Pith. sign in

REVIEW 4 major objections 5 minor 68 references

Exploring the latent space of diffusion models directly through singular value decomposition

T0 review · 4 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

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

Pith's one-line read Applying SVD to diffusion latent codes reveals three structure properties that enable attribute editing without extra data.

desk verdict The SVD-of-latents idea is genuinely new and the editing results are plausible, but the identity-fidelity theorem is broken and the three properties are only qualitatively supported; this needs major revision, not acceptance. read the letter →

arxiv 2502.02225 v1 pith:DBDIJ6RJ submitted 2025-02-04 cs.CV cs.AIcs.MM

Li Wang , Boyan Gao , Yanran Li , Zhao Wang , Xiaosong Yang , David A. Clifton , Jun Xiao This is my paper · ORCID
classification cs.CVcs.AIcs.MM
keywords diffusionmodelslatentspaceinterpretabilitysingularvaluedecompositionimageeditingStableattributevectorstext-guided
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

This paper claims that the latent space of diffusion models, studied directly through singular value decomposition (SVD), has three properties across denoising time steps: singular-vector subspaces stay semantically similar in a small neighborhood, attributes are encoded in singular-vector values and their singular-value magnitudes, and singular-vector ranks show mobility as time steps change. Based on these properties, the paper introduces Attribute Vector Integration, which splices left and right singular vectors from an original latent code and a target latent code (produced by two different text prompts) and uses a small MLP to predict reweighting singular values. The result is a latent code that introduces a desired attribute, such as 'young' or 'male', while preserving the identity of the original image, all without collecting any training data. If these claims hold, the latent code itself becomes a direct, interpretable editing interface for Stable Diffusion models.

What carries the argument

The central object is the channel-wise SVD of latent codes, written as UxSxVx = SVD(x) and UzSzVz = SVD(z). The identity-carrying operation is the concatenation step: Uhat is formed by appending the reversed top-k columns of Uz after the top-k columns of Ux, and Vhat is formed similarly row-wise; the edited latent is y = Uhat · S · Vhat, with S predicted by a small MLP and a residual Δs. The key mechanism is the mobility property, which justifies using latents from different time steps (Tx and Tx+Δτ) and reversing the target singular-vector order so that coarse-grained attributes from the target ascend to higher ranks. A Grassmannian geodesic distance measures subspace similarity, and four loss terms (L1–L4) balance target alignment, original identity, and singular-value regularization.

What would settle it

Train Attribute Vector Integration on one seed pair for a fixed prompt pair and then apply it to several different seed pairs with the same prompts; if the learned singular-value reweighting fails to transfer the attribute across seeds, the 'one pair of latent codes' learning claim collapses. A second check is to measure the Grassmannian geodesic distance between the subspace of the spliced latent and the original latent at the same time step: if it exceeds the small-neighborhood scale reported (around 4.3e-4), the manifold-preservation assumption is violated.

Watch

Extended reading notes

Core claim

The central discovery is that the latent space of diffusion models, when inspected via SVD, exhibits three time-step-independent properties: (1) small neighborhood—the subspaces spanned by left and right singular vectors remain semantically similar across all denoising steps; (2) attribute encoding—semantic attributes live in the singular-vector values and their paired singular-value magnitudes, so changing existing magnitudes adjusts attributes but cannot create new ones without adding new singular vectors; and (3) order mobility—singular vectors reorder across time steps, with coarse-grained attribute vectors ranking higher at later steps and descending at earlier steps while fine-grained vectors ascend. On this basis, the paper proposes Attribute Vector Integration: given an original latent x at time Tx and a target latent z at time Tx+Δτ, it concatenates the top-k singular vectors of x with the reversed top-k singular vectors of z (both column-wise and row-wise), predicts a singular-value matrix S and an adjustment Δs via an MLP, and forms edited latents y and y~. Four loss terms balance fidelity to x and z, and a theorem (Theorem 3.1) argues the integrated attribute vectors remain closer to the original singular vectors, supporting identity preservation.

Load-bearing premise

The method assumes that a semantic attribute such as 'young' is carried by a small set of singular vectors of the target latent, and that splicing those vectors into the original latent, with MLP-predicted singular values, yields a point the diffusion denoiser can still process—a premise the paper does not test statistically.

Editorial extensions

If this is right

  • A single pair of text prompts (original and target) is sufficient to learn an attribute such as 'young' or 'male' for editing Stable Diffusion images, eliminating the need for per-attribute datasets.
  • Editing happens at one specific denoising time step, making the process fast (about 6.7 seconds in the reported setup) and much cheaper than fine-tuning approaches.
  • Identity fidelity of the original image is preserved because the integrated attribute vectors stay closer to the original singular vectors, as supported by Theorem 3.1 and LPIPS comparisons.
  • The MLP-based singular-value prediction keeps the edited latent on the model's latent manifold, so the framework is model-agnostic and reusable across diffusion models.
  • The interpolation weight ρ provides a simple continuous control over attribute strength, yielding roughly disentangled transitions between original and target attributes.

