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Diffusion models (such as Stable Diffusion) predict, during the denoising process, the probability that a pixel belongs to a certain content, and the final decoder weights and averages these probabilities into pixel values, so line edges are continuous grayscale transitions rather than binary masks.
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10/16/2026
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VerifiedStable Diffusion is based on the Latent Diffusion Model and generates high-quality images by performing a denoising process in a compressed latent space78% similarUnverifiedThe mathematical essence of diffusion models is based on stochastic differential equations and score matching theory, underpinning image generation systems such as Stable Diffusion and DALL-E.76% similarUnverifiedThe denoising sampling process of a diffusion model can be regarded as the solution of a stochastic differential equation, comprising a deterministic drift term and a stochastic diffusion term.75% similarUnverifiedStable Diffusion 基于去噪扩散概率模型（DDPM）理论，将扩散过程从像素空间转移到低维潜在空间，由VAE负责编解码，CLIP模型将文本提示编码为语义向量75% similarUnverifiedThe paper "Finite-Sample Guarantees for Fully Discrete Conditional Diffusion Models" establishes finite-sample error guarantees for conditional diffusion models that are learned from data and implemented with a finite number of denoising steps.73% similar
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