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Improving Compositional Generation with Diffusion Models Using Lift Scores

T0 review · 5 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read A diffusion model can grade its own output: reject images that its own denoiser does not reconstruct better when told the prompt, and compositional prompt-following improves, with no retraining and no external verifier.

desk verdict CompLift is a genuinely useful training-free rejection filter for compositional diffusion with broad experiments, but its headline gains rest on hyperparameters tuned on the evaluation sets, so the generalizable-criterion claim needs a validation-split rerun. read the letter →

arxiv 2505.13740 v2 pith:RZXAPTIA submitted 2025-05-19 cs.LG cs.AIcs.CV

Chenning Yu , Sicun Gao This is my paper · ORCID
classification cs.LGcs.AIcs.CV
keywords compositionalgenerationdiffusionmodelsliftscoresrejectionsamplingtraining-freemethodstext-to-imagesynthesisELBOapproximationresamplingcriterion
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 tries to establish that a diffusion model can check its own output against a prompt: before trusting an image, ask the model whether knowing the condition actually lowers its reconstruction error. The lift score — the expected gap between unconditional and conditional denoising error — is positive exactly when the condition is doing work, and per-condition scores composed by AND/OR/NOT logic decide whether the whole prompt is satisfied. The authors report that this training-free acceptance rule improves condition alignment over the Composable Diffusion baseline across 2D synthetic compositions, CLEVR position tasks, and text-to-image generation with three Stable Diffusion backbones, at near-zero extra cost once denoising predictions are cached. If the claim holds, any conditional diffusion model gains a built-in test-time self-critic, without an external scorer, a verifier, or additional training.

What carries the argument

The load-bearing object is the lift score, $\operatorname{lift}(x|c) \approx \mathbb{E}_{t,\epsilon}\big[\|\epsilon - \epsilon_\theta(x_t,\emptyset)\|^2 - \|\epsilon - \epsilon_\theta(x_t,c)\|^2\big]$ (Eq. 3): the expected gap between unconditional and conditional denoising error, which is positive precisely when the condition improves reconstruction of the sample. Around this quantity the paper assembles three further pieces. The Compose function converts any prompt algebra into conjunctive normal form and combines the signs of per-condition lift scores into a single boolean accept/reject verdict, covering product, mixture, and negation. The image-space estimator (Eq. 5) replaces the random noise $\epsilon$ with the composed-prompt prediction $\epsilon_\theta(z_t, c_{\mathrm{compose}})$, which is assumed to share the model's bias and therefore cancel it, sharply reducing per-pixel variance. The activated-pixel count (Eq. 4) then detects object presence as the number of latent pixels with positive lift minus a threshold $\tau$. Finally, the cached variant (Algorithm 2) reuses the denoising predictions made during ordinary generation, so under classifier-free guidance with Composable Diffusion the number of extra forward passes is zero.

What would settle it

Two checks would settle the claim. First, measure the variance of the Eq. (3) estimator across trials on a fixed image: if the standard error of the lift score is comparable to its mean, the accept/reject boundary is dominated by estimation noise rather than condition alignment. Second, freeze all constants before seeing the test distribution — pick $\tau$ on held-out validation prompts from object classes not in the benchmark and sample the timestep by the training distribution rather than the single best observed value — and re-run the text-to-image experiments; if the CLIP and minCLIP gains largely disappear, the reported improvement is attributable to the tuned constants rather than to the lift-score principle itself.

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Extended reading notes

Core claim

The central claim is that the lift score, $\log p(x|c) - \log p(x)$, approximated by the difference between unconditional and conditional denoising losses (Eq. 3), is a dependable per-condition test of whether a generated sample matches a condition. A product prompt such as 'a black car and a white clock' is accepted only when every object's lift is positive; mixtures and negations are composed from the same per-condition scores by logical rules. The paper reports large accuracy gains from applying this filter: from 56.5% to 99.9% on 2D product compositions, from 78.7% to 90.3% at five simultaneous constraints on CLEVR, and consistent CLIP and ImageReward improvement in text-to-image across SD 1.4, 2.1, and XL, with an older backbone plus the filter approaching the next generation's vanilla performance. For text-to-image, the estimator is stabilized by comparing the composed-prompt denoising prediction against the unconditional and per-condition predictions (Eq. 5), and object presence is read off as the count of latent pixels with positive lift above a threshold (Eq. 4). The paper also states the scope of its claims explicitly: the criterion inherits the quality of the underlying generator, and OR/NOT algebra is tested on 2D data but not on text-to-image.

