Pith. sign in

REVIEW 4 major objections 6 minor 1 cited by

StochSync: Stochastic Diffusion Synchronization for Image Generation in Arbitrary Spaces

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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

Pith's one-line read StochSync fuses two rival diffusion strategies into a zero-shot generator that beats finetuned models at 360° panoramas, and reveals the two methods are one algorithm.

desk verdict Useful algorithmic combination with clean ablations, but the main table omits the author's own better configuration, so the headline ranking is shakier than the method itself. read the letter →

arxiv 2501.15445 v2 pith:77JTNF7Y submitted 2025-01-26 cs.CV cs.AI

Kyeongmin Yeo , Jaihoon Kim , Minhyuk Sung This is my paper · ORCID
classification cs.CVcs.AI
keywords StochSyncdiffusionsynchronizationscoredistillationsamplingzero-shotimagegeneration360-degreepanoramameshtexturingmaximumstochasticityarbitrary-space
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 two strategies for generating images in non-standard spaces with a pretrained diffusion model—diffusion synchronization, which runs the reverse process jointly across projected views and averages their clean-sample predictions in a canonical space, and score distillation sampling, which updates the canonical sample by gradient descent—are two ends of a single algorithmic spectrum, and it identifies the spectrum's key parameter: the stochasticity of each denoising step. The proposed method, StochSync, sits at the combination previous work had not tried: maximum stochasticity (which gives cross-view coherence), multi-step denoising of the clean-sample estimate (which restores realism), and alternating sets of non-overlapping views (which keeps the views consistent over time without averaging them into blur). The paper's claim is that, with no finetuning and no image conditioning, this combination produces the best 360° panoramas—including better scores than models finetuned on panorama datasets—and mesh textures comparable to the best depth-conditioned baselines. If true, one pretrained image model can be pointed at spheres, meshes, and other topologies without collecting target-domain data, and the two competing research lines collapse into one design space.

What carries the argument

The load-bearing identity is the DDIM posterior mean under maximum stochasticity. In the reverse step, $x_{t-1}$ is drawn from a Gaussian with mean $\mu_{\sigma_t}(x_0, \epsilon_t) = \sqrt{\alpha_{t-1}}\, x_0 + \sqrt{1-\alpha_{t-1}-\sigma_t^2}\,\epsilon_t$; setting $\sigma_t = \sqrt{1-\alpha_{t-1}}$ cancels the $\epsilon_t$ term, so each step is just a scaled clean-sample prediction plus fresh Gaussian noise, and the next clean prediction comes from denoising that sample. This makes StochSync an iteration of SDEdit, which is why the loop can stop early at $T_{\text{stop}} \gg 0$. The other two components carry the realism: $G(x_t)$, a multi-step deterministic denoiser that replaces the one-step Tweedie estimate $\psi(x_t, \epsilon_t)$, and two alternating sets of five non-overlapping views, whose overlap with the previous step's views is what the paper says keeps the canonical sample synchronized over time.

What would settle it

Destroy the temporal overlap while keeping the other two components: at each step, draw the five views from a fixed grid that is randomly re-shifted by an amount large enough that its regions no longer overlap the previous step's view footprints (for the equirectangular setup, a shift greater than the view field of view). If the output panoramas stay seam-free and repetition-free, the temporal-overlap mechanism is not doing the work; if seams and repeated objects reappear, it is confirmed. A subtler quantitative variant samples panoramas at several shift values between 0° and 72° and plots seam-boundary error against shift size.

