Unverified50% confidenceFactExact time
Compared with earlier GAN-based methods, diffusion models show significant improvements in image quality and diversity, but suffer from slow inference speed and high randomness in generated results.
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9/8/2026
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UnverifiedCompared to earlier generative adversarial networks (GANs), diffusion models have significant advantages in the stability, diversity, and controllability of generation quality, but require higher computational costs.79% similarUnverifiedDiffusion models produce high-quality, detail-rich outputs but have slower inference speeds, while Transformer architectures are faster but often fall short of diffusion models in detail accuracy and realism.75% similarUnverifiedGANs have long suffered from the mode collapse problem, where generated images lack diversity, while the stochastic sampling nature of diffusion models naturally avoids this defect.72% similarUnverified扩散模型通过逐步去噪的生成机制产生的频域异常比GAN更微弱且分布不规律，使传统基于频域分析或GAN指纹的检测方法效果大打折扣71% similar
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