TeEFusion: Blending Text Embeddings to Distill Classifier-Free Guidance
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}.
6882db3f846d2e78b7548a37
Minghao Fu
6882db3f846d2e78b7548a38
Guo-Hua Wang
claimed_verified
2025-07-25T08:38:39.947Z
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Guo-Hua Wang
user
Flourish
6882db3f846d2e78b7548a39
Xiaohao Chen
6882db3f846d2e78b7548a3a
Qing-Guo Chen
claimed_verified
2025-08-19T10:50:35.712Z
63e1fde9419922d5a6d41a73
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Qing-Guo Chen
user
cqgwin
6882db3f846d2e78b7548a3b
Zhao Xu
6882db3f846d2e78b7548a3c
Weihua Luo
6882db3f846d2e78b7548a3d
Kaifu Zhang
TeEFusion: Blending Text Embeddings to Distill Classifier-Free Guidance
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}.
TeEFusion improves text-to-image synthesis by efficiently distilling classifier-free guidance into text embeddings, enabling faster inference without sacrificing image quality.
sampling strategies classifier-free guidance text embeddings distillation method conditional text embeddings unconditional text embeddings linear operations student model teacher model sophisticated sampling approach inference speeds image quality