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Diffusion Generative Flow Samplers

Diffusion Generative Flow Samplers (DGFS): Improving learning signals through partial trajectory optimization

Dinghuai Zhang, Ricky Tian Qi Chen, Cheng-Hao Liu, Aaron Courville, Yoshua Bengio.

We propose a novel DGFS sampler for continuous space sampling from given unnormalized densities based on stochastic optimal control 🤖 formulation and the probabilistic 🎲 GFlowNet framework.

<a href="https://imgse.com/i/pPOmv7T"><img src="https://z1.ax1x.com/2023/10/03/pPOmv7T.md.png" alt="pPOmv7T.png" border="0" /></a>

target/ has the target distribution code. gflownet/ contains the DGFS algorithm code.

Examples

python -m gflownet.main target=gm dt=0.05
python -m gflownet.main target=funnel
python -m gflownet.main target=wells

Dependency

Apart from commonly used torch, torchvision, numpy, scipy, matplotlib, we use the following packages:

pip install hydra-core omegaconf submitit hydra-submitit-launcher
pip install wandb tqdm einops seaborn ipdb