Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
126 tokens/sec
GPT-4o
47 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Rolling Diffusion Models (2402.09470v3)

Published 12 Feb 2024 in cs.LG and stat.ML

Abstract: Diffusion models have recently been increasingly applied to temporal data such as video, fluid mechanics simulations, or climate data. These methods generally treat subsequent frames equally regarding the amount of noise in the diffusion process. This paper explores Rolling Diffusion: a new approach that uses a sliding window denoising process. It ensures that the diffusion process progressively corrupts through time by assigning more noise to frames that appear later in a sequence, reflecting greater uncertainty about the future as the generation process unfolds. Empirically, we show that when the temporal dynamics are complex, Rolling Diffusion is superior to standard diffusion. In particular, this result is demonstrated in a video prediction task using the Kinetics-600 video dataset and in a chaotic fluid dynamics forecasting experiment.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (51)
  1. Fitvid: Overfitting in pixel-level video prediction. arXiv preprint arXiv:2106.13195, 2021.
  2. Align your latents: High-resolution video synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  22563–22575, 2023.
  3. Quo vadis, action recognition? a new model and the kinetics dataset. In proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.  6299–6308, 2017.
  4. A short note about kinetics-600. arXiv preprint arXiv:1808.01340, 2018.
  5. Adversarial video generation on complex datasets. arXiv preprint arXiv:1907.06571, 2019.
  6. Learning to correct spectral methods for simulating turbulent flows. 2022. doi: 10.48550/ARXIV.2207.00556. URL https://arxiv.org/abs/2207.00556.
  7. Self-supervised visual planning with temporal skip connections. CoRL, 12:16, 2017.
  8. E3 tts: Easy end-to-end diffusion-based text to speech. In 2023 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp.  1–8. IEEE, 2023.
  9. Preserve your own correlation: A noise prior for video diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.  22930–22941, 2023.
  10. Photorealistic video generation with diffusion models. arXiv preprint arXiv:2312.06662, 2023.
  11. Flexible diffusion modeling of long videos. Advances in Neural Information Processing Systems, 35:27953–27965, 2022.
  12. Latent video diffusion models for high-fidelity video generation with arbitrary lengths. arXiv preprint arXiv:2211.13221, 2022.
  13. Denoising diffusion probabilistic models. In Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., and Lin, H. (eds.), Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS, 2020.
  14. Imagen video: High definition video generation with diffusion models. arXiv preprint arXiv:2210.02303, 2022a.
  15. Video diffusion models. arXiv:2204.03458, 2022b.
  16. simple diffusion: End-to-end diffusion for high resolution images. arXiv preprint arXiv:2301.11093, 2023.
  17. Scalable adaptive computation for iterative generation. CoRR, abs/2212.11972, 2022.
  18. Imagic: Text-based real image editing with diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  6007–6017, 2023.
  19. The kinetics human action video dataset. arXiv preprint arXiv:1705.06950, 2017.
  20. Variational diffusion models. CoRR, abs/2107.00630, 2021.
  21. Machine learning–accelerated computational fluid dynamics. Proceedings of the National Academy of Sciences, 118(21), 2021. ISSN 0027-8424. doi: 10.1073/pnas.2101784118. URL https://www.pnas.org/content/118/21/e2101784118.
  22. Turbulent flow simulation using autoregressive conditional diffusion models. arXiv preprint arXiv:2309.01745, 2023.
  23. DiffWave: A versatile diffusion model for audio synthesis. In 9th International Conference on Learning Representations, ICLR, 2021.
  24. Ccvs: context-aware controllable video synthesis. Advances in Neural Information Processing Systems, 34:14042–14055, 2021.
  25. Diffusion-lm improves controllable text generation. Advances in Neural Information Processing Systems, 35:4328–4343, 2022.
  26. Fourier neural operator for parametric partial differential equations. arXiv preprint arXiv:2010.08895, 2020.
  27. Pde-refiner: Achieving accurate long rollouts with neural pde solvers. arXiv preprint arXiv:2308.05732, 2023.
  28. Transformation-based adversarial video prediction on large-scale data. arXiv preprint arXiv:2003.04035, 2020.
  29. On distillation of guided diffusion models. CoRR, abs/2210.03142, 2022.
  30. Transframer: Arbitrary frame prediction with generative models. arXiv preprint arXiv:2203.09494, 2022.
  31. Gencast: Diffusion-based ensemble forecasting for medium-range weather. arXiv preprint arXiv:2312.15796, 2023.
  32. Hierarchical text-conditional image generation with CLIP latents. CoRR, abs/2204.06125, 2022.
  33. High-resolution image synthesis with latent diffusion models. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022, pp.  10674–10685. IEEE, 2022.
  34. Photorealistic text-to-image diffusion models with deep language understanding. CoRR, abs/2205.11487, 2022.
  35. Make-a-video: Text-to-video generation without text-video data. CoRR, abs/2209.14792, 2022.
  36. Deep unsupervised learning using nonequilibrium thermodynamics. In Bach, F. R. and Blei, D. M. (eds.), Proceedings of the 32nd International Conference on Machine Learning, ICML, 2015.
  37. Generative modeling by estimating gradients of the data distribution. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS, 2019.
  38. StabilityAI. Introducing stable video diffusion. Nov 2023. URL https://stability.ai/news/stable-video-diffusion-open-ai-video-model. Accessed: 2024-01-25.
  39. A neural pde solver with temporal stencil modeling. arXiv preprint arXiv:2302.08105, 2023.
  40. Csdi: Conditional score-based diffusion models for probabilistic time series imputation. Advances in Neural Information Processing Systems, 34:24804–24816, 2021.
  41. Fvd: A new metric for video generation. 2019.
  42. Phenaki: Variable length video generation from open domain textual description. arXiv preprint arXiv:2210.02399, 2022.
  43. Scaling autoregressive video models. arXiv preprint arXiv:1906.02634, 2019.
  44. Nüwa: Visual synthesis pre-training for neural visual world creation. In European conference on computer vision, pp.  720–736. Springer, 2022.
  45. Ar-diffusion: Auto-regressive diffusion model for text generation. arXiv preprint arXiv:2305.09515, 2023.
  46. Videogpt: Video generation using vq-vae and transformers. arXiv preprint arXiv:2104.10157, 2021.
  47. Diffusion probabilistic modeling for video generation. Entropy, 25(10):1469, 2023.
  48. Scaling autoregressive models for content-rich text-to-image generation. CoRR, abs/2206.10789, 2022.
  49. Magvit: Masked generative video transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  10459–10469, 2023a.
  50. Language model beats diffusion–tokenizer is key to visual generation. arXiv preprint arXiv:2310.05737, 2023b.
  51. Video probabilistic diffusion models in projected latent space. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.  18456–18466, 2023c.
Citations (12)
View on Semantic Scholar

