Instructions to use kvablack/ddpo-alignment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use kvablack/ddpo-alignment with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("kvablack/ddpo-alignment", dtype=torch.bfloat16, device_map="cuda") prompt = "a horse playing chess" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
Download unet/diffusion_pytorch_model.bin from kvablack/ddpo-alignment: direct link, hf CLI and curl.
- Browser
- Download file 3.44 GB
-
https://huggingface.co/kvablack/ddpo-alignment/resolve/main/unet/diffusion_pytorch_model.bin
- Command line
-
hf download hf://kvablack/ddpo-alignment/unet/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/kvablack/ddpo-alignment/resolve/main/unet/diffusion_pytorch_model.bin
3.44 GB
- Xet hash:
- 354e975d313a1dcc410d93606e4611958091b96c9422e83d533adc4a7270069c
- Size of remote file:
- 3.44 GB
- SHA256:
- 510278605ca9242b6a3d20d1520c6894e06f6c7e92f37a4a58fb6b8cbeff306f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.