Instructions to use LYAWWH/DreamLight with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use LYAWWH/DreamLight with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("LYAWWH/DreamLight", torch_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:
- 1f3660afa87865f8fd39a09cd8d3a862d658aba899b5754b7a62c8ef48301c7a
- Size of remote file:
- 188 MB
- SHA256:
- 5f22283b1f60b56098d14b785f201dc977c41f080b079d0679f8974a52555ce6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.