I made FLUX 1.16 faster on an M5 Max. Then I found out my quality gate was measuring the wrong thing.
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I spent a few weeks benchmarking training-free block-residual caching for FLUX.1-dev on an Apple M5 Max. The timing result was real: a stable ~1.16× speedup, tight variance, reproducible across prompts. Then the quality sweep came back and every policy failed my gate. Median PSNR of 14.27 dB against a gate of 30 dB. Decisive failure. I almost published that as "block-residual caching doesn't preserve quality on Apple Silicon." That would have been wrong — not because the numbers were wrong, but…
1Key Takeaways
- I spent a few weeks benchmarking training-free block-residual caching for FLUX.1-dev on an Apple M5 Max.
- The timing result was real: a stable ~1.16× speedup, tight variance, reproducible across prompts.
- Then the quality sweep came back and every policy failed my gate.
- Median PSNR of 14.27 dB against a gate of 30 dB.
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3Why it matters
Coding AI shifts how fast software ships and how much human review each change needs. DEV — ML reports that i spent a few weeks benchmarking training-free block-residual caching for FLUX.1-dev on an Apple M5 Max.
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