---
title: "UniCM — paper by Xu et al. (2026) — summary and ideas"
description: "The authors propose UniCM, which extends consistency distillation to such models. 75 references."
authors: ["Chenkai Xu", "Xu Wang", "Zhenyi Liao", "Yishun Li", "TianQi Hou", "Zhijie Deng"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.906/"
doi: "10.18653/v1/2026.findings-acl.906"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/xu-2026-unicm-unified-consistency-model-efficient/"
---
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# UniCM: A Unified Consistency Model For Efficient Multimodal Generation and Understanding

**Authors:** Chenkai Xu, [Xu Wang](https://md.research.elamur.ai/free/en/authors/xu-wang/), Zhenyi Liao, Yishun Li, TianQi Hou and [Zhijie Deng](https://md.research.elamur.ai/free/en/authors/zhijie-deng/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.906/) · [PDF](https://aclanthology.org/2026.findings-acl.906.pdf) · [DOI 10.18653/v1/2026.findings-acl.906](https://doi.org/10.18653/v1/2026.findings-acl.906) · [OpenAlex](https://openalex.org/W7166827917)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Unified multimodal models, which handle both text-to-image generation and image understanding in one backbone, are slow because both directions require many iterative decoding steps. The authors propose UniCM, which extends consistency distillation to such models. The obstacle is that consistency training requires a denoising-style trajectory for both modalities. They solve this by representing images and text as discrete tokens and giving each modality its own trajectory:
- **Images:** standard masked (discrete) diffusion.
- **Text:** the parallel (Jacobi-style) decoding trace of an autoregressive model, following CLLMs, rather than diffusion language models, which the authors consider weaker.

UniCM is initialized from Show-o and fine-tuned on trajectories collected from it, using COCO 2017 prompts for text-to-image and LLaVA instruction data for image-to-text. A single objective trains the model to map any trajectory point to the same endpoint. Trajectory segmentation and regularization keep training stable.

The resulting model produces competitive 512×512 images in 2–4 steps without classifier-free guidance and speeds up text generation. This shows that few-step inference can be achieved for generation and understanding inside one shared model.

## Key points
- **Training and evaluation setup:** training took 36 hours on 8 A100-40GB GPUs. Latency was measured on one RTX 4090 GPU with batch size 1.
- **Text-to-image results:** UniCM beats SD3 on GenEval, Image Reward and CLIP Score at roughly 1/8 of SD3's sampling time. In 2–8 step sampling it also outperforms SDXL-Turbo, Hyper-SD3, Emu3-Gen and Chameleon on GenEval.
- **Image-to-text results:** UniCM exceeds Show-o on MMMU and ScienceQA-IMG and is about 1.5× faster on NoCaps. With a well-chosen parallel-decoding block size, the speedup reaches up to 2.3× on long outputs.
- **Caption quality trade-off:** captioning scores on NoCaps and Flickr30K drop slightly. The authors attribute this to error accumulation in few-step sampling and to the small distillation set (about 120k COCO prompts).
- **Ablations:**
  - Four trajectory segments work better than one or two.
  - Removing the regularization terms (β=0, γ=0) makes training collapse.
  - Top-k sampling noticeably improves 2- and 4-step image generation.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Shanghai Jiao Tong University](https://md.research.elamur.ai/free/en/universities/shanghai-jiao-tong-university/) — University, Shanghai, China · 212 works · 19,544 citations
- [Huawei Technologies](https://md.research.elamur.ai/free/en/companies/huawei-technologies/) — Company, Shenzhen, China · 96 works · 15,432 citations
- [Tongji University](https://md.research.elamur.ai/free/en/universities/tongji-university/) — University, Shanghai, China · 33 works · 2,993 citations

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## Cite

Cite as: Chenkai Xu, Xu Wang, Zhenyi Liao, Yishun Li, TianQi Hou and Zhijie Deng. 2026. UniCM: A Unified Consistency Model For Efficient Multimodal Generation and Understanding. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{xu2026unicm,
  title = {{UniCM: A Unified Consistency Model For Efficient Multimodal Generation and Understanding}},
  author = {Chenkai Xu, Xu Wang, Zhenyi Liao, Yishun Li, TianQi Hou and Zhijie Deng},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.906},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.906}
}
```

- [← previous UniCite: A Dataset and Unified Hierarchical Taxonomy for Multi-Dimensional Citation Analysis](https://md.research.elamur.ai/free/en/papers/mourky-2026-unicite-dataset-unified-hierarchical-taxonomy/)
- [next → UniDataBench: Evaluating Data Analytics Agents Across Structured and Unstructured Data](https://md.research.elamur.ai/free/en/papers/weng-2026-unidatabench-evaluating-data-analytics-agents/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/55/)

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*Updated 2 November 2026*