datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
FLUX-Reason-6M
FLUX-Reason-6M
FLUX-Reason-6M is a massive, 6-million-scale text-to-image dataset engineered to instill complex reasoning capabilities in generative models. This dataset was created to bridge the performance gap between open-source and leading closed-source text-to-image systems.
This dataset contains:
6 million high-quality, reasoning-focused images synthesized by the state-of-the-art FLUX.1-dev model.
20 million bilingual (English and Chinese) descriptions, providing a rich… See the full description on the dataset page: https://huggingface.co/datasets/LucasFang/FLUX-Reason-6M.flux_generateddatacorp-cnpj-dataFLUX.2-klein-base-9B_samples_Best_ofThis dataset is a highly diverse set of high quality images generated with FLUX.2 [klein] 9B Base.
NOTE: The Base is not intended for image generation, so do not use these images to judge the quality of the model.
Base is intended for training, as are the samples in this dataset as they can be used for regularization.
Possible uses
Regularization images for training models based on FLUX.2 [klein] 9B Base
Quality testing
Data source
This dataset is derived from… See the full description on the dataset page: https://huggingface.co/datasets/stablellama/FLUX.2-klein-base-9B_samples_Best_of.Hunyuan3D-FLUX-Gen
Orient Anything V2 Dataset
Project Page | Paper | GitHub
Orient Anything V2 is an enhanced foundation model for unified understanding of object 3D orientation and rotation from single or paired images. This dataset repository supports the model by providing assets for orientation estimation, 6DoF pose estimation, and object symmetry recognition.
Data Preparation
You can download the absolute orientation, relative rotation, and symm-orientation test datasets using the… See the full description on the dataset page: https://huggingface.co/datasets/Viglong/Hunyuan3D-FLUX-Gen.fluxlorafluent_speech_commands_synth
Dataset Card for "fluent_speech_commands_synth"
More Information needed
curated-danbooru-2026-512px-flux2-vaemjnj_flux32FLUX.2-klein-base-9B_samplesThis dataset is a highly diverse set of high quality images generated with FLUX.2 [klein] 9B Base.
NOTE: The Base is not intended for image generation, so do not use these images to judge the quality of the model.
Base is intended for training, as are the samples in this dataset as they can be used for regularization.
Possible uses
Regularization images for training models based on FLUX.2 [klein] 9B Base
Quality testing
Data source
The images were created in ComfyUI… See the full description on the dataset page: https://huggingface.co/datasets/stablellama/FLUX.2-klein-base-9B_samples.700k_Human_Preference_Dataset_FLUX_SD3_MJ_DALLE3
NOTE: A newer version of this dataset is available Imagen3_Flux1.1_Flux1_SD3_MJ_Dalle_Human_Preference_Dataset
Rapidata Image Generation Preference Dataset
This Dataset is a 1/3 of a 2M+ human annotation dataset that was split into three modalities: Preference, Coherence, Text-to-Image Alignment.
Link to the Coherence dataset: https://huggingface.co/datasets/Rapidata/Flux_SD3_MJ_Dalle_Human_Coherence_Dataset
Link to the Text-2-Image Alignment dataset:… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/700k_Human_Preference_Dataset_FLUX_SD3_MJ_DALLE3.curated-danbooru-2026-256px-flux2-vaeTHCHS-30-tests
THCHS-30 Test Set
THCHS-30 test split for Mandarin Chinese speech recognition benchmarking.
Dataset Info
Language: Mandarin Chinese (zh-CN)
Samples: 2,495
Speakers: 10
Sample Rate: 16 kHz
License: Apache 2.0
Usage
from datasets import load_dataset
# After uploading to HuggingFace
dataset = load_dataset("your-username/thchs30-test")
# Example
print(dataset['train'][0])
# {
# 'audio': {'array': [...], 'sampling_rate': 16000, 'path': 'audio/D11_750.wav'},
#… See the full description on the dataset page: https://huggingface.co/datasets/FluidInference/THCHS-30-tests.Flux_SD3_MJ_Dalle_Human_Alignment_Dataset
NOTE: A newer version of this dataset is available Imagen3_Flux1.1_Flux1_SD3_MJ_Dalle_Human_Alignment_Dataset
Rapidata Image Generation Alignment Dataset
This Dataset is a 1/3 of a 2M+ human annotation dataset that was split into three modalities: Preference, Coherence, Text-to-Image Alignment.
