Pith. sign in

REVIEW 2 major objections 6 minor 41 references

One-step inversion with brief adversarial LoRA training beats 50-step DDIM on watermark robustness for diffusion images.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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

T0 review · grok-4.5

2026-07-30 22:56 UTC pith:6UXI4UAW

load-bearing objection Solid systems paper: one-step adversarially LoRA-tuned inversion beats 50-step DDIM on watermark robustness with ~20 min fine-tune; curvature observation is the real hook. the 2 major comments →

arxiv 2607.26723 v1 pith:6UXI4UAW submitted 2026-07-29 cs.CR cs.AI

FARI: Robust One-Step Inversion for Watermarking in Diffusion Models

Jindong Yang , Han Fang , Weiming Zhang , Nenghai Yu , Kejiang Chen This is my paper
classification cs.CR cs.AI
keywords diffusion modelswatermarkinginversionDDIMLoRAadversarial trainingone-step inversionrobustness
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Inversion-based watermarks can authenticate diffusion images, but extracting them requires running the model backward, which is slow and brittle under real-world distortions like JPEG or blur. This paper argues that the backward path has much lower curvature than the forward generation path, so it can be collapsed into a single step without losing the signal that matters for watermark checks. That speed-up then makes end-to-end adversarial fine-tuning cheap enough to run in minutes on one GPU. The resulting method, FARI, recovers Tree-Ring and Gaussian Shading marks more reliably under common distortions than a 50-step DDIM baseline while cutting the number of model calls from 50 to 1. A reader who cares about practical provenance for AI images gets both the speed and the robustness that high-step inverters never delivered together.

Core claim

The DDIM inversion trajectory has markedly lower curvature than the forward generation path, making it compressible to one step; because external channel distortions already dominate internal truncation error in the watermark setting, that one-step map can be adversarially LoRA-fine-tuned end-to-end and still outperform 50-step DDIM on verification robustness after roughly twenty minutes of training.

What carries the argument

FARI: a single-step inversion that reconstructs initial noise from a (possibly distorted) latent by evaluating a LoRA-adapted denoiser at t≈0, trained with an MSE loss against ground-truth noise under random distortions; the LoRA is enabled only at extraction so generation quality is unchanged.

Load-bearing premise

That the lower curvature of inversion paths remains usable under real distortions, so one linear step plus light fine-tuning can absorb leftover error without erasing the watermark.

What would settle it

Measure bit accuracy (Gaussian Shading) and TPR at FPR 1e-3 (Tree-Ring) for one-step FARI versus 50-step DDIM on held-out distortion types or higher intensities after the same training budget; if FARI falls below the baseline, the robustness claim fails.

Watch this falsifier. Get emailed when new claim-graph text bears on it.

If this is right

  • Watermark verification for diffusion images can run in one denoiser call instead of dozens.
  • End-to-end adversarial training of inverters becomes practical on a single GPU in minutes.
  • LoRA can carry inversion robustness without altering the base model’s generation quality.
  • High-NFE inversion methods built for editing are the wrong default for watermark extraction under channel noise.
  • Cheaper inversion graphs also make gradient-based watermark removal attacks easier to study.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same low-curvature observation may let other verification tasks such as tamper localization drop to few-step inversion.
  • Once inversion is one step, white-box adaptive attacks on inversion watermarks become routinely affordable, shifting the arms race.
  • Generation-oriented distillation methods systematically underperform on inversion because the input distribution is reversed (structured image versus pure noise).
  • Dependence on ODE samplers leaves open whether similar compressibility holds for SDE or flow-matching trajectories.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 6 minor

Summary. The paper addresses the practical bottleneck of slow, error-prone inversion in inversion-based watermarking for diffusion models. It observes that (i) DDIM inversion trajectories have substantially lower curvature than generation trajectories and are therefore more compressible to low NFE, and (ii) external channel distortions dominate internal truncation error for watermark verification, so high-NFE optimization of internal error is misaligned. Building on this, FARI collapses inversion to a single step via LoRA fine-tuning of the denoiser, trained end-to-end with an MSE objective to the ground-truth initial noise under a suite of random distortions. LoRA is activated only at extraction, preserving generation quality. Empirically, after ~20 minutes of training on one A6000, one-step FARI outperforms 50-step DDIM (and several specialized inversion and acceleration baselines) on Gaussian Shading bit accuracy and Tree-Ring TPR@FPR=1e-3 across nine distortions on SD v1.5 and v2.1, with additional generalization and ablation studies.