Load-bearing premise

The method assumes that the difference in denoising error with and without the condition, averaged over a small number of Monte Carlo trials, is an accurate and unbiased measure of whether the condition truly matches the image, and it leans on several constants tuned on the evaluation tasks themselves ($\alpha = 0.9$ in 2D, $t = 928$ on CLEVR, $\tau = 250$ for text-to-image).

Editorial extensions

If this is right

  • Any diffusion model that can be run both with and without a condition inherits a free self-verifier: when classifier-free guidance and composable sampling are already in use, the cached version adds zero extra forward passes.
  • On the CLEVR position task the accuracy gap widens as the prompt grows harder (78.7% to 90.3% at five constraints), so the criterion degrades much more gracefully than the baseline as compositional load increases.
  • The filter transfers across backbones: SD 1.4 plus CompLift approaches vanilla SD 2.1, and SD 2.1 plus CompLift approaches vanilla SDXL on parts of the Attend-and-Excite benchmark, meaning part of a newer model's advantage can be recovered at test time.
  • Rejection trims diversity: CLEVR FID worsens modestly as accuracy improves, so the method trades sample variety for prompt fidelity.

Reading between the lines

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

  • The per-condition scores carry more than a verdict: the non-positive lift identifies which object is missing, so the filter could drive targeted repair (resampling or editing only the failing object) instead of discarding the whole image; the paper does not pursue this.
  • Because the constants $\alpha = 0.9$, $t = 928$, and $\tau = 250$ are tuned on the evaluation tasks themselves, the sharpest test of the principle would fix those constants before seeing any test data and measure whether the gains persist.
  • The recipe is domain-agnostic: any diffusion model with conditionable denoising — audio, video, molecules — inherits the same accept/reject check without retraining, extending the paper's stated future direction toward video and music generation.
  • The reported Pearson correlations between activated-pixel count and CLIP score (0.68–0.86) suggest the lift criterion could serve as a reward signal for self-training or reinforcement-learning fine-tuning, a use the paper does not explore.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. The paper proposes CompLift, a training-free rejection/resampling criterion for compositional generation with diffusion models. The lift score log p(x|c)/p(x) is approximated by the difference between two ELBO-based denoising errors, with and without condition c (Eq. 3). The Compose function combines per-condition lift scores according to product, mixture, or negation algebra (Table 1). An optimized cached variant reuses intermediate predictions from Composable Diffusion and classifier-free guidance (Algorithm 2) to reduce the computational overhead. Experiments are reported on 2D synthetic distributions, the CLEVR Position dataset, and the Attend-and-Excite text-to-image benchmark, with accuracy, Chamfer distance, FID, CLIP, and ImageReward metrics. The central claim is that lift scores, computed only from the original diffusion model, significantly improve compositional generation without additional training or external modules.

Significance. If the criterion is robust, CompLift would be a useful, model-agnostic, training-free addition to compositional generation, and the cached variant shows a concrete path to near-zero extra inference cost under the Composable Diffusion framework. The paper provides a broad experimental sweep, including new TIFA and minCLIP results in the rebuttal appendix, a controlled comparison against a CAS-like whole-prompt scorer (Appendix G.3), and an ablation on the Attend-and-Excite generator (Table 8). The strengths are the simplicity of the criterion, the caching optimization, and the breadth of evaluation across three very different settings. However, the empirical significance is currently limited because key hyperparameters are selected on the evaluation tasks themselves, and because the theoretical link between the ELBO difference and the lift score is not established quantitatively, as the paper itself concedes in Appendix A.

