arxiv preprint - Generate Anything Anywhere in Any Scene

In this episode we discuss Generate Anything Anywhere in Any Scene by Yuheng Li, Haotian Liu, Yangming Wen, Yong Jae Lee. The paper proposes a data augmentation training strategy for personalized object generation in text-to-image diffusion models. They also introduce a plug-and-play adapter layers approach to control the location and size of the generated personalized objects. Additionally, a regionally-guided sampling technique is introduced to maintain image quality during inference. The proposed model shows promising results in terms of fidelity for personalized objects, making it suitable for applications in art, entertainment, and advertising design.