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:
- 4a9337740cfc0e3bdd47e2794df8168f29268866fe0cf0ade20772124a132a67
- Size of remote file:
- 2.88 GB
- SHA256:
- 4acc98f0ce1aedb1c18a500f489a8cd4135e2a5d753903e546d8b89e1d35c319
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