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 Seeed-Projects/Seeed-Jetson-DevelopTool --skill local-llm-text-to-imagegit clone --depth 1 https://github.com/Seeed-Projects/Seeed-Jetson-DevelopToolWrote 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/seeed-projects/seeed-jetson-developtool/local-llm-text-to-image)<a href="https://agentmods.dev/skills/seeed-projects/seeed-jetson-developtool/local-llm-text-to-image"><img src="https://agentmods.dev/badge/skills/seeed-projects/seeed-jetson-developtool/local-llm-text-to-image/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/seeed-projects/seeed-jetson-developtool/local-llm-text-to-image"><img src="https://agentmods.dev/badge/skills/seeed-projects/seeed-jetson-developtool/local-llm-text-to-image.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00076 | $0.01908 |
| Opus 5 | $0.00038 | $0.00954 |
| Sonnet 5 | $0.00015 | $0.00382 |
| Haiku 4.5 | $0.00008 | $0.00191 |
Grade D, and why
local-llm-text-to-image scanned grade D with 3 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 10d 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
sudo apt install python3.8-venv Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
| Model download fails | Check disk space: `df -h`. Clear cache: `rm -rf ~/.cache/huggingface`. | Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
wget -P $MODEL_DIR https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/resolve/main/sd_xl_base_1.0.safetensors How it starts
The opening of the file, as written. The whole thing — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Text-to-Image with Stable Diffusion on Jetson
Generate images from text prompts locally on Jetson using Stable Diffusion. Three deployment paths: KerasCV with TensorFlow, Hugging Face diffusers with PyTorch, and NVIDIA's containerized AUTOMATIC1111 WebUI.
Execution model
Run one phase at a time. After each phase:
- Relay all output to the user.
- If output contains
[STOP]→ stop, consult the failure decision tree. - If output ends with
[OK]→ tell the user "Phase N complete" and proceed.
Prerequisites
| Requirement | Detail |
|---|---|
| Hardware | Jetson Orin NX 16GB (e.g. reComputer J4012) |
| JetPack | 5.1.1+ |
| Storage | ~20 GB free (models are large) |
| RAM tips | Disable desktop GUI to save ~800 MB. Consider disabling ZRAM and using swap. |
Path A — KerasCV Stable Diffusion
Phase A1 — Create virtual environment and install TensorFlow (~10 min)
sudo apt install python3.8-venv
python3 -m venv kerasStableEnvironment
source kerasStableEnvironment/bin/activate
cd kerasStableEnvironment
pip install -U pip
pip install -U numpy grpcio absl-py py-cpuinfo psutil portpicker six mock requests gast h5py astor termcolor protobuf keras-applications keras-preprocessing wrapt google-pasta setuptools testresources
Install TensorFlow for your JetPack version (example for JP 5.1.1):
pip install --extra-index-url https://developer.download.nvidia.com/compute/redist/jp/v511 tensorflow==2.12.0+nv23.05
Verify:
python -c "import tensorflow as tf; print(tf.config.list_physical_devices('GPU'))"
[OK] when GPU device is listed.
Phase A2 — Install PyTorch and KerasCV (~5 min)
sudo apt install libopenblas-dev
pip install --no-cache https://developer.download.nvidia.com/compute/redist/jp/v511/pytorch/torch-2.0.0+nv23.05-cp38-cp38-linux_aarch64.whl
pip install keras-cv==0.5.1 keras==2.12.0 Pillow
Verify PyTorch CUDA:
python -c "import torch; print(torch.cuda.is_available())"
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 216 lines · 76 tokens per session scan D 5fa6380144e9
local-llm-text-to-image is a skill published in the GitHub repository Seeed-Projects/Seeed-Jetson-DevelopTool (54 stars, last pushed today), licensed MIT. It adds 76 tokens to every session and 1,908 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it D with 3 findings (asks for root, recursive force delete, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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