  Advances in text-based image generation and editing have revolutionized
content creation, enabling users to create impressive content from imaginative
text prompts. However, existing methods are not designed to work well with the
oversimplified prompts that are often encountered in typical scenarios when
users start their editing with only vague or abstract purposes in mind. Those
scenarios demand elaborate ideation efforts from the users to bridge the gap
between such vague starting points and the detailed creative ideas needed to
depict the desired results. In this paper, we introduce the task of Image
Editing Recommendation (IER). This task aims to automatically generate diverse
creative editing instructions from an input image and a simple prompt
representing the users' under-specified editing purpose. To this end, we
introduce Creativity-Vision Language Assistant~(Creativity-VLA), a multimodal
framework designed specifically for edit-instruction generation. We train
Creativity-VLA on our edit-instruction dataset specifically curated for IER. We
further enhance our model with a novel 'token-for-localization' mechanism,
enabling it to support both global and local editing operations. Our
experimental results demonstrate the effectiveness of \ours{} in suggesting
instructions that not only contain engaging creative elements but also maintain
high relevance to both the input image and the user's initial hint.
