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
| 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 | |
| <Gallery /> | |
| ## Trigger words | |
| You should use `n7adia` to trigger the image generation. | |
| ## Download model | |
| [Download](/yobuntuvh/n7adia/tree/main) them in the Files & versions tab. | |