Training-Free Adaptive Diffusion with Bounded Difference Approximation Strategy

Summary

The paper presents a novel approach titled "AdaptiveDiffusion" for accelerating diffusion models used in high-quality image and video synthesis. The core issue addressed is the high computational cost and latency associated with existing denoising techniques in diffusion models, which are typically based on step-by-step noise predictions. **Contributions of the Paper:** 1. **Adaptive Diffusion Process**: The paper introduces AdaptiveDiffusion, which adaptively reduces the number of noise prediction steps during the denoising process. This is achieved by skipping steps where the potential redundancy is high, guided by the third-order latent difference that indicates stability between timesteps. 2. **Plug-and-Play Criterion**: A new criterion is proposed to decide whether to infer new noise predictions or reuse previous results based on the third-order difference distribution. This allows for an adaptive acceleration paradigm that is prompt-dependent. 3. **Extensive Experiments**: The method's effectiveness is demonstrated through extensive experiments on both image and video diffusion models. The results show significant speedups of 2 to 5 times on average in the denoising process without quality degradation. 4. **Error Analysis**: The paper provides a theoretical analysis of the upper bound of the error induced by the step-skipping strategy, ensuring that the quality of the final output is maintained. 5. **Adaptive Acceleration**: The approach is designed to be adaptive to different input prompts, offering a practical solution to the high computational costs of sequential denoising techniques. 6. **Generalization Capability**: AdaptiveDiffusion shows a strong generalization capability, being able to adapt to different models and tasks, including text-to-image, image-to-video, and text-to-video generation. In summary, the paper offers a substantial advancement in efficient diffusion model acceleration, with the potential to enable real-time and interactive applications of diffusion models.

Strengths

### Strengths Assessment of the Paper #### Originality The paper demonstrates a high degree of originality through the introduction of the AdaptiveDiffusion method, which offers a novel perspective on accelerating diffusion models. The approach创造性地 addresses the computational inefficiency inherent in traditional denoising techniques by adaptively reducing noise prediction steps. This innovation is not just a technical tweak but a strategic rethinking of the denoising process itself. The use of the third-order latent difference as a criterion for deciding when to skip steps is an ingenious way to balance efficiency and quality, which has not been explored in prior works. #### Quality The quality of the paper is reflected in its rigorous theoretical foundation and comprehensive empirical validation. The authors have provided a detailed error analysis to support their method's robustness, ensuring that the acceleration does not compromise the output quality. The experiments are thorough, covering a range of models and tasks, which substantiates the method's effectiveness and generalizability. The paper also discusses the relationship between different orders of latent differences and the optimal skipping path, which adds depth to the understanding of the proposed technique. #### Clarity The paper is well-structured, with a clear progression from the introduction of the problem to the explanation of the proposed solution, followed by a detailed methodology and extensive experimental results. The figures and tables are used effectively to illustrate the method and results, enhancing the readability and comprehension of the paper. The theoretical proofs and algorithm descriptions are presented in a manner that is accessible to readers with a background in the field. #### Significance The significance of this paper lies in its potential to transform the applicability of diffusion models. By significantly reducing the computational cost and latency, AdaptiveDiffusion opens up new possibilities for real-time and interactive applications of diffusion models, which are currently limited by their resource-intensive nature. The ability to tailor the denoising process to different prompts is also significant, as it allows for more flexible and responsive generative models that can cater to diverse content creation needs. In summary, the paper is a substantial contribution to the field of generative modeling, offering a creative, high-quality, and clearly articulated solution to a pressing problem. Its significance extends beyond technical advancement, promising to enable new applications and use cases for diffusion models.

Weaknesses

While the paper presents a compelling approach to accelerating diffusion models, there are areas where it could be further strengthened: ### Theoretical Depth - **Assumption Limitations**: The paper relies on certain assumptions for its theoretical analysis, such as the Lipschitz continuity of the noise prediction model. It would be beneficial to discuss how violations of these assumptions might impact the method's effectiveness and under what conditions these assumptions hold true. ### Experimental Scope - **Diversity of Models**: Although the method is tested on various tasks, the paper could benefit from testing on a broader range of diffusion models to further establish the generalizability of AdaptiveDiffusion. - **Real-World Applications**: Demonstrating the method's effectiveness in real-world applications or use cases would provide additional context and significance to the work. ### Hyperparameter Sensitivity - **Threshold δ and Cmax**: The paper discusses the impact of these hyperparameters on performance but could provide more guidance on how to select these values in practice, especially given their critical role in balancing speed and quality. ### Computational Complexity - **Memory Usage**: While the method aims to reduce computational cost, it would be insightful to have a discussion on memory usage, especially since diffusion models can be memory-intensive. ### Long-Term Viability - **Adaptability to Model Updates**: The paper could address how well AdaptiveDiffusion might adapt to future updates in diffusion model architectures or training regimes. ### Societal Impact Consideration - **Ethical Considerations**: Although the paper does not explicitly discuss societal impacts, it would be beneficial to include a brief discussion on potential ethical considerations, especially given the generative capabilities of the models involved. ### Reproducibility - **Code and Data Availability**: Ensuring that the code and data used for experiments are publicly available would greatly enhance the reproducibility of the results. ### Documentation - **Algorithm Pseudocode**: Providing pseudocode or flowcharts for the algorithms could help readers better understand the step-skipping strategy and its integration into the overall process. ### Future Work - **Extensions and Limitations**: While the paper outlines future directions, a more detailed discussion on the limitations and potential extensions of the current work would be valuable. By addressing these points, the paper could provide a more comprehensive understanding of AdaptiveDiffusion's capabilities and limitations, setting the stage for further research and development in this area.

