Instructions to use Cubort/Radiencev2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Cubort/Radiencev2 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Cubort/Radiencev2", 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
- Local Apps Settings
- Draw Things
- DiffusionBee
Download vae/diffusion_pytorch_model.bin from Cubort/Radiencev2: direct link, hf CLI and curl.
- Browser
- Download file 134 Bytes
-
https://huggingface.co/Cubort/Radiencev2/resolve/main/vae/diffusion_pytorch_model.bin
- Command line
-
hf download hf://Cubort/Radiencev2/vae/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/Cubort/Radiencev2/resolve/main/vae/diffusion_pytorch_model.bin
134 Bytes
- Xet hash:
- fe8c22728cf94d57adff130bd952e5e0d92d59412cd39c09a3e1150054dcf9c0
- Size of remote file:
- 134 Bytes
- SHA256:
- eb2dcef5951f2f23c3c708eb7e9c2d972b83c25e5b2c6b6a5009269678ab1730
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.