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int_hyper `natural` (wai-rs/tests/vectors/int_hyper/natural_*, record natural_set.json): a mean-scale hyperprior, 64 synthesis and 64 latent channels, trained by tools/wai_int_hyper_train.py at661048492from seed 20261112 on the 599 training pictures of research/training-pictures/natural-1.json in 256 x 256 crops: 32,000 steps of 8, Adam 5e-4 then 5e-5 for the last quarter, lambda 0.0130, clip 1, deterministic algorithms, on the Linux x86_64 build host's integrated GPU (PCI 0x1002, gfx1151) through ROCm 7.2 and torch 2.14.0+rocm7.2, in 90 min (03:21:58-04:52:14Z); last logged step 0.5152 bpp at 26.23 dB (loss 2.53). Listing (its identity) 3b3b56053917bf0b4a49b7011f4c725e3d413da865caec2b6bd57b953c115fb2. Coded picture: the dead-leaves picture of that seed, stream 0, 128 x 128: 1,540 rANS bytes, 0.752 bpp, 18.74 dB, decode 08b6bf5b...; WIH1 7922be10.... int_mlicpp `natural` (wai-rs/tests/vectors/int_mlicpp_natural/, record natural_set.json): 64 channels in 4 slices, a 48-channel hyper-latent, a 3 x 3 entropy network, trained by tools/wai_int_mlicpp_train.py at661048492by the same recipe side by side on the same GPU, in 99 min (to 05:01:24Z); last logged loss 2.5833 (0.5009 bpp, 26.08 dB). model.wmb 27643187da6ba44cd5c8e3fc67ff4cf7e26026942414965cfcbd3a4b18bd4285 (1,988,549 B), listing f37e2302bb2b26bb9a5483bf1f17e0210ecc2cf88b2b55e8b0aaaa6f4e79964b, prior id int_mlicpp/natural. Coded picture: 1,512 rANS bytes, 0.7383 bpp, 18.65 dB, decode 1bbd9de6.... The synthetic vector is unchanged under its own identity. Both records read Open (check --open: tools at their blobs of661048492, the manifest by its digest under the licence rule); self-test 16 of 16 (int_hyper) and 14 of 14 (int_mlicpp). Held: int-payload-conformance `natural` (WIH1); byte-exact goldens wih1_natural_set_is_golden and int_mlicpp_golden::natural (by the sink's decode and through the lowering, reference and fast kernels); wai-ops goldens (stage operators); ops-conformance certificates, both acc64, first acc32 failure h_s input range at 43 bits, listed after the synthetic sets (committed_after) so the index only gains entries; module corpus 123 cases; the browser crate's Node check; the GPU capability test (ops_gpu.rs) with both mlicpp vectors. verifiers/trained-sets.sh runs the manifest's check and self-test and both records' check --open and self-test. Run through rb, macOS x86_64 (iMac): gens; wai_int_payload_vectors verify, wai-verify vectors verify, wai_ops_check.py, int_payload_verify.py, corpus.py --check, all 0; wai-rs tests int_hyper_ops, int_kernels_vectors, corpus_provenance, int_payload, int_mlicpp, int_mlicpp_ops (release, no-fail-fast) and int_hyper_ops, int_payload (debug) 0; byte-exact-conformance (release) 0; wai-ops tests 0; clippy -D warnings clean for neural_int and for the ops_gpu set (no new lint in any other set: the union set's and byte-exact's and wai-ops's are pre-existing, none in a touched line). Linux x86_64 (Framework): ops_gpu on gpu-vulkan-1002 and swgpu-vulkan-10005, int_hyper 15 cases and int_mlicpp 2, graphs_differ 0, errors 0; the 48 test-picture payloads decode by wai_det_cost to the trainers' recorded digests. Plants caught: the fast kernels' IGDN one off on 64-channel layers fails both trained sets' goldens (3 tests) while 116 pass; one byte changed in leaves_g_s.bin and in int_mlicpp/model.wmb (changed bytes under an existing set's name) each fail `wai breaking` as an unacknowledged wire break; wai breaking against88b0bdb4cpasses (25 compatible changes). Co-Authored-By: Claude Opus 5.5 <[email protected]>
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24 lines
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# Committed vectors
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The vectors the reference implementation's tests decode, and the conformance corpora
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under `wai/*-conformance/` and the cross-architecture goldens are built on. A vector
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changes only through its generator (CONTRIBUTING, *Vectors*).
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**Provenance.** Each directory's row names the generator that writes it and the
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generator's input. `tests/corpus_provenance.rs` requires a row for every directory.
