diff --git a/nerfactory/cameras/cameras.py b/nerfactory/cameras/cameras.py
index 54398109e..66cfc2ad8 100644
--- a/nerfactory/cameras/cameras.py
+++ b/nerfactory/cameras/cameras.py
@@ -256,8 +256,8 @@ class Cameras:
 
         Args:
             camera_idx: Index of the camera to convert.
-            image: An image in range [0, 1] that is encoded to a base64 string. Defaults to None.
-            max_size: Max size to resize the image to. Defaults to None.
+            image: An image in range [0, 1] that is encoded to a base64 string.
+            max_size: Max size to resize the image to if present.
 
         Returns:
             A JSON representation of the camera
diff --git a/nerfactory/configs/base.py b/nerfactory/configs/base.py
index 84a9bcf46..3db6903e8 100644
--- a/nerfactory/configs/base.py
+++ b/nerfactory/configs/base.py
@@ -128,7 +128,7 @@ class LocalWriterConfig(InstantiateConfig):
         """Instantiate local writer
 
         Args:
-            banner_messages: List of strings that always print at the bottom of screen. Defaults to None.
+            banner_messages: List of strings that always print at the bottom of screen.
         """
         return self._target(self, banner_messages=banner_messages, **kwargs)
 
@@ -257,9 +257,9 @@ class ModelConfig(InstantiateConfig):
     loss_coefficients: Dict[str, float] = to_immutable_dict({"rgb_loss_coarse": 1.0, "rgb_loss_fine": 1.0})
     """Loss specific weights."""
     num_coarse_samples: int = 64
-    """Number of samples in coarse field evaluation. Defaults to 64"""
+    """Number of samples in coarse field evaluation"""
     num_importance_samples: int = 128
-    """Number of samples in fine field evaluation. Defaults to 128"""
+    """Number of samples in fine field evaluation"""
     enable_density_field: bool = False
     """Whether to create a density field to filter samples."""
     density_field_params: Dict[str, Any] = to_immutable_dict(
@@ -292,20 +292,20 @@ class NerfWModelConfig(ModelConfig):
     )
     """Loss specific weights."""
     num_coarse_samples: int = 64
-    """Number of samples in coarse field evaluation. Defaults to 64,"""
+    """Number of samples in coarse field evaluation."""
     num_importance_samples: int = 64
-    """Number of samples in fine field evaluation. Defaults to 128,"""
+    """Number of samples in fine field evaluation."""
     uncertainty_min: float = 0.03
     """This is added to the end of the uncertainty
     rendering operation. It's called 'beta_min' in other repos.
     This avoids calling torch.log() on a zero value, which would be undefined.
-    Defaults to 0.03."""
+    """
     num_images: int = 10000  # TODO: don't hardcode this
     """How many images exist in the dataset."""
     appearance_embedding_dim: int = 48
-    """Dimension of appearance embedding. Defaults to 48."""
+    """Dimension of appearance embedding."""
     transient_embedding_dim: int = 16
-    """Dimension of transient embedding. Defaults to 16."""
+    """Dimension of transient embedding."""
 
 
 @dataclass
diff --git a/nerfactory/datamanagers/dataloaders.py b/nerfactory/datamanagers/dataloaders.py
index 495d8e186..b70e0625f 100644
--- a/nerfactory/datamanagers/dataloaders.py
+++ b/nerfactory/datamanagers/dataloaders.py
@@ -36,9 +36,9 @@ class CacheImageDataloader(DataLoader):
 
     Args:
         dataset: Dataset to sample from.
-        num_samples_to_collate: How many images to sample rays for each batch. Defaults to all images.
-        num_times_to_repeat_images: How often to collate new images. Defaults to every iteration.
-        device: Device to perform computation. Defaults to "cpu".
+        num_samples_to_collate: How many images to sample rays for each batch. -1 for all images.
+        num_times_to_repeat_images: How often to collate new images.
+        device: Device to perform computation.
     """
 
     def __init__(
diff --git a/nerfactory/datamanagers/dataparsers/instant_ngp_parser.py b/nerfactory/datamanagers/dataparsers/instant_ngp_parser.py
index 17ca0c4af..a98dfd23c 100644
--- a/nerfactory/datamanagers/dataparsers/instant_ngp_parser.py
+++ b/nerfactory/datamanagers/dataparsers/instant_ngp_parser.py
@@ -42,7 +42,7 @@ class InstantNGPDataParserConfig(DataParserConfig):
     scale_factor: float = 1.0
     """How much to scale the camera origins by."""
     scene_scale: float = 0.33
-    """How much to scale the scene. Defaults to 0.33"""
+    """How much to scale the scene."""
 
