# Krea 2 Encode

Encodes a Krea 2 prompt pair and attaches reference latents, in one node.

Wire a VAE and a reference image and the reference is encoded and appended to
the positive conditioning. Leave them unwired and this is a plain two-prompt
encoder. The negative comes out of the same node, so a turbo graph running at
CFG 1.0 still has something to plug in without a second text encode sitting
there doing nothing.

For an outpaint with the AnyPaint LoRA, also wire the **mask** from the node
that padded your picture. Without it, a dark or very colourful picture can
come back with the new area still gray, like a gray frame or a gray corner.

## Controls

- **clip**: The Krea 2 text encoder.
- **prompt**: Describe the whole finished canvas, not just the new area — the
  model generates all of it and matches the reference where it must.
- **negative_prompt**: Ignored at CFG 1.0, which is where turbo runs. Empty is
  the normal case there.
- **vae**: Needed to turn the reference images into latents. Without it the
  references are skipped and only the prompts are encoded.
- **reference**: The image the result follows. For AnyPaint that is Load
  Image + Pad 🆎's padded `image`; for Registered Outpaint, its unpadded
  `reference` output. Any image works; it is fitted to a multiple of 16 first.
- **extra_image**: A second reference, e.g. a style or character plate.
- **VLM reference** (`vlm_reference`): Also show the references to the vision
  tower, so the text encoder describes them. On for AnyPaint with the padded
  canvas as the reference; off for Registered Outpaint with the unpadded
  source, where the description tends to pull the result toward a paraphrase
  of the source. The Krea 2 Outpaint Model Patch page covers both setups.
- **mask**: The area to paint: the `mask` output of Image Crop + Rotate +
  Pad 🆎 or Load Image + Pad 🆎, or the painted mask of an inpaint. Wire it
  for AnyPaint. The reference then shows that area in your picture's own
  colour instead of the gray fill, and the model paints it.

## Outputs

- **positive**: Prompt conditioning with the reference latents attached.
- **negative**: Negative prompt conditioning.

## Notes and limitations

- **References are downscaled to a 384px long edge and snapped to a multiple
  of 16.** The VAE downsamples by 8 and the DiT patchifies by 2, so an edge off
  a multiple of 16 lands on a partial patch and the token grid stops lining up
  with the canvas grid. The cap is deliberate — a reference is there to say
  "this is the picture", and the canvas latent carries the detail.
- Attaching reference latents alone does not tell the model *where* they go.
  Pair this with Krea 2 Outpaint Model Patch 🆎 for outpainting.
- On a core without the Krea 2 prompt template, `vlm_reference` still works —
  it falls back to tokenizing without the template.

## Technical details: why the mask matters

AnyPaint was trained with the new area of its reference filled with the
median colour of the rest of the picture, never with a fixed gray. The
padded canvas carries a flat `#808080` gray instead. Next to a picture whose
median is close to mid-gray that makes no difference, but next to a dark or
saturated picture the model reads the gray as part of the scene and paints
it back, as a flat gray frame or corner. The mask's new area (where it is
above 0.5) is refilled with the median colour of everything else before the
reference goes to the VAE and to the vision tower. The canvas the rest of
the graph uses keeps its gray, so the prompt writer and the stitch are
unchanged. Only `reference` is refilled; `extra_image` is left as it is.