Instructions to use yobuntuvh/n7adia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use yobuntuvh/n7adia 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("yobuntuvh/n7adia") prompt = "-" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
metadata
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/200ec093b1704f048a7466ad202fc6d6.jpeg
text: '-'
base_model: Tongyi-MAI/Z-Image
instance_prompt: n7adia
license: apache-2.0
n7adia

- Prompt
- -
Trigger words
You should use n7adia to trigger the image generation.
Download model
Download them in the Files & versions tab.