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5c50fa8588
Trains a real fal.ai LoRA fine-tune (fal-ai/flux-lora-fast-training) on frames extracted from the real fal.ai-generated room-visualization MP4s already captured this session, so display-layer generation has a specific trained style target instead of prompt engineering against a stock model. Architecture boundary: this model only ever produces supplementary synthetic visual/pixel output. It has no path into measured/derived RF, pose, vitals, identity, or confidence values, which remain local and authoritative. Real run: request 01a060b2-c940-7972-abf9-ea48847eb2ae, 1000 steps, 27 training images, ~$2.04 spend, validated with a real inference call against the trained LoRA (assets/fal-visual-teacher-sample.png).
95 lines
3.1 KiB
Python
95 lines
3.1 KiB
Python
#!/usr/bin/env python3
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"""Run a real validation inference call against the trained visual-teacher LoRA.
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Loads the trained model reference written by train_lora.py, calls
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fal-ai/flux-lora with the trained LoRA weights, and saves the generated
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image as evidence that training actually produced a working style LoRA.
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This inference call, and the LoRA it exercises, produce SYNTHETIC display
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imagery only (ADR-353) -- never a source of measured RF/pose/vitals/identity
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or confidence values.
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Requires FAL_KEY in the environment.
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"""
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import argparse
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import json
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import os
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import sys
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import urllib.request
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from pathlib import Path
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REPO_ROOT = Path(__file__).resolve().parents[2]
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SCRIPT_DIR = Path(__file__).resolve().parent
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INFERENCE_ENDPOINT = "fal-ai/flux-lora"
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DEFAULT_PROMPT = (
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"ruviewstyle room visualization, a living room with a glowing cyan pose "
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"skeleton overlay, wifi signal arcs radiating across the space, a vitals "
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"readout panel, dark tech aesthetic, synthetic sensor visualization"
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)
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def main() -> int:
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parser = argparse.ArgumentParser(description=__doc__)
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parser.add_argument(
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"--training-result", default=str(SCRIPT_DIR / "output" / "training-result.json"),
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)
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parser.add_argument("--prompt", default=DEFAULT_PROMPT)
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parser.add_argument("--scale", type=float, default=1.0, help="LoRA application strength")
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parser.add_argument(
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"--out", default=str(REPO_ROOT / "assets" / "fal-visual-teacher-sample.png"),
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)
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args = parser.parse_args()
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if not os.environ.get("FAL_KEY"):
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print("ERROR: FAL_KEY not set in environment", file=sys.stderr)
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return 1
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import fal_client
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training_result_path = Path(args.training_result)
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record = json.loads(training_result_path.read_text(encoding="utf-8"))
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lora_url = record["result"]["diffusers_lora_file"]["url"]
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print(f"Using trained LoRA: {lora_url}")
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print(f"Submitting validation inference to {INFERENCE_ENDPOINT}...")
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result = fal_client.subscribe(
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INFERENCE_ENDPOINT,
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arguments={
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"prompt": args.prompt,
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"loras": [{"path": lora_url, "scale": args.scale}],
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"image_size": "landscape_16_9",
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"num_images": 1,
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"output_format": "png",
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},
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with_logs=True,
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)
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images = result.get("images", [])
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if not images:
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print(f"ERROR: no images in result: {result}", file=sys.stderr)
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return 1
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image_url = images[0]["url"]
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print(f"Generated image: {image_url}")
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out_path = Path(args.out)
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out_path.parent.mkdir(parents=True, exist_ok=True)
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urllib.request.urlretrieve(image_url, out_path)
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print(f"Saved validation sample to {out_path}")
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meta_path = out_path.with_suffix(".json")
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meta_path.write_text(json.dumps({
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"endpoint": INFERENCE_ENDPOINT,
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"lora_url": lora_url,
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"prompt": args.prompt,
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"scale": args.scale,
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"source_image_url": image_url,
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}, indent=2), encoding="utf-8")
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print(f"Saved validation metadata to {meta_path}")
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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