| --- |
| license: apache-2.0 |
| library_name: diffusers |
| pipeline_tag: unconditional-image-generation |
| tags: |
| - diffusers |
| - priming |
| - image-generation |
| - class-conditional |
| - one-step |
| - imagenet |
| widget: |
| - text: golden retriever |
| output: |
| url: demo.png |
| language: |
| - en |
| --- |
| |
| # Priming-diffusers |
|
|
| Native [diffusers](https://github.com/huggingface/diffusers) checkpoints for **Priming** ([Are We Overthinking One-Step Generation?](https://arxiv.org/abs/2610.20331)). Code: [JaeyeonKim01/priming](https://github.com/JaeyeonKim01/priming). Each variant folder is self-contained: |
|
|
| - `pipeline.py` — `PrimingPipeline` |
| - `scheduler/scheduling_priming.py` — `PrimingScheduler` (one evaluation; the transformer predicts the clean image) |
| - `transformer/transformer_priming.py` — `PrimingTransformer2DModel` |
|
|
| Load with PyPI `diffusers` and `trust_remote_code=True`. No extra Priming package is required. Sampling is one forward pass in RGB at 256×256 with `guidance_scale=1`. Weights are the released post-training EMA. The paper reports ImageNet-256 FID 1.38 for Priming-B and 1.19 for Priming-L. |
|
|
| ## Available checkpoints |
|
|
| | Checkpoint | Path | Resolution | Steps | Guidance | |
| | --- | --- | --- | ---: | ---: | |
| | Priming-B | [`Priming-B/`](Priming-B/) | 256×256 | 1 | 1.0 | |
| | Priming-L | [`Priming-L/`](Priming-L/) | 256×256 | 1 | 1.0 | |
|
|
| ## Demo |
|
|
|  |
|
|
| Class: *golden retriever* (ImageNet 207) — 256×256, 1 step, `guidance_scale=1.0`, `torch_dtype=bfloat16`, seed 1. |
|
|
| ## ImageNet class labels |
|
|
| Each variant keeps an English `id2label` map in `model_index.json`. |
|
|
| - `pipe.id2label` — id → English label |
| - `pipe.labels` — synonym → id |
| - `pipe.get_label_ids("golden retriever")` |
| - `pipe(class_labels="golden retriever", ...)` — string labels resolve automatically |
|
|
| `height` and `width` default to 256. Other sizes divisible by the patch size of 16 interpolate the learned positions and rebuild 2-D rotary frequencies. |
|
|
| ## Inference |
|
|
| ```python |
| import torch |
| from diffusers import DiffusionPipeline |
| |
| pipe = DiffusionPipeline.from_pretrained( |
| "BiliSakura/Priming-diffusers", |
| subfolder="Priming-B", # or "Priming-L" |
| custom_pipeline="pipeline.py", |
| trust_remote_code=True, |
| torch_dtype=torch.bfloat16, |
| ).to("cuda") |
| |
| print(pipe.id2label[207]) |
| image = pipe( |
| class_labels="golden retriever", |
| num_inference_steps=1, |
| guidance_scale=1.0, |
| generator=torch.Generator(device="cuda").manual_seed(1), |
| ).images[0] |
| image.save("demo.png") |
| ``` |
|
|
| Local checkout: |
|
|
| ```python |
| from pathlib import Path |
| import torch |
| from diffusers import DiffusionPipeline |
| |
| model_dir = Path("Priming-diffusers/Priming-B") |
| pipe = DiffusionPipeline.from_pretrained( |
| str(model_dir), |
| local_files_only=True, |
| custom_pipeline=str(model_dir / "pipeline.py"), |
| trust_remote_code=True, |
| torch_dtype=torch.bfloat16, |
| ).to("cuda") |
| ``` |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{park2026are, |
| title = {Are We Overthinking One-Step Generation?}, |
| author = {Park, Woobin and Kim, Jaeyeon and Du, Yilun and Chen, Sitan and Han, Bohyung}, |
| year = {2026}, |
| eprint = {2610.20331}, |
| archivePrefix = {arXiv}, |
| primaryClass = {cs.LG}, |
| url = {https://arxiv.org/abs/2610.20331} |
| } |
| ``` |
|
|
