diff --git a/src/safeds/data/image/_utils/_image_transformation_error_and_warning_checks.py b/src/safeds/data/image/_utils/_image_transformation_error_and_warning_checks.py
index a800d54be..807a72f9c 100644
--- a/src/safeds/data/image/_utils/_image_transformation_error_and_warning_checks.py
+++ b/src/safeds/data/image/_utils/_image_transformation_error_and_warning_checks.py
@@ -13,20 +13,13 @@ def _check_resize_errors(new_width: int, new_height: int) -> None:
     _check_bounds("new_height", new_height, lower_bound=_ClosedBound(1))
 
 
-def _check_crop_errors_and_warnings(
+def _check_crop_warnings(
     x: int,
     y: int,
-    width: int,
-    height: int,
     min_width: int,
     min_height: int,
     plural: bool,
 ) -> None:
-    _check_bounds("x", x, lower_bound=_ClosedBound(0))
-    _check_bounds("y", y, lower_bound=_ClosedBound(0))
-    _check_bounds("width", width, lower_bound=_ClosedBound(1))
-    _check_bounds("height", height, lower_bound=_ClosedBound(1))
-
     if x >= min_width or y >= min_height:
         warnings.warn(
             f"The specified bounding rectangle does not contain any content of {'at least one' if plural else 'the'} image. Therefore {'these images' if plural else 'the image'} will be blank.",
@@ -35,6 +28,18 @@ def _check_crop_errors_and_warnings(
         )
 
 
+def _check_crop_errors(
+    x: int,
+    y: int,
+    width: int,
+    height: int,
+) -> None:
+    _check_bounds("x", x, lower_bound=_ClosedBound(0))
+    _check_bounds("y", y, lower_bound=_ClosedBound(0))
+    _check_bounds("width", width, lower_bound=_ClosedBound(1))
+    _check_bounds("height", height, lower_bound=_ClosedBound(1))
+
+
 def _check_adjust_brightness_errors_and_warnings(factor: float, plural: bool) -> None:
     _check_bounds("factor", factor, lower_bound=_ClosedBound(0))
     if factor == 1:
diff --git a/src/safeds/data/image/containers/_empty_image_list.py b/src/safeds/data/image/containers/_empty_image_list.py
index 025a56716..48531c965 100644
--- a/src/safeds/data/image/containers/_empty_image_list.py
+++ b/src/safeds/data/image/containers/_empty_image_list.py
@@ -10,7 +10,7 @@
     _check_adjust_color_balance_errors_and_warnings,
     _check_adjust_contrast_errors_and_warnings,
     _check_blur_errors_and_warnings,
-    _check_crop_errors_and_warnings,
+    _check_crop_errors,
     _check_remove_images_with_size_errors,
     _check_resize_errors,
     _check_sharpen_errors_and_warnings,
@@ -161,15 +161,7 @@ def convert_to_grayscale(self) -> ImageList:
 
     def crop(self, x: int, y: int, width: int, height: int) -> ImageList:
         _EmptyImageList._warn_empty_image_list()
-        _check_crop_errors_and_warnings(
-            x,
-            y,
-            width,
-            height,
-            x + 1,
-            y + 1,
-            plural=True,
-        )  # Disable x|y >= min_width|min_height check with min_width|min_height=x|y+1
+        _check_crop_errors(x, y, width, height)
         return _EmptyImageList()
 
     def flip_vertically(self) -> ImageList:
diff --git a/src/safeds/data/image/containers/_image.py b/src/safeds/data/image/containers/_image.py
index b1ce3abd8..1aecf60b0 100644
--- a/src/safeds/data/image/containers/_image.py
+++ b/src/safeds/data/image/containers/_image.py
@@ -14,7 +14,8 @@
     _check_adjust_color_balance_errors_and_warnings,
     _check_adjust_contrast_errors_and_warnings,
     _check_blur_errors_and_warnings,
-    _check_crop_errors_and_warnings,
+    _check_crop_errors,
+    _check_crop_warnings,
     _check_resize_errors,
     _check_sharpen_errors_and_warnings,
 )
@@ -46,7 +47,7 @@ def _filter_edges_kernel() -> Tensor:
 
         if Image._filter_edges_kernel_cache is None:
             Image._filter_edges_kernel_cache = (
-                torch.tensor([[-1.0, -1.0, -1.0], [-1.0, 8.0, -1.0], [-1.0, -1.0, -1.0]])
+                torch.tensor([[-1.0, -1.0, -1.0], [-1.0, 8.0, -1.0], [-1.0, -1.0, -1.0]], dtype=torch.float16)
                 .unsqueeze(dim=0)
                 .unsqueeze(dim=0)
                 .to(_get_device())
@@ -118,7 +119,13 @@ def from_bytes(data: bytes) -> Image:
         return Image(image_tensor=torchvision.io.decode_image(input_tensor).to(_get_device()))
 
     def __init__(self, image_tensor: Tensor) -> None:
-        self._image_tensor: Tensor = image_tensor
+        import torch
+
+        self._image_tensor: Tensor
+        if image_tensor.dtype != torch.uint8:
+            self._image_tensor = torch.clamp(image_tensor, 0, 255).to(torch.uint8)
+        else:
+            self._image_tensor = image_tensor
 
     def __eq__(self, other: object) -> bool:
         """
@@ -444,18 +451,20 @@ def convert_to_grayscale(self) -> Image:
 
         _init_default_device()
 
-        if self.channel == 4:
+        if self.channel == 1:
+            return self
+        elif self.channel == 4:
             return Image(
                 torch.cat(
                     [
                         func2.rgb_to_grayscale(self._image_tensor[0:3], num_output_channels=3),
-                        self._image_tensor[3].unsqueeze(dim=0),
+                        self._image_tensor[3:4],
                     ],
                 ),
             )
-        else:
+        else:  # channel == 3
             return Image(
-                func2.rgb_to_grayscale(self._image_tensor[0:3], num_output_channels=self.channel),
+                func2.rgb_to_grayscale(self._image_tensor[0:3], num_output_channels=3),
             )
 
     def crop(self, x: int, y: int, width: int, height: int) -> Image:
@@ -489,7 +498,8 @@ def crop(self, x: int, y: int, width: int, height: int) -> Image:
 
