Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling
By Xiaokang Chen, Zhiyu Wu, Xingchao Liu, Zizheng Pan, Wen Liu, Zhenda Xie, Xingkai Yu, Chong Ruan. arXiv.org, 2025
812 citations recorded by the source corpus.
In this work, we introduce Janus-Pro, an advanced version of the previous work Janus. Specifically, Janus-Pro incorporates (1) an optimized training strategy, (2) expanded training data, and (3) scaling to larger model size. With these improvements, Janus-Pro achieves significant advancements in both multimodal understanding and text-to-image instruction-following capabilities, while also enhancing the stability of text-to-image generation. We hope this work will inspire further exploration in the field. Code and models are publicly available.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/3ef9dbd95a174d4c175fcd603f62b8cc197e8d7e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Mike Zheng Shou
- Tong He
- Zheng Zhang
- Tianjun Xiao
- Alan L. Yuille
- Yuxin Peng
- Pietro Zanuttigh
- Umberto Michieli
Citations in the corpus
- Hallucination of Multimodal Large Language Models: A Survey (2024, positive): “This integration of human feedback into the training loop has demonstrated effectiveness in enhancing the alignment of LLMs.”
- Qwen-Image Technical Report (2025, neutral): “Janus-Pro (Chen et al., 2025b) 0.019 0.006 BLIP3-o (Chen et al., 2025a) 0.021 0.018 Kolors 2.0 (team, 2025) 0.258 0.329 BAGEL (Deng et al., 2025) 0.373 0.310 OmniGen2 (Wu et al., 2025b) 0.561 0.059 X-Omni (Geng et al., 2025) 0.900 0.814 HiDream-I1-Full (Cai et al., 2025) 0.543 0.024 FLUX.1 [Dev] (Bl”
- Emerging Properties in Unified Multimodal Pretraining (2025, neutral): “Typical design choices for unified multi-modal generation and understanding models include: Quantized AR. Autoregressive visual generation [11, 48, 59, 70, 79, 83–85, 90 ] Unfortunately, the visual generation quality of autoregressive models is empirically inferior to diffusion-based models.”
- Language-Agnostic Visual Embeddings for Cross-Script Handwriting Retrieval (2026, unknown): “…Models With the rapid evolution of deep learning, General Visual-Language Models (GVLMs) [ Abdin et al. , 2024; AI, 2024; Yao et al. , 2024; Chen et al. , 2025; Bai et al. , 2025; Wang et al. , 2025 ] have exhibited powerful capabilities in handwriting understanding driven by robust…”