Instructions to use Srivathsava/ddpm-celb-faces with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Srivathsava/ddpm-celb-faces with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Srivathsava/ddpm-celb-faces", 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
Download unet/diffusion_pytorch_model.bin from Srivathsava/ddpm-celb-faces: direct link, hf CLI and curl.
- Browser
- Download file 455 MB
-
https://huggingface.co/Srivathsava/ddpm-celb-faces/resolve/main/unet/diffusion_pytorch_model.bin
- Command line
-
hf download hf://Srivathsava/ddpm-celb-faces/unet/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/Srivathsava/ddpm-celb-faces/resolve/main/unet/diffusion_pytorch_model.bin
455 MB
- Xet hash:
- 09de56d6db6d7d24bbc89cacdf02961e82730758ac103bacbda0dd825c8132f1
- Size of remote file:
- 455 MB
- SHA256:
- 558d8f225662b93bee41943e151dfc6760e111619cc075040b9fa0ab32c18735
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.