major comments (5)
  1. [Section 6.2, Figure 3] The timestep t=928 is chosen by inspecting the accuracy curve in Figure 3, which is computed on the CLEVR Position evaluation combinations. The same combinations are then used for the reported results in Figure 9 and Table 5. This makes the reported accuracy gains potentially a consequence of test-set selection rather than a general property of the lift-score criterion. Please rerun the evaluation with the timestep selected on a validation split of position combinations (or with a principled timestep-sampling distribution), and report both the validation-selected and test results.
  2. [Section 5.1, Eq. (4)] The activated-pixel threshold tau=250 is set as the median activated-pixel count 'among all images,' and the paper states that tests at the 25th and 75th percentiles showed the median works best. Because these images are the test images of the Attend-and-Excite benchmark, the threshold is fitted to the evaluation set. Table 3 therefore does not by itself establish that a fixed tau generalizes. Please define a validation-based selection procedure (e.g., a separate set of prompts and images) and report the sensitivity of Table 3 to tau.
  3. [Section 6.1] The 2D experiments define the unconditional prediction as epsilon_theta(xt, empty)=0.9 * epsilon_theta(xt, c), a task-specific choice that changes the lift criterion itself. The paper calls this 'an effective strategy' but provides no derivation or sensitivity analysis. Since the large gains in Table 2 (e.g., product accuracy rising from 56.5 to 99.5) depend on this alpha, the 2D results do not currently support the claimed generalizability of the lift-score criterion. Please provide a proper unconditional estimate or, failing that, a sensitivity study over alpha.
  4. [Eq. (3) and Appendix A] The approximation in Eq. (3) replaces log p(x|c)/p(x) with a difference of ELBO terms. Because the ELBO is a lower bound, the sign of Eq. (3) is not guaranteed to match the sign of the true lift score. The paper itself notes in Appendix A that 'we will lose the theoretical guarantee of CompLift using ELBO estimation,' but no estimate of the approximation error or of false-accept/false-reject rates is provided. Since the acceptance threshold is exactly zero, a bias or overlap analysis (e.g., extending the Figure 17 score histograms to the text-to-image setting) is needed to connect the theoretical framing to the empirical procedure.
  5. [Section 5.2, Eq. (5)] The variance-reduction substitution of the true noise epsilon by epsilon_theta(zt, c_compose) in Eq. (5) is heuristic. Figure 6 shows a qualitative reduction in noise, but there is no quantitative variance comparison, no bias analysis, and no comparison with alternative variance-reduction baselines. Because Eq. (5) is the core of the text-to-image results, the paper should report the variance of Eq. (3) versus Eq. (5), and show that acceptance decisions are stable across the number of trials T.
minor comments (6)
  1. [Section 7] The word 'sysmatic' should be 'systematic'.
  2. [Abstract/Code Link] The code URL 'rainorangelemon.github.io/complift' lacks a protocol and appears as plain text; please format it as a proper hyperlink.
  3. [Figure 3] The x-axis label 'Timestep Used (1 Trial)' is ambiguous; please clarify that the ELBO is estimated with a single sampled timestep.
  4. [Algorithm 1, line 7] The loop variable is ck but the appended score is written as liftj(x0|ci), which is confusing; please unify the notation.
  5. [Section 5.1, Eq. (4)] Reusing the symbol lift for a pixel-count criterion with units of pixels is confusing after Eq. (3) defines a scalar score; please introduce a distinct symbol such as N_act(x, ci).
  6. [Appendix H] The appendix titled 'Q&A during Rebuttal' contains reviewer-facing language ('We appreciate the feedback from anonymous reviewers'); this text is not appropriate for a published manuscript and should be removed or rewritten.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: lift-score criterion is an independent ELBO-based test statistic; tuned hyperparameters are a generalization risk, not a definitional circularity.