 
 @dataclass
diff --git a/nerfactory/datamanagers/dataparsers/mipnerf_parser.py b/nerfactory/datamanagers/dataparsers/mipnerf_parser.py
index e4890aaa2..967f1e59a 100644
--- a/nerfactory/datamanagers/dataparsers/mipnerf_parser.py
+++ b/nerfactory/datamanagers/dataparsers/mipnerf_parser.py
@@ -38,13 +38,13 @@ class MipNerf360DataParserConfig(DataParserConfig):
     data_directory: Path = Path("data/mipnerf_360/garden")
     """directory specifying location of data"""
     downscale_factor: int = 1
-    """How much to downscale images. Defaults to 1."""
+    """How much to downscale images."""
     val_skip: int = 8
-    """1/val_skip images to use for validation. Defaults to 8."""
+    """1/val_skip images to use for validation."""
     auto_scale: bool = True
-    """Scale based on pose bounds. Defaults to True."""
+    """Scale based on pose bounds."""
     aabb_scale: float = 4
-    """Scene scale, Defaults to 1.0."""
+    """Scene scale."""
 
 
 @dataclass
diff --git a/nerfactory/datamanagers/dataparsers/nerfactory_parser.py b/nerfactory/datamanagers/dataparsers/nerfactory_parser.py
index cd476e554..0f2b6e40c 100644
--- a/nerfactory/datamanagers/dataparsers/nerfactory_parser.py
+++ b/nerfactory/datamanagers/dataparsers/nerfactory_parser.py
@@ -42,15 +42,13 @@ class NerfactoryDataParserConfig(DataParserConfig):
     scale_factor: float = 1.0
     """How much to scale the camera origins by."""
     downscale_factor: int = 1
-    """How much to downscale images. Defaults to 1."""
+    """How much to downscale images."""
     scene_scale: float = 4.0
-    """How much to scale the scene. Defaults to 4.0"""
+    """How much to scale the scene."""
     orientation_method: Literal["pca", "up"] = "up"
-    """The method to use for orientation. Either "pca" or "up"."""
+    """The method to use for orientation."""
     train_split_percentage: float = 0.9
-    """
-    The percent of images to use for training.
-    The remaining images are for eval. Defaults to 0.9.
+    """The percent of images to use for training. The remaining images are for eval.
     """
 
 
diff --git a/nerfactory/datamanagers/dataparsers/record3d_parser.py b/nerfactory/datamanagers/dataparsers/record3d_parser.py
index 5c33f9c08..28be87a09 100644
--- a/nerfactory/datamanagers/dataparsers/record3d_parser.py
+++ b/nerfactory/datamanagers/dataparsers/record3d_parser.py
@@ -40,14 +40,14 @@ class Record3DDataParserConfig(DataParserConfig):
     data_directory: Path = Path("data/record3d/garden")
     """Location of data"""
     val_skip: int = 8
-    """1/val_skip images to use for validation. Defaults to 8."""
+    """1/val_skip images to use for validation."""
     aabb_scale: float = 4.0
-    """Scene scale, Defaults to 4.0."""
+    """Scene scale."""
     orientation_method: Literal["pca", "up"] = "up"
-    """The method to use for orientation. Either "pca" or "up"."""
+    """The method to use for orientation"""
     max_dataset_size: int = 150
     """Max number of images to train on. If the dataset has
-    more, images will be sampled approximately evenly. Defaults to 150."""
+    more, images will be sampled approximately evenly."""
 
