Pith. sign in

REVIEW 3 major objections 5 minor 36 references

FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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

Pith's one-line read Probabilistic per-token Gaussian noise injected during fine-tuning reduces replication in text-to-image diffusion models by 28.78% on average while keeping image quality at or better than baseline.

desk verdict Plausible incremental mitigation method, but the headline 28.78% reduction is a max, not an average, and hyperparameters are tuned on the same evaluation set used for the final numbers. read the letter →

arxiv 2505.21848 v1 pith:NVPD3N2U submitted 2025-05-28 cs.CV

Jingqi Xu , Chenghao Li , Yuke Zhang , Peter A. Beerel This is my paper
classification cs.CV
keywords diffusionmodelsreplicationmitigationtokenembeddingsprobabilisticnoiseinjectionfine-tuningtext-to-imagegenerationmemorizationimagequality
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

To curb the tendency of text-to-image diffusion models to copy training images, this paper proposes a fine-tuning intervention called Fine-grained Probabilistic Addition of Noise (FPAN). The method adds Gaussian noise of a chosen intensity to each token embedding independently, with a fixed probability, during every training step, rather than adding noise to the whole caption or to the image. The paper's central claim is that this simple intervention lowers the replication score by an average of 28.78% relative to the baseline, beats the prior consistent-magnitude noise-addition approach by 26.51%, and preserves or improves FID. A sympathetic reader would care because FPAN is a training-time-only change that leaves the model architecture untouched and composes with existing mitigation methods to reach replication scores as low as 0.357.

What carries the argument

The load-bearing object is the FPAN noise distribution $\xi_i \sim z_i \cdot \mathcal{N}(0, W^2 I)$ with $z_i \sim \mathrm{Bernoulli}(P)$, applied to each token embedding $\tau_i$ of the CLIP text embedding. The paper shows that this scheme preserves the mean of the token-embedding distribution (keeping caption semantics intact) while inflating its variance (reducing the frequency of identical embeddings the model sees), and contrasts it with Random Masking, which shrinks both mean magnitude and variance. The R-FID curve, fitted to measured (R, FID) pairs as the probability P varies, is the instrument used to select W=1.7 and the favorable P range 0.3-0.6.

What would settle it

Fine-tune the same Stable Diffusion 2.1 setup on a held-out split of the same 10,000-image LAION subset with W=1.7, P=0.6 and several random seeds, and measure R and FID on prompts not used in hyperparameter selection; if the mean replication score is not clearly below the baseline's 0.615 (for instance, not below roughly 0.46) or the FID is not within a small margin of 18.24, the central claim fails.

Watch

Extended reading notes

Core claim

The paper's central claim is that the effect of token-embedding noise on a fine-tuned diffusion model is not a monotonically degrading trade-off; it has three stages. At low noise intensities the model overfits, at intermediate intensities it is 'well-fitting' with the lowest FID local minimum, and at high intensities it underfits. FPAN takes the largest noise intensity that still belongs to the well-fitting stage (W=1.7) and applies it per-token with a Bernoulli probability P, so that a fraction of tokens get strong noise while the rest are unchanged. In the paper's experiments, this yields a replication score of R=0.438 at W=1.7, P=0.6 versus R=0.615 for the baseline, with FID improving from 18.24 to 17.96; the same procedure combines with LoyalDiffusion to reach R=0.357.

Load-bearing premise

The load-bearing assumption is that the W=1.7 and P in {0.3, 0.4, 0.5, 0.6} settings, chosen by inspecting R-FID curves computed with the same evaluation that produces the headline results, generalize beyond those curves; the paper provides no held-out validation, error bars, or multiple-seed variation.

