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--- |
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license: apache-2.0 |
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library_name: diffusers |
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pipeline_tag: unconditional-image-generation |
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base_model: shallowdream204/BitDance-ImageNet |
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language: |
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- en |
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tags: |
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- bitdance |
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- imagenet |
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- class-conditional |
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- custom-pipeline |
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- diffusers |
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--- |
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# BitDance-ImageNet (Diffusers) |
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Diffusers-compatible BitDance ImageNet checkpoints for class-conditional generation at `256x256`. |
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## Available Subfolders |
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- `BitDance_B_1x` (`parallel_num=1`) |
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- `BitDance_B_4x` (`parallel_num=4`) |
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- `BitDance_B_16x` (`parallel_num=16`) |
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- `BitDance_L_1x` (`parallel_num=1`) |
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- `BitDance_H_1x` (`parallel_num=1`) |
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All variants include a custom `BitDanceImageNetPipeline` and support ImageNet class IDs (`0-999`). |
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## Requirements |
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- `flash-attn` is required for model execution and sampling. |
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- Install it in your environment before loading the pipeline. |
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## Quickstart (native diffusers) |
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```python |
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import torch |
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from diffusers import DiffusionPipeline |
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repo_id = "BiliSakura/BitDance-ImageNet-diffusers" |
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subfolder = "BitDance_B_1x" # or BitDance_B_4x, BitDance_B_16x, BitDance_L_1x, BitDance_H_1x |
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pipe = DiffusionPipeline.from_pretrained( |
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repo_id, |
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subfolder=subfolder, |
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trust_remote_code=True, |
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torch_dtype=torch.float16, |
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).to("cuda") |
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# ImageNet class 207 = golden retriever |
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out = pipe( |
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class_labels=207, |
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num_images_per_label=1, |
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sample_steps=100, |
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cfg_scale=4.6, |
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) |
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out.images[0].save("bitdance_imagenet.png") |
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``` |
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## Local Path Note |
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When loading from a local clone, do not point `from_pretrained` to the repo root unless you also provide `subfolder=...`. |
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Each variant folder contains its own `model_index.json`, so the most reliable local usage is to load the variant directory directly: |
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```python |
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from diffusers import DiffusionPipeline |
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pipe = DiffusionPipeline.from_pretrained( |
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"/path/to/BitDance-ImageNet-diffusers/BitDance_B_1x", |
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trust_remote_code=True, |
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) |
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``` |
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## Model Metadata |
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- Pipeline class: `BitDanceImageNetPipeline` |
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- Diffusers version in configs: `0.36.0` |
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- Resolution: `256x256` |
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- Number of classes: `1000` |
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- Autoencoder class: `BitDanceImageNetAutoencoder` |
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## Citation |
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If you use this model, please cite BitDance and Diffusers: |
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```bibtex |
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@article{ai2026bitdance, |
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title = {BitDance: Scaling Autoregressive Generative Models with Binary Tokens}, |
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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}, |
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journal = {arXiv preprint arXiv:2602.14041}, |
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year = {2026} |
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} |
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@inproceedings{von-platen-etal-2022-diffusers, |
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title = {Diffusers: State-of-the-art diffusion models}, |
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author = {Patrick von Platen and Suraj Patil and Anton Lozhkov and Damar Jablonski and Hernan Bischof and Thomas Wolf}, |
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booktitle = {GitHub repository}, |
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year = {2022}, |
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url = {https://github.com/huggingface/diffusers} |
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} |
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``` |
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## License |
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This repository is distributed under the Apache-2.0 license, consistent with the upstream BitDance release. |
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