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lite-infer
/
z-image-turbo-nunchaku-lite-int4_r32-bnb4-text-encoder

Text-to-Image
Diffusers
Safetensors
ZImagePipeline
z-image
nunchaku
svdquant
int4
quantization
8-bit precision
Model card Files Files and versions
xet
Community

Instructions to use lite-infer/z-image-turbo-nunchaku-lite-int4_r32-bnb4-text-encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Diffusers

    How to use lite-infer/z-image-turbo-nunchaku-lite-int4_r32-bnb4-text-encoder with Diffusers:

    pip install -U diffusers transformers accelerate
    import torch
    from diffusers import DiffusionPipeline
    
    # switch to "mps" for apple devices
    pipe = DiffusionPipeline.from_pretrained("lite-infer/z-image-turbo-nunchaku-lite-int4_r32-bnb4-text-encoder", torch_dtype=torch.bfloat16, device_map="cuda")
    
    prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
    image = pipe(prompt).images[0]
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • Draw Things
  • DiffusionBee
z-image-turbo-nunchaku-lite-int4_r32-bnb4-text-encoder
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  • 1 contributor
History: 2 commits
rootonchair's picture
rootonchair
Upload converted Nunchaku Lite pipeline
8fdcb1d verified 12 days ago
  • scheduler
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  • text_encoder
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  • tokenizer
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  • transformer
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  • vae
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  • .gitattributes
    1.64 kB
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  • README.md
    2.09 kB
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  • model_index.json
    467 Bytes
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  • output_comparison.png
    2.31 MB
    xet
    Upload converted Nunchaku Lite pipeline 12 days ago