Editorial extensions

If this is right

  • At W=1.7 with P=0.6, replication falls from 0.615 to 0.438 while FID improves from 18.24 to 17.96, so the method has a setting that is better than baseline on both metrics.
  • Because FPAN beats the consistent-magnitude Gaussian noise baseline by 26.51%, the probabilistic per-token mechanism, not simply adding more noise, is what drives the improvement.
  • Combining FPAN with prior mitigation methods yields further replication reductions (up to 16.82%) and reaches R=0.357 when paired with LoyalDiffusion, showing the interventions are complementary.
  • FPAN requires no architectural change and touches only the text-embedding input during fine-tuning, so it can be layered onto existing checkpoints and training pipelines.
  • The well-fitting noise window (0.7 < W <= 1.7) offers a concrete operating range for practitioners who want replication mitigation without FID degradation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The headline 28.78% reduction is the best of four reported probabilities; the average over P=0.3, 0.4, 0.5, 0.6 is about 25.9%, so a conservative restatement is 'roughly a quarter reduction'.
  • If the three-stage overfitting/well-fitting/underfitting curve is real, then W and P need not be fixed: a training curriculum that anneals noise intensity downward as training progresses could reach the same replication suppression with even better final FID.
  • A natural next experiment is token-level importance weighting, perturbing the tokens most responsible for replication more aggressively; the paper notes this would require token-level importance estimation.
  • The mean-preserving property suggests FPAN should be tested on datasets with heavy caption duplication; on already-deduplicated data the achievable replication reduction may be smaller.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. FPAN is a fine-tuning-time intervention for text-to-image diffusion models: at each training iteration, each token embedding independently receives Gaussian noise of intensity W with probability P, otherwise remains unchanged. The paper first studies deterministic noise intensity W, identifies overfitting/well-fitting/underfitting regimes from polynomial fits of R and FID, and then proposes probabilistic injection with W=1.7 and P in {0.3,...,0.6}. On a 10,000-image LAION subset with Stable Diffusion 2.1, the authors report replication score reductions relative to baseline (R=0.438 at P=0.6 versus 0.615) with FID slightly improved, and further reductions when FPAN is combined with existing methods. The main quantitative claim in the abstract is an average 28.78% replication reduction.

Significance. If validated, FPAN is an attractive mitigation method because it is simple, architecture-agnostic, and combines with existing techniques; the three-stage FID/R characterization and the probabilistic mechanism provide a plausible explanation for why occasional high-intensity noise can outperform uniform small noise. The ablation against CPAN and RM is thoughtful, and the Appendix A.3 mean/variance derivation gives a concrete mechanistic rationale. However, the paper's central quantitative evidence is currently single-run, and the operating point is chosen from the same evaluation data, so the magnitude of the effect and its generalizability are not yet established.

major comments (3)
  1. [Sec. 4.2 / Table 2 / Fig. 3] The headline result is not robustly supported because W=1.7 and P in {0.3,...,0.6} are selected by inspecting R-FID curves computed from the same 10,000 generated images later used to report the final numbers, and no held-out split, multiple seeds, or error bars are provided. The reported R values at the four selected operating points (0.491, 0.452, 0.452, 0.438) differ by at most 0.053, which is comparable to the run-to-run variation observed in fine-tuning studies of this scale; selection on the evaluation set can therefore account for a material part of the claimed 28.78% reduction. Similarly, the stage boundaries in Figure 1 are inferred from third-order polynomial fits to the same runs. Please add a validation protocol (separate search and evaluation sets, several seeds, and confidence intervals) or explicitly present the numbers as single-run, exploratory findings.
  2. [Abstract / Sec. 4.3 / Table 2] The abstract's 'average of 28.78%' is not derivable from the reported data. For W=1.7 with P=0.3, 0.4, 0.5, and 0.6, the relative reductions in R are approximately 20.2%, 26.5%, 26.5%, and 28.8%, whose mean is about 25.5%; the 28.78% figure is the maximum, obtained only at P=0.6. The body text correctly says 'up to 28.78%' in Section 4.3, so the abstract overstates the result. Please correct the abstract and any other summary statements.
  3. [Sec. 3.2 / Fig. 1] The classification into overfitting, well-fitting, and underfitting stages relies on a single threshold tau_CLIP computed from one pretrained reference model, and the boundaries W=0.7 and W=1.7 are asserted from fitted curves without uncertainty quantification. Since W=1.7 is then used as the fixed intensity for FPAN, the stage analysis is load-bearing for the method's design. A sensitivity analysis of the boundary locations, or an independent criterion for the well-fitting regime, would strengthen this part of the paper.
minor comments (5)
  1. [Sec. 3.1 / Sec. 3.2 / Title] There are typographical errors: 'embeddigns' in Section 3.1, 'exprimental' in Section 3.2, and an unwanted space in 'F ine-Grained' in the title.
  2. [Sec. 4.3] The sentence beginning 'The above findings suggest that our method outperforms most other methods, when deployed as a standalone strategy, offers similar if not improved trade-off' has a grammatical break and should be rephrased.
  3. [Fig. 1] Figure 1 would benefit from showing raw data points with confidence intervals or error bars in addition to the polynomial fits, because the stage boundaries are read off the fitted curves.
  4. [References] The reference list contains a formatting error in [13], where page numbers '3, 1' appear; please check all citation metadata for similar issues.
  5. [Table 1 / Fig. 3] The labels A1 through A4 are defined only in the caption of Figure 3, but Table 1 references them first; please define the labels in Table 1 as well, or add a note to Table 1.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: FPAN's replication reductions are direct empirical measurements; W/P selection on the same evaluation set is a validation concern, not a circular reduction.

