An Empirical Study of GPT-4o Image Generation Capabilities
The landscape of image generation has rapidly evolved, from early GAN-based
approaches to diffusion models and, most recently, to unified generative
architectures that seek to bridge understanding and generation tasks. Recent
advances, especially the GPT-4o, have demonstrated the feasibility of
high-fidelity multimodal generation, their architectural design remains
mysterious and unpublished. This prompts the question of whether image and text
generation have already been successfully integrated into a unified framework
for those methods. In this work, we conduct an empirical study of GPT-4o's
image generation capabilities, benchmarking it against leading open-source and
commercial models. Our evaluation covers four main categories, including
text-to-image, image-to-image, image-to-3D, and image-to-X generation, with
more than 20 tasks. Our analysis highlights the strengths and limitations of
GPT-4o under various settings, and situates it within the broader evolution of
generative modeling. Through this investigation, we identify promising
directions for future unified generative models, emphasizing the role of
architectural design and data scaling.
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Lei Zhu
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Lu Qi
An Empirical Study of GPT-4o Image Generation Capabilities
The landscape of image generation has rapidly evolved, from early GAN-based
approaches to diffusion models and, most recently, to unified generative
architectures that seek to bridge understanding and generation tasks. Recent
advances, especially the GPT-4o, have demonstrated the feasibility of
high-fidelity multimodal generation, their architectural design remains
mysterious and unpublished. This prompts the question of whether image and text
generation have already been successfully integrated into a unified framework
for those methods. In this work, we conduct an empirical study of GPT-4o's
image generation capabilities, benchmarking it against leading open-source and
commercial models. Our evaluation covers four main categories, including
text-to-image, image-to-image, image-to-3D, and image-to-X generation, with
more than 20 tasks. Our analysis highlights the strengths and limitations of
GPT-4o under various settings, and situates it within the broader evolution of
generative modeling. Through this investigation, we identify promising
directions for future unified generative models, emphasizing the role of
architectural design and data scaling.
An empirical study of GPT-4o's image generation capabilities across multiple tasks reveals its strengths and limitations compared to other models, highlighting the importance of architectural design and data scaling in unified generative frameworks.
GAN diffusion models unified generative architectures text-to-image image-to-image image-to-3D image-to-X