         _init_default_device()
 
-        _check_crop_errors_and_warnings(x, y, width, height, self.width, self.height, plural=False)
+        _check_crop_errors(x, y, width, height)
+        _check_crop_warnings(x, y, self.width, self.height, plural=False)
         return Image(func2.crop(self._image_tensor, y, x, height, width))
 
     def flip_vertically(self) -> Image:
@@ -552,22 +562,31 @@ def adjust_brightness(self, factor: float) -> Image:
             If factor is smaller than 0.
         """
         import torch
-        from torchvision.transforms.v2 import functional as func2
 
         _init_default_device()
 
         _check_adjust_brightness_errors_and_warnings(factor, plural=False)
-        if self.channel == 4:
-            return Image(
-                torch.cat(
-                    [
-                        func2.adjust_brightness(self._image_tensor[0:3], factor * 1.0),
-                        self._image_tensor[3].unsqueeze(dim=0),
-                    ],
-                ),
-            )
+        if self._image_tensor.size(dim=-3) != 4:
+            if factor == 0:
+                return Image(torch.zeros(self._image_tensor.size(), dtype=torch.uint8))
+            else:
+                temp_tensor = self._image_tensor * torch.tensor([factor * 1.0], dtype=torch.float16)
+                torch.clamp(temp_tensor, 0, 255, out=temp_tensor)
+                return Image(temp_tensor.to(torch.uint8))
         else:
-            return Image(func2.adjust_brightness(self._image_tensor, factor * 1.0))
+            img_tensor = torch.empty(self._image_tensor.size(), dtype=torch.uint8)
+            img_tensor[3] = self._image_tensor[3]
+            if factor == 0:
+                torch.zeros(
+                    (3, self._image_tensor.size(dim=-2), self._image_tensor.size(dim=-1)),
+                    dtype=torch.uint8,
+                    out=img_tensor[:, 0:3],
+                )
+            else:
+                temp_tensor = self._image_tensor[0:3] * torch.tensor([factor * 1.0], dtype=torch.float16)
+                torch.clamp(temp_tensor, 0, 255, out=temp_tensor)
+                img_tensor[0:3] = temp_tensor[:]
+            return Image(img_tensor)
 
     def add_noise(self, standard_deviation: float) -> Image:
         """
@@ -595,9 +614,12 @@ def add_noise(self, standard_deviation: float) -> Image:
         _init_default_device()
 
         _check_add_noise_errors(standard_deviation)
-        return Image(
-            self._image_tensor + torch.normal(0, standard_deviation, self._image_tensor.size()).to(_get_device()) * 255,
-        )
+        float_tensor = torch.empty(self._image_tensor.size(), dtype=torch.float16)
+        torch.normal(0, standard_deviation, self._image_tensor.size(), out=float_tensor)
+        float_tensor *= 255
+        float_tensor += self._image_tensor
+        torch.clamp(float_tensor, 0, 255, out=float_tensor)
+        return Image(float_tensor.to(torch.uint8))
 
     def adjust_contrast(self, factor: float) -> Image:
         """
@@ -624,22 +646,26 @@ def adjust_contrast(self, factor: float) -> Image:
             If factor is smaller than 0.
         """
         import torch
-        from torchvision.transforms.v2 import functional as func2
 
         _init_default_device()
 
         _check_adjust_contrast_errors_and_warnings(factor, plural=False)
+
+        factor *= 1.0
+        adjusted_factor = (1 - factor) / factor
+        gray_tensor = self.convert_to_grayscale()._image_tensor[0]
+        mean = torch.mean(gray_tensor, dim=(-2, -1), dtype=torch.float16)
+        del gray_tensor
+        mean *= torch.tensor(adjusted_factor, dtype=torch.float16)
+        tensor = mean.repeat(min(self.channel, 3), self._image_tensor.size(dim=-2), self._image_tensor.size(dim=-1))
+        tensor += self._image_tensor[0 : min(self.channel, 3)]
+        tensor *= factor
+        torch.clamp(tensor, 0, 255, out=tensor)
+
         if self.channel == 4:
-            return Image(
-                torch.cat(
-                    [
-                        func2.adjust_contrast(self._image_tensor[0:3], factor * 1.0),
-                        self._image_tensor[3].unsqueeze(dim=0),
-                    ],
-                ),
-            )
+            return Image(torch.cat([tensor.to(torch.uint8), self._image_tensor[3:4]], dim=0))
         else:
-            return Image(func2.adjust_contrast(self._image_tensor, factor * 1.0))
+            return Image(tensor.to(torch.uint8))
 
     def adjust_color_balance(self, factor: float) -> Image:
         """
@@ -665,10 +691,20 @@ def adjust_color_balance(self, factor: float) -> Image:
         OutOfBoundsError
             If factor is smaller than 0.
         """
+        import torch
+
         _check_adjust_color_balance_errors_and_warnings(factor, self.channel, plural=False)
-        return Image(
-            self.convert_to_grayscale()._image_tensor * (1.0 - factor * 1.0) + self._image_tensor * (factor * 1.0),
-        )
+
+        factor *= 1.0
+        if factor == 0:
+            return self.convert_to_grayscale()
+        else:
+            adjusted_factor = (1 - factor) / factor
+            tensor = self.convert_to_grayscale()._image_tensor * torch.tensor(adjusted_factor, dtype=torch.float16)
+            tensor += self._image_tensor
+            tensor *= factor
+            torch.clamp(tensor, 0, 255, out=tensor)
+            return Image(tensor.to(torch.uint8))
 
     def blur(self, radius: int) -> Image:
         """
@@ -692,12 +728,30 @@ def blur(self, radius: int) -> Image:
         OutOfBoundsError
             If radius is smaller than 0 or equal or greater than the smaller size of the image.
         """
-        from torchvision.transforms.v2 import functional as func2
+        import torch
 
         _init_default_device()
 
