workflow-templates-broken-zap-2026-06-08/site/knowledge/concepts/vae.md

VAE (Variational Autoencoder)

The VAE encodes pixel images into a compact latent representation and decodes latents back into pixel images. All diffusion in Stable Diffusion and Flux happens in latent space — the VAE is the bridge between the images you see and the mathematical space where the model actually works. Every generation workflow ends with a VAE decode step to produce a viewable image.

How It Works in Hanzo Studio

• Key nodes: VAEDecode (latent → image), VAEEncode (image → latent), VAEDecodeTiled (for large images to avoid out-of-memory errors), VAELoader (load a standalone VAE file)
• Typical workflow pattern: Most checkpoints include a built-in VAE, so the VAEDecode node can pull directly from the loaded checkpoint. To use a different VAE, add a VAELoader node and connect it to VAEDecode instead.

Key Settings

• tile_size (for VAEDecodeTiled): Size of each tile when decoding in chunks. Default is 512; reduce if you still encounter memory issues
• VAE choice: VAE files are model-specific. Use sdxl_vae.safetensors for SDXL, ae.safetensors for Flux. Place files in HanzoStudio/models/vae/

Tips

• If colors look washed out or slightly off, try loading an external VAE — the VAE baked into a checkpoint is not always optimal, especially for community fine-tunes
• Use VAEDecodeTiled for images larger than ~2048 px on either side to prevent out-of-memory crashes
• SDXL and Flux each have their own VAE architecture — using the wrong one will produce corrupted output
• When doing img2img or inpainting, the VAEEncode node converts your input image into the latent space the model expects