Torchebm
🍓 Build and train energy-based and diffusion models in PyTorch ⚡.
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What is Torchebm?
TorchEBM is a CUDA-accelerated PyTorch library for Energy-Based Models (EBMs) that provides efficient implementations of sampling, inference, and learning algorithms for modeling complex probability distributions. It enables researchers and practitioners to build and train energy-based models, diffusion models, flow-matching models, and Schrödinger bridges with a focus on scalability and high performance on GPU hardware.
Key features include composable tools for defining energy landscapes (Gaussian, Double Well, Rosenbrock, and custom energy functions), multiple training methods (Contrastive Divergence, Persistent CD, Score Matching, Denoising Score Matching, Sliced Score Matching), and advanced samplers (Langevin Dynamics, Hamiltonian Monte Carlo). The library also supports sampling via MCMC, optimization, or learned continuous-time dynamics including flow-based methods, with seamless integration into the PyTorch ecosystem.
TorchEBM is designed for machine learning researchers, AI practitioners, and developers working on generative modeling, density estimation, unsupervised representation learning, out-of-distribution detection, and structured prediction. It addresses common EBM challenges like intractable partition functions and computationally intensive MCMC sampling through CUDA-accelerated parallel implementations.
Torchebm pricing
Pricing model: Freemium
Free and open-source. Available on PyPI as torchebm (current version 0.2.6) and on GitHub. No paid tiers or commercial licensing required.
Torchebm pros
- CUDA-accelerated parallel computing for fast sampling and training
- Seamless PyTorch integration with familiar API patterns
- Support for multiple training methods (CD, PCD, Score Matching variants)
- Advanced MCMC samplers including Langevin Dynamics and HMC
- Built-in standard energy functions (Gaussian, Double Well, Rosenbrock)
- Base class for creating custom energy functions easily
- Supports diffusion models and flow-matching methods
- Supports Schrödinger bridges for advanced modeling
- Focus on scalability for high-dimensional problems
- Gradient clipping built-in for stable training
- Visualization tools for monitoring energy landscapes during training
- Parameter scheduling support for learning rate, step size, noise scale
- Comprehensive tutorials and documentation for beginners
- Practical examples that are fully tested
- Open-source and actively maintained by developer
Torchebm cons
- Partition function Z remains intractable to compute directly
- MCMC sampling still computationally intensive despite CUDA acceleration
- Score Matching requires computing Hessian diagonals in high dimensions
- CD-k training requires careful hyperparameter balancing
- Persistent CD chains can get stuck in metastable states
- Score Matching has high computational cost in high dimensions
- Performance depends on noise distribution for Denoising Score Matching
- Sliced Score Matching accuracy depends on number of projections
Frequently asked questions about Torchebm
What are Energy-Based Models and how does TorchEBM help?
Energy-Based Models define a probability distribution through an energy function that assigns scalar energy values to configurations, with lower energy indicating higher probability. The probability density is proportional to the negative exponential of energy. TorchEBM addresses EBM challenges by providing CUDA-accelerated samplers, training methods like contrastive divergence, and seamless PyTorch integration for efficient sampling, inference, and learning.
What samplers does TorchEBM support?
TorchEBM implements Langevin Dynamics (gradient-based MCMC with stochastic updates), Hamiltonian Monte Carlo (efficient exploration using Hamiltonian dynamics), and various specialized samplers. All samplers are CUDA-accelerated for parallel computation and can be used for sampling via MCMC, optimization, or learned continuous-time dynamics.
What training methods are available in TorchEBM?
TorchEBM supports Contrastive Divergence (CD), Persistent CD (PCD), Score Matching, Denoising Score Matching, and Sliced Score Matching. CD is simple and efficient but may not converge to true gradient. PCD offers better mode exploration. Score Matching avoids MCMC but requires Hessian computation. Denoising Score Matching is more efficient for high-dimensional data. Sliced Score Matching has linear complexity and scales well.
Can I create custom energy functions in TorchEBM?
Yes, TorchEBM provides a base class BaseEnergyFunction for creating custom energy functions. The library includes standard energy functions like Gaussian, Double Well, and Rosenbrock as examples, but you can subclass BaseEnergyFunction to define your own energy landscapes for specific applications.
What tasks can I use TorchEBM for?
TorchEBM can be applied to generative modeling, density estimation, unsupervised representation learning, out-of-distribution detection, and structured prediction. It supports modeling complex dependencies between variables and capturing multimodal distributions across these tasks.
How do I get started with TorchEBM?
Follow this learning path: 1) Install TorchEBM and read the Introduction guide to understand basic concepts, 2) Read the Energy Models API to understand model implementations, 3) Explore the Samplers guide to learn how to generate samples, 4) Study the Training guide to learn how to train models. The tutorials section provides comprehensive guidance for beginners.
What are best practices for training EBMs with TorchEBM?
Key tips include: start with simple energy functions and datasets before increasing complexity, monitor energy values for collapse indicators, adjust MCMC step size and noise scale for effective exploration, use Persistent CD for complex distributions, visualize energy landscapes frequently, always use gradient clipping, implement parameter scheduling for learning rate/step size/noise scale, consider batch normalization, and use ensemble methods for better results.
Does TorchEBM support diffusion models?
Yes, TorchEBM supports diffusion models along with flow-matching methods and Schrödinger bridges. EBMs are theoretically related to diffusion models, and the library provides composable tools for both energy-based modeling and these related generative modeling approaches.
How does TorchEBM handle the intractable partition function problem?
The partition function Z is inherently intractable to compute directly for most EBMs. TorchEBM addresses this through specialized training methods like Contrastive Divergence and Score Matching that don't require explicit computation of Z. These methods estimate and minimize the difference between model and data distributions using approximations or alternative objectives.
Is TorchEBM suitable for high-dimensional data?
TorchEBM is designed with scalability and performance for high-dimensional problems. Sliced Score Matching is specifically recommended for high-dimensional problems due to its linear computational complexity. Denoising Score Matching also works well for high-dimensional continuous distributions like images. However, standard Score Matching has high computational cost in high dimensions due to Hessian computation requirements.