Reading between the lines

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

  • If the three SVD properties generalize beyond the tested models (Stable Diffusion 2.1 and a few unconditional models), SVD could become a standard interpretability tool for diffusion latent spaces, analogous to interpretable directions found in GANs, but obtained without any training data.
  • The mobility property suggests a principled way to choose time-step intervals for targeting coarse versus fine attributes; a potential extension is to map specific attributes to rank positions as a function of denoising time, which the paper leaves implicit.
  • The method's sensitivity to seeds, noted in the discussion, implies that a single latent pair may not reliably transfer an attribute across different random seeds; a promising testable extension is to normalize singular vectors and values to stabilize the editing interface.
  • The paper relies on manual selection of text-prompt pairs; an automated extension could use a language model or CLIP scoring to propose prompt pairs, making the framework fully automatic.
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

4 major / 5 minor

Summary. The paper proposes to apply Singular Value Decomposition directly to latent codes of diffusion models and claims three properties: (1) singular-vector subspaces remain semantically similar in a small neighbourhood across diffusion time steps, (2) attributes are encoded in singular vector values and singular value magnitudes, and (3) singular vectors exhibit order mobility across time steps. Based on these properties, the authors introduce an Attribute Vector Integration (AVI) framework that combines top-k singular vectors from an original latent and a target latent at different time steps, uses an MLP to predict singular values, and feeds the resulting latent back into the denoising process to edit images without data collection. Experiments on Stable Diffusion 2.1 are reported for four attributes (Female, Male, Old, Young), with qualitative results and a quantitative comparison to Stable Diffusion and the Riemannian-geometry method of Park et al.

Significance. If the three properties and the AVI framework were rigorously established, the paper would offer an interpretable, lightweight, and training-data-free editing interface for diffusion models, which is a valuable goal. The paper also contains an interesting idea: directly studying the latent space rather than an auxiliary feature space. However, the evidence provided is not sufficient to support the central claims. The only formal result, Theorem 3.1, is invalid as stated, the main empirical support is qualitative inspection of a few figures, and the MLP is trained by directly minimizing reconstruction losses toward the target and original latents, so the 'learned attribute' is fitted rather than predicted. The potential significance is real, but the current manuscript does not substantiate it.

major comments (4)
  1. [Section 3.2.1, Theorem 3.1] Theorem 3.1 is the sole formal justification for identity preservation, but the proof is logically invalid: from the two triangle-inequality upper bounds ||Û - Ux|| ≤ ||Û|| + σmax(Ux) and ||Û - Uz|| ≤ ||Û|| + σmax(Uz), it does not follow that ||Û - Ux|| ≤ ||Û - Uz||. Furthermore, since Ux and Uz are orthogonal matrices with all singular values equal to 1, the assumption σUx_max ≤ σUz_max is trivially true and carries no information. The theorem also bounds a matrix norm on singular-vector matrices, not the distance ||ŷ - x|| between reconstructed latent codes, and certainly not identity fidelity of the decoded image. The proof's empirical support refers to a missing 'Figure x', which does not appear in the manuscript. This invalidates the paper's theoretical guarantee of identity preservation.
  2. [Section 3.2.2, Algorithm 1] The MLP Φ is trained per prompt pair by minimizing L1(ŷ, z), L2(ỹ, x), L3(S, Sz), and L4(S + Δs, Sx), which directly push the synthesized latents toward the target latent z and the original latent x. Thus the 'learned attribute' is fitted to the specific pair of latent codes used in training, and the editing success is partly guaranteed by construction. The claim that AVI can 'learn arbitrary attributes from one pair of latent codes' is therefore circular unless the authors provide a held-out test showing that the trained Φ generalizes to unseen prompts or images. The statement that ypred stays in the latent manifold 'due to the property of Φ' is also asserted without proof or any empirical manifold-validity test.
  3. [Section 4.1-4.3] The three core properties are supported only by qualitative inspection of Figures 2-4 and a single average geodesic distance in Figure 5. There is no quantitative definition of 'semantically similar', no statistical test of subspace distance across time steps, no error bars or variance numbers despite '30 samples', and no baseline comparison to random subspaces or other decompositions. Property (2) and the 'mobility' property (3) are inferred from selected examples, and Section 4.2 even states, without supporting evidence, that replacing a single singular vector does not introduce the corresponding semantic attribute. Since these properties are the foundation of the AVI algorithm, the central scientific claim of the paper is not established by the presented evidence.
  4. [Section 5, Table 1] The experimental validation is too limited to support the claim of 'arbitrary attributes'. Only four attributes are evaluated, all on Stable Diffusion 2.1, and the reported FID, CLIP, and LPIPS values are given without confidence intervals, statistical significance tests, or multiple trials. The proposed method does not achieve the best LPIPS on Young or the best FID on Old, and no held-out attributes or cross-prompt generalization experiments are reported. The runtime comparison is also not a controlled comparison because the methods use different optimization or training procedures. These limitations should be addressed before the editing results can be considered a reliable demonstration of the method's effectiveness.
minor comments (5)
  1. [Section 4.1] The phrase 'residential attributes' should be 'residual attributes'; the repeated typo obscures the intended meaning.
  2. [Section 3.2.1] The proof of Theorem 3.1 refers to a missing 'Figure x'; either include the figure or remove the reference.
  3. [Section 3.2.2] The text says 'the general idea of the training and inference is shown in Alg 4', but only Algorithm 1 is present in the manuscript; the reference should be corrected.
  4. [Section 3.2.2] The phrase 'decreasing order prosperity in S' appears to be a typo for 'decreasing order property' and should be clarified.
  5. [Section 3.2.2] The statement 'Theorem 3.1 can also be applied to the inference phase' is unclear because the algorithm's inference phase does not reconstruct ŷ from predicted singular values in the same way as training; the argument should be stated explicitly.

