# @package _group_ name: stylenerf_ffhq 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: 0 resolution_vol: 128 resolution_start: 128 rgb_out_dim: 32 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, 1.12] fov: 12 gaussian_camera: True angular_camera: True depth_transform: ~ dists_normalized: True ray_align_corner: False bg_start: 0.5 renderer_kwargs: n_ray_samples: 32 abs_sigma: False hierarchical: True no_background: True foreground_kwargs: downscale_p_by: 1 use_style: "StyleGAN2" predict_rgb: False use_viewdirs: False add_rgb: True n_blocks: 0 input_kwargs: output_mode: 'tri_plane_reshape' input_mode: 'random' in_res: 4 out_res: 256 out_dim: 32 upsampler_kwargs: no_2d_renderer: False no_residual_img: False block_reses: ~ shared_rgb_style: False upsample_type: "bilinear" progressive: True # reuglarization n_reg_samples: 0 reg_full: False encoder_kwargs: class_name: "training.stylenerf.Encoder" num_fp16_res: ${num_fp16_res} channel_base: ${spec.fmaps} channel_max: 512 conv_clamp: 256 architecture: skip progressive: ${..synthesis_kwargs.progressive} lowres_head: ${..synthesis_kwargs.resolution_start} upsample_type: "bilinear" model_kwargs: output_mode: "W+" predict_camera: False 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 predict_camera: True 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]