Instructions to use EnD-Diffusers/Slime_Tutorial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use EnD-Diffusers/Slime_Tutorial with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("EnD-Diffusers/Slime_Tutorial", dtype=torch.bfloat16, device_map="cuda") prompt = "deetz1" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
- Xet hash:
- 8fd276eda75285284dcae3dfd12470091e2695098bc8196620fa8be295b0e195
- Size of remote file:
- 2.13 GB
- SHA256:
- 49ae549c690f5c9784cde9f8dc507b77aaa899c76ddf0d21d5126b297f734dad
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.