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The dataset generation failed
Error code: DatasetGenerationError
Exception: UnicodeDecodeError
Message: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1872, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/text/text.py", line 98, in _generate_tables
batch = f.read(self.config.chunksize)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/file_utils.py", line 844, in read_with_retries
out = read(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "<frozen codecs>", line 322, in decode
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte
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/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1739, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1922, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
text string |
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MIT License |
Copyright (c) 2025 Hang Guo |
Permission is hereby granted, free of charge, to any person obtaining a copy |
of this software and associated documentation files (the "Software"), to deal |
in the Software without restriction, including without limitation the rights |
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell |
copies of the Software, and to permit persons to whom the Software is |
furnished to do so, subject to the following conditions: |
The above copyright notice and this permission notice shall be included in all |
copies or substantial portions of the Software. |
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
SOFTWARE. |
<p align="center"> |
<img src="assets/logo.jpg" width="700"> |
</p> |
<div align="center"> |
**2K resolution image generation with on single 3090 GPU** 🏔️ |
<img src="assets/teaser.jpg" style="border-radius: 15px"> |
<h2> |
FastVAR: Linear Visual Autoregressive Modeling via Cached Token Pruning (ICCV25) |
</h2> |
[Hang Guo](https://csguoh.github.io/), [Yawei Li](https://yaweili.bitbucket.io/), [Taolin Zhang](https://github.com/taolinzhang), [Jiangshan Wang](https://scholar.google.com.hk/citations?user=HoKoCv0AAAAJ&hl=zh-CN&oi=ao), [Tao Dai](https://scholar.google.com.hk/citations?user=MqJNdaAAAAAJ&hl=zh-CN&oi=ao), [Shu-Tao Xia](https://scholar.google.com.hk/citations?hl=zh-CN&user=koAXTXgAAAAJ), [Luca Benini](https://ee.ethz.ch/the-department/people-a-z/person-detail.luca-benini.html) |
 |
[](https://arxiv.org/pdf/2503.23367) |
</div> |
> **Abstract:** Visual Autoregressive (VAR) modeling has gained popularity for its shift towards next-scale prediction. However, existing VAR paradigms process the entire token map at each scale step, leading to the complexity and runtime scaling dramatically with image resolution. To address this challenge, we propose FastVAR, a post-training acceleration method for efficient resolution scaling with VARs. Our key finding is that the majority of latency arises from the large-scale step where most tokens have already converged. Leveraging this observation, we develop the cached token pruning strategy that only forwards pivotal tokens for scalespecific modeling while using cached tokens from previous scale steps to restore the pruned slots. This significantly reduces the number of forwarded tokens and improves the efficiency at larger resolutions. Experiments show the proposed FastVAR can further speedup FlashAttentionaccelerated VAR by 2.7× with negligible performance drop of <1%. We further extend FastVAR to zero-shot generation of higher resolution images. In particular, FastVAR can generate one 2K image with 15GB memory footprints in 1.5s on a single NVIDIA 3090 GPU. |
⭐If this work is helpful for you, please help star this repo. Thanks!🤗 |
## ✨ Highlights |
1️⃣ **Faster VAR Generation without Perceptual Loss** |
<p align="center"> |
<img src="assets/visual.jpg" style="border-radius: 15px"> |
</p> |
2️⃣ **High-resolution Image Generation (even 2K image on single 3090 GPU)** |
<p align="center"> |
<img src="assets/high_resolution.jpg" style="border-radius: 15px"> |
</p> |
3️⃣ **Promising Resolution Scalibility (almost linear complexity)** |
<p align="center"> |
<img src="assets/efficiency.jpg" width="600" style="border-radius: 15px"> |
</p> |
## 📑 Contents |
- [News](#news) |
- [Pipeline](#pipeline) |
- [TODO](#todo) |
- [Results](#results) |
- [Citation](#cite) |
## <a name="news"></a> 🆕 News |
- **2025-03-30:** arXiv paper available. |
- **2025-04-04:** This repo is released. |
- **2025-06-26:** Congrats! Our FastVAR has been accepted by ICCV2025 😊 |
- **2025-06-29:** We have open sourced all our code. |
## <a name="todo"></a> ☑️ TODO |
- [x] arXiv version available |
- [x] Release code |
- [ ] Further improvements |
## <a name="pipeline"></a> 👀 Pipeline |
Our FastVAR introduces the **"cached token pruning"** which works on the large-scale steps of the VAR models, which is **training-free** and **generic** for various VAR backbones. |
<p align="center"> |
<img src="assets/pipeline.jpg" style="border-radius: 15px"> |
</p> |
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