Pith. sign in

REVIEW 5 major objections 5 minor 36 references

Leapfrog Latent Consistency Model (LLCM) for Medical Images Generation

T0 review · 5 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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

Pith's one-line read A leapfrog solver lets a latent consistency model generate 512x512 medical images in as few as 1-4 inference steps, reaching FID 145.68 at 4 steps on the MedImgs test set, below the FID of stable diffusion (249.18), Dreambooth (300.15)…

desk verdict Useful medical image dataset, but the few-step generation claims rest on an uncontrolled comparison and an algorithm that does not match the equations. read the letter →

arxiv 2411.15084 v1 pith:R5EFR7EX submitted 2024-11-22 eess.IV cs.CVcs.LG

Lakshmikar R. Polamreddy , Kalyan Roy , Sheng-Han Yueh , Deepshikha Mahato , Shilpa Kuppili , Jialu Li , Youshan Zhang This is my paper · ORCID
classification eess.IVcs.CVcs.LG
keywords medicalimagegenerationlatentconsistencymodelleapfrogsolverprobabilityflowODEfew-stepinferenceMedImgsdatasetFIDevaluation
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

The paper introduces MedImgs, a dataset of over 250,127 medical images across 61 disease types and 159 human and animal classes, and a Leapfrog Latent Consistency Model (LLCM) distilled from a diffusion model retrained on MedImgs. LLCM solves the reverse diffusion PF-ODE in latent space with a leapfrog integrator, enabling high-resolution image generation in 1-4 steps. The central claim is that LLCM achieves state-of-the-art FID scores at few-step inference, notably 145.68 at 4 steps, outperforming stable diffusion, Dreambooth, and the original latent consistency model. If this holds, real-time, high-quality medical image synthesis becomes practical on limited compute.

What carries the argument

The leapfrog solver for the PF-ODE (Eq. 16), which sets the midpoint velocity to double the current velocity (v1/2 = 2vt), lets one solver step advance many noise-schedule intervals; the consistency distillation loss (Eq. 12) then trains the model to make its output consistent across time steps k apart, so few-step sampling matches a longer deterministic trajectory.

What would settle it

Train LLCM with the same setup but replace the leapfrog update with a standard Euler step (v1/2 = vt) while keeping everything else identical; if FID at 4 steps does not worsen, then the leapfrog mechanism is not the reason for the improvement. Additionally, recompute the consistency loss using z_hat_tn from Eq. 14 instead of the exact z_tn and compare FID; a significant drop would indicate that the shortcut in the loss, rather than the leapfrog solver, drives the reported gain.

Watch

Extended reading notes

Core claim

LLCM treats the reverse diffusion process in latent space as a probability-flow ODE and approximates it with a leapfrog solver that uses a midpoint velocity v1/2 = 2vt to jump several noise-schedule steps at once. The model is trained with a consistency distillation loss that pushes fθ(zt_{n+k}, tn+k) toward fθ−(zt_n, tn), ultimately mapping noise directly to a clean image. At 4 inference steps the model reaches FID 145.68 on 35 test classes, better than every baseline at every step count tested, and it generalizes to unseen dog cardiac X-ray images.

Load-bearing premise

The leapfrog update v1/2 = 2vt is a valid approximation of the reverse PF-ODE, and the training loss that uses exact z_tn rather than the solver's z_hat_tn still produces a consistency model that matches the leapfrog solution; if the factor 2 is arbitrary or the omitted solver term is essential, the reported FID improvements may not be caused by the leapfrog mechanism.

Editorial extensions

If this is right

  • Medical image generation at 4 steps could enable near-real-time synthesis for clinical decision support and data augmentation.
  • The MedImgs dataset provides a broad benchmark for evaluating generative models across 159 disease classes, including rare conditions.
  • LLCM can be fine-tuned on custom medical datasets, so hospitals could adapt it to their own imaging protocols without sharing patient data.
  • Fewer inference steps translate directly to lower latency and energy cost, making deployment on modest hardware more feasible.

