Helios: Real Real-Time Long Video Generation Model

TL;DR

Helios is a groundbreaking 14B video generation model capable of real-time, minute-scale long video synthesis on a single GPU, overcoming key challenges in robustness, efficiency, and training scalability.

Contribution

The paper introduces Helios, the first 14B model supporting real-time long video generation without common heuristics or acceleration techniques, and with training that scales efficiently without parallelism.

Findings

Outperforms prior methods on short- and long-video tasks

Achieves 19.5 FPS on a single NVIDIA H100 GPU

Supports minute-scale video generation with high quality

Abstract

We introduce Helios, the first 14B video generation model that runs at 19.5 FPS on a single NVIDIA H100 GPU and supports minute-scale generation while matching the quality of a strong baseline. We make breakthroughs along three key dimensions: (1) robustness to long-video drifting without commonly used anti-drifting heuristics such as self-forcing, error-banks, or keyframe sampling; (2) real-time generation without standard acceleration techniques such as KV-cache, sparse/linear attention, or quantization; and (3) training without parallelism or sharding frameworks, enabling image-diffusion-scale batch sizes while fitting up to four 14B models within 80 GB of GPU memory. Specifically, Helios is a 14B autoregressive diffusion model with a unified input representation that natively supports T2V, I2V, and V2V tasks. To mitigate drifting in long-video generation, we characterize typical failure modes and propose simple yet effective training strategies that explicitly simulate drifting during training, while eliminating repetitive motion at its source. For efficiency, we heavily compress the historical and noisy context and reduce the number of sampling steps, yielding computational costs comparable to -- or lower than -- those of 1.3B video generative models. Moreover, we introduce infrastructure-level optimizations that accelerate both inference and training while reducing memory consumption. Extensive experiments demonstrate that Helios consistently outperforms prior methods on both short- and long-video generation. We plan to release the code, base model, and distilled model to support further development by the community.