Compositional VQ Sampling for Efficient and Accurate Conditional Image Generation
image generation;compositional generalization
Yes
No ethics review needed.
- It is unclear how equation (3) follows from equation (2). Could this approach face the same issues highlighted in [4]?

[4] Du, Yilun, Conor Durkan, Robin Strudel, Joshua B. Tenenbaum, Sander Dieleman, Rob Fergus, Jascha Sohl-Dickstein, Arnaud Doucet, and Will Sussman Grathwohl. "Reduce, reuse, recycle: Compositional generation with energy-based diffusion models and mcmc." In International conference on machine learning, pp. 8489-8510. PMLR, 2023.
- The study addresses a relatively unexplored area of compositional generation within discrete diffusion and masked generative models, complementing the majority of prior work focused on EBMs or continuous diffusion models. This contributes meaningfully to the field.
- The proposed approach is straightforward, effective, and intuitive, producing promising empirical results across various datasets.
The paper introduces an composition algorithm that enables compositional generation (like score composition in EBM [1] or diffusion [2]) for discrete diffusion models or masked generation models. The method is straightforward, effective, and aligns well with intuitive understanding. The authors evaluate their approach on diverse datasets, including Positional CLEVR, Relational CLEVR, and FFHQ, demonstrating strong results.


[1] Du, Yilun, Shuang Li, and Igor Mordatch. "Compositional visual generation with energy based models." Advances in Neural Information Processing Systems 33 (2020): 6637-6647.

[2] Liu, Nan, Shuang Li, Yilun Du, Antonio Torralba, and Joshua B. Tenenbaum. "Compositional visual generation with composable diffusion models." In European Conference on Computer Vision, pp. 423-439. Cham: Springer Nature Switzerland, 2022.
- Lines 191-192 use the phrase “statistically independent of each other,” which may be ambiguous and potentially misleading. It could imply a graphical model with $c_i$ nodes pointing to $x$. My understanding is that equation (1) is derived directly from the product of experts assumption, similar to equation (4) in [1].
- The method essentially adopts a specialized form of classifier-free guidance (CFG), which has been explored in [3] and has been widely implemented in autoregressive and discrete diffusion models. This raises questions about the novelty of the approach.
- The paper provides results for compositional operations like AND and NOT, but does not address OR, leaving a gap in the evaluation.

[3] Nair, Nithin Gopalakrishnan, Wele Gedara Chaminda Bandara, and Vishal M. Patel. "Unite and conquer: Plug & play multi-modal synthesis using diffusion models." In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6070-6079. 2023.
Yes
No ethics review needed.
1.	How does the proposed compositional VQ sampling method perform when applied to autoregressive-based models?
2.	Regarding the influence of weighting values on attributes:
 * Is the range of weights (e.g., -3 to +3) shown in Figure 6 applicable to all attributes, or does it require tuning for each attribute?
* When involving multiple components, how does varying the weight for each component affect the final generated result? Specifically, how do different weight values interact across components?
1. Novelty: The approach is the first of its kind to apply compositional generation to discrete latent spaces, offering potential for increased efficiency and interpretability over traditional continuous sampling.
2. Applicability: Can be integrated with pretrained text-to-image models without fine-tuning, showing its broad applicability and ease of use.
3. Efficiency: The method is computationally efficient, delivering significant speed-ups over continuous methods, making it more practical for real-time or large-scale applications.
The paper introduces a new approach for compositional image generation for discrete generative processes, leveraging a compositional code sampling framework applied to discrete spaces with Vector-Quantization technique, and masked token prediction method for efficient image generation. The proposed method combines log-probabilities of discrete generative models, allowing control over compositional generation conditions without specialized training loss. Results across various settings show SOTA performance in terms of accuracy and FID, with notable efficiency improvements compared to continuous methods. The method also integrates seamlessly with pre-trained text-to-image generation models, offering practical applications for controllable image generation.
1. Although this work introduces the first approach for compositional image generation in discrete space, the methodology itself appears similar to ‘concept conjunction’ used in compositional diffusion models [1] within continuous space. Additionally, the rationale behind why the composition technique from continuous space models cannot be directly applied to discrete space remains unclear (as both approaches appear to rely on the composition of log-probabilities). Furthermore, the trade-offs mentioned in the introduction lack sufficient clarity and specificity.
2.	Limited Scope in Evaluated Datasets: The datasets are somewhat limited in complexity (CLEVR, FFHQ). More complex datasets with more varied attributes could better test the method’s generalization. 

