Instructions to use jbern3812947/Space-Image-ZIT-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use jbern3812947/Space-Image-ZIT-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("jbern3812947/Space-Image-ZIT-LoRA") prompt = "-" image = pipe(prompt).images[0] - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
Space Image ZIT LoRA

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Model description
Attempt at producing space images (although base ZIT does fairly well).
Recommended LoRA strength <= 0.9
Optional trigger word: Sp11
I recommend using simple terms like: galaxy, nebula, cosmic dust, stars
Trained on 32 Hubble (and similar) images.
Credit to ESA/Hubble for the images used in the dataset (CC BY 4.0).
Download model
Download them in the Files & versions tab.
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Model tree for jbern3812947/Space-Image-ZIT-LoRA
Base model
Tongyi-MAI/Z-Image-Turbo