ausboss-ComfyUI-AusBoss/js/docs/AUSBOSS_NODES_Krea2Encode.md

Krea 2 Encode

The prompt node for Krea 2. Type your prompt and negative prompt here, and optionally give it a picture for the result to follow, all in one node.

Wire a VAE and a reference image and the reference is attached to the positive output. 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.
• The big text box (prompt): Describe the whole finished canvas, not just the new area — the model generates all of it and matches the reference where it must. The two boxes have no labels, only gray hint text.
• The small text box (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 no reference is attached to the result. If VLM reference is on, the text encoder still looks at the pictures.
• reference: The image the result follows. For AnyPaint that is the padded image from Image Crop + Rotate + Pad 🆎 or Load Image + Pad 🆎; for Registered Outpaint, Load Image + Pad 🆎's 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, off by default): 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.