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lightx2v
/
Z-Image-Turbo-Quantized

Text-to-Image
Diffusers
Diffusion Single File
comfyui
distillation
Z-Image-Turbo
Model card Files Files and versions
xet
Community
3

Instructions to use lightx2v/Z-Image-Turbo-Quantized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Diffusers

    How to use lightx2v/Z-Image-Turbo-Quantized with Diffusers:

    pip install -U diffusers transformers accelerate
    import torch
    from diffusers import DiffusionPipeline
    
    # switch to "mps" for apple devices
    pipe = DiffusionPipeline.from_pretrained("lightx2v/Z-Image-Turbo-Quantized", dtype=torch.bfloat16, device_map="cuda")
    
    prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k"
    image = pipe(prompt).images[0]
  • Diffusion Single File

    How to use lightx2v/Z-Image-Turbo-Quantized with Diffusion Single File:

    # No code snippets available yet for this library.
    
    # To use this model, check the repository files and the library's documentation.
    
    # Want to help? PRs adding snippets are welcome at:
    # https://github.com/huggingface/huggingface.js
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps
  • Draw Things
  • DiffusionBee
Z-Image-Turbo-Quantized
12.3 GB
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  • 1 contributor
History: 4 commits
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lightx2v
Update README.md
89fd15f verified 3 months ago
  • .gitattributes
    1.52 kB
    initial commit 3 months ago
  • README.md
    3.5 kB
    Update README.md 3 months ago
  • z_image_turbo_int8.safetensors
    6.17 GB
    xet
    Upload folder using huggingface_hub 3 months ago
  • z_image_turbo_scaled_fp8_e4m3fn.safetensors
    6.17 GB
    xet
    Upload folder using huggingface_hub 3 months ago