| --- |
| license: apache-2.0 |
| datasets: |
| - Alex11556666/Reason_Tuning |
| base_model: |
| - Qwen/Qwen2.5-VL-3B-Instruct |
| pipeline_tag: text-to-image |
| --- |
| |
| # 💡 DeepGen 1.0: A Lightweight Unified Multimodal Model for Advancing Image Generation and Editing |
| <p align="left"> |
| <a href="http://arxiv.org/abs/2602.12205"> |
| <img |
| src="https://img.shields.io/badge/DeepGen 1.0-Paper-red?logo=arxiv&logoColor=red" style="display: inline-block; vertical-align: middle;" |
| alt="DeepGen 1.0 Paper on arXiv" |
| /> |
| </a> |
| <a href="https://github.com/deepgenteam/deepgen" target="_blank" style="margin: 2px;"> |
| <img |
| alt="Github" src="https://img.shields.io/badge/DeepGen 1.0-Codebase-536af5?color=536af5&logo=github" style="display: inline-block; vertical-align: middle;" |
| alt="DeepGen 1.0 Codebase" |
| /> |
| </a> |
| <a href="https://deepgenteam.github.io/" target="_blank" style="margin: 2px;"> |
| <img |
| alt="Github" src="https://img.shields.io/badge/Website-project page-orange" style="display: inline-block; vertical-align: middle;" |
| alt="DeepGen 1.0 page" |
| /> |
| </a> |
| </p> |
| DeepGen 1.0 is a lightweight unified multimodal model with only 5B parameters (3B VLM + 2B DiT). It integrates five core capabilities—general image generation, general image editing, reasoning image generation, reasoning image editing, and text rendering—within a single model. Across multiple authoritative benchmarks, DeepGen 1.0 is competitive with competitive with or surpassing the state-of-the-art unified multimodal models that are 3× to 16× larger, achieving comprehensive performance, demonstrating that massive scaling is not the sole path to high-performance multimodal generation. |
| <p align="left"><img src="bubble_chart.png" width="80%"></p> |
| |
| ## 🧠 Method |
| Our core observation is that a lightweight model, when empowered by synergistic architecture design and data-centric training strategies, can achieve comprehensive capabilities competitive with or even surpassing much larger counterparts. |
| To overcome the limitations of lightweight 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) **Alignment Pre-training** on large-scale image-text pairs and editing triplets to synchronize VLM and DiT representations, (2) **Joint Supervised Fine-tuning** on a high-quality mixture of generation, editing, and reasoning tasks to foster omni-capabilities, and (3) **Reinforcement Learning with MR-GRPO**, which leverages a mixture of reward functions and supervision signals, resulting in substantial gains in generation quality and alignment with human preferences, while maintaining stable training progress and avoiding visual artifacts. |
|
|
| <p align="left"><img src="arch.png" width="80%"></p> |
|
|
| ## 📊 Benchmarks |
|
|
| ### 1. General Image Generation |
| | Model | Params | Geneval ↑ | DPGBench ↑ | UniGenBench ↑ | |
| | --------------------- | ----------- | ----------- | ------------ | ------------- | |
| | OmniGen2 | 3B + 4B | 0.80 | 83.57 | 63.09 | |
