Scale RAE
Collection
Collection for "Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders"
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6 items
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Updated
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Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 289, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 83, in _split_generators
raise ValueError(
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 343, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 294, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Scale RAE Data
Project Page | Paper | Code
This repository contains data associated with the paper "Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders".
The dataset is used for training and evaluating Scale-RAE, a framework that investigates scaling Representation Autoencoders (RAEs) for large-scale, freeform text-to-image (T2I) generation. It includes data used for scaling RAE decoders beyond ImageNet, featuring web, synthetic, and text-rendering data, as well as high-quality instruction datasets for fine-tuning.
Citation
If you find this work useful, please cite:
@article{scale-rae-2026,
title={Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders},
author={Shengbang Tong and Boyang Zheng and Ziteng Wang and Bingda Tang and Nanye Ma and Ellis Brown and Jihan Yang and Rob Fergus and Yann LeCun and Saining Xie},
journal={arXiv preprint arXiv:2601.16208},
year={2026}
}
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