--- license: apache-2.0 library_name: diffusers pipeline_tag: text-to-image base_model: shallowdream204/BitDance-14B-16x language: - en tags: - bitdance - text-to-image - custom-pipeline - diffusers - qwen --- # BitDance-14B-16x (Diffusers) Diffusers-converted checkpoint for BitDance-14B-16x with bundled custom pipeline code (`bitdance_diffusers`) so it can be loaded directly with `DiffusionPipeline`. ## Quickstart (native diffusers) ```python import torch from diffusers import DiffusionPipeline repo_id = "BiliSakura/BitDance-14B-16x-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_16x.png") ``` ## Model Metadata - Pipeline class: `BitDanceDiffusionPipeline` - Diffusers version in config: `0.36.0` - Parallel prediction factor: `16` - 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.