System and method for accelerating diffusion sampling with progressive consistency training

A system for performing a truncated consistency model training framework, comprising:

• one or more processors;
• and
• a non-transitory computer-readable medium having processor-executable instructions stored thereon, wherein the processor-executable instructions, when executed by the one or more processors, facilitate:
• obtaining one or more first time step samples based on sampling from a plurality of time steps;
• determining one or more second time step samples based on the one or more first time step samples;
• training a consistency model at a first stage using the one or more first time step samples and the one or more second time step samples;
• subsequent to training the consistency model at the first stage, obtaining a truncated time range associated with a subset of the plurality of time steps, wherein the truncated time range does not include all of the time steps from the plurality of time steps;
• obtaining one or more third time step samples based on sampling from the truncated time range;
• determining one or more fourth time step samples based on the one or more third time step samples and a time step difference;
• and
• training the consistency model at a second stage using the one or more third time step samples and the one or more fourth time step samples.

A non-transitory computer-readable medium having processor-executable instructions stored thereon for performing a truncated consistency model training framework, wherein the processor-executable instructions, when executed, facilitate:

• obtaining one or more first time step samples based on sampling from a plurality of time steps;
• determining one or more second time step samples based on the one or more first time step samples;
• training a consistency model at a first stage using the one or more first time step samples and the one or more second time step samples;
• subsequent to training the consistency model at the first stage, obtaining a truncated time range associated with a subset of the plurality of time steps, wherein the truncated time range does not include all of the time steps from the plurality of time steps;
• obtaining one or more third time step samples based on sampling from the truncated time range;
• determining one or more fourth time step samples based on the one or more third time step samples and a time step difference;
• and
• training the consistency model at a second stage using the one or more third time step samples and the one or more fourth time step samples.