Instructions to use wcde/Z-Image-Turbo-DeJPEG-Lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wcde/Z-Image-Turbo-DeJPEG-Lora 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("wcde/Z-Image-Turbo-DeJPEG-Lora") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Z-Image-Turbo-DeJPEG-Lora

- Prompt
- -

- Prompt
- -
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Model tree for wcde/Z-Image-Turbo-DeJPEG-Lora
Base model
Tongyi-MAI/Z-Image-Turbo