| --- |
| license: cc-by-4.0 |
| pretty_name: GenScale |
| task_categories: |
| - text-to-image |
| - image-to-image |
| - image-classification |
| language: |
| - en |
| size_categories: |
| - 1K<n<10K |
| configs: |
| - config_name: entries |
| data_files: |
| - split: train |
| path: data/genscale_entries.parquet |
| - config_name: pairs |
| data_files: |
| - split: train |
| path: data/genscale_pairs.parquet |
| - config_name: task3_edit_plans |
| data_files: |
| - split: train |
| path: data/genscale_task3_edit_plans.parquet |
| --- |
| |
| # GenScale |
|
|
| **GenScale: A Benchmark for Relative Object Scale in Image Generation and Editing** |
| Lingxiao Li, Max Whitton, Ledell Wu, and Boqing Gong. |
|
|
| [Project page](https://lingxiao-li.github.io/genscale.github.io/) · [Paper](https://arxiv.org/abs/2609.00525) · [Code](https://github.com/lingxiao-li/GenScale) · [Rescale checkpoint](https://huggingface.co/lingxiao2049/Rescale) |
|
|
| ## Contents and loading |
|
|
| | Configuration | Rows | Meaning | |
| |---|---:|---| |
| | `entries` | 900 | Generation/editing prompts and metadata | |
| | `pairs` | 1,643 | Pairwise scale evaluation units | |
| | `task3_edit_plans` | 100 | Precise S5 editing instructions | |
|
|
| ```python |
| from datasets import load_dataset |
| entries = load_dataset("lingxiao2049/GenScale", "entries", split="train") |
| pairs = load_dataset("lingxiao2049/GenScale", "pairs", split="train") |
| ``` |
|
|
| The `train` split name is a storage convention, not a prescribed training split. CSV mirrors are provided alongside Parquet. For images and the complete JSON, use `huggingface_hub.snapshot_download(repo_id="lingxiao2049/GenScale", repo_type="dataset", local_dir="GenScale")`. |
|
|
| `GenScale_Benchmark.json` is the canonical JSON entry point. It is byte-identical to the retained `GenScale_Benchmark_anonymous_clean.json` from the anonymous review release. Scenario counts are S1=200, S2=200, S3=300, S4=100, S5=100 entries; corresponding pair counts are 570, 573, 300, 100, 100. S4 and S5 share 100 source images. The 433 unique included images comprise 294 Task 2 references, 100 Task 3 inputs, and 39 Task 3 references. Every nonempty input/reference path in the benchmark JSON resolves within this release. |
|
|
| The two `metadata/` CSVs contain the 100-category physical-size knowledge base and the 1,000-product ABO candidate pool. Candidate-pool `abo_images/original/...` paths identify upstream archive assets, which are not bundled; benchmark inputs use the included `images/...` paths. `scripts/make_genscale_parquet.py` regenerates tables from the frozen JSON. `SHA256SUMS` records checksums; `VALIDATION.json` records release checks. |
|
|
| ## Scale convention |
|
|
| `object_a`, `object_b`, and `target_ratio_a_to_b` correspond to the key `object_a_to_object_b`: the intended physical characteristic-length ratio **length(A) / length(B)**. `acceptable_ratio_min/max` use the same direction. The judge code uses A as anchor, B as target, and estimates **B/A**; take the reciprocal when converting between them. Physical-scale metadata and generated prose can disagree on individual entries; preserve and inspect both rather than inferring a new ground truth from prompt text. |
|
|
| ## Collection, processing, and responsible use |
|
|
| This benchmark combines a common-object physical-size knowledge base, ABO-derived product dimensions/catalog reference images, API-assisted image-generation prompts, and selected generated inputs for scale editing. The frozen JSON and conversion script define the released records; `PROCESSING_REPORT.md` preserves the original release counts. Local absolute paths were replaced with relative asset paths. Missing scenario-specific fields are represented as null/empty cells and have not been imputed. Ratios and annotations are preserved from the review release. |
|
|
| The intended use is research evaluation of relative object scale in image generation and editing. The benchmark is small, uses English prompts and selected everyday objects/products, and is not representative of all cultures, bodies, physical environments, or object categories. Metadata dimensions may be imperfect, prompt text can conflict with metadata, and generated inputs can contain artifacts. A plausible scale score does not establish visual quality, factual correctness, or suitability for safety-critical measurement. Human-product prompts use simplified fixed body-size anchors; these are evaluation assumptions, not population statistics. |
|
|
| The public release contains prompts, object/product metadata, and images. It does not include private human annotation forms, contact details, or rater identities. Product imagery and synthesized people may depict faces or brands; no biometric labels or person-identification ground truth are supplied. No independent privacy, demographic fairness, or harmful-content audit is claimed. Human-study compensation and ethics-review status are outside this dataset release and are not inferred here. |
|
|
| Machine-assisted construction metadata records Gemini model identifiers where available. The paper's scoring pipeline is provided in the code repository; benchmark rows are not themselves the complete per-model judge-output archive. Trained Rescale checkpoints are distributed separately at [lingxiao2049/Rescale](https://huggingface.co/lingxiao2049/Rescale) under the FLUX non-commercial license. This dataset does not bundle those weights, all generated model outputs, or raw human-study responses. Report bugs through the linked GitHub repository; cite a Hugging Face commit revision for reproducible use. |
|
|
| [`croissant.json`](croissant.json) is the canonical, author-maintained Croissant metadata file for all three structured tables. It includes all eight minimum RAI topics in the [NeurIPS 2026 hosting guidance](https://neurips.cc/Conferences/2026/EvaluationsDatasetsHosting): limitations, biases, personal/sensitive information, intended uses and construct validity, social impact, a synthetic-data boolean, source datasets, and collection/preprocessing/annotation provenance. Synthetic source prompts and available generator identifiers are documented; missing generation seeds and backend revisions are explicitly identified. CSV sources are pinned to an immutable public dataset revision and have SHA-256 checksums. |
|
|
| The exact file passed all four checks in the [conference-linked Croissant checker](https://huggingface.co/spaces/JoaquinVanschoren/croissant-checker) (JSON, schema, RAI fields, and record generation). See [CROISSANT_VALIDATION_REPORT.txt](CROISSANT_VALIDATION_REPORT.txt) and [RAI_REQUIREMENTS.json](RAI_REQUIREMENTS.json). The checker currently tests six RAI keys; the two required PROV source/activity fields were additionally reviewed against the conference guidance. Local `mlcroissant` validation loaded all 900 / 1,643 / 100 records. **For OpenReview, upload this repository's `croissant.json`, which contains the authored RAI information; an automatically generated metadata export may omit these additions.** |
|
|
| ## License and attribution |
|
|
| Dataset: **CC BY 4.0**, preserving the anonymous release's license. See [LICENSE](LICENSE) and [THIRD_PARTY_NOTICES.md](THIRD_PARTY_NOTICES.md) for ABO attribution and external-asset scope. Code is separately MIT licensed. Model weights retain their upstream terms. |
|
|
| Please cite [CITATION.bib](CITATION.bib). |
|
|
