Discrete Noise Inversion for Next-scale Autoregressive Text-based Image
  Editing
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.
68b7b78e295f15ff60911302
Quan Dao
claimed_verified
2025-09-03T08:27:17.401Z
63e083e6f351dc0745745d17
https://cdn-avatars.huggingface.co/v1/production/uploads/63e083e6f351dc0745745d17/N0GE4uLrkm14blAQMnm2E.jpeg
Quan Dao
user
quandao10
68b7b78e295f15ff60911303
Xiaoxiao He
68b7b78e295f15ff60911304
Ligong Han
68b7b78e295f15ff60911305
Ngan Hoai Nguyen
68b7b78e295f15ff60911306
Amin Heyrani Nobar
68b7b78e295f15ff60911307
Faez Ahmed
68b7b78e295f15ff60911308
Han Zhang
68b7b78e295f15ff60911309
Viet Anh Nguyen
68b7b78e295f15ff6091130a
Dimitris Metaxas
Discrete Noise Inversion for Next-scale Autoregressive Text-based Image
  Editing
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.
VARIN, a noise inversion-based editing technique for visual autoregressive models, enables precise image editing aligned with textual prompts while preserving original details.
Visual autoregressive models VAR diffusion models text-to-image generation prompt-guided image editing Visual AutoRegressive Inverse Noise VARIN Location-aware Argmax Inversion LAI inverse Gumbel noises argmax sampling