How to train Ref-NeRF on real captured dataset correctly? about multinerf HOT 3 CLOSED

Config.dataset_loader = 'llff'
Config.batching = 'single_image'
Config.near = 0.2
Config.far = 1e6
Config.factor = 4
Config.batch_size = 256
Config.eval_render_interval = 5
Config.render_chunk_size = 256
Config.compute_normal_metrics = False
Config.data_loss_type = 'mse'
Config.distortion_loss_mult = 0.0
Config.orientation_loss_mult = 0.1
Config.orientation_loss_target = 'normals_pred'
Config.predicted_normal_loss_mult = 3e-4
Config.orientation_coarse_loss_mult = 0.01
Config.predicted_normal_coarse_loss_mult = 3e-5
Config.interlevel_loss_mult = 0.0
Config.data_coarse_loss_mult = 0.1
Config.adam_eps = 1e-8

Model.raydist_fn = @jnp.reciprocal
Model.opaque_background = True

Model.num_levels = 2
Model.single_mlp = True
Model.num_prop_samples = 128  # This needs to be set despite single_mlp = True.
Model.num_nerf_samples = 128
Model.anneal_slope = 0.
Model.dilation_multiplier = 0.
Model.dilation_bias = 0.
Model.single_jitter = False
Model.resample_padding = 0.01

PropMLP.warp_fn = @coord.contract
PropMLP.net_depth = 4
PropMLP.net_width = 256

NerfMLP.warp_fn = @coord.contract
NerfMLP.net_depth = 8
NerfMLP.net_width = 256
NerfMLP.net_depth_viewdirs = 8
NerfMLP.basis_shape = 'octahedron'
NerfMLP.basis_subdivisions = 1
NerfMLP.disable_density_normals = False
NerfMLP.enable_pred_normals = True
NerfMLP.use_directional_enc = True
NerfMLP.use_reflections = True
NerfMLP.deg_view = 5
NerfMLP.enable_pred_roughness = True
NerfMLP.use_diffuse_color = True
NerfMLP.use_specular_tint = True
NerfMLP.use_n_dot_v = True
NerfMLP.bottleneck_width = 128
NerfMLP.density_bias = 0.5
NerfMLP.max_deg_point = 16