run.py

#!/usr/bin/env python3
"""
Thera MLX — Arbitrary-scale super-resolution on Apple Silicon.

Usage:
    python run.py                         # Launch web UI (default port 5005)
    python run.py --port 8080             # Custom port
    python run.py run input.png out.png --scale 4 --model pro
    python run.py run input.png out.png --scale 2 --model air --ensemble --tiles 2
    python run.py convert --model air     # Download + convert weights
"""

import argparse
import sys
import os

# Make sure this directory is on the path so all modules import cleanly
sys.path.insert(0, os.path.dirname(__file__))


def main():
    parser = argparse.ArgumentParser(
        prog="thera-mlx",
        description="Thera MLX — arbitrary-scale super-resolution on Apple Silicon",
        formatter_class=argparse.RawDescriptionHelpFormatter,
        epilog=__doc__,
    )
    sub = parser.add_subparsers(dest="command")

    # ── gui (default) ────────────────────────────────────────────────────────
    gui_p = sub.add_parser("gui", help="Launch web UI (default)")
    gui_p.add_argument("--port", type=int, default=5005, help="Port (default: 5005)")
    gui_p.add_argument("--host", type=str, default="127.0.0.1")

    # ── run ──────────────────────────────────────────────────────────────────
    run_p = sub.add_parser("run", help="Upscale a single image from the CLI")
    run_p.add_argument("input",  help="Input image path")
    run_p.add_argument("output", help="Output image path")
    scale_g = run_p.add_mutually_exclusive_group(required=True)
    scale_g.add_argument("--scale", type=float, help="Scale factor  (e.g. 2, 4)")
    scale_g.add_argument("--size",  type=int, nargs=2, metavar=("H", "W"),
                          help="Exact target size  height width")
    run_p.add_argument("--model",    choices=["air", "pro"], default="air",
                        help="Model variant: air (fast) or pro (quality). Default: air")
    run_p.add_argument("--weights",  default=None,
                        help="Path to weights file (optional — uses bundled weights by default)")
    run_p.add_argument("--ensemble", action="store_true",
                        help="Geometric self-ensemble (4 rotations) — higher quality, slower")
    run_p.add_argument("--tiles",    type=int, choices=[2, 3, 4], default=None,
                        help="Tile NxN to reduce RAM usage (2, 3, or 4)")

    # ── convert ──────────────────────────────────────────────────────────────
    conv_p = sub.add_parser("convert", help="Download + convert weights from Flax format")
    conv_p.add_argument("--model",  choices=["air", "pro"], default="air")
    conv_p.add_argument("--input",  default=None,
                         help="Path to model.pkl (downloads from HuggingFace if omitted)")
    conv_p.add_argument("--no-jax", action="store_true",
                         help="Load pickle without JAX installed")

    args = parser.parse_args()

    # Default: no subcommand → launch GUI
    if args.command is None or args.command == "gui":
        _cmd_gui(args if args.command else argparse.Namespace(host="127.0.0.1", port=5005))
    elif args.command == "run":
        _cmd_run(args)
    elif args.command == "convert":
        _cmd_convert(args)


# ── Command implementations ───────────────────────────────────────────────────

def _cmd_gui(args):
    import logging
    logging.getLogger("werkzeug").setLevel(logging.ERROR)
    from ui import app
    print(f"\n  Thera MLX  →  http://{args.host}:{args.port}\n")
    app.run(host=args.host, port=args.port, debug=False, threaded=True)


def _cmd_run(args):
    from upscale import upscale_file
    upscale_file(
        args.input, args.output,
        scale=args.scale, size=args.size,
        model_size=args.model, weights_path=args.weights,
        ensemble=args.ensemble, tiles=args.tiles,
    )


def _cmd_convert(args):
    from pathlib import Path
    from upscale import WEIGHTS_DIR
    from convert import (
        REPO_IDS, download_model, convert_flax_to_mlx,
        load_pickle_with_jax, load_pickle_without_jax,
        save_safetensors, save_npz,
    )

    output_path = str(WEIGHTS_DIR / f"weights-{args.model}.safetensors")

    if args.input is None:
        repo = REPO_IDS[args.model]
        print(f"Downloading model from HuggingFace ({repo})...")
        pkl_path = download_model(args.model)
    else:
        pkl_path = args.input

    flax_params = (load_pickle_without_jax if args.no_jax else load_pickle_with_jax)(pkl_path)

    print("Converting weights...")
    mlx_weights = convert_flax_to_mlx(flax_params, size=args.model)

    total_params = sum(w.size for w in mlx_weights.values())
    print(f"  Parameters : {total_params:,}")
    print(f"  Weight keys: {len(mlx_weights)}")

    if output_path.endswith(".safetensors"):
        try:
            save_safetensors(mlx_weights, output_path)
        except ImportError:
            output_path = output_path.replace(".safetensors", ".npz")
            print("safetensors not installed — saving as .npz instead")
            save_npz(mlx_weights, output_path)
    else:
        save_npz(mlx_weights, output_path)

    print(f"Saved → {output_path}")


if __name__ == "__main__":
    main()