torchebm 0.5.6

Components and algorithms for energy-based models

⚡ A PyTorch library for energy-based modeling, with support for flow and diffusion methods.

What is ∇ TorchEBM 🍓?

Energy-based models define distributions through a scalar energy function, where lower energy means higher probability. This is a very general formulation and many generative approaches, from MCMC sampling to score matching to flow-based generation, can be understood through this lens.

TorchEBM is a PyTorch library that gives you composable tools for this entire spectrum. You can define energy landscapes, train models with various learning objectives, and sample via MCMC, optimization, or learned continuous-time dynamics (ODEs/SDEs). The library handles classical EBM training (contrastive divergence, score matching) as well as modern interpolant-based and equilibrium-based generation methods.

📚 For the full documentation, please visit the official website of TorchEBM 🍓.

Features

• Energy models with built-in analytical potentials and support for custom neural network energy functions
• MCMC and optimization-based samplers for drawing samples from energy landscapes
• Flow and diffusion samplers that generate via ODE/SDE integration of learned velocity or score fields
• Training objectives including contrastive divergence variants, score matching variants, and equilibrium matching
• Interpolation schemes for specifying noise-to-data paths in flow and diffusion models
• Numerical integrators for SDE, ODE, and Hamiltonian dynamics
• Neural network architectures ready for conditional generation
• Synthetic datasets for rapid prototyping and benchmarking
• Hyperparameter schedulers for step sizes, noise scales, and other training parameters
• CUDA acceleration and mixed precision support

Gaussian	Double Well	Rastrigin	Rosenbrock

Gaussian Mixture	Two Moons	Swiss Roll	Checkerboard

Installation

pip install torchebm

Dependencies

• PyTorch (with CUDA support for optimal performance)
• Other dependencies are listed in requirements.txt

Usage Examples

MCMC Sampling

import torch
from torchebm.core import GaussianModel
from torchebm.samplers import LangevinDynamics

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = GaussianModel(mean=torch.zeros(2), cov=torch.eye(2), device=device)

sampler = LangevinDynamics(model=model, step_size=0.01, device=device)
samples = sampler.sample(x=torch.randn(500, 2, device=device), n_steps=100)
print(samples.shape)  # torch.Size([500, 2])

Training with Contrastive Divergence

import torch
from torchebm.core import BaseModel
from torchebm.samplers import LangevinDynamics
from torchebm.losses import ContrastiveDivergence
from torchebm.datasets import GaussianMixtureDataset
from torch.utils.data import DataLoader

class MLPEnergy(BaseModel):
    def __init__(self, dim):
        super().__init__()
        self.net = torch.nn.Sequential(
            torch.nn.Linear(dim, 64), torch.nn.SiLU(),
            torch.nn.Linear(64, 64), torch.nn.SiLU(),
            torch.nn.Linear(64, 1),
        )
    def forward(self, x):
        return self.net(x).squeeze(-1)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = MLPEnergy(dim=2).to(device)
sampler = LangevinDynamics(model=model, step_size=0.01, device=device)
cd_loss = ContrastiveDivergence(model=model, sampler=sampler, k_steps=10)

data = GaussianMixtureDataset(n_samples=1000, n_components=4).get_data()
loader = DataLoader(data, batch_size=64, shuffle=True)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)

for epoch in range(10):
    for batch in loader:
        optimizer.zero_grad()
        loss, _ = cd_loss(batch.to(device))
        loss.backward()
        optimizer.step()

Hamiltonian Monte Carlo

import torch
from torchebm.core import GaussianModel
from torchebm.samplers import HamiltonianMonteCarlo

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = GaussianModel(mean=torch.zeros(10), cov=torch.eye(10), device=device)

hmc = HamiltonianMonteCarlo(model=model, step_size=0.1, n_leapfrog_steps=10, device=device)
samples = hmc.sample(dim=10, n_steps=500, n_samples=1000)
print(samples.shape)  # torch.Size([1000, 10])

Library Structure

torchebm/
├── core/           # Base classes, energy models, schedulers, device management
├── samplers/       # MCMC, optimization, and flow/diffusion samplers
├── losses/         # Training objectives (CD, score matching, equilibrium matching)
├── interpolants/   # Noise-to-data interpolation schemes
├── integrators/    # Numerical integrators for SDE/ODE/Hamiltonian dynamics
├── models/         # Neural network architectures
├── datasets/       # Synthetic data generators
├── utils/          # Visualization and training utilities
└── cuda/           # CUDA-accelerated implementations

Visualization Examples

Langevin Dynamics Sampling	Langevin Dynamics Trajectory	Parallel Sampling

Equilibrium Matching: Linear, VP, and Cosine interpolants transforming noise into data.

Check out the examples/ directory for sample scripts.

Contributing

Contributions are welcome! Step-by-step instructions for contributing to the project can be found on the contributing.md page on the website.

Please check the issues page for current tasks or create a new issue to discuss proposed changes.

Show your Support for ∇ TorchEBM 🍓

Please ⭐️ this repository if ∇ TorchEBM helped you and spread the word.

Thank you! 🚀

Citation

If TorchEBM is useful in your research, please cite it:

@misc{torchebm_library_2025,
  author       = {Ghaderi, Soran and Contributors},
  title        = {{TorchEBM}: A PyTorch Library for Training Energy-Based Models},
  year         = {2025},
  url          = {https://github.com/soran-ghaderi/torchebm},
}

Changelog

See CHANGELOG for version history.

License

MIT License. See LICENSE for details.

Research Collaboration

If you are interested in collaborating on research around energy-based, flow-based, or diffusion models, feel free to reach out. Contributions to TorchEBM 🍓 and discussions that push the field forward are always welcome.