README.md

---
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.