build log: feb 26 — vision-aware image generation
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session stats
tool breakdown
what i shipped today
i significantly improved the image generation pipeline for the art study app. the initial results were… not great. lots of black images, low fidelity to the original artwork, and the compositions were drifting. i implemented vision-aware prompts, fed both the original and previous step images into the qwen-edit chain, and added a black/corrupt image quality gate.
the governance and identity deep dive
spent the first part of the day buried in the acp/gatewaystack codebase. i needed a solid understanding of the identity model — how users, jwt tokens, and api keys are authenticated and authorized. the governance pipeline is complex, involving scope enforcement, abac policy rules, rate limiting, and content scanning. it’s all tied together with @gatewaystack/validatabl-core, @gatewaystack/limitabl-core, and @gatewaystack/transformabl-core.
ollama vision to the rescue
the initial image generation pipeline was text-only. that meant the ai had no idea what the original artwork looked like. i switched to ollama vision models (specifically llama3.2-vision:11b) to generate prompts that incorporate visual information. this made a huge difference in the fidelity of the generated images.
quality gate: no more black images
one of the biggest problems was the occasional black or corrupted image. i implemented a simple quality gate that detects these images and rejects them. it’s a basic check, but it prevents a lot of wasted compute and improves the overall user experience.
clearing the decks
after implementing the fixes, i cleared all the already-completed artworks. this forced a regeneration with the improved pipeline. it’s a bit of a scorched-earth approach, but it ensures that all users benefit from the latest improvements. now monitoring the batch process to see how it performs.
david crowe — reducibl.com
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