BitDance: Scaling Autoregressive Generative Models with Binary Tokens
Yuang Ai*, Jiaming Han*, Shaobin Zhuang*, Weijia Mao, Xuefeng Hu, Ziyan Yang, Zhenheng Yang, Huaibo Huang†, Xiangyu Yue†, Hao Chen*†‡
* Equal Contribution † Corresponding Author ‡ Project Lead
For visual generation, discrete autoregressive models often struggle with poor tokenizer reconstruction, difficulties in sampling from large vocabularies, and slow token-by-token generation speeds. We present BitDance, which addresses these challenges via a large-vocabulary binary tokenizer, a binary diffusion head for sampling in large discrete space, and a next-patch diffusion paradigm that enables efficient multitoken prediction. BitDance is an open-source discrete autoregressive foundation model with 14B parameters, trained on large-scale multimodal tokens. While maintaining the standard language modeling paradigm for text tokens, BitDance employs a next-patch diffusion paradigm for visual tokens to predict multiple tokens in parallel—up to 64 per step. This unified multimodal framework is simple, scalable, and capable of efficiently generating high-resolution, photorealistic images.
⚡ Quick Start
1️⃣ Create Conda Environment and Install Package
git clone https://github.com/shallowdream204/BitDance.git
cd BitDance
conda create -n bitdance python=3.11 -y
conda activate bitdance
pip install -r requirements.txt
pip install flash_attn==2.8.2 --no-build-isolation
2️⃣ Download Model Weights
We offer two models, BitDance-14B-64x and BitDance-14B-16x, which can predict 64 and 16 tokens in parallel at each step, respectively.
| Model | #Token per Step | Step (1024px) | Supported Size | Huggingface |
|---|---|---|---|---|
| BitDance-14B-64x | 64 | 64 | 1024px | BitDance-14B-64x |
| BitDance-14B-16x | 16 | 256 | 512&1024px | BitDance-14B-16x |
Run the following scripts to download all T2I models.
hf download shallowdream204/BitDance-14B-64x --local-dir models/BitDance-14B-64x --max-workers=16
hf download shallowdream204/BitDance-14B-16x --local-dir models/BitDance-14B-16x --max-workers=16
3️⃣ T2I Inference (check here for the supported image resolution)
# example_t2i.py
from modeling.t2i_pipeline import BitDanceT2IPipeline
model_path = 'models/BitDance-14B-64x'
# model_path = 'models/BitDance-14B-16x'
device = 'cuda'
pipe = BitDanceT2IPipeline(model_path=model_path, device=device)
prompt = "A close-up portrait in a cinematic photography style, capturing a girl-next-door look on a sunny daytime urban street. She wears a khaki sweater, with long, flowing hair gently draped over her shoulders. Her head is turned slightly, revealing soft facial features illuminated by realistic, delicate sunlight coming from the left. The sunlight subtly highlights individual strands of her hair. The image has a Canon film-like color tone, evoking a warm nostalgic atmosphere."
image = pipe.generate(
prompt=prompt,
height=1024,
width=1024,
num_sampling_steps=50, # may adjust to 25 steps for faster inference, but may slightly reduce quality
guidance_scale=7.5,
num_images=1,
seed=42
)[0]
image.save("example.png")
🤗 Demo
🔥 Try the Huggingface Space demo to start playing with BitDance: BitDance-Demo
You can also run the demo locally:
python app.py
📊 Model Performance
🪪 License
BitDance is licensed under the Apache 2.0 license.
📖 Citation
If you find our work useful for your research, please consider citing our paper:
@article{ai2026bitdance,
title = {BitDance: Scaling Autoregressive Generative Models with Binary Tokens},
author = {Ai, Yuang and Han, Jiaming and Zhuang, Shaobin and Hu, Xuefeng and Yang, Ziyan and Yang, Zhenheng and Huang, Huaibo and Yue, Xiangyu and Chen, Hao},
journal = {arXiv preprint arXiv:2602.14041},
year = {2026}
}