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| directory | provenance |
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|---|---|
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| `generate/` | Generated: `tools/wai_generate_model.py` writes the spectral model from an integer sine table and a word list. |
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| `int_audio/` | Generated: `tools/wai_int_audio_export.py` trains the codec on a signal it generates from seed 1 and codes another signal from the same seed; it reads no audio file ([`int_audio/README.md`](int_audio/README.md)). |
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| `int_cond/` | Generated: `tools/wai_int_cond_export.py` (seed 20261109) trains the conditional video codec on seeded procedural clips it draws itself (panning gratings and moving shapes), quantises it after training, and codes a held-out procedural clip from seed 20261110 with the integer networks; it reads no image or video file. |
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| `int_hyper/` | Generated. `tiny_*`: `tools/wai_int_hyper_tiny_vector.py` draws the tiny conditional codec's integer weights from a seeded generator; no image. `leaves_*`: `tools/wai_int_hyper_train.py` trains a set (seed 20261110) on the dead-leaves pictures `tools/wai_int_synth_real.py` generates from that seed, streams 1 to 4096, and codes stream 0 with it through `tools/wai_sota_int_synth_export.py`; it reads no image file. `leaves_set.json` records the set, its seeds and the commit it was trained at, and `python3 -I tools/wai_int_hyper_train.py check --own wai-rs/tests/vectors/int_hyper leaves` reads it. `natural_*`: `tools/wai_int_hyper_train.py` trains a set (seed 20261112) on the `train` pictures of `research/training-pictures/natural-1.json`, openly licensed pictures that are **not** generated and are not in the repository (each one's licence, credit, URL, retrieval time and SHA-256 are in that manifest; decisions D60), and codes stream 0 of the dead-leaves pictures of that seed with it, so the committed bitstream and decode are of a generated picture. `natural_set.json` records the set, and `python3 -I tools/wai_int_hyper_train.py check --open wai-rs/tests/vectors/int_hyper natural` reads it. |
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| `int_mlicpp/` | Generated: `tools/wai_int_mlicpp_export.py` (seed 20260929) codes an integer pattern of ramps, an xor texture and a disc with seeded weights; `tools/wai_int_mlicpp_set_form.py` writes its set-form twin. |
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| `int_mlicpp_natural/` | Trained: `tools/wai_int_mlicpp_train.py` (seed 20261112) trains a set on the `train` pictures of `research/training-pictures/natural-1.json` (openly licensed, not generated, not in the repository; decisions D60), exports it through `tools/wai_int_mlicpp_export.py`'s integer path and codes stream 0 of the dead-leaves pictures of that seed with it, so the committed payload and decode are of a generated picture; `tools/wai_int_mlicpp_set_form.py` writes its set-form twin. `natural_set.json` records the set, and `python3 -I tools/wai_int_mlicpp_train.py check --open wai-rs/tests/vectors/int_mlicpp_natural natural` reads it. |
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| `int_splat/` | Generated: `tools/wai_int_splat_export.py` trains on a seeded procedural cloud. |
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| `int_video/` | `int_video.*` is generated by `tools/wai_int_video_encode.py` (a disc over a gradient), and `video_nk.*` by `tools/wai_int_video_keyframe_encode.py` (the `int_hyper` tiny keyframe, panned). `video_real.*` is **not** generated: `tools/wai_int_video_real_encode.py` pans over a third-party picture that was not made for this repository and has no recorded licence for redistribution. It is to be replaced by generated content. |
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| `int_world/` | Generated: `tools/wai_int_world_export.py` trains on a seeded procedural world. |
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| `int_world2/` | Generated: `tools/wai_int_world2_export.py` trains on seeded procedural backgrounds. |
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| `jpegai/` | `test256.bin` is the output of the ISO/IEC 6048-1 reference encoder (BSD-3) for a 256×256 generated test image of gradients and blocks ([`jpegai/README.md`](jpegai/README.md)). |
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| `neural_audio/` | Generated: `tools/wai_neural_audio_vectors.py` writes, from fixed dyadic parameters and SplitMix64 codes (seeds 1 to 4), a small decoder with each model's geometry, an envelope of seeded codes and the decode it computes itself ([`neural_audio/README.md`](neural_audio/README.md)). No audio, no model weights. |
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| `staged_bounds/` | Constructed here for the bound tests: grey frames from a residual stream of zeros, and envelopes whose capability is refinement-only or companion-only ([`staged_bounds/README.md`](staged_bounds/README.md)). No image. |
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