Hyper-parameter tuning for text guided image editing

TL;DR

This paper introduces Forgedit, a test-time finetuning method for text-guided image editing that efficiently adapts to individual images and effectively solves overfitting issues present in previous state-of-the-art techniques.

Contribution

It presents a hyper-parameter tuning strategy for Forgedit that enhances editing quality and efficiency without increasing workflow complexity.

Findings

Forgedit can adapt to images in 30 seconds using fixed hyper-parameters.

The method effectively addresses overfitting in text-guided image editing.

Hyper-parameter tuning improves the quality of image edits.

Abstract

The test-time finetuning text-guided image editing method, Forgedit, is capable of tackling general and complex image editing problems given only the input image itself and the target text prompt. During finetuning stage, using the same set of finetuning hyper-paramters every time for every given image, Forgedit remembers and understands the input image in 30 seconds. During editing stage, the workflow of Forgedit might seem complicated. However, in fact, the editing process of Forgedit is not more complex than previous SOTA Imagic, yet completely solves the overfitting problem of Imagic. In this paper, we will elaborate the workflow of Forgedit editing stage with examples. We will show how to tune the hyper-parameters in an efficient way to obtain ideal editing results.