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Hongjie Wang

3 accepted papers

2025

LinGen: Towards High-Resolution Minute-Length Text-to-Video Generation with Linear Computational Complexity

CVPR 2025poster

Text-to-video generation enhances content creation but is highly computationally intensive: The computational cost of Diffusion Transformers (DiTs) scales quadratically in the number of pixels. This makes minute-length video generation extremely expensive, limiting most existing models to generatin…

2024

Attention-Driven Training-Free Efficiency Enhancement of Diffusion Models

CVPR 2024poster

Diffusion models (DMs) have exhibited superior performance in generating high-quality and diverse images. However this exceptional performance comes at the cost of expensive generation process particularly due to the heavily used attention module in leading models. Existing works mainly adopt a retr…

Cited by 15SourcePDFScholar
2024

Zero-TPrune: Zero-Shot Token Pruning through Leveraging of the Attention Graph in Pre-Trained Transformers

CVPR 2024poster

Deployment of Transformer models on edge devices is becoming increasingly challenging due to the exponentially growing inference cost that scales quadratically with the number of tokens in the input sequence. Token pruning is an emerging solution to address this challenge due to its ease of deployme…

Cited by 31SourcePDFScholar