Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add artokun/comfyui-mcp --skill krea2-txt2imggit clone --depth 1 https://github.com/artokun/comfyui-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/artokun/comfyui-mcp/krea2-txt2img)<a href="https://agentmods.dev/skills/artokun/comfyui-mcp/krea2-txt2img"><img src="https://agentmods.dev/badge/skills/artokun/comfyui-mcp/krea2-txt2img/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/artokun/comfyui-mcp/krea2-txt2img"><img src="https://agentmods.dev/badge/skills/artokun/comfyui-mcp/krea2-txt2img.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00053 | $0.02275 |
| Opus 5 | $0.00026 | $0.01137 |
| Sonnet 5 | $0.00011 | $0.00455 |
| Haiku 4.5 | $0.00005 | $0.00228 |
Grade A, and why
krea2-txt2img scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
- Krea 2 Raw is the base checkpoint before extra post-training. For fine-tuning / maximum fidelity, more steps.
- 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 (theMANUAL PROMPTnode).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)
| 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 |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 143 lines · 53 tokens per session scan A ce5164426caf
krea2-txt2img is a skill published in the GitHub repository artokun/comfyui-mcp (740 stars, last pushed 2d ago), licensed MIT. It adds 53 tokens to every session and 2,275 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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