Conceptos Básicos
Ground-A-Score introduces a model-agnostic image editing approach that effectively handles complex editing prompts by breaking them down into individual modification subtasks.
Resumen
The Ground-A-Score methodology focuses on enhancing image editing outcomes by incorporating grounding during score distillation. The approach ensures precise reflection of intricate prompt requirements, leading to high-quality results respecting original attributes. The content is structured into sections covering Introduction, Related Works, Methodology, Experimental Results, Conclusion, and Additional Details.
Introduction:
- Ground-A-Score addresses challenges in multi-attribute image editing.
Related Works:
- Various diffusion models and methods for text-to-image synthesis are discussed.
Method: Ground-A-Score:
- Aggregation of multiple editing guidance and null-text penalty explained.
Experimental Results:
- Qualitative and quantitative comparisons with other baseline models presented.
Conclusion:
- Ground-A-Score's effectiveness in modifying objects as intended is highlighted.
Additional Details:
- Information on the optimization process, full-prompt guidance, null-text penalty, detailed editing prompts provided.
Estadísticas
"We used StableDiffusion 1.5 [30] as the base T2I diffusion model."
"CLIP score[↑]: GLIGEN [19] - 30.34"
Citas
"We show that Ground-A-Score outperforms the existing image editing models."
"Ground-A-Score achieved a better image quality with small LPIPS conceptual loss compared to other methods."
"Through these demonstrations, we concluded that our method most appropriately modifies the object as intended in the prompt compared to existing methods."