ai-image-editing_skill
- Python
21
GitHub Stars
1
Bundled Files
2 months ago
Catalog Refreshed
4 months ago
First Indexed
Readme & install
Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.
Installation
Preview and clipboard use veilstrat where the catalogue uses aiagentskills.
npx veilstrat add skill omer-metin/skills-for-antigravity --skill ai-image-editing- SKILL.md1.3 KB
Overview
This skill provides expert patterns and actionable guidance for AI-powered image editing workflows, including inpainting, outpainting, ControlNet-guided edits, and image-to-image transforms. It targets integration with popular APIs and models (Replicate, Stability AI, ComfyUI/Flux/SDXL) and enforces safe, validated editing patterns. The goal is to help practitioners produce predictable, high-quality edits and avoid common failure modes.
How this skill works
The skill inspects a proposed editing task and maps it to proven patterns for mask creation, prompt engineering, ControlNet conditioning, and model selection. It validates inputs against strict constraints and warns about known failure modes (artifacts, identity drift, inconsistent lighting). Finally, it outputs a step-by-step recipe and API call patterns that match the chosen provider and model.
When to use it
- Remove unwanted objects while preserving surrounding texture and lighting
- Extend images (outpainting) to match composition and perspective
- Convert sketches or rough drafts into photoreal or stylized images via image-to-image
- Apply precise structural control using ControlNet (pose, edges, scribbles)
- Integrate image editing into pipelines using Replicate, Stability AI, or ComfyUI/Flux
Best practices
- Always provide a clean, tightly-fit mask for inpainting; avoid overly large masked areas in a single pass
- Use multi-step edits: coarse structure first, then refine details and color consistency
- Pin a reference for identity-critical edits and validate face fidelity when altering people
- Condition ControlNet with aligned guidance images (same scale and orientation) to reduce artifacts
- Validate output against objective checks (resolution, aspect ratio, color shift, and artifact thresholds) before deployment
Example use cases
- Remove a powerline from a landscape photo while preserving sky gradients and edge continuity
- Outpaint a product shot to create additional negative space for marketing layouts
- Use ControlNet with a sketch to generate a concept art composition that matches a pose reference
- Iteratively refine a portrait with SDXL: fix silhouette, then restore skin texture and color balance
- Automate an image-editing API pipeline that runs mask generation, model selection, and quality validation
FAQ
High-capacity SDXL variants typically deliver the best detail and texture continuity for inpainting; use smaller models for speed or when artifacts must be minimized.
How do I avoid identity drift when editing faces?
Provide a clear reference image, limit edit scope, and run validation checks comparing facial landmarks and color profiles to the reference.
Can I use ControlNet with outpainting?
Yes — provide aligned guidance extended to the outpaint area. Keep scale and orientation consistent to preserve structure.
