feat(cnn_v3/training): add --single-sample option + doc fixes

- train_cnn_v3.py: --single-sample <dir> implies --full-image + --batch-size 1
- cnn_v3_utils.py: CNNv3Dataset accepts single_sample= kwarg (explicit override)
- HOWTO.md: document --single-sample workflow, fix pack_photo_sample.py usage (--target required)
- HOW_TO_CNN.md: fix GBufferEffect seq input (prev_cnn→source), fix binary name (demo→demo64k), add --resume to flag table, remove stale "pack without target" block

handoff(Gemini): --single-sample <dir> added to train_cnn_v3.py; docs audited and corrected

4 files changed, 64 insertions, 26 deletions

diff --git a/cnn_v3/docs/HOWTO.md b/cnn_v3/docs/HOWTO.md
index 58f09ed..1aead68 100644
--- a/cnn_v3/docs/HOWTO.md
+++ b/cnn_v3/docs/HOWTO.md
@@ -233,12 +233,13 @@ channel-dropout training.
 
 ```bash
 python3 cnn_v3/training/pack_photo_sample.py \
-    --photo cnn_v3/training/input/photo1.jpg \
+    --photo  input/photo1.jpg \
+    --target target/photo1_styled.png \
     --output dataset/photos/sample_001/
 ```
 
-The output `target.png` defaults to the input photo (no style). Copy in
-your stylized version as `target.png` before training.
+`--target` is required and must be a stylized ground-truth image at the same
+resolution as the photo. The script writes it as `target.png` in the sample dir.
 
 ### Dataset layout
 
@@ -285,10 +286,31 @@ python3 train_cnn_v3.py \
     --patch-size 32 --detector random
 ```
 
+### Single-sample training
+
+Use `--single-sample <dir>` to train on one specific sample directory.
+Implies `--full-image` and `--batch-size 1` automatically.
+
+```bash
+# Pack input/target pair into a sample directory first
+python3 pack_photo_sample.py \
+    --photo  input/photo1.png \
+    --target target/photo1_styled.png \
+    --output dataset/simple/sample_001/
+
+# Train on that sample only
+python3 train_cnn_v3.py \
+    --single-sample dataset/simple/sample_001/ \
+    --epochs 500
+```
+
+All other flags (`--epochs`, `--lr`, `--checkpoint-dir`, `--enc-channels`, etc.) work normally.
+
 ### Key flags
 
 | Flag | Default | Notes |
 |------|---------|-------|
+| `--single-sample DIR` | — | Train on one sample dir; implies `--full-image`, `--batch-size 1` |
 | `--input DIR` | `training/dataset` | Root with `full/` or `simple/` subdirs |
 | `--input-mode` | `simple` | `simple`=photos, `full`=Blender G-buffer |
 | `--patch-size N` | `64` | Patch crop size |
diff --git a/cnn_v3/docs/HOW_TO_CNN.md b/cnn_v3/docs/HOW_TO_CNN.md
index 624deaa..f5f1b1a 100644
--- a/cnn_v3/docs/HOW_TO_CNN.md
+++ b/cnn_v3/docs/HOW_TO_CNN.md
@@ -107,15 +107,6 @@ The network learns the mapping `albedo → target`. If you pass the same image a
 input and target, the network learns identity (useful as sanity check, not for real
 training). Confirm `target.png` looks correct before running training.
 
-**Alternative — pack without a target yet:**
-```bash
-python3 pack_photo_sample.py \
-    --photo /path/to/photo.png \
-    --output dataset/simple/sample_001/
-# target.png defaults to a copy of the input; replace it before training:
-cp my_stylized_version.png dataset/simple/sample_001/target.png
-```
-
 **Batch packing:**
 ```bash
 for f in photos/*.png; do
@@ -336,10 +327,22 @@ uv run train_cnn_v3.py \
     --epochs 500
 ```
 
+**Single-sample training (overfit on one input/target pair):**
+```bash
+# Pack first
+./gen_sample.sh input/photo.png target/photo_styled.png dataset/simple/sample_001/
+
+# Train — --full-image and --batch-size 1 are implied
+uv run train_cnn_v3.py \
+    --single-sample dataset/simple/sample_001/ \
+    --epochs 500
+```
+
 ### Flag reference
 
 | Flag | Default | Notes |
 |------|---------|-------|
+| `--single-sample DIR` | — | Train on one sample dir; implies `--full-image`, `--batch-size 1` |
 | `--input DIR` | `training/dataset` | Dataset root; always set explicitly |
 | `--input-mode` | `simple` | `simple`=photos, `full`=Blender G-buffer |
 | `--epochs N` | 200 | 500 recommended for full-image mode |
@@ -356,6 +359,7 @@ uv run train_cnn_v3.py \
 | `--film-cond-dim N` | 5 | Must match `CNNv3FiLMParams` field count in C++ |
 | `--checkpoint-dir DIR` | `checkpoints/` | Set per-experiment |
 | `--checkpoint-every N` | 50 | 0 to disable intermediate checkpoints |
+| `--resume [CKPT]` | — | Resume from checkpoint path; if path missing, uses latest in `--checkpoint-dir` |
 
 ### Architecture at startup
 
@@ -564,10 +568,12 @@ It owns:
 