+        float_dtype = torch.float32 if _get_device() != torch.device("cuda") else torch.float16
+
         _check_blur_errors_and_warnings(radius, min(self.width, self.height), plural=False)
-        return Image(func2.gaussian_blur(self._image_tensor, [radius * 2 + 1, radius * 2 + 1]))
+
+        kernel = torch.full(
+            (self._image_tensor.size(dim=-3), 1, radius * 2 + 1, radius * 2 + 1),
+            1 / (radius * 2 + 1) ** 2,
+            dtype=float_dtype,
+        )
+        tensor = torch.nn.functional.conv2d(
+            torch.nn.functional.pad(
+                self._image_tensor.to(float_dtype),
+                (radius, radius, radius, radius),
+                mode="replicate",
+            ),
+            kernel,
+            padding="valid",
+            groups=self._image_tensor.size(dim=-3),
+        ).to(torch.uint8)
+        return Image(tensor)
 
     def sharpen(self, factor: float) -> Image:
         """
@@ -734,7 +788,7 @@ def sharpen(self, factor: float) -> Image:
                 torch.cat(
                     [
                         func2.adjust_sharpness(self._image_tensor[0:3], factor * 1.0),
-                        self._image_tensor[3].unsqueeze(dim=0),
+                        self._image_tensor[3:4],
                     ],
                 ),
             )
@@ -759,7 +813,7 @@ def invert_colors(self) -> Image:
 
         if self.channel == 4:
             return Image(
-                torch.cat([func2.invert(self._image_tensor[0:3]), self._image_tensor[3].unsqueeze(dim=0)]),
+                torch.cat([func2.invert(self._image_tensor[0:3]), self._image_tensor[3:4]]),
             )
         else:
             return Image(func2.invert(self._image_tensor))
@@ -813,20 +867,19 @@ def find_edges(self) -> Image:
 
         _init_default_device()
 
-        edges_tensor = torch.clamp(
-            torch.nn.functional.conv2d(
-                self.convert_to_grayscale()._image_tensor.float()[0].unsqueeze(dim=0),
-                Image._filter_edges_kernel(),
-                padding="same",
-            ).squeeze(dim=1),
-            0,
-            255,
-        ).to(torch.uint8)
+        edges_tensor_float16 = torch.nn.functional.conv2d(
+            self.convert_to_grayscale()._image_tensor.to(torch.float16)[0:1],
+            Image._filter_edges_kernel(),
+            padding="same",
+        )
+        torch.clamp(edges_tensor_float16, 0, 255, out=edges_tensor_float16)
+        if self.channel == 1:
+            return Image(edges_tensor_float16.to(torch.uint8))
+        edges_tensor = edges_tensor_float16.to(torch.uint8)
+        del edges_tensor_float16
         if self.channel == 3:
             return Image(edges_tensor.repeat(3, 1, 1))
-        elif self.channel == 4:
+        else:  # self.channel == 4
             return Image(
-                torch.cat([edges_tensor.repeat(3, 1, 1), self._image_tensor[3].unsqueeze(dim=0)]),
+                torch.cat([edges_tensor.repeat(3, 1, 1), self._image_tensor[3:4]]),
             )
-        else:
-            return Image(edges_tensor)
diff --git a/src/safeds/data/image/containers/_multi_size_image_list.py b/src/safeds/data/image/containers/_multi_size_image_list.py
index f919776e9..186d6b79a 100644
--- a/src/safeds/data/image/containers/_multi_size_image_list.py
+++ b/src/safeds/data/image/containers/_multi_size_image_list.py
@@ -9,7 +9,9 @@
 from safeds._utils import _structural_hash
 from safeds.data.image._utils._image_transformation_error_and_warning_checks import (
     _check_blur_errors_and_warnings,
+    _check_crop_errors,
     _check_remove_images_with_size_errors,
+    _check_resize_errors,
 )
 from safeds.data.image.containers import Image, ImageList
 from safeds.exceptions import (
@@ -374,7 +376,7 @@ def add_images(self, images: list[Image] | ImageList) -> ImageList:
                         key=sorted(range(len(new_indices)), key=old_indices.__getitem__).__getitem__,
                     )
                 ]
-                fixed_ims._calc_new_indices_to_tensor_positions()
+                fixed_ims._indices_to_tensor_positions = fixed_ims._calc_new_indices_to_tensor_positions()
                 image_list._image_list_dict[size] = fixed_ims
             else:
                 image_list._image_list_dict[size] = _SingleSizeImageList._create_image_list(
@@ -435,9 +437,13 @@ def remove_images_with_size(self, width: int, height: int) -> ImageList:
         if (width, height) not in self._image_list_dict:
             return self
         if len(self._image_list_dict) == 2:
-            single_size_image_list = self._image_list_dict[
-                next(iter([key for key in list(self._image_list_dict.keys()) if key != (width, height)]))
-            ]._as_single_size_image_list()
+            single_size_image_list = (
+                self._image_list_dict[
+                    next(iter([key for key in list(self._image_list_dict.keys()) if key != (width, height)]))
+                ]
+                ._clone()
+                ._as_single_size_image_list()
+            )
             single_size_image_list._tensor_positions_to_indices = torch.sort(
                 torch.Tensor(single_size_image_list._tensor_positions_to_indices),
             )[1].tolist()
@@ -506,14 +512,18 @@ def resize(self, new_width: int, new_height: int) -> ImageList:
 
         from safeds.data.image.containers._single_size_image_list import _SingleSizeImageList
 
-        image_list_tensors = []
+        _check_resize_errors(new_width, new_height)
+
         image_list_indices = []
+        image_list = _SingleSizeImageList()
+        image_list._tensor = torch.empty(len(self), self.channel, new_height, new_width, dtype=torch.uint8)
+        current_start_index = 0
         for image_list_original in self._image_list_dict.values():
             image_list_new = image_list_original.resize(new_width, new_height)._as_single_size_image_list()
-            image_list_tensors.append(image_list_new._tensor)
+            end = current_start_index + len(image_list_original)
+            image_list._tensor[current_start_index:end] = image_list_new._tensor
             image_list_indices += image_list_new._tensor_positions_to_indices
-        image_list = _SingleSizeImageList()
-        image_list._tensor = torch.cat(image_list_tensors, dim=0)
+            current_start_index = end
         image_list._tensor_positions_to_indices = image_list_indices
         image_list._indices_to_tensor_positions = image_list._calc_new_indices_to_tensor_positions()
         return image_list
@@ -531,14 +541,18 @@ def crop(self, x: int, y: int, width: int, height: int) -> ImageList:
 
         from safeds.data.image.containers._single_size_image_list import _SingleSizeImageList
 