Watch

Extended reading notes

Core claim

The central discovery is a unification plus a recipe. On the unification side, the paper shows that a score distillation step is exactly one DDIM denoising refinement run with maximum stochasticity, $\sigma_t = \sqrt{1-\alpha_{t-1}}$, on a randomly sampled timestep, with a single gradient-descent step in place of the synchronization's full least-squares averaging; StochSync makes the reverse move, converting SDS into a synchronization by using a decreasing time schedule and fully minimizing the $\ell^2$ loss. On the recipe side, the paper claims that three changes to the base synchronization method—setting $\sigma_t$ to its maximum so the posterior mean becomes $\sqrt{\alpha_{t-1}}\, x_{0|t}$ plus fresh noise, replacing the one-step Tweedie clean-sample estimate with a multi-step deterministic denoiser $G(x_t)$, and sampling non-overlapping views that alternate between two shifted sets—jointly remove the seams that appear when no depth or image conditioning is available while keeping the fine detail that pure SDS loses. The paper reports FID, IS, GIQA, and CLIP scores for text-only 360° panorama generation that beat the finetuned baselines, and mesh-texturing scores on par with the best prior synchronization method.

Load-bearing premise

The load-bearing premise is that temporal overlap—each new set of non-overlapping views sharing regions with the previous step's views—is enough to keep the canonical sample synchronized, even though the views within a single step never overlap spatially; Section 6 asserts this but no experiment isolates it.

Editorial extensions

If this is right

  • Text-only zero-shot 360° panorama generation can beat finetuning-based methods (MVDiffusion, PanFusion) and the inpainting-based L-MAGIC on FID, IS, GIQA, and CLIP, without collecting panorama data or training a target-space model.
  • Because StochSync reads as iterated SDEdit, the denoising loop can be truncated (Tstop = 270 instead of 0, or with DPM-Solver from 50 to 20 ODE steps), putting its runtime below the fastest previously reported baselines.
  • The same three-component recipe transfers to other canonical spaces: mesh surfaces, spheres and tori without depth maps, 3D Gaussians, and 8K resolution panoramas, suggesting it is a general mechanism rather than a per-task trick.
  • Under maximum stochasticity, refining with more steps does not improve quality but degrades it, because the forward process at $\sigma_t = \sqrt{1-\alpha_{t-1}}$ fails to converge to an SDE as the step interval shrinks—so the standard 'more steps is better' intuition of DDIM does not apply at this operating point.

Reading between the lines

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

  • The temporal-overlap claim predicts a quantitative trade-off: shrink the overlap between consecutive step view sets and seams should reappear; measuring seam error or cross-view agreement as a function of the angular shift between the two alternating sets would give a direct test the paper does not run.
  • The unification opens a continuous design space between SDS and DS; intermediate points (partial stochasticity, partial gradient steps, partially overlapping views) are natural targets for a systematic study that the paper leaves implicit.
  • The SDEdit reading suggests StochSync is also a refinement operator: re-running the loop on an already-generated canonical sample, as done for 8K panoramas, could serve as a general seam-removal post-process for any multi-view generation pipeline.
  • If the DS–SDS unification holds, a main practical consequence is that gradient-descent step sizes in SDS variants are replaceable by a parameter-free least-squares projection, removing a fragile hyperparameter from distillation-style generation.
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 / 6 minor

Summary. The paper proposes StochSync, a zero-shot method for generating data in canonical spaces such as 360-degree panoramas and 3D mesh surfaces using a pretrained image diffusion model. The method builds on a diffusion-synchronization base (SyncTweedies) and introduces three components: maximum stochasticity in the DDIM posterior, multi-step clean-sample prediction G(xt) instead of a single Tweedie estimate, and non-overlapping view sampling that is claimed to maintain synchronization over time through overlap of views across steps. The paper also presents a reinterpretation of score distillation sampling as one-step maximum-stochasticity DDIM refinement and positions StochSync as a hybrid of diffusion synchronization and score distillation. Experiments cover panorama generation, mesh texturing, high-resolution panoramas, and 3D Gaussian texturing, with ablations, a user study, and runtime comparisons; Appendix E reports a faster and quantitatively better configuration, StochSync*.