 
 @dataclass
diff --git a/nerfactory/engine/trainer.py b/nerfactory/engine/trainer.py
index e00f77ff3..65aa25b89 100644
--- a/nerfactory/engine/trainer.py
+++ b/nerfactory/engine/trainer.py
@@ -66,8 +66,8 @@ class Trainer:
 
     Args:
         config: The configuration object.
-        local_rank: Local rank of the process. Defaults to 0.
-        world_size: World size of the process. Defaults to 1.
+        local_rank: Local rank of the process.
+        world_size: World size of the process.
     """
 
     def __init__(self, config: cfg.Config, local_rank: int = 0, world_size: int = 1):
diff --git a/nerfactory/fields/modules/encoding.py b/nerfactory/fields/modules/encoding.py
index 4dd5be992..ebd588a92 100644
--- a/nerfactory/fields/modules/encoding.py
+++ b/nerfactory/fields/modules/encoding.py
@@ -68,8 +68,8 @@ class ScalingAndOffset(Encoding):
 
     Args:
         in_dim: Input dimension of tensor
-        scaling: Scaling applied to tensor. Defaults to 1.0.
-        offset: Offset applied to tensor. Defaults to 0.0.
+        scaling: Scaling applied to tensor.
+        offset: Offset applied to tensor.
     """
 
     def __init__(self, in_dim: int, scaling: float = 1.0, offset: float = 0.0) -> None:
@@ -127,7 +127,7 @@ class NeRFEncoding(Encoding):
 
         Args:
             in_tensor: For best performance, the input tensor should be between 0 and 1.
-            covs: Covariances of input points. Defaults to None.
+            covs: Covariances of input points.
         Returns:
             Output values will be between -1 and 1
         """
@@ -186,7 +186,7 @@ class RFFEncoding(Encoding):
 
         Args:
             in_tensor: For best performance, the input tensor should be between 0 and 1.
-            covs: Covariances of input points. Defaults to None.
+            covs: Covariances of input points.
 
         Returns:
             Output values will be between -1 and 1
@@ -212,12 +212,12 @@ class HashEncoding(Encoding):
     """Hash encoding
 
     Args:
-        num_levels: Number of feature grids. Defaults to 16.
-        min_res: Resolution of smallest feature grid. Defaults to 16.
-        max_res: Resolution of largest feature grid. Defaults to 1024.
-        hash_table_size: Size of hash table. Defaults to 2**19.
-        features_per_level: Number of features per level. Defaults to 2.
-        hash_init_scale: Value to initialize hash grid. Defaults to 0.001.
+        num_levels: Number of feature grids.
+        min_res: Resolution of smallest feature grid.
+        max_res: Resolution of largest feature grid.
+        hash_table_size: Size of hash table.
+        features_per_level: Number of features per level.
+        hash_init_scale: Value to initialize hash grid.
     """
 
     def __init__(
@@ -313,9 +313,9 @@ class TensorCPEncoding(Encoding):
     """Learned CANDECOMP/PARFAC (CP) decomposition encoding used in TensoRF
 
     Args:
-        resolution: Resolution of grid. Defaults to 256.
-        num_components: Number of components per dimension. Defaults to 24.
-        init_scale: Initialization scale. Defaults to 0.1.
+        resolution: Resolution of grid.
+        num_components: Number of components per dimension.
+        init_scale: Initialization scale.
     """
 
     def __init__(self, resolution: int = 256, num_components: int = 24, init_scale: float = 0.1) -> None:
@@ -363,9 +363,9 @@ class TensorVMEncoding(Encoding):
     """Learned vector-matrix encoding proposed by TensoRF
 
     Args:
-        resolution: Resolution of grid. Defaults to 256.
-        num_components: Number of components per dimension. Defaults to 24.
-        init_scale: Initialization scale. Defaults to 0.1.
+        resolution: Resolution of grid.
+        num_components: Number of components per dimension.
+        init_scale: Initialization scale.
     """
 
     plane_coef: TensorType[3, "num_components", "resolution", "resolution"]
@@ -427,7 +427,7 @@ class SHEncoding(Encoding):
     """Spherical harmonic encoding
 
     Args:
-        levels: Number of spherical hamonic levels to encode. Defaults to 4.
+        levels: Number of spherical hamonic levels to encode.
     """
 
     def __init__(self, levels: int = 4) -> None:
diff --git a/nerfactory/fields/modules/field_heads.py b/nerfactory/fields/modules/field_heads.py
index 9f816d2ac..fc25f2272 100644
--- a/nerfactory/fields/modules/field_heads.py
+++ b/nerfactory/fields/modules/field_heads.py
@@ -117,9 +117,9 @@ class SHFieldHead(FieldHead):
 
