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tflite/README.md

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+# Stable Audio 3 — LiteRT / TFLite models (variable-length, fp32, self-contained)
+
+Portable CPU (and browser-via-WASM) LiteRT builds of the SA3 pipeline, parallel to
+the `mlx/`, `onnx/`, and TensorRT releases. Runtime: `ai_edge_litert` (LiteRT); the
+XNNPACK delegate accelerates all of these.
+
+Every model is **variable-length** — a single graph runs any sequence length via
+`interpreter.resize_tensor_input(...)` — except T5Gemma (fixed 256 text tokens, which
+is correct: it encodes the prompt, independent of audio duration). The pre/post glue
+(conditioning, patch/unpatch) is **baked into the graphs**, matching the `onnx/` tree,
+so these are drop-in self-contained (no host-side conditioner/patch code needed).
+All fp32 except T5Gemma (fp16 — its bf16 source makes fp16 lossless).
+
+## Files
+
+| path | component | precision | I/O (dynamic axis = L latent tokens; N = L·4096 audio samples) |
+|---|---|---|---|
+| `sa3-sm-music/dit_fp32.tflite` | DiT small (sa3-sm-music) | fp32 | `x[1,256,L]`, `t[1]`, `t5_hidden[1,256,768]`, `t5_mask[1,256]`, `seconds_total[1]`, `local_add_cond[1,257,L]` → `velocity[1,256,L]` |
+| `sa3-m/dit_fp32.tflite` | DiT medium (sa3-medium) | fp32 | (same 6-in → velocity) |
+| `sa3-sm-sfx/dit_fp32.tflite` | DiT small-SFX (sa3-sm-sfx) | fp32 | (same 6-in → velocity) |
+| `same-s/enc_fp32.tflite` | SAME-S encoder | fp32 | audio `[1,2,N]` → latents `[1,256,L]` |
+| `same-s/dec_fp32.tflite` | SAME-S decoder | fp32 | latents `[1,256,L]` → audio `[1,2,N]` |
+| `same-l/enc_fp32.tflite` | SAME-L encoder | fp32 | audio `[1,2,N]` → latents `[1,256,L]` |
+| `same-l/dec_fp32.tflite` | SAME-L decoder | fp32 | latents `[1,256,L]` → audio `[1,2,N]` |
+| `t5gemma/encoder_fp16.tflite` | T5Gemma text encoder | fp16 | `input_ids[1,256]` i32, `attention_mask[1,256]` i32 → `[1,256,768]` (**fixed 256**) |
+
+Conditioning is baked into the DiT (prompt padding + seconds embed + concat), so the
+DiT takes the raw T5Gemma output directly — I/O identical to `onnx/…/dit.onnx`.
+`local_add_cond` is a zeros input `[1,257,L]` (resize its dim-2 to L along with `x`).
+
+Sizes: small/sfx DiT 1.7 GB · medium DiT 5.4 GB · SAME-S enc/dec 0.2 GB each ·
+SAME-L enc/dec 1.7 GB each · T5Gemma 0.5 GB. Total ≈ 13 GB.
+
+## Variable-length usage
+
+```python
+from ai_edge_litert import interpreter as tfl
+it = tfl.Interpreter(model_path="same-s/dec_fp32.tflite", num_threads=4)
+xi = it.get_input_details()[0]["index"]              # latents [1,256,L]
+it.resize_tensor_input(xi, [1, 256, L]); it.allocate_tensors()
+it.set_tensor(xi, latents); it.invoke()
+audio = it.get_tensor(it.get_output_details()[0]["index"])   # [1,2,L*4096]
+```
+
+The graphs recompute RoPE / attention masks in-graph from the live length, so
+**XNNPACK stays fully delegated** and per-length latency matches the equivalent
+fixed-shape export — no dynamic-shape penalty.
+
+## Per-model notes (all validated lossless vs the fp32 / MLX reference)
+- **DiT** (small/medium/sfx): >100 dB vs both the torch reference and the ONNX-ORT
+  refs, at every length 16 → 4096 latent tokens (~1.5 s → 6:20), incl. odd lengths.
+  Whole-sequence, unmasked attention → ~linear cost. Latents are softnorm (std≈1).
+- **SAME-S** enc/dec: ~linear, run whole. ~89–98 dB vs reference.
+- **SAME-L** enc/dec: dense sliding-window-attention mask → cost is **O(L²)** on
+  whole-sequence runs. For long clips, **chunk with overlap = 8 latent tokens**
+  (accuracy is constant for any chunk ≥ the window; ~64 latent tokens is the
+  throughput sweet spot). 98–112 dB vs reference. SAME-S needs only overlap = 2.
+- **T5Gemma**: fixed 256 by design (prompt, padded+masked; independent of audio
+  duration). Matches the MLX (padded) and TRT (static) paths.
+
+## Running the full pipeline
+`prompt → T5Gemma → DiT (8-step rectified-flow) → SAME decoder → WAV` (conditioning
+and wave-conversion are now in-graph). A reference CLI (`sa3_tflite.py` in the
+`speed-metal` repo) drives it and prints per-stage timing. The only host-side pieces
+left are the SentencePiece tokenizer (prompt → input_ids) and the 8-step sampler loop.
+Encoders run the reverse (reference audio → latents).