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 text_encoder/pytorch_model.bin from Cubort/Radiencev2: direct link, hf CLI and curl.
- Browser
- Download file 134 Bytes
-
https://huggingface.co/Cubort/Radiencev2/resolve/main/text_encoder/pytorch_model.bin
- Command line
-
hf download hf://Cubort/Radiencev2/text_encoder/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Cubort/Radiencev2/resolve/main/text_encoder/pytorch_model.bin
134 Bytes
- Xet hash:
- d3284095bf09547b60c1933136c95bf6c0267db9fafa06ce452bb743ad1ad2a4
- Size of remote file:
- 134 Bytes
- SHA256:
- ef807ef2e4bb28d1005658f1532b60ef5a2891c6ccf55e4da0a4c70b111d4a80
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