Effective Real Image Editing with Accelerated Iterative Diffusion
  Inversion
Zhihong Pan Riccardo Gherardi Xiufeng Xie Stephen Huang
Despite all recent progress, it is still challenging to edit and manipulate
natural images with modern generative models. When using Generative Adversarial
Network (GAN), one major hurdle is in the inversion process mapping a real
image to its corresponding noise vector in the latent space, since its
necessary to be able to reconstruct an image to edit its contents. Likewise for
Denoising Diffusion Implicit Models (DDIM), the linearization assumption in
each inversion step makes the whole deterministic inversion process unreliable.
Existing approaches that have tackled the problem of inversion stability often
incur in significant trade-offs in computational efficiency. In this work we
propose an Accelerated Iterative Diffusion Inversion method, dubbed AIDI, that
significantly improves reconstruction accuracy with minimal additional overhead
in space and time complexity. By using a novel blended guidance technique, we
show that effective results can be obtained on a large range of image editing
tasks without large classifier-free guidance in inversion. Furthermore, when
compared with other diffusion inversion based works, our proposed process is
shown to be more robust for fast image editing in the 10 and 20 diffusion
steps' regimes.