| --- |
| license: apache-2.0 |
| library_name: diffusers |
| pipeline_tag: unconditional-image-generation |
| base_model: shallowdream204/BitDance-ImageNet |
| language: |
| - en |
| tags: |
| - bitdance |
| - imagenet |
| - class-conditional |
| - custom-pipeline |
| - diffusers |
| --- |
| |
| # BitDance-ImageNet (Diffusers) |
|
|
| Diffusers-compatible BitDance ImageNet checkpoints for class-conditional generation at `256x256`. |
|
|
| ## Available Subfolders |
|
|
| - `BitDance_B_1x` (`parallel_num=1`) |
| - `BitDance_B_4x` (`parallel_num=4`) |
| - `BitDance_B_16x` (`parallel_num=16`) |
| - `BitDance_L_1x` (`parallel_num=1`) |
| - `BitDance_H_1x` (`parallel_num=1`) |
|
|
| All variants include a custom `BitDanceImageNetPipeline` and support ImageNet class IDs (`0-999`). |
|
|
| ## Requirements |
|
|
| - `flash-attn` is required for model execution and sampling. |
| - Install it in your environment before loading the pipeline. |
|
|
| ## Quickstart (native diffusers) |
|
|
| ```python |
| import torch |
| from diffusers import DiffusionPipeline |
| |
| repo_id = "BiliSakura/BitDance-ImageNet-diffusers" |
| subfolder = "BitDance_B_1x" # or BitDance_B_4x, BitDance_B_16x, BitDance_L_1x, BitDance_H_1x |
| |
| pipe = DiffusionPipeline.from_pretrained( |
| repo_id, |
| subfolder=subfolder, |
| trust_remote_code=True, |
| torch_dtype=torch.float16, |
| ).to("cuda") |
| |
| # ImageNet class 207 = golden retriever |
| out = pipe( |
| class_labels=207, |
| num_images_per_label=1, |
| sample_steps=100, |
| cfg_scale=4.6, |
| ) |
| out.images[0].save("bitdance_imagenet.png") |
| ``` |
|
|
| ## Local Path Note |
|
|
| When loading from a local clone, do not point `from_pretrained` to the repo root unless you also provide `subfolder=...`. |
| Each variant folder contains its own `model_index.json`, so the most reliable local usage is to load the variant directory directly: |
|
|
| ```python |
| from diffusers import DiffusionPipeline |
| |
| pipe = DiffusionPipeline.from_pretrained( |
| "/path/to/BitDance-ImageNet-diffusers/BitDance_B_1x", |
| trust_remote_code=True, |
| ) |
| ``` |
|
|
| ## Model Metadata |
|
|
| - Pipeline class: `BitDanceImageNetPipeline` |
| - Diffusers version in configs: `0.36.0` |
| - Resolution: `256x256` |
| - Number of classes: `1000` |
| - Autoencoder class: `BitDanceImageNetAutoencoder` |
|
|
| ## 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. |
|
|
