deepgenteam/DeepGen-1.0

💡 DeepGen 1.0: A Lightweight Unified Multimodal Model for Advancing Image Generation and Editing

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.

🧠 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.

📊 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

🛠️ 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.

# Merge zip
cat DeepGen_CKPT.zip.part-* > DeepGen_CKPT.zip
# Unzip DeepGen checkpoints 
unzip DeepGen_CKPT.zip