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Ttio2
/
Z-Image-Turbo-Ghibli-Style

Text-to-Image
Diffusers
lora
template:diffusion-lora
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xet
Community
1

Instructions to use Ttio2/Z-Image-Turbo-Ghibli-Style with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Diffusers

    How to use Ttio2/Z-Image-Turbo-Ghibli-Style 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("Ttio2/Z-Image-Turbo-Ghibli-Style")
    
    prompt = "an image of a young woman sitting outdoors on grass. She appears to be in her 20s with brown hair tied back in a ponytail. She is wearing a dark blue sports bra-style crop top that shows her midriff, paired with high-waisted olive green cargo pants. She has a white shirt or jacket draped over her shoulders. Around her neck is a silver chain necklace, and she is wearing small hoop earrings.  "
    image = pipe(prompt).images[0]
  • Inference
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  • Google Colab
  • Kaggle
  • Local Apps Settings
  • Draw Things
  • DiffusionBee
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Is it possible to retrain a new LoRA using z‑image‑base?

#1 opened 4 months ago by
makisekurisu-jp
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