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"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "a24d02a2",
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"source": [
"import json\n",
"import pandas as pd\n",
"import numpy as np\n",
"from pathlib import Path\n",
"from datetime import datetime\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"# ============================================================================\n",
"# SECTION 1: Load and Parse Results from GPU Tuning Runs\n",
"# ==========================-==================================================\n",
"print(\"=\" * 80)\n",
"print(\"LOADING TUNING RESULTS FROM GPU RUNS\")\n",
"print(\"=\" * 80)\n",
"\n",
"results_dir = Path(\"RESULTS_TURNING/run_2\")\n",
"all_experiments = []\n",
"baseline_metrics = None\n",
"\n",
"# Collect results from all GPU runs\n",
"for gpu_id in range(8):\n",
" gpu_dir = results_dir / f\"gpu_{gpu_id}\"\n",
" results_file = gpu_dir / \"tuning_results.json\"\n",
" \n",
" if results_file.exists():\n",
" with open(results_file, 'r') as f:\n",
" data = json.load(f)\n",
" \n",
" # Extract baseline (same across all GPUs)\n",
" if baseline_metrics is None and \"baseline\" in data:\n",
" baseline_metrics = data[\"baseline\"][\"metrics\"]\n",
" print(f\"\\n📊 Baseline Metrics (cfg_scale=5.0):\")\n",
" for metric, value in baseline_metrics.items():\n",
" print(f\" {metric:15s}: {value:.6f}\")\n",
" \n",
" # Collect all experiments\n",
" if \"experiments\" in data:\n",
" all_experiments.extend(data[\"experiments\"])\n",
" print(f\"✓ GPU {gpu_id}: {len(data['experiments'])} results loaded\")\n",
"\n",
"print(f\"\\n✓ Total experiments loaded: {len(all_experiments)}\")\n",
"\n",
"# ============================================================================\n",
"# SECTION 2: Filter Top Configs with Improvements Across All Metrics\n",
"# ============================================================================\n",
"print(\"\\n\" + \"=\" * 80)\n",
"print(\"FILTERING CONFIGURATIONS WITH IMPROVEMENTS IN ALL METRICS\")\n",
"print(\"=\" * 80)\n",
"\n",
"# Define improvement metrics to track (using ImageReward instead of Reward)\n",
"improvement_metrics = [\n",
" \"aesthetic_improvement\", \n",
" \"imagereward_improvement\", \n",
" \"clip_improvement\", \n",
" \"pickscore_improvement\", \n",
" \"hpsv2_improvement\"\n",
" ]\n",
"\n",
"# Filter experiments with improvements in ALL metrics\n",
"top_configs = []\n",
"\n",
"for exp in all_experiments:\n",
" if \"improvements\" not in exp or \"config\" not in exp or \"metrics\" not in exp:\n",
" continue\n",
" \n",
" improvements = exp[\"improvements\"]\n",
" config = exp[\"config\"]\n",
" metrics = exp[\"metrics\"]\n",
" \n",
" # Check if ALL improvements are positive (>0)\n",
" all_positive = all(improvements.get(metric, -1) > 0 for metric in improvement_metrics)\n",
" \n",
" if all_positive:\n",
" # Calculate aggregate improvement score\n",
" avg_improvement = np.mean([improvements.get(metric, 0) for metric in improvement_metrics])\n",
" \n",
" top_configs.append({\n",
" \"config\": config,\n",
" \"metrics\": metrics,\n",
" \"improvements\": improvements,\n",
" \"avg_improvement\": avg_improvement\n",
" })\n",
"\n",
"print(f\"✓ Found {len(top_configs)} configurations with improvements in ALL metrics\")\n",
"\n",
"# Sort by average improvement\n",
"top_configs.sort(key=lambda x: x[\"avg_improvement\"], reverse=True)\n",
"\n",
"# Get top 10\n",
"top_10 = top_configs[:10]\n",
"print(f\"✓ Extracted top 10 best performing configurations\")\n",
"\n",
"# ============================================================================\n",
"# SECTION 3: Create Comprehensive Results Table\n",
"# ============================================================================\n",
"print(\"\\n\" + \"=\" * 80)\n",
"print(\"CREATING COMPREHENSIVE RESULTS TABLE\")\n",
"print(\"=\" * 80)\n",
