vidax: A Unified JAX Framework for Video Generative Models on Accelerator Meshes

Congyue Deng

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arXiv · 2609.18077

vidax: A Unified JAX Framework for Video Generative Models on Accelerator Meshes

Congyue Deng

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Abstract

Open-source video generative models ship almost exclusively as PyTorch/CUDA reference implementations. This leaves Cloud TPU pods without a production-ready inference path, despite offering large, cost-effective accelerator memory pools ideal for long-sequence spatiotemporal attention. We present vidax, an open-source JAX/Flax inference engine and zero-copy PyTorch-to-JAX weight translator for modern video generation architectures. vidax covers a diverse set of spatiotemporal models --- including Diffusion Transformers, omnimodal Mixture-of-Transformers, 3D VAEs, text encoders, and native samplers --- with zero PyTorch dependency in the execution path. The framework unifies 1D tensor parallelism with DeepSpeed-Ulysses sequence parallelism on a single JAX sharding mesh, integrates TPU flash-attention kernels, and implements per-layer weight offloading to support reference resolutions that exceed single-device memory. We benchmark compile times, latency, and peak memory utilization on TPU v4-8 hardware, and document real-world numerical bugs surfaced during checkpoint translation. vidax is released open-source as a baseline for JAX and TPU video generation research.

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BibTeX RIS

Congyue Deng. 2026-09-16. vidax: A Unified JAX Framework for Video Generative Models on Accelerator Meshes. https://arxiv.org/abs/2609.18077

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