datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
OmniContext
Introduction
As part of OmniGen2, we introduce a new benchmark for in-context generation, OmniContext, which aims to provide a more comprehensive evaluation of models' in-context generation abilities. It incorporates a diverse set of input images and instructions, and utilizes GPT-4.1 for interpretable, metric-driven assessment.
Project Page: https://vectorspacelab.github.io/OmniGen2
Github Repo for OmniContext: https://github.com/VectorSpaceLab/OmniGen2… See the full description on the dataset page: https://huggingface.co/datasets/OmniGen2/OmniContext.omnigen2-azimuth-ladder-anny-20260901
omnigen2-azimuth-ladder-anny-20260901
An image-edit ladder in the EditScore dataset shape: a candidate measured against a baseline
on the same prompts, one row per (source, edited, instruction) with the per-pair
measurement beside the images. The baseline is OmniGen2; the candidate is the same model
after a camera-control LoRA.
This ladder has no EditScore score. The runs measured recovered azimuth — where the
body actually faces in the generated view — not EditScore's pf / sc /… See the full description on the dataset page: https://huggingface.co/datasets/chibifire/omnigen2-azimuth-ladder-anny-20260901.rung0-omnigen2-anny-depth-train
rung0-omnigen2-anny-depth
Rung 0 smoke invocation of the depth-conditioned generation pipeline: one
ANNY render's depth map conditioning one OmniGen2 image. A pipeline record,
not a training corpus.
Five zstd parquet tables in Essential Tuple Normal Form sharing primary
keys: run (one row per invocation), run_params and prompt (satellites),
render (camera and framing of the depth control), asset (image bytes:
the depth control and the generated output).
Generator:… See the full description on the dataset page: https://huggingface.co/datasets/chibifire/rung0-omnigen2-anny-depth-train.genshin_woman_Formal_Outfit_omnigen2_extractedOmniGen2