Unverified50% confidenceFactExact time
Current mainstream diffusion models tend to select the highest-probability denoising path at each step, causing generated results to gravitate toward the statistical center of the training data distribution and exhibit the most averaged visual presentation.
1
Sources
50%
Confidence
Long-term
Relevance
9/8/2026
First Seen
Sources
Related Entities
Related Claims
Verified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76% similarUnverifiedDiffusion 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.73% similarUnverifiedLLM天然倾向于输出最符合训练数据分布的平均化表达，这是AI内容高度同质化的根本原因72% similarUnverifiedDiffusion models have a natural tendency to generate high-frequency details and textures present in the training data distribution; if certain elements are not explicitly removed, the model will by default fill in content it considers 'should be there'.72% 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.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/877503API
curl https://kongchang.com/api/v1/knowledge/claims/877503MCP
get_claim(id=877503)