---
library_name: diffusers
pipeline_tag: unconditional-image-generation
tags:
  - diffusers
  - sit
  - image-generation
  - class-conditional
  - imagenet
license: mit
inference: true
---

# SiT-diffusers

Diffusers-ready checkpoints for **Scalable Interpolant Transformers (SiT)**, converted for local/offline use.

This root folder is a model collection that contains:

- `SiT-S-2-256-diffusers`
- `SiT-B-2-256-diffusers`
- `SiT-L-2-256-diffusers`
- `SiT-XL-2-256-diffusers`
- `SiT-XL-2-512-diffusers`

Each subfolder is a self-contained Diffusers model repo with:

- `pipeline.py`
- `transformer/transformer_sit.py`
- `scheduler/scheduling_flow_match_sit.py`
- `transformer/diffusion_pytorch_model.safetensors`
- `vae/diffusion_pytorch_model.safetensors`

## Model Paths

Use paths relative to this root README:

| Model | Resolution | Local path |
|---|---:|---|
| SiT-S/2 | 256x256 | `./SiT-S-2-256-diffusers` |
| SiT-B/2 | 256x256 | `./SiT-B-2-256-diffusers` |
| SiT-L/2 | 256x256 | `./SiT-L-2-256-diffusers` |
| SiT-XL/2 | 256x256 | `./SiT-XL-2-256-diffusers` |
| SiT-XL/2 | 512x512 | `./SiT-XL-2-512-diffusers` |

## Inference Demo (Diffusers)

### 1) Load a local subfolder checkpoint

```python
import torch
from diffusers import DiffusionPipeline

model_path = "./SiT-XL-2-512-diffusers"  # change to any path in the table above
device = "cuda" if torch.cuda.is_available() else "cpu"

pipe = DiffusionPipeline.from_pretrained(
    model_path,
    trust_remote_code=True,
).to(device)

generator = torch.Generator(device=device).manual_seed(0)

# ImageNet class example: 207 = golden retriever
result = pipe(
    class_labels=207,
    height=512,
    width=512,
    num_inference_steps=250,  # official SiT comparisons commonly use 250 steps
    guidance_scale=4.0,
    generator=generator,
)

image = result.images[0]
image.save("sit_xl_512_demo.png")
```

### 2) Quick variant switch (256 models)

```python
model_path = "./SiT-S-2-256-diffusers"
# model_path = "./SiT-B-2-256-diffusers"
# model_path = "./SiT-L-2-256-diffusers"
# model_path = "./SiT-XL-2-256-diffusers"

pipe = DiffusionPipeline.from_pretrained(model_path, trust_remote_code=True).to(device)
image = pipe(
    class_labels=207,
    height=256,
    width=256,
    num_inference_steps=250,
    guidance_scale=4.0,
    generator=generator,
).images[0]
image.save("sit_256_demo.png")
```

## FID Reference (from Official SiT Results)

The table below summarizes widely cited SiT numbers from the official project materials for class-conditional ImageNet generation.

| Model / setting | Resolution | FID-50K (lower is better) |
|---|---:|---:|
| SiT-S (400K steps) | 256x256 | 57.6 |
| SiT-B (400K steps) | 256x256 | 33.5 |
| SiT-L (400K steps) | 256x256 | 17.2 |
| SiT-XL (400K steps) | 256x256 | 8.6 |
| SiT-XL (cfg=1.5, ODE) | 256x256 | 2.15 |
| SiT-XL (cfg=1.5, SDE, `w(t)=sigma_t`) | 256x256 | 2.06 |
| SiT-XL (sample showcase) | 512x512 | Not reported in the same benchmark table |

> Note: FID depends on training recipe, sampler choice (ODE/SDE), guidance scale, and evaluation protocol. Treat this table as a reference to official SiT reports, not as guaranteed reproducibility for every conversion/export.

## Source and Paper

- Official SiT code: [willisma/SiT](https://github.com/willisma/SiT)
- Project page: [scalable-interpolant.github.io](https://scalable-interpolant.github.io/)
- Paper (arXiv): [SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers](https://arxiv.org/abs/2401.08740)

## Citation

If you use SiT in your work, please cite:

```bibtex
@inproceedings{ma2024sit,
  title={SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers},
  author={Ma, Nanye and Goldstein, Mark and Albergo, Michael S. and Boffi, Nicholas M. and Vanden-Eijnden, Eric and Xie, Saining},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2024},
  note={Accepted to ECCV 2024}
}
```