Significance. If the empirical claims hold, StochSync is a useful and simple zero-shot recipe that extends pretrained image diffusion models to several non-square output spaces, and the explicit DS-SDS connection is conceptually interesting for future algorithm design. The paper's strengths include component-wise ablations in Table 2, a user study against L-MAGIC, and demonstrations on panoramas, mesh textures, 8K outputs, and 3D Gaussians. However, the headline empirical claim is under-specified: the paper's own appendix reports a better configuration than the one used in the main comparison, all quantitative tables are single runs without variance or significance testing, and the load-bearing temporal-overlap mechanism is not isolated by an ablation. These are fixable within the scope of a revision, but they currently prevent the definitive ranking stated in the abstract from being fully supported.

major comments (4)
  1. [Appendix E, Table 4 vs. Table 1] The central claim that StochSync provides the best performance in 360-degree panorama generation is supported in Table 1 by FID 57.88, but Appendix E, Table 4 reports StochSync*, the same method with Tstop=700 and 8 denoising steps, achieving FID 47.24, IS 10.80, GIQA 21.41, and CLIP 31.07, which is better on every metric; StochSync*+DPM-S also reaches FID 47.59 with a much shorter runtime. No protocol is given for selecting Tstop and the number of denoising steps, or for deciding which configuration is reported in the main table. The headline comparison is therefore configuration-dependent and under-specified. Please move the best configuration into the main comparison, explain the configuration-selection procedure, and justify the configuration used for the abstract's ranking claim.
  2. [Tables 1-3 and Appendix E] All quantitative comparisons are single-run point estimates without variance, confidence intervals, or significance tests. This is especially consequential for mesh texturing: in Table 3, SyncTweedies has better FID (21.76 vs. 22.29) and better CLIP (28.89 vs. 28.57), while StochSync is better only on KID (1.31 vs. 1.46), so the paper's 'comparable' wording is appropriate but no uncertainty measure supports it. In addition, the mesh-texture baseline numbers are copied from SyncTweedies rather than re-run, and the L-MAGIC prompt results in Table 8 show materially smaller margins than the PanFusion-prompt results. Please report multiple seeds with means and variances, clearly state which numbers are re-computed versus inherited, and avoid definitive ranking statements based on single runs.
  3. [Sec. 6, Non-Overlapping View Sampling; Table 2] The synchronization-over-time mechanism is load-bearing for the method, but it is not isolated experimentally. The justification that newly sampled non-overlapping views are synchronized through their overlap with views from previous steps is plausible but remains an assumption. Table 2 changes multiple components at once: row 5 (Max sigma_t + N.O. Views, without Impr. x0|t) has FID 117.09, while row 4 (Max sigma_t + Impr. x0|t, overlapping views) has FID 78.56, and only row 6 with all three components reaches 57.88. No experiment varies the degree of overlap between consecutive view sets while holding the other components fixed. Please add an ablation that varies temporal overlap directly, for example by alternating view sets with no overlap, partial overlap, and full overlap at fixed compute.
  4. [Sec. 7.1, evaluation protocol] The reference set for the panorama metrics is generated by Stable Diffusion 2.1, the same base model used by StochSync. This makes the FID, IS, and GIQA numbers measures of closeness to the base model's distribution rather than absolute panorama realism, and it may systematically penalize finetuned or inpainting-based baselines that deviate from that prior. The paper should explicitly acknowledge this limitation and, where possible, supplement the automated metrics with a reference set from real panorama data or with additional human evaluation beyond the L-MAGIC comparison.
minor comments (6)
  1. [Algorithm 4] The indentation of lines 10-13 under the `for i = 1 . . . N` loop is inconsistent with the surrounding pseudocode; please fix the layout for clarity.
  2. [Appendix D.1] The 'informal proof' that maximum stochasticity cannot be approximated by an SDE as the timestep interval goes to zero should be clearly labeled as a heuristic argument, or expanded with precise assumptions and a rigorous statement; as written it is not a proof.
  3. [Sec. 6 and Appendix B] The multi-step denoiser G(xt) is described only loosely in the main text, and details such as the RePaint-style boundary blending appear only in Appendix B; a precise and self-contained definition of G(·) and its step-count schedule would improve reproducibility.
  4. [Appendix E, Tables 4-7] The notation for configurations is inconsistent, with StochSync, StochSync*, StochSync*+DPM-S, and StochSync+DPM-S used in slightly different forms across tables; please align the notation and define it once.
  5. [Reproducibility Statement] The paper says code 'will be released publicly' but provides no repository link or version; please provide an anonymized or public link, or state the exact release conditions.
  6. [Title and Abstract] The phrase 'images in arbitrary spaces' is broader than the demonstrated settings; the method requires a known differentiable projection from the canonical space to the instance space, and this boundary condition should be stated in the abstract or introduction.

