Unconditional Image Generation
Diffusers
Safetensors
sit
image-generation
class-conditional
imagenet
Instructions to use BiliSakura/SiT-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/SiT-diffusers with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("BiliSakura/SiT-diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 325 Bytes
4c42d10 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | {
"_class_name": [
"pipeline",
"SiTPipeline"
],
"_diffusers_version": "0.36.0",
"scheduler": [
"scheduling_flow_match_sit",
"SiTFlowMatchScheduler"
],
"transformer": [
"transformer_sit",
"SiTTransformer2DModel"
],
"vae": [
"diffusers",
"AutoencoderKL"
]
}
|