Significance. If the empirical claims hold, FARI removes a major deployment obstacle for inversion-based watermarks by simultaneously cutting NFE from 50 to 1 and improving robustness under realistic distortions, at negligible fine-tuning cost and without degrading the base generator. The geometric asymmetry observation (lower inversion curvature) and the dual-benefit argument (speed enabling end-to-end adversarial training) are useful conceptual contributions beyond the specific method. Strengths include public code and pretrained models, head-to-head evaluation on two watermark schemes with their native metrics, multiple strong baselines (EDICT, BELM, ExactDPM, adversarially-tuned AMED, LCM-LoRA, DMD2), and ablations on LoRA rank, training NFE, unseen distortions, guidance scales, and samplers. Limitations (ODE-only; poor clean reconstruction PSNR; unsuitable for editing) are stated and orthogonal to the watermark-extraction claim.

major comments (2)
  1. [§3.1, Figure 1] §3.1 and Fig. 1 (middle): the curvature comparison that motivates one-step compressibility is measured on clean, unconditional trajectories. While Fig. 1 (right), Fig. 3, Table 1, and Fig. 5 (left) provide strong downstream empirical support that external error remains dominant after consolidation, the manuscript would be tighter if it also reported discrete curvature (or an analogous local linearity measure) on trajectories starting from distorted latents, to directly confirm that the geometric premise survives the channel noise the method is designed to handle.
  2. [§3.2, Equations (9)–(10)] §3.2, Eqs. (9)–(10): setting the one-step inversion timestep to t=0 (rather than t=T) is presented as an empirical fix for the violated piecewise-linear assumption. The appendix ablation (Table 10) shows t≈0 is best, but the main text should briefly state the range of t that works and whether this choice interacts with the noise schedule or VAE latent scale, so that the one-step map is reproducible without consulting the appendix.
minor comments (6)
  1. [Figure 1] Figure 1 (left) PCA visualization of inversion residual is helpful but the caption should note the number of components retained and whether the same projection is used for generation vs. inversion for fair visual comparison.
  2. [Table 1] Table 1: the Adv. column is not defined in the main caption; clarify whether it is a fixed composite attack or the average over the training distortion set.
  3. [§4.1] §4.1: state explicitly that training is watermark-agnostic (MSE to z_T) so that the same LoRA applies to both Tree-Ring and Gaussian Shading; this is clear from the method but easy to miss.
  4. [§4 / Appendix E.1] Appendix E.1 inference-time note on LoRA overhead and merge strategy is important for practitioners; a one-sentence pointer in the main efficiency discussion would help.
  5. [Throughout] Typos / formatting: title line breaks (“ROBUSTONE-STEP”, “WATER-MARKING”); “T olerance” in Fig. 1; occasional missing spaces before citations. Minor polish only.
  6. [§5] §5 Limitations correctly flags SDE failure and editing unsuitability; consider one sentence on whether multi-step FARI (NFE=2–4) could recover intermediate features for editing without losing the robustness gains (Fig. 5 right already suggests NFE>1 does not help watermark metrics).

