Compression Asymmetry and Trajectory Binding in Noise-Anchored Diffusion Inversion
Real-image diffusion inversion faces a quality-cost trade-off. This paper reveals two mechanisms: element-wise compression asymmetry and trajectory binding, leading to Noise-Anchored Reverse Correction (NARC), a training-free method that outperforms baselines with drastically reduced storage.
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[Submitted on 8 Jul 2026]
Title:Compression Asymmetry and Trajectory Binding in Noise-Anchored Diffusion Inversion
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Abstract:Real-image diffusion inversion is governed by a tight quality-cost trade-off, with costs incurred in computation, storage, or per-image optimization. We study this trade-off through the forward Gaussian noise anchor that defines a diffusion trajectory and isolate two mechanisms behind effective stored-noise inversion. First, diffusion noise exhibits an element-wise compression asymmetry: int8 full-dimensional anchors preserve reconstruction, whereas low-dimensional subspace summaries are much less reliable, often collapsing even at comparable or smaller payloads; the element-wise over subspace ordering persists across five stored-noise inversion methods. Second, inversion is trajectory-bound and score-prior coupled: the matched forward anchor and a trained score network are both necessary, arguing against a purely algebraic-identity explanation. Together, these findings specify what to store and how to use it. They lead to Noise-Anchored Reverse Correction (NARC), a training-free inversion primitive that stores a single int8 latent anchor and reuses it with a fixed, noise-level-dependent anchor-weight schedule: strong anchoring when the reverse trajectory is noise-dominated, then relaxed anchoring as image detail emerges. On PIE-Bench++ with Stable Diffusion 1.5, NARC outperforms five modern non-exact baselines and improves PSNR by +3.24 dB over PnP DirectInv while using about 400x less inversion storage than PnP DirectInv. The compression asymmetry, anchor specificity, and editing plug-in also transfer to SDXL 1024^2.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Information Theory (cs.IT)
Cite as: arXiv:2607.09784 [cs.CV]
(or arXiv:2607.09784v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.09784
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yongseong Park [view email] [v1] Wed, 8 Jul 2026 13:30:44 UTC (12,495 KB)
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