases from left to right, the style gradually shifts towards Style-2 from Style-1. (b) exhibits the generated parameters' trajectory at different time steps during the inference stage using t-SNE from five random noise start points in image-transfer tasks.
• Figure 5: Cond P-Diff framework in style-transfer tasks.