Circularity Check

0 steps flagged

No significant circularity: empirical method paper; training MSE and curvature measurements do not force downstream watermark metrics.

full rationale

FARI is a systems/method paper, not a first-principles derivation. The load-bearing chain is: (i) discrete curvature of DDIM inversion trajectories is measured lower than generation (Fig. 1 middle, κ_t = θ_t/s_t on unconditional SD trajectories); (ii) therefore a one-step map is trainable; (iii) LoRA is optimized by MSE to ground-truth initial noise under random distortions (Eq. 11); (iv) success is reported on external watermark metrics (GS bit accuracy; TR TPR@FPR=1e-3) against independent inversion baselines (DDIM, EDICT, BELM, ExactDPM, AMED, LCM-LoRA, DMD2) in Table 1. None of these steps is definitional or algebraically forced: the MSE objective does not determine bit-accuracy or TPR; curvature plots are measurements, not fits to watermark scores; and evaluation uses held-out prompts and distortion intensities (Fig. 4) plus partial transfer to unseen distortions (Fig. 5 left). Self-use of Gaussian Shading (overlapping authors) is only as one of two downstream benchmarks alongside independent Tree-Ring; it does not justify the method or uniqueness of the result. Limitations (ODE-only; poor clean PSNR) are stated openly. No self-definitional loop, fitted-as-prediction, or load-bearing self-citation chain exists.

Axiom & Free-Parameter Ledger

5 free parameters · 5 axioms · 1 invented entities

Load-bearing content is empirical ML practice plus standard diffusion/DDIM mathematics. No new physical entities. Free parameters are ordinary training and architecture choices; axioms are domain assumptions of latent diffusion watermarking under ODE samplers.

free parameters (5)
  • LoRA rank r = 8 (default)
    Chosen low-rank adapter size; default r=8, ablated down to 1 with small gains beyond rank 1.
  • Inversion timestep t in one-step map = t=0
    Set to t=0 (not t=T) because piecewise-linear assumption fails in one step; ablated vs 100/500/999.
  • Adversarial distortion set T and intensities = 9 types (Fig. 6)
    Nine hand-chosen augmentations (JPEG QF=25, 60% crop, 80% drop, resize, blurs, noises, brightness) define the robustness objective.
  • Training hyperparameters (lr, steps, batch, gen NFE) = 1e-4 / 1000 / 4 / 20
    lr 1e-4, 1000 steps, batch 4, 20-step DDIM generation during training; early stop ~300 steps already beats baseline.
  • Guidance and sampler settings for gen/eval = gs=7.5, NFE_gen=50
    Default guidance 7.5, 50-step DDIM generation; unconditional inversion (scale 1.0, null prompt).
axioms (5)
  • domain assumption DDIM defines a deterministic invertible ODE path so z_T can be recovered from z_0 in the clean case (Song et al. 2021).
    Foundation of all inversion-based watermarks used as downstream tasks (§2.2–2.3).
  • ad hoc to paper External channel distortions dominate accumulated inversion error relative to internal truncation error in the watermark setting.
    Core motivation for abandoning high-NFE internal-error optimization; supported empirically (Fig. 3, Table 1) but not proved in general (§1, §3).
  • ad hoc to paper Discrete trajectory curvature κ_t = θ_t / s_t is a valid proxy for numerical compressibility / truncation-error tolerance.
    Used to justify one-step distillation from Fig. 1 measurements (Appendix C); standard numerical-analysis intuition, not a theorem for neural ODE denoisers.
  • domain assumption Watermark verification metrics (GS bit accuracy; TR TPR at FPR=1e-3) are the right success criteria; clean PSNR/editing fidelity may be sacrificed.
    Explicit task choice; paper reports clean PSNR drop (Table 2) and editing incompatibility (§5).
  • domain assumption ODE-based sampling is used at generation and inversion time; SDE samplers are out of scope.
    Stated limitation (§5); method fails under SDE sampling by construction of DDIM-style invertibility.
invented entities (1)
  • FARI one-step asymmetric robust inverter (LoRA-augmented denoiser map) independent evidence
    purpose: Collapse multi-step DDIM inversion to one NFE and absorb distortion-induced error via adversarial LoRA.
    Engineering construct (Eqs. 9–11, Fig. 2), not a new physical object; independent evidence is the public code and reported tables.

pith-pipeline@v1.2.0-daily-grok45 · 38157 in / 3666 out tokens · 74651 ms · 2026-07-30T22:56:36.398109+00:00 · methodology