Reading between the lines

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

  • The leapfrog solver is domain-agnostic, so the same 1-4 step speedup likely transfers to non-medical image generation tasks if the distillation is repeated on other data.
  • The factor 2 in v1/2 = 2vt could be interpreted as a second-order midpoint approximation; replacing it with a learnable parameter might further improve FID at even lower step counts.
  • A direct comparison using the solver's estimated z_hat_tn in the training loss, rather than the exact noised z_tn, would isolate whether the reported gain genuinely comes from the leapfrog mechanism.
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

5 major / 5 minor

Summary. MedImgs, a medical image collection of 250,127 images across 61 disease types and 159 classes, is presented together with LLCM, a latent consistency model distilled from a re-trained stable diffusion model. The reverse PF-ODE is solved with a leapfrog solver in latent space, aiming for 1–4 step generation of 512x512 medical images. The paper claims state-of-the-art FID (145.68 at 4 steps) on the MedImgs test classes and a qualitative improvement on unseen dog cardiac X-rays.

Significance. The dataset has practical scale and the few-step generation goal is timely. The paper also ships source code and describes a clear training-device budget. If the leapfrog solver were shown to be the actual cause of the improvement under controlled comparison, LLCM would be a useful contribution to medical image generation. At present, however, the empirical package is not sufficient to support the state-of-the-art claim because the baselines are not described as being trained on MedImgs, the training algorithm does not match the consistency loss derivation, and the only out-of-domain evaluation is qualitative.

major comments (5)
  1. [Section 6, Table 3] Table 3 compares LLCM against Stable Diffusion, Dreambooth, and LCM, but the text never states whether any of these baselines were fine-tuned or retrained on the MedImgs training distribution. Dreambooth is a subject-specific fine-tuning procedure, so using it as a fixed baseline is not defined without specifying its prompt set and training images. Since LLCM is the only method explicitly trained on MedImgs, the FID gap in Table 3 can be explained entirely by in-domain dataset exposure rather than by the leapfrog mechanism; the authors should retrain all baselines on the same MedImgs split or clearly restrict the claim to out-of-the-box baselines.
  2. [Section 5, Eqs. 12–14 and Algorithm 1] The consistency distillation loss in Eq. 12 requires the target f_theta-(z_hat^Psi,omega_tn, ...), where z_hat is the one-step solver estimate from Eqs. 13–14. Algorithm 1, however, computes the loss using the exactly noised z_tn and never applies the leapfrog solver; the line L(theta, theta-) <- d(f_theta(ztn+k, ...), f_theta-(ztn, ...)) does not match Eq. 12. In addition, Eq. 13 integrates forward from tn to tn+k but the left side is z_tn - z_tn+k, which reverses the orientation of the reverse-time ODE. The paper should correct Eq. 13 and either revise Algorithm 1 to include the solver step or explain why the exact-z_tn target still enforces consistency with the leapfrog solution.
  3. [Section 5, Eqs. 15–16] The leapfrog derivation is incomplete: Eq. 15 is the standard position-velocity leapfrog update for a second-order system, but the PF-ODE in Eq. 10 is first-order, and the text does not specify how F(x) is obtained from the ODE right-hand side. The setting v1/2 = 2v_t in Eq. 16 is stated without derivation or justification, and since this factor multiplies the entire one-step displacement it changes the numerical solution; no ablation over this coefficient is reported. If the factor 2 is not a consequence of the midpoint rule, the FID improvement cannot be attributed to a faithful leapfrog discretization.
  4. [Table 3] In Table 3, LLCM's FID at Step 1 equals 198.32 and at Step 10 also equals 198.32, which is implausible unless the model is deterministic with respect to step count or the number is a typo or rounding artifact; the paper also reports no error bars, seeds, or statistics across runs. At minimum, the authors should correct the table and report mean plus or minus standard deviation over several seeds for at least the key step counts (1, 4, 10); otherwise the headline number 145.68 is not statistically assessable.
  5. [Section 6, Figure 4] The claim that LLCM outperforms existing models on unseen dog cardiac X-ray images is supported only by a qualitative figure; no quantitative metric, such as FID on generated dog X-rays or downstream cardiomegaly classification accuracy, is given. Since the cited dog dataset [14] is from the authors' prior work, the paper must also clarify how those images were held out from the MedImgs animal classes and provide an exact evaluation protocol; otherwise the generalization claim is not verifiable.
minor comments (5)
  1. [Abstract] The abstract says 'over 250,127 images' but Section 3's train and test counts sum to exactly 250,127; please align the wording.
  2. [Table 3] The header contains 'Steps2' instead of 'Step 2,' and 'Step1' should read 'Step 1'; also the row labels mix number and word forms, such as 'Step1' versus 'Steps20.'
  3. [Section 4.3] The symbol epsilon is used both as the small positive offset in the consistency function f(x_t,t) mapping to x_epsilon and as the Gaussian noise epsilon in Algorithm 1; please use distinct notation.
  4. [Eq. (11)] The functions c_skip(t) and c_out(t) are not fully specified; please give the explicit schedules rather than only the boundary conditions c_skip(0)=1 and c_out(0)=0.
  5. [Section 6] The training description says '55 epochs' with '184 batches per epoch' and also 'the entire training process encompasses 10,000 iterations'; these numbers do not reconcile exactly, since 55 times 184 equals 10,120, so please clarify the intended schedule.

