This project introduces Echo-4o, a novel approach to image generation that leverages large-scale synthetic datasets derived from proprietary models like GPT-4o. It addresses the challenge of achieving high identity consistency across diverse and complex image editing scenarios. Targeted at researchers and engineers in AI image generation, Echo-4o offers improved performance and transferability by utilizing synthetic data for robust instruction-following and creative generation tasks.

How It Works

Echo-4o utilizes synthetic data generated by GPT-4o to construct large-scale datasets, notably Echo-4o-Image (~180K samples), designed for enhanced identity consistency. This approach allows for the generation of rare scenarios and provides pure supervision for instruction-following tasks. The project also highlights Nano-consistent-150k, a dataset exceeding 150K samples built with Nano-Banana, emphasizing consistent human identity across numerous editing outputs. The Echo-4o model itself is fine-tuned on Echo-4o-Image, extending the capabilities of the Bagel architecture. This strategy aims to improve image generation quality and enable seamless linking of multiple editing tasks around the same individual.

Quick Start & Requirements

Setup involves preparing the environment by following instructions for the Bagel project. Data preparation should adhere to Bagel's documentation, ensuring multi-reference data follows the specified format. Training scripts and inference processes are designed to be compatible with Bagel's existing commands and parameters. Key resources include the Echo-4o model on Hugging Face, the Echo-4o-Image dataset on Hugging Face, and the associated research paper.

• Primary Install/Run: Based on Bagel's framework.
• Prerequisites: Bagel environment setup, specific multi-reference data formatting.
• Links:
  • Paper: https://arxiv.org/abs/2508.09987
  • Dataset: Echo-4o-Image on Hugging Face
  • Model: Echo-4o on Hugging Face
  • Blog/News: https://arxiv.org/abs/2508.09987

Highlighted Details

• Introduces Nano-consistent-150k (150K+ samples) and Echo-4o-Image (~180K GPT-4o-generated samples).
• Echo-4o-Image comprises 38K surreal fantasy, 73K multi-reference, and 68K complex instruction-following samples.
• Fine-tuning Bagel on Echo-4o-Image yields Echo-4o, achieving state-of-the-art performance.
• Echo-4o-Image demonstrates strong transferability, enhancing models like OmniGen2 and BLIP3-o.
• Proposes two new benchmarks: Geneval++ (increased instruction complexity) and Imagine-Bench (fantasy tasks, creativity assessment).

Maintenance & Community

The project acknowledges contributions from the open-source communities of Bagel, BLIP3o, and OmniGen2. No specific details regarding active maintainers, community channels (e.g., Discord, Slack), or a public roadmap are provided in the README.

Licensing & Compatibility

The provided README text does not specify the software license for the Echo-4o code or the datasets. Compatibility for commercial use or linking with closed-source projects cannot be determined without explicit licensing information.

Limitations & Caveats

Documentation links for setup, data examples, and inference procedures within the README point back to the README itself, suggesting potential incompleteness or immaturity in the documentation structure. The project's reliance on external architectures like Bagel implies potential dependency management complexities. No other explicit limitations or known issues are mentioned.