full rationale

No circular step is present in the claimed derivation chain. The lift-score criterion in Eq. (3) is the standard ELBO-based approximation of log p(x|c)/p(x), and the acceptance rule (positive lift) is not defined in terms of the external evaluation metrics (CLIP, ImageReward, SAM2, TIFA) used in the experiments. The text-to-image variant (Eqs. 4-5) is an empirically motivated variance-reduction estimator that replaces the sampled noise with the model's composed-prompt prediction; while self-referential, it is not equivalent to its inputs by construction, and Appendix A explicitly concedes that the theoretical ELBO guarantee is lost in practice. The 2D simplification ϵθ(xt,∅)=αϵθ(xt,c) with α=0.9 (Section 6.1), the CLEVR timestep t=928 chosen from Figure 3 (Section 6.2), and the threshold τ=250 chosen as the test-set median (Section 5.1) are tuned hyperparameters; selecting them on the evaluation sets is a generalization risk requiring validation-split reruns, not a formal circular dependency, because the reported improvements are still measured by external verifiers. All load-bearing method citations (Diffusion Classifier, Composable Diffusion, CAS) are external prior work; there is no self-citation chain or imported uniqueness theorem.

Assumptions & free parameters 3 free parameters · 3 assumptions · 0 invented entities

The method relies on the diffusion model's own ELBO estimates, introduces several data-dependent constants (α, τ, t), and uses a heuristic composed-prediction substitution for noise; no new physical or model entities are introduced.

free parameters (3)
  • alpha_unconditional_scale_2d = 0.9
    In 2D synthetic tasks, the unconditional prediction is defined as α times the conditional prediction; α=0.9 is chosen by the authors without a principled derivation (Section 6.1).
  • threshold_activated_pixels_tau = 250
    Median activated pixel count across all test images; used to decide whether an object is present (Section 5.1).
  • clevr_timestep_t = 928
    Best single timestep chosen from Figure 3 for ELBO estimation in the CLEVR task (Section 6.2).
assumptions (3)
  • standard math ELBO provides a valid lower bound for log p(x|c)
    Used in Eqs. (1) and (2) of Section 3.2 to approximate conditional and unconditional densities.
  • domain assumption The difference of squared denoising errors with and without condition approximates log p(x|c)/p(x)
    This is the core of the lift approximation in Eq. (3); no error analysis is provided.
  • ad hoc to paper The composed prediction εθ(z_t,c_compose) is a good proxy for the true noise ε
    Used in Eq. (5) to reduce estimation variance; no theoretical justification is given.

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Cite this review

Pith. "Pith review of Improving Compositional Generation with Diffusion Models Using Lift Scores." pith.science (2026). https://pith.science/paper/RZXAPTIA

@misc{pith2026250513740,
  author       = {Pith},
  title        = {Pith review of: Improving Compositional Generation with Diffusion Models Using Lift Scores},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RZXAPTIA}},
  note         = {Machine review of arXiv:2505.13740}
}
read the original abstract

We introduce a novel resampling criterion using lift scores, for improving compositional generation in diffusion models. By leveraging the lift scores, we evaluate whether generated samples align with each single condition and then compose the results to determine whether the composed prompt is satisfied. Our key insight is that lift scores can be efficiently approximated using only the original diffusion model, requiring no additional training or external modules. We develop an optimized variant that achieves relatively lower computational overhead during inference while maintaining effectiveness. Through extensive experiments, we demonstrate that lift scores significantly improved the condition alignment for compositional generation across 2D synthetic data, CLEVR position tasks, and text-to-image synthesis. Our code is available at http://rainorangelemon.github.io/complift.

Figures

Figures reproduced from arXiv: 2505.13740 by the authors.

