Instructions to use nphSi/Z-Image-Lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use nphSi/Z-Image-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,Tongyi-MAI/Z-Image-Turbo", dtype=torch.bfloat16, device_map="cuda") pipe.load_lora_weights("nphSi/Z-Image-Lora") prompt = "Alexandra Chando (vrtlAlexandraChando)" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
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README.md
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license: creativeml-openrail-m
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2025/12/17:
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Onetrainer support for Z-Image de-turbo is officially available now. My first trained Lora at 1024 is online (vrtlalbtraum).
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Feedback welcome.
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History:
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Added Annemarie Carpendale, Mareile Hoeppner, Annalena Baerbock, Ashley Williams, Alexandra Chando, Billie Piper.
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license: creativeml-openrail-m
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History:
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Added Annemarie Carpendale, Mareile Hoeppner, Annalena Baerbock, Ashley Williams, Alexandra Chando, Billie Piper.
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