ShareGPT-4o-Image: Aligning Multimodal Models with GPT-4o-Level Image
  Generation
Authors: name: Junying Chen, name: Zhenyang Cai, name: Pengcheng Chen, name: Shunian Chen, name: Ke Ji, name: Xidong Wang, name: Yunjin Yang, name: Benyou Wang

Recent advances in multimodal generative models have unlocked photorealistic,
instruction-aligned image generation, yet leading systems like GPT-4o-Image
remain proprietary and inaccessible. To democratize these capabilities, we
present ShareGPT-4o-Image, the first dataset comprising 45K text-to-image and
46K text-and-image-to-image data, all synthesized using GPT-4o's image
generation capabilities for distilling its advanced image generation abilities.
Leveraging this dataset, we develop Janus-4o, a multimodal large language model
capable of both text-to-image and text-and-image-to-image generation. Janus-4o
not only significantly improves text-to-image generation over its predecessor,
Janus-Pro, but also newly supports text-and-image-to-image generation. Notably,
it achieves impressive performance in text-and-image-to-image generation from
scratch, using only 91K synthetic samples and 6 hours of training on an 8
A800-GPU machine. We hope the release of ShareGPT-4o-Image and Janus-4o will
foster open research in photorealistic, instruction-aligned image generation.