The Auto-Tagger Pipeline: Closing the Feedback Loop
Most people's workflow goes in one direction: prompt → image. They write a prompt, generate an image, and if they like it, they move on. If they don't, they rewrite the prompt ...
Related Tutorials
Tame Your SD Output Library: Sort by Model, Then Browse
Two small scripts for anyone drowning in generated images: sort a mixed output folder into per-model subdirectories by reading each file's embedded metadata, then page through the result in a keyboard-driven 2x2 grid.
Diagnosing Intermittent Checkpoint Failures with a Tensor Health Scan
A repeatable way to track down a checkpoint that fails on some seeds and not others: write a NaN/Inf/magnitude scan, always run a control group, and learn why a clean static scan points straight at a dynamic fp16 cause.
Production-Grade Blind Evaluation: Four Pipeline Gotchas That Will Bite You
You wrote a script that auto-generates images across N checkpoints and feeds them into a blind eval. It works once. It breaks the next time. The failure modes are subtle: filename gaps that shift every subsequent image's label by one; old prompt-dirs from yesterday's run leaking into today's; new checkpoints invisible because the API cached its model list at startup; non-realism checkpoints saturating to black on prompts with heavy double-parens. None of these announce themselves; you just get a result that's quietly wrong. Here are the four gotchas, exactly what each one does, and the specific code fix for each.