---

name: ic-edit description: "Route ICEdit image-editing, Gradio demo, and LoRA training workflows." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

ICEdit

Use this skill when the user wants to edit an image with ICEdit, launch the browser demo, or understand and prepare the shipped LoRA / MoE LoRA training paths.

Route map

• sub-skills/inference/SKILL.md — one-shot CLI editing, seed control, 512-width normalization, CPU-offload guidance, and normal or MoE inference.
• sub-skills/gradio/SKILL.md — browser demo launch, share and port control, GGUF inputs, bundled presets, and UI troubleshooting.
• sub-skills/training/SKILL.md — config-driven LoRA and MoE LoRA training, dataset prep, launch-command construction, wandb, and checkpointing.

Read first

• references/repo-provenance.md when you need the repository snapshot or want to check staleness.
• references/installation.md before preparing or reusing a Python environment.
• scripts/check_icedit_env.py to confirm CUDA and the installed import surface.
• references/model-assets.md for model ids, width rules, and bundled preset assets.
• references/troubleshooting.md for cross-cutting install, GPU, model, and download failures.
• references/repo-routing-metadata.json only when validating the managed area-family placement; it is not workflow guidance.

All commands below are cwd-independent when they use an absolute skill root. Set it once if convenient:

export ICEDIT_SKILL=/path/to/ic-edit-skill

Operating notes

• ICEdit is GPU-first. The primary workflows expect a CUDA-capable torch build.
• The bundled helpers preserve the fixed diptych prompt template and the 512-pixel width rule from the repo scripts.
• Normal inference and normal Gradio are standalone helpers in this skill tree; their default Hub ids still download model weights.
• MoE inference/demo require an ICEdit checkout supplied with --repo-root (including its vendored icedit/ package).
• Training requires a checkout's train/src and train/train/config; training source and configs are not bundled here.
• The training helper is dry-run by default; --execute is the explicit GPU/network action.

Installation summary

1. Create or reuse a Python 3.11 environment with a CUDA-enabled torch wheel.
2. Install the base editing stack and the extra training support libraries listed in references/installation.md.
3. Run the environment checker with its rooted path, for example python "$ICEDIT_SKILL/scripts/check_icedit_env.py".

Fast intent routing

• Edit an image, change the seed, or honor the 512-width rule -> inference
• Open a browser UI, share a link, try GGUF, or adjust LoRA scale -> gradio
• Build or troubleshoot a training launch, config, or dataset path -> training

Notes for future agents

• Read the provenance file before deciding whether this skill is stale for a new checkout.
• Keep runtime links inside this skill tree.
• Prefer the sub-skill that matches the user's immediate intent instead of a broader generic image-generation skill.