-        image_list_tensors = []
+        _check_crop_errors(x, y, width, height)
+
         image_list_indices = []
+        image_list = _SingleSizeImageList()
+        image_list._tensor = torch.empty(len(self), self.channel, width, height, dtype=torch.uint8)
+        current_start_index = 0
         for image_list_original in self._image_list_dict.values():
             image_list_new = image_list_original.crop(x, y, width, height)._as_single_size_image_list()
-            image_list_tensors.append(image_list_new._tensor)
+            end = current_start_index + len(image_list_original)
+            image_list._tensor[current_start_index:end] = image_list_new._tensor
             image_list_indices += image_list_new._tensor_positions_to_indices
-        image_list = _SingleSizeImageList()
-        image_list._tensor = torch.cat(image_list_tensors, dim=0)
+            current_start_index = end
         image_list._tensor_positions_to_indices = image_list_indices
         image_list._indices_to_tensor_positions = image_list._calc_new_indices_to_tensor_positions()
         return image_list
diff --git a/src/safeds/data/image/containers/_single_size_image_list.py b/src/safeds/data/image/containers/_single_size_image_list.py
index 6081acd24..459e82f28 100644
--- a/src/safeds/data/image/containers/_single_size_image_list.py
+++ b/src/safeds/data/image/containers/_single_size_image_list.py
@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import copy
+import math
 import random
 import sys
 from pathlib import Path
@@ -14,7 +15,8 @@
     _check_adjust_color_balance_errors_and_warnings,
     _check_adjust_contrast_errors_and_warnings,
     _check_blur_errors_and_warnings,
-    _check_crop_errors_and_warnings,
+    _check_crop_errors,
+    _check_crop_warnings,
     _check_remove_images_with_size_errors,
     _check_resize_errors,
     _check_sharpen_errors_and_warnings,
@@ -132,34 +134,36 @@ def _create_image_list(images: list[Tensor], indices: list[int]) -> ImageList:
             return _EmptyImageList()
 
         image_list = _SingleSizeImageList()
-        images_ready_to_concat: list[tuple[Tensor, int]] = []
-        images_with_correct_channel: list[tuple[Tensor, int]] = []
-        images_with_less_channels: list[tuple[Tensor, int]] = []
+        image_list._tensor_positions_to_indices = []
+        images_with_channels: dict[int, list[Tensor]] = {}
+        indices_with_channels: dict[int, list[int]] = {}
         max_channel = 0
         for image, index in zip(images, indices, strict=False):
-            if max_channel < image.size(dim=-3):  # all images have to increase their channel
-                images_with_less_channels += images_with_correct_channel
-                images_with_correct_channel = [(image, index)]
-                max_channel = image.size(dim=-3)
-            elif max_channel > image.size(dim=-3):  # current image has to increase its channel
-                images_with_less_channels.append((image, index))
-            else:  # current image has same channel as max_channel
-                images_with_correct_channel.append((image, index))
-        for image, index in images_with_correct_channel:
-            images_ready_to_concat.append((image.unsqueeze(dim=0), index))
-        for image, index in images_with_less_channels:
-            if max_channel == 3:  # image channel 1 and max channel 3
-                image_to_append = torch.cat([image, image, image], dim=0)
-            elif image.size(dim=0) == 1:  # image channel 1 and max channel 4
-                image_to_append = torch.cat(
-                    [image, image, image, torch.full(image.size(), 255, device=image.device)],
-                    dim=0,
+            current_channel = image.size(dim=-3)
+            if max_channel < current_channel:
+                max_channel = current_channel
+            if current_channel not in images_with_channels:
+                images_with_channels[current_channel] = [image]
+                indices_with_channels[current_channel] = [index]
+            else:
+                images_with_channels[current_channel].append(image)
+                indices_with_channels[current_channel].append(index)
+
+        height = images[0].size(dim=-2)
+        width = images[0].size(dim=-1)
+        image_list._tensor = torch.empty(len(images), max_channel, height, width, dtype=torch.uint8)
+        current_start_index = 0
+        for current_channel, ims in images_with_channels.items():
+            for index, image in enumerate(ims):
+                image_list._tensor[index + current_start_index, 0 : max(current_channel, min(max_channel, 3))] = image
+            if max_channel == 4 and current_channel != 4:
+                torch.full(
+                    (len(ims), 1, height, width),
+                    255,
+                    out=image_list._tensor[current_start_index : current_start_index + len(ims), 3:4],
                 )
-            else:  # image channel 3 and max channel 4
-                image_to_append = torch.cat([image, torch.full(image[0:1].size(), 255, device=image.device)], dim=0)
-            images_ready_to_concat.append((image_to_append.unsqueeze(dim=0), index))
-        image_list._tensor = torch.cat([image for image, index in images_ready_to_concat])
-        image_list._tensor_positions_to_indices = [index for image, index in images_ready_to_concat]
+            current_start_index += len(ims)
+            image_list._tensor_positions_to_indices += indices_with_channels[current_channel]
         image_list._indices_to_tensor_positions = image_list._calc_new_indices_to_tensor_positions()
 