     Args:
         in_dim: input dimension. If not defined in constructor, it must be set later.
-        levels: Number of spherical harmonics layers. Defaults to 3.
+        levels: Number of spherical harmonics layers.
         channels: Number of channels. Defaults to 3 (ie RGB).
-        activation: Output activation. Defaults to None.
+        activation: Output activation.
     """
 
     def __init__(
diff --git a/nerfactory/fields/modules/mlp.py b/nerfactory/fields/modules/mlp.py
index 3af30d012..de7c5dd03 100644
--- a/nerfactory/fields/modules/mlp.py
+++ b/nerfactory/fields/modules/mlp.py
@@ -31,9 +31,9 @@ class MLP(FieldModule):
         in_dim: Input layer dimension
         num_layers: Number of network layers
         layer_width: Width of each MLP layer
-        out_dim: Ouput layer dimension. Defaults to layer_width
-        activation: intermediate layer activation function. Defaults to nn.ReLU.
-        out_activation: output activation function. Defaults to None.
+        out_dim: Ouput layer dimension. Uses layer_width if None.
+        activation: intermediate layer activation function.
+        out_activation: output activation function.
     """
 
     def __init__(
diff --git a/nerfactory/fields/nerf_field.py b/nerfactory/fields/nerf_field.py
index ec0f000c9..48e80c12f 100644
--- a/nerfactory/fields/nerf_field.py
+++ b/nerfactory/fields/nerf_field.py
@@ -38,15 +38,15 @@ class NeRFField(Field):
     """NeRF Field
 
     Args:
-        position_encoding: Position encoder. Defaults to Identity(in_dim=3).
-        direction_encoding: Direction encoder. Defaults to Identity(in_dim=3).
-        base_mlp_num_layers: Number of layers for base MLP. Defaults to 8.
-        base_mlp_layer_width: Width of base MLP layers. Defaults to 256.
-        head_mlp_num_layers: Number of layer for ourput head MLP. Defaults to 2.
-        head_mlp_layer_width: Width of output head MLP layers. Defaults to 128.
-        skip_connections: Where to add skip connection in base MLP. Defaults to (4,).
-        use_integrated_encoding: Used integrated samples as encoding input, Defaults to False.
-        spatial_distortion: Spatial distortion. Defaults to None.
+        position_encoding: Position encoder.
+        direction_encoding: Direction encoder.
+        base_mlp_num_layers: Number of layers for base MLP.
+        base_mlp_layer_width: Width of base MLP layers.
+        head_mlp_num_layers: Number of layer for ourput head MLP.
+        head_mlp_layer_width: Width of output head MLP layers.
+        skip_connections: Where to add skip connection in base MLP.
+        use_integrated_encoding: Used integrated samples as encoding input.
+        spatial_distortion: Spatial distortion.
     """
 
     def __init__(
diff --git a/nerfactory/fields/nerfw_field.py b/nerfactory/fields/nerfw_field.py
index 481977d4e..a33313b1e 100644
--- a/nerfactory/fields/nerfw_field.py
+++ b/nerfactory/fields/nerfw_field.py
@@ -40,15 +40,15 @@ class VanillaNerfWField(Field):
 
     Args:
         num_images: How many images exist in the dataset.
-        position_encoding: Position encoder. Defaults to Identity(in_dim=3).
-        direction_encoding: Direction encoder. Defaults to Identity(in_dim=3).
-        base_mlp_num_layers: Number of layers for base MLP. Defaults to 8.
-        base_mlp_layer_width: Width of base MLP layers. Defaults to 256.
-        head_mlp_num_layers: Number of layer for ourput head MLP. Defaults to 2.
-        head_mlp_layer_width: Width of output head MLP layers. Defaults to 128.
-        appearance_embedding_dim:: Dimension of appearance embedding. Defaults to 48.
-        transient_embedding_dim:: Dimension of transient embedding. Defaults to 16.
-        skip_connections: Where to add skip connection in base MLP. Defaults to (4,).
+        position_encoding: Position encoder.
+        direction_encoding: Direction encoder.
+        base_mlp_num_layers: Number of layers for base MLP.
+        base_mlp_layer_width: Width of base MLP layers.
+        head_mlp_num_layers: Number of layer for ourput head MLP.
+        head_mlp_layer_width: Width of output head MLP layers.
+        appearance_embedding_dim:: Dimension of appearance embedding.
+        transient_embedding_dim:: Dimension of transient embedding.
+        skip_connections: Where to add skip connection in base MLP.
     """
 
     def __init__(
@@ -121,7 +121,7 @@ class VanillaNerfWField(Field):
 
         Args:
             ray_samples (RaySamples): Ray samples.
-            density_embedding (TensorType[..., "embedding_size"], optional): Density embedding. Defaults to None.
+            density_embedding (TensorType[..., "embedding_size"], optional): Density embedding.
 
