cmoyates 57c3aff913 feat(phase6): optimization, benchmarking, and tiled inference ... - Cache nn.Upsample instances in DecoderHead/GreenFormer __init__ (eliminated ~7 allocations per forward pass) - Add mx.compile() support via load_model(compile=True) - Benchmark harness: eager vs compiled, multi-resolution, parity checks - Tiled inference with overlap blending for large images - Profiling utilities with forced mx.eval for accurate timing - Reference comparison script (scripts/compare_reference.py) - 12 new tests (compiled consistency + tiling) - README performance section with Apple Silicon guidance Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>		2026-03-01 06:24:10 -03:30
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2026-03-01-feat-corridorkey-mlx-inference-port-plan.md	feat(phase6): optimization, benchmarking, and tiled inference	2026-03-01 06:24:10 -03:30
2026-03-01-fix-converter-review-feedback-plan.md	feat(phase3): PyTorch→MLX weight converter (#1)	2026-03-01 05:16:56 -03:30
2026-03-01-phase4-hiera-backbone-plan.md	feat(phase4): MLX Hiera backbone port (#2)	2026-03-01 05:55:24 -03:30