Link to the Coherence dataset: https://huggingface.co/datasets/Rapidata/Flux_SD3_MJ_Dalle_Human_Coherence_Dataset
Link to the Preference dataset:… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Flux_SD3_MJ_Dalle_Human_Alignment_Dataset.ami-corpus-mirror
AMI Corpus Mirror
Mirror of the subset of the AMI Meeting Corpus used by
FluidAudio diarization benchmarks. Hosted here so CI and
local benchmark runs do not depend on the availability of the upstream groups.inf.ed.ac.uk server
(see FluidAudio#752).
Contents
annotations/ami_public_manual_1.6.2.zip — AMI public manual annotations v1.6.2
(repackaged from the official archive; identical content, including segments/, words/,
corpusResources/meetings.xml)… See the full description on the dataset page: https://huggingface.co/datasets/FluidInference/ami-corpus-mirror.HUGGER-Unified-Gravity-Fluid-Framework
🌍 H.U.G.G.E.R: Heuristic Universal Grid & Gravity Equilibrium Rendering Tensor
This repository serves as an open academic archive and tensor-specification benchmark for generalized tensor standards, designed to resolve non-linear computational collapse and topological pole singularities in high-performance CFD and planetary atmospheric models.
It acts as the Macroscopic Gravitational Backbone, perfectly entangled with the microscopic Topological Zero Tensor (TZT)… See the full description on the dataset page: https://huggingface.co/datasets/jskresearch/HUGGER-Unified-Gravity-Fluid-Framework.Flux-2-pro_t2i_human_preference
Rapidata Flux 2 Pro Preference
This T2I dataset contains over ~400'000 human responses from over ~50'000 individual annotators, collected in less than 7h using the Rapidata Python API, accessible to anyone and ideal for large scale evaluation.
Evaluating Flux 2 Pro (version from 25.11.25) across three categories: preference, coherence, and alignment.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Flux-2-pro_t2i_human_preference.BibleMMSThe Dataset associated with the Paper "Meta Learning Text-to-Speech Synthesis in over 7000 Languages" by Florian Lux, Sarina Meyer, Lyonel Behringer, Frank Zalkow, Phat Do, Matt Coler, Emanuël A. P. Habets and Ngoc Thang Vu (Interspeech 2024).
We generate 2000 spoken utterances per language using the subsets of the eBible dataset [1] that are under free licenses as the text input to the MMS TTS models [2].
The languages associated with the following ISO-639-3 codes are represented in this… See the full description on the dataset page: https://huggingface.co/datasets/Flux9665/BibleMMS.mmlu
Dataset Card for MMLU
Dataset Summary
Measuring Massive Multitask Language Understanding by Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt (ICLR 2021).
This is a massive multitask test consisting of multiple-choice questions from various branches of knowledge. The test spans subjects in the humanities, social sciences, hard sciences, and other areas that are important for some people to learn. This covers 57 tasks… See the full description on the dataset page: https://huggingface.co/datasets/flunardelli/mmlu.improved-flux-prompts-photoreal-portrait
Photo Portrait Prompt Dataset for FLUX
Overview
This dataset contains a curated collection of prompts specifically designed for generating photo portraits using FLUX.1, an advanced text-to-image model. These prompts are crafted to produce high-quality, lifelike portraits by leveraging sophisticated prompting techniques and best practices.
Latest Version
Improved on October 3, 2024.
This version has undergone curation and improvement. What is new?
Cleaned up… See the full description on the dataset page: https://huggingface.co/datasets/k-mktr/improved-flux-prompts-photoreal-portrait.flux-schnell-teacher-latents
Flux Schnell Teacher Latents
Pre-computed latents, decoded images, and text embeddings from FLUX.1-schnell for distillation and research.
