Instructions to use BiliSakura/BitDance-14B-64x-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/BitDance-14B-64x-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BiliSakura/BitDance-14B-64x-diffusers", torch_dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
metadata
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)
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 (seemodel_index.json)
Citation
If you use this model, please cite BitDance and Diffusers:
@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.