Nano-consistent-150k

Echo: Harnessing Proprietary Models’ Synthetic Images for Improved Image Generation

📰 News

• [2025.9.18] 🔥 We have released Nano-consistent-150k. — the first dataset constructed using Nano-Banana that exceeds 150k high-quality samples, uniquely designed to preserve consistent human identity across diverse and complex editing scenarios. Check out the [ Blog; Dataset; Awesome; ].

• [2025.8.13] 🔥 We have released Echo: Harnessing Proprietary Models’ Synthetic Images for Improved Image Generation. Check out the [ Paper; Dataset; Model; Code) ].

🏆 Contributions

• ⁉️ Why use synthetic data instead of real-world data?: We analyze and summarize the advantages of synthetic data over real-world images, highlighting its ability to generate rare scenarios and to provide pure, long-tailed supervision for instruction-following tasks.
• 🔧 How to generate synthetic data? We curate Echo-4o-Image, a synthetic dataset of ~180K samples generated using GPT-4o. Echo-4o-Image includes 38K surreal fantasy samples, 73K multi-reference image generation samples, and 68K complex instruction-following samples.
• ✨ Does synthetic data work? We fine-tune the Bagel model on Echo-4o-Image, yielding model Echo-4o, which achieves state-of-the-art performance across multiple benchmarks. Furthermore, Echo-4o-Image consistently enhances other backbone models such as OmniGen2 and BLIP3-o, demonstrating strong transferability.
• 📐 How to evaluate performance? We propose two new evaluation benchmarks: Geneval++ increases instruction complexity to alleviate score saturation in text-to-image evaluation. Imagine-Bench targets fantasy tasks and is designed to assess both understanding and generation of imaginative content.

🎨Echo-4o-Image

🔗 Dataset on Hugging Face: Echo-4o-Image

We introduce Echo-4o-Image, a large-scale synthetic dataset distilled from GPT-4o.It contains approximately 179,000 samples spanning three distinct task types: 38K surreal fantasy generation tasks, 73K multi-reference image generation tasks, and 68K complex instruction execution tasks.

For better visualization, we provide an online gallery showcasing representative samples from our dataset: Online Gallery

🤖 Echo-4o

First, prepare your environment by following the setup instructions in the Bagel environments.

Training

Our training code extends Bagel's capabilities to support multi-reference datasets for training.

Data Preparation:

• Follow the same data preparation process as outlined in Bagel's documentation
• Ensure your multi-reference data follows the expected format in our data example here

Training Process:

Our training scripts use the same interface and parameters as Bagel, so you can use the existing training commands and configurations of Bagel without modification.

Inference

Please first download Echo-4o here

• Text-to-Image Tasks

  For standard text-to-image generation, use the same inference process as Bagel:

  Refer to the text-to-image generation example here

• Multi-Reference Tasks

  For tasks involving multiple references, refer to the multi-reference example here

📏 GenEval++ & Imagine-Bench

To rigorously evaluate the model’s instruction-following and imaginative generation, we further introduce two novel benchmarks: Geneval++ and Imagine-Bench. Geneval++ incorporates an automated evaluator powered by GPT-4.1 and significantly increases the difficulty and compositional complexity of test instructions, addressing the limitations of scoring saturation and insufficient accuracy found in existing text-to-image evaluations. Imagine-Bench focuses on imaginative generation, offering a comprehensive evaluation of conceptual creativity and visual consistency across three dimensions: fantasy fulfillment, identity preservation, and aesthetic quality.

We provide benchmark guides for GenEval++ and Imagine-Bench. For more details, see EVAL.

❤️ Acknowledgements

We would like to thank the following open-source projects and research works:

• Bagel
• BLIP3o
• OmniGen2

😊 We'd love to hear from you—feel free to reach out anytime if you have any questions!

📕 BibTeX

@article{ye2025echo4o,
      title={Echo-4o: Harnessing the Power of GPT-4o Synthetic Images for Improved Image Generation}, 
      author={Junyan Ye, Dongzhi Jiang, Zihao Wang, Leqi Zhu, Zhenghao Hu, Zilong Huang, Jun He, Zhiyuan Yan, Jinghua Yu, Hongsheng Li, Conghui He, Weijia Li},
      journal={https://arxiv.org/abs/2508.09987},
      year={2025},
}