  Denoising diffusion models have shown outstanding performance in image
editing. Existing works tend to use either image-guided methods, which provide
a visual reference but lack control over semantic coherence, or text-guided
methods, which ensure faithfulness to text guidance but lack visual quality. To
address the problem, we propose the Zero-shot Inversion Process (ZIP), a
framework that injects a fusion of generated visual reference and text guidance
into the semantic latent space of a \textit{frozen} pre-trained diffusion
model. Only using a tiny neural network, the proposed ZIP produces diverse
content and attributes under the intuitive control of the text prompt.
Moreover, ZIP shows remarkable robustness for both in-domain and out-of-domain
attribute manipulation on real images. We perform detailed experiments on
various benchmark datasets. Compared to state-of-the-art methods, ZIP produces
images of equivalent quality while providing a realistic editing effect.
