Upload 2 files

LICENSE

@@ -0,0 +1,38 @@
+Academic and Non-Commercial Research Use Only License
+
+Copyright (c) 2026 Jaekyun Ko, Byung Wan Lim, Soomin Lee, Dongjin Kim, Tae Hyun Kim
+(Department of Computer Science, Hanyang University)
+
+This repository (source code, configuration files, and any accompanying
+pretrained model checkpoints distributed alongside it, e.g. via Hugging Face)
+is provided for ACADEMIC AND NON-COMMERCIAL RESEARCH PURPOSES ONLY.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated files (the "Software"), to use, copy,
+modify, merge, and distribute copies of the Software for non-commercial
+research and educational purposes, subject to the following conditions:
+
+1. The above copyright notice and this license notice shall be included in
+   all copies or substantial portions of the Software.
+2. The Software, in whole or in part, and any derivative works, may NOT be
+   used for any commercial purpose without prior written permission from the
+   authors. Commercial purposes include, without limitation, selling,
+   licensing, or incorporating the Software into a commercial product or
+   service, or using it to provide a commercial service.
+3. Any publication or public disclosure of results produced using the
+   Software shall cite the associated paper:
+
+     Jaekyun Ko, Byung Wan Lim, Soomin Lee, Dongjin Kim, and Tae Hyun Kim.
+     "YeTI: You Only Need Two Noisy Images for Real-World sRGB Noise
+     Generation." arXiv:2607.09193, 2026.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
+FROM, OUT OF, OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER
+DEALINGS IN THE SOFTWARE.
+
+For commercial licensing inquiries, please contact the corresponding author
+(Tae Hyun Kim, Department of Computer Science, Hanyang University).

README.md

@@ -0,0 +1,70 @@
+---
+license: other
+license_name: academic-use-only
+license_link: https://github.com/ByungWanLim/YeTI/blob/main/LICENSE
+tags:
+- pytorch
+- image-denoising
+- noise-generation
+- diffusion
+- computer-vision
+- image-to-image
+---
+
+# 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)
+
+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>
+— <sup>1</sup>Department of Computer Science, Hanyang University, <sup>2</sup>Mobile eXperience (MX) Division, Samsung Electronics
+(<sup>\*</sup>equal contribution, <sup>†</sup>corresponding author)
+
+## 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).
+
+## Files in this repository
+
+| File | Description | Size |
+|---|---|---|
+| `rae.ckpt` | Reconstruction AutoEncoder — disentangles structure / noise latents | ~145 MB |
+| `c_dit.ckpt` | Conditional Diffusion Transformer — main noise generation model | ~817 MB |
+| `apbsn.ckpt` | AP-BSN self-supervised denoiser (baseline) | ~45 MB |
+| `apbsn_mix.ckpt` | AP-BSN denoiser trained with YeTI-generated noisy data added | ~46 MB |
+| `mmbsn.ckpt` | MM-BSN self-supervised denoiser (baseline) | ~81 MB |
+| `mmbsn_mix.ckpt` | MM-BSN denoiser trained with YeTI-generated noisy data added | ~81 MB |
+
+## 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).