Instructions to use dilightnet/DiLightNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use dilightnet/DiLightNet with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline from diffusers.utils import load_image # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("dilightnet/DiLightNet", dtype=torch.bfloat16, device_map="cuda") prompt = "Turn this cat into a dog" input_image = load_image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png") image = pipe(image=input_image, prompt=prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 293f7f0096f64e2a9f7f660f8c957d74174e0ee70183a614984af87189d8106e
- Size of remote file:
- 1.46 GB
- SHA256:
- f8ed6f8b13bf03648346787734d9f5d9ba2b1be416931096e90650ab30f184da
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