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Text-conditioned image editing is a recently emerged and highly practicaltask, and its potential is immeasurable. However, most of the concurrentmethods are unable to perform action editing, i.e. they can not produce resultsthat conform to the action semantics of the editing prompt and preserve thecontent of the original image. To solve the problem of action editing, wepropose KV Inversion, a method that can achieve satisfactory reconstructionperformance and action editing, which can solve two major problems: 1) theedited result can match the corresponding action, and 2) the edited object canretain the texture and identity of the original real image. In addition, ourmethod does not require training the Stable Diffusion model itself, nor does itrequire scanning a large-scale dataset to perform time-consuming training.

KV Inversion: KV Embeddings Learning for Text-Conditioned Real Image Action Editing