PackForcing: Short Video Training Suffices for Long Video Sampling and Long Context Inference
Autoregressive video diffusion models have demonstrated remarkable progress, yet they remain bottlenecked by intractable linear KV-cache growth, temporal repetition, and compounding errors during long-video generation. To address these challenges, we present PackForcing, a unified framework that efficiently manages the generation history through a novel three-partition KV-cache strategy. Specifically, we categorize the historical context into three distinct types: (1) Sink tokens, which preserve early anchor frames at full resolution to maintain global semantics; (2) Mid tokens, which achieve a massive spatiotemporal compression (32x token reduction) via a dual-branch network fusing progressive 3D convolutions with low-resolution VAE re-encoding; and (3) Recent tokens, kept at full resolution to ensure local temporal coherence. To strictly bound the memory footprint without sacrificing quality, we introduce a dynamic top-k context selection mechanism for the mid tokens, coupled with a continuous Temporal RoPE Adjustment that seamlessly re-aligns position gaps caused by dropped tokens with negligible overhead. Empowered by this principled hierarchical context compression, PackForcing can generate coherent 2-minute, 832x480 videos at 16 FPS on a single H200 GPU. It achieves a bounded KV cache of just 4 GB and enables a remarkable 24x temporal extrapolation (5s to 120s), operating effectively either zero-shot or trained on merely 5-second clips. Extensive results on VBench demonstrate state-of-the-art temporal consistency (26.07) and dynamic degree (56.25), proving that short-video supervision is sufficient for high-quality, long-video synthesis. https://github.com/ShandaAI/PackForcing
69c686ff8508d7e37629b4a3
Xiaofeng Mao
69c686ff8508d7e37629b4a4
Shaohao Rui
admin_assigned
2026-03-30T10:20:48.309Z
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Shaohao Rui(SII)
shaohao011
user
shaohao011
69c686ff8508d7e37629b4a5
Kaining Ying
admin_assigned
2026-03-30T10:20:56.576Z
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Kaining Ying
Kaining
user
Kaining
69c686ff8508d7e37629b4a6
Bo Zheng
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Chuanhao Li
69c686ff8508d7e37629b4a8
Mingmin Chi
69c686ff8508d7e37629b4a9
Kaipeng Zhang
claimed_verified
2026-03-30T08:59:03.192Z
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kaipeng
kpzhang996
user
kpzhang996
PackForcing: Short Video Training Suffices for Long Video Sampling and Long Context Inference
Autoregressive video diffusion models have demonstrated remarkable progress, yet they remain bottlenecked by intractable linear KV-cache growth, temporal repetition, and compounding errors during long-video generation. To address these challenges, we present PackForcing, a unified framework that efficiently manages the generation history through a novel three-partition KV-cache strategy. Specifically, we categorize the historical context into three distinct types: (1) Sink tokens, which preserve early anchor frames at full resolution to maintain global semantics; (2) Mid tokens, which achieve a massive spatiotemporal compression (32x token reduction) via a dual-branch network fusing progressive 3D convolutions with low-resolution VAE re-encoding; and (3) Recent tokens, kept at full resolution to ensure local temporal coherence. To strictly bound the memory footprint without sacrificing quality, we introduce a dynamic top-k context selection mechanism for the mid tokens, coupled with a continuous Temporal RoPE Adjustment that seamlessly re-aligns position gaps caused by dropped tokens with negligible overhead. Empowered by this principled hierarchical context compression, PackForcing can generate coherent 2-minute, 832x480 videos at 16 FPS on a single H200 GPU. It achieves a bounded KV cache of just 4 GB and enables a remarkable 24x temporal extrapolation (5s to 120s), operating effectively either zero-shot or trained on merely 5-second clips. Extensive results on VBench demonstrate state-of-the-art temporal consistency (26.07) and dynamic degree (56.25), proving that short-video supervision is sufficient for high-quality, long-video synthesis. https://github.com/ShandaAI/PackForcing
PackForcing enables efficient long-video generation through hierarchical KV-cache management and spatiotemporal compression while maintaining temporal consistency and reducing memory usage.
autoregressive video diffusion models KV-cache temporal repetition compounding errors sink tokens mid tokens recent tokens dual-branch network progressive 3D convolutions low-resolution VAE re-encoding dynamic top-k context selection Temporal RoPE Adjustment hierarchical context compression temporal extrapolation VBench