---

name: evaluation-conversion-deployment description: "Plan Sana metrics, checkpoint conversion/export, Hugging Face upload/download utilities, sana-run SLURM launches, and deployment routes without running heavy jobs." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Evaluation, Conversion, and Deployment

Use this sub-skill when you need to plan a Sana evaluation, export, upload, or deployment command without executing the benchmark or conversion job.

Use for

• Metric planning for FID/CLIP, GenEval, DPG-Bench, and ImageReward.
• Checkpoint export planning for image, video, and SVDQuant/Nunchaku pipelines.
• Safe inspection of sana-run and sana-upload.
• Deployment route selection for SGLang, ComfyUI, and Gradio demos.

Do not use for

• Actual image/video generation; route to the image or video inference sub-skills.
• Training or dataset schema work; route to training-data-configs.

Start here

• references/metrics-and-benchmarks.md
• references/conversion-and-export.md
• references/cli-and-deployment.md
• references/troubleshooting.md

Safe planners

• scripts/plan_metrics_command.py
• scripts/plan_conversion_command.py
• scripts/inspect_sana_cli.py

Operating notes

• Prefer a dry, command-planning pass before any benchmark, upload, or conversion.
• Require separate metric environments and datasets when a benchmark needs them.
• Treat HF tokens, SLURM credentials, and upload destinations as secrets.
• If a requested path is missing or a model family/precision combo is inconsistent, stop at planning and explain the gap rather than guessing.

Provenance labels used while distilling

• docs/metrics_toolkit.md
• docs/sglang.md
• docs/ComfyUI/comfyui.md
• docs/model_zoo.md
• docs/4bit_sana.md
• scripts/bash_run_inference_metric*.sh
• scripts/inference_*metric*.py
• tools/metrics/*
• tools/convert_scripts/*.py
• sana/cli/run.py
• sana/cli/upload2hf.py
• .github/workflows/ci.yaml