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14 hoursdocs: consolidate and sync docs with current codebase stateskal
- PROJECT_CONTEXT.md: fix effect count (12→18), shader count (27→37), update CNN v3 pipeline description, tighten Next Up section - TODO.md: fix priority numbering, restore GPU PCM synthesis as pending, streamline CNN v3 section, consolidate Future items - doc/SEQUENCE.md: effect count 12→18 - cnn_v3/README.md: phases 1–7→1–9, test count 36→38, add phases 8–9 - cnn_v3/docs/HOWTO.md: fix dataset layout blender/photos→full/simple, update test counts 36→38 throughout - doc/COMPLETED.md: archive FFT/timing/OLA fixes, remove false GPU PCM claim - src/audio/audio_engine.cc: fix step comment numbering (6→5) - src/audio/synth.cc: remove stale fractional_pos tempo-scaling comment handoff(Gemini): docs now accurate — 18 effects, 37 shaders, 38/38 tests, GPU PCM synthesis back in TODO as pending, CNN v3 dataset layout corrected.
7 daysfeat(cnn_v3): add G-buffer visualizer + web sample loader (Phase 7)skal
C++ GBufViewEffect: renders all 20 feature channels from feat_tex0/feat_tex1 in a 4×5 tiled grid. Custom BGL with WGPUTextureSampleType_Uint; bind group rebuilt per frame via wgpuRenderPipelineGetBindGroupLayout. Web tool: "Load sample directory" button — webkitdirectory picker, FULL_PACK_SHADER compute (matches gbuf_pack.wgsl packing), runFromFeat() skips photo-pack step, computePSNR() readback + comparison vs target.png side-by-side. 36/36 tests pass. Docs updated: HOWTO.md §9, README, PROJECT_CONTEXT, TODO, COMPLETED. handoff(Gemini): CNN v3 Phase 7 done. Next: run train_cnn_v3.py (see HOWTO §3).
10 daysfeat(cnn_v3): Phase 1 complete - GBufferEffect integrated + HOWTO playbookskal
- 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.
10 daysdocs(cnn_v3): full design doc — U-Net + FiLM architecture planskal
- CNN_V3.md: complete design document - U-Net enc_channels=[4,8], ~5 KB f16 weights - FiLM conditioning (5D → γ/β per level, CPU-side MLP) - 20-channel feature buffer, 32 bytes/pixel: two rgba32uint textures - feat_tex0: albedo.rgb, normal.xy, depth, depth_grad.xy (f16) - feat_tex1: mat_id, prev.rgb, mip1.rgb, mip2.rgb, shadow, transp (u8) - 4-pass G-buffer: raster MRT + SDF compute + lighting + pack - Per-pixel parity framework: PyTorch / HTML WebGPU / C++ WebGPU (≤1/255) - Training pipelines: Blender full G-buffer + photo-only (channel dropout) - train_cnn_v3_full.sh spec (modelled on v2 script) - HTML tool adaptation plan from cnn_v2/tools/cnn_v2_test/index.html - Binary format v3 header spec - 8-phase ordered implementation checklist - TODO.md: add CNN v3 U-Net+FiLM future task with phases - cnn_v3/README.md: update status to design phase handoff(Gemini): CNN v3 design complete. Phase 0 (stub G-buffer) unblocks all other phases — one compute shader writing feat_tex0+feat_tex1 with synthetic values from the current framebuffer. See cnn_v3/docs/CNN_V3.md Implementation Checklist.
2026-02-15feat(cnn): add CNN v3 directory structure with training dataskal
Initialize CNN v3 subdirectory with training pipeline layout: - docs/, scripts/, shaders/, src/, tools/, weights/ for organization - training/input/ with sample images - training/target_1/, target_2/ for multi-style training - README.md documenting structure Training images tracked in repo for easy collaboration. Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>