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

Recognition: no theorem link

SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations

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

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

0 comments

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.

Desk Editor's Note private letter to a colleague

SDEdit adapts pre-trained diffusion SDEs for training-free guided editing by noising then denoising user inputs, with large reported human-study gains over GAN baselines, though the no-tuning claim depends on how the starting noise level is chosen.

read the letter

SDEdit adapts a pre-trained diffusion model for guided image synthesis and editing. You add noise to the user's input guide, whatever form it takes, then run the fixed SDE denoising process to produce a more realistic output while aiming to respect the guide. This avoids the conditional training or inversion steps common in the GAN methods the abstract cites. The core procedure is straightforward and does not introduce new parameters fitted to the editing tasks themselves. The human perception study supplies the main evidence, with the paper claiming large margins in realism and overall satisfaction across stroke-based synthesis, editing, and compositing. That is the practical contribution: a general way to leverage an existing generative prior without task-specific retraining or losses. The method stays non-circular because the performance gains trace back to the pre-trained diffusion model rather than quantities optimized inside this work. The soft spots sit in the balance claim and the evaluation details. The starting noise level directly controls how much guide structure is preserved versus how much gets corrected for realism. If different levels are used for stroke editing versus compositing, or tuned per image, that choice performs the balancing work the paper presents as happening naturally. The abstract reports specific percentage improvements from the human study but gives no participant numbers, rating instructions, controls, or statistical tests, so the strength of those numbers is hard to judge from the summary alone. This paper is for computer vision researchers and practitioners who want controllable generation tools that work with off-the-shelf diffusion models. Readers looking to implement or extend editing methods without heavy retraining would find the procedure and comparisons useful to examine. It has enough substance and a distinct technical angle to merit a serious referee, even if the reviewers will press on the noise schedule and study reporting. I would send it out for peer review with requests to clarify how the starting timestep is selected in practice and to supply full human-study details.

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)

[§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.
[§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)

[§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.
[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

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
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)

Forward citations

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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  ...

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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...

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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...

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We focus on editing hairstyles and adding glasses

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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 ...

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Which image do you think is more realistic

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