AI image-production workflow
From prompt to production
A workflow for keeping identity, brand and delivery requirements intact after the model has made the image.
The premise
The output was never the whole product.
Generative image models can produce a striking frame. Real delivery asks for a sequence that remains on-brand, a person who still looks like themselves, a transparent asset with clean edges and a file that will not slow the website down.
I built the missing production path around the model: reference hierarchy, approved outputs that become the next reference, background recovery and automatic optimisation for delivery.
- Role
- Creator & workflow designer
- Context
- Creative production
- Output
- Web-ready image assets
- Public surface
- Curated showcase
The production path
Generation is one moment.
The system is everything around it.
- 01
Establish what must not drift.
Separate the person, product, brand and visual style into a deliberate hierarchy of references.
- 02
Use approval as memory.
An accepted image becomes part of the next decision, so continuity compounds across the set.
- 03
Finish what the model cannot.
A two-pass difference-matting process recovers transparency when native background removal is unavailable.
- 04
Deliver for the place it will live.
Final assets are compressed and prepared for actual interfaces, not left as oversized generation files.
Why it stands out
The intelligence sits
between the tools.
“The useful question is not: can the model make it? It is: can the system make it again, keep it right and deliver it properly?”
The public repository shows the workflow and example output. The reusable implementation remains private while its packaging, testing and release boundaries are prepared.
View the public showcase ↗The interesting part is always the problem underneath.
If you have one that crosses commercial and technical boundaries, start there.
Bring the problem ↗