conf.yml

# model
IMAGE_SIZE : [128, 128]   # load image size, if it's train mode, it will be randomly cropped to IMAGE_SIZE. If it's test mode, it will be resized to IMAGE_SIZE.
CHANNEL_X : 3             # input channel
CHANNEL_Y : 3             # output channel
TIMESTEPS : 100           # diffusion steps
SCHEDULE : 'linear'       # linear or cosine
MODEL_CHANNELS : 32       # basic channels of Unet
NUM_RESBLOCKS : 1         # number of residual blocks
CHANNEL_MULT : [1,2,3,4]  # channel multiplier of each layer
NUM_HEADS : 1

MODE : 1                  # 1 Train, 0 Test
PRE_ORI : 'True'          # if True, predict $x_0$, else predict $\epsilon$.


# train
PATH_GT : ''              # path of ground truth
PATH_IMG : ''             # path of input
BATCH_SIZE : 32           # training batch size
NUM_WORKERS : 16           # number of workers
ITERATION_MAX : 1000000   # max training iteration
LR : 0.0001               # learning rate
LOSS : 'L2'               # L1 or L2
EMA_EVERY : 100           # update EMA every EMA_EVERY iterations
START_EMA : 2000          # start EMA after START_EMA iterations
SAVE_MODEL_EVERY : 10000  # save model every SAVE_MODEL_EVERY iterations
EMA: 'True'               # if True, use EMA
CONTINUE_TRAINING : 'False'               # if True, continue training
CONTINUE_TRAINING_STEPS : 10000           # continue training from CONTINUE_TRAINING_STEPS
PRETRAINED_PATH_INITIAL_PREDICTOR : ''    # path of pretrained initial predictor
PRETRAINED_PATH_DENOISER : ''             # path of pretrained denoiser
WEIGHT_SAVE_PATH : './checksave'          # path to save model
TRAINING_PATH : './Training'              # path of training data
BETA_LOSS : 50            # hyperparameter to balance the pixel loss and the diffusion loss
HIGH_LOW_FREQ : 'True'    # if True, training with frequency separation


# test
NATIVE_RESOLUTION : 'False'               # if True, test with native resolution
DPM_SOLVER : 'False'      # if True, test with DPM_solver
DPM_STEP : 20             # DPM_solver step
BATCH_SIZE_VAL : 1        # test batch size
TEST_PATH_GT : ''         # path of ground truth
TEST_PATH_IMG : ''        # path of input
TEST_INITIAL_PREDICTOR_WEIGHT_PATH : ''   # path of initial predictor
TEST_DENOISER_WEIGHT_PATH : ''            # path of denoiser
TEST_IMG_SAVE_PATH : './results'          # path to save results