---
license: other
license_name: academic-use-only
license_link: https://github.com/ByungWanLim/YeTI/blob/main/LICENSE
datasets:
- BWLim/YeTI
tags:
- pytorch
- pytorch-lightning
- image-denoising
- noise-generation
- diffusion
- computer-vision
- image-to-image
---

<div align="center">

# YeTI: You Only Need Two Noisy Images for Real-World sRGB Noise Generation

**ECCV 2026** · [Paper (arXiv:2607.09193)](https://arxiv.org/abs/2607.09193) · [Code (GitHub)](https://github.com/ByungWanLim/YeTI) · [Dataset](https://huggingface.co/datasets/BWLim/YeTI)

Jaekyun Ko<sup>1,2\*</sup>, Byung Wan Lim<sup>1\*</sup>, Soomin Lee<sup>1</sup>, Dongjin Kim<sup>1</sup>, Tae Hyun Kim<sup>1†</sup>
<br><sup>1</sup>Department of Computer Science, Hanyang University &nbsp;<sup>2</sup>Mobile eXperience (MX) Division, Samsung Electronics
<br><sup>\*</sup>equal contribution &nbsp;<sup>†</sup>corresponding author

</div>

## Model Description

YeTI is a real-world sRGB noise generation framework that learns to synthesize realistic, signal-dependent sensor noise from only **two noisy observations of the same scene** — no clean ground truth or camera metadata required. It uses a Reconstruction Autoencoder (RAE) to disentangle scene structure from noise characteristics, and a one-step Conditional Diffusion Transformer (C-DiT) trained with consistency objectives to model the latent noise distribution. At inference time, YeTI takes a single noisy image and generates additional realistic noisy samples of the same scene, which can be used to train downstream (self-supervised) denoisers such as AP-BSN and MM-BSN.

Full details, training recipe, and evaluation protocol are in the [paper](https://arxiv.org/abs/2607.09193) and the [official code repository](https://github.com/ByungWanLim/YeTI).

## Intended Uses & Limitations

**Intended use**: academic research on real-world sRGB noise modeling / synthesis, and as a data-augmentation source for training self-supervised image denoisers. Requires the [YeTI code repository](https://github.com/ByungWanLim/YeTI) to load and run — these checkpoints are not standalone `transformers`/`diffusers` models.

**Limitations**: trained primarily on smartphone sRGB noise (SIDD) and MAI2021 data; noise realism may degrade on sensors, ISO ranges, or capture pipelines far outside the training distribution. Released for **academic / non-commercial research use only** — see License below.

## Files in this repository

| File | Description | Size |
|---|---|---|
| `rae.ckpt` | Reconstruction AutoEncoder — disentangles structure / noise latents | ~148 MB |
| `c_dit.ckpt` | Conditional Diffusion Transformer — main noise generation model | ~856 MB |
| `apbsn.ckpt` | AP-BSN self-supervised denoiser, trained on YeTI-generated noisy data only | ~46 MB |
| `apbsn_mix.ckpt` | AP-BSN denoiser trained on a 50:50 mix of YeTI-generated and real noisy data | ~46 MB |
| `mmbsn.ckpt` | MM-BSN self-supervised denoiser, trained on YeTI-generated noisy data only | ~68 MB |
| `mmbsn_mix.ckpt` | MM-BSN denoiser trained on a 50:50 mix of YeTI-generated and real noisy data | ~68 MB |
| `config.json` | Machine-readable checkpoint index | — |

## Training Data

Trained on the public **[SIDD](https://www.eecs.yorku.ca/~kamel/sidd/)** (Smartphone Image Denoising Dataset) and **MAI2021** benchmarks. Preprocessed archives matching this codebase's expected format are hosted at **[BWLim/YeTI (dataset)](https://huggingface.co/datasets/BWLim/YeTI)**.

## How to use

These checkpoints are PyTorch Lightning `.ckpt` files meant to be used with the official [YeTI code repository](https://github.com/ByungWanLim/YeTI), which defines the model architectures (`yeti/archs`) and LightningModules (`yeti/models`) needed to load them.

```bash
# 1) Clone the code
git clone https://github.com/ByungWanLim/YeTI.git
cd YeTI

# 2) Download these weights into ckpt/
huggingface-cli download BWLim/YeTI --local-dir ckpt

# 3) Run validation / generation, e.g.
python main.py --config configs/val/C-DiT/val_lit_c-dit.yaml --ckpt ckpt/c_dit.ckpt
```

See the repository README for the full training / evaluation / noise-generation usage (environment setup, dataset preparation, and all `run_*.sh` scripts).

## Citation

```bibtex
@article{ko2026yeti,
  title   = {YeTI: You Only Need Two Noisy Images for Real-World sRGB Noise Generation},
  author  = {Ko, Jaekyun and Lim, Byung Wan and Lee, Soomin and Kim, Dongjin and Kim, Tae Hyun},
  journal = {arXiv preprint arXiv:2607.09193},
  year    = {2026}
}
```

## License

These model weights are released for **academic / non-commercial research use only**. See [LICENSE](./LICENSE) for full terms. For commercial licensing inquiries, please contact the corresponding author (Tae Hyun Kim, Hanyang University).