arXiv:2509.24223v2 Announce Type: replace Abstract: Editing the content of an image with a pretrained text-to-image model remains challenging. Existing methods often distort fine details or introduce unintended artifacts. We propose using \emph{coupled stochastic differential equations} (coupled SDEs) to guide the sampling process of any pre-trained generative model that can be sampled by solving an SDE, including diffusion and rectified flow models. By driving both the source image and the edited image with the same correlated noise, our approach steers new samples toward the desired semantic
The rapid advancement in generative AI models, particularly text-to-image, necessitates more precise and artifact-free content editing methods to enhance their utility and commercial application.
This development addresses a critical challenge in generative AI, enabling more effective image manipulation and content creation with reduced distortion, which is vital for professional and creative applications.
The ability to generate and edit images with greater control and fewer artifacts allows for more sophisticated and commercially viable content generation workflows, moving beyond rudimentary model outputs.
- · AI content creators
- · Creative industries
- · Generative AI model developers
- · E-commerce platforms
- · Traditional image editing software reliant on manual processes
- · Generative AI models with poor editing capabilities
- · Content creators using less sophisticated tools
Improved and more accessible tools for semantic image editing become widely available, democratizing professional-quality content creation.
The demand for high-quality, customized visual content accelerates across various industries, from marketing to virtual reality.
Enhanced generative editing capabilities could lead to new forms of media and interactive experiences, blurred the lines between real and synthetic visuals.
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Read at arXiv cs.LG