Instructions to use Onise/zbase-emma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Onise/zbase-emma with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("Tongyi-MAI/Z-Image", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("Onise/zbase-emma") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
- Xet hash:
- 1477e674ede1184c0b09addcdd7938ec58febeb7948973ef22bdd9ed376925ab
- Size of remote file:
- 170 MB
- SHA256:
- 96777d6c29b14797a3a28d5625faf9d4b6babcdb95eb8806a8130b1252e26630
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.