int_synth's determinism was proven on synthetic/constructed models; this adds
a model TRAINED ON REAL IMAGES decoding a REAL PHOTOGRAPH, and proves the same
bit-exactness on it. tools/wai_int_synth_real.py trains an autoencoder on real
photos, post-training-quantizes the decoder with per-layer activation-scale
calibration (the working recipe — pure QAT through the >> shift can't learn),
encodes a held-out image to an int8 latent, and decodes it through the EXPORTED
integer model in numpy (the same i64 scatter-accumulate / +bias / arithmetic
>> shift / clamp that int_synth.rs runs) as the reference.
New corpus vector `photo`: a 128×128 macOS Sonoma landscape → a 6,156-byte
int8 latent (96×8×8) + a four-layer trained integer decoder, ~21.6 dB at ~3
bpp. Honest scope: a pure integer transposed-conv decoder with no entropy
model — quality is modest BY DESIGN; the point is that the decode is
bit-identical on every machine. wai_int_synth_vectors `gen` cross-checks the
Rust decode against the python integer-forward (ref_rgb.bin) to max|Δ|=0 and
freezes the rgb_hash; the four-platform conformance CI then enforces the same
bytes everywhere. A real trained model, a real photo, decoded identically on
every machine — the int_synth thesis, demonstrated end to end.
Co-Authored-By: Claude Opus 4.8 <[email protected]>
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