Circularity Check

2 steps flagged · score 2.0 of 10

No definitional circularity; the FID result is measured rather than fitted, but a mild self-citation and an internal equation/algorithm mismatch weaken attribution without making the result equivalent to its inputs.

  1. other [Section 5, Eq. (12)-(14) vs Algorithm 1]
    "LCD(θ, θˆ; Ψ ) = Ez,ω,c,n h d fθ(ztn+k , ω, c, tn+k), fθ− (ˆzΨ,ω tn , ω, c, tn) i ... L(θ, θ−) ← d(fθ(ztn+k, tn+k, c, w), fθ− (ztn, tn, c, w))"

    The stated consistency-distillation loss uses the Leapfrog solver output z_hat (Eqs. 13-14), but Algorithm 1 replaces z_hat with the exact noised latent ztn, which is generated from the same z and the same Gaussian noise as ztn+k. By the paper's own definitions, ztn is not the leapfrog PF-ODE estimate, so the Leapfrog solver does not enter the fitted objective. The reported FID cannot, on the paper's own construction, be attributed to the leapfrog mechanism; the named component is absent from the algorithm that produced the trained model.

  2. other [Section 6, Results; Reference [14]]
    "To test the generalization ability of our model, we applied our LLCM model in an unseen dog heart X-ray dataset [14], and we achieved remarkable results compared to state-of-the-art generation models."

    Reference [14] is the prior work of co-authors Jialu Li and Youshan Zhang, so the 'unseen' generalization evidence is sourced from the authors' own dataset. This is a minor self-support issue rather than load-bearing circularity: the dataset is a data source, not a theorem, and the main FID comparison in Table 3 does not depend on this citation. It does, however, make the external check partly self-referential.

full rationale

The paper's central quantitative claim, LLCM reaching the lowest FID at Step 4, is a measured evaluation on MedImgs test classes, not a quantity derived from a fitted parameter. The consistency model is trained on MedImgs and the FID is computed on held-out test images, so the headline result does not reduce to a fit by construction. The uncontrolled baseline comparison (off-the-shelf Stable Diffusion, Dreambooth, and LCM apparently not retrained on MedImgs) is a fairness and correctness concern, but it is not circularity. The leapfrog update's factor 2 in Eq. 16 is a hand-set ansatz rather than a derived first-principles coefficient; this weakens the theoretical derivation and makes the mechanism's benefit untested, but it does not make the FID value equivalent to the ansatz. The more serious internal problem is that Algorithm 1's training loss omits the Leapfrog solver estimate that Eq. 12 claims to use, so the trained model may not implement the described leapfrog distillation. That is an internal inconsistency and an attribution gap, not a case where a prediction is equal to its input by definition. Finally, the unseen dog cardiac X-ray evaluation cites a dataset from two of the paper's own authors, a mild self-citation that is not load-bearing for the main FID comparison. Overall, no self-definitional, fitted-input-called-prediction, or uniqueness-imported circularity is present; the score of 2 reflects the minor self-citation and the supportive but non-circular weaknesses.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central method rests on several unverified premises: the validity of the leapfrog update with an unexplained factor 2, a training objective that may omit the solver, and an aggregated dataset with undisclosed composition. No new physical or conceptual entities are introduced.

