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:
- 41bf8006302b84d664c29988cd84b1c39294d26e11290ee51be281df093336ca
- Size of remote file:
- 4.71 MB
- SHA256:
- 626f52da619c466802ccb1b7b797cfe85e49e8f27f87fe98ae2fa66c940008c6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.