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license: apache-2.0
library_name: diffusers
pipeline_tag: text-to-image
base_model: shallowdream204/BitDance-14B-64x
language:
- en
tags:
- bitdance
- text-to-image
- custom-pipeline
- diffusers
- qwen
---
# BitDance-14B-64x (Diffusers)
Diffusers-converted checkpoint for BitDance-14B-64x with bundled custom pipeline code (`bitdance_diffusers`) for direct loading with `DiffusionPipeline`.
## Quickstart (native diffusers)
```python
import torch
from diffusers import DiffusionPipeline
repo_id = "BiliSakura/BitDance-14B-64x-diffusers"
pipe = DiffusionPipeline.from_pretrained(
repo_id,
trust_remote_code=True,
torch_dtype=torch.bfloat16,
).to("cuda")
result = pipe(
prompt="A cinematic landscape photo of snowy mountains at sunrise.",
height=1024,
width=1024,
num_inference_steps=50,
guidance_scale=7.5,
)
result.images[0].save("bitdance_14b_64x.png")
```
## Model Metadata
- Pipeline class: `BitDanceDiffusionPipeline`
- Diffusers version in config: `0.36.0`
- Parallel prediction factor: `64`
- Text stack: `Qwen3ForCausalLM` + `Qwen2TokenizerFast`
- Supported resolutions include `1024x1024`, `1280x768`, `768x1280`, `2048x512`, and more (see `model_index.json`)
## Citation
If you use this model, please cite BitDance and Diffusers:
```bibtex
@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}
}
@inproceedings{von-platen-etal-2022-diffusers,
title = {Diffusers: State-of-the-art diffusion models},
author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Damar Jablonski and Hernan Bischof and Thomas Wolf},
booktitle = {GitHub repository},
year = {2022},
url = {https://github.com/huggingface/diffusers}
}
```
## License
This repository is distributed under the Apache-2.0 license, consistent with the upstream BitDance release.
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