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| # @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 | |
| keep_posenc: -1 | |
| keep_nerf_latents: False | |
| 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 | |
| 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] |