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New methods accelerate Diffusion Transformers for faster image generation · 2 papers

By PulseAugur Editorial · [2 sources] ·

Two new research papers propose methods to accelerate Diffusion Transformers (DiTs), which are computationally intensive for high-resolution image and video generation. The first paper, Shiva-DiT, introduces a residual-based differentiable top-k selection method that allows for end-to-end learnability and deterministic token counts, achieving up to 1.54x speedup on SD3-Medium with competitive fidelity. The second paper, BaryCache, presents a training-free acceleration technique using a barycentric extrapolator that forecasts sampling steps, offering a favorable trade-off between memory usage and perceptual quality, with up to 3.30x speedup. AI

IMPACT These methods could significantly reduce the computational cost and latency of generating high-resolution images and videos with Diffusion Transformers.

RANK_REASON Two academic papers published on arXiv proposing novel methods for accelerating Diffusion Transformers.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New methods accelerate Diffusion Transformers for faster image generation · 2 papers

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Jiaji Zhang, Hailiang Zhao, Jiaju Wu, Ruichao Sun, Xinkui Zhao, Shuiguang Deng ·

    Shiva-DiT: Residual-Based Differentiable Top-$k$ Selection for Efficient Diffusion Transformers

    arXiv:2602.05605v2 Announce Type: replace-cross Abstract: Diffusion Transformers (DiTs) are costly at high resolution because self-attention scales quadratically with token sequence length. Existing pruning methods do not jointly provide end-to-end learnability, low training over…

  2. arXiv cs.CV TIER_1 English(EN) · Chengjie Lu, Tianchi Deng, Zhengqi He, Zhijian Gao, Huisi Wu, Xueliang Li ·

    Memory-Efficient Training-Free Acceleration of Diffusion Transformers with BaryCache

    arXiv:2608.28670v1 Announce Type: new Abstract: Diffusion Transformers achieve high-fidelity image and video generation, but their iterative sampling remains expensive, for each denoising step requires large matrix operations. Existing cache-based acceleration reduces redundant c…

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