datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
flux-attn-tsFLUX-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-ffn-tsflux_generatedFluxVLAData
FluxVLA Engine 🚀
FluxVLA Engine is an integrated engineering platform designed for embodied intelligence applications. It follows the core design principles of unified configuration, standardized interfaces, module decoupling, and deployability, forming a complete engineering loop from data collection to real-world deployment. With a focus on building a "standardized industrial-academic-research foundation," FluxVLA significantly lowers the engineering threshold for VLA (Visual… See the full description on the dataset page: https://huggingface.co/datasets/limxdynamics/FluxVLAData.flux-2-klein-modelsflux.2-dev-synthetic-2M
Flux2.dev Synthetic: 2.2M Text-to-Image Pairs at 512×512
Dataset Summary
This dataset contains ~2.2 million large-scale synthetic image–caption pairs generated using the FLUX.2-dev diffusion model:
Model: black-forest-labs/FLUX.2-dev
Caption source: Text2Image-2M
Total samples: 2,282,665 (571 shards × ~4000 samples)
Resolution: 512 × 512
Image format: PNG (lossless)
Total shards: 571
Samples per shard: 4000 (last shard: 2665)
Total size: ~865 GB
Each sample consists of:… See the full description on the dataset page: https://huggingface.co/datasets/KempnerInstituteAI/flux.2-dev-synthetic-2M.flux-dev-modelsHunyuan3D-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.i1-fluxreason-tfrecordi1: A Simple and Fully Open Recipe for Strong Text-to-Image Models
Boya Zeng, Tianze Luo, Shu Pu, Jucheng Shen, Taiming Lu, Gabriel Sarch, Zhuang Liu
Princeton University
[arXiv][code][model][project page]
Overview
To prepare the dataset for training, we store the image-caption pairs as TFRecords.
This HuggingFace dataset contains the TFRecords corresponding to the fluxreason dataset at 256×256 resolution.
It also serves as an example of what a dataset processed using… See the full description on the dataset page: https://huggingface.co/datasets/zlab-princeton/i1-fluxreason-tfrecord.pdm3-ht-20260528-flux2-vae-latents-public
PDM-3-HT FLUX.2 VAE latents for ImageNet-256 train
Public research artifact for PDM-3-HT VAE-backend experiments. This repository contains latent cache shards only. It intentionally does not contain raw ImageNet images, ADM-cropped uint8 images, PAE latents, PAE checkpoints, or training checkpoints.
Source and preprocessing
Source dataset: ImageNet-1k train via ILSVRC/imagenet-1k; access requires accepting the upstream ImageNet terms.
Image preprocessing before VAE… See the full description on the dataset page: https://huggingface.co/datasets/LAXMAYDAY/pdm3-ht-20260528-flux2-vae-latents-public.fluxloraFLUX.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.curated-danbooru-2026-512px-flux2-vaei1-fluxreason-512-resolution-1m-tfrecordi1: A Simple and Fully Open Recipe for Strong Text-to-Image Models
Boya Zeng, Tianze Luo, Shu Pu, Jucheng Shen, Taiming Lu, Gabriel Sarch, Zhuang Liu
Princeton University
[arXiv][code][model][project page]
Overview
To prepare the dataset for training, we store the image-caption pairs as TFRecords.
This HuggingFace dataset contains the TFRecords corresponding to the fluxreason dataset at 512×512 resolution. Concretely, we only retain raw images with a shorter edge of at… See the full description on the dataset page: https://huggingface.co/datasets/i1-datasets/i1-fluxreason-512-resolution-1m-tfrecord.flux_vgg50k_inv28_infer28_uncondIDTruei1-fluxreason-1024-resolution-1m-tfrecordi1: A Simple and Fully Open Recipe for Strong Text-to-Image Models
Boya Zeng, Tianze Luo, Shu Pu, Jucheng Shen, Taiming Lu, Gabriel Sarch, Zhuang Liu
Princeton University
[arXiv][code][model][project page]
Overview
To prepare the dataset for training, we store the image-caption pairs as TFRecords.
This HuggingFace dataset contains the TFRecords corresponding to the fluxreason dataset at 1024×1024 resolution. Concretely, we only retain raw images with a shorter edge of at… See the full description on the dataset page: https://huggingface.co/datasets/i1-datasets/i1-fluxreason-1024-resolution-1m-tfrecord.FLUX.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.mjnj_flux32flux1-backup-202501flux1-backup-202507700k_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_DALLE3.5_synt_flux_street_selected_single_validated_1011curated-danbooru-2026-256px-flux2-vaeWSM_v1_Latent_WSG_v1.6_for_Flux2flux1-backup-202508flux1-backup-202409imagenet-256-flux2-vae-latents
ImageNet-256 FLUX.2 VAE Latents
Pre-computed deterministic, model-facing encodings from the
FLUX.2 VAE (black-forest-labs/FLUX.2-dev)
for the full ImageNet-1K training set at 256x256 resolution, stored as Parquet
shards. Each example includes latents for both the original and horizontally
flipped image, enabling flip augmentation without re-encoding at training time.
Dataset Description
Each example contains:
Column
Shape
Stored type
Description… See the full description on the dataset page: https://huggingface.co/datasets/yuanchenyang/imagenet-256-flux2-vae-latents.flux1-backup-202410Flux_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.