Circularity Check

2 steps flagged · score 6.0 of 10

The per-pair MLP is trained by minimizing reconstruction losses toward exactly the target and source latents, so the edited latent's resemblance to both is a fitted consequence, not a prediction; the central 'learn attributes' claim is partially circular.

  1. fitted input called prediction [Section 3.2.1-3.2.2, Eq. L1-L4 and Algorithm 1]
    "L1(ˆy, z) = ∥ˆy − z∥2_F, ˆy = ˆU · S · ˆV ... L2(˜y, x) = ∥˜y − x∥2_F ... L3(S, Sz) = ∥S − Sz∥2_F ... L4(S + ∆s, Sx) = ∥S + ∆s − Sx∥2_F ... [T]he final objective function for learning Φ is defined as follows: LAVI(ϕ) := L(ϕ, ˆy, ˜y, S, St, ∆s) = λ1L1(ˆy, z)+λ2L2(˜y, x)+λ3L3(S, Sz)+λ4L4(S+∆s, Sx)"

    The MLP that is said to predict singular values is trained on the same pair (x,z) that the edit must reproduce: L1 directly forces the integrated latent ŷ toward the target latent z, L2 forces a reversed reconstruction toward the source x, and L3/L4 pin S to Sz and Sx. The experiments train on one pair and then apply the framework to that same pair ('N is set to 5000 when provided only one pair of latent codes'), so resemblance of the output to the target attribute and source identity is guaranteed by the loss construction rather than predicted from the SVD properties. The claimed ability to 'learn arbitrary attributes from one pair of latent codes' is thus a per-pair fit, not an independent prediction.

  2. other [Section 3.2.1, Theorem 3.1 and proof; 'Figure x' missing]
    "Given σUx max ≤ σUz max ... The following inequality holds: ||ˆU − Ux|| ≤ ||ˆU|| + ||Ux|| ≤ ||ˆU|| + σUx max, then: ||ˆU − Uz|| ≤ ||ˆU|| + ||Uz|| ≤ ||ˆU|| + σUz max. ■ Figure x shows that the maximum singular values from Ux and Uz increases along with the time steps, which indicates the assumption in the proof is satisfied."

    This is not circularity but a load-bearing proof defect that the reviewing rules require flagging. The two displayed upper bounds do not imply the claimed comparison ||ˆU − Ux|| ≤ ||ˆU − Uz||; and because Ux and Uz are orthogonal SVD factors, their singular values are all 1, making the premise vacuous and carrying no information about latent codes. The proof also refers to a 'Figure x' that does not appear in the manuscript. Consequently the paper's theoretical identity-fidelity guarantee is unsupported; only the fitted L2 loss (the previous step) remains to enforce closeness to x.

full rationale

The derivation chain has one genuine circular step: the per-pair MLP predictor is optimized with losses that directly minimize the distance from the edited latent to both the target latent z and the source latent x, and the method is trained and evaluated on the same pair. Therefore the 'learned attribute' and preserved identity are partly guaranteed by construction, making the framework's central promise of prediction from one pair partially circular. The three SVD properties themselves are empirical observations (geodesic distance plots, singular-vector manipulation figures) and are not derived from the method, so they retain independent content; this is why the paper is not wholly circular. There is no load-bearing self-citation: citations to prior latent-space work (Kwon et al., Park et al., Yue et al.) are from other authors and do not smuggle in the paper's own assumptions. Separately, Theorem 3.1's proof is logically invalid and cites a missing 'Figure x'; this is a correctness risk (the identity guarantee is unsupported) rather than a circularity, and it lowers confidence in the theoretical claims. Overall, the fitted-to-target construction affects the central editing claim, giving a partial circularity score of 6.

