Verified80% confidenceFactTime unknown
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
18
Sources
80%
Confidence
Long-term
Relevance
5/31/2026
First Seen
Sources
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Related Claims
UnverifiedDiffusion models are a deep learning architecture that generates images by learning a progressive denoising process, and the scale of training data typically reaches billions of images.84% similarVerifiedThe 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.83% similarVerifiedThe output randomness of image generation models fundamentally stems from the initial noise sampling in the diffusion process, with the Seed (random number seed) controlling the initial noise.83% similarPartially 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.81% similar
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