Instructions to use wxwhj/lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use wxwhj/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("wxwhj/lora") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
File size: 342 Bytes
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tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/z__00001_pqaxq_1764670350.png
text: '-'
base_model: Tongyi-MAI/Z-Image-Turbo
instance_prompt: null
license: apache-2.0
---
# pussy
<Gallery />
## Download model
[Download](/wxwhj/lora/tree/main) them in the Files & versions tab.
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