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arxiv: 2108.01073 · v2 · pith:B4TMQ75Inew · submitted 2021-08-02 · 💻 cs.CV · cs.AI

SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations

Chenlin Meng , Yutong He , Yang Song , Jiaming Song , Jiajun Wu , Jun-Yan Zhu , Stefano Ermon This is my paper

Pith reviewed 2026-05-12 22:24 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords guided image synthesisimage editingdiffusion modelsstochastic differential equationsSDEditimage compositingrealism vs faithfulness
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The pith

SDEdit adds noise to any user guide then denoises it with a pre-trained diffusion model to produce realistic edits without task-specific training.

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

The paper introduces SDEdit for guided image synthesis and editing tasks such as turning hand-drawn strokes into photos or compositing elements into scenes. It starts with an input that contains the user's guide, adds noise to it, and then runs the iterative denoising process from a stochastic differential equation learned by a diffusion model to improve realism. This avoids the extra training data, loss functions, or model inversions that GAN methods usually need for each new application. Human raters found SDEdit outputs substantially more realistic and satisfying than prior approaches across multiple editing scenarios. If the method works as described, it offers a simpler way to let everyday users create controlled, photo-like images from loose instructions.

Core claim

SDEdit synthesizes realistic images by iteratively denoising through a stochastic differential equation prior after first adding noise to an input image containing a user guide of any type. The approach requires no task-specific training or inversions and naturally balances faithfulness to the guide with realism, outperforming state-of-the-art GAN-based methods by up to 98.09% on realism and 91.72% on overall satisfaction in human perception studies.

What carries the argument

The noise-addition step followed by iterative SDE denoising using a pre-trained diffusion model generative prior, which removes noise while respecting the structure present in the noised guide.

If this is right

  • Enables stroke-based synthesis and editing plus image compositing using the same pre-trained model for all tasks.
  • Removes the need for per-application loss functions or additional training data that GAN methods require.
  • Produces images rated up to 98 percent more realistic than current GAN baselines in direct human comparisons.
  • Supports editing with any form of user guide without performing model inversion steps.

Where Pith is reading between the lines

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

  • The same noise-then-denoise pattern could be tested on other control signals such as text descriptions or depth maps if suitable diffusion priors exist.
  • Creative tools might become easier to build and maintain because one diffusion model could replace many task-tuned GANs.
  • Higher-resolution or video versions would need checks on whether the added-noise step still preserves fine user details.

Load-bearing premise

Adding noise to an arbitrary user guide and then applying the pre-trained diffusion denoising process will keep the result faithful to that guide while making it look realistic.

What would settle it

A human preference test on outputs from SDEdit applied to detailed or conflicting user guides that shows lower faithfulness or realism ratings than competing GAN methods.

read the original abstract

Guided image synthesis enables everyday users to create and edit photo-realistic images with minimum effort. The key challenge is balancing faithfulness to the user input (e.g., hand-drawn colored strokes) and realism of the synthesized image. Existing GAN-based methods attempt to achieve such balance using either conditional GANs or GAN inversions, which are challenging and often require additional training data or loss functions for individual applications. To address these issues, we introduce a new image synthesis and editing method, Stochastic Differential Editing (SDEdit), based on a diffusion model generative prior, which synthesizes realistic images by iteratively denoising through a stochastic differential equation (SDE). Given an input image with user guide of any type, SDEdit first adds noise to the input, then subsequently denoises the resulting image through the SDE prior to increase its realism. SDEdit does not require task-specific training or inversions and can naturally achieve the balance between realism and faithfulness. SDEdit significantly outperforms state-of-the-art GAN-based methods by up to 98.09% on realism and 91.72% on overall satisfaction scores, according to a human perception study, on multiple tasks, including stroke-based image synthesis and editing as well as image compositing.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes SDEdit, a method for guided image synthesis and editing that leverages a pre-trained diffusion model. Given a user guide (e.g., strokes or composite), it adds noise according to an SDE and then runs the fixed denoising process to produce a realistic output. The central claim is that this procedure naturally balances faithfulness to the guide and image realism without task-specific training, inversions, or additional losses, and that it significantly outperforms GAN-based baselines (up to 98.09% on realism and 91.72% on satisfaction) in human studies across stroke-based synthesis/editing and compositing tasks.

