Unconditional Image Generation
Diffusers
Safetensors
English
bitdance
imagenet
class-conditional
custom-pipeline
Instructions to use BiliSakura/BitDance-ImageNet-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/BitDance-ImageNet-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/BitDance-ImageNet-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
- Xet hash:
- caca26784b7d5ea26218d1bad16ff8277f03b6a8a340d7ffbef396f7e05deddd
- Size of remote file:
- 2.88 GB
- SHA256:
- 26ae06fe6a6fc25073f447f1f9c96601d8dda9ca2724a3bb2c15c4ecd79dbeae
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.