[2605.26064] Paris 2.0: A Decentralized Diffusion Model for Video Generation
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Computer Science > Computer Vision and Pattern Recognition
arXiv:2605.26064 (cs)
[Submitted on 25 May 2026 (v1), last revised 27 May 2026 (this version, v2)]
Title:Paris 2.0: A Decentralized Diffusion Model for Video Generation
Authors:Ali Rouzbayani, Bidhan Roy, Marcos Villagra, Zhiying Jiang<br>View a PDF of the paper titled Paris 2.0: A Decentralized Diffusion Model for Video Generation, by Ali Rouzbayani and 3 other authors
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Abstract: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.
Comments:<br>6 pages, 5 figures
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
ACM classes:<br>I.2.10; I.2.11
Cite as:<br>arXiv:2605.26064 [cs.CV]
(or<br>arXiv:2605.26064v2 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2605.26064
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arXiv-issued DOI via DataCite
Submission history<br>From: Marcos Villagra [view email]<br>[v1]<br>Mon, 25 May 2026 17:27:22 UTC (2,417 KB)
[v2]<br>Wed, 27 May 2026 11:28:25 UTC (3,047 KB)
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