free parameters (4)
  • Midpoint velocity multiplier in leapfrog update = 2 (v1/2 = 2·v_t)
    Introduced without derivation in Eq. 16; the central update uses this multiplier and the FID results depend on it.
  • Jumping interval k = 20
    Set by ablation on one Alzheimer's class (Fig. 3); no justification for all classes is given.
  • CFG scale range [wmin, wmax] = Not reported
    The consistency distillation samples omega uniformly from [wmin, wmax], but the actual interval used in training is not stated.
  • EMA decay = 0.95
    Chosen hyperparameter for training stability; no sensitivity analysis is provided.
assumptions (4)
  • domain assumption The reverse diffusion process can be accurately solved as a PF-ODE in latent space (Eqs. 8-10).
    Follows from Song et al. [30] under regularity conditions that are assumed without verification for medical image latents.
  • ad hoc to paper The leapfrog discretization of Eqs. 15-16 is a valid PF-ODE solver for the latent diffusion ODE.
    The update x_{t-1} = x_t + h·v1/2 with v1/2 = 2·v_t is stated without proof and is not the standard leapfrog or DDIM update.
  • standard math The consistency distillation objective with the target model's output on z_hat_tn (Eq. 12) minimizes the distance along the PF-ODE trajectory.
    Borrowed from Song et al. [29]; accepted as background.
  • domain assumption The FID computed on 175,000 generated images against the MedImgs test set is a valid measure of medical image quality.
    FID is a general-purpose distributional metric; its suitability for medical images is not interrogated.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Leapfrog Latent Consistency Model (LLCM) for Medical Images Generation." pith.science (2026). https://pith.science/paper/R5EFR7EX

@misc{pith2026241115084,
  author       = {Pith},
  title        = {Pith review of: Leapfrog Latent Consistency Model (LLCM) for Medical Images Generation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/R5EFR7EX}},
  note         = {Machine review of arXiv:2411.15084}
}
read the original abstract

The scarcity of accessible medical image data poses a significant obstacle in effectively training deep learning models for medical diagnosis, as hospitals refrain from sharing their data due to privacy concerns. In response, we gathered a diverse dataset named MedImgs, which comprises over 250,127 images spanning 61 disease types and 159 classes of both humans and animals from open-source repositories. We propose a Leapfrog Latent Consistency Model (LLCM) that is distilled from a retrained diffusion model based on the collected MedImgs dataset, which enables our model to generate real-time high-resolution images. We formulate the reverse diffusion process as a probability flow ordinary differential equation (PF-ODE) and solve it in latent space using the Leapfrog algorithm. This formulation enables rapid sampling without necessitating additional iterations. Our model demonstrates state-of-the-art performance in generating medical images. Furthermore, our model can be fine-tuned with any custom medical image datasets, facilitating the generation of a vast array of images. Our experimental results outperform those of existing models on unseen dog cardiac X-ray images. Source code is available at https://github.com/lskdsjy/LeapfrogLCM.

Figures

Figures reproduced from arXiv: 2411.15084 by the authors.

Figure 1
Figure 1. Comparison of LLCM different steps generated medical images of [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Flowchart of Image Generation with Leapfrog Latent Consistency Model. [PITH_FULL_IMAGE:figures/full_fig_p009_2.png] view at source ↗
Figure 3
Figure 3. Comparison of LLCM generated medical images of ( [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Comparison results of unseen large dog heart X-ray images generated by [PITH_FULL_IMAGE:figures/full_fig_p011_4.png]
Figure 5
Figure 5. Figure 5: Generated images results with our LLCM model of ( [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

36 extracted references · 20 canonical work pages

  1. [14]

    Scientific Reports 14(1), 1539 (2024)

    Li, J., Zhang, Y.: Regressive vision transformer for dog cardiomegaly assessment. Scientific Reports 14(1), 1539 (2024)

    work page 2024

  2. [1]

    Computerized medical imaging and graphics 79, 101684 (2020)

    Armanious, K., Jiang, C., Fischer, M., K¨ ustner, T., Hepp, T., Nikolaou, K., Gatidis, S., Yang, B.: Medgan: Medical image translation using gans. Computerized medical imaging and graphics 79, 101684 (2020)

    work page 2020

  3. [2]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Bhunia, A.K., Khan, S., Cholakkal, H., Anwer, R.M., Laaksonen, J., Shah, M., Khan, F.S.: Person image synthesis via denoising diffusion model. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5968–5976 (2023)

    work page 2023

  4. [3]

