Datasets:
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ef222b5 d895b3e ef222b5 d895b3e ef222b5 d895b3e ef222b5 d895b3e ef222b5 d895b3e ef222b5 d895b3e ef222b5 d895b3e ef222b5 d895b3e ef222b5 1b3319a ef222b5 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 | from __future__ import annotations
import csv
import json
from collections import Counter, defaultdict
from pathlib import Path
from typing import Any
def csv_count(path: Path) -> tuple[int, set[str]]:
with path.open("r", encoding="utf-8", newline="") as stream:
rows = list(csv.DictReader(stream))
return len(rows), set(rows[0]) if rows else set()
def verify(config: dict[str, Any], results_dir: Path) -> dict[str, Any]:
failures = []
expected_runs = len(config["prompts"]) * int(config["campaign"]["replicates"]) * len(config["formats"])
expected_per_format = len(config["prompts"]) * int(config["campaign"]["replicates"])
expected_noise_groups = len(config["prompts"]) * int(config["campaign"]["replicates"])
expected_trajectory_rows = (len(config["formats"]) - 1) * expected_per_format * int(config["controls"]["steps"]) * 2
expected_weight_rows = (len(config["formats"]) - 1) * 430
def require(condition: bool, message: str) -> None:
if not condition:
failures.append(message)
manifest = json.loads((results_dir / "manifest.json").read_text(encoding="utf-8"))
require(manifest["config_sha256"] == config["_config_sha256"], "Manifest configuration hash differs from current configuration")
for name, expected in config["integrity"]["expected_sha256"].items():
model = manifest["models"].get(name, {})
require(model.get("sha256") == expected, f"Publisher hash mismatch or missing hash for {name}")
require(model.get("publisher_hash_match") is True, f"Publisher match flag is not true for {name}")
for name in ("preflight.json", "campaign_complete.json", "report_complete.json"):
require((results_dir / name).is_file(), f"Missing completion marker {name}")
require(json.loads((results_dir / "preflight.json").read_text(encoding="utf-8"))["passed"] is True, "Preflight marker is not passed")
require(json.loads((results_dir / "campaign_complete.json").read_text(encoding="utf-8"))["scored_runs"] == expected_runs, f"Campaign marker does not declare {expected_runs} runs")
if config.get("_base_results_dir"):
require((results_dir / "extension_import.json").is_file(), "Extension import marker is missing")
require((results_dir / "metrics" / "performance_bridge.json").is_file(), "Performance bridge evidence is missing")
matrix = json.loads((results_dir / "run_matrix.json").read_text(encoding="utf-8"))
require(len(matrix) == expected_runs, f"Run matrix has {len(matrix)} rows instead of {expected_runs}")
require(len({row["run_id"] for row in matrix}) == expected_runs, "Run matrix contains duplicate run ids")
counts = Counter(row["format_id"] for row in matrix)
require(all(counts[format_id] == expected_per_format for format_id in config["formats"]), f"Format counts are wrong: {dict(counts)}")
noise_groups = defaultdict(list)
for row in matrix:
directory = results_dir / "captures" / row["run_id"]
required_files = ["metadata.json", "image.png", "decoded_float32.npy", "final_latent_float32.npy", "trajectory.npz"]
require(all((directory / name).is_file() for name in required_files), f"Incomplete capture: {row['run_id']}")
if (directory / "metadata.json").is_file():
metadata = json.loads((directory / "metadata.json").read_text(encoding="utf-8"))
require({"sampling", "vae_decode", "output"}.issubset(metadata), f"Metadata sections missing: {row['run_id']}")
noise_groups[(row["prompt_id"], row["replicate"])].append(metadata["sampling"]["noise_sha256"])
require(len(noise_groups) == expected_noise_groups, f"Expected {expected_noise_groups} prompt-seed noise groups, found {len(noise_groups)}")
for key, values in noise_groups.items():
require(len(values) == len(config["formats"]) and len(set(values)) == 1, f"Noise mismatch for {key}: {values}")
for format_id in config["formats"]:
hashes = []
for repeat in range(3):
metadata_path = results_dir / "captures" / f"repeatability__{format_id}__{repeat}" / "metadata.json"
require(metadata_path.is_file(), f"Missing repeatability result {format_id}/{repeat}")
if metadata_path.is_file():
hashes.append(json.loads(metadata_path.read_text(encoding="utf-8"))["output"]["image_sha256"])