full rationale

The paper's claimed effect is empirical: FPAN changes the training-time token-embedding distribution (Eqs. 4-6) and the paper reports measured R and FID on 10,000 generated images. There is no equation in which the conclusion is algebraically identical to an input. The three-stage 'overfitting/well-fitting/underfitting' interpretation and the choice W=1.7, P in {0.3,...,0.6} are inferred from R-FID curves on the same evaluation protocol that later yields the headline reductions (Section 4.2, Figure 3, Table 1); this is hyperparameter selection on the evaluation set, a validity threat, not circular reasoning. The abstract's 'average of 28.78%' is not the mean of the Table 2 relative reductions (which is about 25.5%) and conflicts with the body's 'up to 28.78%'; this is an internal numerical inconsistency, not a circular derivation. The paper cites its own prior work [14,15] for the replication-score definition and uses those methods as baselines, but it does not rely on them as proof of FPAN's effectiveness; SSCD and CLIP are external. Hence no step reduces to its own inputs.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The central empirical claim depends on two tuned hyperparameters (W and P), which are selected by inspecting R-FID curves computed on the same evaluation protocol that produces the headline numbers. No new physical or conceptual entities are introduced.

free parameters (2)
  • W (noise intensity) = 1.7 (final); 1.5 and 1.6 also tested
    Chosen by inspecting R-FID curves on the same evaluation protocol that produces the headline numbers. Section 4.2 and Table 1.
  • P (probability of adding noise to each token) = 0.3 to 0.6 (reported points); swept from 0 to 1.0
    Selected as favorable points on the W=1.7 R-FID curve; no held-out validation or error bars reported. Section 4.2 and Table 1.
assumptions (3)
  • domain assumption Replication score R, computed as the 95th percentile of SSCD dot-product similarity to the training set, is a valid proxy for unwanted replication.
    Section 2.2, Eqs. (2)-(3). All headline claims are quantified through R; if R misses the copying behavior that matters for privacy, the measured mitigations are not meaningful.
  • domain assumption The pretrained Stable Diffusion 2.1 model's SimCLIP against the fine-tuning set (threshold 0.472) marks the boundary between overfitting and well-fitting for this training setup.
    Section 3.2 and Figure 1(c). The three-stage interpretation and the choice of W=1.7 as the end of the 'well-fitting' stage rely on this threshold.
  • domain assumption Training-time noise injection to text embeddings preserves semantic alignment because the mean of token embeddings is unchanged, and mean preservation implies quality preservation.
    Appendix A.3, Eqs. (7)-(8). The mean/variance computation is standard math, but the assumption that mean preservation maintains generation quality is a domain assumption about how CLIP embeddings behave.

how reviews work

0 comments
Cite this review

Pith. "Pith review of FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings." pith.science (2026). https://pith.science/paper/NVPD3N2U

