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": "SiTTransformer2DModel", | |
| "_diffusers_version": "0.36.0", | |
| "class_dropout_prob": 0.1, | |
| "depth": 12, | |
| "hidden_size": 768, | |
| "in_channels": 4, | |
| "input_size": 32, | |
| "learn_sigma": true, | |
| "mlp_ratio": 4.0, | |
| "num_classes": 1000, | |
| "num_heads": 12, | |
| "patch_size": 2 | |
| } | |