Instructions to use Kolrasp/Pau with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use Kolrasp/Pau 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("Kolrasp/Pau") prompt = "-" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| tags: | |
| - text-to-image | |
| - lora | |
| - diffusers | |
| - template:diffusion-lora | |
| widget: | |
| - output: | |
| url: images/images - 2026-06-30T133611.643.jpeg | |
| text: '-' | |
| base_model: Tongyi-MAI/Z-Image | |
| instance_prompt: Pauli | |
| # Pau | |
| <Gallery /> | |
| ## Model description | |
| Pau | |
| ## Trigger words | |
| You should use `Pauli` to trigger the image generation. | |
| ## Download model | |
| [Download](/Kolrasp/Pau/tree/main) them in the Files & versions tab. | |