xsl
ab6b0cb0bb
报告图片已压缩为内嵌 JPG(缩放至300px宽,质量70),体积从 ~45MB 降至 ~3MB: - static/benchmark_report.html: 4模型×3分辨率矩阵对比 (2.7MB) - static/hairstyle_report.html: 3图×5发型×10组合对比 (3.1MB) 同步更新报告生成脚本支持图片压缩,并从 .gitignore 移除报告文件排除。
165 lines
6.7 KiB
Python
165 lines
6.7 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""把 benchmark_out/matrix/results.json 生成 HTML 报告。
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每个组合一行：原图 + 3次生发图 + 耗时/显存。
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图片用 base64 内嵌（自包含单文件，便于部署）。
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"""
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import base64
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import json
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import os
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from pathlib import Path
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OUT = Path("/home/ubuntu/hair/benchmark_out/matrix")
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RESULTS = OUT / "results.json"
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HTML = OUT / "report.html"
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RES_LABEL = {"orig": "原图", "640": "640", "896": "896(默认)"}
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MODEL_LABEL = {
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"4b-fp8": "4B fp8 (3.8G)",
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"9b-fp8": "9B fp8 (8.8G)",
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"9b-Q5": "9B Q5_K_M (6.6G)",
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"9b-Q4": "9B Q4_K_M (5.6G)",
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}
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MODEL_ORDER = ["4b-fp8", "9b-Q4", "9b-Q5", "9b-fp8"]
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def img_b64(path, max_w=320, quality=72):
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"""读图片，缩放到 max_w 宽、JPEG 降质后转 base64 data URI（压缩体积便于入 git）。"""
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if not path or not os.path.isfile(path):
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return None
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from io import BytesIO
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from PIL import Image
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im = Image.open(path).convert("RGB")
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if im.width > max_w:
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nh = round(im.height * max_w / im.width)
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im = im.resize((max_w, nh), Image.LANCZOS)
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buf = BytesIO()
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im.save(buf, format="JPEG", quality=quality, optimize=True)
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b = base64.b64encode(buf.getvalue()).decode()
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return f"data:image/jpeg;base64,{b}"
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def thumb(data_uri, alt="", cls=""):
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if not data_uri:
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return f'<div class="na {cls}">⚠ 失败</div>'
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return f'<img class="{cls}" src="{data_uri}" alt="{alt}" loading="lazy">'
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def main():
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data = json.load(open(RESULTS, encoding="utf-8"))
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# 预读原图 base64（3 张图）
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orig_b64 = {}
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for c in data:
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ip = c["img_path"]
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if c["img"] not in orig_b64:
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orig_b64[c["img"]] = img_b64(ip, 200)
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# 统计：每个模型的平均耗时、平均峰值显存
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stats = {}
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for c in data:
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m = c["model"]
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stats.setdefault(m, {"times": [], "peaks": []})
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for r in c["runs"]:
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if not r["error"]:
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stats[m]["times"].append(r["elapsed"])
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stats[m]["peaks"].append(r["gpu_peak"])
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rows_html = []
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# 按模型顺序、分辨率顺序、图片顺序排列
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for m in MODEL_ORDER:
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mdata = [c for c in data if c["model"] == m]
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for rlabel in ["orig", "640", "896"]:
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for ilabel in ["asdf", "qwer", "girl5"]:
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c = next((x for x in mdata if x["res"] == rlabel and x["img"] == ilabel), None)
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if not c:
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continue
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# 3 次结果图
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run_cells = []
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for i, r in enumerate(c["runs"]):
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uri = img_b64(r["grown_path"]) if not r["error"] else None
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if uri:
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run_cells.append(
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f'<div class="run-cell"><div class="run-label">第{i+1}次 · {r["elapsed"]:.1f}s</div>'
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f'{thumb(uri, f"r{i+1}", "result-img")}</div>'
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)
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else:
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run_cells.append(
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f'<div class="run-cell"><div class="run-label">第{i+1}次 · 失败</div>'
