The problem
Why this system needed to exist.
Design teams often need many controlled variations of a familiar asset type. Generic image generation can drift away from the established visual language, lose prompt and seed provenance, and create more review work than it removes.
The useful system is not only a fine-tuned model. It needs repeatable training, traceable generation, quality filtering, ranking, and a handoff designers can actually use.
The system
How the pieces work together.
Reference assets are captioned with BLIP and prepared for SDXL LoRA adaptation using Diffusers, PEFT, and Accelerate. MLflow tracks parameters, metrics, artifacts, and adapter weights.
A Delta-backed prompt library drives seeded batch generation. Vision scoring filters obvious failures, human review controls approval, and the handoff exports selected assets with manifests, contact sheets, provenance, and line-art previews. The original workflow helped designers accelerate repetitive exploration while preserving final human judgment.