Instructions to use cjd616289/1Detest with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use cjd616289/1Detest with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("cjd616289/1Detest", 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:
- 0c73b552041954d1497f3b7daa10845fa941575f07ea9070de22e4d7e6ea80b1
- Size of remote file:
- 492 MB
- SHA256:
- 6dd3993066a4984b1be03f2541bd9c6c0e6e55b48f145852980712213bc044c1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.