artokun/krea2-txt2img
Build Krea 2 Turbo txt2img workflows — native krea2 CLIPLoader, Qwen3-VL encoder, Qwen image VAE, 8-step turbo settings, and Ideogram-style JSON prompting
npx skills add https://github.com/artokun/comfyui-mcp --skill krea2-txt2img
Krea 2 is a 12B-parameter Diffusion Transformer from Krea.ai (released June
2026, weights open-sourced under the Krea 2 Community License — free commercial
use up to 50 seats). Two variants:
fine-tuning / maximum fidelity, more steps.
cfg 1**. This is what the krea2 txt2img packs ship.
Sliced from the KREA2 ULTRA V2 monolith into standalone single-pipeline packs
— pick by how you prompt / what you want:
krea2-txt2img-manual — plain prose prompt (the MANUAL PROMPT node).krea2-txt2img-json — Ideogram-4-style structured JSON / area prompting(Ideogram4PromptBuilderKJ).
krea2-combo — two-pass detail boost: a first pass then a low-denoiserefine (denoise 0.3), with the krea2 turbo LoRA @0.2 on both passes +
(optional) the IdeoKrea LoRA. JSON/Ideogram-style prompting; saves both
passes to compare.
Each pack's one prompt source is active (no prompt-mode bypass to flip).
ImageSharpenKJ runs before SaveImage. V2 adds the Krea2T-Enhancer MODEL
detail-boost patch (ships active) and drops v1's ConditioningKrea2Rebalance.
RBG_Smart_Seed_Variance ships bypassed (optional, see below).
Krea 2 has native ComfyUI support (comfy/text_encoders/krea2.py, ComfyUI ≥
v0.26.0): the CLIPLoader uses type=krea2, with a Qwen3-VL 4B text
encoder and the Qwen image VAE. The Qwen3-VL encoder drives strong prompt
adherence and structured-JSON prompts.
Aitrepreneur/FLX mirror; official: krea/Krea-2-Turbo)| Slot | File | Notes |
|---|---|---|
| diffusion_models/ | krea2_turbo_fp8.safetensors | 12B Turbo, fp8 — RTX 4000/3000/2000 |
| diffusion_models/ | krea2_turbo_mxfp8.safetensors | RTX 5000 (Blackwell) native fp8 |
| text_encoders/ | qwen3vl_4b_fp8_scaled.safetensors | Qwen3-VL 4B encoder |
| vae/ | qwen_image_vae.safetensors | Qwen image VAE |
| loras/ | krea2_turbo_lora_rank_64_bf16.safetensors | turbo LoRA — combo only, @0.2 both passes |
| loras/ | IdeoKrea-test.safetensors | OPTIONAL Ideogram-style LoRA (Aitrepreneur/IdeoKrea) — combo add-in |
UNETLoader (krea2_turbo) → CLIPLoader (type=krea2) → VAELoader(qwen_image_vae), wired via KJNodes SetNode/GetNode buses into a subgraph
(CLIPTextEncode → KSampler → VAEDecode). An rgthree Any Switch sits in
front of the encoder; in each pack only that pack's prompt source is wired to it
(manual node in -manual, JSON builder in -json).
Ideogram4PromptBuilderKJ, ImageSharpenKJ, INTConstant.capitan01R): Krea2T-Enhancer — V2 MODEL→MODELdetail-boost patch, wired in the model path (PowerLora → Krea2T-Enhancer →
sampler). Ships active; bypass to compare against the un-boosted result.
RBG_Smart_Seed_Variance — optional, shipsbypassed in the positive-conditioning loop.
cubiq): ImageResize+ — combo only (the two-passVAE-roundtrip resize).
er_sde, scheduler simple — the verified defaults.prompt-mode bypass to flip.
Krea2T-Enhancer) + comboKrea2T-Enhancer is a MODEL→MODEL patch (the V2 "massive detail boost"). Itsits inline in the model path and ships active in all three packs. Widgets
are [on, strength, …]; bypass it (or toggle on) to A/B the boost.
krea2-combo is the showcase: a two-pass refine — FIRST PASS (8 steps,er_sde, denoise 1) → VAE roundtrip → SECOND PASS (4 steps, euler, denoise
0.3) — with the turbo LoRA @0.2 on both passes. It SAVES BOTH passes so
you can see the boost. The IdeoKrea LoRA is downloaded but NOT wired by
default — drop it into the Power Lora Loader's empty slot (start ~0.5–1.0; it's a
test LoRA) for the turbo + IdeoKrea Ideogram-style combo.
All packs leave RBG_Smart_Seed_Variance in the positive-conditioning loop
bypassed (passthrough). Un-bypass on the live canvas with panel_set_node_mode
(or in the UI) for controlled variations of the same prompt without changing the
composition — set its seed mode to randomize and tune the variance mode (e.g.
🌿 Balanced) / strength widgets. Leave bypassed for a deterministic result.
Like Ideogram 4, Krea 2's Qwen3-VL encoder reads structured prompts (per-region
desc + bounding boxes + palettes). For structured prompting just use the
krea2-txt2img-json pack — its Ideogram4PromptBuilderKJ drives the encoder
directly (no bypass to flip). After the render, VERIFY the image matches the JSON
you set (view it) BEFORE continuing; if it doesn't, a field is probably stale —
fix and rerun. Gotchas learned the hard way:
background,technical, style, lighting (widgets 3/5/6/7). Leaving stale values leaks
content (a leftover celebrity portrait bled into a tea still-life).
literal color-swatch strip down the edge of the image. Empty palette: []
(top-level and per-box) gives a clean full-frame result.
style for object/landscapescenes — Krea 2 follows it well.
-manual / -json core graph): render-verified — crisp 1920×1080 / 8steps / cfg 1 / er_sde (snow-leopard prose + tea-still-life JSON with each object
in its bbox).
Krea2T-Enhancer active patch + the krea2-combo two-pass)are statically validated (clean slice + structural lint) but **not yet
live-rendered** — they need the ComfyUI-Krea2T-Enhancer node + the turbo/IdeoKrea
LoRAs installed and a healthy ComfyUI. Re-run scripts/verify-render.mjs once
those are present.
ImageSharpenKJ (rcas 0.55) before SaveImage is active —bypassing it drops the image link (a converter gap: bypass-passthrough doesn't
cross a subgraph IMAGE output), and the contrast-adaptive sharpen suits Krea 2's
crisp look anyway.
CLIPLoader: 'krea2' not in list → ComfyUI too old; update to ≥ v0.26.0.Torch not compiled with CUDA enabled → reinstall torch for your CUDA tag(--index-url https://download.pytorch.org/whl/cu128).
Take artokun/krea2-txt2img from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.