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<div align="center">
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# Entering the Era of Discrete Diffusion Models: A Benchmark for Schrödinger Bridges and Entropic Optimal Transport
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[Xavier Aramayo](https://scholar.google.com/citations?user=1B9UIYoAAAAJ),
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[Grigoriy Ksenofontov](https://scholar.google.com/citations?user=e0mirzYAAAAJ), [Aleksei Leonov](https://scholar.google.com/citations?user=gzj9nOcAAAAJ), [Iaroslav Koshelev](https://scholar.google.com/citations?user=gmaJRL4AAAAJ), [Alexander Korotin](https://scholar.google.ru/citations?user=1rIIvjAAAAAJ)
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[](https://arxiv.org/abs/2509.23348)
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[](https://openreview.net/forum?id=XcPDT615Gd)
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[](https://github.com/gregkseno/catsbench)
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[](https://huggingface.co/gregkseno/catsbench)
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</div>
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This repository contains the benchmark checkpoints associated with the paper *"Entering the Era of Discrete Diffusion Models: A Benchmark for Schrödinger Bridges and Entropic Optimal Transport"*, accepted at **ICLR 2026**.
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<!--  -->
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## 📦 CatSBench (Package)
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Benchmark usage is provided via `catsbench`, a standalone package that includes benchmark definitions, evaluation metrics, and reusable utilities, including a Triton-optimized log-sum-exp (LSE) matmul kernel.
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### 📥 Installation
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Install the benchmark package via `pip`:
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```bash
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pip install catsbench
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```
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### 🚀 Quickstart
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Load a benchmark definition and its assets from a pretrained repository:
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```python
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from catsbench import BenchmarkHD
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bench = BenchmarkHD.from_pretrained(
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"gregkseno/catsbench",
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"hd_d2_s50_gaussian_a0.02_gaussian",
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init_benchmark=False, # skip heavy initialization at load time
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)
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```
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To sample marginals $p_0$ and $p_1$:
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```python
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x_start, x_end = bench.sample_input_target(32) # ([B=32, D=2], [B=32, D=2])
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```
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Or sample them separately:
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```python
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x_start = bench.sample_input(32) # [B=32, D=2]
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x_end = bench.sample_target(32) # [B=32, D=2]
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```
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> [!IMPORTANT]
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> Both examples above sample independently, i.e., $(x_0, x_1) \sim p_0(x_0),p_1(x_1)$.
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To sample from the ground-truth EOT/SB coupling, i.e., $(x_0, x_1) \sim p_0(x_0),q^*(x_1 | x_0)$, use:
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```python
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x_start = bench.sample_input(32) # [B=32, D=2]
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x_end = bench.sample(x_start) # [B=32, D=2]
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```
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> [!NOTE]
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> See the end-to-end benchmark workflow (initialization, evaluation, metrics, plotting) in [`notebooks/benchmark_usage.ipynb`](https://github.com/gregkseno/catsbench/blob/master/notebooks/benchmark_usage.ipynb).
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### 🎓 Citation
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```bibtex
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@misc{
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carrasco2025enteringeradiscretediffusion,
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title={Entering the Era of Discrete Diffusion Models: A Benchmark for {Schr\"odinger} Bridges and Entropic Optimal Transport},
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author={Xavier Aramayo Carrasco and Grigoriy Ksenofontov and Aleksei Leonov and Iaroslav Sergeevich Koshelev and Alexander Korotin},
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year={2025},
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eprint={2509.23348},
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archivePrefix={arXiv},
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primaryClass={cs.LG},
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url={https://arxiv.org/abs/2509.23348},
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}
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```
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## 🙏 Credits
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- [Comet ML](https://www.comet.com) — experiment-tracking and visualization toolkit;
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- [Inkscape](https://inkscape.org/) — an excellent open-source editor for vector graphics;
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- [Hydra/Lightning template](https://github.com/ashleve/lightning-hydra-template) - project template used as a starting point.
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