@misc{pith2026250521848,
  author       = {Pith},
  title        = {Pith review of: FPAN: Mitigating Replication in Diffusion Models through the Fine-Grained Probabilistic Addition of Noise to Token Embeddings},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NVPD3N2U}},
  note         = {Machine review of arXiv:2505.21848}
}
read the original abstract

Diffusion models have demonstrated remarkable potential in generating high-quality images. However, their tendency to replicate training data raises serious privacy concerns, particularly when the training datasets contain sensitive or private information. Existing mitigation strategies primarily focus on reducing image duplication, modifying the cross-attention mechanism, and altering the denoising backbone architecture of diffusion models. Moreover, recent work has shown that adding a consistent small amount of noise to text embeddings can reduce replication to some degree. In this work, we begin by analyzing the impact of adding varying amounts of noise. Based on our analysis, we propose a fine-grained noise injection technique that probabilistically adds a larger amount of noise to token embeddings. We refer to our method as Fine-grained Probabilistic Addition of Noise (FPAN). Through our extensive experiments, we show that our proposed FPAN can reduce replication by an average of 28.78% compared to the baseline diffusion model without significantly impacting image quality, and outperforms the prior consistent-magnitude-noise-addition approach by 26.51%. Moreover, when combined with other existing mitigation methods, our FPAN approach can further reduce replication by up to 16.82% with similar, if not improved, image quality.

Figures

Figures reproduced from arXiv: 2505.21848 by the authors.

Figure 1
Figure 1. Results under different noise intensities. (a) [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Overview of our proposed framework. (a) Probabilistic selection of noise. (b) Generation of noisy token embeddings. (c) [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. R-FID curves corresponding to different values of [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: Comparison of R-FID curves between FPAN and RM. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 4
Figure 4. Figure 4: Comparison of R-FID curves between FPAN and CPAN [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 6
Figure 6. Figure 6: FID curve of FPAN as we vary P for three different values of W = 1.5, 1.6, 1.7. below 0.2, the number of function word embeddings sub￾jected to strong noise continues to decrease. Although func￾tion words individually encode limited semantic content, perturbing them in…
Figure 7
Figure 7. Figure 7: Comparison of token embedding distributions. (a) Orig [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

36 extracted references · 26 canonical work pages

  1. [1]

    GPT-4 technical report

    Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ah- mad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. GPT-4 technical report. arXiv preprint arXiv:2303.08774 ,

    arXiv

  2. [2]

    Extracting training data from diffu- sion models

    Nicolas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagiel- ski, Vikash Sehwag, Florian Tramer, Borja Balle, Daphne Ip- polito, and Eric Wallace. Extracting training data from diffu- sion models. In 32nd USENIX Security Symposium (USENIX Security 23), pages 5253–5270, 2023. 1

    work page 2023

  3. [3]

    Exploring local memorization in diffusion models via bright ending attention

    Chen Chen, Daochang Liu, Mubarak Shah, and Chang Xu. Exploring local memorization in diffusion models via bright ending attention. arXiv preprint arXiv:2410.21665, 2024. 3

    arXiv 2024

  4. [4]

    Towards memorization-free diffusion models

    Chen Chen, Daochang Liu, and Chang Xu. Towards memorization-free diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8425–8434, 2024. 1, 3

    work page 2024

  5. [5]

    ELECTRA: Pre-training text encoders as discriminators rather than generators

    Kevin Clark, Minh-Thang Luong, Quoc V Le, and Christo- pher D Manning. ELECTRA: Pre-training text encoders as discriminators rather than generators. arXiv preprint arXiv:2003.10555, 2020. 1

    arXiv 2003

  6. [6]

    Diffusion models beat GANs on image synthesis

    Prafulla Dhariwal and Alexander Nichol. Diffusion models beat GANs on image synthesis. Advances in neural informa- tion processing systems, 34:8780–8794, 2021. 1

    work page 2021

  7. [7]

    On the inherent regulariza- tion effects of noise injection during training

    Oussama Dhifallah and Yue Lu. On the inherent regulariza- tion effects of noise injection during training. In Interna- tional Conference on Machine Learning , pages 2665–2675. PMLR, 2021. 4

    work page 2021

  8. [8]

