BlockVid: Block Diffusion for High-Quality and Consistent Minute-Long Video Generation

TL;DR

BlockVid introduces a novel block diffusion framework with semantic-aware caching and specialized training strategies to generate high-quality, coherent minute-long videos, addressing long-horizon errors and coherence evaluation.

Contribution

The paper proposes BlockVid, a new block diffusion method with semantic-aware sparse KV cache and chunk-wise noise scheduling, plus a new benchmark LV-Bench for long-video coherence evaluation.

Findings

Outperforms existing methods in quality and coherence metrics.

Achieves 22.2% improvement on VDE Subject.

Achieves 19.4% improvement on VDE Clarity.

Abstract

Generating minute-long videos is a critical step toward developing world models, providing a foundation for realistic extended scenes and advanced AI simulators. The emerging semi-autoregressive (block diffusion) paradigm integrates the strengths of diffusion and autoregressive models, enabling arbitrary-length video generation and improving inference efficiency through KV caching and parallel sampling. However, it yet faces two enduring challenges: (i) KV-cache-induced long-horizon error accumulation, and (ii) the lack of fine-grained long-video benchmarks and coherence-aware metrics. To overcome these limitations, we propose BlockVid, a novel block diffusion framework equipped with semantic-aware sparse KV cache, an effective training strategy called Block Forcing, and dedicated chunk-wise noise scheduling and shuffling to reduce error propagation and enhance temporal consistency. We further introduce LV-Bench, a fine-grained benchmark for minute-long videos, complete with new metrics evaluating long-range coherence. Extensive experiments on VBench and LV-Bench demonstrate that BlockVid consistently outperforms existing methods in generating high-quality, coherent minute-long videos. In particular, it achieves a 22.2% improvement on VDE Subject and a 19.4% improvement on VDE Clarity in LV-Bench over the state of the art approaches. Project website: https://ziplab.co/BlockVid. Inferix (Code): https://github.com/alibaba-damo-academy/Inferix.