         Returns:
             Dict[FieldHeadNames, TensorType]: Outputs of the NeRF-W field.
diff --git a/nerfactory/fields/semantic_nerf_field.py b/nerfactory/fields/semantic_nerf_field.py
index 99edf6e15..b0334c41d 100644
--- a/nerfactory/fields/semantic_nerf_field.py
+++ b/nerfactory/fields/semantic_nerf_field.py
@@ -38,13 +38,13 @@ class SemanticNerfField(Field):
 
     Args:
         num_semantic_classes: Number of distinct semantic classes.
-        position_encoding: Position encoder. Defaults to Identity(in_dim=3).
-        direction_encoding: Direction encoder. Defaults to Identity(in_dim=3).
-        base_mlp_num_layers: Number of layers for base MLP. Defaults to 8.
-        base_mlp_layer_width: Width of base MLP layers. Defaults to 256.
-        head_mlp_num_layers: Number of layer for ourput head MLP. Defaults to 2.
-        head_mlp_layer_width: Width of output head MLP layers. Defaults to 128.
-        skip_connections: Where to add skip connection in base MLP. Defaults to (4,).
+        position_encoding: Position encoder.
+        direction_encoding: Direction encoder.
+        base_mlp_num_layers: Number of layers for base MLP.
+        base_mlp_layer_width: Width of base MLP layers.
+        head_mlp_num_layers: Number of layer for ourput head MLP.
+        head_mlp_layer_width: Width of output head MLP layers.
+        skip_connections: Where to add skip connection in base MLP.
     """
 
     def __init__(
diff --git a/nerfactory/models/modules/ray_sampler.py b/nerfactory/models/modules/ray_sampler.py
index 7c3a772fb..9993c9e9d 100644
--- a/nerfactory/models/modules/ray_sampler.py
+++ b/nerfactory/models/modules/ray_sampler.py
@@ -239,11 +239,11 @@ class PDFSampler(Sampler):
 
     Args:
         num_samples: Number of samples per ray
-        train_stratified: Randomize location within each bin during training. Defaults to True
-        include_original: Add original samples to ray. Defaults to True
+        train_stratified: Randomize location within each bin during training.
+        include_original: Add original samples to ray.
         density_field: Density grid. If provides, samples below weight_threshold as set as invalid.
         weight_threshold: Removes samples below threshold weight. Only used if density field is provided.
-        histogram_padding: Amount to weights prior to computing PDF. Defaults to 0.01.
+        histogram_padding: Amount to weights prior to computing PDF.
     """
 
     def __init__(
@@ -276,7 +276,7 @@ class PDFSampler(Sampler):
             ray_samples: Existing ray samples
             weights: Weights for each bin
             num_samples: Number of samples per ray
-            eps: Small value to prevent numerical issues. Defaults to 1e-5
+            eps: Small value to prevent numerical issues.
 
         Returns:
             Positions and deltas for samples along a ray
diff --git a/nerfactory/models/nerfw.py b/nerfactory/models/nerfw.py
index bbb97775b..73c555c17 100644
--- a/nerfactory/models/nerfw.py
+++ b/nerfactory/models/nerfw.py
@@ -65,7 +65,6 @@ class NerfWModel(Model):
             uncertainty_min (float, optional): This is added to the end of the uncertainty
                 rendering operation. It's called 'beta_min' in other repos.
                 This avoids calling torch.log() on a zero value, which would be undefined.
-                Defaults to 0.03.
         """
         self.field_coarse = None
         self.field_fine = None
diff --git a/nerfactory/optimizers/schedulers.py b/nerfactory/optimizers/schedulers.py
index a493a5622..5863195d5 100644
--- a/nerfactory/optimizers/schedulers.py
+++ b/nerfactory/optimizers/schedulers.py
@@ -29,8 +29,8 @@ class ExponentialDecaySchedule(LambdaLR):
         lr_init: The initial learning rate.
         lr_final: The final learning rate.
         max_steps: The maximum number of steps.
-        lr_delay_steps: The number of steps to delay the learning rate. Default is 0.
-        lr_delay_mult: The multiplier for the learning rate after the delay. Default is 1.0.
+        lr_delay_steps: The number of steps to delay the learning rate.
+        lr_delay_mult: The multiplier for the learning rate after the delay.
     """
 
