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StyleNeRF
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# @package _group_
name: stylenerf_ffhq_warped_depth

G_kwargs:
    class_name: "training.networks.Generator"
    z_dim: 512
    w_dim: 512

    mapping_kwargs:
        num_layers: ${spec.map}

    synthesis_kwargs:
        # global settings
        num_fp16_res: ${num_fp16_res}
        channel_base: 1
        channel_max: 1024
        conv_clamp: 256
        kernel_size: 1
        architecture: skip
        upsample_mode: "nn_cat"

        z_dim_bg: 0
        z_dim: 0
        resolution_vol: 32
        resolution_start: 32
        rgb_out_dim: 256

        use_noise: False
        module_name: "training.stylenerf.NeRFSynthesisNetwork"
        no_bbox: True
        margin: 0
        magnitude_ema_beta: 0.999

        camera_kwargs:
            range_v: [1.4157963267948965, 1.7257963267948966]
            range_u: [-0.3, 0.3]
            range_radius: [1.0, 1.0]
            depth_range: [0.88, 3.2]
            fov: 12
            gaussian_camera: True
            angular_camera: True
            depth_transform:  InverseWarp
            dists_normalized: True
            ray_align_corner: False
            bg_start: 0.5
        
        renderer_kwargs:
            n_bg_samples: 0
            n_ray_samples: 48
            abs_sigma: False
            hierarchical: True
            no_background: True
            
        foreground_kwargs:
            positional_encoding: "normal"
            downscale_p_by: 1
            use_style: "StyleGAN2"
            predict_rgb: True
            use_viewdirs: False

        upsampler_kwargs:
            channel_base: ${model.G_kwargs.synthesis_kwargs.channel_base}
            channel_max:  ${model.G_kwargs.synthesis_kwargs.channel_max}
            no_2d_renderer: False
            no_residual_img: False
            block_reses: ~
            shared_rgb_style: False
            upsample_type: "bilinear"
        
        progressive: True

        # reuglarization
        n_reg_samples: 16
        reg_full: True

D_kwargs:
    class_name: "training.stylenerf.Discriminator"
    epilogue_kwargs:
        mbstd_group_size: ${spec.mbstd}

    num_fp16_res: ${num_fp16_res}
    channel_base: ${spec.fmaps}
    channel_max: 512
    conv_clamp: 256
    architecture: skip
    progressive: ${model.G_kwargs.synthesis_kwargs.progressive}
    lowres_head: ${model.G_kwargs.synthesis_kwargs.resolution_start}
    upsample_type: "bilinear"
    resize_real_early: True

# loss kwargs
loss_kwargs:
    pl_batch_shrink: 2
    pl_decay: 0.01
    pl_weight: 2
    style_mixing_prob: 0.9
    curriculum: [500,5000]