Verified90% confidenceFactTime unknown
Compared to GANs, diffusion models are more stable to train, generate greater diversity, and are less prone to mode collapse.
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5/31/2026
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VerifiedCompared to GANs, diffusion models offer more stable training, higher generation diversity, and easier integration with text conditioning.89% similarVerifiedGANs improve generation quality through adversarial interplay between a generator and a discriminator, but are prone to mode collapse; diffusion models have more stable training objectives and naturally avoid mode collapse.84% similarUnverified扩散模型的训练目标为简单的去噪均方误差，相比GAN避免了对抗训练的不稳定性，降低了模式崩塌的风险81% similarUnverified与GAN不同，扩散模型不依赖对抗训练，生成过程更稳定，输出图像在像素级和频域上更接近真实照片的统计分布77% similarUnverifiedCompared to MoE architectures, dense models have more uniform gradient flow during training, are less prone to expert collapse, and exhibit more deterministic inference behavior.76% similar
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