Circularity Check

2 steps flagged · score 2.0 of 10

Minor self-promotional circularity (novelty claim contradicted by own appendix; mesh baselines imported from authors' prior paper), but the central StochSync derivation is an empirical composition tested against external baselines.

  1. renaming known result [Abstract; Sec. 6 'Comparisons to SDS Variants'; Appendix A]
    "In this paper, we reveal for the first time the interconnection between these two methods while highlighting their differences. ... Previous works (Kim et al., 2024b; Lukoianov et al., 2024) have also made a similar observation."

    The paper's headline conceptual contribution, 'reveal for the first time the interconnection' between Diffusion Synchronization and Score Distillation Sampling, is immediately contradicted by its own Appendix A, which states that prior works already made a similar observation. The interconnection is presented as a novel first-time discovery, but the appendix shows it is a known equivalence, re-expressed as the paper's Sec. 5.2/6 analysis. This does not make the empirical StochSync algorithm circular, but the claimed novel insight reduces to renaming a previously observed connection.

  2. self citation load bearing [Sec. 7.2 (3D Mesh Texturing, caption of Tab. 3)]
    "Note that the results from other baseline methods are sourced from Kim et al. (2024a)."

    The mesh-texturing comparison supporting the claim that StochSync 'outperforms all other baselines' (Tab. 3) imports the baseline numbers for Paint-it, Paint3D, TEXTure, and Text2Tex from the authors' own prior SyncTweedies paper rather than measuring them in this work. The favorable ranking in that table is therefore load-bearing on a self-citation that is not independently re-verified here. This is not a definitional circularity, but it is a self-citation that directly supports one of the two headline application claims.

full rationale

The central StochSync method is an empirical combination of three well-specified modifications to a diffusion-synchronization base, evaluated with ablations and compared against external baselines in the panorama task (Tab. 1, Tab. 2). No target metric is used as a fitted constant, and no core derivation reduces to its own input; the panorama 'best performance' claim is supported by numbers measured against L-MAGIC, MVDiffusion, and PanFusion rather than by construction. The circularity score is therefore low. It is not zero because (i) the 'first time' novelty claim about the DS-SDS interconnection is contradicted by the paper's own appendix, and (ii) the mesh-texturing comparison reuses baseline numbers from the authors' prior SyncTweedies paper, making that particular ranking depend on a self-citation. The Appendix E StochSync* configuration discrepancy (better FID with a different Tstop) is a robustness and selection-protocol concern, not a circularity, and would belong in a correctness review rather than this pass.

Assumptions & free parameters 5 free parameters · 6 assumptions · 0 invented entities

StochSync introduces no new physical or ontological entities; it operates on existing pretrained models and projection maps. The ledger instead shows hand-chosen schedules, view configurations, and load-bearing assumptions about stochasticity and temporal overlap, plus the Lipschitz assumption used in the theoretical motivation.

