ORBIS: Output-Guided Token Reduction with Distribution-Aware Matching for Video Diffusion Acceleration

2026-05-22 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Expert, extended

Summary

ORBIS is a software-hardware co-designed accelerator specifically developed to address the high computational costs of video Diffusion Transformers (DiT). Video DiT models, which use 3D Spatio-Temporal Attention, suffer from increased token length and quadratic attention complexity, leading to prolonged inference times, such as over 0.5 hours for a 129-frame 544p video on an NVIDIA A100 GPU. Existing token reduction methods offer only marginal improvements due to inaccurate similarity estimates and inefficient matching algorithms. ORBIS introduces Output-Guided Matching (OGM), which utilizes previous timestep output activations for more accurate inter-token similarity, and a Distribution-Aware Token Matching (DATM) algorithm that minimizes token-pair loss. A specialized, deeply pipelined hardware accelerator, utilizing 4-bit quantization, fully hides DATM's latency while occupying only 2.4% of the total area. Experiments show ORBIS achieves approximately 2x higher token reduction ratio than AsymRnR, delivering up to 4.5x speedup and 79.3% energy reduction compared to an NVIDIA A100 GPU.

Key takeaway

For AI Hardware Engineers or Machine Learning Engineers deploying video Diffusion Transformers, you should consider hardware-software co-design approaches like ORBIS. This method significantly boosts inference speed and energy efficiency by utilizing output-guided token reduction and specialized hardware. You can achieve up to 4.5x speedup and 79.3% energy reduction compared to an NVIDIA A100 GPU. Evaluate integrating similar output-guided, distribution-aware token reduction techniques with custom accelerators to overcome current computational bottlenecks in video generation.

Key insights

SW-HW co-design with output-guided, distribution-aware token reduction significantly accelerates video Diffusion Transformers by exploiting temporal consistency.

Principles

• Output activation from previous timesteps provides superior token similarity for reduction.
• Explicitly minimizing token-pair loss with distribution-aware matching improves reduction quality.
• Specialized hardware can fully hide complex algorithm latency for end-to-end speedup.

Method

ORBIS employs Output-Guided Matching using previous timestep's output activation and a Distribution-Aware Token Matching (DATM) algorithm that iteratively minimizes token-pair L2 loss. A deeply pipelined hardware engine with 4-bit quantization executes DATM.

In practice

• Quantize token matching algorithms (e.g., 4-bit) when relative relationships are key, not absolute values.
• Schedule multiple Reduced Computation timesteps after each Full Computation step, reusing token index pairs.

Topics

• Video Diffusion Models
• Diffusion Transformers
• Token Reduction
• SW-HW Co-Design
• Inference Acceleration
• Hardware Accelerators

Code references

• genmoai/models

Best for: AI Engineer, Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Hardware Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.