# deepinv

> DeepInverse is the leading open-source PyTorch-based library for solving imaging inverse problems with deep learning. It provides imaging operators, pretrained reconstruction networks and denoisers, plug-and-play and unfolded optimization, sampling algorithms, training losses and datasets.


deepinverse contributors 2026

## Pages in this subsection

- [Diffusion & MCMC](https://deepinv.org/auto_examples/sampling/index.html.md): <div id='sg-tag-list' class='sphx-glr-tag-list'></div><div class="sphx-glr-thumbnails">
- [NOTE](https://deepinv.org/auto_examples/sampling/demo_custom_kernel.html.md): New to DeepInverse? Get started with the basics with the
- [NOTE](https://deepinv.org/auto_examples/sampling/demo_diffusion_sde.html.md): New to DeepInverse? Get started with the basics with the
- [NOTE](https://deepinv.org/auto_examples/sampling/demo_dps.html.md): New to DeepInverse? Get started with the basics with the
- [NOTE](https://deepinv.org/auto_examples/sampling/demo_flow_matching.html.md): New to DeepInverse? Get started with the basics with the
- [NOTE](https://deepinv.org/auto_examples/sampling/demo_ddrm.html.md): New to DeepInverse? Get started with the basics with the
- [NOTE](https://deepinv.org/auto_examples/sampling/demo_diffpir.html.md): New to DeepInverse? Get started with the basics with the
- [NOTE](https://deepinv.org/auto_examples/sampling/demo_noisy_data_fidelity.html.md): New to DeepInverse? Get started with the basics with the
- [NOTE](https://deepinv.org/auto_examples/sampling/demo_sampling.html.md): New to DeepInverse? Get started with the basics with the
- [NOTE](https://deepinv.org/auto_examples/sampling/demo_diffusers.html.md): New to DeepInverse? Get started with the basics with the

## Optional

- [Top-level llms.txt](https://deepinv.org/llms.txt): Complete documentation index.