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
| { | |
| "_class_name": [ | |
| "pipeline", | |
| "SiTPipeline" | |
| ], | |
| "_diffusers_version": "0.36.0", | |
| "scheduler": [ | |
| "scheduling_flow_match_sit", | |
| "SiTFlowMatchScheduler" | |
| ], | |
| "transformer": [ | |
| "transformer_sit", | |
| "SiTTransformer2DModel" | |
| ], | |
| "vae": [ | |
| "diffusers", | |
| "AutoencoderKL" | |
| ] | |
| } | |