Unverified50% confidenceFactExact time
FSD models compress the tens to hundreds of sampling steps of standard diffusion models down to 1–8 steps through knowledge distillation.
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10/9/2026
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UnverifiedFSDP shards model parameters, gradients, and optimizer states across multiple GPUs, and is the mainstream solution for training models with hundreds of billions of parameters71% similarUnverifiedConsistency models attempt to compress the multi-step denoising process of diffusion models into 1–4 steps, theoretically capable of improving inference speed by 10–50 times.69% similarVerifiedLightweight models are compressed in scale through knowledge distillation and model pruning techniques; knowledge distillation has the small model mimic the output probability distribution of the large model, while pruning removes network weights that contribute least to the output.69% similarUnverifiedCombining knowledge distillation with quantization can enable a billion-parameter-scale model to retain 70–85% of the original large model's capabilities while achieving a 5–10× improvement in inference speed.69% similarUnverified在扩散模型中，UNet的任务是在每一步采样中预测并去除潜空间中的噪声，经多步迭代还原图像68% similar
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