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| # PGC (Zhou et al., 2026): the official ProGAN training recipe, reproduced in SynthScope. | |
| # data ForenSynths / CNNDetection ProGAN train, all 20 classes (720k images = the UniversalFakeDetect train set) | |
| # train flip -> ColorJitter(0.15, 0.15, 0.15, 0.05) p0.2 -> pad + RandomCrop 224 (no resize: the YCbCr residual | |
| # is resolution-sensitive) -> ImageNet normalization; the residual is computed inside the model | |
| # optimizer AdamW lr 5e-5 (constant over this budget), weight decay 0.05, label smoothing 0.1, batch 32 x 4 | |
| # accumulation on ONE GPU, gradient clipping 1.0, seed 42 | |
| # budget 2000 micro-batches with a checkpoint every 100 (model_step<N>.pth): the released checkpoint is ~400 steps | |
| # in and transfer to unseen generators erodes within the first epoch, so the early curve is evaluated | |
| # Evaluate: python benchmark/bench_eval.py --model pgc --ckpt <save_path>/model_step<N>.pth --benchmark forensynths --data <CNNDetection/test> | |
| export TRITON_CACHE_DIR=${TRITON_CACHE_DIR:-/nvmestore/ssalehi/.triton} | |
| export HF_HOME=${HF_HOME:-/nvmestore/ssalehi/.cache/huggingface} | |
| export TORCH_HOME=${TORCH_HOME:-/nvmestore/ssalehi/.cache/torch} | |
| export CUDA_VISIBLE_DEVICES=${CUDA_VISIBLE_DEVICES:-0} | |
| export OMP_NUM_THREADS=1 | |
| DATA=${DATA:-/nvmestore/ssalehi/SynthScope/CNNDetection} | |
| SAVE=${SAVE:-/nvmestore/ssalehi/SynthScope/models/pgc} | |
| # Plain single-process run (no `accelerate launch`): gradient accumulation and clipping are done by the trainer's | |
| # step budget exactly as in the official code. | |
| /home/ssalehi/miniconda3/bin/conda run --no-capture-output -n SynthScope python main.py \ | |
| trainer@TrainerConfig=image/pgc \ | |
| model@ModelConfig=image/pgc \ | |
| dataset@DatasetConfig=image/cnndetection \ | |
| DatasetConfig.root_dir=$DATA \ | |
| DatasetConfig.train_batch_size=32 \ | |
| DatasetConfig.val_batch_size=64 \ | |
| "DatasetConfig.train_transform_list=[random_horizontal_flip,color_jitter_pgc,pad_random_crop_224,to_dtype,normalize_dinov2]" \ | |
| "DatasetConfig.eval_transform_list=[center_crop_224,to_dtype,normalize_dinov2]" \ | |
| TrainerConfig.save_path=$SAVE \ | |
| "metrics=[real_accuracy,fake_accuracy]" \ | |
| logger.name=pgc \ | |
| logger.mode=offline \ | |
| "logger.tags=[SynthScope,PGC,progan20]" | |