LoRA Stacking 101: How I Combine LoRAs for Better Images
Most people use LoRAs one at a time. Download a character LoRA, add it to the prompt, generate. That works fine. But there's a lot more you can do once you start stacking th...
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Reading Blind Eval Results
How to read a blind eval scoreboard without lying to yourself: placement frequency, head-to-head vs aggregate scoring, and why a 6-4 win in 10 prompts is closer to a coin flip than a verdict.
How to Run a Checkpoint Comparison Sweep
Blind eval methodology for crowning (or dethroning) a checkpoint: prompt pool, blind HTML, scoring schemes, and how to know when one round of testing is enough.
The 10% Accent Rule: Composites That Beat Their Ingredients
You ran a graft-comparison round at 30%. One candidate placed surprisingly high in a small early eval, then collapsed when you verified with more prompts — but the model has a real visual character you don't want to lose. Most people drop it and pick from the remaining survivors. The better move: keep it as a 10% accent on top of the survivors. The composite usually beats every ingredient including itself at 30%. Here's the rule, when it applies, and why a primary-secondary-accent split at roughly 70/20/10 is the structure that works.