Upload folder using huggingface_hub

LICENSE

@@ -0,0 +1,12 @@
+Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
+
+Copyright (c) 2026 the authors of "Learning Illumination Control in Diffusion Models"
+(Nishit Anand, Manan Suri, Christopher Metzler, Dinesh Manocha, Ramani Duraiswami).
+
+This Hugging Face dataset bundle is licensed under CC BY-NC-SA 4.0, consistent with
+the GitHub repository and FFHQ-derived research use. See the repository README and
+https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
+
+Flickr-Faces-HQ (FFHQ): training splits in this project trace to FFHQ. Comply with
+https://github.com/NVlabs/ffhq-dataset and NVIDIA's CC BY-NC-SA 4.0 terms for the FFHQ
+dataset distribution; cite Karras, Laine, Aila (StyleGAN / FFHQ) as required.

README.md

@@ -0,0 +1,108 @@
+---
+license: cc-by-nc-sa-4.0
+language:
+  - en
+tags:
+  - diffusion
+  - computer-vision
+  - image-editing
+  - faces
+  - dataset
+pretty_name: Image relighting diffusion (research data)
+size_categories:
+  - 10K<n<100K
+task_categories:
+  - image-to-image
+---
+
+# Learning Illumination Control in Diffusion Models — Dataset (HF)
+
+Public **data and evaluation assets** for [*Learning Illumination Control in Diffusion Models*](https://arxiv.org/abs/2604.24877) (ReALM-GEN @ ICLR 2026).
+
+| | |
+|--|--|
+| **Code** | [github.com/nishitanand/image-relighting-diffusion](https://github.com/nishitanand/image-relighting-diffusion) |
+| **Model weights** | [huggingface.co/nishitanand/sd-image-relighting-model](https://huggingface.co/nishitanand/sd-image-relighting-model) |
+| **Paper** | [arxiv.org/abs/2604.24877](https://arxiv.org/abs/2604.24877) |
+| **Project site** | [nishitanand.github.io/relighting-diffusion-website](https://nishitanand.github.io/relighting-diffusion-website) |
+
+---
+
+## Download (CLI)
+
+Install the [Hugging Face CLI](https://huggingface.co/docs/huggingface_hub/guides/cli) (`pip install -U "huggingface_hub[cli]"`), then:
+
+```bash
+huggingface-cli download nishitanand/image-relighting-diffusion-data \
+  --repo-type dataset \
+  --local-dir ./image-relighting-diffusion-data
+```
+
+You can also browse files on the [dataset page](https://huggingface.co/datasets/nishitanand/image-relighting-diffusion-data) and download subsets manually.
+
+---
+
+## Contents
+
+### Training & test tensors / metadata
+
+| Folder | Description |
+|--------|-------------|
+| `data-train/` | Synthetic degraded inputs + paired metadata for **SD1.5** fine-tuning |
+| `data-test/` | Held-out **test** split for quantitative evaluation |
+| `data_hf_train/` | (Optional) Pre-sharded `datasets` format for faster dataloader startup |
+| `data-val/` | (Optional) Extra split folder if you mirror the paper’s three-way split on disk |
+
+### OOD qualitative pack
+
+| Path | Description |
+|------|-------------|
+| `qualitative_comparison/selected-64/` | 64 face crops for the paper’s out-of-distribution qualitative evaluation |
+| `qualitative_comparison/ood_test_64.csv` | **64 rows** — one `editing_instruction` per image (paper Figure 6 qualitative protocol) |
+| `qualitative_comparison/ood-64-results/` | (Optional) Archived run outputs + `ood_results.json` |
+
+Paths in `ood_test_64.csv` are relative to the **`qualitative_comparison/`** directory (e.g. `selected-64/img000-lat349.png`).
+
+### Optional evaluation bundles
+
+| Path | Description |
+|------|-------------|
+| `evaluation/evaluation_results_comparison/` | Saved comparisons (our model vs SD1.5 baseline) + JSON |
+| `evaluation/baseline_sdxl_long_descriptions/` | SDXL baseline outputs |
+| `evaluation/baseline_flux_long_descriptions/` | FLUX baseline outputs |
+
+---
+
+## Using with the GitHub code
+
+1. Clone **[image-relighting-diffusion](https://github.com/nishitanand/image-relighting-diffusion)**.  
+2. Download this dataset to e.g. `./hf_dataset` (command above).  
+3. **Training:** point `--data_dir` (or symlink) at `hf_dataset/data_hf_train` or rebuild triplets from CSVs in the code repo — see the GitHub **README** “Download prebuilt data” and “Full pipeline”.  
+4. **OOD:** copy `hf_dataset/qualitative_comparison/selected-64/` and `ood_test_64.csv` into the clone’s `qualitative_comparison/` next to `process_ood_test.py`.  
+5. **Quantitative eval:** CSVs in the code repo use paths relative to the **repository root**; keep the same relative layout or rewrite prefixes.
+
+**FFHQ** originals are **not** part of this dataset; obtain FFHQ under its license from [NVlabs/ffhq-dataset](https://github.com/NVlabs/ffhq-dataset) and cite the StyleGAN / FFHQ paper and NVIDIA terms as required.
+
+---
+
+## Citation (BibTeX)
+
+```bibtex
+@article{anand2026learning,
+  title={Learning Illumination Control in Diffusion Models},
+  author={Anand, Nishit and Suri, Manan and Metzler, Christopher and Manocha, Dinesh and Duraiswami, Ramani},
+  journal={arXiv preprint arXiv:2604.24877},
+  year={2026},
+  note={ReALM-GEN @ ICLR 2026}
+}
+```
+
+---
+
+## License
+
+This dataset bundle on Hugging Face is released under [**CC BY-NC-SA 4.0**](https://creativecommons.org/licenses/by-nc-sa/4.0/). See [`LICENSE`](LICENSE) in this repository.
+
+**FFHQ.** Curated training splits in the paper trace to **Flickr-Faces-HQ (FFHQ)**. Individual FFHQ images were published on Flickr under licenses such as CC BY 2.0, CC BY-NC 2.0, and public-domain marks; the **FFHQ dataset distribution** (metadata, scripts, documentation) is provided by NVIDIA under **CC BY-NC-SA 4.0**. See [NVlabs/ffhq-dataset](https://github.com/NVlabs/ffhq-dataset).
+
+Cite [**arXiv:2604.24877**](https://arxiv.org/abs/2604.24877) when publishing results built on this bundle.