0 comments
read the original abstract

Inversion-based watermarking is a promising approach to authenticate diffusion-generated images, yet practical use is bottlenecked by inversion that is both slow and error-prone. While the primary challenge in the watermarking setting is robustness against external distortions, existing approaches over-optimize internal truncation error, and because that error scales with the sampler step size, they are inherently confined to high-NFE (number of function evaluations) regimes that cannot meet the dual demands of speed and robustness. In this work, we have two key observations: (i) the inversion trajectory has markedly lower curvature than the forward generation path does, making it highly compressible and amenable to low-NFE approximation; and (ii) in inversion for watermark verification, the trade-off between speed and truncation error is less critical, since external distortions dominate the error. A faster inverter provides a dual benefit: it is not only more efficient, but it also enables end-to-end adversarial training to directly target robustness, a task that is computationally prohibitive for the original, lengthy inversion trajectories. Building on this, we propose \textbf{FARI} (\textbf{F}ast \textbf{A}symmetric \textbf{R}obust \textbf{I}nversion), a one-step inversion framework paired with lightweight adversarial LoRA fine-tuning of the denoiser for watermark extraction. While consolidation slightly increases internal error, FARI delivers large gains in both speed and robustness: with approximately 20 minutes of fine-tuning on a single NVIDIA RTX A6000 GPU, it surpasses 50-step DDIM inversion on watermark-verification robustness while dramatically reducing inference time. Code and pretrained models are available at https://github.com/0xD009/FARI.

Figures

Figures reproduced from arXiv: 2607.26723 by Han Fang, Jindong Yang, Kejiang Chen, Nenghai Yu, Weiming Zhang.

Figure 1
Figure 1. Figure 1: Left: Visualization of the inversion error, where latent vectors are projected down to 3 channels via PCA for display. Middle: Curvature of generation and inversion trajectories across diffusion timesteps. Discrete curvature estimated using 100 unconditionally generated images from Stable Diffusion v2.1. Right: The resulting error for generation and inversion when reducing the NFE, compared with a 50-step … view at source ↗
Figure 2
Figure 2. Figure 2: The framework of FARI. FARI simultaneously performs one-step distillation and adver [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Inversion trajectory error of FARI and DDIM on clean and JPEG￾compressed images. Specifically, we fine-tune the denoising network of the diffusion model using Low-Rank Adaptation (LoRA) (Hu et al., 2022), a parameter-efficient fine-tuning technique that updates pretrained weight matrices through low-rank decomposition. Given a weight matrix W0 ∈ R d×k , the update is represented as W0 +∆W = W0 +BA, where B… view at source ↗
Figure 4
Figure 4. Figure 4: The results of experiments on various guidance scales and noise intensities. [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Left: Performance of FARI when trained under different noise settings, measured in mean squared error (MSE), where lower values are better. Middle: The effect of different LoRA ranks on FARI’s performance. Right: The impact of using a higher NFE for end-to-end training on FARI’s final performance. 4.4 ABLATION STUDY In this section, we present ablation studies of FARI’s training settings. LoRA Ranks. We ex… view at source ↗
Figure 6
Figure 6. Figure 6: Visualization of the distortion set used in our experiments. (a) Clean image or identity [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Left: The curvature of the conditional generation, unconditional inversion, and conditional inversion trajectories over the diffusion timesteps. Right: The curvature of the generation, inversion, and regeneration trajectories over the diffusion timesteps. 0 200 400 600 800 1000 Timestep 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Curvature Curvature over Sampling (COCO dataset) generation w/ condition inversion w/ conditi… view at source ↗
Figure 8
Figure 8. Figure 8: Left: The curvature of the the conditional generation, unconditional inversion, and condi￾tional inversion trajectories over the diffusion timesteps, measured using SD v2.1 on the COCO dataset . Right: The curvature of the generation and inversion trajectories over the diffusion timesteps, measured using SD v3.5M. D EXTENDED APPLICATION ON IMAGE RECONSTRUCTION AND EDITING Although FARI is specifically desi… view at source ↗
Figure 9
Figure 9. Figure 9: The loss tendency and tested performance on downstream watermarking tasks over the [PITH_FULL_IMAGE:figures/full_fig_p018_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Performance of DDIM inversion and FARI under different attacks. [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: FARI reduces reconstruction error, especially under distortion. Naive DDIM single-step [PITH_FULL_IMAGE:figures/full_fig_p022_11.png] view at source ↗

