reference
LTX-2.5-Multiple-Subject-Referencerefcontrol-FLUX.2-klein-9B-reference-pose-loraLTX-2.3-Multiple-Subject-Referencekrea2_turbo_style_referencerefcontrol-FLUX.2-klein-9B-reference-depth-lorakiwi-edit-5b-instruct-reference-diffusersFLUX.2-klein-9B-manga-colorization-by-reference-LORArefcontrol-FLUX.2-klein-9B-reference-canny-lora
agents-last-exam-reference
Agents Last Exam — Reference (Ground-Truth) Data
⚠️ Gated dataset. This repo contains the ground-truth / reference outputs
used to score the Agents Last Exam (ALE) benchmark. Access requires login,
agreement to the terms on the access-request form, and manual approval.
Note (06/16/26): This repository was accidentally deleted and has been recreated. The
previous list of approved requesters could not be restored, so even if you
were granted access before, you will need to… See the full description on the dataset page: https://huggingface.co/datasets/agents-last-exam/agents-last-exam-reference.tadabur-align-references
tadabur-align-references
Precomputed reference embeddings powering tadabur-align — word-level timestamp extraction for Quranic recitation via DTW alignment transfer (no ASR).
What this is
For 5,481 of the Quran's 6,236 ayahs, this dataset holds frame-level tadabur-embedding features for up to 8 reference reciters, plus each reference's word-level timestamps and internal-pause intervals. No audio is included — only model outputs and timing data. tadabur-align… See the full description on the dataset page: https://huggingface.co/datasets/FaisaI/tadabur-align-references.terrain-referenced-3d-glacier-mapping-product
Terrain-referenced Glacier Mapping Product
This repository provides the terrain-referenced glacier-area mapping product generated for the manuscript. The product is openly available through Hugging Face with DOI: 10.57967/hf/9900.
The archive contains regional mapping outputs, oblique terrain-visualization products, metadata files, and tabular glacier-area summaries. Glacier masks generated by Prithvi-SDT are linked with Copernicus DEM terrain information and RGI 7.0 glacier… See the full description on the dataset page: https://huggingface.co/datasets/yyhw/terrain-referenced-3d-glacier-mapping-product.multi_reference_image_editing
Multi-Reference Instruction-Based Image Editing Dataset
Overview
This dataset contains 20,000 high-resolution image pairs and multi-modal instructions designed for training advanced image-to-image editing models. It combines two complementary example types: 10,000 reference-grounded edits, where structural or stylistic changes are driven by up to three provided visual reference images, and 10,000 occlusion-based inpainting/outpainting edits, where the model must… See the full description on the dataset page: https://huggingface.co/datasets/molbal/multi_reference_image_editing.watercolour-reference-pool
Watercolour reference pool
The reference paintings that define the reward in the watercolour RL environment: an
agent writes a p5.brush sketch, the sketch is
rendered, and a vision judge compares the render against paintings sampled from this pool.
What the pool contains is the reward function. Replace it and you have changed what
the environment rewards, without touching a line of code.
178 paintings in two tiers, each with the JavaScript source that produced it.
tier… See the full description on the dataset page: https://huggingface.co/datasets/FineEnvs/watercolour-reference-pool.references
GEM References
What is it?
This repository contains all the reference datasets that are used for running evaluation on the GEM benchmark. Some of these datasets were originally hosted as a GitHub release on the GEM-metrics repository, but have been migrated to the Hugging Face Hub.
Converting datasets to JSON
We provide a convert_dataset_to_json.py conversion script that converts the datasets in the GEM organisation to the JSON format expected by the… See the full description on the dataset page: https://huggingface.co/datasets/GEM/references.