Assumptions & free parameters 8 free parameters · 5 assumptions · 1 invented entities

The ledger shows that the central claim rests on manually chosen hyperparameters, an untested manifold assumption, and a new interpretive entity ('attribute vectors') with no independent evidence. The three properties are empirical axioms rather than derived theorems.

free parameters (8)
  • Time step Tx = 0.8T (T=1000)
    Chosen manually; defines which latent code is used for the original prompt in all experiments.
  • Time step offset Delta tau = -0.3T (Tz=0.5T)
    Chosen manually; defines the target latent code time step and is central to the mobility property.
  • Top-k singular vector count k = 32 (half of the 64-dimensional per-channel latent)
    Chosen for all experiments; no sensitivity analysis is given.
  • Mixing ratio rho = 1 in training, user-custom at inference
    Controls the strength of introduced attributes; users must set it manually.
  • Loss weights lambda1, lambda2, lambda3, lambda4 = 3, 10, 10, 10
    Chosen by hand to balance identity and target reconstruction; sensitivity deferred to missing supplementary.
  • Sampling count N = 5000 for one pair, 500 for over 5 pairs
    Number of latent samples generated for training; chosen without analysis.
  • Grassmannian subspace dimension p = 4
    Used to compute geodesic distances in Figure 5; no justification is provided.
  • MLP weights Phi = not released
    The network parameters are fit to each prompt pair via losses L1-L4; no pretrained weights or training code are shipped.
assumptions (5)
  • domain assumption The subspaces constructed by singular vectors remain semantically similar in a small neighbourhood across all time steps.
    Section 4.3 measures this with a geodesic distance averaged over 30 samples, but no formal bound or statistical test is provided.
  • ad hoc to paper Attributes are encoded in singular vector values and in the magnitude of their paired singular values, and residual attributes cannot be changed without adding new singular vectors.
    Section 4.2 asserts this from qualitative inspection of Figures 3 and 4; no independent test is given.
  • ad hoc to paper Singular vectors have order mobility across time steps while their directions remain stable enough to be transferred.
    Section 4.2 states this property and the method relies on it, but no tracking or formal definition is provided.
  • ad hoc to paper The latent code produced by the MLP stays on the latent manifold.
    Section 3.2.2 states 'due to the property of the Phi that ypred stays in the manifold of latent space' without proof.
  • domain assumption DDIM inversion yields a faithful real-image latent that behaves like a generated latent for editing.
    Standard assumption for inversion-based editing; the paper uses it without validation in this context.
invented entities (1)
  • Attribute vectors (singular vectors interpreted as semantic directions)
    purpose: Justify mixing top-k singular vectors of x with reversed tail singular vectors of z to introduce target attributes.
    The interpretation is supported only by qualitative inspection of Figures 2, 3, and 4; no external benchmark or falsifiable prediction is provided.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Exploring the latent space of diffusion models directly through singular value decomposition." pith.science (2026). https://pith.science/paper/DBDIJ6RJ

@misc{pith2026250202225,
  author       = {Pith},
  title        = {Pith review of: Exploring the latent space of diffusion models directly through singular value decomposition},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DBDIJ6RJ}},
  note         = {Machine review of arXiv:2502.02225}
}
read the original abstract

Despite the groundbreaking success of diffusion models in generating high-fidelity images, their latent space remains relatively under-explored, even though it holds significant promise for enabling versatile and interpretable image editing capabilities. The complicated denoising trajectory and high dimensionality of the latent space make it extremely challenging to interpret. Existing methods mainly explore the feature space of U-Net in Diffusion Models (DMs) instead of the latent space itself. In contrast, we directly investigate the latent space via Singular Value Decomposition (SVD) and discover three useful properties that can be used to control generation results without the requirements of data collection and maintain identity fidelity generated images. Based on these properties, we propose a novel image editing framework that is capable of learning arbitrary attributes from one pair of latent codes destined by text prompts in Stable Diffusion Models. To validate our approach, extensive experiments are conducted to demonstrate its effectiveness and flexibility in image editing. We will release our codes soon to foster further research and applications in this area.

Figures

Figures reproduced from arXiv: 2502.02225 by the authors.

Figure 1
Figure 1. Our framework overview for image editing. (1) During the denoising process, we select one time step [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Impact of singular values on their main singular vectors on Unconditional Diffusion Models (CelebA-HQ dataset on the left and [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Representative examples shown on the attributes that one single singular vector affects across the time steps in Stable Diffusion [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Impact of singular values on their main singular vectors [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Geodesic Distance across subspaces constructed by sin [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 7
Figure 7. Figure 7: Linear interpolation on learned attributes. [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 6
Figure 6. Figure 6: Examples of image edition on various learned attributes. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

68 extracted references · 35 canonical work pages

  1. [1]

    Segdiff: Image segmentation with diffusion proba- bilistic models

    Tomer Amit, Tal Shaharbany, Eliya Nachmani, and Lior Wolf. Segdiff: Image segmentation with diffusion proba- bilistic models. arXiv preprint arXiv:2112.00390, 2021. 1

    arXiv 2021

  2. [2]