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

41 extracted references · 19 linked inside Pith

  1. [1]

    Variational image compression with a scale hyperprior.arXiv preprint arXiv:1802.01436,

    Johannes Ball´e, David Minnen, Saurabh Singh, Sung Jin Hwang, and Nick Johnston. Variational image compression with a scale hyperprior.arXiv preprint arXiv:1802.01436,

    Pith/arXiv arXiv

  2. [2]

    While dis- abling adversarial training (FARI Cln) boosts FARI’s precision on clean inversions, a gap remains when compared to leading editing-oriented methods. Regarding image editing, many techniques require attention map or feature sharing during the intermediate steps of the inversion and regen- eration process to ensure high consistency. As our one-st...

    2022

  3. [4]

    Prompt-to-prompt image editing with cross attention control.arXiv preprint arXiv:2208.01626,

    Amir Hertz, Ron Mokady, Jay Tenenbaum, Kfir Aberman, Yael Pritch, and Daniel Cohen-Or. Prompt-to-prompt image editing with cross attention control.arXiv preprint arXiv:2208.01626,

    Pith/arXiv arXiv

  4. [7]

    Direct inversion: Boosting diffusion-based editing with 3 lines of code.arXiv preprint arXiv:2310.01506,

    Xuan Ju, Ailing Zeng, Yuxuan Bian, Shaoteng Liu, and Qiang Xu. Direct inversion: Boosting diffusion-based editing with 3 lines of code.arXiv preprint arXiv:2310.01506,

    Pith/arXiv arXiv

  5. [8]

    Gaussmarker: Robust dual-domain watermark for diffusion models.arXiv preprint arXiv:2506.11444,

    Kecen Li, Zhicong Huang, Xinwen Hou, and Cheng Hong. Gaussmarker: Robust dual-domain watermark for diffusion models.arXiv preprint arXiv:2506.11444,

    Pith/arXiv arXiv

  6. [9]

    Microsoft coco: Common objects in context

    Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll´ar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. InComputer vision–ECCV 2014: 13th European conference, zurich, Switzerland, September 6-12, 2014, pro- ceedings, part v 13, pp. 740–755. Springer,

    2014

  7. [11]

    Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps

    11 Published as a conference paper at ICLR 2026 Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan LI, and Jun Zhu. Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps. InAdvances in Neural Information Processing Systems, volume 35, pp. 5775–5787, 2022a. Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li,...

    Pith/arXiv arXiv 2026

  8. [12]

    Lcm-lora: A universal stable-diffusion acceleration module

    Simian Luo, Yiqin Tan, Suraj Patil, Daniel Gu, Patrick von Platen, Apolin ´ario Passos, Longbo Huang, Jian Li, and Hang Zhao. Lcm-lora: A universal stable-diffusion acceleration module. arXiv preprint arXiv:2311.05556,

    Pith/arXiv arXiv

  9. [13]

    Negative-prompt inversion: Fast image inversion for editing with text-guided diffusion models

    Daiki Miyake, Akihiro Iohara, Yu Saito, and Toshiyuki Tanaka. Negative-prompt inversion: Fast image inversion for editing with text-guided diffusion models. In2025 IEEE/CVF Winter Con- ference on Applications of Computer Vision (WACV), pp. 2063–2072. IEEE,

    2063

  10. [14]

    Black-box forgery attacks on semantic watermarks for diffusion models.arXiv preprint arXiv:2412.03283,

    Andreas M ¨uller, Denis Lukovnikov, Jonas Thietke, Asja Fischer, and Erwin Quiring. Black-box forgery attacks on semantic watermarks for diffusion models.arXiv preprint arXiv:2412.03283,

    Pith/arXiv arXiv

  11. [15]

