30 Epoch, Consistency Model with 2 step. Using $t_1 = 2, t_2 = 80$.

30 Epoch, Consistency Model with 5 step. Using $t_i \in {5, 10, 20,40, 80}$.

Unofficial Implementation of Consistency Models (paper) in pytorch.

Three days ago, legendary man Yang Song released entirely new set of generative model, called consistency models. There aren't yet any open implementations, so here is my attempt at it.

Diffusion models are amazing, because they enable you to sample high fidelity + high diversity images. Downside is, you need lots of steps, something at least 20.

Progressive Distillation (Salimans & Ho, 2022) solves this with distillating 2-steps of the diffusion model down to single step. Doing this N times boosts sampling speed by $2^N$. But is this the only way? Do we need to train diffusion model and distill it $n$ times? Yang didn't think so. Consistency model solves this by mainly trianing a model to make a consistent denosing for different timesteps (Ok I'm obviously simplifying)

Install the package with

pip install git+https://github.com/cloneofsimo/consistency_models.git

This repo mainly implements consistency training:

$$ L(\theta) = \mathbb{E}[d(f_\theta(x + t_{n + 1}z, t_{n + 1}), f_{\theta_{-}}(x + t_n z, t_n))] $$

And sampling:

$$ \begin{align} z &\sim \mathcal{N}(0, I) \\ x &\leftarrow x + \sqrt{t_n ^2 - \epsilon^2} z \\ x &\leftarrow f_\theta(x, t_n) \\ \end{align} $$

There is a self-contained MNIST training example on the root main.py.

python main.py

• EMA
• CIFAR10 Example
• Samples are sooo fuzzy... try to get a crisp result.
• Consistency Distillation

@misc{https://doi.org/10.48550/arxiv.2303.01469,
  doi = {10.48550/ARXIV.2303.01469},
  
  url = {https://arxiv.org/abs/2303.01469},
  
  author = {Song, Yang and Dhariwal, Prafulla and Chen, Mark and Sutskever, Ilya},
  
  keywords = {Machine Learning (cs.LG), Computer Vision and Pattern Recognition (cs.CV), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Consistency Models},
  
  publisher = {arXiv},
  
  year = {2023},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@misc{https://doi.org/10.48550/arxiv.2202.00512,
  doi = {10.48550/ARXIV.2202.00512},
  
  url = {https://arxiv.org/abs/2202.00512},
  
  author = {Salimans, Tim and Ho, Jonathan},
  
  keywords = {Machine Learning (cs.LG), Artificial Intelligence (cs.AI), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {Progressive Distillation for Fast Sampling of Diffusion Models},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}