zyyyz/ID-Bench

--- dataset_info: features: - name: target_image dtype: image - name: condition_image_1 dtype: image - name: condition_image_2 dtype: image - name: condition_image_3 dtype: image - name: caption dtype: string splits: - name: train num_bytes: 5757742734 num_examples: 2000 download_size: 5736519068 dataset_size: 5757742734 configs: - config_name: default data_files: - split: train path: data/train-* license: cc-by-4.0 task_categories: - image-to-image language: - zh - en size_categories: - 10K<n<100K --- # ID-Bench **ID-Bench** is a real-world benchmark for **multi-reference identity-preserving image generation**. It is built from real-world e-commerce advertising images and organized by **product identity**, with the goal of evaluating whether a model can generate a **novel target image** that both preserves product identity and follows target-specific variation cues. We release, in this repository, the curated benchmark dataset that is consistent with the one used for evaluation in our paper. The full training dataset will be released soon. ## What Makes ID-Bench Different? Recent progress in reference-guided image generation has improved visual quality substantially, but evaluating **multi-reference identity-preserving generation** remains challenging. Many existing settings do not clearly distinguish: - genuine same-identity generation, - trivial copying from the conditioning set, - and target-guided variation control. ID-Bench is designed to address this gap in a real-world product-image setting. ## Data Format Each example contains: - `target_image`: the held-out target image - `condition_image_1`: reference image 1 from the same product identity - `condition_image_2`: reference image 2 from the same product identity - `condition_image_3`: reference image 3 from the same product identity - `caption`: a short target-oriented caption describing the desired image or scene For more information, please visit our project website: [ID-Bench](https://zyyyz.github.io/IDBench/).