Krea2 Txt2img

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 is the base checkpoint before extra post-training. For fine-tuning / maximum fidelity, more steps.
2. Krea 2 Turbo is post-trained and distilled; it 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 and 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 plus the optional 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, the 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 or higher cfg over-cooks it.
• sampler er_sde, scheduler simple are 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). There is 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 full demonstration of the boost, 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 to 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 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 only 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 top-level palette with many colors 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.