         return image_list
@@ -496,25 +500,25 @@ def _add_image_tensor(self, image_tensor: Tensor, index: int) -> ImageList:
         ):
             image_list_single: _SingleSizeImageList = self._clone_without_tensor()
             if image_tensor.size(dim=0) > self.channel:
-                tensor = self.change_channel(image_tensor.size(dim=0))._as_single_size_image_list()._tensor
+                tensor = _SingleSizeImageList._change_channel_of_tensor(self._tensor, image_tensor.size(dim=0))
             else:
                 tensor = self._tensor
             if image_tensor.size(dim=0) < tensor.size(dim=1):
-                if tensor.size(dim=1) == 3:
+                if tensor.size(dim=1) == 3:  # image_tensor channel == 1
                     image_list_single._tensor = torch.cat(
-                        [tensor, torch.cat([image_tensor, image_tensor, image_tensor], dim=0).unsqueeze(dim=0)],
+                        [tensor, torch.stack([image_tensor, image_tensor, image_tensor], dim=1)],
                     )
-                elif image_tensor.size(dim=0) == 1:
+                elif image_tensor.size(dim=0) == 1:  # tensor channel == 4
                     image_list_single._tensor = torch.cat(
                         [
                             tensor,
-                            torch.cat(
+                            torch.stack(
                                 [image_tensor, image_tensor, image_tensor, torch.full(image_tensor.size(), 255)],
-                                dim=0,
-                            ).unsqueeze(dim=0),
+                                dim=1,
+                            ),
                         ],
                     )
-                else:
+                else:  # image_tensor channel == 3; tensor channel == 4
                     image_list_single._tensor = torch.cat(
                         [
                             tensor,
@@ -557,71 +561,279 @@ def add_images(self, images: list[Image] | ImageList) -> ImageList:
         if isinstance(images, _EmptyImageList) or isinstance(images, list) and len(images) == 0:
             return self
 
