Comments (2)
@octadion@Zhendong-Wang
I got the same question when training with the command
python train.py --outdir=training-runs --data="cifar10.zip" --gpus=1 --batch 16 --batch-gpu=16 --cfg fastgan --kimg 50000 --target 0.45 --d_pos first --noise_sd 0.5
then I get the network named "best_model.pkl" in the outdir "training-runs", so I sample and evalution using the network, the metric command as follows:
python calc_metrics.py --metrics=fid50k_full --data=cifar10.zip --mirror=1 --network=best_model.pkl
But the fid is too high, can you tell me why if you know the reason :
{"results": {"fid50k_full": 13.780802051702606}, "metric": "fid50k_full", "total_time": 205.13619828224182, "total_time_str": "3m 25s", "num_gpus": 1, "snapshot_pkl": "best_model.pkl", "timestamp": 1685179674.1273081}
from diffusion-gan.
Hi there,
I rerun my code and it seems reproducing the good results. I guess you use the Diffusion-ProejctedGAN code. I did the follows:
conda env create -f environment.yaml
conda activate pg
python train.py --outdir=training-runs --data="~/cifar10.zip" --gpus=4 --batch 64 --batch-gpu=16 --cfg fastgan --kimg 50000 --target 0.45 --d_pos first --noise_sd 0.5
I show the trainining outputs that I have here. It converges fast.
from diffusion-gan.
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from diffusion-gan.