Questions

1. **Assumption Validity**: Could the authors elaborate on the conditions under which the Lipschitz continuity assumption for the noise prediction model holds? How do they ensure this assumption is valid across different models and datasets? 2. **Generalization Across Models**: The paper demonstrates results on a few models. What steps have been taken to ensure that AdaptiveDiffusion can generalize across a wider variety of diffusion models, especially those that may not conform to the same architectural patterns? 3. **Hyperparameter Selection**: The paper mentions the importance of hyperparameters δ and Cmax. Can the authors provide more detailed guidelines or methods for selecting these hyperparameters in different contexts or suggest any automated tuning processes? 4. **Memory Usage Discussion**: Given that diffusion models can be memory-intensive, could the authors discuss the memory usage implications of AdaptiveDiffusion, especially when scaling up to larger models or datasets? 5. **Ethical Considerations**: Although the paper focuses on a technical advancement, could the authors comment on any potential ethical implications of the work, particularly related to the generative capabilities of the models? 6. **Reproducibility Assurance**: To ensure the reproducibility of the results, will the authors commit to making their code and datasets publicly available, and if so, when? 7. **Algorithm Visualization**: For better understanding, especially for readers who may be less familiar with the proposed methods, can the authors provide pseudocode or flowcharts illustrating the step-skipping strategy? 8. **Long-Term Viability**: How does the authors' method accommodate or adapt to potential future changes in the architecture or training of diffusion models? 9. **Limitation Discussion**: The paper outlines future work but could benefit from a more explicit discussion of current limitations. Are there specific scenarios or model types where AdaptiveDiffusion might underperform? 10. **Statistical Significance**: The paper reports χ2 stats and p-values for the correlation between estimated and optimal paths. Could the authors provide more details on the statistical tests used and the rationale behind choosing these tests? 11. **Real-World Application**: While the method shows promise in controlled experiments, are there any real-world scenarios or use cases where AdaptiveDiffusion has been tested or is planned to be tested? 12. **Societal Impact**: The paper could be strengthened by a brief discussion on the potential societal impacts, both positive and negative, of the technology. This includes considerations of how the method might be used or misused. 13. **Comparison with State-of-the-Art**: How does AdaptiveDiffusion compare with the state-of-the-art in terms of computational efficiency and quality of results? Are there any specific advantages or disadvantages in particular scenarios? 14. **Documentation and API**: For practical adoption, what level of documentation and API support is available or planned for AdaptiveDiffusion to facilitate its integration into existing systems?

Rating

3

Confidence

1

Soundness

3

Presentation

3

Contribution

3

Limitations

Based on the information provided and the typical guidelines of the NeurIPS Paper Checklist, it appears that the authors have made an effort to address limitations and societal impacts. However, I offer general advice on how authors can improve their discussion of these topics: 1. **Clear Acknowledgment**: Authors should explicitly acknowledge the limitations of their work in the main text of the paper. This includes potential constraints on the generalizability of their findings, assumptions made, and any conditions under which the method may not perform as expected. 2. **Depth of Discussion**: While acknowledging limitations, authors should provide a thorough explanation of how these limitations might affect the results and the applicability of their method. This could include a discussion of how the method behaves under different conditions or with different types of data. 3. **Societal Impact Analysis**: Authors should consider the broader societal impacts of their work, including both positive and negative outcomes. This discussion should be grounded in the context of the work and consider potential misuse, ethical concerns, privacy issues, and fairness. 4. **Mitigation Strategies**: If there are potential negative societal impacts, authors should discuss possible mitigation strategies. This could involve suggesting guidelines for the responsible use of the technology, potential regulatory frameworks, or technical safeguards. 5. **Ethical Considerations**: The paper should include a section on ethical considerations, especially if the work involves generative models that could be used to create misleading or harmful content. 6. **Transparency**: Authors should be transparent about any potential conflicts of interest, funding sources, or affiliations that might influence the research or its interpretation. 7. **Openness to Feedback**: Authors should demonstrate a willingness to engage with the community for feedback on the societal impacts of their work and be open to adjusting their approach based on this feedback. 8. **Long-Term Vision**: While discussing limitations, authors could also provide a long-term vision for how they anticipate overcoming these limitations in future work.

