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
Add files using upload-large-folder tool
Browse files
SiT-L-2-256-diffusers/transformer/diffusion_pytorch_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:fd3c19ec1a7675f5648f5200acf4f1a94ae6a44ff5ff81a1011de8242d12edd6
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size 1832436424
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SiT-XL-2-512-diffusers/vae/diffusion_pytorch_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:2aa1f43011b553a4cba7f37456465cdbd48aab7b54b9348b890e8058ea7683ec
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size 334643268
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