| | BAGEL | 14B | 0.82 | 85.10 | 61.53 | |
| | X-Omni | 7B + 12B | 0.83 | 87.65🥉 | 53.77 | |
| | Lumina-DiMOO | 8B | 0.88🥇 | 86.04 | 71.12 | |
| | Hunyuan-Image-3.0 | 80B | 0.72 | 86.10 | — | |
| | Qwen-Image | 7B + 20B | 0.87 🥈 | 88.32 🥇 | 78.81 🥇 | |
| | LongCat-Image | 7B + 6B | 0.87 🥈 | 86.80 | — | |
| | Z-Image-Turbo | 4B + 6B | 0.84 | 85.15 | 71.40 | |
| | GLM-Image | 9B + 7B | — | 84.78 | — | |
| | **DeepGen 1.0 (SFT)** | **3B + 2B** | 0.86 🥉 | 87.05 | 74.18 🥉 | |
| | **DeepGen 1.0 (RL)** | **3B + 2B** | 0.87 🥈 | 87.90 🥈 | 75.74 🥈 | |
|
|
|
|
|
|
| ### 2. General Image Editing |
|
|
| | Model | Params | GEdit-EN ↑ | ImgEdit ↑ | |
| | :--- | :--- | :--- | :--- | |
| | BAGEL | 14B | 6.52 | 3.20 | |
| | Qwen-Image-Edit [2509] | 7B + 20B | 7.54 🥈 | 4.35 🥈 | |
| | LongCat-Image-Edit | 7B + 6B | 7.60 🥇 | 4.50 🥇 | |
| | Mammoth2 | 8B + 3B + 2B | 6.60 | 4.06 | |
| | **DeepGen 1.0 (SFT)** | **3B + 2B** | 7.12 | 4.09 | |
| | **DeepGen 1.0 (RL)** | **3B + 2B** | 7.17 🥉 | 4.14 🥉 | |
|
|
| ### 3. Reasoning Image Generation |
| | Model | Params | WISE ↑ | T2I-CoREBench ↑ | |
| | :--- | :--- | :--- | :--- | |
| | OmniGen2 | 3B + 4B | 0.47 | 36.1 | |
| | BAGEL | 14B | 0.70 🥉 | 41.1 | |
| | Hunyuan-Image-3.0 | 80B | 0.57 | 46.0 | |
| | Qwen-Image | 7B + 20B | 0.62 | 46.3 🥉 | |
| | LongCat-Image | 7B + 6B | 0.65 | 52.2 🥇 | |
| | Z-Image-Turbo | 4B + 6B | - | 43.7 | |
| | **DeepGen 1.0 (SFT)** | **3B + 2B** | 0.72 🥈 | 45.7 | |
| | **DeepGen 1.0 (RL)** | **3B + 2B** | 0.73 🥇 | 46.5 🥈 | |
|
|
| ### 4. Reasoning Image Editing |
|
|
| | Model | Params | RISE ↑ | UniREditBench ↑ | |
| | :--- | :--- | :--- | :--- | |
| | OmniGen2 | 3B + 4B | - | 43.4 | |
| | BAGEL | 14B | 11.9 🥈 | 51.0 | |
| | Qwen-Image-Edit [2509] | 7B + 20B | 8.9 | 56.5 🥉 | |
| | **DeepGen 1.0 (SFT)** | **3B + 2B** | 13.3 🥇 | 77.5 🥇 | |
| | **DeepGen 1.0 (RL)** | **3B + 2B** | 10.8 🥉 | 75.7 🥈 | |
|
|
| ## 🎨 Quantitative results |
| <p align="left"><img src="teaser.png" width="80%"></p> |
|
|
| ## 🛠️ Usage |
|
|
| ### Merge ZIP Files |
| To use the DeepGen checkpoints, please merge the sharded model files first. We release Pre-traning, Supervised Fine-Tuning and Reinforcement Learning checkpoints. |
|
|
| ```bash |
| # Merge zip |
| cat DeepGen_CKPT.zip.part-* > DeepGen_CKPT.zip |
| # Unzip DeepGen checkpoints |
| unzip DeepGen_CKPT.zip |
| ``` |
|
|
| ```text |
| checkpoints/ |
| ├── DeepGen_CKPT |
| ├──Pretrain├──iter_200000.pth |
| ├── SFT├──iter_400000.pth |
| ├──RL├──MR-GDPO_final.pt |
| |
| ``` |
| if you want only final model state please use `model.pt` directly , it is same as `MR-GDPO_final.pt` |
|
|
| ## ⭐ Citation |
| ```bibtex |
| @article{wang2026deepgen, |
| title={DeepGen 1.0: A Lightweight Unified Multimodal Model for Advancing Image Generation and Editing}, |
| author={Wang, Dianyi and Li, Ruihang and Han, Feng and Ma, Chaofan and Song, Wei and Wang, Siyuan and Wang, Yibin and Xin, Yi and Liu, Hongjian and Zhang, Zhixiong and others}, |
| journal={arXiv preprint arXiv:2602.12205}, |
| year={2026} |
| } |
| ``` |
