Ground-A-Score: Scaling Up the Score Distillation for Multi-Attribute
  Editing
Hangeol Chang Jinho Chang Jong Chul Ye
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.