TeEFusion: Blending Text Embeddings to Distill Classifier-Free Guidance
Authors: name: Minghao Fu, name: Guo-Hua Wang, name: Xiaohao Chen, name: Qing-Guo Chen, name: Zhao Xu, name: Weihua Luo, name: Kaifu Zhang

Recent advances in text-to-image synthesis largely benefit from sophisticated
sampling strategies and classifier-free guidance (CFG) to ensure high-quality
generation. However, CFG's reliance on two forward passes, especially when
combined with intricate sampling algorithms, results in prohibitively high
inference costs. To address this, we introduce TeEFusion (Text
Embeddings Fusion), a novel and efficient distillation method
that directly incorporates the guidance magnitude into the text embeddings and
distills the teacher model's complex sampling strategy. By simply fusing
conditional and unconditional text embeddings using linear operations,
TeEFusion reconstructs the desired guidance without adding extra parameters,
simultaneously enabling the student model to learn from the teacher's output
produced via its sophisticated sampling approach. Extensive experiments on
state-of-the-art models such as SD3 demonstrate that our method allows the
student to closely mimic the teacher's performance with a far simpler and more
efficient sampling strategy. Consequently, the student model achieves inference
speeds up to 6times faster than the teacher model, while maintaining image
quality at levels comparable to those obtained through the teacher's complex
sampling approach. The code is publicly available at
https://github.com/AIDC-AI/TeEFusion{github.com/AIDC-AI/TeEFusion}.