[1] Nan Liu, Shuang Li, Yilun Du, Antonio Torralba, and Joshua B Tenenbaum. Compositional visual generation with composable diffusion models. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XVII, pages 423–439. Springer, 2022.
Yes
No ethics review needed.
- Why does the FID score change when the number of components varies that much (particularly for Table 3)?
- For out-of-distribution synthesis, under what conditions does the model fail? Can it generate more than eight objects? 
- What happens in the case of synthesizing both 𝑐  and −𝑐 ?  
- Is it necessary to maintain all conditioning signals until the end of the generation process? For instance, on FFHQ, conditioning on glasses for the first few iterations and switch to no condition?
- Is the current format for references correct?
- The paper demonstrates strong results in terms of error rate reduction, highlighting effective conditioning.
- The method leverages the MaskGIT framework, achieving high throughput (compared to diffusion models) while maintaining a low parameter count.
- The paper also reveals interesting properties for out-of-distribution (OoD) synthesis and negative prompting
The paper investigates multiple conditioning approaches for the Masked Generative Image Transformer, focusing on the composability of multiple predictions. The authors demonstrate that using multiple conditioning signals improves control over the generation process. Their approach shows substantial improvements across three datasets, significantly reducing the error rate in conditional image generation. Additionally, they present interesting properties for negative conditioning and out-of-distribution generation for text-to-image tasks.
- The contribution of conditioning the model alone is already well-established through Classifier-Free Guidance (CFG). Applying this technique for multiple conditions for discrete spaces may be a weak contribution. 
- While the manipulation of text-to-image conditioning appears promising, the lack of quantitative results makes it difficult to fully assess its effectiveness. I think that the complete Figure 13 in the main paper would enhance clarity.
- Similar to other methods (e.g., [7]), this approach requires additional forward passes, which slow down the overall pipeline.
- The method shows a relatively weak FID score on the FFHQ dataset.
Yes
No ethics review needed.
- Please address the novelty concern and other weakness mentioned in the weakness section.
- The paper is well written and easily understood
- The results support the claims
- The conditions are able to generalize to OOD sampling
The paper presents a compositional approach for image generation. The authors proposes to incorporate multiple conditions within a discrete masked transformer generation. Under the assumption that these conditions are independent, each condition contributes to the discrete denoising of the masked latent space at each diffusion time. 
 The conditions are shown to control the specific attributes, both positively and negatively, as well as generate high quality images both quantitatively and qualitatively. In addition, the method is shown to be applicable to pre-train t2i discrete diffusion namely aMUSEd).
My main concern is the novelty of this paper.
Masked diffusion transformers are not new as noted by the author it is used for example in aMUSEd.
In addition compositional conditions was presented in "Compositional Visual Generation with Composable Diffusion Models" which derive the same conjunction and negation attributes.  While their are some differences (the use VQ-VAE vs VQGAN) the method can be seem as a compositional aMUSEd, which is quite limited in novelty.
I wish the authors to better explain what exactly this work proposes that aMUSEd and compositional conditions did not.

- The conditions are entangled with other attributes as can be seen in Fig 6.
- Each condition requires a forward pass, in an already slow diffusion generation process.
- Low resolution image generation.
We propose a probability-theoretic method for multi-condition sampling from the VQ latent space, achieving SOTA generation accuracy and speed on 3 datasets, with competetive FID.
@inproceedings{
anonymous2024compositional,
title={Compositional {VQ} Sampling for Efficient and Accurate Conditional Image Generation},
author={Anonymous},
booktitle={Submitted to The Thirteenth International Conference on Learning Representations},
year={2024},
url={https://openreview.net/forum?id=gKui6QvvfK},
note={under review}
}
Compositional diffusion and energy-based models have driven progress in controllable image generation, however the challenge of composing discrete generative models has remained open, holding the potential for improvements in efficiency, interpretability and generation quality. To this end, we propose a framework for controllable conditional generation of images. We formulate a process for composing discrete generation processes, enabling generation with an arbitrary number of input conditions without the need for any specialised training objective. We adapt this result for parallel token prediction with masked generative transformers, enabling accurate and efficient conditional sampling from the discrete latent space of VQ models. In particular, our method attains an average error rate of 19.3% across nine experiments spanning three datasets (between one and three input conditions for each dataset), representing an average 63.4% reduction in error rate relative to the previous state-of-the-art. Our method also outperforms the next-best approach (ranked by error rate) in terms of FID in seven out of nine settings, with an average FID of $24.23$, and average improvement of $-9.58$. Furthermore, our method offers a $2.3\times$ to $12\times$ speedup over comparable methods. We find that our method can generalise to combinations of input conditions that lie outside the training data (e.g. more objects per image for Positional CLEVR) in addition to offering an interpretable dimension of controllability via concept weighting. Outside of the rigorous quantitative settings, we further demonstrate that our approach can be readily applied to an open pre-trained discrete text-to-image model, demonstrating fine-grained control of text-to-image generation. The accuracy and efficiency of our framework across diverse conditional image generation settings reinforces its theoretical foundations, while opening up practical avenues for future work in controllable and composable image generation.
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image generation compositional generalization
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generative models
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Compositional VQ Sampling for Efficient and Accurate Conditional Image Generation
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