  Language has emerged as a natural interface for image editing. In this paper,
we introduce a method for region-based image editing driven by textual prompts,
without the need for user-provided masks or sketches. Specifically, our
approach leverages an existing pre-trained text-to-image model and introduces a
bounding box generator to identify the editing regions that are aligned with
the textual prompts. We show that this simple approach enables flexible editing
that is compatible with current image generation models, and is able to handle
complex prompts featuring multiple objects, complex sentences, or lengthy
paragraphs. We conduct an extensive user study to compare our method against
state-of-the-art methods. The experiments demonstrate the competitive
performance of our method in manipulating images with high fidelity and realism
that correspond to the provided language descriptions. Our project webpage can
be found at: https://yuanze-lin.me/LearnableRegions_page.