    Multidiffusion: Fusing diffusion paths for controlled image generation

    Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel. Multidiffusion: Fusing diffusion paths for controlled image generation. 2023. 1

    2023

  3. [3]

    Label-efficient se- mantic segmentation with diffusion models

    Dmitry Baranchuk, Ivan Rubachev, Andrey V oynov, Valentin Khrulkov, and Artem Babenko. Label-efficient se- mantic segmentation with diffusion models. arXiv preprint arXiv:2112.03126, 2021. 1

    arXiv 2021

  4. [4]

    High- frequency space diffusion model for accelerated mri

    Chentao Cao, Zhuo-Xu Cui, Yue Wang, Shaonan Liu, Taijin Chen, Hairong Zheng, Dong Liang, and Yanjie Zhu. High- frequency space diffusion model for accelerated mri. IEEE Transactions on Medical Imaging, 2024. 1

    work page 2024

  5. [5]

    Textdiffuser: Diffusion models as text painters

    Jingye Chen, Yupan Huang, Tengchao Lv, Lei Cui, Qifeng Chen, and Furu Wei. Textdiffuser: Diffusion models as text painters. Advances in Neural Information Processing Sys- tems, 36, 2024. 1

    work page 2024

  6. [6]

    Infogan: Interpretable rep- resentation learning by information maximizing generative adversarial nets

    Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, and Pieter Abbeel. Infogan: Interpretable rep- resentation learning by information maximizing generative adversarial nets. Advances in neural information processing systems, 29, 2016. 1

    work page 2016

  7. [7]

    Ilvr: Conditioning method for denoising diffusion probabilistic models

    Jooyoung Choi, Sungwon Kim, Yonghyun Jeong, Youngjune Gwon, and Sungroh Yoon. Ilvr: Conditioning method for denoising diffusion probabilistic models. arXiv preprint arXiv:2108.02938, 2021. 2

    arXiv 2021

  8. [8]

    Do Not Escape From the Manifold: Discovering the Local Coordinates on the Latent Space of GANs

    Jaewoong Choi, Junho Lee, Changyeon Yoon, Jung Ho Park, Geonho Hwang, and Myungjoo Kang. Do not escape from the manifold: Discovering the local coordinates on the latent space of gans. arXiv preprint arXiv:2106.06959, 2021. 7

    work page Pith review arXiv 2021

Show all 68 references
  1. [9]

    Custom-edit: Text-guided image edit- ing with customized diffusion models

    Jooyoung Choi, Yunjey Choi, Yunji Kim, Junho Kim, and Sungroh Yoon. Custom-edit: Text-guided image edit- ing with customized diffusion models. arXiv preprint arXiv:2305.15779, 2023. 2

    2023 arXiv

  2. [10]

    Diffusion models in vision: A survey

    Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, and Mubarak Shah. Diffusion models in vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 45(9):10850–10869, 2023. 1, 3

    2023

  3. [11]

    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, 3

    2021

  4. [12]

    Prompt tuning inversion for text-driven image editing using diffusion models

    Wenkai Dong, Song Xue, Xiaoyue Duan, and Shumin Han. Prompt tuning inversion for text-driven image editing using diffusion models. In Proceedings of the IEEE/CVF Inter- national Conference on Computer Vision, pages 7430–7440,

  5. [13]

    Di- rect inversion: Optimization-free text-driven real image edit- ing with diffusion models

    Adham Elarabawy, Harish Kamath, and Samuel Denton. Di- rect inversion: Optimization-free text-driven real image edit- ing with diffusion models. arXiv preprint arXiv:2211.07825,

    arXiv

  6. [14]

    Concept sliders: Lora adap- tors for precise control in diffusion models

    Rohit Gandikota, Joanna Materzynska, Tingrui Zhou, Anto- nio Torralba, and David Bau. Concept sliders: Lora adap- tors for precise control in diffusion models. arXiv preprint arXiv:2311.12092, 2023. 1

    2023 arXiv

  7. [15]

    Diffusion brush: A latent diffusion model-based editing tool for ai-generated images

    Peyman Gholami and Robert Xiao. Diffusion brush: A latent diffusion model-based editing tool for ai-generated images. arXiv preprint arXiv:2306.00219, 2023. 2

    2023 arXiv

  8. [16]

    Domain targeted synthetic plant style transfer using stable diffusion lora and controlnet

    Zane KJ Hartley, Rob J Lind, Michael P Pound, and An- drew P French. Domain targeted synthetic plant style transfer using stable diffusion lora and controlnet. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5375–5383, 2024. 1

    2024

  9. [17]

    Classifier-free diffusion guidance

    Jonathan Ho and Tim Salimans. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598, 2022. 1

    2022 arXiv

  10. [18]

    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, 3

    2020

  11. [19]