    Sdxl: Improving latent diffusion models for high-resolution image synthesis.arXiv preprint arXiv:2307.01952,

    Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas M ¨uller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion models for high-resolution image synthesis.arXiv preprint arXiv:2307.01952,

    Pith/arXiv arXiv

  12. [16]

    Progressive distillation for fast sampling of diffusion models.arXiv preprint arXiv:2202.00512,

    Tim Salimans and Jonathan Ho. Progressive distillation for fast sampling of diffusion models.arXiv preprint arXiv:2202.00512,

    Pith/arXiv arXiv

  13. [17]

    Lightning-fast image inversion and editing for text-to-image diffusion models

    Dvir Samuel, Barak Meiri, Haggai Maron, Yoad Tewel, Nir Darshan, Shai Avidan, Gal Chechik, and Rami Ben-Ari. Lightning-fast image inversion and editing for text-to-image diffusion models. arXiv preprint arXiv:2312.12540,

    Pith/arXiv arXiv

  14. [20]

    There and back again: On the relation between noise and image inversions in diffusion models.arXiv preprint arXiv:2410.23530,

    Łukasz Staniszewski, Łukasz Kuci ´nski, and Kamil Deja. There and back again: On the relation between noise and image inversions in diffusion models.arXiv preprint arXiv:2410.23530,

    arXiv

  15. [21]

    Edict: Exact diffusion inversion via coupled trans- formations

    12 Published as a conference paper at ICLR 2026 Bram Wallace, Akash Gokul, and Nikhil Naik. Edict: Exact diffusion inversion via coupled trans- formations. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recog- nition, pp. 22532–22541,

    2026

  16. [22]

    Tree-ring watermarks: Fin- gerprints for diffusion images that are invisible and robust.arXiv preprint arXiv:2305.20030,

    Yuxin Wen, John Kirchenbauer, Jonas Geiping, and Tom Goldstein. Tree-ring watermarks: Fin- gerprints for diffusion images that are invisible and robust.arXiv preprint arXiv:2305.20030,

    Pith/arXiv arXiv

  17. [23]

    Gaussian shading++: Rethinking the realistic deployment challenge of performance-lossless image watermark for diffusion models.arXiv preprint arXiv:2504.15026,

    Zijin Yang, Xin Zhang, Kejiang Chen, Kai Zeng, Qiyi Yao, Han Fang, Weiming Zhang, and Nenghai Yu. Gaussian shading++: Rethinking the realistic deployment challenge of performance-lossless image watermark for diffusion models.arXiv preprint arXiv:2504.15026,

    Pith/arXiv arXiv

  18. [24]

    Improved distribution matching distillation for fast image synthesis.Advances in neural information processing systems, 37:47455–47487, 2024a

    Tianwei Yin, Micha ¨el Gharbi, Taesung Park, Richard Zhang, Eli Shechtman, Fredo Durand, and Bill Freeman. Improved distribution matching distillation for fast image synthesis.Advances in neural information processing systems, 37:47455–47487, 2024a. Tianwei Yin, Micha¨el Gharbi, Richard Zhang, Eli Shechtman, Fredo Durand, William T Freeman, and Taesung Pa...

    Pith/arXiv arXiv

  19. [25]

    Fast ode-based sampling for diffusion models in around 5 steps

    Zhenyu Zhou, Defang Chen, Can Wang, and Chun Chen. Fast ode-based sampling for diffusion models in around 5 steps. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7777–7786, 2024a. Zhenyu Zhou, Defang Chen, Can Wang, Chun Chen, and Siwei Lyu. Simple and fast distillation of diffusion models.Advances in Neural Infor...

    2026

  20. [26]

    Notably, these methods do not actually change the result of the initial inversion; their focus is on the reconstruction phase

    also follows this path by decoupling the source and target diffusion branches. Notably, these methods do not actually change the result of the initial inversion; their focus is on the reconstruction phase. Therefore, their performance in a watermarking scenario is nearly identical to that of standard DDIM inversion, and we do not include them in our quant...

