add BODY suite + relation to NERVE

README.md

@@ -20,6 +20,10 @@ A super-resolution architecture that is simple to train, fast to run, and
 works everywhere: **traiNNer-redux**, **chaiNNer**, **ONNX Runtime**,
 **TensorRT**.
 
+Part of the **BODY** suite by **Philip Hofmann** — networks built to be
+real-world usable first, not benchmark-chasing. See
+[Relation to NERVE](#relation-to-nerve) for which one to pick.
+
 > Short version: copy `heart_arch.py` into traiNNer-redux, train with
 > `lq_size: 96`, export to ONNX, use anywhere. Details below.
 
@@ -123,6 +127,22 @@ scripts/export_heart.py  checkpoint → dynamic ONNX exporter
 `examples/inputs/` also contains the original Real-ESRGAN test images, so you
 can inspect full-size inputs and run the models yourself.
 
+## Relation to NERVE
+
+HEART and NERVE are the two members of the **BODY** suite (by Philip Hofmann):
+same philosophy — real-world usability over benchmark scores — different tiers.
+
+| | **HEART** | **NERVE** |
+|---|---|---|
+| size | ~16.7M params | ~1.8M params |
+| design | attention (FlashAttention-friendly) | pure convolution |
+| use when | you want the **best quality** and have the compute | you want **speed, small size, and dead-simple deployment** (mobile/edge/web) |
+| cost | heavier to run | a fraction of the cost |
+
+Rule of thumb: **HEART for maximum quality, NERVE for maximum practicality.**
+Both export to dynamic ONNX; NERVE additionally converts to ncnn cleanly
+because it has no attention ops.
+
 ## License & credits
 
 Apache-2.0 (this repository's code and pretrained weights).