    In: 2009 IEEE conference on computer vision and pattern recognition

    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)

    2009

  5. [4]

    Advances in neural information processing systems 34, 8780–8794 (2021)

    Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. Advances in neural information processing systems 34, 8780–8794 (2021)

    2021

  6. [5]

    Authorea Preprints (2023)

    Dorjsembe, Z., Pao, H.K., Odonchimed, S., Xiao, F.: Conditional diffusion models for semantic 3d medical image synthesis. Authorea Preprints (2023)

    work page 2023

  7. [6]

    In: Proceedings of the AAAI conference on artificial intelligence

    Fan, W.C., Chen, Y.C., Chen, D., Cheng, Y., Yuan, L., Wang, Y.C.F.: Frido: Feature pyramid diffusion for complex scene image synthesis. In: Proceedings of the AAAI conference on artificial intelligence. vol. 37, pp. 579–587 (2023)

    work page 2023

  8. [7]

    Medical Image Analysis 72, 102106 (2021)

    Havaei, M., Mao, X., Wang, Y., Lao, Q.: Conditional generation of medical images via disentangled adversarial inference. Medical Image Analysis 72, 102106 (2021)

    work page 2021

Show all 36 references
  1. [8]

    Advances in neural information processing systems 30 (2017)

    Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017)

    2017

  2. [9]

    Advances in neural information processing systems 33, 6840–6851 (2020)

    Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33, 6840–6851 (2020)

    2020

  3. [10]

    arXiv preprint arXiv:2207.12598 (2022) Leapfrog Latent Consistency Model (LLCM) for Medical Images Generation 15

    Ho, J., Salimans, T.: Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022) Leapfrog Latent Consistency Model (LLCM) for Medical Images Generation 15

    2022 arXiv

  4. [11]

    Bio- engineering 10(11), 1258 (2023)

    Hung, A.L.Y., Zhao, K., Zheng, H., Yan, R., Raman, S.S., Terzopoulos, D., Sung, K.: Med-cdiff: Conditional medical image generation with diffusion models. Bio- engineering 10(11), 1258 (2023)

    2023

  5. [12]

    Scientific Reports 13(1), 7303 (2023)

    Khader, F., M¨ uller-Franzes, G., Tayebi Arasteh, S., Han, T., Haarburger, C., Schulze- Hagen, M., Schad, P., Engelhardt, S., Baeßler, B., Foersch, S., et al.: Denoising diffusion probabilistic models for 3d medical image generation. Scientific Reports 13(1), 7303 (2023)

    2023

  6. [13]

    Advances in neural information processing systems 34, 21696–21707 (2021)

    Kingma, D., Salimans, T., Poole, B., Ho, J.: Variational diffusion models. Advances in neural information processing systems 34, 21696–21707 (2021)

    2021

  7. [15]

    In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision

    Liu, X., Park, D.H., Azadi, S., Zhang, G., Chopikyan, A., Hu, Y., Shi, H., Rohrbach, A., Darrell, T.: More control for free! image synthesis with semantic diffusion guidance. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 289–299 (2023)

    2023

  8. [16]

    arXiv preprint arXiv:2310.04378 (2023)

    Luo, S., Tan, Y., Huang, L., Li, J., Zhao, H.: Latent consistency models: Synthesizing high-resolution images with few-step inference. arXiv preprint arXiv:2310.04378 (2023)

    2023 arXiv

  9. [17]

    In: Proceedings of the 31st ACM International Conference on Multimedia

    Mao, J., Wang, X., Aizawa, K.: Guided image synthesis via initial image editing in diffusion model. In: Proceedings of the 31st ACM International Conference on Multimedia. pp. 5321–5329 (2023)

    2023

  10. [18]

    IEEE Journal of Biomedical and Health Informatics 26(12), 6070–6080 (2022)

    Moon, J.H., Lee, H., Shin, W., Kim, Y.H., Choi, E.: Multi-modal understanding and generation for medical images and text via vision-language pre-training. IEEE Journal of Biomedical and Health Informatics 26(12), 6070–6080 (2022)

    2022

  11. [19]

