Instructions to use RobertML/edge-super-compile-0D with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use RobertML/edge-super-compile-0D with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("RobertML/edge-super-compile-0D", 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
- Xet hash:
- fabc82a1fa7247799064530b04279b5e60c71a67917757eb4e913f73fccd544d
- Size of remote file:
- 9.36 MB
- SHA256:
- 9c07b173fc5324cc6ee6e83f15044bb0938ff69c0bb41ba71de6a9f053db937b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.