free parameters (5)
  • Panorama denoising schedule = T=900, Tstop=270, 25 denoising steps
    Set in Appendix B. Chosen by hand or validation, not derived. Central results in Tab. 1 use this schedule, and Appendix E reports better FID with a different schedule.
  • Multi-step denoising step count for G(xt) = 50 steps initially, decreasing linearly
    Set in Appendix B. Affects realism and coherence under maximum stochasticity.
  • Number and geometry of panorama views = 5 views per set, azimuths [0,72,144,216,288] and [36,108,180,252,324], elevation 0, FoV 72 degrees
    Set in Appendix B and Sec. 6. View count and arrangement are design choices; the mesh setting uses 4 views.
  • Mesh texturing schedule = T=1000, Tstop=270, 4 views
    Set in Appendix B. Chosen by hand for the depth-conditioned case.
  • StochSync* optimized configuration = Tstop=700, 8 denoising steps
    Appendix E says this 'optimal configuration was found' and reports FID 47.24, better than the main StochSync FID of 57.88, indicating sensitivity to hyperparameters and possible selection on the test prompt set.
assumptions (6)
  • domain assumption Pretrained Stable Diffusion 2.1 Base and ControlNet provide a valid image prior for projected perspective views.
    Used in Sec. 7.1 and 7.2. The method inherits all quality from these priors; if the prior is weak for a target space, the method fails.
  • domain assumption Projection operations fc are known and differentiable for equirectangular projection and mesh rendering.
    Stated in Sec. 3 as the mapping from canonical to instance space. Needed for the l2 minimization in Alg. 4 and for gradients.
  • ad hoc to paper DDIM posterior with maximum stochasticity sigma_t = sqrt(1 - alpha_{t-1}) can be used for synchronization and stays near the data distribution at the chosen step counts.
    Given in Eq. 6 and Sec. 6. The paper itself shows in Appendix D that this stochasticity diverges as the step interval approaches zero, so its use at 25 steps is a fragile assumption.
  • domain assumption Multi-step deterministic denoising G(xt) provides a clean estimate aligned with the data manifold.
    Introduced in Sec. 6 under 'Multi-Step x0|t Computation'. Required to restore realism after maximum stochasticity perturbation.
  • ad hoc to paper Non-overlapping views sampled at different steps synchronize through temporal overlap.
    Stated in Sec. 6 under 'Non-Overlapping View Sampling'. No formal argument or isolated experiment supports this assumption; it is the load-bearing premise of the method.
  • domain assumption The noise predictor epsilon_theta is K-Lipschitz for the convergence analysis in Appendix D.
    Assumed in Appendix D.1 and used in Eq. 11 to show that deterministic denoising has slow measurement-error convergence.

how reviews work

0 comments
Cite this review

Pith. "Pith review of StochSync: Stochastic Diffusion Synchronization for Image Generation in Arbitrary Spaces." pith.science (2026). https://pith.science/paper/77JTNF7Y

@misc{pith2026250115445,
  author       = {Pith},
  title        = {Pith review of: StochSync: Stochastic Diffusion Synchronization for Image Generation in Arbitrary Spaces},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/77JTNF7Y}},
  note         = {Machine review of arXiv:2501.15445}
}
read the original abstract

We propose a zero-shot method for generating images in arbitrary spaces (e.g., a sphere for 360{\deg} panoramas and a mesh surface for texture) using a pretrained image diffusion model. The zero-shot generation of various visual content using a pretrained image diffusion model has been explored mainly in two directions. First, Diffusion Synchronization-performing reverse diffusion processes jointly across different projected spaces while synchronizing them in the target space-generates high-quality outputs when enough conditioning is provided, but it struggles in its absence. Second, Score Distillation Sampling-gradually updating the target space data through gradient descent-results in better coherence but often lacks detail. In this paper, we reveal for the first time the interconnection between these two methods while highlighting their differences. To this end, we propose StochSync, a novel approach that combines the strengths of both, enabling effective performance with weak conditioning. Our experiments demonstrate that StochSync provides the best performance in 360{\deg} panorama generation (where image conditioning is not given), outperforming previous finetuning-based methods, and also delivers comparable results in 3D mesh texturing (where depth conditioning is provided) with previous methods.

Figures

Figures reproduced from arXiv: 2501.15445 by the authors.

