File size: 6,113 Bytes
ce2829b | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | from __future__ import annotations
import copy
import math
import statistics
from pathlib import Path
from typing import Any, Iterable
from PIL import Image, ImageDraw, ImageFont
PAIRING_FIELDS = (
"prompt_id",
"prompt",
"seed",
"width",
"height",
"num_inference_steps",
"guidance_scale",
)
def json_safe_loading_info(loading_info: dict[str, Any]) -> dict[str, Any]:
"""Normalize Hugging Face loading diagnostics for stable JSON output."""
result: dict[str, Any] = {}
for key, value in loading_info.items():
if isinstance(value, set):
result[key] = sorted(value)
elif isinstance(value, tuple):
result[key] = list(value)
else:
result[key] = value
return result
def rewrite_modular_component_sources(
model_index: dict[str, Any], source: str
) -> dict[str, Any]:
"""Point component specs in a modular model index at one release tree."""
rewritten = copy.deepcopy(model_index)
for value in rewritten.values():
if (
isinstance(value, list)
and len(value) >= 3
and isinstance(value[0], str)
and value[0] in {"diffusers", "transformers"}
and isinstance(value[2], dict)
and "subfolder" in value[2]
):
value[2]["pretrained_model_name_or_path"] = source
value[2]["revision"] = None
return rewritten
def validate_paired_records(
original: list[dict[str, Any]], orbitquant: list[dict[str, Any]]
) -> None:
if len(original) != len(orbitquant):
raise ValueError(
f"paired record count differs: original={len(original)}, "
f"orbitquant={len(orbitquant)}"
)
for index, (original_row, quantized_row) in enumerate(zip(original, orbitquant)):
for field in PAIRING_FIELDS:
if original_row.get(field) != quantized_row.get(field):
raise ValueError(
f"paired record {index} differs in {field}: "
f"{original_row.get(field)!r} != {quantized_row.get(field)!r}"
)
def summarize_metrics(rows: list[dict[str, Any]]) -> dict[str, Any]:
if not rows:
raise ValueError("cannot summarize an empty metric set")
seconds = [float(row["generation_seconds"]) for row in rows]
hot = seconds[1:] or seconds
result: dict[str, Any] = {
"generated_samples": len(rows),
"first_generation_seconds": seconds[0],
"hot_generation_mean_seconds": statistics.fmean(hot),
"hot_generation_median_seconds": statistics.median(hot),
"generation_mean_seconds": statistics.fmean(seconds),
"generation_median_seconds": statistics.median(seconds),
}
for field in ("gpu_peak_mb", "torch_peak_mb"):
values = [row.get(field) for row in rows if row.get(field) is not None]
result[field] = max(values) if values else None
return result
def _font(label_height: int) -> ImageFont.ImageFont | ImageFont.FreeTypeFont:
size = max(10, label_height // 3)
for name in ("DejaVuSans-Bold.ttf", "Arial Bold.ttf", "Arial.ttf"):
try:
return ImageFont.truetype(name, size=size)
except OSError:
continue
return ImageFont.load_default()
def _open_native_rgb(path: Path, tile_size: tuple[int, int]) -> Image.Image:
with Image.open(path) as source:
if source.size != tile_size:
raise ValueError(
f"{path} has size {source.size}; expected native tile size {tile_size}"
)
return source.convert("RGB")
def create_full_resolution_matrix(
pairs: Iterable[dict[str, Any]],
output_path: str | Path,
*,
tile_size: tuple[int, int] = (2048, 2048),
prompt_pairs_per_row: int = 2,
label_height: int = 96,
) -> dict[str, Any]:
records = list(pairs)
if not records:
raise ValueError("at least one image pair is required")
if prompt_pairs_per_row <= 0:
raise ValueError("prompt_pairs_per_row must be positive")
if label_height <= 0:
raise ValueError("label_height must be positive")
tile_width, tile_height = tile_size
row_count = math.ceil(len(records) / prompt_pairs_per_row)
matrix_width = prompt_pairs_per_row * 2 * tile_width
row_height = label_height + tile_height
matrix_height = row_count * row_height
matrix = Image.new("RGB", (matrix_width, matrix_height), "#111111")
draw = ImageDraw.Draw(matrix)
font = _font(label_height)
for index, record in enumerate(records):
row = index // prompt_pairs_per_row
group = index % prompt_pairs_per_row
x = group * 2 * tile_width
label_y = row * row_height
image_y = label_y + label_height
original = _open_native_rgb(Path(record["original"]), tile_size)
quantized = _open_native_rgb(Path(record["orbitquant"]), tile_size)
matrix.paste(original, (x, image_y))
matrix.paste(quantized, (x + tile_width, image_y))
seed = record.get("seed", "-")
title = str(record.get("title", f"Prompt {index + 1}"))
draw.text(
(x + 12, label_y + 4),
f"{index + 1:02d} {title} 路 BF16 路 seed {seed}",
fill="white",
font=font,
)
draw.text(
(x + tile_width + 12, label_y + 4),
f"{index + 1:02d} {title} 路 OrbitQuant W4A4 路 seed {seed}",
fill="white",
font=font,
)
destination = Path(output_path)
destination.parent.mkdir(parents=True, exist_ok=True)
if destination.suffix.lower() == ".webp":
matrix.save(destination, format="WEBP", lossless=True, quality=100, method=6)
else:
matrix.save(destination)
return {
"matrix_path": str(destination),
"matrix_size": [matrix_width, matrix_height],
"tile_size": [tile_width, tile_height],
"prompt_count": len(records),
"prompt_pairs_per_row": prompt_pairs_per_row,
"label_height": label_height,
"resized": False,
}
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