rewrite Why section: academic tone, trainer motivation, community credit

README.md

@@ -38,30 +38,48 @@ real-world usable first, not benchmark-chasing. See
 
 ## Why HEART?
 
-There are strong SR networks already. Here's the honest case for HEART — and
-when to pick something else.
-
-**One-line pitch:** HAT-class quality at roughly half the memory, in a single
-readable file that trains stably in bf16 and exports to dynamic ONNX.
-
-**Why it's worth your time:**
-- **Quality at lower cost.** Ties HAT on PSNR while using **~half the peak
-  VRAM**, and runs **~1.6x faster in bf16** inference.
-- **FlashAttention / SDPA friendly.** No attention masks, no position-bias
-  table lookups — RIB replaces them with a dot product.
-- **Stable, simple training.** i-LN keeps bf16 AMP stable with no fp32
-  gymnastics.
+There are strong and established SR networks already. This is the honest case
+for when HEART is worth your time — and when it is not.
+
+This project comes out of a trainer's frustration rather than a benchmark goal.
+After training hundreds of SISR models (see
+[Phhofm/models](https://github.com/Phhofm/models)), the recurring annoyance was
+networks that felt like *paper* networks: they chase leaderboard metrics with
+techniques that leave you juggling an unfused and a fused checkpoint, or whose
+dynamic-ONNX conversion is fragile. This network is the opposite of that — built
+so a trainer can pick it up, train it, and convert it to dynamic ONNX without
+headaches.
+
+**Strengths of HEART**
+- **Real-world usability over metric chasing.** OTF models are trained on the
+  full Real-ESRGAN degradation chain and restore real photos, not just clean
+  bicubic downsamples.
+- **Competitive quality at lower cost.** In our tests it matches HAT on PSNR
+  while using roughly half the peak VRAM and running ~1.6x faster in bf16
+  inference.
 - **One file, exports cleanly.** Single-file pure PyTorch; dynamic ONNX on the
   first try; no fusion step or fused/unfused checkpoint pairs.
-- **Real-world capable.** OTF models are trained on the full Real-ESRGAN
-  degradation chain and restore real photos, not just clean bicubic downsamples.
+- **Stable, simple training.** i-LN keeps bf16 AMP stable without fp32
+  gymnastics.
+- **FlashAttention / SDPA friendly.** No attention masks, no position-bias
+  table lookups — RIB replaces them with a dot product.
 
-**When to pick something else:** for maximum benchmark PSNR or a tiny mobile
-model, use NERVE instead — HEART is the desktop / server quality tier.
+**When to pick something else.** For maximum benchmark PSNR or a small mobile
+model, use NERVE or one of the community networks below. HEART is the desktop /
+server quality tier.
 
-Full architecture details, design rationale, and ablation results:
+Full architecture details, design rationale and ablation results:
 [`docs/EXPERIMENTS.md`](docs/EXPERIMENTS.md).
 
+**Credit where it is due.** None of this exists in a vacuum. SISR has many
+strong architectures and generous community members, for example HAT, ATD and
+DAT2, the community network work of [umzi](https://github.com/umzi2) and
+[Kim2091](https://github.com/Kim2091), fdat by
+[sharekhan](https://github.com/stinkybread), and
+[the-database](https://github.com/the-database) (traiNNer-redux, its benchmarks
+and its models). This list is far from complete — apologies to anyone not named;
+the SISR community has a great many people contributing a great deal.
+
 ---
 
 ## Quickstart