    2023

  21. [27]

    However, their design carries an implicit assumption that the entire process is free from external error

    modify the sampler to achieve a smaller theoretical error bound. However, their design carries an implicit assumption that the entire process is free from external error. This holds true for reconstruction but is clearly violated in watermarking, where the image is subject to various distortions after generation that can push it out of the method’s conver...

    2025

  22. [28]

    module. However, its purpose is different: it is designed to adapt to various user-personalized models, enabling accelerated sampling without requiring a separate distillation for each fine-tuned model. DMD2 (Yin et al., 2024a), on the other hand, uses distribution matching distillation. It does not directly learn the teacher’s output but rather its targe...

    2022

  23. [29]

    trailing

    watermarks, we use the official open-source code provided by the authors on GitHub. We use Tree-Ring in itsrandmode, with the watermark embedded in the fourth channel of the latent space. For Gaussian Shading, we adopt the default settings (f ch = 4, fh = 8, fw = 8, for a 256-bit capacity) and use a stream cipher to encrypt the watermark message. Baseline...

    2021

  24. [30]

    The NFE is set to 50 steps, and EDICT’s hyperparameterpis set to 0.93 as recom- mended in the original paper

    and BELM (Wang et al., 2024), we use the implementation pro- vided by BELM. The NFE is set to 50 steps, and EDICT’s hyperparameterpis set to 0.93 as recom- mended in the original paper. For ExactDPM (Hong et al., 2024), we use the official implementation with the Backward Euler method for inversion. To ensure a fair and efficient comparison, we disable th...

    2024

  25. [31]

    We set LCM-LoRA to use two steps (within its allowed 2-8 range) and use a single step for DMD2, which has one-step sampling capabilities

    and DMD2 (Yin et al., 2024a), we use the officially provided weights. We set LCM-LoRA to use two steps (within its allowed 2-8 range) and use a single step for DMD2, which has one-step sampling capabilities. 15 Published as a conference paper at ICLR 2026 Training and Evaluation.For FARI’s training, we use a batch size of 4 with the Adam opti- mizer and a...

    2026

  26. [32]

    (d) 80% area Random Drop (R.Drop)

    (c) 60% area Random Crop (R.Crop). (d) 80% area Random Drop (R.Drop). (e) 25% Resize and restore (Resize). (f) Gaussian Blur,r= 4 (G.Blur). (g) Median Blur,k= 7 (M.Blur). (h) Gaussian Noise,µ= 0,σ= 0.05 (G.Noise). (i) Salt and Pepper Noise,p= 0.05 (S&P). (j) Brightness,f actor= 6 (Bright). C DETAILS ABOUT THECURVATUREEVALUATION. In Section 3.1, we measure...

    2022

  27. [33]

    All other settings remain identical to our main experiments

    For con- ditional generation, we use prompts from the Stable-Diffusion-Prompts dataset. All other settings remain identical to our main experiments. First, we used conditional generation to create an image, and then compared the curvature of the original generation trajectory against two types of inversion: one that was conditionally-aligned and one that ...

    2024

  28. [34]

    0 200 400 600 800 1000 Timestep 0.0 0.5 1.0 1.5 2.0 2.5 3.0Curvature Curvature over Sampling (SDP dataset) generation w/ condition inversion w/ condition inversion w/o condition 0 200 400 600 800 1000 Timestep 0 2 4 6 8 10Curvature Curvature over Sampling generation inversion regeneration Figure 7:Left: The curvature of the conditional generation, uncondi...

    2026

  29. [36]

    While this introduces latency, the total time remains less than that of a two-step inversion

    modules affects the denoiser’s inference efficiency, with the average time per function evaluation on an NVIDIA RTX A6000 GPU increasing from 0.0247 s to 0.0358 s. While this introduces latency, the total time remains less than that of a two-step inversion. To eliminate this overhead during inference, a practical solution is to merge the LoRA weights byW ...

