AI Image Production — Public Showcase

This repository is the public visual showcase for an AI image-production system built by Athar Jameel Ahmed.

It demonstrates a terminal-driven workflow that turns generative image models into a more repeatable production process: references, brand continuity, transparent outputs, optimisation and review are treated as parts of one system rather than separate manual jobs.

Repository scope: this is the public showcase and example output layer. The reusable implementation remains private while its packaging, tests, documentation and release boundaries are prepared.

What the underlying system implements

• Text-to-image generation and natural-language image editing
• Output controls for common aspect ratios and resolutions up to 4K
• Up to 14 input/reference images for style, object and character continuity
• An approval-driven reference chain in which accepted outputs become references for later work
• Two-pass difference matting against white and black backgrounds to recover RGBA transparency
• Automatic TinyPNG optimisation for web delivery, with an opt-out for source and print assets
• Brand extraction and reusable project scaffolding
• Storyboard continuity for motion workflows using shared boundary frames before Veo rendering and final stitching

Why it matters

Generative image APIs solve only part of a production problem. Real delivery also needs:

• repeatable brand decisions;
• reusable visual references;
• review and approval gates;
• reliable transparent assets;
• sensible output sizes; and
• a traceable path from brief to final delivery.

This project explores that operational layer.

Transparency note

Difference matting is a practical technique, not a guarantee of a perfect cutout for every generated image. The white and black generations must remain sufficiently consistent for the recovered alpha channel to be accurate. Public examples should be evaluated as outputs of the workflow, not as a universal quality claim.

Security and privacy

• API keys do not belong in this repository.
• Generated deployment dependencies and local environment files are excluded from version control.
• The full private implementation is not embedded in the showcase.
• Client assets, confidential brand material and private project references should not be committed here.

Project status

Public showcase. The underlying private toolchain is undergoing release-readiness work covering tests, dependency metadata, licensing, examples, privacy/cost guidance and versioned releases.

About Athar

Athar Jameel Ahmed is a forward-deployed AI engineer, founder-operator and product/systems architect focused on turning business problems into intelligent products and operational systems.

• Website: ajahmed.com
• GitHub: github.com/BULMKT