version: "1.0.1" name: krea2-txt2img description: Build Krea 2 Turbo txt2img workflows — native krea2 CLIPLoader, Qwen3-VL encoder, Qwen image VAE, 8-step turbo settings, and Ideogram-style JSON prompting globs:

• "**/*.json"

Krea 2 Text-to-Image Workflows

Overview

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:

1. Krea 2 Raw — the base checkpoint before extra post-training. For

fine-tuning / maximum fidelity, more steps.

1. Krea 2 Turbo — post-trained + distilled; generates in **~8 steps at

cfg 1**. This is what the krea2 txt2img packs ship.

Three packs (V2 — no group toggles)

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-denoise

refine (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.

Models (all from the Aitrepreneur/FLX mirror; official: krea/Krea-2-Turbo)

Node stack

• core: 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).

• rgthree-comfy: Power Lora Loader, Any Switch, Label, Fast Groups.
• ComfyUI-KJNodes: Set/Get, Ideogram4PromptBuilderKJ, ImageSharpenKJ, INTConstant.
• ComfyUI-Krea2T-Enhancer (capitan01R): Krea2T-Enhancer — V2 MODEL→MODEL

detail-boost patch, wired in the model path (PowerLora → Krea2T-Enhancer → sampler). Ships active; bypass to compare against the un-boosted result.

• ComfyUI-RBG-SmartSeedVariance: RBG_Smart_Seed_Variance — optional, ships

bypassed in the positive-conditioning loop.

• ComfyUI_essentials (cubiq): ImageResize+ — combo only (the two-pass

VAE-roundtrip resize).

Settings that matter

• steps 8, cfg 1 — Turbo is distilled; more steps/higher cfg over-cooks it.
• sampler `er_sde`, scheduler `simple` — the verified defaults.
• 1920×1080 default; Krea 2 handles a wide aspect range.
• The prompt source is fixed per pack (manual node vs JSON builder) — no

prompt-mode bypass to flip.

V2 detail boost (Krea2T-Enhancer) + combo

• `Krea2T-Enhancer` is a MODEL→MODEL patch (the V2 "massive detail boost"). It

sits 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.

Optional post-proc (ships bypassed — un-bypass to use)

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.

JSON / area prompting

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:

• Set ALL the builder fields, not just the prompt/boxes — background,

technical, style, lighting (widgets 3/5/6/7). Leaving stale values leaks content (a leftover celebrity portrait bled into a tea still-life).

• Keep palettes minimal or empty. A rich top-level palette can render as a

literal color-swatch strip down the edge of the image. Empty palette: [] (top-level and per-box) gives a clean full-frame result.

• Add "no people / single full-frame photograph" to style for object/landscape

scenes — Krea 2 follows it well.

Verification status

• v1 (`-manual` / `-json` core graph): render-verified — crisp 1920×1080 / 8

steps / cfg 1 / er_sde (snow-leopard prose + tea-still-life JSON with each object in its bbox).

• V2 additions (the 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.

• Note: the 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.

Gotchas

• 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).

Sources

• Official: none found.
• Empirical: sampler values, wiring, and prompt notes from working graphs in packs/ and observed renders; not a vendor prompting guide.