Instructions to use wty500/RL-RIG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wty500/RL-RIG with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("wty500/RL-RIG", 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 adapters/phase2_editor.safetensors from wty500/RL-RIG: direct link, hf CLI and curl.
- Browser
- Download file 189 MB
-
https://huggingface.co/wty500/RL-RIG/resolve/main/adapters/phase2_editor.safetensors
- Command line
-
hf download hf://wty500/RL-RIG/adapters/phase2_editor.safetensors
-
curl -L -o phase2_editor.safetensors https://huggingface.co/wty500/RL-RIG/resolve/main/adapters/phase2_editor.safetensors
189 MB
- Xet hash:
- dde5e8a56078a39e72e111db0f479bed5e1ec5fe3421c627678aff9ebd4382c0
- Size of remote file:
- 189 MB
- SHA256:
- 91402ff09c5f0065924b9869ba1964a96bebc61435027c58fc457c32d072379a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.