Paris 2.0: A Decentralized Diffusion Model for Video Generation
It solves the open problem of temporally coherent video generation under decentralized training, enabling video model training without monolithic GPU clusters.
Paris 2.0 is the first video generation model pre-trained through decentralized computation, achieving a ~2.0x improvement in Frechet Video Distance (from 561.04 to 279.01) over a monolithic model trained with the same compute budget.
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.