Figure 1
Figure 1. Assorted mesh textures and panoramas generated using [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. A comparison of SyncTweedies (Kim et al., 2024a), a synchronization method, SDS (Poole [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Qualitative results of panorama generation using PanFusion (Zhang et al., 2024a) prompts. [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Qualitative result of 3D mesh texturing. [PITH_FULL_IMAGE:figures/full_fig_p010_4.png]
Figure 6
Figure 6. Figure 6: Screenshots of the user study. The main test is shown in (a), and the vigilance test in (b). [PITH_FULL_IMAGE:figures/full_fig_p016_6.png]
Figure 7
Figure 7. Figure 7: Qualitative result of image inpainting. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_7.png]
Figure 8
Figure 8. Figure 8: Qualitative results of image inpainting. [PITH_FULL_IMAGE:figures/full_fig_p019_8.png]
Figure 10
Figure 10. Figure 10: Qualitative results of image gener￾ation with Max. σt. Each image is obtained by running different numbers of steps. Sam￾pling images with Max. σt for a large number of steps fails to generate plausible images. Experiment 2: Effect of Increasing the Number of Steps. T…
Figure 12
Figure 12. Figure 12: Runtime comparison of NVIDIA RTX A6000 and Intel Gaudi-v2 across three different timestep settings in multi-step x0|t computation. Results using Gaudi Intel-v2. Furthermore, we showcase qualitative results for 360◦ panorama generation using Intel Gaudi-v2 in [PITH_FU…
Figure 13
Figure 13. Figure 13: Qualitative results of high resolution panorama generation using [PITH_FULL_IMAGE:figures/full_fig_p022_13.png]
Figure 14
Figure 14. Figure 14: Qualitative results of texturing 3D Gaussians (Kerbl et al., 2023) using [PITH_FULL_IMAGE:figures/full_fig_p023_14.png]
Figure 15
Figure 15. Figure 15: Qualitative comparisons between L-MAGIC (Cai et al., 2024) and [PITH_FULL_IMAGE:figures/full_fig_p024_15.png]
Figure 16
Figure 16. Figure 16: Additional qualitative results of 3D mesh texturing. [PITH_FULL_IMAGE:figures/full_fig_p024_16.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Latent Swap Joint Diffusion for 2D Long-Form Latent Generation

    cs.SD 2025-02 conditional novelty 7.0 of 10

    A training-free latent swap method that replaces averaging with binary swapping in joint diffusion, improving long-form audio spectrum and panorama generation.

Reference graph

Works this paper leans on

29 extracted references · 23 canonical work pages · cited by 1 Pith paper

  1. [4]

    Text-guided texturing by synchronized multi-view diffusion

    Yuxin Liu, Minshan Xie, Hanyuan Liu, and Tien-Tsin Wong. Text-guided texturing by synchronized multi-view diffusion. arXiv preprint arXiv:2311.12891,

    arXiv

  2. [5]

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

    Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps. In NeurIPS, 2022a. 12 Published as a conference paper at ICLR 2025 Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu. Dpm-solver++: Fast solver for guided sampling of diff...

    arXiv 2025

  3. [6]

    Score distillation via reparametrized ddim

    Artem Lukoianov, Haitz Sáez de Ocáriz Borde, Kristjan Greenewald, Vitor Campagnolo Guizilini, Timur Bagautdinov, Vincent Sitzmann, and Justin Solomon. Score distillation via reparametrized ddim. arXiv preprint arXiv:2405.15891,

    arXiv

  4. [8]

    Paint-it: Text-to-Texture Synthesis via Deep Convolutional Texture Map Optimization and Physically-Based Rendering

    Kim Youwang, Tae-Hyun Oh, and Gerard Pons-Moll. Paint-it: Text-to-texture synthesis via deep convolutional texture map optimization and physically-based rendering. arXiv preprint arXiv:2312.11360,

    work page Pith review arXiv

  5. [9]

    5.2 of the main paper is equivalent to the original loss presented in DreamFusion (Poole et al.,

    14 Published as a conference paper at ICLR 2025 APPENDIX A R EFORMULATION OF SDS L OSS Here, we show that the SDS loss introduced in Sec. 5.2 of the main paper is equivalent to the original loss presented in DreamFusion (Poole et al.,

    work page 2025

  6. [11]

    Which image has better quality, fewer seams, fewer distortions, and better alignment with the given text prompt across the panoramic view?

