  Text-guided image editing on real or synthetic images, given only the
original image itself and the target text prompt as inputs, is a very general
and challenging task. It requires an editing model to estimate by itself which
part of the image should be edited, and then perform either rigid or non-rigid
editing while preserving the characteristics of original image. In this paper,
we design a novel text-guided image editing method, named as Forgedit. First,
we propose a vision-language joint optimization framework capable of
reconstructing the original image in 30 seconds, much faster than previous SOTA
and much less overfitting. Then we propose a novel vector projection mechanism
in text embedding space of Diffusion Models, which is capable to control the
identity similarity and editing strength seperately. Finally, we discovered a
general property of UNet in Diffusion Models, i.e., Unet encoder learns space
and structure, Unet decoder learns appearance and identity. With such a
property, we design forgetting mechanisms to successfully tackle the fatal and
inevitable overfitting issues when fine-tuning Diffusion Models on one image,
thus significantly boosting the editing capability of Diffusion Models. Our
method, Forgedit, built on Stable Diffusion, achieves new state-of-the-art
results on the challenging text-guided image editing benchmark: TEdBench,
surpassing the previous SOTA methods such as Imagic with Imagen, in terms of
both CLIP score and LPIPS score. Codes are available at
https://github.com/witcherofresearch/Forgedit
