Latent Operations

Encode and decode images in latent space.

VAEEncode

Converts image to latent representation.

Inputs

Input	Type	Description
pixels	image	Image to encode
vae	vae	VAE model

Outputs

Output	Type	Description
LATENT	latent	Encoded latent

Usage

LoadImage → VAEEncode → KSampler
              ↑
         VAE (from checkpoint)

VAEDecode

Converts latent to visible image.

Inputs

Input	Type	Description
samples	latent	Latent to decode
vae	vae	VAE model

Outputs

Output	Type	Description
IMAGE	image	Decoded image

Usage

KSampler → VAEDecode → SaveImage
              ↑
         VAE (from checkpoint)

EmptyLatentImage

Creates initial noise for generation.

Inputs

Input	Type	Description
(none)	-	-

Parameters

Parameter	Type	Description
width	int	Image width
height	int	Image height
batch_size	int	Number to generate

Outputs

Output	Type	Description
LATENT	latent	Empty latent noise

Common Sizes

Size	Use Case
512x512	Standard
768x768	Higher quality
1024x1024	SDXL native

LatentUpscale

Upscale latent image before decoding.

Inputs

Input	Type	Description
samples	latent	Latent to upscale
method	dropdown	Upscale method

Parameters

Method	Description
nearest	Fast, lower quality
bilinear	Medium
lanczos	Good quality
area	Best quality

Inpainting Nodes

VAEEncode (Inpainting)

Uses mask to encode only unmasked areas.

Mask Handling

LoadImage (RGBA) → VAEEncode
                       ↓
                  Mask preserved for KSampler

Tips

1. Match resolution: Ensure latent size matches desired output
2. Batch generation: Use batch_size for multiple images
3. Inpainting: Use masked encoding for area editing

Next Steps

• KSampler - Sample in latent space
• Image I/O - Load/save images