Building an AI Art Pipeline: Notes and Lessons

Over the past months I experimented with end‑to‑end pipelines for AI art generation—balancing quality, throughput, and cost. This post captures pragmatic notes for future me (and maybe you).

Goals

• High signal‑to‑noise: minimize unusable generations.
• Fast iteration: prompt templates and parameter sets.
• Simple curation: metadata and visual review workflow.

Workflow

1. Prompt design — Start from reference works and distill style/subject constraints.
2. Model selection — Prioritize models with consistent style controls and good negative prompts.
3. Batching — Generate small batches, score, then expand good seeds.
4. Scoring — Use aesthetic/NSFW classifiers and CLIP similarity to prune.
5. Curation — Keep rich metadata (seed, params, prompt) to reproduce and iterate.

Tips

• Use embeddings + vector search to cluster similar outputs and select diverse sets.
• Cache intermediate assets; re‑use latents where supported to save time.
• Create a style guide with do/don’t examples for consistent results.

What’s next

I’m exploring lightweight on‑device upscalers and pose/edge guidance to better control composition while keeping latency low.