Instructions to use DeverStyle/Z-Image-loras with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use DeverStyle/Z-Image-loras 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-Turbo", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("DeverStyle/Z-Image-loras") prompt = "Arcane style samples" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee

- Xet hash:
- cbeec34668bf2dd7f99ea55ea953fefdaed3309215d07e8a53452b83347a5704
- Size of remote file:
- 5.75 MB
- SHA256:
- 494fccf8df10ea0d717003ad925c45d838fd6652038b11743f21880351ee629e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.