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

Shanghai Innovation Institute, DeepGen Team

🔥 News

• Feb 13, 2026: We released DeepGen 1.0, Pre-traning, Supervised Fine-Tuning and Reinforcement Learning checkpoints can be found in Huggingface, support both T2I generation and image editing.
• Feb 13, 2026: We released the training code support Pre-training, Supervised Fine-Tuning, Reinforcement Learning and evaluation code support wide range of benchmarks.
• Feb 13, 2026: We released the DeepGen 1.0 technical report on Arxiv

✨ Introduction

Broader Scenario and Dimension Coverage We propose DeepGen 1.0, 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 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.

💻 Train & Eval

Set up environment

git clone https://github.com/deepgenteam/deepgen.git
cd deepgen
conda create -n deepgen python=3.12 -y
conda activate deepgen
pip install -r requirements.txt
pip install flash_attn==2.8.3 --no-build-isolation
pip install xtuner==0.2.0
pip install transformers==4.56.1
pip install triton==2.3.0
pip install -U opencv-python-headless

Data Prepare

Please See DATA for more details. We provide a detailed description of the data download and usage procedures for both the Pre-traning stage and the Supervised Fine-Tuning stage.

Inference

import torch
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained(
    "deepgenteam/DeepGen-1.0-diffusers",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
)
pipe.to("cuda")

result = pipe(
    prompt="a photo of a blue pizza and a yellow baseball glove",
    height=512, width=512,
    num_inference_steps=50,
    guidance_scale=4.0,
    seed=42,
)
result.images[0].save("output.png")

from PIL import Image

result = pipe(
    prompt="Place this guitar on a sandy beach with the sunset in the background.",
    image=Image.open("guitar.png"),
    negative_prompt="blurry, low quality, low resolution, distorted, deformed, broken content, missing parts, damaged details, artifacts, glitch, noise, pixelated, grainy, compression artifacts, bad composition, wrong proportion, incomplete editing, unfinished, unedited areas.",
    height=512, width=512,
    num_inference_steps=50,
    guidance_scale=4.0,
    seed=42,
)
result.images[0].save("edited.png")

Please refer to INFERENCE for more details, including the native pipeline usage.

Train

See TRAIN for more details. We provide a detailed description of the model and training configs for both the Pre-traning stage and the Supervised Fine-Tuning stage.

Eval

We provide the scripts for evaluating wide range of T2I and Editing benchmarks. Please See EVAL for more details.

📊 Benchmarks

1. General Image Generation

2. General Image Editing

3. Reasoning Image Generation

4. Reasoning Image Editing

📧 Contact

dywang24@m.fudan.edu.cn, wjqdev@gmail.com

🎨 Quantitative results

⭐ Citation

@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}
}

🙏 Acknowledgement

The project builds upon the following pioneering works:

• OpenUni: We thank the OpenUni releasing the elegant and concise code and pretrain dataset.
• UniPic2-SD3.5M-Kontext-2B: We use UniPic2-SD3.5M-Kontext-2B as our diffusion module, considering its efficiency and strong performance on both t2i and editing.
• UnifiedReward-Think: We use UnifiedReward-Think as our reward model for RL, considering its strong performance.
• Qwen2.5 VL: We useQwen2.5 VL-3B as our VLM module, considering its efficiency and strong performance on multimodal understanding abilities.
• BLIP3-o: We thank the BLIP3-o team for releasing the precious high-quality tuning dataset.
• OpenGPT-4o-Image: We thank the OpenGPT-4o-Image team for releasing the precious high-quality tuning dataset.
• ShareGPT-4o-Image: We thank the ShareGPT-4o-Image team for releasing the precious high-quality tuning dataset.
• Echo-4o: We thank the Echo-4o team for releasing the precious high-quality tuning dataset.
• OmniGen2: We thank the OmniGen2 team for releasing the precious high-quality editing tuning dataset and code.
• Uniworld-V1: We thank the Uniworld team for releasing the precious high-quality tuning dataset and code.
• Picobanana: We thank the Picobanana team for releasing the precious high-quality editing tuning dataset.
• Nano-consist: We thank the Nano-consist team for releasing the precious high-quality editing tuning dataset.
• NHR-edit: We thank the NHR-edit team for releasing the precious high-quality editing tuning dataset.
• UniREditBench: We thank the UniREditBench team for releasing the precious high-quality reason-based editing tuning dataset.