Discrete Noise Inversion for Next-scale Autoregressive Text-based Image
  Editing
Authors: name: Quan Dao, name: Xiaoxiao He, name: Ligong Han, name: Ngan Hoai Nguyen, name: Amin Heyrani Nobar, name: Faez Ahmed, name: Han Zhang, name: Viet Anh Nguyen, name: Dimitris Metaxas

Visual autoregressive models (VAR) have recently emerged as a promising class
of generative models, achieving performance comparable to diffusion models in
text-to-image generation tasks. While conditional generation has been widely
explored, the ability to perform prompt-guided image editing without additional
training is equally critical, as it supports numerous practical real-world
applications. This paper investigates the text-to-image editing capabilities of
VAR by introducing Visual AutoRegressive Inverse Noise (VARIN), the first noise
inversion-based editing technique designed explicitly for VAR models. VARIN
leverages a novel pseudo-inverse function for argmax sampling, named
Location-aware Argmax Inversion (LAI), to generate inverse Gumbel noises. These
inverse noises enable precise reconstruction of the source image and facilitate
targeted, controllable edits aligned with textual prompts. Extensive
experiments demonstrate that VARIN effectively modifies source images according
to specified prompts while significantly preserving the original background and
structural details, thus validating its efficacy as a practical editing
approach.