Using an Initial Population

To use a custom initial population for optimization instead of random initialization:

1. Prepare your population directory structure:

   • Choose a population name (e.g., initialized_population)
   • Create the directory structure: results/genetic/pixart_alpha/initialized_population/gen_000/candidates/
   • Place your schedule JSON files in the candidates directory
   • Name them as cand_000.json, cand_001.json, ..., cand_071.json (for a population size of 72)

2. Run optimization with your initial population:

nohup python ecad/genetic/train_nsga2_single_gpu.py \
    --image-generator PixArtAlphaImageGenerator \
    --name initialized_population \
    --num-cycles 50 \
    --all-populations-dir results/genetic/pixart_alpha \
    --all-benchmarks-dir results/benchmark/genetic/pixart_alpha \
    --batch-size 100 \
    --population-size 72 \
    --embedding-dir /path/to/results/embeddings/image_reward/pixart_alpha_embeddings \
    --benchmark-prompts prompts/ImageRewardPrompts.json \
    &> nohup_pixart_alpha_ecad_optimize.out & disown

Important: When using an initial population, you will NOT receive a prompt asking about random initialization. The system will automatically detect and use your provided population.

The single GPU implementation executes the following steps sequentially for each generation:

1. Image generation using the evolved schedules
2. Image quality scoring
3. Computational metrics calculation (MACs/FLOPs)

To see the outputs of each evaluation step during execution, add the --print-eval-outputs flag.