Eta Inversion: Designing an Optimal Eta Function for Diffusion-based
  Real Image Editing
Wonjun Kang Kevin Galim Hyung Il Koo
Diffusion models have achieved remarkable success in the domain of
text-guided image generation and, more recently, in text-guided image editing.
A commonly adopted strategy for editing real images involves inverting the
diffusion process to obtain a noisy representation of the original image, which
is then denoised to achieve the desired edits. However, current methods for
diffusion inversion often struggle to produce edits that are both faithful to
the specified text prompt and closely resemble the source image. To overcome
these limitations, we introduce a novel and adaptable diffusion inversion
technique for real image editing, which is grounded in a theoretical analysis
of the role of $\eta$ in the DDIM sampling equation for enhanced editability.
By designing a universal diffusion inversion method with a time- and
region-dependent $\eta$ function, we enable flexible control over the editing
extent. Through a comprehensive series of quantitative and qualitative
assessments, involving a comparison with a broad array of recent methods, we
demonstrate the superiority of our approach. Our method not only sets a new
benchmark in the field but also significantly outperforms existing strategies.
Our code is available at https://github.com/furiosa-ai/eta-inversion