README.md

---
library_name: diffusers
pipeline_tag: unconditional-image-generation
tags:
  - diffusers
  - sit
  - image-generation
  - class-conditional
  - imagenet
license: mit
inference: true
widget:
  - output:
      url: SiT-XL-2-512/demo.png
language:
  - en
---

# 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`
- `SiT-B-2-256`
- `SiT-L-2-256`
- `SiT-XL-2-256`
- `SiT-XL-2-512`

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

- `pipeline.py`
- `transformer/transformer_sit.py`
- `scheduler/scheduler_config.json` (`FlowMatchEulerDiscreteScheduler`)
- `transformer/diffusion_pytorch_model.safetensors`
- `vae/diffusion_pytorch_model.safetensors`

Each variant embeds English `id2label` directly in `model_index.json` (DiT-style), so class labels can be passed as
ImageNet ids or English synonym strings.

## Demo

![SiT-XL-2-512 demo](SiT-XL-2-512/demo.png)

Class-conditional sample (ImageNet class **207**, golden retriever), `SiT-XL/2` at 512×512, 250 steps, CFG 4.0, seed 0.

## Model Paths

Use paths relative to this root README:

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

## Inference Demo (Diffusers)

### 1) Load a local subfolder checkpoint

```python
import torch
from diffusers import DiffusionPipeline

model_path = "./SiT-XL-2-512"  # 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
print(pipe.id2label[207])
print(pipe.get_label_ids("golden retriever"))  # [207]
result = pipe(
    class_labels="golden retriever",
    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"
# model_path = "./SiT-B-2-256"
# model_path = "./SiT-L-2-256"
# model_path = "./SiT-XL-2-256"

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")
```