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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.09828 |
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| _version_ | 1866916521615294464 |
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| author | Xu, Xunnong Cao, Mengying |
| author_facet | Xu, Xunnong Cao, Mengying |
| contents | Diffusion transformers enable flexible generative modeling for video. However, it is still technically challenging and computationally expensive to generate high-resolution videos with rich semantics and complex motion. Similar to languages, video data are also auto-regressive by nature, so it is counter-intuitive to use attention mechanism with bi-directional dependency in the model. Here we propose a Multi-Scale Causal (MSC) framework to address these problems. Specifically, we introduce multiple resolutions in the spatial dimension and high-low frequencies in the temporal dimension to realize efficient attention calculation. Furthermore, attention blocks on multiple scales are combined in a controlled way to allow causal conditioning on noisy image frames for diffusion training, based on the idea that noise destroys information at different rates on different resolutions. We theoretically show that our approach can greatly reduce the computational complexity and enhance the efficiency of training. The causal attention diffusion framework can also be used for auto-regressive long video generation, without violating the natural order of frame sequences. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_09828 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MSC: Multi-Scale Spatio-Temporal Causal Attention for Autoregressive Video Diffusion Xu, Xunnong Cao, Mengying Computer Vision and Pattern Recognition Diffusion transformers enable flexible generative modeling for video. However, it is still technically challenging and computationally expensive to generate high-resolution videos with rich semantics and complex motion. Similar to languages, video data are also auto-regressive by nature, so it is counter-intuitive to use attention mechanism with bi-directional dependency in the model. Here we propose a Multi-Scale Causal (MSC) framework to address these problems. Specifically, we introduce multiple resolutions in the spatial dimension and high-low frequencies in the temporal dimension to realize efficient attention calculation. Furthermore, attention blocks on multiple scales are combined in a controlled way to allow causal conditioning on noisy image frames for diffusion training, based on the idea that noise destroys information at different rates on different resolutions. We theoretically show that our approach can greatly reduce the computational complexity and enhance the efficiency of training. The causal attention diffusion framework can also be used for auto-regressive long video generation, without violating the natural order of frame sequences. |
| title | MSC: Multi-Scale Spatio-Temporal Causal Attention for Autoregressive Video Diffusion |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.09828 |