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AutomatosX
/
AX-Image-2.1-Turbo-AXQ-NVFP4

Text-to-Image
Diffusers
Safetensors
AXQReferenceDiffusionPipeline
axquant
axq
nvfp4
reference-storage
research-only
development
Model card Files Files and versions
xet
Community

Instructions to use AutomatosX/AX-Image-2.1-Turbo-AXQ-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Diffusers

    How to use AutomatosX/AX-Image-2.1-Turbo-AXQ-NVFP4 with Diffusers:

    pip install -U diffusers transformers accelerate
    import torch
    from diffusers import DiffusionPipeline
    
    # switch to "mps" for apple devices
    pipe = DiffusionPipeline.from_pretrained("AutomatosX/AX-Image-2.1-Turbo-AXQ-NVFP4", 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
AX-Image-2.1-Turbo-AXQ-NVFP4 / processor
11.4 MB
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  • 1 contributor
History: 1 commit
AutomatosX's picture
AutomatosX
Publish research-only AXQuant NVFP4 reference storage with explicit BF16 loader
aad6de7 verified 14 days ago
  • chat_template.jinja
    5.29 kB
    Publish research-only AXQuant NVFP4 reference storage with explicit BF16 loader 14 days ago
  • processor_config.json
    1.3 kB
    Publish research-only AXQuant NVFP4 reference storage with explicit BF16 loader 14 days ago
  • tokenizer.json
    11.4 MB
    xet
    Publish research-only AXQuant NVFP4 reference storage with explicit BF16 loader 14 days ago
  • tokenizer_config.json
    705 Bytes
    Publish research-only AXQuant NVFP4 reference storage with explicit BF16 loader 14 days ago