Instruction-based image editing for Krea 2 in ComfyUI — the node pack that powers the Krea 2 Identity Edit LoRA. Turns Krea 2 (Raw or Turbo) into an image editor with dual conditioning: the source image is injected both as VAE latent tokens (appearance) and into the Qwen3-VL text encoder (semantic grounding), matching how the LoRA was trained.

☕ Support on Ko-fi — all tips go straight to GPU compute for future versions.

See CHANGELOG.md — v1.2 is recommended (better face likeness, plus the new fit reference geometry and ref_boost fidelity dial).

cd ComfyUI/custom_nodes
git clone https://github.com/lbouaraba/comfyui-krea2edit
# restart ComfyUI

Requirements: a ComfyUI version with native Krea 2 support, the Krea 2 model (Raw or Turbo), the Qwen3-VL 4B text encoder used by Krea 2, and the Krea 2 Identity Edit LoRA (krea2_identity_edit_v1_2.safetensors). No extra Python dependencies.

Wraps the diffusion model so the VAE-encoded source image is prepended as clean in-context tokens (RoPE frame 1). Inputs:

• model — Krea 2 (LoRA already applied)
• source_latent — VAEEncode of the image being edited
• source_latent_b (optional) — second reference (RoPE frame 2) for two-input edits (e.g. person + scene)
• vae + source_image (optional, recommended) — the blur-proof pixel path: give the raw image (and VAE) and the node fits it to the target grid in pixel space. Required for fit_mode: fit.
• fit_mode (default fit) — how a source fits a mismatched output aspect ratio. fit = training-matched resample at a centered offset (v1.2); crop = center-crop, the v1/v1.1-legacy geometry (use with older weights).
• ref_boost (default 1.0) — reference-fidelity dial; >1 pulls harder toward the reference's appearance, <1 loosens. ref_boost_a is the same dial for the scene ref in two-ref edits.

Image-grounded instruction encoding — the text encoder sees the image while reading your instruction, exactly as during training. Inputs:

• clip — the Krea 2 CLIP (Qwen3-VL, loaded with type: krea2)
• prompt — the edit instruction ("recolor the car to matte black")
• image — the same source image
• image_b (optional) — second reference for two-input edits
• grounding_px — grounding resolution (default 768; trained range 512–1536). This is a quality dial: lower = stronger edit adherence, higher = stronger identity/likeness. Try 1024+ for people, 512 for stubborn scene changes.

Both nodes are required. With a stock CLIPTextEncode the model never sees the image semantically and quality drops sharply, especially for scene-referential instructions ("the man on the left").

LoadImage ─┬─ VAEEncode ── Krea2EditModelPatch.source_latent
           └─ Krea2EditGroundedEncode.image     (+ your prompt)
UNETLoader ── LoraLoaderModelOnly (krea2_identity_edit_v1_2 @1.0) ── Krea2EditModelPatch.model
Krea2EditModelPatch ── KSampler.model
Krea2EditGroundedEncode ── KSampler.positive
Krea2EditGroundedEncode (empty prompt, same image) ── KSampler.negative
EmptySD3LatentImage ── KSampler.latent_image

Example workflow in workflows/: krea2_identity_edit.json — single-image editor by default; enable group 2 (toggle its Bypass off) for two-image person-into-scene edits.

1. Aspect ratio. With fit_mode: fit (default in v1.2) and vae + source_image connected, mismatched source/output aspect ratios are handled — the source is resampled to the target grid. On crop/legacy weights, still match the AR: a mismatched AR is out of distribution and degrades identity/preservation.
2. Turbo, 8 steps, CFG 1 is the fast path (~1 min at 2MP) and works for most edits: recolor, add/insert, attribute changes, restyles, scene translation.
3. Removals and other "delete salient content" edits need real guidance: use the Raw model at CFG 3, ~20 steps. Distilled Turbo at CFG 1 will usually re-render the subject instead of removing it.
4. At CFG > 1, ground the negative too: a second Krea2EditGroundedEncode with an empty prompt and the same image (this is the trained unconditional).
5. Two-input edits: scene image → source_latent/image, subject image → source_latent_b/image_b. Leave the b-inputs unconnected for single-image use.
6. Generate at ≤2MP. Above the trained range, source content can bleed into the output or subjects duplicate.
7. Two distinct people: place both references in a single pass (scene/subject A on the main inputs, subject B on the _b inputs) rather than adding them one at a time — simultaneous placement is currently more reliable than chaining separate edits. Face separation is still imperfect and a focus for future versions.

Nodes: Apache-2.0. The Krea 2 Identity Edit weights ship separately under the Krea 2 Community License Agreement (see the model card, LICENSE.pdf, and NOTICE in the weights repo). Built on Krea 2 by Krea AI; text encoder Qwen3-VL (Alibaba).

This is a solo project, made a lot better by the community. Thank you to:

• stablellama — the MIT-licensed head/face/eye/person swap dataset behind those capabilities in v1.2.
• CeciliaXCIX — tireless, high-quality community support in the discussions.
• akashzeno — node engineering: diagnosing the ComfyUI compatibility break and contributing the regression test.
• SubtleShader — testing the training code and consumer-GPU feedback.

Want to help? Contributions of training data and node/code work are welcome, see the discussions.

Krea 2 Identity Edit is an identity-preserving character restaging model, trained only on SFW data. It is not trained on any NSFW concepts, and I have no plans to add or support NSFW data in current or future versions.

I do not endorse or support using this model to produce non-consensual, harmful, or sexual imagery of real people, including deepfakes. Please use it responsibly and respect the consent and likeness of anyone you depict.