Echo-4o: Harnessing the Power of GPT-4o Synthetic Images for Improved
  Image Generation
Recently, GPT-4o has garnered significant attention for its strong
performance in image generation, yet open-source models still lag behind.
Several studies have explored distilling image data from GPT-4o to enhance
open-source models, achieving notable progress. However, a key question
remains: given that real-world image datasets already constitute a natural
source of high-quality data, why should we use GPT-4o-generated synthetic data?
In this work, we identify two key advantages of synthetic images. First, they
can complement rare scenarios in real-world datasets, such as surreal fantasy
or multi-reference image generation, which frequently occur in user queries.
Second, they provide clean and controllable supervision. Real-world data often
contains complex background noise and inherent misalignment between text
descriptions and image content, whereas synthetic images offer pure backgrounds
and long-tailed supervision signals, facilitating more accurate text-to-image
alignment. Building on these insights, we introduce Echo-4o-Image, a 180K-scale
synthetic dataset generated by GPT-4o, harnessing the power of synthetic image
data to address blind spots in real-world coverage. Using this dataset, we
fine-tune the unified multimodal generation baseline Bagel to obtain Echo-4o.
In addition, we propose two new evaluation benchmarks for a more accurate and
challenging assessment of image generation capabilities: GenEval++, which
increases instruction complexity to mitigate score saturation, and
Imagine-Bench, which focuses on evaluating both the understanding and
generation of imaginative content. Echo-4o demonstrates strong performance
across standard benchmarks. Moreover, applying Echo-4o-Image to other
foundation models (e.g., OmniGen2, BLIP3-o) yields consistent performance gains
across multiple metrics, highlighting the datasets strong transferability.
689d6033b083e610d741ea4c
Junyan Ye
689d6033b083e610d741ea4d
Dongzhi Jiang
claimed_verified
2025-08-14T13:37:21.218Z
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Dongzhi Jiang
user
CaraJ
689d6033b083e610d741ea4e
Zihao Wang
689d6033b083e610d741ea4f
Leqi Zhu
689d6033b083e610d741ea50
Zhenghao Hu
689d6033b083e610d741ea51
Zilong Huang
claimed_verified
2025-08-14T13:37:23.470Z
6487e158f675b4a7867f45fa
https://cdn-avatars.huggingface.co/v1/production/uploads/6487e158f675b4a7867f45fa/J0sls6zZ682o-SH7iQs7B.jpeg
Zilong Huang
user
SereinH
689d6033b083e610d741ea52
Jun He
689d6033b083e610d741ea53
Zhiyuan Yan
689d6033b083e610d741ea54
Jinghua Yu
689d6033b083e610d741ea55
Hongsheng Li
689d6033b083e610d741ea56
Conghui He
689d6033b083e610d741ea57
Weijia Li
Echo-4o: Harnessing the Power of GPT-4o Synthetic Images for Improved
  Image Generation
Recently, GPT-4o has garnered significant attention for its strong
performance in image generation, yet open-source models still lag behind.
Several studies have explored distilling image data from GPT-4o to enhance
open-source models, achieving notable progress. However, a key question
remains: given that real-world image datasets already constitute a natural
source of high-quality data, why should we use GPT-4o-generated synthetic data?
In this work, we identify two key advantages of synthetic images. First, they
can complement rare scenarios in real-world datasets, such as surreal fantasy
or multi-reference image generation, which frequently occur in user queries.
Second, they provide clean and controllable supervision. Real-world data often
contains complex background noise and inherent misalignment between text
descriptions and image content, whereas synthetic images offer pure backgrounds
and long-tailed supervision signals, facilitating more accurate text-to-image
alignment. Building on these insights, we introduce Echo-4o-Image, a 180K-scale
synthetic dataset generated by GPT-4o, harnessing the power of synthetic image
data to address blind spots in real-world coverage. Using this dataset, we
fine-tune the unified multimodal generation baseline Bagel to obtain Echo-4o.
In addition, we propose two new evaluation benchmarks for a more accurate and
challenging assessment of image generation capabilities: GenEval++, which
increases instruction complexity to mitigate score saturation, and
Imagine-Bench, which focuses on evaluating both the understanding and
generation of imaginative content. Echo-4o demonstrates strong performance
across standard benchmarks. Moreover, applying Echo-4o-Image to other
foundation models (e.g., OmniGen2, BLIP3-o) yields consistent performance gains
across multiple metrics, highlighting the datasets strong transferability.
Echo-4o-Image, a synthetic dataset generated by GPT-4o, enhances image generation models by addressing rare scenarios and providing clean supervision, leading to improved performance and transferability.
GPT-4o synthetic dataset rare scenarios surreal fantasy multi-reference image generation clean supervision complex background noise text-to-image alignment unified multimodal generation Bagel Echo-4o GenEval++ Imagine-Bench OmniGen2 BLIP3-o