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 unet/diffusion_pytorch_model.bin from Cubort/Radiencev2: direct link, hf CLI and curl.
- Browser
- Download file 135 Bytes
-
https://huggingface.co/Cubort/Radiencev2/resolve/main/unet/diffusion_pytorch_model.bin
- Command line
-
hf download hf://Cubort/Radiencev2/unet/diffusion_pytorch_model.bin
-
curl -L -o diffusion_pytorch_model.bin https://huggingface.co/Cubort/Radiencev2/resolve/main/unet/diffusion_pytorch_model.bin
135 Bytes
- Xet hash:
- aa86ac4aa32e6fd361e05961406a3d8afb37b13227aa8b6584a04fbf15f96345
- Size of remote file:
- 135 Bytes
- SHA256:
- 30ce0584ca87962127048aa2995f14b201b6a0750d43e92a1dbb6e63c9eb555b
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