Research line · ascendant
Unified understanding and generation
Build one model that both interprets images and produces them, instead of separate understanding and generation systems.
What defines membership
Understanding and generation should share parameters, and doing so makes both better.
How the line developed
Read top to bottom: what came before, the idea itself, the evidence for it, what improved, and where it breaks.
Before
Emu2 · 2023-12core
Autoregressive prediction of visual embeddings alongside text at multimodal LLM scale.
The idea
Janus-Pro · 2025-01core
Decouples the encoder used for understanding from the one used for generation while sharing the language backbone.
Evidence
BLIP3-o · 2025-05core
Systematic ablation of CLIP-versus-VAE features and flow-matching-versus-MSE objectives, fully open.
Improvement
MetaQuery · 2025-04core
Connects a fully frozen multimodal LLM to a diffusion decoder with learnable queries, so understanding ability cannot be damaged.
Show-o2 · 2025-06core
Autoregressive text and diffusion image generation in one set of weights, now on 3D-VAE latents.
OmniGen2 · 2025-06core
Two decoding pathways with unshared parameters rather than one forced shared path.
At scale
HunyuanImage 3.0 · 2025-09core
An 80B mixture-of-experts unified model with native chain-of-thought before image output.
What it gets right
- One model to train, serve, and improve
- Understanding can inform generation, enabling instruction following and reasoning about the requested image
- The direction every frontier lab is visibly pursuing
Where it is weak
- Generation training frequently degrades understanding ability
- What is actually shared varies enormously between systems, which makes the label 'unified' nearly meaningless without qualification
- Evaluation is immature: few benchmarks test both capabilities together
Also in this line
BAGEL, Chameleon, Emu3, Emu3.5, Janus, STARFlow2, Transfusion