Unverified50% confidenceFactExact time
Classical diffusion models (such as DDPM and Score-Based Generative Model) rely on Brownian motion driven by Gaussian noise as the forward corruption process.
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10/16/2026
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VerifiedThe denoising process of diffusion models is based on the DDPM principle, which involves progressively adding Gaussian noise to data during training and then training a neural network to learn the reverse denoising process.79% similarVerifiedIn 2020, DDPM (Denoising Diffusion Probabilistic Models) pioneered a paradigm in the image generation field of achieving high-quality generation through progressive noise addition followed by progressive denoising.77% similarVerified扩散模型的核心原理分为前向扩散过程（逐步添加高斯噪声）和反向去噪过程（从噪声中还原图像）两个阶段75% similarVerified扩散模型是一类基于马尔可夫链的生成模型，核心思想是通过前向加噪和反向去噪来捕捉数据分布75% similarUnverified扩散模型推理速度较慢，通常需要数十步去噪迭代才能生成一张图像，这催生了DDIM、DPM-Solver等加速采样算法71% similar
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