Verified80% confidenceFactExact time
The core idea of diffusion models originates from non-equilibrium thermodynamics: during the training phase, Gaussian noise is gradually added to clear images, and during the inference phase, noise is progressively removed from random noise in reverse to restore the image.
7
Sources
80%
Confidence
Long-term
Relevance
7/12/2026
First Seen
Sources
In-Depth Comparison of AI Image Generation Tools: How to Choose Between Flux, Midjourney, and Gemini?
redditr/learnmachinelearning7/11/2026
Related Claims
VerifiedDiffusion models are inspired by non-equilibrium thermodynamics and employ a two-step training process: the forward process gradually adds Gaussian noise to an image until it becomes pure random noise, and the reverse process trains a neural network to progressively denoise from pure noise back to a clear image.93% similarVerified扩散模型通过在海量图像数据上训练，学会了从随机噪声中逐步去噪生成图像的能力83% similarUnverifiedThe technical foundations of diffusion models originate from stochastic processes in non-equilibrium thermodynamics, comprising two stages: a forward diffusion process and a reverse denoising process.83% similarUnverified扩散模型的训练过程分为加噪与去噪两个阶段，推理阶段的条件可引入额外视觉信号（如参考视频、深度图、骨骼图）约束去噪方向82% 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82% similar
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