molbal-ComfyUI-Krea2-MultiRef/js/docs/Krea2ReferenceEncoder.md

Krea2 Reference Encoder

Prepares the reference latents for a Krea2-patched model and appends them to your existing text conditioning. The adapter uses RoPE (Rotary Position Embedding) to attend to the references during diffusion without overwriting your text prompt.

How the slots work

The adapter was trained with a fixed slot convention:

Slot	Input	Resolution	Role
1	base_latent	Must match output	The image being edited, inpainted, or outpainted
2	reference_latent_2	Any	Style / content reference (optional)
3	reference_latent_3	Any	Style / content reference (optional)
4	reference_latent_4	Any	Style / content reference (optional)

Slot 1 is always the base image — the canvas the model will modify. Slots 2–4 are optional visual references that guide style or content.

Inputs

Name	Type	Required	Description
positive	CONDITIONING	✅	Positive text conditioning from a CLIP Text Encode node. The reference payload is appended — your prompt is never overwritten.
negative	CONDITIONING	✅	Negative text conditioning. Receives the same reference payload as positive.
base_latent	LATENT	✅	The image to edit / inpaint, VAE-encoded. Must be the same resolution as the generation output.
strength	FLOAT	✅	Scales all reference latents before storing them. Range: 0.0 – 2.0, default 1.0.
reference_latent_2	LATENT	➖	Optional style / content reference, VAE-encoded.
reference_latent_3	LATENT	➖	Optional style / content reference, VAE-encoded.
reference_latent_4	LATENT	➖	Optional style / content reference, VAE-encoded.

Outputs

Name	Type	Description
positive	CONDITIONING	Positive conditioning with all reference latents embedded under reference_latents.
negative	CONDITIONING	Negative conditioning with the same reference payload.

Usage

Instruct-style editing

Edit an existing image by describing only the change you want.

1. Load Image → VAE Encode → base_latent
2. Write a prompt describing the change (e.g. "replace the sky with a stormy sunset")
3. Connect positive / negative to a KSampler whose model comes from Krea2 Model Patcher

Inpainting / outpainting

Fill a masked or extended region while keeping the rest of the image intact.

1. Prepare the source image with the unknown region masked (neutral grey or noise)
2. VAE Encode the masked image → base_latent
3. Prompt for the content that should fill the masked area

Reference-guided generation

Generate a new image that draws style or content from reference images.

1. Provide a blank or noise latent (same size as the desired output) → base_latent
2. Load style / content reference images → VAE Encode → reference_latent_2 (and optionally _3, _4)
3. Write a prompt describing the desired output

Notes

• base_latent must match the output resolution — this is a hard constraint from the training data. Mismatched resolutions will produce degraded results.
• strength above 1.0 amplifies the reference signal and can cause the model to over-fit to the references at the expense of prompt adherence.
• Chaining two Krea2 Reference Encoder nodes is supported — the second call accumulates its references onto the first via torch.cat.
• The downstream sampler must use a model patched with Krea2 Model Patcher. An unpatched model will silently ignore the reference_latents key in the conditioning metadata.