Unverified50% confidenceFactExact time
The deterministic mapping enables consistency distillation, training a student model to directly map any noise level to the final sample, thereby compressing the number of sampling steps to 1-4.
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10/30/2026
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VerifiedConsistency models achieve direct prediction of the endpoint of the denoising trajectory by training the network, theoretically enabling single-step generation.77% similarUnverifiedThe technical principle behind four-step generation typically comes from accelerated sampling paradigms such as Consistency Models or Flow Matching, which use distillation training to enable the model to accurately predict the denoising direction at sparse time steps.76% similarUnverified扩散模型的训练过程分为加噪与去噪两个阶段，推理阶段的条件可引入额外视觉信号（如参考视频、深度图、骨骼图）约束去噪方向75% similarVerifiedFlow matching models generate images by learning a continuous probability flow (ODE trajectory) from the noise distribution to the data distribution, and are generally more stable to train and require fewer inference steps.75% similarVerified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75% similar
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