    Generative adversarial networks

    Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Commu- nications of the ACM, 63(11):139–144, 2020. 1

    2020

Show all 36 references
  1. [9]

    On memorization in diffusion models

    Xiangming Gu, Chao Du, Tianyu Pang, Chongxuan Li, Min Lin, and Ye Wang. On memorization in diffusion models. arXiv preprint arXiv:2310.02664, 2023. 1

    2023 arXiv

  2. [10]

    Finding NeMo: Localizing neurons responsible for memorization in diffu- sion models

    Dominik Hintersdorf, Lukas Struppek, Kristian Kersting, Adam Dziedzic, and Franziska Boenisch. Finding NeMo: Localizing neurons responsible for memorization in diffu- sion models. Advances in Neural Information Processing Systems, 37:88236–88278, 2024. 3

    2024

  3. [11]

    Denoising dif- fusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising dif- fusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020. 1, 2

    2020

  4. [12]

    An introduction to variational autoencoders

    Diederik P Kingma, Max Welling, et al. An introduction to variational autoencoders. Foundations and Trends® in Ma- chine Learning, 12(4):307–392, 2019. 1, 2

    2019

  5. [13]

    Learning to perturb word embeddings for out-of-distribution QA

    Seanie Lee, Minki Kang, Juho Lee, and Sung Ju Hwang. Learning to perturb word embeddings for out-of-distribution QA. arXiv preprint arXiv:2105.02692, 2021. 3, 1

    2021 arXiv

  6. [14]

    Mitigate replication and copying in diffusion mod- els with generalized caption and dual fusion enhancement

    Chenghao Li, Dake Chen, Yuke Zhang, and Peter A Beerel. Mitigate replication and copying in diffusion mod- els with generalized caption and dual fusion enhancement. In ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages 72...

    2024

  7. [15]

    LoyalDiffusion: A diffusion model guarding against data replication

    Chenghao Li, Yuke Zhang, Dake Chen, Jingqi Xu, and Pe- ter A Beerel. LoyalDiffusion: A diffusion model guarding against data replication. arXiv preprint arXiv:2412.01118 ,

    arXiv

  8. [16]

    Are GANs created equal? A large-scale study

    Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet. Are GANs created equal? A large-scale study. Advances in neural information process- ing systems, 31, 2018. 4, 6

    2018

  9. [17]

    Enhancing DreamBooth with LoRA for generating unlimited characters with Stable Dif- fusion

    Rub ´en Pascual, Adri ´an Maiza, Mikel Sesma-Sara, Daniel Paternain, and Mikel Galar. Enhancing DreamBooth with LoRA for generating unlimited characters with Stable Dif- fusion. In 2024 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE, 2024. 2, 4

    2024

  10. [18]

    A self-supervised descriptor for image copy detection

    Ed Pizzi, Sreya Dutta Roy, Sugosh Nagavara Ravindra, Priya Goyal, and Matthijs Douze. A self-supervised descriptor for image copy detection. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages 14532–14542, 2022. 2

    2022

  11. [19]

    Learning transferable visual models from natural language supervi- sion

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervi- sion. In International conference on machine learning, ...

    2021

  12. [20]

    Zero-shot text-to-image generation

    Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea V oss, Alec Radford, Mark Chen, and Ilya Sutskever. Zero-shot text-to-image generation. In International confer- ence on machine learning, pages 8821–8831. Pmlr, 2021. 1

    2021

  13. [21]

    Unveiling and mitigating mem- orization in text-to-image diffusion models through cross at- tention

    Jie Ren, Yaxin Li, Shenglai Zeng, Han Xu, Lingjuan Lyu, Yue Xing, and Jiliang Tang. Unveiling and mitigating mem- orization in text-to-image diffusion models through cross at- tention. In European Conference on Computer Vision, pages 340–356. Springer, 2024. 1, 2, 3, 7

    2024

  14. [22]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bj ¨orn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022. 1, 2, 4, 5

    2022

  15. [23]

    U- Net: Convolutional networks for biomedical image segmen- tation

    Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U- Net: Convolutional networks for biomedical image segmen- tation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, pa...

