Update all files for BitDance-ImageNet-diffusers

README.md

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+---
+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.