Figure 1
Figure 1. An illustration of product, mixture, and negation compositional models, and the improved sampling performance using CompLift. Left to right: Component distributions, ground truth composed distribution, composable diffusion samples, samples accepted by CompLift. Top: product, center: mixture, bottom: negation. ∅ represents the empty set - no samples are generated or accepted. Each component distribution is trained in… view at source ↗
Figure 2
Figure 2. The accuracy of CompLift with different noise sam￾pling strategies on 2D synthetic dataset. See Section 4.1. 0 200 400 600 800 1000 Timestep Used (1 Trial) 20 40 60 80 100 Accuracy [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Accuracy of acceptance/rejection over a single sam￾pled timestep for pretrained model (Liu et al., 2022) on CLEVR Position dataset. We found that models trained with importance sampling require importance sampling for ELBO estimation. 4.1. Effect of Noise In Line 7 in Algorithm 1, we choose to share the ϵ when estimating both p(x|c) and p(x). To validate this design, we conduct the experiment on a 2D synthetic datas… view at source ↗
Figures from the paper (18 more)
Figure 4
Figure 4. Figure 4: Accepted and rejected SDXL examples using CompLift criterion. Objects in blue are composed through the given prompts. Here we show the images from the text prompts with the most-improved CLIP scores. See more examples in Section G. ComposableDiff Cached CompLift (T=50)…
Figure 5
Figure 5. Figure 5: Average running time on 2D synthetic dataset. T indicates number of trials. Note that our methods can be further optimized to the latency of only 1 forward pass with parallelization, while MCMC methods require sequential computation. See [PITH_FULL_IMAGE:figures/full_…
Figure 7
Figure 7. Figure 7: CompLift for text-to-image compositional task. Pixels with negative scores are masked out. From left to right: original image 1 x, the latent z, the heatmaps of lift score lift(z, ci) for each object component ci. The border color represents the corresponding object. W…
Figure 8
Figure 8. Figure 8: Examples of samples on CLEVR Position dataset. points randomly for each component distribution, and train 1 individual diffusion model for each distribution. We use the diffusion process of 50 timesteps for both training and inference, which means the cached version of…
Figure 9
Figure 9. Figure 9: Quantitative results on CLEVR Position dataset. with CompLift criterion is able to generate empty sets by construction. We show some examples in [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]
Figure 10
Figure 10. Figure 10: The correlation between the number of activated pixels and the CLIP score. The correlation is calculated using the Pearson correlation coefficient. Each point represents a generated image. Prompt: a black car and a white clock. can be boosted to be comparable to their…
Figure 11
Figure 11. Figure 11: 2D synthetic result for product composition with Composable Diffusion and CompLift. = 𝑝1(𝑥) 𝑝2(𝑥) Ground Truth Composable Diffusion + CompLift + = = + + [PITH_FULL_IMAGE:figures/full_fig_p016_11.png]
Figure 12
Figure 12. Figure 12: 2D synthetic result for mixture composition with Composable Diffusion and CompLift. = 𝑝1(𝑥) 𝑝2(𝑥) Ground Truth Composable Diffusion + CompLift = - = ⦰ ⦰ - - [PITH_FULL_IMAGE:figures/full_fig_p016_12.png]
Figure 13
Figure 13. Figure 13: 2D synthetic result for negation composition with Composable Diffusion and CompLift. 16 [PITH_FULL_IMAGE:figures/full_fig_p016_13.png]
Figure 14
Figure 14. Figure 14: 2D synthetic result for product composition with Energy-Based Models and MCMC. The component distributions are the same ones in [PITH_FULL_IMAGE:figures/full_fig_p017_14.png]
Figure 15
Figure 15. Figure 15: 2D synthetic result for mixture composition with Energy-Based Models and MCMC. The component distributions are the same ones in [PITH_FULL_IMAGE:figures/full_fig_p017_15.png]
Figure 16
Figure 16. Figure 16: 2D synthetic result for negation composition with Energy-Based Models and MCMC. The component distributions are the same ones in [PITH_FULL_IMAGE:figures/full_fig_p017_16.png]
Figure 17
Figure 17. Figure 17: shows the histograms of the CompLift scores for each composed 2D synthetic distributions following the order in the [PITH_FULL_IMAGE:figures/full_fig_p018_17.png]
Figure 18
Figure 18. Figure 18: More examples of samples on CLEVR Position dataset. We find that the SAM verifier is more robust and generalizable compared to the pretrained classifier provided by the Composable Diffusion work (Liu et al., 2022). From left to right: the first column shows the origin…
Figure 19
Figure 19. Figure 19: Example of failed samples on CLEVR Position dataset using MCMC. The generated objects in the synthesized images are often in strange shapes which is dissimilar from the trained data. We suspect that the MCMC method may require a more careful tuning of hyperparameters.…
Figure 20
Figure 20. Figure 20: More examples of correlation between CLIP and CompLift. Prompt to generate left images: a turtle with a bow. Prompt to generate right images: a frog and a mouse. Text in blue indicates the object components to compose. G.2. Ablation Study: Improvement on Attend&Excite…
Figure 21
Figure 21. Figure 21: More examples of accepted and rejected images using SDXL + CompLift for text-to-image generation. 25 [PITH_FULL_IMAGE:figures/full_fig_p025_21.png]
Figure 22
Figure 22. Figure 22: More examples of accepted and rejected images using SDXL + CompLift for text-to-image generation. 26 [PITH_FULL_IMAGE:figures/full_fig_p026_22.png]

