Image Generation Analysis¶
Analysis pipeline for studying image generation patterns, regeneration behavior, and prompt effectiveness (April–June 2026).
Scripts¶
| Script | Purpose |
|---|---|
dump_image_gen_jobs.py |
Pulls all image generation jobs from MongoDB (renderboard.assetGenJobs) for a date range and saves to JSON |
image_gen_insights.py |
Computes per-user generation counts and regeneration metrics (mean/median/max cluster sizes) using MinHash/LSH |
prompt_feature_extraction.py |
Extracts 15 structural features from each unique prompt via GPT, correlates features with download success rate |
compare_sketch_vs_manual.py |
Compares platform-generated (generateFromSketch) prompts vs manual user prompts — structural and qualitative |
Setup¶
pip install pymongo datasketch openai
Usage¶
# 1. Dump data from MongoDB
python dump_image_gen_jobs.py
# 2. Regeneration analysis
python image_gen_insights.py
# 3. Feature extraction (uses GPT API, cached after first run)
python prompt_feature_extraction.py
# 4. Platform vs manual comparison
python compare_sketch_vs_manual.py