Ground-A-Score: Scaling Up the Score Distillation for Multi-Attribute Editing
<|reference_start|>Ground-A-Score: Scaling Up the Score Distillation for Multi-Attribute Editing: Despite recent advancements in text-to-image diffusion models facilitating various image editing techniques, complex text prompts often lead to an oversight of some requests due to a bottleneck in processing text information. To tackle this challenge, we present Ground-A-Score, a simple yet powerful model-agnostic image editing method by incorporating grounding during score distillation. This approach ensures a precise reflection of intricate prompt requirements in the editing outcomes, taking into account the prior knowledge of the object locations within the image. Moreover, the selective application with a new penalty coefficient and contrastive loss helps to precisely target editing areas while preserving the integrity of the objects in the source image. Both qualitative assessments and quantitative analyses confirm that Ground-A-Score successfully adheres to the intricate details of extended and multifaceted prompts, ensuring high-quality outcomes that respect the original image attributes.<|reference_end|>
@article{chang2024ground-a-score:,
    title={Ground-A-Score: Scaling Up the Score Distillation for Multi-Attribute
  Editing},
    author={Hangeol Chang, Jinho Chang, and Jong Chul Ye},
    journal={arXiv preprint arXiv:2403.13551},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13551},
    primaryClass={cs.CV cs.LG}
}