DisCa：面向视频扩散 Transformer 的蒸馏兼容可学习特征缓存加速
DisCa: Accelerating Video Diffusion Transformers with Distillation-Compatible Learnable Feature Caching
用轻量可学习神经预测器替代无训练启发式特征缓存，并与步数蒸馏联合优化，可直接迁移到任意视频扩散 Transformer 的少步推理管线中以降低采样开销。
原文依据 We employ a lightweight learnable neural predictor instead of traditional training-free heuristics for diffusion models
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论尽管扩散模型在视频生成领域取得了巨大成功，但这一进展伴随着迅速攀升的计算负担。在现有加速方法中，特征缓存因其无需训练的特性与可观的加速性能而广受欢迎，但在进一步压缩时不可避免地出现语义与细节丢失。另一种被广泛采用的方法——训练感知的步数蒸馏——虽然在图像生成中取得成功，但在视频生成中步数极少时同样出现剧烈退化。此外，由于采样步数更为稀疏，将无需训练的特征缓存直接应用于步数蒸馏模型时，质量损失会更为严重。本文首次创新性地提出一种蒸馏兼容的可学习特征缓存机制。我们采用轻量可学习的神经预测器替代扩散模型中传统的无需训练启发式方法，从而更准确地捕捉高维特征的演化过程。此外，我们探究了在大规模视频模型上进行高压缩蒸馏所面临的挑战，并提出一种保守的受限 MeanFlow 方法，以实现更稳定、无损的蒸馏。通过上述举措，我们在保持生成质量的同时将加速边界进一步推进至 11.8 倍。大量实验验证了本方法的有效性。
英文原文摘要 1433 字符
While diffusion models have achieved great success in the field of video generation, this progress is accompanied by a rapidly escalating computational burden. Among the existing acceleration methods, Feature Caching is popular due to its training-free property and considerable speedup performance, but it inevitably faces semantic and detail drop with further compression. Another widely adopted method, training-aware step-distillation, though successful in image generation, also faces drastic degradation in video generation with a few steps. Furthermore, the quality loss becomes more severe when simply applying training-free feature caching to the step-distilled models, due to the sparser sampling steps. This paper novelly introduces a distillation-compatible learnable feature caching mechanism for the first time. We employ a lightweight learnable neural predictor instead of traditional training-free heuristics for diffusion models, enabling a more accurate capture of the high-dimensional feature evolution process. Furthermore, we explore the challenges of highly compressed distillation on large-scale video models and propose a conservative Restricted MeanFlow approach to achieve more stable and lossless distillation. By undertaking these initiatives, we further push the acceleration boundaries to 11.8 times while preserving generation quality. Extensive experiments demonstrate the effectiveness of our method.