Instructions to use wolfer45/cfgqualityboost-zit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wolfer45/cfgqualityboost-zit 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", torch_dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("wolfer45/cfgqualityboost-zit") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
metadata
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/31.jpg
text: '-'
base_model: Tongyi-MAI/Z-Image-Turbo
instance_prompt: cfgqualityboost-zit
cfgqualityboost-zit

- Prompt
- -
Model description
cfgqualityboost-zit
Trigger words
You should use cfgqualityboost-zit to trigger the image generation.
Download model
Download them in the Files & versions tab.