require(len(hashes) == 3 and len(set(hashes)) == 1, f"Repeatability mismatch for {format_id}: {hashes}")
expected_tables = {
"image_core.csv": expected_runs,
"image_advanced.csv": expected_runs,
"latency_components.csv": expected_runs,
"performance_runs.csv": expected_runs,
"trajectory.csv": expected_trajectory_rows,
"weight_parameters_all.csv": expected_weight_rows,
}
for name, expected_count in expected_tables.items():
path = results_dir / "metrics" / name
require(path.is_file(), f"Missing metric table {name}")
if path.is_file():
count, _ = csv_count(path)
require(count == expected_count, f"Metric table {name} has {count} rows, expected {expected_count}")
advanced_count, advanced_fields = csv_count(results_dir / "metrics" / "image_advanced.csv")
required_advanced = {
"lpips_alex",
"lpips_vgg",
"dists",
"ms_ssim",
"dinov2_large_image_cosine_to_bf16",
"openclip_vith14_prompt_score",
"pickscore_v1",
"hpsv2_1",
"pyiqa_musiq",
"pyiqa_maniqa",
"pyiqa_topiq_nr",
"pyiqa_niqe",
"pyiqa_brisque",
"ocr_expected_phrase_exact_fraction",
}
require(required_advanced.issubset(advanced_fields), f"Advanced metric columns missing: {sorted(required_advanced - advanced_fields)}")
for folder in ("full", "differences", "details"):
count = len(list((results_dir / "comparison_sheets" / folder).glob("*.png")))
require(count == 30, f"Comparison sheet folder {folder} has {count} PNGs instead of 30")
require((results_dir / "comparison_sheets" / "contact_sheet_replicate0.png").is_file(), "Master contact sheet is missing")
for format_id in config["formats"]:
require((results_dir / "telemetry" / f"{format_id}.csv").is_file(), f"GPU telemetry missing for {format_id}")
require((results_dir / "telemetry" / f"system_{format_id}.csv").is_file(), f"System telemetry missing for {format_id}")
equivalence_path = results_dir / "validation" / "sampler_equivalence.json"
require(equivalence_path.is_file(), "Sampler equivalence evidence is missing")
if equivalence_path.is_file():
equivalence = json.loads(equivalence_path.read_text(encoding="utf-8"))
require(equivalence["latent_bit_exact"] and equivalence["image_bit_exact"], "Custom sampler is not bit-exact with standard KSampler")
require(equivalence["latent_max_abs"] == 0.0 and equivalence["image_max_abs"] == 0.0, "Sampler equivalence has nonzero error")
scored_errors = []
with (results_dir / "runs.jsonl").open("r", encoding="utf-8") as stream:
for line in stream:
row = json.loads(line)
if row.get("scored") and row.get("status") == "error":
scored_errors.append(row["run_id"])
require(not scored_errors, f"Scored run ledger contains errors: {scored_errors}")
report_path = results_dir / "TECHNICAL_REPORT.md"
require(report_path.is_file(), "Technical report is missing")
if report_path.is_file():
report = report_path.read_text(encoding="utf-8")
for phrase in ("Best quantized BF16 fidelity", "INT8 ConvRot", "INT4 ConvRot", "GGUF Q8_0", "GGUF Q4_K_M", "OCR on prompts", "bit-identical", "Primary sources"):
require(phrase in report, f"Technical report is missing required section/claim: {phrase}")
workflows = [Path(config["_config_path"]).parent / "workflows" / "krea2_benchmark_api.json", Path(config["_config_path"]).parent / "workflows" / "krea2_benchmark_interactive.json"]
has_gguf = any(fmt.startswith("gguf") for fmt in config.get("formats", {}))
if has_gguf:
workflows.extend([
Path(config["_config_path"]).parent / "workflows" / "krea2_benchmark_api_gguf.json",
Path(config["_config_path"]).parent / "workflows" / "krea2_benchmark_interactive_gguf.json"
])
for workflow in workflows:
require(workflow.is_file(), f"Workflow is missing: {workflow.name}")
if workflow.is_file():
json.loads(workflow.read_text(encoding="utf-8"))
result = {
"passed": not failures,
"failures": failures,
"scored_runs": len(matrix),
"format_counts": dict(counts),
"noise_groups": len(noise_groups),
"metric_tables_checked": expected_tables,
"advanced_metric_columns_checked": sorted(required_advanced),
"comparison_sheet_groups": 30,
}
output_path = results_dir / "validation" / "completion_audit.json"
output_path.parent.mkdir(parents=True, exist_ok=True)
output_path.write_text(json.dumps(result, indent=2, sort_keys=True), encoding="utf-8")
if failures:
raise RuntimeError("Completion audit failed:\n- " + "\n- ".join(failures))
return result
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