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

# SiT-B-2-256-diffusers

Self-contained Diffusers checkpoint repo for SiT.

## Usage

```python
import torch
from diffusers import DiffusionPipeline

pipe = DiffusionPipeline.from_pretrained("./").to("cuda" if torch.cuda.is_available() else "cpu")
generator = torch.Generator(device=pipe.device).manual_seed(0)

image = pipe(
    class_labels=207,
    height=256,
    width=256,
    num_inference_steps=250,
    guidance_scale=4.0,
    generator=generator,
).images[0]
image.save("demo.png")
```

## Components

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