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
# 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")
Test Running
Run tests from the model directory in your active Python environment:
python test_bitdance.py
VRAM Usage by Resolution
Measured on NVIDIA A100-SXM4-80GB using:
dtype=torch.bfloat16num_inference_steps=30guidance_scale=7.5- 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.
| Resolution | Peak Allocated VRAM (GiB) | Peak Reserved VRAM (GiB) | Time (s) | Status |
|---|---|---|---|---|
| 512x512 | 39.60 | 40.62 | 4.08 | ok |
| 1024x1024 | 41.21 | 50.15 | 15.79 | ok |
| 1280x768 | 40.88 | 49.52 | 14.78 | ok |
| 768x1280 | 40.88 | 49.52 | 14.75 | ok |
| 1536x640 | 40.88 | 49.52 | 14.76 | ok |
| 2048x512 | 41.21 | 50.15 | 15.85 | ok |
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.