-        first_new_index = max(self._tensor_positions_to_indices) + 1
+        next_index = max(self._tensor_positions_to_indices) + 1
+
+        max_channel = self.channel
+        self_size = (self.widths[0], self.heights[0])
+
         if isinstance(images, list):
-            images = ImageList.from_images(images)
-        if isinstance(images, _MultiSizeImageList):
-            image_list_multi: _MultiSizeImageList = _MultiSizeImageList()
-            image_list_multi._image_list_dict[(self.widths[0], self.heights[0])] = self
-            for index in self._tensor_positions_to_indices:
-                image_list_multi._indices_to_image_size_dict[index] = (self.widths[0], self.heights[0])
-            return image_list_multi.add_images(images)
-        else:
-            images_as_single_size_image_list: _SingleSizeImageList = images._as_single_size_image_list()
-            new_indices = [
-                index + first_new_index for index in images_as_single_size_image_list._tensor_positions_to_indices
-            ]
-            if (
-                images_as_single_size_image_list.widths[0] == self.widths[0]
-                and images_as_single_size_image_list.heights[0] == self.heights[0]
-            ):
-                image_list_single: _SingleSizeImageList = self._clone_without_tensor()._as_single_size_image_list()
-                image_list_single._tensor_positions_to_indices += new_indices
-                if self.channel > images_as_single_size_image_list.channel:
-                    image_list_single._tensor = torch.cat(
-                        [
+            new_image_lists: dict[tuple[int, int], ImageList] = {}
+
+            images_with_sizes_with_channel: dict[tuple[int, int], dict[int, list[Image]]] = {}
+            images_with_sizes_count: dict[tuple[int, int], int] = {}
+            indices_with_sizes_with_channel: dict[tuple[int, int], dict[int, list[int]]] = {}
+            for image in images:
+                current_size = (image.width, image.height)
+                current_channel = image.channel
+                if max_channel < current_channel:
+                    max_channel = current_channel
+                if current_size not in images_with_sizes_with_channel:
+                    images_with_sizes_with_channel[current_size] = {}
+                    indices_with_sizes_with_channel[current_size] = {}
+                    images_with_sizes_count[current_size] = 1
+                else:
+                    images_with_sizes_count[current_size] += 1
+                if current_channel not in images_with_sizes_with_channel[current_size]:
+                    images_with_sizes_with_channel[current_size][current_channel] = [image]
+                    indices_with_sizes_with_channel[current_size][current_channel] = [next_index]
+                else:
+                    images_with_sizes_with_channel[current_size][current_channel].append(image)
+                    indices_with_sizes_with_channel[current_size][current_channel].append(next_index)
+                next_index += 1
+            if self_size not in images_with_sizes_with_channel:
+                if self.channel != max_channel:
+                    new_image_lists[self_size] = self.change_channel(max_channel)
+                else:
+                    new_image_lists[self_size] = self
+            for size in images_with_sizes_with_channel:
+                if size == self_size:
+                    new_tensor = torch.empty(
+                        len(self) + images_with_sizes_count[size],
+                        max_channel,
+                        size[1],
+                        size[0],
+                        dtype=torch.uint8,
+                    )
+                    new_indices = self._tensor_positions_to_indices
+                    if self.channel != max_channel:
+                        new_tensor[0 : len(self)] = _SingleSizeImageList._change_channel_of_tensor(
                             self._tensor,
-                            _SingleSizeImageList._change_channel_of_tensor(
-                                images_as_single_size_image_list._tensor,
-                                self.channel,
-                            ),
-                        ],
+                            max_channel,
+                        )
+                    else:
+                        new_tensor[0 : len(self)] = self._tensor
+                    current_index = len(self)
+                    for channel in images_with_sizes_with_channel[size]:
+                        new_indices += indices_with_sizes_with_channel[size][channel]
+                        number_in_current_channel = len(images_with_sizes_with_channel[size][channel])
+                        end_index = current_index + number_in_current_channel
+                        if channel < max_channel:
+                            if channel == 3:
+                                torch.stack(
+                                    [img._image_tensor for img in images_with_sizes_with_channel[size][channel]],
+                                    dim=0,
+                                    out=new_tensor[current_index:end_index, 0:3],
+                                )
+                            else:  # channel == 1
+                                torch.stack(
+                                    [img._image_tensor for img in images_with_sizes_with_channel[size][channel]],
+                                    dim=0,
+                                    out=new_tensor[current_index:end_index, 0:1],
+                                )
+                                torch.stack(
+                                    [img._image_tensor for img in images_with_sizes_with_channel[size][channel]],
+                                    dim=0,
+                                    out=new_tensor[current_index:end_index, 1:2],
+                                )
+                                torch.stack(
+                                    [img._image_tensor for img in images_with_sizes_with_channel[size][channel]],
+                                    dim=0,
+                                    out=new_tensor[current_index:end_index, 2:3],
+                                )
+                            if max_channel == 4:
+                                torch.full(
+                                    (number_in_current_channel, 1, size[1], size[0]),
+                                    255,
+                                    dtype=torch.uint8,
+                                    out=new_tensor[current_index:end_index, 3:4],
+                                )
+                        else:
+                            torch.stack(
+                                [img._image_tensor for img in images_with_sizes_with_channel[size][channel]],
+                                dim=0,
+                                out=new_tensor[current_index:end_index, :],
+                            )
+                        current_index = end_index
+                    new_image_list = _SingleSizeImageList()
+                    new_image_list._tensor = new_tensor
+                    new_image_list._tensor_positions_to_indices = new_indices
+                    new_image_list._indices_to_tensor_positions = new_image_list._calc_new_indices_to_tensor_positions()
+                    new_image_lists[size] = new_image_list
+                else:
+                    new_tensor = torch.empty(
+                        images_with_sizes_count[size],
+                        max_channel,
+                        size[1],
+                        size[0],
+                        dtype=torch.uint8,
                     )
-                elif self.channel < images_as_single_size_image_list.channel:
-                    image_list_single._tensor = torch.cat(
+                    new_indices = []
+                    current_index = 0
+                    for channel in images_with_sizes_with_channel[size]:
+                        new_indices += indices_with_sizes_with_channel[size][channel]
+                        number_in_current_channel = len(images_with_sizes_with_channel[size][channel])
+                        end_index = current_index + number_in_current_channel
+                        if channel < max_channel:
+                            if channel == 3:
+                                torch.stack(
+                                    [img._image_tensor for img in images_with_sizes_with_channel[size][channel]],
+                                    dim=0,
+                                    out=new_tensor[current_index:end_index, 0:3],
+                                )
+                            else:  # channel == 1
+                                torch.stack(
+                                    [img._image_tensor for img in images_with_sizes_with_channel[size][channel]],
+                                    dim=0,
+                                    out=new_tensor[current_index:end_index, 0:1],
+                                )
+                                torch.stack(
+                                    [img._image_tensor for img in images_with_sizes_with_channel[size][channel]],
+                                    dim=0,
+                                    out=new_tensor[current_index:end_index, 1:2],
+                                )
+                                torch.stack(
+                                    [img._image_tensor for img in images_with_sizes_with_channel[size][channel]],
+                                    dim=0,
+                                    out=new_tensor[current_index:end_index, 2:3],
+                                )
+                            if max_channel == 4:
+                                torch.full(
+                                    (number_in_current_channel, 1, size[1], size[0]),
+                                    255,
+                                    dtype=torch.uint8,
+                                    out=new_tensor[current_index:end_index, 3:4],
+                                )
+                        else:
+                            torch.stack(
+                                [img._image_tensor for img in images_with_sizes_with_channel[size][channel]],
+                                dim=0,
+                                out=new_tensor[current_index:end_index, :],
+                            )
+                        current_index = end_index
+                    new_image_list = _SingleSizeImageList()
+                    new_image_list._tensor = new_tensor
+                    new_image_list._tensor_positions_to_indices = new_indices
+                    new_image_list._indices_to_tensor_positions = new_image_list._calc_new_indices_to_tensor_positions()
+                    new_image_lists[size] = new_image_list
+            if len(new_image_lists) == 1:
+                return new_image_lists[next(iter(new_image_lists))]
+            else:
+                multi_image_list = _MultiSizeImageList()
+                multi_image_list._image_list_dict = new_image_lists
+                multi_image_list._indices_to_image_size_dict = {}
+                for size, im_list in new_image_lists.items():
+                    for index in im_list._as_single_size_image_list()._tensor_positions_to_indices:
+                        multi_image_list._indices_to_image_size_dict[index] = size
+                return multi_image_list
+        else:  # images is of type ImageList
+            max_channel = max(max_channel, images.channel)
+            index_offset = max(self._tensor_positions_to_indices)
+            if isinstance(images, _SingleSizeImageList):
+                return _SingleSizeImageList._combine_two_single_size_image_lists(self, images)
+            else:  # images is of type _MultiSizeImageList
+                images = images._as_multi_size_image_list()
+                multi_image_list = _MultiSizeImageList()
+                multi_image_list._image_list_dict = {}
+                multi_image_list._indices_to_image_size_dict = {}
+                if self_size in images._image_list_dict:
+                    new_self_im_list = _SingleSizeImageList._combine_two_single_size_image_lists(
+                        self,
+                        images._image_list_dict[self_size]._as_single_size_image_list(),
+                    )
+                elif self.channel != max_channel:
+                    new_self_im_list = self.change_channel(max_channel)
+                else:
+                    new_self_im_list = self
+                multi_image_list._image_list_dict[self_size] = new_self_im_list
+                for index in new_self_im_list._as_single_size_image_list()._tensor_positions_to_indices:
+                    multi_image_list._indices_to_image_size_dict[index] = self_size
+                for im_size, im_list in images._image_list_dict.items():
+                    if im_size == self_size:
+                        continue
+                    if im_list.channel != max_channel:
+                        new_im_list = im_list.change_channel(max_channel)._as_single_size_image_list()
+                    else:
+                        new_im_list = im_list._as_single_size_image_list()
+                    new_im_list._tensor_positions_to_indices = [
+                        old_index + index_offset
+                        for old_index in im_list._as_single_size_image_list()._tensor_positions_to_indices
+                    ]
+                    new_im_list._indices_to_tensor_positions = new_im_list._calc_new_indices_to_tensor_positions()
+                    multi_image_list._image_list_dict[im_size] = new_im_list
+                    for new_index in new_im_list._tensor_positions_to_indices:
+                        multi_image_list._indices_to_image_size_dict[new_index] = im_size
+                return multi_image_list
+
+    @staticmethod
+    def _combine_two_single_size_image_lists(
+        image_list_1: _SingleSizeImageList,
+        image_list_2: _SingleSizeImageList,
+    ) -> ImageList:
+        import torch
+
+        from safeds.data.image.containers._multi_size_image_list import _MultiSizeImageList
+
+        max_channel = max(image_list_1.channel, image_list_2.channel)
+        im1_size = (image_list_1.widths[0], image_list_1.heights[0])
+        im2_size = (image_list_2.widths[0], image_list_2.heights[0])
+        index_offset = max(image_list_1._tensor_positions_to_indices) + 1
+
+        if im1_size == im2_size:
+            if image_list_2.channel == image_list_1.channel:
+                new_image_list = _SingleSizeImageList()
+                new_image_list._tensor = torch.cat([image_list_1._tensor, image_list_2._tensor], dim=0)
+                new_image_list._tensor_positions_to_indices = image_list_1._tensor_positions_to_indices + [
+                    old_index + index_offset for old_index in image_list_2._tensor_positions_to_indices
+                ]
+                new_image_list._indices_to_tensor_positions = new_image_list._calc_new_indices_to_tensor_positions()
+                return new_image_list
+            else:
+                new_image_list = _SingleSizeImageList()
+                if image_list_2.channel < max_channel:
+                    new_image_list._tensor = torch.cat(
                         [
-                            _SingleSizeImageList._change_channel_of_tensor(
-                                self._tensor,
-                                images_as_single_size_image_list.channel,
-                            ),
-                            images_as_single_size_image_list._tensor,
+                            image_list_1._tensor,
+                            _SingleSizeImageList._change_channel_of_tensor(image_list_2._tensor, max_channel),
                         ],
+                        dim=0,
                     )
                 else:
-                    image_list_single._tensor = torch.cat([self._tensor, images_as_single_size_image_list._tensor])
-                image_list_single._indices_to_tensor_positions = (
-                    image_list_single._calc_new_indices_to_tensor_positions()
-                )
-                return image_list_single
-            else:
-                image_list_multi = _MultiSizeImageList()
-                image_list_multi._image_list_dict[(self.widths[0], self.heights[0])] = self
-                image_list_multi._image_list_dict[
-                    (images_as_single_size_image_list.widths[0], images_as_single_size_image_list.heights[0])
-                ] = images_as_single_size_image_list
-                for index in self._tensor_positions_to_indices:
-                    image_list_multi._indices_to_image_size_dict[index] = (self.widths[0], self.heights[0])
-                for index in images_as_single_size_image_list._tensor_positions_to_indices:
-                    image_list_multi._indices_to_image_size_dict[index + first_new_index] = (
-                        images_as_single_size_image_list.widths[0],
-                        images_as_single_size_image_list.heights[0],
+                    new_image_list._tensor = torch.cat(
+                        [
+                            _SingleSizeImageList._change_channel_of_tensor(image_list_1._tensor, max_channel),
+                            image_list_2._tensor,
+                        ],
+                        dim=0,
                     )
-
-                if images_as_single_size_image_list.channel != self.channel:
-                    image_list_multi = image_list_multi.change_channel(
-                        max(images_as_single_size_image_list.channel, self.channel),
-                    )._as_multi_size_image_list()
-                return image_list_multi
+                new_image_list._tensor_positions_to_indices = image_list_1._tensor_positions_to_indices + [
+                    old_index + index_offset for old_index in image_list_2._tensor_positions_to_indices
+                ]
+                new_image_list._indices_to_tensor_positions = new_image_list._calc_new_indices_to_tensor_positions()
+                return new_image_list
+        else:
+            multi_image_list = _MultiSizeImageList()
+            multi_image_list._image_list_dict = {}
+            im_l_2 = _SingleSizeImageList()
+            if image_list_2.channel == image_list_1.channel:
+                multi_image_list._image_list_dict[im1_size] = image_list_1
+                im_l_2._tensor = image_list_2._tensor
+            elif image_list_2.channel < image_list_1.channel:
+                multi_image_list._image_list_dict[im1_size] = image_list_1
+                im_l_2._tensor = _SingleSizeImageList._change_channel_of_tensor(image_list_2._tensor, max_channel)
+            else:
+                multi_image_list._image_list_dict[im1_size] = image_list_1.change_channel(max_channel)
+                im_l_2._tensor = image_list_2._tensor
+            im_l_2._tensor_positions_to_indices = [
+                old_index + index_offset for old_index in image_list_2._tensor_positions_to_indices
+            ]
+            im_l_2._indices_to_tensor_positions = im_l_2._calc_new_indices_to_tensor_positions()
+            multi_image_list._image_list_dict[im2_size] = im_l_2
+            multi_image_list._indices_to_image_size_dict = {}
+            for index in image_list_1._tensor_positions_to_indices:
+                multi_image_list._indices_to_image_size_dict[index] = im1_size
+            for index in image_list_2._tensor_positions_to_indices:
+                multi_image_list._indices_to_image_size_dict[index + index_offset] = im2_size
+            return multi_image_list
 
