  Visual encoding constitutes the basis of large multimodal models (LMMs) in
understanding the visual world. Conventional LMMs process images in fixed sizes
and limited resolutions, while recent explorations in this direction are
limited in adaptivity, efficiency, and even correctness. In this work, we first
take GPT-4V and LLaVA-1.5 as representative examples and expose systematic
flaws rooted in their visual encoding strategy. To address the challenges, we
present LLaVA-UHD, a large multimodal model that can efficiently perceive
images in any aspect ratio and high resolution. LLaVA-UHD includes three key
components: (1) An image modularization strategy that divides native-resolution
images into smaller variable-sized slices for efficient and extensible
encoding, (2) a compression module that further condenses image tokens from
visual encoders, and (3) a spatial schema to organize slice tokens for LLMs.
Comprehensive experiments show that LLaVA-UHD outperforms established LMMs
trained with 2-3 orders of magnitude more data on 9 benchmarks. Notably, our
model built on LLaVA-1.5 336x336 supports 6 times larger (i.e., 672x1088)
resolution images using only 94% inference computation, and achieves 6.4
accuracy improvement on TextVQA. Moreover, the model can be efficiently trained
in academic settings, within 23 hours on 8 A100 GPUs (vs. 26 hours of
LLaVA-1.5). We make the data and code publicly available at
https://github.com/thunlp/LLaVA-UHD.
