BiliSakura/BitDance-14B-64x-diffusers

import torch
from diffusers import DiffusionPipeline

# Local path (recommended - no trust_remote_code needed)
model_path = "BiliSakura/BitDance-14B-64x-diffusers"
pipe = DiffusionPipeline.from_pretrained(
    model_path,
    custom_pipeline=model_path,
    torch_dtype=torch.bfloat16,
).to("cuda")

result = pipe(
    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.",
    height=1024,
    width=1024,
    num_inference_steps=50,
    guidance_scale=7.5,
)
result.images[0].save("bitdance_14b_64x.png")

python test_bitdance.py

@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}
}