Significance. If the no-tuning claim and human-study results hold under scrutiny, the work would be significant: it offers a simple, training-free way to repurpose unconditional diffusion priors for controllable editing, sidestepping the optimization and data requirements of GAN inversion or conditional training. The approach is general across guide types and could accelerate adoption of diffusion models for interactive image tasks.

major comments (2)
  1. [§3.2 and §4] §3.2 (editing procedure) and §4 (experiments): the starting timestep t that controls the noise level is not fixed but appears selected per task (stroke editing vs. compositing) and per image to achieve the reported balance; this selection is equivalent to task-specific hyperparameter tuning and directly contradicts the claim that the method 'naturally' balances faithfulness and realism with a fixed pre-trained SDE and no tuning.
  2. [§5] §5 (human perception study): the reported 98.09% realism and 91.72% satisfaction improvements are presented without participant count, study design details, statistical tests, confidence intervals, or controls for bias/order effects; these omissions make it impossible to assess whether the margins are robust or whether they reflect per-task optimization of t for SDEdit while baselines receive no analogous adjustment.
minor comments (2)
  1. [§3.1] Notation for the SDE (e.g., the precise form of the forward process and the starting noise schedule) should be stated explicitly in §3.1 rather than referenced only to prior diffusion papers, to allow readers to reproduce the exact editing procedure.
  2. [Figure 4 and Table 2] Figure 4 and Table 2: axis labels and caption text are too small; enlarge them and add error bars or per-image t values to clarify how the quantitative metrics were obtained.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments on our manuscript. We provide point-by-point responses to the major comments below and indicate the revisions we will make to address them.

read point-by-point responses

  1. Referee: [§3.2 and §4] §3.2 (editing procedure) and §4 (experiments): the starting timestep t that controls the noise level is not fixed but appears selected per task (stroke editing vs. compositing) and per image to achieve the reported balance; this selection is equivalent to task-specific hyperparameter tuning and directly contradicts the claim that the method 'naturally' balances faithfulness and realism with a fixed pre-trained SDE and no tuning.

    Authors: We appreciate the referee's observation regarding the starting timestep t. In the SDEdit method, t determines the amount of noise added to the input guide, thereby controlling the degree of faithfulness to the user input versus the realism imposed by the diffusion model's prior. While different values of t are used for different tasks (e.g., lower t for stroke editing to preserve more of the guide, higher t for compositing to allow more synthesis), this choice is made once per task type based on the nature of the guide and is not optimized per individual image or through any training procedure. This is distinct from task-specific tuning in the sense of the paper's claims, which refer to the absence of conditional training, GAN inversion optimization, or additional loss functions. The pre-trained SDE is fixed, and the balance emerges from the stochastic denoising process. To clarify this, we will revise the description in §3.2 to emphasize that t is a controllable parameter for the trade-off, and update §4 to specify the t values used for each task without implying per-image selection. We believe this resolves the apparent contradiction. revision: partial

  2. Referee: [§5] §5 (human perception study): the reported 98.09% realism and 91.72% satisfaction improvements are presented without participant count, study design details, statistical tests, confidence intervals, or controls for bias/order effects; these omissions make it impossible to assess whether the margins are robust or whether they reflect per-task optimization of t for SDEdit while baselines receive no analogous adjustment.

    Authors: We acknowledge that the human perception study in §5 is not described with sufficient detail. In the revised version of the manuscript, we will provide the number of participants involved in the study, a full description of the study design including how pairs were presented and any randomization to mitigate order effects, the statistical tests used to compute the reported percentages, confidence intervals for the results, and any measures taken to control for bias. With respect to the concern that the results may stem from per-task optimization of t, we clarify that t was selected qualitatively for each task category to achieve a reasonable balance, and the same selection criterion was applied uniformly across all images in that task. The baselines were implemented and evaluated following their respective publications. We will add this clarification and the statistical details to strengthen the presentation of the human study results. revision: yes

Circularity Check

0 steps flagged

No significant circularity in the derivation chain

full rationale

The paper presents SDEdit as the direct application of an existing pre-trained diffusion model prior: noise is added to an arbitrary user guide at a chosen timestep, after which the fixed SDE denoising process is run to produce the output. No equations, self-definitions, or fitted parameters inside the paper reduce the claimed balance between faithfulness and realism, or the human-study performance margins, to quantities that are tautological with the method's own inputs. The procedure is framed as a zero-shot use of the external generative prior without task-specific training, inversions, or additional losses, and the empirical results are reported from separate human evaluations rather than derived by construction from the editing steps themselves.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the assumption that a pre-trained diffusion model supplies a sufficiently strong generative prior for any user guide after noise perturbation. No new free parameters or invented entities are introduced in the abstract.

axioms (1)
  • domain assumption A pre-trained diffusion model provides a generative prior that, after controlled noise addition, can be denoised to produce realistic images faithful to an arbitrary input guide.
    Invoked to justify why the SDE denoising step balances realism and faithfulness without task-specific training.

pith-pipeline@v0.9.0 · 5538 in / 1241 out tokens · 38005 ms · 2026-05-12T22:24:55.228647+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

18 extracted references · 18 canonical work pages · cited by 91 Pith papers · 6 internal anchors

  1. [1]

    Demystifying MMD GANs

    Mikołaj Bi´nkowski, Danica J Sutherland, Michael Arbel, and Arthur Gretton. Demystifying mmd gans. arXiv preprint arXiv:1801.01401,

    work page internal anchor Pith review arXiv

  2. [2]

    arXiv preprint arXiv:2108.02938 , year=

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

    work page arXiv

  3. [3]

    Diffusion Models Beat GANs on Image Synthesis

    Prafulla Dhariwal and Alex Nichol. Diffusion models beat gans on image synthesis. arXiv preprint arXiv:2105.05233,

    work page internal anchor Pith review arXiv

  4. [4]

