feat(cnn_v3): upgrade architecture to enc_channels=[8,16]

Double encoder capacity: enc0 4→8ch, enc1 8→16ch, bottleneck 16→16ch,
dec1 32→8ch, dec0 16→4ch. Total weights 2476→7828 f16 (~15.3 KB).
FiLM MLP output 40→72 params (L1: 16×40→16×72).

16-ch textures split into _lo/_hi rgba32uint pairs (enc1, bottleneck).
enc0 and dec1 textures changed from rgba16float to rgba32uint (8ch).
GBUF_RGBA32UINT node gains CopySrc for parity test readback.

- WGSL shaders: all 5 passes rewritten for new channel counts
- C++ CNNv3Effect: new weight offsets/sizes, 8ch uniform structs
- Web tool (shaders.js + tester.js): matching texture formats and bindings
- Parity test: readback_rgba32uint_8ch helper, updated vector counts
- Training scripts: default enc_channels=[8,16], updated docstrings
- Docs + architecture PNG regenerated

handoff(Gemini): CNN v3 [8,16] upgrade complete. All code, tests, web
tool, training scripts, and docs updated. Next: run training pass.

1 files changed, 16 insertions, 16 deletions

diff --git a/cnn_v3/docs/HOW_TO_CNN.md b/cnn_v3/docs/HOW_TO_CNN.md
index 09db97c..11ed260 100644
--- a/cnn_v3/docs/HOW_TO_CNN.md
+++ b/cnn_v3/docs/HOW_TO_CNN.md
@@ -358,7 +358,7 @@ uv run train_cnn_v3.py \
 
 The model prints its parameter count:
 ```
-Model: enc=[4, 8]  film_cond_dim=5  params=3252  (~6.4 KB f16)
+Model: enc=[8, 16]  film_cond_dim=5  params=9148  (~17.9 KB f16)
 ```
 
 If `params` is much higher, `--enc-channels` was changed; update C++ constants accordingly.
@@ -492,12 +492,12 @@ WEIGHTS_CNN_V3_FILM_MLP, BINARY, weights/cnn_v3_film_mlp.bin, "CNN v3 FiLM MLP w
 
 | Layer | f16 count | Bytes |
 |-------|-----------|-------|
-| enc0 Conv(20→4,3×3)+bias | 724 | — |
-| enc1 Conv(4→8,3×3)+bias | 296 | — |
-| bottleneck Conv(8→8,3×3,dil=2)+bias | 584 | — |
-| dec1 Conv(16→4,3×3)+bias | 580 | — |
-| dec0 Conv(8→4,3×3)+bias | 292 | — |
-| **Total** | **2476 f16** | **4952 bytes** |
+| enc0 Conv(20→8,3×3)+bias | 1448 | — |
+| enc1 Conv(8→16,3×3)+bias | 1168 | — |
+| bottleneck Conv(16→16,3×3,dil=2)+bias | 2320 | — |
+| dec1 Conv(32→8,3×3)+bias | 2312 | — |
+| dec0 Conv(16→4,3×3)+bias | 580 | — |
+| **Total** | **7828 f16** | **15656 bytes** |
 
 **`cnn_v3_film_mlp.bin`** — FiLM MLP weights as raw f32, row-major:
 
@@ -505,9 +505,9 @@ WEIGHTS_CNN_V3_FILM_MLP, BINARY, weights/cnn_v3_film_mlp.bin, "CNN v3 FiLM MLP w
 |-------|-------|-----------|
 | L0 weight | (16, 5) | 80 |
 | L0 bias | (16,) | 16 |
-| L1 weight | (40, 16) | 640 |
-| L1 bias | (40,) | 40 |
-| **Total** | | **776 f32 = 3104 bytes** |
+| L1 weight | (72, 16) | 1152 |
+| L1 bias | (72,) | 72 |
+| **Total** | | **1320 f32 = 5280 bytes** |
 
 The FiLM MLP is for CPU-side inference (future — see §4d). The U-Net weights in
 `cnn_v3_weights.bin` are what you need immediately.
@@ -524,16 +524,16 @@ The export script produces this layout: `u32 = u16[0::2] | (u16[1::2] << 16)`.
 
 ```
 Checkpoint: epoch=200  loss=0.012345
-  enc_channels=[4, 8]  film_cond_dim=5
+  enc_channels=[8, 16]  film_cond_dim=5
 
 cnn_v3_weights.bin
-  2476 f16 values → 1238 u32 → 4952 bytes
-  Upload via CNNv3Effect::upload_weights(queue, data, 4952)
+  7828 f16 values → 3914 u32 → 15656 bytes
+  Upload via CNNv3Effect::upload_weights(queue, data, 15656)
 
 cnn_v3_film_mlp.bin
   L0: weight (16, 5) + bias (16,)
-  L1: weight (40, 16) + bias (40,)
-  776 f32 values → 3104 bytes
+  L1: weight (72, 16) + bias (72,)
+  1320 f32 values → 5280 bytes
 ```
 
 ### Pitfalls
@@ -542,7 +542,7 @@ cnn_v3_film_mlp.bin
   assertion in the export script fires. The C++ weight-offset constants (`kEnc0Weights` etc.)
   in `cnn_v3_effect.cc` must also be updated to match.
 - **Old checkpoint missing `config`:** if `config` key is absent (checkpoint from a very early
-  version), the script defaults to `enc_channels=[4,8], film_cond_dim=5`.
+  version), the script defaults to `enc_channels=[8,16], film_cond_dim=5`.
 - **`weights_only=True`:** requires PyTorch ≥ 2.0. If you get a warning, upgrade torch.
 
 ---