Compressing Image Style Training into a Single Model Forward
Researchers propose i2L (image-to-LoRA), a framework that amortizes style LoRA training into a single forward pass, eliminating per-style optimization. Using an image encoder, learnable LoRA queries, and compressed decoding heads, i2L predicts LoRA weights and outperforms baselines on Z-Image, FLUX.2, and Hidream-O1 in style fidelity, prompt alignment, and perceptual quality.
[2606.13809] Compressing Image Style Training into a Single Model Forward
[Submitted on 11 Jun 2026]
Title:Compressing Image Style Training into a Single Model Forward
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Abstract:Diffusion-based style transfer must balance inference efficiency with stylization fidelity. Adapter-based methods are efficient, but they inject style as an external condition and can either weaken reference-specific appearance or copy reference semantics into the generated image. Optimization-based personalization methods such as LoRA internalize style more effectively, but require a separate training process for every new style. We introduce i2L (image-to-LoRA), a framework that amortizes style LoRA training into a single forward pass. Given one or more reference images, i2L predicts LoRA weights for a text-to-image model, enabling immediate style instantiation without per-style optimization. The architecture combines an image encoder, learnable LoRA queries, and compressed decoding heads that generate adapted matrices. Training on semantically diverse style pairs encourages the predictor to preserve appearance cues while suppressing reference-content copying. Experiments on Z-Image, FLUX.2, and Hidream-O1 show that i2L improves style fidelity, prompt alignment, and perceptual quality over existing baselines. Because i2L produces explicit LoRA weights, it also supports asymmetric classifier-free guidance, multi-reference style fusion, and composition with controllable-generation modules.
Comments: 11 pages, 9 figures
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.13809 [cs.CV]
(or arXiv:2606.13809v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.13809
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zhongjie Duan [view email] [v1] Thu, 11 Jun 2026 18:21:26 UTC (8,522 KB)
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