Summary

  • The paper introduces Rolling Diffusion Models, reparameterizing diffusion time per frame to distinctly capture temporal dynamics.
  • It employs a local sliding window denoising process that reduces computational load while enhancing sequence prediction.
  • Empirical results on video prediction and chaotic fluid dynamics demonstrate improved performance over standard diffusion models.

Exploring Temporal Dynamics with Rolling Diffusion Models: A New Framework for Sequential Data Generation

Introduction to Rolling Diffusion Models

The advent of diffusion models has significantly advanced the capabilities of generative modeling, with broad applications spanning from generating static images to complex text-to-speech synthesis. These models employ a process that gradually adds noise to data and learns to reverse this process to generate new data instances from noise. However, applying diffusion models to sequential or temporal data, such as videos or time-series, introduces unique challenges, particularly in handling the temporal dynamics inherent in such data. This paper introduces Rolling Diffusion Models, a novel framework designed to better capture and generate the temporal evolution of data through a unique approach to the diffusion and denoising processes.

Diffusion Models for Temporal Data

Sequential data presents a rich area for applying generative modeling, given its wide-ranging applications across various disciplines. Standard diffusion models, while effective for static data, encounter limitations when extended to sequences. These models typically treat time as an additional dimension, conflating the inherent temporal dynamics with spatial dimensions, leading to increased demands on memory and computation. Moreover, the equal treatment of all frames during generation overlooks the progressive nature of time, where future states inherently carry more uncertainty than immediate ones. This paper argues for a more nuanced approach, one that explicitly accounts for the temporal ordering and varying degrees of uncertainty across frames.

Rolling Diffusion: A Local Sequential Denoising Process

The proposed Rolling Diffusion framework innovates by reparameterizing diffusion time on a per-frame basis, effectively allowing each frame within a sequence to have its own local diffusion time. This reparameterization facilitates a sliding window mechanism, where a model focuses on a subset of frames at any given moment, applying a progressively noisier diffusion process as it "rolls" forward in time. This approach introduces several key advantages:

  • It enables the model to capture the progressive increase in uncertainty inherent in predicting future states.
  • By focusing on a local subset of frames, it reduces the computational load compared to models that operate on entire sequences simultaneously.
  • It allows for indefinite sequence generation, given its local processing nature.

Empirical validation on two challenging domains, video prediction using the Kinetics-600 dataset and chaotic fluid dynamics prediction, demonstrates the superior capability of Rolling Diffusion models in capturing complex temporal dynamics compared to standard diffusion models.

Theoretical and Practical Implications

From a theoretical standpoint, Rolling Diffusion models offer a more refined understanding of incorporating temporal dynamics into the diffusion process. The introduction of a sliding window denoising process, coupled with a frame-specific reparameterization of diffusion time, represents a significant departure from traditional approaches to sequence generation models. Practically, this methodology opens up new possibilities in areas where accurate long-term prediction and generation of sequential data are crucial, such as in forecasting natural phenomena with fluid mechanics simulations or creating realistic video content.

Looking Ahead: Future Directions in Sequential Generative Modeling

The research on Rolling Diffusion models marks a promising step toward more sophisticated and capable generative models for sequential data. Future work may explore various aspects such as optimizing the sliding window mechanism, extending the framework to other types of sequential data beyond video, and improving the efficiency and quality of generated sequences. As this field continues to evolve, we anticipate seeing these models play a pivotal role in applications that require a nuanced understanding and generation of temporal dynamics.

Conclusion

This paper presents Rolling Diffusion models as an innovative approach to generating sequential data, addressing some of the inherent limitations in applying standard diffusion models to temporal datasets. By reimagining the diffusion process through a temporally-aware lens, this framework sets a new standard for the creation of dynamic, realistic sequences, offering valuable insights and tools for researchers and practitioners in generative modeling and its many applications.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown
Youtube Logo Streamline Icon: https://streamlinehq.com

YouTube