NearID-SDXL_1024 — Near-Identity Distractors (Stable Diffusion XL inpainting)

This dataset contains near-identity distractors generated by Stable Diffusion XL inpainting at 1024×1024 resolution as part of the NearID project.

Each sample contains up to 3 distractor images (nimg1, nimg2, nimg3): different but visually similar instances inpainted into the exact same background/context as the corresponding anchor in the base Aleksandar/NearID dataset. These distractors are used to train and evaluate identity embeddings that distinguish true identity from contextual shortcuts.

Quick Start

from datasets import load_dataset

# Load this negative source
ds = load_dataset("Aleksandar/NearID-SDXL_1024")

# Load base positives for anchor/positive pairs
positives = load_dataset("Aleksandar/NearID")

Dataset Structure

Column	Type	Description
id	int64	Sample ID (matches the base NearID dataset)
category	string	Object category (rigid)
category_description	string	Natural language description of the object
nimg1, nimg2, nimg3	image	Near-identity distractor images (up to 3 per sample)
n_images	int64	Number of valid distractor images
objaverse_id	string	Source Objaverse object identifier
prompts1, prompts2, prompts3	string	Generation prompts for each distractor
quality	string	Quality label

How the Distractors Were Generated

1. For each anchor identity in the base NearID dataset, a semantically similar but different object instance was retrieved.
2. The distractor instance was inpainted into the same background as the anchor using Stable Diffusion XL inpainting.
3. Resolution: 1024×1024 pixels.

This creates a controlled test: a model must rely on intrinsic identity features, not background context, to distinguish anchor from distractor.

All NearID Datasets

Dataset	Description	Resolution
Aleksandar/NearID	Multi-view positives (anchor + positive views)	Base
Aleksandar/NearID-Flux	Near-identity distractors via FLUX.1 inpainting	512×512
Aleksandar/NearID-Flux_1024	Near-identity distractors via FLUX.1 inpainting	1024×1024
Aleksandar/NearID-FluxC	Near-identity distractors via FLUX.1 Canny-guided inpainting	512×512
Aleksandar/NearID-FluxC_1024	Near-identity distractors via FLUX.1 Canny-guided inpainting	1024×1024
Aleksandar/NearID-PowerPaint	Near-identity distractors via PowerPaint inpainting	512×512
Aleksandar/NearID-Qwen	Near-identity distractors via Qwen-based inpainting	512×512
Aleksandar/NearID-Qwen_1328	Near-identity distractors via Qwen-based inpainting	1328×1328
Aleksandar/NearID-SDXL	Near-identity distractors via Stable Diffusion XL inpainting	512×512
Aleksandar/NearID-SDXL_1024	Near-identity distractors via Stable Diffusion XL inpainting	1024×1024

Related

• Model: Aleksandar/nearid-siglip2 — NearID identity embedding model
• Paper: NearID: Identity Representation Learning via Near-identity Distractors
• Code: github.com/Gorluxor/NearID

License & Attribution

This dataset is released under CC-BY-4.0. It is derived from the SynCD dataset (MIT License, Copyright 2022 SynCD). If you use this dataset, please cite both NearID and SynCD.

Citation

@article{cvejic2026nearid,
  title={NearID: Identity Representation Learning via Near-identity Distractors},
  author={Cvejic, Aleksandar and Abdal, Rameen and Eldesokey, Abdelrahman and Ghanem, Bernard and Wonka, Peter},
  journal={arXiv preprint arXiv:2604.01973},
  year={2026}
}