configs/cifar10_ve_cd.py [40:74]:
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    data = config.data
    data.dataset = "CIFAR10"

    # model
    model = config.model
    model.name = "ncsnpp"
    model.ema_rate = 0.9999
    model.normalization = "GroupNorm"
    model.nonlinearity = "swish"
    model.nf = 128
    model.ch_mult = (2, 2, 2)
    model.num_res_blocks = 4
    model.attn_resolutions = (16,)
    model.resamp_with_conv = True
    model.conditional = True
    model.fir = True
    model.fir_kernel = [1, 3, 3, 1]
    model.skip_rescale = True
    model.resblock_type = "biggan"
    model.progressive = "none"
    model.progressive_input = "residual"
    model.progressive_combine = "sum"
    model.attention_type = "ddpm"
    model.init_scale = 0.0
    model.fourier_scale = 16
    model.conv_size = 3
    model.rho = 7.0
    model.data_std = 0.5
    model.num_scales = 18
    model.dropout = 0.0

    # optimization
    optim = config.optim
    optim.weight_decay = 0.0
    optim.optimizer = "radam"
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configs/cifar10_ve_ct_adaptive.py [44:79]:
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    data = config.data
    data.dataset = "CIFAR10"

    # model
    model = config.model
    model.name = "ncsnpp"
    model.ema_rate = 0.9999
    model.normalization = "GroupNorm"
    model.nonlinearity = "swish"
    model.nf = 128
    model.ch_mult = (2, 2, 2)
    model.num_res_blocks = 4
    model.attn_resolutions = (16,)
    model.resamp_with_conv = True
    model.conditional = True
    model.fir = True
    model.fir_kernel = [1, 3, 3, 1]
    model.skip_rescale = True
    model.resblock_type = "biggan"
    model.progressive = "none"
    model.progressive_input = "residual"
    model.progressive_combine = "sum"
    model.attention_type = "ddpm"
    model.init_scale = 0.0
    model.fourier_scale = 16
    model.conv_size = 3
    model.rho = 7.0
    model.data_std = 0.5
    model.num_scales = 18
    # model.dropout = 0.13
    model.dropout = 0.0

    # optimization
    optim = config.optim
    optim.weight_decay = 0.0
    optim.optimizer = "radam"
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