Usage
from datasets import load_dataset
# Load specific subset
ds = load_dataset("AbstractPhil/flux-schnell-teacher-latents", "train_512")
ds = load_dataset("AbstractPhil/flux-schnell-teacher-latents", "train_2_512")
ds = load_dataset("AbstractPhil/flux-schnell-teacher-latents", "train_3_512")
Subsets
Config… See the full description on the dataset page: https://huggingface.co/datasets/AbstractPhil/flux-schnell-teacher-latents.goes-xray-flux
GOES Solar X-Ray Flux (1-Minute)
Credit: NASA/SDO
Part of a dataset collection on Hugging Face.
Dataset description
Solar soft X-ray flux from the GOES X-Ray Sensor (XRS), the operational backbone of solar flare monitoring. Updated daily from NOAA SWPC, growing incrementally at 1-minute cadence.
The GOES (Geostationary Operational Environmental Satellite) X-Ray Sensor measures the Sun's soft X-ray irradiance in two wavelength bands: a "short" 0.05-0.4 nm… See the full description on the dataset page: https://huggingface.co/datasets/juliensimon/goes-xray-flux.fluent_speech_commands_femalefluorescence_prediction
Dataset Card for Fluorescence Prediction Dataset
Dataset Summary
The Fluorescence Prediction task focuses on predicting the fluorescence intensity of green fluorescent protein mutants, a crucial function in biology that allows researchers to infer the presence of proteins within cell lines and living organisms. This regression task utilizes training and evaluation datasets that feature mutants with three or fewer mutations, contrasting the testing dataset, which comprises… See the full description on the dataset page: https://huggingface.co/datasets/proteinglm/fluorescence_prediction.Persona-Fluxed-10k-2608
Persona Fluxed 10k
Synthetic persona portraits rendered with FLUX.2-klein-4b (8-step, 1024x1024) from the NVIDIA Nemotron-Personas-* datasets.
Each persona is grounded in real-world demographic, geographic and
personality-trait distributions for its country (CC BY 4.0 source; no real people).
Currently Nemotron-Personas exist for:
USA — English
Japan — Japanese
India — English, Hindi
Brazil — Portuguese
Singapore — English
France — French
Korea — Korean
El Salvador — Spanish… See the full description on the dataset page: https://huggingface.co/datasets/retowyss/Persona-Fluxed-10k-2608.Flux_SD3_MJ_Dalle_Human_Coherence_Dataset
NOTE: A newer version of this dataset is available: Imagen3_Flux1.1_Flux1_SD3_MJ_Dalle_Human_Coherence_Dataset
Rapidata Image Generation Coherence Dataset
This Dataset is a 1/3 of a 2M+ human annotation dataset that was split into three modalities: Preference, Coherence, Text-to-Image Alignment.
Link to the Preference dataset: https://huggingface.co/datasets/Rapidata/700k_Human_Preference_Dataset_FLUX_SD3_MJ_DALLE3
Link to the Text-2-Image Alignment dataset:… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/Flux_SD3_MJ_Dalle_Human_Coherence_Dataset.flux-kontext-ipa-datasetfluxdev_controlnet_16kTime-Series-Library
Time-Series-Library (TSLib)
TSLib is an open-source library for deep learning researchers, especially for deep time series analysis.
We provide a neat code base to evaluate advanced deep time series models or develop your model, which covers five mainstream tasks: long- and short-term forecasting, imputation, anomaly detection, and classification.
This benchmark collection is designed to evaluate and develop advanced deep time-series models. For an in-depth exploration of… See the full description on the dataset page: https://huggingface.co/datasets/fluxae/Time-Series-Library.OpenSeeSimE-Fluid
OpenSeeSimE-Fluid: Engineering Simulation Visual Question Answering Benchmark
Dataset Summary
OpenSeeSimE-Fluid is a large-scale benchmark dataset for evaluating vision-language models on computational fluid dynamics (CFD) simulation interpretation tasks. It contains approximately 98,000 question-answer pairs across parametrically-varied fluid simulations including turbulent flow, heat transfer, and complex flow patterns.
Purpose
While vision-language models… See the full description on the dataset page: https://huggingface.co/datasets/cmudrc/OpenSeeSimE-Fluid.