Diffusion-Generative-Flow-Samplers

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Diffusion-Generative-Flow-Samplers

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PyTorch implementation for our ICLR 2024 paper "Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization"

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// repository documentation

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# Diffusion Generative Flow Samplers

[Diffusion Generative Flow Samplers (DGFS):
Improving learning signals through partial trajectory optimization](https://arxiv.org/abs/2310.02679)

[Dinghuai Zhang](https://zdhnarsil.github.io/), 
[Ricky Tian Qi Chen](https://rtqichen.github.io//),
[Cheng-Hao Liu](https://pchliu.github.io/), 
Aaron Courville, 
[Yoshua Bengio](https://yoshuabengio.org/).

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.

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

## Examples

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

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