Effective Real Image Editing with Accelerated Iterative Diffusion Inversion

Understand

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.