     def __init__(self, optimizer, lr_init, lr_final, max_steps, lr_delay_steps=0, lr_delay_mult=1.0) -> None:
diff --git a/nerfactory/renderers/renderers.py b/nerfactory/renderers/renderers.py
index fa9d7a3b1..67b6a6e69 100644
--- a/nerfactory/renderers/renderers.py
+++ b/nerfactory/renderers/renderers.py
@@ -27,7 +27,7 @@ Example:
 
 """
 import math
-from typing import Optional
+from typing import Literal, Optional
 
 import nerfacc
 import torch
@@ -42,7 +42,7 @@ class RGBRenderer(nn.Module):
     """Standard volumetic rendering.
 
     Args:
-        background_color: Background color as RGB. Defaults to random.
+        background_color: Background color as RGB. Uses random colors if None.
     """
 
     def __init__(self, background_color: Optional[TensorType[3]] = None) -> None:
@@ -63,7 +63,7 @@ class RGBRenderer(nn.Module):
         Args:
             rgb: RGB for each sample
             weights: Weights for each sample
-            background_color: Background color as RGB. Defaults to random.
+            background_color: Background color as RGB. Uses random colors if None.
             ray_indices: Ray index for each sample, used when samples are packed.
             num_rays: Number of rays, used when samples are packed.
 
@@ -115,8 +115,8 @@ class SHRenderer(nn.Module):
     """Render RGB value from spherical harmonics.
 
     Args:
-        background_color: Background color as RGB. Defaults to random.
-        activation: Output activation. Defaults to Sigmoid().
+        background_color: Background color as RGB. Uses random colors if None
+        activation: Output activation.
     """
 
     def __init__(
@@ -191,13 +191,11 @@ class DepthRenderer(nn.Module):
     """Calculate depth along ray.
 
     Args:
-        method (str, optional): Depth calculation method. Defaults to 'expected'.
+        method (str, optional): Depth calculation method.
     """
 
-    def __init__(self, method: str = "expected") -> None:
+    def __init__(self, method: Literal["expected"] = "expected") -> None:
         super().__init__()
-        if method not in {"expected"}:
-            raise ValueError(f"{method} is an invalid depth calculation method")
         self.method = method
 
     def forward(
diff --git a/nerfactory/utils/io.py b/nerfactory/utils/io.py
index c6e5d4882..9681ab9b2 100644
--- a/nerfactory/utils/io.py
+++ b/nerfactory/utils/io.py
@@ -75,7 +75,7 @@ def get_git_root(path: str, dirs: Tuple[str] = (".git",), default: str = "") ->
 
     Args:
         path: The path to start from.
-        dirs: The directories to look for. Defaults to (".git",).
+        dirs: The directories to look for.
         default: The default value to return if no git root is found.
 
     Returns:
diff --git a/nerfactory/utils/visualization.py b/nerfactory/utils/visualization.py
index fcaa777cc..3e44e22e0 100644
--- a/nerfactory/utils/visualization.py
+++ b/nerfactory/utils/visualization.py
@@ -28,7 +28,7 @@ def apply_colormap(image: TensorType["bs":..., 1], cmap="viridis") -> TensorType
 
     Args:
         image: Single channel image.
-        cmap: Colormap for image. Defaults to 'turbo'.
+        cmap: Colormap for image.
 
     Returns:
         TensorType: Colored image
@@ -55,10 +55,10 @@ def apply_depth_colormap(
 
     Args:
         depth: Depth image.
-        accumulation: Ray accumulation used for masking vis. Defaults to None.
-        near_plane: Closest depth to consider. If None, use min image value. Defaults to None.
-        far_plane: Furthest depth to consider. If None, use max image value. Defaults to None.
-        cmap: Colormap to apply. Defaults to "turbo".
+        accumulation: Ray accumulation used for masking vis.
+        near_plane: Closest depth to consider. If None, use min image value.
+        far_plane: Furthest depth to consider. If None, use max image value.
+        cmap: Colormap to apply.
 
