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[2602.12205] DeepGen 1.0: A Lightweight Unified Multimodal Model for Advancing Image Generation and Editing
Summary
DeepGen 1.0 is a lightweight unified multimodal model designed for image generation and editing, achieving competitive performance with only 5 billion parameters.
Why It Matters
This research addresses the growing need for efficient AI models that reduce training costs and deployment challenges while maintaining high performance. By introducing innovative techniques like Stacked Channel Bridging and a data-centric training strategy, DeepGen 1.0 democratizes access to advanced multimodal capabilities, making it relevant for both researchers and developers in the AI field.
Key Takeaways
- DeepGen 1.0 uses only 5 billion parameters, outperforming larger models.
- Introduces Stacked Channel Bridging for enhanced semantic understanding.
- Employs a three-stage training strategy for improved generation quality.
- Open-sourcing the model promotes accessibility in multimodal research.
- Achieves significant performance gains on various benchmarks.
Computer Science > Computer Vision and Pattern Recognition arXiv:2602.12205 (cs) [Submitted on 12 Feb 2026 (v1), last revised 13 Feb 2026 (this version, v2)] Title:DeepGen 1.0: A Lightweight Unified Multimodal Model for Advancing Image Generation and Editing Authors:Dianyi Wang, Ruihang Li, Feng Han, Chaofan Ma, Wei Song, Siyuan Wang, Yibin Wang, Yi Xin, Hongjian Liu, Zhixiong Zhang, Shengyuan Ding, Tianhang Wang, Zhenglin Cheng, Tao Lin, Cheng Jin, Kaicheng Yu, Jingjing Chen, Wenjie Wang, Zhongyu Wei, Jiaqi Wang View a PDF of the paper titled DeepGen 1.0: A Lightweight Unified Multimodal Model for Advancing Image Generation and Editing, by Dianyi Wang and 19 other authors View PDF Abstract:Current unified multimodal models for image generation and editing typically rely on massive parameter scales (e.g., >10B), entailing prohibitive training costs and deployment footprints. In this work, we present DeepGen 1.0, a lightweight 5B unified model that achieves comprehensive capabilities competitive with or surpassing much larger counterparts. To overcome the limitations of compact models in semantic understanding and fine-grained control, we introduce Stacked Channel Bridging (SCB), a deep alignment framework that extracts hierarchical features from multiple VLM layers and fuses them with learnable 'think tokens' to provide the generative backbone with structured, reasoning-rich guidance. We further design a data-centric training strategy spanning three progressive stages: (1)...
