PEA-Diffusion: Parameter-Efficient Adapter with Knowledge Distillation in non-English Text-to-Image Generation

TL;DR

This paper introduces PEA-Diffusion, a lightweight adapter trained via knowledge distillation that enables non-English text-to-image diffusion models to generate culturally relevant images without extensive retraining.

Contribution

It proposes a parameter-efficient adapter method that significantly improves non-English text-to-image generation using minimal additional parameters and knowledge distillation.

Findings

Freezing UNet still yields high performance on language-specific prompts.

PEA approaches English model performance on general prompts.

Adapter enhances cross-lingual text-to-image tasks.

Abstract

Text-to-image diffusion models are well-known for their ability to generate realistic images based on textual prompts. However, the existing works have predominantly focused on English, lacking support for non-English text-to-image models. The most commonly used translation methods cannot solve the generation problem related to language culture, while training from scratch on a specific language dataset is prohibitively expensive. In this paper, we are inspired to propose a simple plug-and-play language transfer method based on knowledge distillation. All we need to do is train a lightweight MLP-like parameter-efficient adapter (PEA) with only 6M parameters under teacher knowledge distillation along with a small parallel data corpus. We are surprised to find that freezing the parameters of UNet can still achieve remarkable performance on the language-specific prompt evaluation set, demonstrating that PEA can stimulate the potential generation ability of the original UNet. Additionally, it closely approaches the performance of the English text-to-image model on a general prompt evaluation set. Furthermore, our adapter can be used as a plugin to achieve significant results in downstream tasks in cross-lingual text-to-image generation. Code will be available at: https://github.com/OPPO-Mente-Lab/PEA-Diffusion