Consistency Distillation
A diffusion model acceleration distillation technique that trains a student model to produce outputs close to the final result in very few inference steps, substantially reducing the number of sampling steps while maintaining generation quality.
Core Facts
Timeline (last 90 days)
Common implementation approaches for distillation-based acceleration include consistency distillation and progressive distillation
Acceleration LoRAs use distillation training to enable video models to achieve near-original quality with fewer denoising steps, completing sampling in 3 to 4 steps, which can reduce inference time to approximately one-tenth of the original.
The core technologies behind few-step video generation are typically consistency distillation or flow matching acceleration.
Common inference acceleration techniques include consistency distillation, flow matching, and diffusion model step compression methods such as LCM and SDXL-Turbo.
Acceleration approaches such as Turbo and Lightning introduce consistency distillation or adversarial distillation techniques, using a teacher model to supervise a student model in approximating the final output within very few steps.
This experiment adopts trajectory-level distillation, which belongs to the category of behavioral cloning, directly having the student model imitate the external behavioral sequences of the teacher model.
Consistency 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.
Model distillation technology can compress the number of sampling steps from 50–100 steps down to 4–8 steps or even fewer through methods such as consistency distillation or progressive distillation.
All Facts (8)
This experiment adopts trajectory-level distillation, which belongs to the category of behavioral cloning, directly having the student model imitate the external behavioral sequences of the teacher model.
75%UnverifiedConsistency 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.
60%UnverifiedCommon implementation approaches for distillation-based acceleration include consistency distillation and progressive distillation
50%UnverifiedAcceleration LoRAs use distillation training to enable video models to achieve near-original quality with fewer denoising steps, completing sampling in 3 to 4 steps, which can reduce inference time to approximately one-tenth of the original.
50%UnverifiedThe core technologies behind few-step video generation are typically consistency distillation or flow matching acceleration.
50%UnverifiedCommon inference acceleration techniques include consistency distillation, flow matching, and diffusion model step compression methods such as LCM and SDXL-Turbo.
50%UnverifiedAcceleration approaches such as Turbo and Lightning introduce consistency distillation or adversarial distillation techniques, using a teacher model to supervise a student model in approximating the final output within very few steps.
50%UnverifiedModel distillation technology can compress the number of sampling steps from 50–100 steps down to 4–8 steps or even fewer through methods such as consistency distillation or progressive distillation.
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