feat(cnn_v3): Phase 4 complete — CNNv3Effect C++ + FiLM uniform upload

- cnn_v3/src/cnn_v3_effect.{h,cc}: full Effect subclass with 5 compute
  passes (enc0→enc1→bottleneck→dec1→dec0), shared weights storage buffer,
  per-pass uniform buffers, set_film_params() API
- Fixed WGSL/C++ struct alignment: vec3u has align=16, so CnnV3Params4ch
  is 64 bytes and CnnV3ParamsEnc1 is 96 bytes (not 48/80)
- Weight offsets computed as explicit formulas (e.g. 20*4*9+4) for clarity
- Registered in CMake, shaders.h/cc, demo_effects.h, test_demo_effects.cc
- 35/35 tests pass

handoff(Gemini): CNN v3 Phase 5 next — parity validation (Python ref vs WGSL)

1 files changed, 1 insertions, 1 deletions

diff --git a/TODO.md b/TODO.md
index 86c3e37..e33072f 100644
--- a/TODO.md
+++ b/TODO.md
@@ -76,7 +76,7 @@ PyTorch / HTML WebGPU / C++ WebGPU.
    - Howto: `cnn_v3/docs/HOWTO.md`
 2. ✅ Training infrastructure: `blender_export.py`, `pack_blender_sample.py`, `pack_photo_sample.py`
 3. ✅ WGSL shaders: cnn_v3_common (snippet), enc0, enc1, bottleneck, dec1, dec0
-4. C++ CNNv3Effect + FiLM uniform upload
+4. ✅ C++ CNNv3Effect + FiLM uniform upload
 5. Parity validation (test vectors, ≤1/255 per pixel)
 
 ## Future: CNN v2 8-bit Quantization