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Reference graph

Works this paper leans on

24 extracted references · 11 canonical work pages

  1. [1]

    K., Wang, Y .-X., and Hebert, M

    Bao, Z., Li, Y ., Singh, K. K., Wang, Y .-X., and Hebert, M. Separate-and-enhance: Compositional finetuning for text- to-image diffusion models. InACM SIGGRAPH 2024 Conference Papers, pp. 1–10,

    work page 2024

  2. [3]

    Here, we provide another perspective to interpret the numbers

    A: The seemingly trivial CLIP improvement is due to the low magnitude of CLIP scores. Here, we provide another perspective to interpret the numbers. We compare the CompLift selector to the perfect best-of-n selector, which has direct access to the metric function. The percentage gain is calculated as (CompLift metric - baseline metric) / (perfect selector...

    work page

  3. [4]

    E., and Wang, W

    Feng, W., He, X., Fu, T.-J., Jampani, V ., Akula, A., Narayana, P., Basu, S., Wang, X. E., and Wang, W. Y . Training-free structured diffusion guidance for compositional text-to-image synthesis.arXiv preprint arXiv:2212.05032,

    arXiv

  4. [5]

    and Salimans, T

    Ho, J. and Salimans, T. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598,

    arXiv

  5. [7]

    J., Kang, W., and Moon, I.- C

    Kim, D., Kim, Y ., Kwon, S. J., Kang, W., and Moon, I.- C. Refining generative process with discriminator guid- ance in score-based diffusion models.arXiv preprint arXiv:2211.17091,

    arXiv

  6. [9]

    Inference-time scaling for diffusion models beyond scaling denoising steps.arXiv preprint arXiv:2501.09732,

    Ma, N., Tong, S., Jia, H., Hu, H., Su, Y .-C., Zhang, M., Yang, X., Li, Y ., Jaakkola, T., Jia, X., et al. Inference-time scaling for diffusion models beyond scaling denoising steps.arXiv preprint arXiv:2501.09732,

    arXiv

  7. [11]

    Sdxl: Im- proving latent diffusion models for high-resolution image synthesis.arXiv preprint arXiv:2307.01952,

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

    arXiv

  8. [12]

    Not all noises are cre- ated equally: Diffusion noise selection and optimization

    Qi, Z., Bai, L., Xiong, H., and Xie, Z. Not all noises are cre- ated equally: Diffusion noise selection and optimization. arXiv preprint arXiv:2407.14041,

    arXiv

Show all 24 references
  1. [13]

    Sam 2: Segment anything in images and videos.arXiv preprint arXiv:2408.00714,

    Ravi, N., Gabeur, V ., Hu, Y .-T., Hu, R., Ryali, C., Ma, T., Khedr, H., R ¨adle, R., Rolland, C., Gustafson, L., et al. Sam 2: Segment anything in images and videos.arXiv preprint arXiv:2408.00714,

    arXiv

  2. [14]

    Grounded sam: Assembling open-world models for diverse visual tasks

    Ren, T., Liu, S., Zeng, A., Lin, J., Li, K., Cao, H., Chen, J., Huang, X., Chen, Y ., Yan, F., et al. Grounded sam: Assembling open-world models for diverse visual tasks. arXiv preprint arXiv:2401.14159,

    arXiv

  3. [16]

    Section A discusses the connection between our method and Classifier-Free Guidance (CFG) (Ho & Salimans, 2022; Liu et al., 2022)

    11 Improving Compositional Generation with Diffusion Models Using Lift Scores In this appendix, we provide additional details on the experiments and results presented in the main paper. Section A discusses the connection between our method and Classifier-Free Guidance (CFG) (H...

