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 baselines/robossm_train_s1_final.pth from ChengYangYang/bpp-reading-pointer-weights: direct link, hf CLI and curl.
- Browser
- Download file 22.9 MB
-
https://huggingface.co/ChengYangYang/bpp-reading-pointer-weights/resolve/main/baselines/robossm_train_s1_final.pth
- Command line
-
hf download hf://ChengYangYang/bpp-reading-pointer-weights/baselines/robossm_train_s1_final.pth
-
curl -L -o robossm_train_s1_final.pth https://huggingface.co/ChengYangYang/bpp-reading-pointer-weights/resolve/main/baselines/robossm_train_s1_final.pth
22.9 MB
- Xet hash:
- dd32c8d6e5de34b50f79d80c4e20b616107ee2546c37f42c47bceefce8436767
- Size of remote file:
- 22.9 MB
- SHA256:
- 74c42eb4a4f1993b2644b008e2157fecd51dcd4715b8462e8f4757b82e2fb972
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