    In: International Conference on Machine Learning

    Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: International Conference on Machine Learning. pp. 8162–8171. PMLR (2021)

    2021

  12. [20]

    Physics in Medicine & Biology 68(10), 105004 (2023)

    Pan, S., Wang, T., Qiu, R.L., Axente, M., Chang, C.W., Peng, J., Patel, A.B., Shel- ton, J., Patel, S.A., Roper, J., et al.: 2d medical image synthesis using transformer- based denoising diffusion probabilistic model. Physics in Medicine & Biology 68(10), 105004 (2023)

    2023

  13. [21]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Phung, Q., Ge, S., Huang, J.B.: Grounded text-to-image synthesis with attention refocusing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 7932–7942 (2024)

    2024

  14. [22]

    In: MICCAI Workshop on Deep Generative Models

    Pinaya, W.H., Tudosiu, P.D., Dafflon, J., Da Costa, P.F., Fernandez, V., Nachev, P., Ourselin, S., Cardoso, M.J.: Brain imaging generation with latent diffusion models. In: MICCAI Workshop on Deep Generative Models. pp. 117–126. Springer (2022)

    2022

  15. [23]

    arXiv preprint arXiv:2307.01952 (2023)

    Podell, D., English, Z., Lacey, K., Blattmann, A., Dockhorn, T., M¨ uller, J., Penna, J., Rombach, R.: Sdxl: Improving latent diffusion models for high-resolution image synthesis. arXiv preprint arXiv:2307.01952 (2023)

    2023 arXiv

  16. [24]

    In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10684–10695 (2022)

    2022

  17. [25]

    arXiv preprint arXiv:2207.13038 (2022)

    Rombach, R., Blattmann, A., Ommer, B.: Text-guided synthesis of artistic images with retrieval-augmented diffusion models. arXiv preprint arXiv:2207.13038 (2022)

    2022 arXiv

  18. [26]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., Aberman, K.: Dream- booth: Fine tuning text-to-image diffusion models for subject-driven generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 22500–22510 (2023)

    2023

  19. [27]

    large-scale dataset for training next generation image-text models

    Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., et al.: Laion-5b: An open 16 Polamreddy et al. large-scale dataset for training next generation image-text models. Advances in Neural Information Pro...

    2022

  20. [28]

    arXiv preprint arXiv:2010.02502 (2020)

    Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)

    2020 arXiv

  21. [29]

    Song, Y., Dhariwal, P., Chen, M., Sutskever, I.: Consistency models (2023)

    2023

  22. [30]

    arXiv preprint arXiv:2011.13456 (2020)

    Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score- based generative modeling through stochastic differential equations. arXiv preprint arXiv:2011.13456 (2020)

    2020 arXiv

  23. [31]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Tao, M., Bao, B.K., Tang, H., Xu, C.: Galip: Generative adversarial clips for text- to-image synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14214–14223 (2023)

    2023

  24. [32]

    In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part VI 22

    Uzunova, H., Ehrhardt, J., Jacob, F., Frydrychowicz, A., Handels, H.: Multi- scale gans for memory-efficient generation of high resolution medical images. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd International Conference, Shenzhen, China...

    2019

  25. [33]

    In: Proceedings of the AAAI Conference on Artificial Intelligence

    Wang, R., Chen, Z., Chen, C., Ma, J., Lu, H., Lin, X.: Compositional text-to-image synthesis with attention map control of diffusion models. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 38, pp. 5544–5552 (2024)

    2024

  26. [34]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision

    Wu, Q., Liu, Y., Zhao, H., Bui, T., Lin, Z., Zhang, Y., Chang, S.: Harnessing the spatial-temporal attention of diffusion models for high-fidelity text-to-image synthesis. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 7766–7776 (2023)

    2023

  27. [35]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision

    Xie, J., Li, Y., Huang, Y., Liu, H., Zhang, W., Zheng, Y., Shou, M.Z.: Boxdiff: Text-to-image synthesis with training-free box-constrained diffusion. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 7452–7461 (2023)

    2023

  28. [36]

    In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition

    Xue, H., Huang, Z., Sun, Q., Song, L., Zhang, W.: Freestyle layout-to-image synthesis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14256–14266 (2023)

    2023

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.