Unverified50% confidenceFactExact time
The 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.
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9/15/2026
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UnverifiedConsistency models achieve direct prediction of the endpoint of the denoising trajectory by training the network, theoretically enabling single-step generation.77% similarUnverifiedCommon inference acceleration techniques include consistency distillation, flow matching, and diffusion model step compression methods such as LCM and SDXL-Turbo.76% similarUnverifiedTurbo version models are typically based on techniques such as consistency distillation or flow matching, compressing the tens of denoising inference steps of standard diffusion models to 4 to 8 steps or even fewer76% 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.72% similarUnverifiedConsistency Distillation was implemented by Song et al. in the consistency models proposed in 2023; its core idea is to train the model so that the predicted outputs of any two points on the same denoising trajectory remain consistent, thereby enabling single-step or few-step generation.72% similar
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