Instructions to use QuasarHash/Mavrelya1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use QuasarHash/Mavrelya1 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("QuasarHash/Mavrelya1") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
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Download README.md from QuasarHash/Mavrelya1: direct link, hf CLI and curl.
- Browser
- Download file 414 Bytes
-
https://huggingface.co/QuasarHash/Mavrelya1/resolve/main/README.md
- Command line
-
hf download hf://QuasarHash/Mavrelya1/README.md
-
curl -L -o README.md https://huggingface.co/QuasarHash/Mavrelya1/resolve/main/README.md
414 Bytes
metadata
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- output:
url: images/2026-08-31 12.20.23.jpg
text: '-'
base_model: Tongyi-MAI/Z-Image
instance_prompt: Mavrelya
Mavrelya

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