    Cascaded diffu- sion models for high fidelity image generation

    Jonathan Ho, Chitwan Saharia, William Chan, David J Fleet, Mohammad Norouzi, and Tim Salimans. Cascaded diffu- sion models for high fidelity image generation. Journal of Machine Learning Research, 23(47):1–33, 2022. 1

    2022

  12. [20]

    Kv inversion: Kv embeddings learning for text-conditioned real image action editing

    Jiancheng Huang, Yifan Liu, Jin Qin, and Shifeng Chen. Kv inversion: Kv embeddings learning for text-conditioned real image action editing. In Chinese Conference on Pattern Recognition and Computer Vision (PRCV) , pages 172–184. Springer, 2023. 2

    2023

  13. [21]

    Diff- styler: Controllable dual diffusion for text-driven image styl- ization

    Nisha Huang, Yuxin Zhang, Fan Tang, Chongyang Ma, Haibin Huang, Weiming Dong, and Changsheng Xu. Diff- styler: Controllable dual diffusion for text-driven image styl- ization. IEEE Transactions on Neural Networks and Learn- ing Systems, 2024. 2

    2024

  14. [22]

    Inverse problems in atmospheric science and their applica- tion

    Sixun Huang, Jie Xiang, Huadong Du, and Xiaoqun Cao. Inverse problems in atmospheric science and their applica- tion. In Journal of Physics: Conference Series, page 45. IOP Publishing, 2005. 1

    2005

  15. [23]

    Diffusion model-based image editing: A survey

    Yi Huang, Jiancheng Huang, Yifan Liu, Mingfu Yan, Jiaxi Lv, Jianzhuang Liu, Wei Xiong, He Zhang, Shifeng Chen, and Liangliang Cao. Diffusion model-based image editing: A survey. arXiv preprint arXiv:2402.17525, 2024. 2

    2024 arXiv

  16. [24]

    An edit friendly ddpm noise space: Inversion and manipulations

    Inbar Huberman-Spiegelglas, Vladimir Kulikov, and Tomer Michaeli. An edit friendly ddpm noise space: Inversion and manipulations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12469– 12478, 2024. 2

    2024

  17. [25]

    Imagic: Text-based real image editing with diffusion models

    Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov, Huiwen Chang, Tali Dekel, Inbar Mosseri, and Michal Irani. Imagic: Text-based real image editing with diffusion models. InPro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6007–6017, 2023. 1, 2

    2023

  18. [26]

    Diffu- sionclip: Text-guided diffusion models for robust image ma- nipulation

    Gwanghyun Kim, Taesung Kwon, and Jong Chul Ye. Diffu- sionclip: Text-guided diffusion models for robust image ma- nipulation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2426–2435,

  19. [27]

    User- friendly image editing with minimal text input: Leverag- ing captioning and injection techniques

    Sunwoo Kim, Wooseok Jang, Hyunsu Kim, Junho Kim, Yunjey Choi, Seungryong Kim, and Gayeong Lee. User- friendly image editing with minimal text input: Leverag- ing captioning and injection techniques. arXiv preprint arXiv:2306.02717, 2023. 2

    2023 arXiv

  20. [28]

    Adam: A method for stochastic opti- mization

    Diederik P Kingma. Adam: A method for stochastic opti- mization. arXiv preprint arXiv:1412.6980, 2014. 8

    2014 arXiv

  21. [29]

    Diffusion models already have a semantic latent space

    Mingi Kwon, Jaeseok Jeong, and Youngjung Uh. Diffusion models already have a semantic latent space. arXiv preprint arXiv:2210.10960, 2022. 1, 2

    2022 arXiv

  22. [30]

    Diffusion models already have a semantic latent space

    Mingi Kwon, Jaeseok Jeong, and Youngjung Uh. Diffusion models already have a semantic latent space. In ICLR, 2023. 1

    2023

  23. [31]

    Srdiff: Single image super-resolution with diffusion probabilistic models

    Haoying Li, Yifan Yang, Meng Chang, Shiqi Chen, Huajun Feng, Zhihai Xu, Qi Li, and Yueting Chen. Srdiff: Single image super-resolution with diffusion probabilistic models. Neurocomputing, 479:47–59, 2022. 1

    2022

  24. [32]

    Self-discovering interpretable diffusion latent di- rections for responsible text-to-image generation

    Hang Li, Chengzhi Shen, Philip Torr, V olker Tresp, and Jin- dong Gu. Self-discovering interpretable diffusion latent di- rections for responsible text-to-image generation. In Pro- ceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12006–1201...

