cleanup: remove legacy models, obsolete TRT caches, update archive

Removed:
- trt_cache/sm89/trt10.14_ort1.24_v3/ (manual merge, replaced by v2)
- trt_cache/sm89/trt10.14_ort1.24/ (first version, no suffix)
- trt_cache/sm89/trt10.9_ort1.24/ (old TRT 10.9)
- 14 root-level *.onnx duplicates (actual models in models/)
- buffalo_l/ root (InsightFace legacy: genderage, 2d106det, 1k3d68, det_10g_fp16)
- dfl_xseg.onnx (old DeepFaceLab XSeg, replaced by xseg_1/2/3)
- scripts/convert_scrfd_fp16.py (one-time converter)

Updated:
- archives/trt-cache-sm89.tar.zst (rebuilt from v2 production cache)

Kept:
- models/ (15 active ONNX models)
- trt_cache/sm89/trt10.14_ort1.24_v2/ (production TRT cache, 23 engines)
- deploy/cuda-12.9-py312/ (app_code + runtime_kit)
- gfpgan/ (face restoration weights)
- archives/models-core-masks.tar.zst

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scripts/convert_scrfd_fp16.py

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-#!/usr/bin/env python3
-"""Convert SCRFD det_10g.onnx from FP32 to FP16.
-
-Usage:
-    pip install onnx onnxconverter-common
-    python scripts/convert_scrfd_fp16.py \
-        --input /path/to/det_10g.onnx \
-        --output /path/to/det_10g_fp16.onnx
-
-Design decisions:
-- op_block_list=['BatchNormalization'] — epsilon 1e-5 underflows to 0 in FP16 → NaN.
-  Keeping BN in FP32 prevents this while still converting ~95% of ops to FP16.
-- keep_io_types=True — Input/output remain float32 for compatibility.
-  No preprocessing changes needed in SCRFD pipeline.
-- onnx.checker validates structural integrity after conversion.
-"""
-
-import argparse
-import sys
-from pathlib import Path
-
-
-def convert_fp16(input_path: str, output_path: str) -> None:
-    """Convert ONNX model from FP32 to FP16."""
-    try:
-        import onnx
-        from onnxconverter_common import float16
-    except ImportError:
-        print("Missing dependencies. Install:")
-        print("  pip install onnx onnxconverter-common")
-        sys.exit(1)
-
-    input_file = Path(input_path)
-    if not input_file.exists():
-        print(f"Input file not found: {input_path}")
-        sys.exit(1)
-
-    print(f"Loading {input_path} ...")
-    model = onnx.load(input_path)
-
-    input_size_mb = input_file.stat().st_size / (1024 * 1024)
-    print(f"  Input size: {input_size_mb:.1f} MB")
-    print(f"  Opset version: {model.opset_import[0].version}")
-
-    # Convert to FP16 with BatchNormalization excluded
-    # BN epsilon (1e-5) underflows to 0 in FP16 → division by zero → NaN
-    print("Converting to FP16 (excluding BatchNormalization) ...")
-    model_fp16 = float16.convert_float_to_float16(
-        model,
-        op_block_list=["BatchNormalization"],
-        keep_io_types=True,
-    )
-
-    # Validate
-    print("Validating converted model ...")
-    onnx.checker.check_model(model_fp16)
-
-    # Save
-    output_file = Path(output_path)
-    output_file.parent.mkdir(parents=True, exist_ok=True)
-    onnx.save(model_fp16, output_path)
-
-    output_size_mb = output_file.stat().st_size / (1024 * 1024)
-    ratio = output_size_mb / input_size_mb * 100
-    print(f"  Output size: {output_size_mb:.1f} MB ({ratio:.0f}% of original)")
-    print(f"  Saved to: {output_path}")
-    print("Done.")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Convert SCRFD det_10g.onnx FP32 → FP16")
-    parser.add_argument("--input", required=True, help="Path to FP32 det_10g.onnx")
-    parser.add_argument("--output", required=True, help="Output path for FP16 model")
-    args = parser.parse_args()
-
-    convert_fp16(args.input, args.output)

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