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Negative-prompt Inversion: Fast Image Inversion for Editing with Text-guided Diffusion Models

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arxiv 2305.16807 v2 pith:TW3FJROG submitted 2023-05-26 cs.CV

Daiki Miyake , Akihiro Iohara , Yu Saito , Toshiyuki Tanaka This is my paper
classification cs.CV
keywords inversionreconstructionfidelitydiffusioneditingimagemethodmodels
verification ladder T0 review T1 audit T2 compute T3 formal
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In image editing employing diffusion models, it is crucial to preserve the reconstruction fidelity to the original image while changing its style. Although existing methods ensure reconstruction fidelity through optimization, a drawback of these is the significant amount of time required for optimization. In this paper, we propose negative-prompt inversion, a method capable of achieving equivalent reconstruction solely through forward propagation without optimization, thereby enabling ultrafast editing processes. We experimentally demonstrate that the reconstruction fidelity of our method is comparable to that of existing methods, allowing for inversion at a resolution of 512 pixels and with 50 sampling steps within approximately 5 seconds, which is more than 30 times faster than null-text inversion. Reduction of the computation time by the proposed method further allows us to use a larger number of sampling steps in diffusion models to improve the reconstruction fidelity with a moderate increase in computation time.

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Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Consistent-Inversion: Reverse Consistency Guidance for Structure-Preserving Visual Editing

    cs.CV 2026-06 unverdicted novelty 7.0 of 10

    Consistent-Inversion introduces reverse consistency guidance that corrects early target denoising steps by checking reversibility toward the source inversion trajectory under the original prompt.

  2. Delta Rectified Flow Sampling for Text-to-Image Editing

    cs.CV 2025-09 unverdicted novelty 7.0 of 10

    DRFS is a new inversion-free editing technique for rectified flow models that models source-target velocity discrepancies and applies a time-dependent shift to improve fidelity and unify prior methods like DDS and FlowEdit.

  3. Root-Selecting Fixed-Point Inversion for Rectified Flows via Trajectory Straightness

    cs.CV 2026-06 unverdicted novelty 6.0 of 10

    SelFix selects straighter-trajectory fixed-point solutions for rectified flow inversion to improve real-image reconstruction and source-preserving editing.

  4. Training-free image inversion for one-step diffusion models

    cs.CV 2026-05 unverdicted novelty 6.0 of 10

    TFinv proposes iterative noise alignment and suffix learning to enable training-free inversion and editing for one-step diffusion models, achieving SOTA performance and higher efficiency than multistep methods.

  5. Discrete Noise Inversion for Next-scale Autoregressive Text-based Image Editing

    cs.CV 2025-09 conditional novelty 6.0 of 10

    VARIN uses a Location-aware Argmax Inversion pseudo-inverse of Gumbel-max sampling to extract editable discrete noises, enabling training-free prompt-guided editing for visual autoregressive models.

  6. FlowSonic: Stable Zero-Shot Music Editing via High-Order Trajectory Integration

    cs.SD 2026-07 reject novelty 4.0 of 10

    FlowSonic combines deterministic rectified-flow inversion, cached cross-attention injection, and a 'seeded' third-order Adams-Bashforth solver to report better timbre and genre edits on small datasets.

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