    2024

  25. [33]

    Sdedit: Image synthesis and editing with stochastic differen- tial equations

    C Meng, Y Song, J Song, J Wu, JY Zhu, and S Ermon. Sdedit: Image synthesis and editing with stochastic differen- tial equations. arxiv 2021. arXiv preprint arXiv:2108.01073. 2

    2021 arXiv

  26. [34]

    Dragondiffusion: Enabling drag-style manipula- tion on diffusion models

    Chong Mou, Xintao Wang, Jiechong Song, Ying Shan, and Jian Zhang. Dragondiffusion: Enabling drag-style manipula- tion on diffusion models. arXiv preprint arXiv:2307.02421,

    arXiv

  27. [35]

    Clustergan: Latent space clustering in generative adversarial networks

    Sudipto Mukherjee, Himanshu Asnani, Eugene Lin, and Sreeram Kannan. Clustergan: Latent space clustering in generative adversarial networks. In Proceedings of the AAAI conference on artificial intelligence, pages 4610–4617,

  28. [36]

    Contrastive denoising score for text-guided latent diffusion image editing

    Hyelin Nam, Gihyun Kwon, Geon Yeong Park, and Jong Chul Ye. Contrastive denoising score for text-guided latent diffusion image editing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9192–9201, 2024. 2

    2024

  29. [37]

    Improved denoising diffusion probabilistic models

    Alexander Quinn Nichol and Prafulla Dhariwal. Improved denoising diffusion probabilistic models. In International conference on machine learning, pages 8162–8171. PMLR,

  30. [38]

    Shape-guided diffusion with inside-outside atten- tion

    Dong Huk Park, Grace Luo, Clayton Toste, Samaneh Azadi, Xihui Liu, Maka Karalashvili, Anna Rohrbach, and Trevor Darrell. Shape-guided diffusion with inside-outside atten- tion. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 4198–4207...

    2024

  31. [39]

    Understanding the latent space of diffusion models through the lens of riemannian geometry

    Yong-Hyun Park, Mingi Kwon, Jaewoong Choi, Junghyo Jo, and Youngjung Uh. Understanding the latent space of diffusion models through the lens of riemannian geometry. Advances in Neural Information Processing Systems , 36: 24129–24142, 2023. 1, 2, 8, 9

    2023

  32. [40]

    Understanding the latent space of dif- fusion models through the lens of riemannian geometry

    Yong-Hyun Park, Mingi Kwon, Jaewoong Choi, Junghyo Jo, and Youngjung Uh. Understanding the latent space of dif- fusion models through the lens of riemannian geometry. In NeurIPS, 2023. 6

    2023

  33. [41]

    Enhancing dreambooth with lora for generating unlimited characters with stable diffusion

    Rub ´en Pascual, Adri ´an Maiza, Mikel Sesma-Sara, Daniel Paternain, and Mikel Galar. Enhancing dreambooth with lora for generating unlimited characters with stable diffusion. In 2024 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE, 2024. 1

    2024

  34. [42]

    Localizing object-level shape variations with text-to-image diffusion models

    Or Patashnik, Daniel Garibi, Idan Azuri, Hadar Averbuch- Elor, and Daniel Cohen-Or. Localizing object-level shape variations with text-to-image diffusion models. In Proceed- ings of the IEEE/CVF International Conference on Com- puter Vision, pages 23051–23061, 2023. 2

    2023

  35. [43]

    Unsupervised representation learning with deep convolutional generative adversarial networks

    Alec Radford. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434, 2015. 1

    2015 arXiv

  36. [44]

    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

  37. [45]

    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. 8, 9

    2022

  38. [46]

    Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation

    Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , pages 2250...

    2023

  39. [47]

    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

  40. [48]

    In- terpreting the latent space of gans for semantic face editing

    Yujun Shen, Jinjin Gu, Xiaoou Tang, and Bolei Zhou. In- terpreting the latent space of gans for semantic face editing. In Proceedings of the IEEE/CVF conference on computer vi- sion and pattern recognition, pages 9243–9252, 2020. 1

    2020

  41. [49]

    Dragdiffusion: Harnessing diffusion models for interactive point-based image editing

    Yujun Shi, Chuhui Xue, Jun Hao Liew, Jiachun Pan, Han- shu Yan, Wenqing Zhang, Vincent YF Tan, and Song Bai. Dragdiffusion: Harnessing diffusion models for interactive point-based image editing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recogniti...

    2024

  42. [50]

    Denoising diffusion implicit models

    Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502, 2020. 1, 3, 8

    2010 arXiv

  43. [51]

    Score-based generative modeling through stochastic differential equa- tions

    Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Ab- hishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equa- tions. arXiv preprint arXiv:2011.13456, 2020. 1, 3

    2011 arXiv

  44. [52]

    Unitune: Text-driven image editing by fine tuning a diffusion model on a single image

    Dani Valevski, Matan Kalman, Eyal Molad, Eyal Segalis, Yossi Matias, and Yaniv Leviathan. Unitune: Text-driven image editing by fine tuning a diffusion model on a single image. ACM Transactions on Graphics (TOG), 42(4):1–10,

  45. [53]

    Unsupervised discov- ery of interpretable directions in the gan latent space

    Andrey V oynov and Artem Babenko. Unsupervised discov- ery of interpretable directions in the gan latent space. In International conference on machine learning, pages 9786–