    and the other by StochSync. They were asked to answer the following question: “Which image has better quality, fewer seams, fewer distortions, and better alignment with the given text prompt across the panoramic view?” In each user study, 25 panoramic images were shown in a shuffled order, including five vigilance tests. For the vigilance tests, participa...

    work page 2025

  7. [12]

    Under these assumptions, the reformulation of a one-step denoising process from the perspective of x0|t yields the following

    • The pretrained noise prediction network ϵθ(·, ·) is K-Lipschitz, satisfying |ϵθ(xt, t) − ϵθ(xt−∆t, t− ∆t)| < K|xt − xt−∆t| for some constant K. Under these assumptions, the reformulation of a one-step denoising process from the perspective of x0|t yields the following. x0|t−∆t = x0|t + s 1 − αt−∆t αt−∆t (ϵt − ϵt−∆t) . (10) ∴ |x0|t−∆t − x0|t| = s 1 − αt−...

    work page 2025

  8. [14]

    A bowl of cereal with a spoon on a kitchen counter

    The images are obtained by solving the ODE, G(xt), initialized from the same random noise xT . Red boxes are used to highlight the convergence of x0|t to y. As illustrated, methods with maximum stochasticity (Max. σt and StochSync) converge significantly faster than σt = 0, a trend also reflected in the measurement error plot (Fig. 9). Additionally, Stoch...

    work page 2024

Show all 29 references
  1. [15]

    Graffiti-covered alleyway with street art murals

    Instead, it can stop at t = Tstop ≫ 0, effectively reducing the number of denoising steps. The optimal configuration was found to be Tstop = 700 with 8 denoising steps, which we denote as StochSync∗. Further improvements in efficiency were achieved by incorporating advanced OD...

    2024

  2. [16]

    Quirky steampunk workshop filled with gears and gadgets

    (Fig. 14). High Resolution Panorama Generation. To extend StochSync to high-resolution panorama generation, we modify the original panorama generation setup by narrowing the field of view for individual views and increasing the number of samples, resulting in a higher-resoluti...

    2021

  3. [17]

    Table 8: Quantitative results of panorama gener- ation using the prompts provided in L-MAGIC (Cai et al

    using StochSync. Table 8: Quantitative results of panorama gener- ation using the prompts provided in L-MAGIC (Cai et al. (2024)). GIQA is scaled by

    2024

  4. [19]

    A photo of a savanna in Tanzaniawith horizon

    More qualitative results of 360◦ panorama generation are presented in the following pages. 23 Published as a conference paper at ICLR 2025 “A photo of a savanna in Tanzaniawith horizon.” “A photo of a sunflower field in Kansas with horizon.” “A photo of a tropical island in th...

    2025

  5. [20]

    Desert canyon, sculpted sandstone

    Text2Tex (Chen et al., 2023a) StochSync Figure 16: Additional qualitative results of 3D mesh texturing. 24 Published as a conference paper at ICLR 2025 G.1 A DDITIONAL 360◦ PANORAMA GENERATION RESULTS USING PANFUSION PROMPTS “Desert canyon, sculpted sandstone.” SDS (Poole et al.,

    2025

  6. [21]

    Desert dunes, endless golden waves

    StochSync 25 Published as a conference paper at ICLR 2025 “Desert dunes, endless golden waves.” SDS (Poole et al.,

    2025

  7. [22]

    Moonlit beach, waves whispering secrets

    StochSync 26 Published as a conference paper at ICLR 2025 “Moonlit beach, waves whispering secrets.” SDS (Poole et al.,

    2025

  8. [23]

    Nestled in a canyon, a pueblo village stands against the red earth

    StochSync 27 Published as a conference paper at ICLR 2025 “Nestled in a canyon, a pueblo village stands against the red earth.” SDS (Poole et al.,

    2025

  9. [24]

    The interior of a historic library, filled with rows of antique books, leather-bound and dust-covered

    StochSync 28 Published as a conference paper at ICLR 2025 “The interior of a historic library, filled with rows of antique books, leather-bound and dust-covered.” SDS (Poole et al.,