 ```
 SEQUENCE 0 0 "Scene with CNN v3"
-  EFFECT + GBufferEffect prev_cnn -> gbuf_feat0 gbuf_feat1  0 60
-  EFFECT + CNNv3Effect   gbuf_feat0 gbuf_feat1 -> sink       0 60
+  EFFECT + GBufferEffect source -> gbuf_feat0 gbuf_feat1  0 60
+  EFFECT + CNNv3Effect   gbuf_feat0 gbuf_feat1 -> sink    0 60
 ```
 
+Temporal feedback (`prev_cnn`) is wired automatically by `wire_dag()` — no explicit input needed in the `.seq` file.
+
 Or direct C++:
 ```cpp
 #include "cnn_v3/src/cnn_v3_effect.h"
@@ -658,7 +664,7 @@ Do not reference them from outside the effect unless debugging.
 ```bash
 cmake -B build -DCMAKE_BUILD_TYPE=Release
 cmake --build build -j4
-./build/demo
+./build/demo64k
 ```
 
 ### Expected visual output
diff --git a/cnn_v3/training/cnn_v3_utils.py b/cnn_v3/training/cnn_v3_utils.py
index bef4091..50707a2 100644
--- a/cnn_v3/training/cnn_v3_utils.py
+++ b/cnn_v3/training/cnn_v3_utils.py
@@ -286,7 +286,8 @@ class CNNv3Dataset(Dataset):
                  channel_dropout_p: float = 0.3,
                  detector: str = 'harris',
                  augment: bool = True,
-                 patch_search_window: int = 0):
+                 patch_search_window: int = 0,
+                 single_sample: str = ''):
         self.patch_size          = patch_size
         self.patches_per_image   = patches_per_image
         self.image_size          = image_size
@@ -296,16 +297,18 @@ class CNNv3Dataset(Dataset):
         self.augment             = augment
         self.patch_search_window = patch_search_window
 
-        root       = Path(dataset_dir)
-        subdir     = 'full' if input_mode == 'full' else 'simple'
-        search_dir = root / subdir
-        if not search_dir.exists():
-            search_dir = root
-
-        self.samples = sorted([
-            d for d in search_dir.iterdir()
-            if d.is_dir() and (d / 'albedo.png').exists()
-        ])
+        if single_sample:
+            self.samples = [Path(single_sample)]
+        else:
+            root       = Path(dataset_dir)
+            subdir     = 'full' if input_mode == 'full' else 'simple'
+            search_dir = root / subdir
+            if not search_dir.exists():
+                search_dir = root
+            self.samples = sorted([
+                d for d in search_dir.iterdir()
+                if d.is_dir() and (d / 'albedo.png').exists()
+            ])
         if not self.samples:
             raise RuntimeError(f"No samples found in {search_dir}")
 
diff --git a/cnn_v3/training/train_cnn_v3.py b/cnn_v3/training/train_cnn_v3.py
index de10d6a..31cfd9d 100644
--- a/cnn_v3/training/train_cnn_v3.py
+++ b/cnn_v3/training/train_cnn_v3.py
@@ -104,6 +104,10 @@ def train(args):
     enc_channels = [int(c) for c in args.enc_channels.split(',')]
     print(f"Device: {device}")
 
+    if args.single_sample:
+        args.full_image  = True
+        args.batch_size  = 1
+
     dataset = CNNv3Dataset(
         dataset_dir=args.input,
         input_mode=args.input_mode,
@@ -115,6 +119,7 @@ def train(args):
         detector=args.detector,
         augment=True,
         patch_search_window=args.patch_search_window,
+        single_sample=args.single_sample,
     )
     loader = DataLoader(dataset, batch_size=args.batch_size, shuffle=True,
                         num_workers=0, drop_last=False)
@@ -222,6 +227,8 @@ def main():
     p = argparse.ArgumentParser(description='Train CNN v3 (U-Net + FiLM)')
 
     # Dataset
+    p.add_argument('--single-sample', default='', metavar='DIR',
+                   help='Train on a single sample directory; implies --full-image and --batch-size 1')
     p.add_argument('--input', default='training/dataset',
                    help='Dataset root (contains full/ or simple/ subdirs)')
     p.add_argument('--input-mode', default='simple', choices=['simple', 'full'],