  46. [54]

    Stylediffusion: Controllable disentangled style transfer via diffusion models

    Zhizhong Wang, Lei Zhao, and Wei Xing. Stylediffusion: Controllable disentangled style transfer via diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7677–7689, 2023. 2

    2023

  47. [55]

    De- blurring via stochastic refinement

    Jay Whang, Mauricio Delbracio, Hossein Talebi, Chitwan Saharia, Alexandros G Dimakis, and Peyman Milanfar. De- blurring via stochastic refinement. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16293–16303, 2022. 1

    2022

  48. [56]

    Uncovering the disentanglement capability in text- to-image diffusion models

    Qiucheng Wu, Yujian Liu, Handong Zhao, Ajinkya Kale, Trung Bui, Tong Yu, Zhe Lin, Yang Zhang, and Shiyu Chang. Uncovering the disentanglement capability in text- to-image diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , p...

    1900

  49. [57]

    The ocean circulation inverse problem

    Carl Wunsch. The ocean circulation inverse problem. Cam- bridge University Press, 1996. 1

    1996

  50. [58]

    Open-vocabulary panop- tic segmentation with text-to-image diffusion models

    Jiarui Xu, Sifei Liu, Arash Vahdat, Wonmin Byeon, Xiao- long Wang, and Shalini De Mello. Open-vocabulary panop- tic segmentation with text-to-image diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 2955–2966, 2023. 7

    2023

  51. [59]

    Raphael: Text-to-image generation via large mixture of diffusion paths

    Zeyue Xue, Guanglu Song, Qiushan Guo, Boxiao Liu, Zhuo- fan Zong, Yu Liu, and Ping Luo. Raphael: Text-to-image generation via large mixture of diffusion paths. Advances in Neural Information Processing Systems, 36, 2024. 1

    2024

  52. [60]

    Paint by example: Exemplar-based image editing with diffusion mod- els

    Binxin Yang, Shuyang Gu, Bo Zhang, Ting Zhang, Xuejin Chen, Xiaoyan Sun, Dong Chen, and Fang Wen. Paint by example: Exemplar-based image editing with diffusion mod- els. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition , pages 18381–18391,

  53. [61]

    Diffusion models: A comprehensive survey of methods and applications

    Ling Yang, Zhilong Zhang, Yang Song, Shenda Hong, Run- sheng Xu, Yue Zhao, Wentao Zhang, Bin Cui, and Ming- Hsuan Yang. Diffusion models: A comprehensive survey of methods and applications. ACM Computing Surveys, 56(4): 1–39, 2023. 1, 3

    2023

  54. [62]

    Magicremover: Tuning-free text-guided image inpainting with diffusion models

    Siyuan Yang, Lu Zhang, Liqian Ma, Yu Liu, JingJing Fu, and You He. Magicremover: Tuning-free text-guided image inpainting with diffusion models. arXiv preprint arXiv:2310.02848, 2023. 2

    2023 arXiv

  55. [63]

    Exploring diffusion time- steps for unsupervised representation learning

    Zhongqi Yue, Jiankun Wang, Qianru Sun, Lei Ji, Eric I Chang, Hanwang Zhang, et al. Exploring diffusion time- steps for unsupervised representation learning. arXiv preprint arXiv:2401.11430, 2024. 1, 4, 5, 6

    2024 arXiv

  56. [64]

    Text-to-image diffusion models in gener- ative ai: A survey

    Chenshuang Zhang, Chaoning Zhang, Mengchun Zhang, and In So Kweon. Text-to-image diffusion models in gener- ative ai: A survey. arXiv preprint arXiv:2303.07909, 2023. 1, 3

    2023 arXiv

  57. [65]

    Adding conditional control to text-to-image diffusion models

    Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 3836–3847, 2023. 1

    2023

  58. [66]

    Sine: Single image editing with text- to-image diffusion models

    Zhixing Zhang, Ligong Han, Arnab Ghosh, Dimitris N Metaxas, and Jian Ren. Sine: Single image editing with text- to-image diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages 6027–6037, 2023. 1

    2023

  59. [67]

    Uni-controlnet: All-in-one control to text-to-image diffusion models

    Shihao Zhao, Dongdong Chen, Yen-Chun Chen, Jianmin Bao, Shaozhe Hao, Lu Yuan, and Kwan-Yee K Wong. Uni-controlnet: All-in-one control to text-to-image diffusion models. Advances in Neural Information Processing Sys- tems, 36, 2024. 1

    2024

  60. [68]

    Conditional text image generation with diffu- sion models

    Yuanzhi Zhu, Zhaohai Li, Tianwei Wang, Mengchao He, and Cong Yao. Conditional text image generation with diffu- sion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 14235– 14245, 2023. 1

    2023

Pith tools

Reviewed August 9, 2026 · model on record in the stance chip above.