  Recent text-driven image editing in diffusion models has shown remarkable
success. However, the existing methods assume that the user's description
sufficiently grounds the contexts in the source image, such as objects,
background, style, and their relations. This assumption is unsuitable for
real-world applications because users have to manually engineer text prompts to
find optimal descriptions for different images. From the users' standpoint,
prompt engineering is a labor-intensive process, and users prefer to provide a
target word for editing instead of a full sentence. To address this problem, we
first demonstrate the importance of a detailed text description of the source
image, by dividing prompts into three categories based on the level of semantic
details. Then, we propose simple yet effective methods by combining prompt
generation frameworks, thereby making the prompt engineering process more
user-friendly. Extensive qualitative and quantitative experiments demonstrate
the importance of prompts in text-driven image editing and our method is
comparable to ground-truth prompts.

Computer Vision and Pattern Recognition