Instructions to use ChengYangYang/bpp-reading-pointer-weights with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ChengYangYang/bpp-reading-pointer-weights with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ChengYangYang/bpp-reading-pointer-weights", 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 earlier_lines/posefirst_ckpt/frames.pt from ChengYangYang/bpp-reading-pointer-weights: direct link, hf CLI and curl.
- Browser
- Download file 142 MB
-
https://huggingface.co/ChengYangYang/bpp-reading-pointer-weights/resolve/main/earlier_lines/posefirst_ckpt/frames.pt
- Command line
-
hf download hf://ChengYangYang/bpp-reading-pointer-weights/earlier_lines/posefirst_ckpt/frames.pt
-
curl -L -o frames.pt https://huggingface.co/ChengYangYang/bpp-reading-pointer-weights/resolve/main/earlier_lines/posefirst_ckpt/frames.pt
142 MB
- Xet hash:
- 5ef373756545a97809040549e821e4470eb1e620419c46ec82e4d8dfa11cd4b1
- Size of remote file:
- 142 MB
- SHA256:
- 82d2e2ec70259efd4573b122d8193ba8997b423488efe097bdc63f032db97258
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.