Update all files for BitDance-Tokenizer-diffusers

README.md

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+---
+license: apache-2.0
+library_name: diffusers
+pipeline_tag: image-to-image
+base_model: shallowdream204/BitDance-Tokenizer
+language:
+  - en
+tags:
+  - bitdance
+  - tokenizer
+  - autoencoder
+  - custom-architecture
+  - diffusers
+---
+
+# BitDance-Tokenizer (Diffusers)
+
+Diffusers-formatted BitDance tokenizer autoencoders (AE) converted from the upstream BitDance tokenizer checkpoints.
+
+## Available Autoencoders
+
+- `ae_d16c32` (`z_channels=32`, `gan_decoder=false`)
+- `ae_d32c128` (`z_channels=128`, `gan_decoder=true`)
+- `ae_d32c256` (`z_channels=256`, `gan_decoder=true`)
+
+Each subfolder includes:
+
+- `config.json` with the autoencoder architecture
+- `conversion_metadata.json` documenting the source checkpoint and config
+
+## Quickstart (native diffusers)
+
+```python
+import torch
+from diffusers import DiffusionPipeline
+
+# Load a BitDance diffusers pipeline first (provides BitDanceAutoencoder class).
+pipe = DiffusionPipeline.from_pretrained(
+    "BiliSakura/BitDance-14B-16x-diffusers",
+    trust_remote_code=True,
+    torch_dtype=torch.bfloat16,
+).to("cuda")
+
+# Swap in a tokenizer autoencoder from this repository.
+pipe.autoencoder = pipe.autoencoder.__class__.from_pretrained(
+    "BiliSakura/BitDance-Tokenizer-diffusers",
+    subfolder="ae_d16c32",
+).to("cuda")
+
+image = pipe(
+    prompt="A watercolor painting of a red fox in a snowy forest.",
+    height=1024,
+    width=1024,
+).images[0]
+image.save("bitdance_with_custom_tokenizer.png")
+```
+
+> Note: this repository stores tokenizer autoencoder components; use `trust_remote_code=True` with a BitDance runtime repo when loading custom classes.
+
+## 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.