  Text-guided image editing faces significant challenges to training and
inference flexibility. Much literature collects large amounts of annotated
image-text pairs to train text-conditioned generative models from scratch,
which is expensive and not efficient. After that, some approaches that leverage
pre-trained vision-language models are put forward to avoid data collection,
but they are also limited by either per text-prompt optimization or
inference-time hyper-parameters tuning. To address these issues, we investigate
and identify a specific space, referred to as CLIP DeltaSpace, where the CLIP
visual feature difference of two images is semantically aligned with the CLIP
textual feature difference of their corresponding text descriptions. Based on
DeltaSpace, we propose a novel framework called DeltaEdit, which maps the CLIP
visual feature differences to the latent space directions of a generative model
during the training phase, and predicts the latent space directions from the
CLIP textual feature differences during the inference phase. And this design
endows DeltaEdit with two advantages: (1) text-free training; (2)
generalization to various text prompts for zero-shot inference. Extensive
experiments validate the effectiveness and versatility of DeltaEdit with
different generative models, including both the GAN model and the diffusion
model, in achieving flexible text-guided image editing. Code is available at
https://github.com/Yueming6568/DeltaEdit.
