Instructions to use JosephusCheung/ACertainModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use JosephusCheung/ACertainModel with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("JosephusCheung/ACertainModel", dtype=torch.bfloat16, device_map="cuda") prompt = "masterpiece, best quality, 1girl, brown hair, green eyes, colorful, autumn, cumulonimbus clouds, lighting, blue sky, falling leaves, garden" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee

- Xet hash:
- 746ff5831206b8ffffadcce223ffde6fd425fbb3e8c17b79759b49f602ab1ef3
- Size of remote file:
- 2.17 MB
- SHA256:
- 9ca55c894d6dc8ae67913bdc435ec67954f6d4bec86d4055713e06799b42376d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.