Instructions to use AuraDiffusion/16ch-vae with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use AuraDiffusion/16ch-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("AuraDiffusion/16ch-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
VAE latent after normalization not in N(0,1)
#2
by Eyalgut - opened
Usually when training the latent given from vae.encode it is first normalized to ~N(0,1)
latents = (latents - vae.config.shift_factor) * vae.config.scaling_factor
At least that is what happens on sd3 vae(and probably sdxl also).
Here the latent after normalization is ~N(0.014,0.16^2), not even close to N(0,1).
Isn't the normalization meant to be ~N(0,1) as in SD3 vae ?
*** Verified on imagenet resized to 128,128 and normalized to [-1,1] as usual before vae.encode
Eyalgut changed discussion status to closed
Eyalgut changed discussion title from VAE Normalization to VAE after normalization not in N(0,1)
Eyalgut changed discussion title from VAE after normalization not in N(0,1) to VAE latent after normalization not in N(0,1)
Eyalgut changed discussion status to open