BRACE: Taming Sharp Irregularities via Barycentric Rational Forecasting for Fast Diffusion Transformers Inference

• URL: http://arxiv.org/abs/2608.07572v1
• Date: Tue, 04 Aug 2026 11:24:53 GMT
• Title: BRACE: Taming Sharp Irregularities via Barycentric Rational Forecasting for Fast Diffusion Transformers Inference
• Abstract summary: Diffusion Transformers (DiTs) have demonstrated exceptional performance in high-fidelity image and video generation.<n>To alleviate their massive computational overhead, temporal feature caching has been proposed to bypass redundant computations.<n>We propose Barycentric Forecasting with Chebyshev Enhancement (BRACE) to address this bottleneck.
• Score: 7.024073219145713
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion Transformers (DiTs) have demonstrated exceptional performance in high-fidelity image and video generation. To alleviate their massive computational overhead, temporal feature caching has been proposed to bypass redundant computations. However, existing cache-then-forecast methods driven by derivative-based polynomials often cause severe quality degradation under high acceleration due to unstable long-step predictions. To address this bottleneck, we propose Barycentric Rational Forecasting with Chebyshev Enhancement (BRACE). Motivated by the observation that DiT feature trajectories are globally smooth yet frequently exhibit sharp irregularities and local non-smoothness, BRACE shifts the paradigm from derivative-driven polynomial extrapolation to feature-driven rational forecasting. Specifically, it maintains a local sliding window to cache sparse historical features and leverages adapted Chebyshev weights to formulate a barycentric rational function, directly aggregating these raw features to ensure numerical stability. Extensive experiments demonstrate that BRACE achieves state-of-the-art quality-efficiency trade-offs across various DiT architectures with negligible computational overhead.

Related papers

• ChebBooster: A Training-Free Approach for Efficient Diffusion Transformer Inference via Chebyshev-Inspired Extrapolation [6.871887763122593]
  Diffusion Transformers (DiTs) have shown strong performance in high-fidelity image generation, but their sampling process remains computationally intensive due to full model execution at every timestep.<n>We propose ChebBooster, a training-free extrapolation framework based on Chebyshev theory that achieves stable and efficient acceleration for DiTs.
  arXiv  Detail & Related papers  (2026-08-24T16:06:11Z)
• ResilPhase: Plug-and-Play Phase Mapping and Noise-Resilient Macro-Trajectory Extrapolation for Diffusion Acceleration [10.972902209477844]
  We introduce a derivative-free barycentric Lagrange extrapolator to bypass derivative instability and approximation error.<n> Experiments on FLUX.1-dev and HunyuanVideo demonstrate state-of-the-art fidelity under aggressive acceleration ratios.
  arXiv  Detail & Related papers  (2026-06-25T08:54:17Z)
• Taming Curvature: Architecture Warm-Up for Stable Transformer Training [52.6838951211596]
  We introduce a fast online estimator of the largest (preconditioned) Hessian eigenvalue (i.e., curvature) based on a warm-started variant for power with Hessian-vector products.<n>We find that training instabilities coincide with surges in preconditioned curvature and that curvature grows with depth.<n> Experiments on large Transformers validate that our approach enables efficient curvature tracking and reduces instabilities.
  arXiv  Detail & Related papers  (2026-06-15T14:16:56Z)
• Clipping Bottleneck: Stabilizing RLVR via Stochastic Recovery of Near-Boundary Signals [83.0127582612634]
  Near-boundary Rescue (NSR) is a minimal, plug-and-play modification that retains out-of-bound tokens to recover lost signals.<n>NSR substantially improves training stability and delivers consistent gains over strong baselines such as DAPO and GSPO.
  arXiv  Detail & Related papers  (2026-05-21T16:45:31Z)
• Ride the Wave: Precision-Allocated Sparse Attention for Smooth Video Generation [9.132288507881592]
  Video Diffusion Transformers have revolutionized high-fidelity video generation but suffer from the massive computational burden of self-attention.<n>We propose Precision-Allocated Sparse Attention (PASA), a training-free framework designed for highly efficient and temporally smooth video generation.
  arXiv  Detail & Related papers  (2026-04-14T02:51:52Z)
• DOC-GS: Dual-Domain Observation and Calibration for Reliable Sparse-View Gaussian Splatting [80.43237927269575]
  In this paper, we revisit sparse-view 3DGS reconstruction from a new perspective.<n>We identify the core challenge as the unobservability of Gaussian primitive reliability.<n>Motivated by this observation, we propose a unified Dual-domain Observation and geometrics in rendered images framework.
  arXiv  Detail & Related papers  (2026-04-08T07:01:24Z)
• Adaptive Spectral Feature Forecasting for Diffusion Sampling Acceleration [58.19554276924402]
  We propose spectral diffusion feature forecaster (Spectrum) to enable global, long-range feature reuse with tightly controlled error.<n>We achieve up to 4.79$times$ speedup on FLUX.1 and 4.67$times$ speedup on Wan2.1-14B, while maintaining much higher sample quality compared with the baselines.
  arXiv  Detail & Related papers  (2026-03-02T08:59:11Z)
• GTS: Inference-Time Scaling of Latent Reasoning with a Learnable Gaussian Thought Sampler [54.10960908347221]
  We model latent thought exploration as conditional sampling from learnable densities and instantiate this idea as a Gaussian Thought Sampler (GTS)<n>GTS predicts context-dependent perturbation distributions over continuous reasoning states and is trained with GRPO-style policy optimization while keeping the backbone frozen.
  arXiv  Detail & Related papers  (2026-02-15T09:57:47Z)
• Forecast the Principal, Stabilize the Residual: Subspace-Aware Feature Caching for Efficient Diffusion Transformers [9.698781486878206]
  Diffusion Transformer (DiT) models have achieved unprecedented quality in image and video generation, yet their iterative sampling process remains computationally prohibitive.<n>We propose SVD-Cache, a subspace-aware caching framework that decomposes diffusion features via Singular Value Decomposition (SVD)<n>Our code is in supplementary material and will be released on Github.
  arXiv  Detail & Related papers  (2026-01-12T10:30:12Z)
• HiCache: Training-free Acceleration of Diffusion Models via Hermite Polynomial-based Feature Caching [19.107716099809707]
  HiCache is a training-free acceleration framework that improves feature prediction.<n>We introduce a dual-scaling mechanism that ensures numerical stability while preserving predictive accuracy.
  arXiv  Detail & Related papers  (2025-08-23T10:35:16Z)
• Sortblock: Similarity-Aware Feature Reuse for Diffusion Model [9.749736545966694]
  Diffusion Transformers (DiTs) have demonstrated remarkable generative capabilities.<n>DiTs' sequential denoising process results in high inference latency.<n>We propose Sortblock, a training-free inference acceleration framework.
  arXiv  Detail & Related papers  (2025-08-01T08:10:54Z)
• Towards Stabilized and Efficient Diffusion Transformers through Long-Skip-Connections with Spectral Constraints [51.83081671798784]
  Diffusion Transformers (DiT) have emerged as a powerful architecture for image and video generation, offering superior quality and scalability.<n>DiT's practical application suffers from inherent dynamic feature instability, leading to error amplification during cached inference.<n>We propose Skip-DiT, an image and video generative DiT variant enhanced with Long-Skip-Connections (LSCs) - the key efficiency component in U-Nets.
  arXiv  Detail & Related papers  (2024-11-26T17:28:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.