{
  "schema": "studycommons.hero.provenance.v2",
  "asset_role": "Study Commons public Daily Read hero",
  "slug": "paper-bridge-fair-test-evidence",
  "generation_id": "img-69bffa4dd732382ebdac804c",
  "generator": "QwenImage21Pipeline",
  "model": "Qwen/Qwen-Image-2.1-Turbo",
  "model_revision": "fc13ba1986fcb905854dc8de2f52eee395f49b5b",
  "model_license": "Qwen Research License, non-commercial research/evaluation only; commercial use requires separate permission",
  "model_index_sha256": "97e2febeb19d93cb41fa44807cde8bd42757950b5dffeacdb0331fb2d390cf20",
  "checkpoint_sha256": "37230cd77d377c0be0acc06d628fd8a6e978e23eb2c530b73fa98df54ef62557",
  "renderer_source_sha256": "18b5d04715eadcb1d3672ba2b4f1af5da95fb9f8ebfdea97b3add250b08712c9",
  "prompt": "A photorealistic three-quarter front angle classroom science experiment showing one paper bridge made from a SINGLE WHITE A4 SHEET that has been visibly folded lengthwise into a stiff accordion corrugation. The paper spans a clean air gap between TWO IDENTICAL HARDBACK BOOKS on a wooden classroom tabletop. CRITICAL GEOMETRY: along the exposed FRONT EDGE of the bridge, show at least FIVE SHARP ALTERNATING V-SHAPED PEAKS and troughs, like a real concertina or corrugated cardboard cross-section. These ridges are LONGITUDINAL: straight parallel creases run all the way across the gap from the left book to the right book. The sheet must NOT look flat or smoothly curved. The accordion folds have straight crisp edges, visibly alternating mountain and valley folds across the WIDTH of the bridge; there is NO flat unfolded strip. Show one single small unmarked silver metal washer resting stably at the center of the corrugated bridge to indicate a simple load, without flattening the zigzag folds. Two books only, parallel supports of equal height, realistic paper thickness and gravity, clear air gap beneath paper, natural daylight, educational museum-quality photograph, neutral background, wide landscape 16:9, subject fully visible with safe crop margins, no humans. Absolutely no legible text, letters, numbers, logos, arrows, charts, equations, graphs, labels, fake captions, extra books, floating parts, magical support, or smoothed-out flat paper. This is an illustrative setup, not empirical measurement.",
  "prompt_sha256": "10044ed78ef550e59523e5c0ea36d34910d2729cbbdcb509d0e54f6acac3ccfa",
  "negative_prompt": "",
  "seed": 1233790170,
  "width": 1312,
  "height": 736,
  "num_inference_steps": 8,
  "true_cfg_scale": 1.0,
  "scheduler": "FlowMatchEulerDiscreteScheduler",
  "sampling": "explicit-checkpoint-8-sigma",
  "sample_sigmas": [
    1.0,
    0.978453,
    0.95418,
    0.926626,
    0.89508,
    0.845148,
    0.704534,
    0.414568
  ],
  "generator_device": "cpu",
  "generator_algorithm": "torch.Generator('cpu').manual_seed(seed)",
  "torch_dtype": "bfloat16",
  "cpu_offload": "model",
  "use_kv_cache": true,
  "vae_tiling": true,
  "lora": null,
  "reference_images": [],
  "gpu": "NVIDIA CUDA (hardware-specific machine details withheld)",
  "runtime": {
    "torch": "2.14.0+cu130",
    "diffusers": "0.41.0.dev0",
    "transformers": "5.17.0",
    "cuda": "13.0"
  },
  "job_completed_at": "2026-10-13T20:09:41.090079+00:00",
  "output_png_sha256": "69bffa4dd732382ebdac804ccf8c0344e1bdb1a403e8d5bff2205eb408479b9c",
  "output_png_dimensions": [
    1312,
    736
  ],
  "postprocess": {
    "format": "AVIF",
    "ffmpeg_version": "ffmpeg version 6.1.1-3ubuntu5",
    "filter": "scale=1280:720:force_original_aspect_ratio=increase:flags=lanczos,crop=1280:720",
    "encoder": "libaom-av1",
    "still_picture": 1,
    "crf": 1,
    "bitrate": "0",
    "cpu_used": 6,
    "pix_fmt": "yuv420p",
    "frames": 1,
    "source_png_sha256": "69bffa4dd732382ebdac804ccf8c0344e1bdb1a403e8d5bff2205eb408479b9c",
    "output_avif_sha256": "1b2702172c41739e69d2c866b4db8bf145350a916b756409d16fd0e62265b44c",
    "output_avif_width": 1280,
    "output_avif_height": 720,
    "output_avif_bytes": 204316
  },
  "reproduce": {
    "recipe": "QwenImage21Pipeline.from_pretrained(model_path, dtype=torch.bfloat16); enable_model_cpu_offload(); vae.enable_tiling(); pipeline(prompt=prompt, width=width, height=height, true_cfg_scale=1.0, num_inference_steps=8, sigmas=sample_sigmas, use_kv_cache=True, generator=torch.Generator('cpu').manual_seed(seed)); encode PNG to AVIF using postprocess fields",
    "model_source": "Hugging Face model ID and exact model_revision above, subject to the stated license",
    "verification": "Compare generated image and encoded AVIF with the corresponding SHA-256 values above. Pixel-identical results are not guaranteed across devices or software builds.",
    "script": "tools/reproduce_hero.py"
  },
  "provenance_note": "This is an exact generation-parameter record and cryptographic integrity chain, not a legal ownership certificate or guarantee of third-party rights. Full private machine/job metadata is excluded.",
  "reproduction_verification": {
    "verified_at": "2026-10-13T20:14:13.473882+00:00",
    "method": "independent deterministic rerun from the public provenance parameters",
    "png_byte_identical": true,
    "avif_byte_identical": true,
    "replayed_png_sha256": "69bffa4dd732382ebdac804ccf8c0344e1bdb1a403e8d5bff2205eb408479b9c",
    "replayed_avif_sha256": "1b2702172c41739e69d2c866b4db8bf145350a916b756409d16fd0e62265b44c",
    "note": "Bitwise identity was verified on the recorded CUDA and software stack. Reproducibility may vary across devices."
  }
}