     def remove_image_by_index(self, index: int | list[int]) -> ImageList:
         if isinstance(index, int):
@@ -653,7 +865,7 @@ def _remove_image_by_index_ignore_invalid(self, index: int | list[int]) -> Image
         image_list = _SingleSizeImageList()
         image_list._tensor = self._tensor[
             [i for i, v in enumerate(self._tensor_positions_to_indices) if v not in index]
-        ].clone()
+        ]
         image_list._tensor_positions_to_indices = [
             i - len([k for k in index if k < i]) for i in self._tensor_positions_to_indices if i not in index
         ]
@@ -676,6 +888,8 @@ def remove_duplicate_images(self) -> ImageList:
             dim=0,
             return_inverse=True,
         )  # Works somehow faster on cpu
+        if tensor_cpu_unique.size(dim=-4) == self._tensor.size(dim=-4):  # no duplicates
+            return self
         image_list._tensor = tensor_cpu_unique.to(self._tensor.device)
         indices, indices_to_remove = [], []
         offset_indices: list[int] = []
@@ -717,6 +931,8 @@ def resize(self, new_width: int, new_height: int) -> ImageList:
         return image_list
 
     def convert_to_grayscale(self) -> ImageList:
+        if self.channel == 1:
+            return self
         image_list = self._clone_without_tensor()
         image_list._tensor = _SingleSizeImageList._convert_tensor_to_grayscale(self._tensor)
         return image_list
@@ -728,20 +944,23 @@ def _convert_tensor_to_grayscale(tensor: Tensor) -> Tensor:
 
