LoMOE: Localized Multi-Object Editing via Multi-Diffusion
<|reference_start|>LoMOE: Localized Multi-Object Editing via Multi-Diffusion: Recent developments in the field of diffusion models have demonstrated an exceptional capacity to generate high-quality prompt-conditioned image edits. Nevertheless, previous approaches have primarily relied on textual prompts for image editing, which tend to be less effective when making precise edits to specific objects or fine-grained regions within a scene containing single/multiple objects. We introduce a novel framework for zero-shot localized multi-object editing through a multi-diffusion process to overcome this challenge. This framework empowers users to perform various operations on objects within an image, such as adding, replacing, or editing $\textbf{many}$ objects in a complex scene $\textbf{in one pass}$. Our approach leverages foreground masks and corresponding simple text prompts that exert localized influences on the target regions resulting in high-fidelity image editing. A combination of cross-attention and background preservation losses within the latent space ensures that the characteristics of the object being edited are preserved while simultaneously achieving a high-quality, seamless reconstruction of the background with fewer artifacts compared to the current methods. We also curate and release a dataset dedicated to multi-object editing, named $\texttt{LoMOE}$-Bench. Our experiments against existing state-of-the-art methods demonstrate the improved effectiveness of our approach in terms of both image editing quality and inference speed.<|reference_end|>
@article{chakrabarty2024lomoe:,
    title={LoMOE: Localized Multi-Object Editing via Multi-Diffusion},
    author={Goirik Chakrabarty, Aditya Chandrasekar, Ramya Hebbalaguppe, Prathosh
  AP},
    journal={arXiv preprint arXiv:2403.00437},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.00437},
    primaryClass={cs.CV cs.AI cs.GR cs.LG}
}