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English(EN) BudgetPix: Compute-Adaptive Tokenization for Pixel-Space Image Diffusion

BudgetPix框架为图像扩散模型实现自适应计算

研究人员推出了一种新颖的图像扩散模型框架BudgetPix,该框架根据视觉复杂度自适应地分配计算资源。这种方法允许单个模型检查点在广泛的计算预算范围内运行,优化了质量-成本权衡。评估表明,BudgetPix在使用的计算量显著减少的情况下,可以实现与MiniT2I-L和PixelDiT等现有模型相当的保真度,并且在类别条件生成任务中表现出接近零的质量下降。 AI

影响 通过根据内容复杂度动态调整计算量,实现更高效的图像生成。

排序理由 介绍图像扩散模型新技术框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

BudgetPix框架为图像扩散模型实现自适应计算

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介绍图像扩散模型新技术框架的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ozgur Kara, Yujia Chen, Daniel Watson, David Forsyth, James Matthew Rehg, Wen-Sheng Chu, Du Tran ·

    BudgetPix:像素空间图像扩散的计算自适应分词

    arXiv:2610.12307v1 Announce Type: new Abstract: Most image generation models rely on uniform tokenization, allocating the exact same computational budget to equally-sized image patches. This static paradigm cannot adapt to different resource constraints at inference time, and yie…