    2026

  30. [37]

    Since SD v3.5M does not support DDIM sampler, we implement inver- sion for this model via naive Euler Method

    and SDXL- Turbo (Podell et al., 2023). Since SD v3.5M does not support DDIM sampler, we implement inver- sion for this model via naive Euler Method. SDXL-Turbo inherently supports one-step generation, so we compare the performance of one-step DDIM inversion against FARI. Given that SD v3.5 has 16 latent channels, we setf ch = 2andf h =f w = 8for Gaussian ...

    2023

  31. [39]

    We therefore opted for the more parameter- efficient default setting

    Although training more modules yields a marginal improvement, the increase is not significant. We therefore opted for the more parameter- efficient default setting. /uni0000003d/uni0000004b/uni00000044/uni00000052/uni00000015/uni00000013/uni00000015/uni00000017/uni00000046/uni0000004b/uni00000048/uni00000051/uni0000004a/uni00000015/uni00000013/uni00000015...

    2026

  32. [40]

    and Tree-Ring (Wen et al., 2023), we directly measure and report the MSE of FARI’s reconstructed latent noise in Table 11, with visual results shown in Fig- ure

    2023

  33. [41]

    DM Methods NFE Clean Adv

    Table 11: The MSE of inversion methods under various image distortions. DM Methods NFE Clean Adv. Jpeg R.Crop R.Drop Resize G.Blur M.Blur G.Noise S&P Bright SD v1.5 DDIM 50 0.20030.92850.8740 0.8734 0.8834 0.7671 0.9706 0.6920 1.1860 1.2963 0.81391 0.7108 0.90160.8981 0.9646 0.9621 0.8294 0.8987 0.8157 0.9067 0.9316 0.9074EDICT 50 0.2646 1.04771.0400 1.04...

    arXiv 2065

  34. [300]

    (2020) and Ball´e et al

    For Cheng et al. (2020) and Ball´e et al. (2018), we set the quality parameter to 3 (representing the highest attack strength). For the adversarial optimization attack (Lukas et al., 2023), we use the following settings:ϵ= 4/255, Adam optimizer, learning rate of 0.01, and 5 optimization steps per image, all of which are con- sistent with the default confi...

    2020

  35. [2014]

    Pseudo numerical methods for diffusion models on manifolds.arXiv preprint arXiv:2202.09778,

    Luping Liu, Yi Ren, Zhijie Lin, and Zhou Zhao. Pseudo numerical methods for diffusion models on manifolds.arXiv preprint arXiv:2202.09778,

    Pith/arXiv arXiv

  36. [2020]

    Consistency models.arXiv preprint arXiv:2303.01469,

    Yang Song, Prafulla Dhariwal, Mark Chen, and Ilya Sutskever. Consistency models.arXiv preprint arXiv:2303.01469,

    Pith/arXiv arXiv

  37. [2021]

    Score-based generative modeling through stochastic differential equations.arXiv preprint arXiv:2011.13456,

    Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations.arXiv preprint arXiv:2011.13456,

    Pith/arXiv arXiv 2011

  38. [2022]

    Classifier-free diffusion guidance.arXiv preprint arXiv:2207.12598,

    Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance.arXiv preprint arXiv:2207.12598,

    Pith/arXiv arXiv

  39. [2023]

    Scaling rectified flow transformers for high-resolution image synthesis

    10 Published as a conference paper at ICLR 2026 Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas M ¨uller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling rectified flow transformers for high-resolution image synthesis. InForty-first international conference on machine learning,

    2026

  40. [2024]

    An undetectable watermark for generative image models.arXiv preprint arXiv:2410.07369,

    Sam Gunn, Xuandong Zhao, and Dawn Song. An undetectable watermark for generative image models.arXiv preprint arXiv:2410.07369,

    Pith/arXiv arXiv

  41. [2025]

    Huayang Huang, Yu Wu, and Qian Wang

    URLhttps: //arxiv.org/abs/2501.14195. Huayang Huang, Yu Wu, and Qian Wang. Robin: Robust and invisible watermarks for diffusion models with adversarial optimization.Advances in Neural Information Processing Systems, 37: 3937–3963,

    Pith/arXiv arXiv