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ostris
/
primal_vae

Diffusers
vae
autoencoder
latent-diffusion
custom_code
Model card Files Files and versions
xet
Community
2

Instructions to use ostris/primal_vae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Diffusers

    How to use ostris/primal_vae with Diffusers:

    pip install -U diffusers transformers accelerate
    import torch
    from diffusers import DiffusionPipeline
    
    # switch to "mps" for apple devices
    pipe = DiffusionPipeline.from_pretrained("ostris/primal_vae", 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
primal_vae
651 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 6 commits
ostris's picture
ostris
Add gradient checkpointing to the vae
5dcf26e verified about 21 hours ago
  • __pycache__
    Add gradient checkpointing to the vae about 21 hours ago
  • assets
    Update weights for 2x1 to latest step 1 day ago
  • .gitattributes
    1.68 kB
    Primal VAE 2x1 and 2x2 1 day ago
  • LICENSE
    11.3 kB
    Apache 2.0 license about 22 hours ago
  • README.md
    8.49 kB
    Add gradient checkpointing to the vae about 21 hours ago
  • config.json
    223 Bytes
    patchify takes total compression, unpatchify infers it 1 day ago
  • modeling_primal_vae.py
    10.2 kB
    Add gradient checkpointing to the vae about 21 hours ago
  • primal_vae_2x1.safetensors
    321 MB
    xet
    Update weights for 2x1 to latest step 1 day ago
  • primal_vae_2x2.safetensors
    321 MB
    xet
    Primal VAE 2x1 and 2x2 1 day ago