🔥 LongLive-2.0: An NVFP4 Parallel Infrastructure for Long Video Generation

💡 The paper introduces LongLive-2.0, a parallel infrastructure for long video generation that addresses training and inference bottlenecks. The problem with existing methods is that they are slow and require a lot of memory, especially for long videos. To solve this, the authors propose a sequence-parallel autoregressive training method called Balanced SP, which pairs clean-history and noisy-target temporal chunks on each rank, enabling efficient teacher-forcing and reducing GPU memory cost.

The method also uses NVFP4 precision to accelerate GEMM computation during training. Additionally, the authors tune a diffusion model into a long, multi-shot, interactive auto-regressive diffusion model, which can be converted to real-time generation with standalone LoRA weights. For inference, the authors enable W4A4 NVFP4 inference, quantize KV cache into NVFP4 for memory savings, and boost end-to-end throughput with asynchronous streaming VAE decoding.

The results show that LongLive-2.0 achieves up to 2.15x speedup in training and 1.84x in inference. The LongLive-2.0-5B model achieves 45.7 FPS inference while attaining strong performance on benchmarks. The authors claim that LongLive-2.0 is the first NVFP4 training and inference system for long video generation, making it a significant contribution to the field. Overall, the paper presents a novel parallel infrastructure that addresses the speed and memory bottlenecks in long video generation, making it possible to generate high-quality videos in real-time.

📅 Published on May 18

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2605.18739
• PDF: https://arxiv.org/pdf/2605.18739
• Project Page: https://nvlabs.github.io/LongLive/LongLive2/

🤖 Models citing this paper:
• https://huggingface.co/Efficient-Large-Model/LongLive-2.0-5B
• https://huggingface.co/Efficient-Large-Model/LongLive-2.0-5B-NVFP4-S4
• https://huggingface.co/Efficient-Large-Model/LongLive-2.0-5B-NVFP4-S2

📊 Datasets citing this paper:
• https://huggingface.co/datasets/Efficient-Large-Model/LongLive2.0-Toy-Dataset

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📢 By: https://xn--r1a.website/PaperNexus

#LongVideoGeneration #ParallelInfrastructure #NVFP4 #AutoregressiveTraining #DiffusionModeling