    Implicit generation and generalization in energy-based models.arXiv preprint arXiv:1903.08689, 2019

    Yilun Du and Igor Mordatch. Implicit generation and generalization in energy-based models. arXiv preprint arXiv:1903.08689,

    work page arXiv 1903

  5. [5]

    Denoising Diffusion Probabilistic Models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. arXiv preprint arXiv:2006.11239,

    work page internal anchor Pith review Pith/arXiv arXiv 2006

  6. [6]

    Progressive Growing of GANs for Improved Quality, Stability, and Variation

    Tero Karras, Timo Aila, Samuli Laine, and Jaakko Lehtinen. Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196,

    work page internal anchor Pith review Pith/arXiv arXiv

  7. [7]

    Training Generative Adversarial Networks with Limited Data

    Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila. Training generative adversarial networks with limited data. arXiv preprint arXiv:2006.06676, 2020a. Tero Karras, Samuli Laine, Miika Aittala, Janne Hellsten, Jaakko Lehtinen, and Timo Aila. Analyzing and improving the image quality of stylegan. In IEEE Conference on ...

    work page arXiv 2006

  8. [8]

    Pivotal tuning for latent-based editing of real images

    Daniel Roich, Ron Mokady, Amit H Bermano, and Daniel Cohen-Or. Pivotal tuning for latent-based editing of real images. arXiv preprint arXiv:2106.05744,

    work page arXiv

  9. [9]

    Deep Unsupervised Learning using Nonequilibrium Thermodynamics

    Jascha Sohl-Dickstein, Eric A Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. arXiv preprint arXiv:1503.03585,

    work page internal anchor Pith review arXiv

  10. [10]

    Denoising Diffusion Implicit Models

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

    work page internal anchor Pith review Pith/arXiv arXiv 2010

  11. [11]

    Song and S

    Yang Song and Stefano Ermon. Improved techniques for training score-based generative models.arXiv preprint arXiv:2006.09011,

    work page arXiv 2006

  12. [12]

    arXiv preprint arXiv:2101.03288 , year=

    Yang Song and Diederik P Kingma. How to train your energy-based models.arXiv preprint arXiv:2101.03288,

    work page arXiv

  13. [13]

    with probability at least (1 −δ), x(g) − SDEdit(x(g);t0,θ )  2 2 ≤σ2(t0)(Cσ2(t0) +d + 2 √ −d · logδ − 2 logδ) (5) whered is the number of dimensions of x(g). Proof. Denote x(g)(0) = SDEdit(x(g);t,θ ), then x(g)(t0) − x(g)(0)  2 2 =  ∫ 0 t0 dx(g)(t) dt dt  2 2 (6) =  ∫ 0 t0 [ −d[σ2(t)] dt sθ(x,t ;θ) ] dt + √ d[σ2(t)] dt d ¯w  ...

    work page 2000

  14. [14]

    We observe that SDEdit still outperforms SC-FEGAN using only stroke as the input guide

    using both stroke and extra sketch as the input guide. We observe that SDEdit still outperforms SC-FEGAN using only stroke as the input guide. B.5 C OMPARISON WITH SONG ET AL . (2021) Methods proposed by Song et al. (2021) introduce an extra noise-conditioned classifier for condi- tional generation and the performance of the classifier is critical to the co...

    work page 2021

  15. [15]

    measurement

    Since we do not have a known “measurement” function 18 Preprint. Under review. for user-generated guides, their approach cannot be directly applied to user-guided image synthe- sis or editing in the form of manipulating pixel RGB values. To deal with this limitation, SDEdit initializes the reverse SDE based on user input and modifiest0 accordingly—an appro...

    work page 2021

  16. [16]

    We focus on editing hairstyles and adding glasses

    In general, the masks are simply the pixels the users have copied pixel patches to. We focus on editing hairstyles and adding glasses. We use an SDEdit model pretrained on FFHQ (Karras et al., 2019). We use t0 = 0.35,N = 700,K = 1 for SDEdit (VE). We present more results in Appendix E.2. D.2 S YNTHESIZING STROKE PAINTING Human-stroke-simulation algorithm ...

    work page 2019

  17. [17]

    See Appendix D for experiment settings

    We observe that SDEdit can generate both faithful and realistic edited images. See Appendix D for experiment settings. Attribute classification with stroke-based generation. In order to further evaluate how the mod- els convey user intents with high level user guide, we perform attribute classification on stroke-based generation for human faces. We use the ...

    work page 2021

  18. [18]

    Which image do you think is more realistic

    4https://github.com/Azure-Samples/cognitive-services-quickstart-code/ tree/master/python/Face 23 Preprint. Under review. (a) Dataset image (b) User guide (c) GAN output (d) GAN blending Figure 16: Post-processing samples from GANs by masking out undesired changes, yet the artifacts are strong at the boundaries even with blending. Methods Gender Glasses Ha...

    work page 2021