Paris 2.0: A Decentralized Diffusion Model for Video Generation
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866913170453430272 |
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| author | Rouzbayani, Ali Roy, Bidhan Villagra, Marcos Jiang, Zhiying |
| author_facet | Rouzbayani, Ali Roy, Bidhan Villagra, Marcos Jiang, Zhiying |
| contents | We present Paris 2.0, the first video generation model pre-trained through decentralized computation. Its training recipe builds upon Paris 1.0 (arXiv:2510.03434), the first ever open-weight Decentralized Diffusion Model (DDM), which showed that image generation can be trained without a monolithic GPU cluster. However, temporally coherent video generation had remained an open problem under decentralized training, and Paris 2.0 closes it.
In low-resolution text-to-video training, against a monolithic model trained on the same data under a matched total compute budget, Paris 2.0 cuts Frechet Video Distance (FVD) from 561.04 to 279.01, a ~2.0x improvement, and lifts CLIP text-video similarity and aesthetic score. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_26064 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Paris 2.0: A Decentralized Diffusion Model for Video Generation Rouzbayani, Ali Roy, Bidhan Villagra, Marcos Jiang, Zhiying Computer Vision and Pattern Recognition Machine Learning I.2.10; I.2.11 We present Paris 2.0, the first video generation model pre-trained through decentralized computation. Its training recipe builds upon Paris 1.0 (arXiv:2510.03434), the first ever open-weight Decentralized Diffusion Model (DDM), which showed that image generation can be trained without a monolithic GPU cluster. However, temporally coherent video generation had remained an open problem under decentralized training, and Paris 2.0 closes it. In low-resolution text-to-video training, against a monolithic model trained on the same data under a matched total compute budget, Paris 2.0 cuts Frechet Video Distance (FVD) from 561.04 to 279.01, a ~2.0x improvement, and lifts CLIP text-video similarity and aesthetic score. |
| title | Paris 2.0: A Decentralized Diffusion Model for Video Generation |
| topic | Computer Vision and Pattern Recognition Machine Learning I.2.10; I.2.11 |
| url | https://arxiv.org/abs/2605.26064 |