Unconditional Image Generation
Diffusers
Safetensors
image-generation
class-conditional
imagenet
dico
latent-diffusion
convnet
Instructions to use BiliSakura/DiCo-diffusers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use BiliSakura/DiCo-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/DiCo-diffusers", dtype=torch.bfloat16, device_map="cuda") prompt = "golden retriever" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 3b0c43a7d8a602d50f727adea60e345f158bb766126648d23debcfff9cff8619
- Size of remote file:
- 117 kB
- SHA256:
- a6093a73928029e09dd9a59d22ed993c66cb7be9bfd8c21e3c967947764eab95
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