| title | L2P: Unlocking Latent Potential for Pixel Generation |
|---|---|
| authors | Zhennan Chen, Junwei Zhu, Xu Chen, Jiangning Zhang, Jiawei Chen, Zhuoqi Zeng, Wei Zhang, Chengjie Wang, Jian Yang, Ying Tai |
| date | 2026-05-12 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2605.12013 |
| upvotes | 36 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | Pixel diffusion models have recently regained attention for visual generation. However, training advanced pixel-space models from scratch demands prohibitive computational and data resources. To address this, we propose the Latent-to-Pixel (L2P) transfer paradigm, an efficient framework that directly harnesses the rich knowledge of pre-trained LDMs to build powerful pixel-space models. Specifically, L2P discards the VAE in favor of large-patch tokenization and freezes the source LDM's intermediate layers, exclusively training shallow layers to learn the latent-to-pixel transformation. By utilizing LDM-generated synthetic images as the sole training corpus, L2P fits an already smooth data manifold, enabling rapid convergence with zero real-data collection. This strategy allows L2P to seamlessly migrate massive latent priors to the pixel space using only 8 GPUs. Furthermore, eliminating the VAE memory bottleneck unlocks native 4K ultra-high resolution generation. Extensive experiments across mainstream LDM architectures show that L2P incurs negligible training overhead, yet performs on par with the source LDM on DPG-Bench and reaches 93% performance on GenEval. |
cite
@misc{chen2026l,
title={L2P: Unlocking Latent Potential for Pixel Generation},
author={Zhennan Chen and Junwei Zhu and Xu Chen and Jiangning Zhang and Jiawei Chen and Zhuoqi Zeng and Wei Zhang and Chengjie Wang and Jian Yang and Ying Tai},
year={2026},
eprint={2605.12013},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2605.12013}
}