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library_name: diffusers
pipeline_tag: unconditional-image-generation
tags:
- diffusers
- sit
- image-generation
- class-conditional
inference: true
---
# SiT-L-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`
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