Summary

## Summary * This paper propose a greedy approach that accelerate the Probability Flow ODE solvers for txt to image diffusion models. Empirical results on SD 1.5 and SD XL with mulitple solvers (DDIM, DPM, Euler) demonstrate the advantage of their approach over previous acceleration techniques.

Strengths

## Strength * The idea of prompt adaptative acceleration is interesting and promising. As previous solvers designed for more general PF-ODE are not adaptative to different prompts. It is quite natural for a prompt adaptative approach to obtain better result. * The empirical advantage over previous accelerations is obvious, especially in terms of image quality such as PSNR and LPIPS.

Weaknesses

## Weakness * A new trend in image generation diffusion model is the adoptation of Flow matching optimal transport (FM-OT) / Rectified flow (RF) (See Learning to Generate and Transfer Data with Rectified Flow). Those lines of works adopt a forward SDE that is neither VP nor VE. Their special SDE has a constant velocity in the corresponding PF ODE. Rectified flow can even achieve single step sampling with this PF ODE. Their PF-ODE has a path that is very close to straight line and be solved with less steps compared with VP / VE SDE. The latest Stable Diffusion 3 already adopt this type of diffusion. Those efforts in diffusion community also speed up sampling, not by solvers but by different models. Those line of works should be discussed as they have the same goal with this paper.

Questions

## Questions * For now the results are reported on 3 different ODE solvers. Can the proposed approach be applied to SDE solvers too? As sometimes the SDE has some advantage over ODE in terms of sample quality (See Closing the ODE-SDE gap in score-based diffusion models through the Fokker-Planck equation).

Rating

6

Confidence

4

Soundness

3

Presentation

3

Contribution

3

Limitations

Yes

Summary

This paper proposes a strategy to speed up image and video diffusion generative models. The speed-up is achieved by skipping denoising steps. The authors suggest implementing an adaptive skipping schedule, where the decision of which steps to skip depends on the processed image or video. Specifically, the proposed algorithm calculates the norm of the third-order derivative in the latent space. It then skips denoising steps when the norm of the third-order derivative is below a predefined threshold while limiting the number of consecutive skips to a set maximum. The authors evaluate the proposed skipping strategy through various experiments with image and video diffusion models, comparing the speed-up and quality of the generated content with DeepCache [1] and Adaptive DPM-Solver [2]. In most experiments, the proposed skipping strategy achieves higher speed-up and better image and video quality than competitors. [1] Xinyin Ma, Gongfan Fang, and Xinchao Wang. Deepcache: Accelerating diffusion models for free. In Conference on Computer Vision and Pattern Recognition (CVPR), 2024 [2] 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. arXiv preprint arXiv:2206.00927, 2022.

Strengths

The proposed skipping strategy leverages the generated content to speed up image and video diffusion generative models through an adaptive skipping scheme. In experiments with image and video generation, the authors demonstrate that this adaptive skipping scheme can achieve higher speed and better quality than competitors. In my opinion, the concept of using an adaptive skipping scheme to speed up diffusion generative models is potentially valuable and could be of interest to the research community.

Weaknesses

The choice of employing the norm of the third-order derivative as a criterion for skipping denoising steps was made empirically. Theorem 1 (Equation 3) explains why it makes sense to consider the first-order derivative as a criterion for skipping. However, the authors empirically demonstrate that the first-order and second-order derivatives barely correlate with an optimal skipping schedule found by a greedy search. There is a lack of mathematical justification for choosing the third-order derivative.

Questions

It would be helpful if the authors could provide any mathematical justification or intuition (in addition to the empirical experiments in the paper) for their choice to employ third-order derivatives as the criterion for skipping denoising steps.

Rating

6

Confidence

4

Soundness

3

Presentation

3

Contribution

3

Limitations

Yes.

Summary

To enhance the sampling speed in diffusion models, this paper introduces the AdaptiveDiffusion framework, which utilizes a skipping strategy. Specifically, this strategy is guided by the third-order latent difference, assessing the stability between timesteps throughout the denoising process. Experiments results on image and video diffusion models demonstrate the superiority of the proposed adaptively sampling framework.

Strengths

1. The motivation is reasonable: to accelerate the sampling speed by reducing redundant time steps. 2. The contribution is helpful in diffusion community. 3. The figures are pretty and the presentation is readable. 4. The proposed AdaptiveDiffusion is effective on multitask.

Weaknesses

1. In my humble opinion, some improvements are marginal to me, especially on ImageNet 256×256. 2. I am not sure the Theorem one is guaranteed even in larger sampling step size. 3. Since many methods investigate reducing sampling steps by employing higher-order solutions, this approach lacks novelty.

Questions

1. Can you derive the Theorem 1 when the step size is large? Since large step size will magnify the upper bound on the error, and fast sampling method always equals to large step size sampling. 2. Since the proposed method aims to accelerate the sampling speed, can it reduce the NFEs? 3. Is the proposed method effective on pure image generation?

Rating

4

Confidence

3

Soundness

2

Presentation

3

Contribution

2

Limitations

Please see in Weaknesses and Questions. If all of my concerns are addressed, I will definitely improve my score.