14B model generates minute-scale videos at 19.5 FPS without sacrificing quality or requiring specialized acceleration techniques.
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.
Helios generates minute-long videos at 19.5 FPS on a single GPU using a 14B model while matching larger model quality.
The model eliminates drifting through unified history injection and guidance attention without requiring self-forcing or error-banks.
Real-time performance achieved through multi-term memory patchification and hierarchical distillation reducing sampling steps from 50 to 3.
Method requires high-end hardware like NVIDIA H100 GPU for optimal performance at stated speeds.
Approach still relies on autoregressive generation which may limit theoretical maximum coherence in extremely long sequences.
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