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arXiv · 2610.23653

A Language-Informed Loss for Visual Generation

Suchir Agarwal·Keshigeyan Chandrasegaran·Ziyu Chen·Kyle Sargent·Juan Carlos Niebles·Li Fei-Fei·Jiajun Wu
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Abstract

End-to-end visual generation with pixel-space loss has recently achieved competitive performance without the need for learned image tokenizers. In this work, we study whether language-informed supervision can provide a richer training signal than pixel loss for visual generation. We introduce LILIE (pronounced "lily"), a Language-Informed and Lossless Image Encoder. LILIE is an invertible image transform trained such that a vision-language model can recover captions from noised features. The encoder uses a normalizing flow transformer trained jointly with a vision-language model via next token prediction. We then use the encoder to extract a simple language-informed loss for text-to-image generation. We train JiT models with LILIE loss, reducing gFD by 14 points (66.84 $\to$ 53.01) against a pixel-only baseline on the GPIC dataset. Our code, encoders, and generative models are available at https://suchiragarwal.com/lilie.

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Suchir Agarwal, Keshigeyan Chandrasegaran, Ziyu Chen, Kyle Sargent, Juan Carlos Niebles, Li Fei-Fei, Jiajun Wu. 2026-10-16. A Language-Informed Loss for Visual Generation. https://arxiv.org/abs/2610.23653

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