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| author | skal <pascal.massimino@gmail.com> | 2026-03-20 09:22:18 +0100 |
|---|---|---|
| committer | skal <pascal.massimino@gmail.com> | 2026-03-20 09:22:18 +0100 |
| commit | a10cabbe3a5ae05730c2e76493e42554ee6037ba (patch) | |
| tree | 5c2fbb717f53dc701b1536b4f98685bc042820e1 /cnn_v3/README.md | |
| parent | f74bcd843c631f82daefe543fca7741fb5bb71f4 (diff) | |
feat(cnn_v3): Phase 1 complete - GBufferEffect integrated + HOWTO playbook
- Wire GBufferEffect into demo build: assets.txt, DemoSourceLists.cmake,
demo_effects.h, shaders.h/cc. ShaderComposer::Compose() applied to
gbuf_raster.wgsl (resolves #include "common_uniforms").
- Add GBufferEffect construction test. 35/35 passing.
- Write cnn_v3/docs/HOWTO.md: G-buffer wiring, training data prep,
training plan, per-pixel validation workflow, phase status table,
troubleshooting guide.
- Add project hooks: remind to update HOWTO.md on cnn_v3/ edits;
warn on direct str_view(*_wgsl) usage bypassing ShaderComposer.
- Update PROJECT_CONTEXT.md and TODO.md: Phase 1 done,
Phase 3 (WGSL U-Net shaders) is next active.
handoff(Gemini): CNN v3 Phase 3 is next - WGSL enc/dec/bottleneck/FiLM
shaders in cnn_v3/shaders/. See cnn_v3/docs/CNN_V3.md Architecture
section and cnn_v3/docs/HOWTO.md section 3 for spec. GBufferEffect
outputs feat_tex0 + feat_tex1 (rgba32uint, 20ch, 32 bytes/pixel).
C++ CNNv3Effect (Phase 4) takes those as input nodes.
Diffstat (limited to 'cnn_v3/README.md')
| -rw-r--r-- | cnn_v3/README.md | 4 |
1 files changed, 3 insertions, 1 deletions
diff --git a/cnn_v3/README.md b/cnn_v3/README.md index a22d823..f161bf4 100644 --- a/cnn_v3/README.md +++ b/cnn_v3/README.md @@ -31,7 +31,9 @@ Add images directly to these directories and commit them. ## Status -**Design phase.** Architecture defined, G-buffer prerequisite pending. +**Phase 1 complete.** G-buffer integrated (raster + pack), 35/35 tests pass. +Training infrastructure ready. U-Net WGSL shaders are next. +See `cnn_v3/docs/HOWTO.md` for the practical playbook. See `cnn_v3/docs/CNN_V3.md` for full design. See `cnn_v2/` for reference implementation. |