"\n",
"# Build detailed table data\n",
"table_data = []\n",
"\n",
"for rank, result in enumerate(top_10, 1):\n",
" cfg = result[\"config\"]\n",
" metrics = result[\"metrics\"]\n",
" improvements = result[\"improvements\"]\n",
" \n",
" row = {\n",
" \"Rank\": rank,\n",
" \"CFG Scale\": cfg.get(\"cfg_scale\", \"N/A\"),\n",
" \"Grad Config\": cfg.get(\"grad_config\", \"N/A\"),\n",
" \"Steps\": cfg.get(\"num_grad_steps\", \"N/A\"),\n",
" \"LR\": cfg.get(\"grad_step_size\", \"N/A\"),\n",
" \"Momentum\": cfg.get(\"momentum\", \"N/A\"),\n",
" \"ImageReward\": f\"{metrics.get('imagereward', 0):.6f}\",\n",
" \"ImageReward ↑\": f\"{improvements.get('imagereward_improvement', 0):+.2f}%\",\n",
" \"CLIP\": f\"{metrics.get('clip', 0):.4f}\",\n",
" \"CLIP ↑\": f\"{improvements.get('clip_improvement', 0):+.2f}%\",\n",
" \"Aesthetic\": f\"{metrics.get('aesthetic', 0):.4f}\",\n",
" \"Aesthetic ↑\": f\"{improvements.get('aesthetic_improvement', 0):+.2f}%\",\n",
" \"PickScore\": f\"{metrics.get('pickscore', 0):.4f}\",\n",
" \"PickScore ↑\": f\"{improvements.get('pickscore_improvement', 0):+.2f}%\",\n",
" \"HPSv2\": f\"{metrics.get('hpsv2', 0):.4f}\",\n",
" \"HPSv2 ↑\": f\"{improvements.get('hpsv2_improvement', 0):+.2f}%\",\n",
" \"Avg Improvement\": f\"{result['avg_improvement']:+.2f}%\",\n",
" }\n",
" \n",
" table_data.append(row)\n",
"\n",
"df_top_10 = pd.DataFrame(table_data)\n",
"\n",
"print(\"\\n📋 TOP 10 CONFIGURATIONS WITH IMPROVEMENTS IN ALL METRICS:\")\n",
"print(\"=\" * 180)\n",
"print(df_top_10.to_string(index=False))\n",
"print(\"=\" * 180)\n",
"\n",
"# ============================================================================\n",
"# SECTION 4: Visualize and Summary Statistics\n",
"# ============================================================================\n",
"print(\"\\n\" + \"=\" * 80)\n",
"print(\"SUMMARY STATISTICS\")\n",
"print(\"=\" * 80)\n",
"\n",
"# Extract numeric improvement values for analysis\n",
"improvement_summary = []\n",
"for result in top_10:\n",
" improvements = result[\"improvements\"]\n",
" for metric in [\"imagereward_improvement\", \"clip_improvement\", \"aesthetic_improvement\", \n",
" \"pickscore_improvement\", \"hpsv2_improvement\"]:\n",
" metric_name = metric.replace(\"_improvement\", \"\").upper()\n",
" improvement_summary.append({\n",
" \"Metric\": metric_name,\n",
" \"Improvement %\": improvements.get(metric, 0)\n",
" })\n",
"\n",
"df_summary = pd.DataFrame(improvement_summary)\n",
"\n",
"print(\"\\n📊 Average Improvements by Metric (Top 10):\")\n",
"metric_stats = df_summary.groupby(\"Metric\")[\"Improvement %\"].agg([\"mean\", \"std\", \"min\", \"max\"])\n",
"print(metric_stats.round(2))\n",
"\n",
"print(\"\\n📈 Best Configuration Details:\")\n",
"best = top_10[0]\n",
"best_cfg = best[\"config\"]\n",
"best_metrics = best[\"metrics\"]\n",
"best_improvements = best[\"improvements\"]\n",
"\n",
"print(f\"\\n✓ RANK #1 - Best Performing Configuration:\")\n",
"print(f\" Configuration:\")\n",
"print(f\" • CFG Scale: {best_cfg.get('cfg_scale')}\")\n",
"print(f\" • Gradient Config: {best_cfg.get('grad_config')}\")\n",
"print(f\" • Gradient Steps: {best_cfg.get('num_grad_steps')}\")\n",
"print(f\" • Step Size: {best_cfg.get('grad_step_size')}\")\n",
"print(f\" • Momentum: {best_cfg.get('momentum')}\")\n",
"print(f\"\\n Metrics:\")\n",
"for metric in [\"imagereward\", \"clip\", \"aesthetic\", \"pickscore\", \"hpsv2\"]:\n",
" baseline_val = baseline_metrics.get(metric, 0)\n",
" current_val = best_metrics.get(metric, 0)\n",
" improvement = best_improvements.get(f\"{metric}_improvement\", 0)\n",
" print(f\" • {metric:12s}: {current_val:8.6f} (baseline: {baseline_val:8.6f}) ↑ {improvement:+6.2f}%\")\n",
"\n",
"print(\"\\n\" + \"=\" * 80)\n",
"print(\"✓ ANALYSIS COMPLETE - TOP 10 CONFIGURATIONS IDENTIFIED\")\n",
"print(\"=\" * 80)"
]
}
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