    2022

  4. [17]

    The two formulations are equivalent in the Lagrangian sense, but CFG is not guaranteed to strictly satisfy the constraints in practice

    A brief summary is that our method tries to solve the primal form as Equation (6), while CFG tries to solve the dual form using soft Lagrangian regularization to satisfy the constraint as Equation (9). The two formulations are equivalent in the Lagrangian sense, but CFG is not...

    2022

  5. [18]

    (9) In this formulation: • The first term,logp generator(x0), is our original objective

    , λ i≥0. (9) In this formulation: • The first term,logp generator(x0), is our original objective. • The second term penalizes any violation of the constraint logp(x 0|c i)−logp(x 0)≥0 . If the constraint is violated, the penalty term will lower the overall value of the Lagrang...

    2022

  6. [19]

    The figures show the generated samples for product, mixture, and negation compositions, respectively

    17 Improving Compositional Generation with Diffusion Models Using Lift Scores In Figures 11 to 16, we provide additional visualizations of the 2D synthetic distribution compositions. The figures show the generated samples for product, mixture, and negation compositions, respec...

    2023

  7. [20]

    Prompt to generate right images: a frog and a mouse

    More examples of correlation between CLIP andCompLift.Prompt to generate left images: a turtle with a bow. Prompt to generate right images: a frog and a mouse. Text in blue indicates the object components to compose. G.2. Ablation Study: Improvement on Attend&Excite Generator ...

    2023

  8. [21]

    More examples of samples on CLEVR Position dataset.We find that the SAM verifier is more robust and generalizable compared to the pretrained classifier provided by the Composable Diffusion work (Liu et al., 2022). From left to right: the first column shows the original images,...

    2022

  9. [22]

    as a generalizable verifier, to check whether an object is in a specified position. We first extract the background mask, which is the mask with the largest area using the automatic mask generator of SAM2 (SAM2AutomaticMaskGenerator), then label a condition as satisfied if the...

    2022

  10. [1000]

    consistently outperforms baseline methods across all scenarios, achieving perfect or near-perfect accuracy in many cases. While all methods perform reasonably well with Product algebra, performance generally degrades with Mixture and especially with Negation algebra, whereComp...

    2000

  11. [2015]

    P., Kumar, A., Er- mon, S., and Poole, B

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

    2011 arXiv

  12. [2017]

    If at first you don’t succeed, try, try again: Faithful diffusion-based text-to-image generation by selection.arXiv preprint arXiv:2305.13308,

    Karthik, S., Roth, K., Mancini, M., and Akata, Z. If at first you don’t succeed, try, try again: Faithful diffusion-based text-to-image generation by selection.arXiv preprint arXiv:2305.13308,

    arXiv

  13. [2021]

    Diffusion rejection sampling.arXiv preprint arXiv:2405.17880,

    Na, B., Kim, Y ., Park, M., Shin, D., Kang, W., and Moon, I.-C. Diffusion rejection sampling.arXiv preprint arXiv:2405.17880,

    arXiv

  14. [2022]

    Kong, X., Liu, O., Li, H., Yogatama, D., and Steeg, G. V . Interpretable diffusion via information decomposition. arXiv preprint arXiv:2310.07972,

    arXiv

  15. [2023]

    Robust classification via a single diffusion model.arXiv preprint arXiv:2305.15241,

    Chen, H., Dong, Y ., Wang, Z., Yang, X., Duan, C., Su, H., and Zhu, J. Robust classification via a single diffusion model.arXiv preprint arXiv:2305.15241,

    arXiv

  16. [2024]

    D., and Tsur, S

    Brin, S., Motwani, R., Ullman, J. D., and Tsur, S. Dynamic itemset counting and implication rules for market basket data. InProceedings of the 1997 ACM SIGMOD interna- tional conference on Management of data, pp. 255–264,

    1997

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