    2025

  10. [25]

    Surreal desert, mirage of shimmering heat, dunes stretching endlessly

    StochSync 29 Published as a conference paper at ICLR 2025 “Surreal desert, mirage of shimmering heat, dunes stretching endlessly.” SDS (Poole et al.,

    2025

  11. [26]

    Alpine village, snow-covered rooftops, nestled between majestic peaks—a picture-perfect scene of winter tranquility

    StochSync 30 Published as a conference paper at ICLR 2025 “Alpine village, snow-covered rooftops, nestled between majestic peaks—a picture-perfect scene of winter tranquility.” SDS (Poole et al.,

    2025

  12. [27]

    Desert canyon, ancient rock formations sculpted by time, a vast expanse of terracotta hues—an arid symphony of textures

    StochSync 31 Published as a conference paper at ICLR 2025 “Desert canyon, ancient rock formations sculpted by time, a vast expanse of terracotta hues—an arid symphony of textures.” SDS (Poole et al.,

    2025

  13. [28]

    Desert under starlit sky

    StochSync 32 Published as a conference paper at ICLR 2025 G.2 M ORE 360◦ PANORAMA GENERATION RESULTS USING L-MAGIC P ROMPTS “Desert under starlit sky” SDS (Poole et al.,

    2025

  14. [29]

    Japanese Zen meditation room

    StochSync 33 Published as a conference paper at ICLR 2025 “Japanese Zen meditation room” SDS (Poole et al.,

    2025

  15. [96]

    For multi-step x0|t computation, the total number of steps is initially set to 50, decreasing linearly as the denoising process progresses

    A linearly decreasing timestep schedule is employed, starting from T = 900 and decreasing to Tstop = 270 , with a total of 25 denoising steps. For multi-step x0|t computation, the total number of steps is initially set to 50, decreasing linearly as the denoising process progre...

    2022

  16. [103]

    The best result in each column is highlighted in bold, and the runner-up is underlined. Method FID ↓ IS ↑ GIQA ↑ CLIP ↑ SDS 163.23 5.60 17.41 30.37 SDI 171.69 5.93 16.42 29.33 ISM 197.10 4.92 16.52 29.44 MVDiffusion 111.12 6.17 20.71 31.07 PanFusion 151.60 5.48 18.19 28.46 L-M...

    2024

  17. [1956]

    σt leads to xt−∆t being placed in low-density regions of the noisy data distribution, ultimately degrading the quality of x0

    often deviate from the clean data manifold, sampling process using Max. σt leads to xt−∆t being placed in low-density regions of the noisy data distribution, ultimately degrading the quality of x0. Inspired by this observation, we note that x0|t should be well-aligned with the...

    2025

  18. [2015]

    Denoising diffusion implicit models

    Jiaming Song, Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. In ICLR, 2021a. Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equations. In ...

    2025 arXiv

  19. [2022]

    Images that sound: Composing images and sounds on a single canvas

    Ziyang Chen, Daniel Geng, and Andrew Owens. Images that sound: Composing images and sounds on a single canvas. arXiv preprint arXiv:2405.12221, 2024b. 11 Published as a conference paper at ICLR 2025 Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli Van...

    2025 arXiv

  20. [2023]

    Synctweedies: A general generative framework based on synchronized diffusions

    Jaihoon Kim, Juil Koo, Kyeongmin Yeo, and Minhyuk Sung. Synctweedies: A general generative framework based on synchronized diffusions. arXiv preprint arXiv:2403.14370, 2024a. Jeongsol Kim, Geon Yeong Park, and Jong Chul Ye. Dreamsampler: Unifying diffusion sampling and score d...

    arXiv

  21. [2024]

    Noise-free score distillation.arXiv preprint arXiv:2310.17590,

    Oren Katzir, Or Patashnik, Daniel Cohen-Or, and Dani Lischinski. Noise-free score distillation.arXiv preprint arXiv:2310.17590,

    arXiv

Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.