    2015

  16. [24]

    Photorealistic text-to-image diffusion models with deep language understanding

    Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. Photorealistic text-to-image diffusion models with deep language understanding. Advances in neural information...

    2022

  17. [25]

    Safe latent diffusion: Mitigating inappro- priate degeneration in diffusion models

    Patrick Schramowski, Manuel Brack, Bj ¨orn Deiseroth, and Kristian Kersting. Safe latent diffusion: Mitigating inappro- priate degeneration in diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 22522–22531, 2023. 1

    2023

  18. [26]

    LAION-5B: An open large-scale dataset for train- ing next generation image-text models

    Christoph Schuhmann, Romain Beaumont, Richard Vencu, Cade Gordon, Ross Wightman, Mehdi Cherti, Theo 9 Coombes, Aarush Katta, Clayton Mullis, Mitchell Worts- man, et al. LAION-5B: An open large-scale dataset for train- ing next generation image-text models. Advances in neural i...

    2022

  19. [27]

    Diffusion art or digital forgery? Investigating data replication in diffusion models

    Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, and Tom Goldstein. Diffusion art or digital forgery? Investigating data replication in diffusion models. In Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 6048–6058, 202...

    2023

  20. [28]

    Understanding and mitigating copying in diffusion models

    Gowthami Somepalli, Vasu Singla, Micah Goldblum, Jonas Geiping, and Tom Goldstein. Understanding and mitigating copying in diffusion models. Advances in Neural Informa- tion Processing Systems, 36:47783–47803, 2023. 1, 2, 3, 4, 6, 7

    2023

  21. [29]

    LLaMA: Open and efficient foundation language models

    Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timoth´ee Lacroix, Baptiste Rozi`ere, Naman Goyal, Eric Hambro, Faisal Azhar, et al. LLaMA: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971, 2023. 3

    2023 arXiv

  22. [30]

    Lexical density and register differentiation

    Jean Ure. Lexical density and register differentiation. Appli- cations of linguistics, 23(7):443–452, 1971. 1

    1971

  23. [31]

    On the de-duplication of LAION-2B

    Ryan Webster, Julien Rabin, Loic Simon, and Frederic Ju- rie. On the de-duplication of LAION-2B. arXiv preprint arXiv:2303.12733, 2023. 1

    2023 arXiv

  24. [32]

    De- tecting, explaining, and mitigating memorization in diffusion models

    Yuxin Wen, Yuchen Liu, Chen Chen, and Lingjuan Lyu. De- tecting, explaining, and mitigating memorization in diffusion models. In The Twelfth International Conference on Learn- ing Representations, 2024. 1

    2024

  25. [33]

    A universal discriminator for zero-shot generalization

    Haike Xu, Zongyu Lin, Jing Zhou, Yanan Zheng, and Zhilin Yang. A universal discriminator for zero-shot generalization. arXiv preprint arXiv:2211.08099, 2022. 1

    2022 arXiv

  26. [34]

    Infusion: Preventing customized text-to-image diffusion from overfitting

    Weili Zeng, Yichao Yan, Qi Zhu, Zhuo Chen, Pengzhi Chu, Weiming Zhao, and Xiaokang Yang. Infusion: Preventing customized text-to-image diffusion from overfitting. In Pro- ceedings of the 32nd ACM International Conference on Mul- timedia, pages 3568–3577, 2024. 4

    2024

  27. [35]

    Forget-Me-Not: Learning to for- get in text-to-image diffusion models

    Gong Zhang, Kai Wang, Xingqian Xu, Zhangyang Wang, and Humphrey Shi. Forget-Me-Not: Learning to for- get in text-to-image diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1755–1764, 2024. 3 10 FPAN: Mitigating Replic...

    2024

  28. [256]

    For the inference process, we generate sam- ples using S = 50 steps, uniformly spacing across the full diffusion process

    Optimization is performed using the Adam optimizer with β1 = 0.9 and β2 = 0.999, along with a weight decay factor of 1e−2. For the inference process, we generate sam- ples using S = 50 steps, uniformly spacing across the full diffusion process. A.2. Analysis of the Sudden Shif...

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.