Instructions to use JAWCF/objects-demo-5000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JAWCF/objects-demo-5000 with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("JAWCF/objects-demo-5000", 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
- Draw Things
- DiffusionBee
- Xet hash:
- af032025f2d4b855201e3ea9053c366630108df92d1780e3dd09f46199610697
- Size of remote file:
- 6.88 GB
- SHA256:
- 180af6eed46217cef7e7ebcb8f372f59d38406bfd543feac089b00c34e36485c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.