| | --- |
| | 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. |
| |
|
