Pith. sign in

REVIEW 5 cited by

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

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

Daiki Miyake , Akihiro Iohara , Yu Saito , Toshiyuki Tanaka This is my paper · ORCID
classification cs.CV
keywords inversionreconstructionfidelitydiffusioneditingimagemethodmodels
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 5 Pith papers

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

  1. DNAEdit: Direct Noise Alignment for Text-Guided Rectified Flow Editing

    cs.CV 2025-06 conditional novelty 7.0 of 10

    Direct Noise Alignment iteratively moves a random Gaussian noise until the model's predicted velocity matches the straight-line velocity to the image, reducing inversion drift and giving the best reported fidelity-edi...

  2. 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.

  3. RefEdit: A Benchmark and Method for Improving Instruction-based Image Editing Model on Referring Expressions

    cs.CV 2025-06 conditional novelty 6.0 of 10

    RefEdit-Bench measures referring-expression image editing; the RefEdit model, trained on 20K synthetic triplets, reports state-of-the-art results over million-scale baselines.

  4. 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.

  5. DCI: Dual-Conditional Inversion for Boosting Diffusion-Based Image Editing

    cs.CV 2025-06 reject novelty 4.0 of 10

    DCI combines reference-guided noise correction with fixed-point latent refinement and reports state-of-the-art reconstruction and editing metrics on PIE-Bench.

Pith tools