Three Creates All: You Only Sample 3 Steps
📰 ArXiv cs.AI
Diffusion models can be optimized for faster inference by sampling only 3 steps using Multi-layer Time Embedding Optimization (MTEO)
Action Steps
- Freeze the pretrained diffusion backbone
- Distill a small set of step-wise, layer-wise time embeddings from reference data
- Optimize the time embeddings for few-step sampling using MTEO
- Evaluate the performance of the optimized model on generation tasks
Who Needs to Know This
AI engineers and researchers can benefit from this technique to improve the efficiency of diffusion models, while ML researchers can apply this to develop more efficient generative models
Key Insight
💡 Standard timestep conditioning is a key bottleneck for few-step sampling in diffusion models, and MTEO can help overcome this limitation
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💡 Diffusion models can be optimized for faster inference by sampling only 3 steps!
Key Takeaways
Diffusion models can be optimized for faster inference by sampling only 3 steps using Multi-layer Time Embedding Optimization (MTEO)
Full Article
Title: Three Creates All: You Only Sample 3 Steps
Abstract:
arXiv:2603.22375v1 Announce Type: cross Abstract: Diffusion models deliver high-fidelity generation but remain slow at inference time due to many sequential network evaluations. We find that standard timestep conditioning becomes a key bottleneck for few-step sampling. Motivated by layer-dependent denoising dynamics, we propose Multi-layer Time Embedding Optimization (MTEO), which freeze the pretrained diffusion backbone and distill a small set of step-wise, layer-wise time embeddings from refer
Abstract:
arXiv:2603.22375v1 Announce Type: cross Abstract: Diffusion models deliver high-fidelity generation but remain slow at inference time due to many sequential network evaluations. We find that standard timestep conditioning becomes a key bottleneck for few-step sampling. Motivated by layer-dependent denoising dynamics, we propose Multi-layer Time Embedding Optimization (MTEO), which freeze the pretrained diffusion backbone and distill a small set of step-wise, layer-wise time embeddings from refer
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