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Efficient Video Generation with the Temporal Pyramid Diffusion Model (TPDiff)

The generation of videos using artificial intelligence has made enormous progress in recent years. Diffusion models have proven to be particularly promising, as they can generate high-quality and realistic videos. However, the high computational cost of these models poses a significant challenge. A new approach, the so-called Temporal Pyramid Diffusion Model (TPDiff), now promises to significantly improve efficiency in both training and inference.

The Problem of Computational Intensity

Conventional video diffusion models require immense computing power, which often limits their use in practice. The diffusion process, in which an image or video is transformed into a pure noise state by gradually adding noise, and the reverse process, denoising, are very complex and computationally intensive. Especially with videos, which contain high temporal resolution and many individual frames, the computational effort increases enormously.

TPDiff: A Stage-wise Approach

The TPDiff model is based on the realization that the reverse process of diffusion, i.e., the removal of noise, is inherently entropy-reducing. In other words, the information in the video becomes increasingly clear and structured throughout the process. Therefore, it is not necessary to maintain the full frame rate in the early, high-entropy phases of the process. TPDiff utilizes this principle by dividing the diffusion into several stages. In the initial stages, the model works with a reduced frame rate, which is then gradually increased until the full frame rate is reached in the final stage. This approach significantly reduces the computational effort without significantly compromising the quality of the final result.

Stage-wise Diffusion: An Optimized Training Procedure

To train the multi-stage diffusion model, a special training procedure called "Stage-wise Diffusion" has been developed. This procedure is based on solving partitioned probability flow ordinary differential equations (ODE) considering aligned data and noise. This approach allows for a more efficient calculation of the transitions between the individual diffusion stages and thus contributes to a further reduction in training effort. Furthermore, Stage-wise Diffusion is applicable to various forms of diffusion, which increases the flexibility of the TPDiff model.

Experimental Results and Outlook

Extensive experimental investigations have confirmed the effectiveness of the TPDiff model. Compared to conventional video diffusion models, the training effort could be reduced by up to 50% and the inference speed increased by a factor of 1.5. These results underscore the potential of TPDiff to make video generation with diffusion models more efficient and accessible. Future research could focus on further optimizing the stage division and the training procedure to further improve the performance of TPDiff. The development of efficient video diffusion models like TPDiff opens up new possibilities for the application of AI in areas such as film creation, animation, and virtual realities.

Bibliographie: Esser, Patrick, et al. "Structure and Content-Guided Video Synthesis with Diffusion Models." Proceedings of the IEEE/CVF International Conference on Computer Vision. 2023. Skorokhodov, Ivan, et al. "Hierarchical Patch Diffusion Models for High-Resolution Video Generation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024. Ran, Lingmin, and Mike Zheng Shou. "TPDiff: Temporal Pyramid Video Diffusion Model." arXiv preprint arXiv:2503.09566 (2025). "Pyramid Discrete Diffusion." Yuheng Li's Homepage, yuheng.ink/project-page/pyramid-discrete-diffusion/. "TPDiff: Temporal Pyramid Video Diffusion Model." Hugging Face, huggingface.co/papers/2503.09566. "TPDiff." GitHub, github.com/showlab/TPDiff. "Papers." Hugging Face, huggingface.co/papers. "WALT." WALT, walt-video-diffusion.github.io/. "ChatPaper." ChatPaper, chatpaper.com/chatpaper/fr?id=4&date=1741795200&page=1. ```