Tikhonov-Stabilized Bezier Representation Forecasting for Training-free Diffusion Acceleration

Key points

• Diffusion models, particularly Diffusion Transformers, achieve strong image and video generation quality but remain expensive at inference time due to repeated denoiser evaluations.1
• Feature caching offers a deployment-friendly acceleration strategy by reusing or predicting intermediate representations without retraining the generator or modifying the sampler.1
• BeziCast estimates B'ezier control points via Tikhonov-stabilized fitting, providing a smooth, capacity-controlled temporal parameterization that avoids explicit high-order derivative estimation and decouples trajectory capacity from cache-query density.1
• To moderate aggressive extrapolation, we further derive equivalent forecasting weights and introduce a convex-hull-inspired guardrail that detects high-risk predictions and softly pulls them toward a conservative simplex-projected estimate.1

Sources (1)

• [1]Tikhonov-Stabilized Bezier Representation Forecasting for Training-free Diffusion Acceleration
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 09:14 AM

  We propose BeziCast, a training-free diffusion acceleration framework that forecasts output-proximal denoising representations using low-order B'ezier trajectories.

  Diffusion models, particularly Diffusion Transformers, achieve strong image and video generation quality but remain expensive at inference time due to repeated denoiser evaluations.

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