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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
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")
```
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