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f'<div class="na">⚠ {r["error"][:30] if r["error"] else ""}</div></div>'
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)
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orig = orig_b64.get(c["img"])
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rows_html.append(f'''
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<div class="combo-row">
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<div class="cell-model">{MODEL_LABEL.get(m, m)}<div class="cell-sub">res={RES_LABEL.get(rlabel, rlabel)}</div></div>
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<div class="cell-img">{thumb(orig, "原图", "orig-img")}<div class="run-label">{ilabel}</div></div>
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<div class="cell-runs">{"".join(run_cells)}</div>
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</div>''')
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# 模型对比汇总
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summary_rows = []
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for m in MODEL_ORDER:
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s = stats.get(m, {"times": [], "peaks": []})
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if s["times"]:
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avg_t = sum(s["times"]) / len(s["times"])
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max_p = max(s["peaks"]) / 1024
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summary_rows.append(
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f"<tr><td>{MODEL_LABEL.get(m,m)}</td><td>{avg_t:.1f}s</td>"
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f"<td>{max_p:.1f} GB</td><td>{len(s['times'])} 成功</td></tr>"
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)
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html = f"""<!DOCTYPE html>
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<html lang="zh-CN">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Flux 模型矩阵测试报告 — 接口2女性花瓣形</title>
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<style>
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* {{ box-sizing: border-box; margin: 0; padding: 0; }}
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body {{ font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif; background: #f5f5f5; color: #333; padding: 20px; }}
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h1 {{ font-size: 22px; margin-bottom: 4px; }}
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.subtitle {{ color: #888; font-size: 13px; margin-bottom: 16px; }}
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.summary {{ background: #fff; border-radius: 10px; padding: 16px 20px; margin-bottom: 20px; box-shadow: 0 1px 4px rgba(0,0,0,.06); }}
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.summary h2 {{ font-size: 16px; margin-bottom: 10px; }}
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.summary table {{ border-collapse: collapse; width: 100%; font-size: 14px; }}
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.summary th, .summary td {{ border: 1px solid #e5e7eb; padding: 8px 12px; text-align: left; }}
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.summary th {{ background: #f9fafb; font-weight: 600; }}
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.combo-row {{ display: flex; align-items: flex-start; gap: 12px; background: #fff; border-radius: 10px;
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padding: 12px 16px; margin-bottom: 10px; box-shadow: 0 1px 3px rgba(0,0,0,.05); }}
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.cell-model {{ min-width: 130px; font-weight: 700; font-size: 14px; padding-top: 6px; }}
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.cell-sub {{ font-weight: 400; font-size: 12px; color: #6b7280; margin-top: 2px; }}
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.cell-img {{ min-width: 160px; text-align: center; }}
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.cell-runs {{ display: flex; gap: 10px; flex: 1; }}
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.run-cell {{ text-align: center; }}
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.run-label {{ font-size: 11px; color: #6b7280; margin-bottom: 4px; }}
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img {{ border-radius: 6px; max-height: 200px; max-width: 100%; object-fit: contain; background: #f9fafb; }}
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.orig-img {{ max-height: 180px; border: 2px solid #e5e7eb; }}
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.result-img {{ max-height: 200px; }}
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.na {{ color: #d1d5db; font-size: 12px; padding: 40px 20px; background: #f9fafb; border-radius: 6px; width: 150px; }}
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</style>
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</head>
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<body>
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<h1>💇 Flux 模型矩阵测试报告</h1>
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<p class="subtitle">接口2女性 · 花瓣形发型 · 4模型 × 3分辨率 × 3图 × 3次 = 108 次 · RTX 3090 24GB</p>
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<div class="summary">
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<h2>📊 模型对比汇总</h2>
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<table>
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<tr><th>模型</th><th>平均耗时</th><th>峰值显存</th><th>成功次数</th></tr>
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{"".join(summary_rows)}
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</table>
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</div>
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<h2 style="font-size:16px;margin:24px 0 12px">🖼️ 各组合对比（每行：原图 + 3次生发结果）</h2>
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{"".join(rows_html)}
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</body>
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</html>"""
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with open(HTML, "w", encoding="utf-8") as f:
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f.write(html)
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print(f"✓ 报告已生成: {HTML} ({HTML.stat().st_size//1024} KB)")
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if __name__ == "__main__":
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main()
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