     Returns:
         Colored depth image
@@ -88,8 +88,8 @@ def apply_boolean_colormap(
 
     Args:
         image: Boolean image.
-        true_color: Color to use for True. Defaults to white.
-        false_color: Color to use for False. Defaults to black.
+        true_color: Color to use for True.
+        false_color: Color to use for False.
 
     Returns:
         Colored boolean image
diff --git a/nerfactory/viewer/server/subprocess.py b/nerfactory/viewer/server/subprocess.py
index f7ddeb384..276db33d9 100644
--- a/nerfactory/viewer/server/subprocess.py
+++ b/nerfactory/viewer/server/subprocess.py
@@ -38,7 +38,7 @@ def run_viewer_bridge_server_as_subprocess(zmq_port: int, websocket_port: int, l
     Args:
         zmq_port: Port to use for the ZMQ server.
         websocket_port: Port to use for the websocket server.
-        log_filename: Filename to use for the log file. Defaults to None. If None, no log file is created.
+        log_filename: Filename to use for the log file. If None, no log file is created.
 
     Returns:
         None
diff --git a/nerfactory/viewer/server/visualizer.py b/nerfactory/viewer/server/visualizer.py
index dced16d8a..dfec24caa 100644
--- a/nerfactory/viewer/server/visualizer.py
+++ b/nerfactory/viewer/server/visualizer.py
@@ -90,8 +90,8 @@ class Viewer:
     """Viewer class for connecting to the bridge server.
 
     Args:
-        zmq_url: Where to connect with ZMQ. Defaults to None.
-        window: An already existing ViewerWindow. Defaults to None.
+        zmq_url: Where to connect with ZMQ.
+        window: An already existing ViewerWindow.
     """
 
     def __init__(self, zmq_url: Optional[str] = None, window: Optional[ViewerWindow] = None):
diff --git a/scripts/debugging/run_actions.py b/scripts/debugging/run_actions.py
index 9e28e9762..67421ddc1 100755
--- a/scripts/debugging/run_actions.py
+++ b/scripts/debugging/run_actions.py
@@ -18,7 +18,7 @@ def run_command(command: str, continue_on_fail: bool = False) -> bool:
 
     Args:
         command: command to run
-        continue_on_fail: whether to continue running commands if the current one fails. Defaults to False.
+        continue_on_fail: whether to continue running commands if the current one fails.
     """
     ret_code = subprocess.call(command, shell=True)
     if ret_code != 0:
diff --git a/scripts/run_eval.py b/scripts/run_eval.py
index effc1509f..920ef90bf 100755
--- a/scripts/run_eval.py
+++ b/scripts/run_eval.py
@@ -110,8 +110,7 @@ def _render_trajectory_video(
         output_filename: Name of the output file.
         rendered_output_name: Name of the renderer output to use.
         rendered_resolution_scaling_factor: Scaling factor to apply to the camera image resolution.
-            Defaults to 1.0.
-        seconds: Number for the output video. Defaults to 5.0.
+        seconds: Number for the output video.
     """
     console.print("[bold green]Creating trajectory video")
     images = []
diff --git a/scripts/run_train.py b/scripts/run_train.py
index 783e979ed..f59c6a5cc 100755
--- a/scripts/run_train.py
+++ b/scripts/run_train.py
@@ -126,10 +126,10 @@ def launch(
         main_func (Callable): function that will be called by the distributed workers
         num_gpus_per_machine (int): number of GPUs per machine
         num_machines (int, optional): total number of machines
-        machine_rank (int, optional): rank of this machine. Defaults to 0.
-        dist_url (str, optional): url to connect to for distributed jobs. Defaults to "auto".
-        config (Config, optional): config file specifying training regimen. Defaults to None.
-        timeout (timedelta, optional): timeout of the distributed workers. Defaults to DEFAULT_TIMEOUT.
+        machine_rank (int, optional): rank of this machine.
+        dist_url (str, optional): url to connect to for distributed jobs.
+        config (Config, optional): config file specifying training regimen.
+        timeout (timedelta, optional): timeout of the distributed workers.
     """
     assert config is not None
     world_size = num_machines * num_gpus_per_machine