4c05a9daa8
wan_video 5B q8 verified clean on M3 Ultra (148.7s) and M2 Max (503.7s), identical 9.2GB max RSS on both; 81GB 'footprint' on m2max identified as MLX opportunistic cache, not demand. Frame-identical output at same seed across machines. HARDWARE.md gains wan_video and ardy_motion rows. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
76 lines
2.9 KiB
Python
76 lines
2.9 KiB
Python
import argparse
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import json
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import subprocess
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import sys
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from pathlib import Path
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ap = argparse.ArgumentParser()
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ap.add_argument("--input", action="append", default=[])
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ap.add_argument("--outdir", required=True)
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ap.add_argument("--params", default="{}")
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a = ap.parse_args()
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p = json.loads(a.params)
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if not a.input:
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print("ERROR: no input image")
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sys.exit(1)
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if not p.get("prompt"):
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print("ERROR: prompt is required — describe the edit you want")
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sys.exit(1)
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cli = Path(sys.executable).parent / "mflux-generate-qwen-edit"
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if not cli.exists():
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print(f"ERROR: {cli} missing. Run scripts/install_mflux.sh")
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sys.exit(1)
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src = Path(a.input[0])
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outdir = Path(a.outdir)
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out = outdir / f"{src.stem}_edited.png"
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# ALL inputs go to Qwen (it composes up to ~3 images: "put the shirt from image 2 on the person
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# in image 1"). Single-image jobs behave exactly as before.
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cmd = [str(cli), "--image-paths", *[str(Path(x).resolve()) for x in a.input],
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"--prompt", p["prompt"],
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"--steps", str(p.get("steps", 25)),
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"--seed", str(p.get("seed", 42)),
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"--guidance", str(p.get("guidance", 4.0)),
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"--output", str(out.resolve())]
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# Qwen-Edit LoRAs: "name" or "name:scale", comma-separated. Names resolve
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# (case-insensitive prefix match) against ~/Documents/localmodels/qwen-loras,
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# which mirrors ultra's civit/_parked-qwen stash. mflux natively takes
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# --lora-paths/--lora-scales; a LoRA for the wrong base is a silent no-op.
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LORA_DIR = Path.home() / "Documents" / "localmodels" / "qwen-loras"
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if p.get("loras"):
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paths, scales = [], []
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avail = {f.name.lower(): f for f in LORA_DIR.rglob("*.safetensors")}
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for item in str(p["loras"]).split(","):
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item = item.strip()
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if not item:
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continue
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name, _, scale = item.partition(":")
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name = name.strip().lower()
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hit = avail.get(name) or avail.get(name + ".safetensors") or next(
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(f for k, f in sorted(avail.items()) if k.startswith(name)), None)
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if not hit:
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print(f"ERROR: no LoRA matching '{name}' under {LORA_DIR}")
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print("available:", ", ".join(sorted(f.stem for f in avail.values())[:40]))
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sys.exit(1)
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paths.append(str(hit))
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scales.append(str(float(scale) if scale.strip() else 1.0))
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if paths:
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cmd += ["--lora-paths", *paths, "--lora-scales", *scales]
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print("+", " ".join(cmd), flush=True)
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print("(first run downloads Qwen-Image-Edit weights)", flush=True)
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res = subprocess.run(cmd, cwd=str(outdir), stdout=sys.stdout, stderr=subprocess.STDOUT)
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if res.returncode != 0:
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sys.exit(res.returncode)
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pngs = sorted(outdir.rglob("*.png"))
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if not pngs:
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print("ERROR: no edited image produced")
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sys.exit(1)
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img = out if out.exists() else pngs[-1]
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(outdir / "result.json").write_text(json.dumps(
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{"outputs": [{"path": img.name, "meta": {"tool": "qwen-image-edit"}}]}))
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print(f"done: {img.name}", flush=True)
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