         _init_default_device()
 
-        if tensor.size(dim=-3) == 4:
+        if tensor.size(dim=-3) == 1:
+            return tensor
+        elif tensor.size(dim=-3) == 4:
             return torch.cat(
-                [func2.rgb_to_grayscale(tensor[:, 0:3], num_output_channels=3), tensor[:, 3].unsqueeze(dim=1)],
+                [func2.rgb_to_grayscale(tensor[:, 0:3], num_output_channels=3), tensor[:, 3:4]],
                 dim=1,
             )
-        else:
-            return func2.rgb_to_grayscale(tensor[:, 0:3], num_output_channels=tensor.size(dim=-3))
+        else:  # channel == 3
+            return func2.rgb_to_grayscale(tensor[:, 0:3], num_output_channels=3)
 
     def crop(self, x: int, y: int, width: int, height: int) -> ImageList:
         from torchvision.transforms.v2 import functional as func2
 
         _init_default_device()
 
-        _check_crop_errors_and_warnings(x, y, width, height, self.widths[0], self.heights[0], plural=True)
+        _check_crop_errors(x, y, width, height)
+        _check_crop_warnings(x, y, self.widths[0], self.heights[0], plural=True)
         image_list = self._clone_without_tensor()
         image_list._tensor = func2.crop(self._tensor, x, y, height, width)
         return image_list
@@ -766,19 +985,26 @@ def flip_horizontally(self) -> ImageList:
 
     def adjust_brightness(self, factor: float) -> ImageList:
         import torch
-        from torchvision.transforms.v2 import functional as func2
 
         _init_default_device()
 
         _check_adjust_brightness_errors_and_warnings(factor, plural=True)
         image_list = self._clone_without_tensor()
-        if self.channel == 4:
-            image_list._tensor = torch.cat(
-                [func2.adjust_brightness(self._tensor[:, 0:3], factor * 1.0), self._tensor[:, 3].unsqueeze(dim=1)],
-                dim=1,
+        image_list._tensor = torch.empty(self._tensor.size(), dtype=torch.uint8)
+        channel = self._tensor.size(dim=-3)
+        if factor == 0:
+            torch.zeros(
+                (self._tensor.size(dim=-4), min(3, channel), self._tensor.size(dim=-2), self._tensor.size(dim=-1)),
+                dtype=torch.uint8,
+                out=image_list._tensor[:, 0 : min(self._tensor.size(dim=-3), 3)],
             )
         else:
-            image_list._tensor = func2.adjust_brightness(self._tensor, factor * 1.0)
+            temp_tensor = self._tensor[:, 0 : min(channel, 3)] * torch.tensor([factor * 1.0], dtype=torch.float16)
+            torch.clamp(temp_tensor, 0, 255, out=temp_tensor)
+            image_list._tensor[:, 0 : min(channel, 3)] = temp_tensor[:, :]
+        if channel == 4:
+            image_list._tensor[:, 3] = self._tensor[:, 3]
+
         return image_list
 
     def add_noise(self, standard_deviation: float) -> ImageList:
@@ -788,44 +1014,100 @@ def add_noise(self, standard_deviation: float) -> ImageList:
 
         _check_add_noise_errors(standard_deviation)
         image_list = self._clone_without_tensor()
-        image_list._tensor = (
-            self._tensor + torch.normal(0, standard_deviation, self._tensor.size()).to(_get_device()) * 255
-        )
+        image_list._tensor = torch.empty(self._tensor.size(), dtype=torch.uint8)
+        float_tensor = torch.empty(self._tensor.size(), dtype=torch.float16)
+        torch.normal(0, standard_deviation, self._tensor.size(), out=float_tensor)
+        float_tensor *= 255
+        float_tensor += self._tensor
+        torch.clamp(float_tensor, 0, 255, out=float_tensor)
+        image_list._tensor[:] = float_tensor[:]
         return image_list
 
     def adjust_contrast(self, factor: float) -> ImageList:
         import torch
-        from torchvision.transforms.v2 import functional as func2
 
         _init_default_device()
 
         _check_adjust_contrast_errors_and_warnings(factor, plural=True)
         image_list = self._clone_without_tensor()
-        if self.channel == 4:
-            image_list._tensor = torch.cat(
-                [func2.adjust_contrast(self._tensor[:, 0:3], factor * 1.0), self._tensor[:, 3].unsqueeze(dim=1)],
-                dim=1,
-            )
-        else:
-            image_list._tensor = func2.adjust_contrast(self._tensor, factor * 1.0)
+        image_list._tensor = torch.empty(self._tensor.size(), dtype=torch.uint8)
+
+        channel = self._tensor.size(dim=-3)
+        factor *= 1.0
+        adjusted_factor = (1 - factor) / factor
+
+        gray_tensor = _SingleSizeImageList._convert_tensor_to_grayscale(self._tensor[:, 0 : min(channel, 3)])
+        mean = torch.mean(gray_tensor, dim=(-3, -2, -1), keepdim=True, dtype=torch.float16)
+        del gray_tensor
+        mean *= torch.tensor(adjusted_factor, dtype=torch.float16)
+        adjusted_tensor = mean.repeat(1, min(channel, 3), self._tensor.size(dim=-2), self._tensor.size(dim=-1))
+        adjusted_tensor += self._tensor[:, 0 : min(channel, 3)]
+        adjusted_tensor *= factor
+        torch.clamp(adjusted_tensor, 0, 255, out=adjusted_tensor)
+        image_list._tensor[:, 0 : min(channel, 3)] = adjusted_tensor[:, :]
+
+        if channel == 4:
+            image_list._tensor[:, 3] = self._tensor[:, 3]
+
         return image_list
 
     def adjust_color_balance(self, factor: float) -> ImageList:
+        import torch
+
         _check_adjust_color_balance_errors_and_warnings(factor, self.channel, plural=True)
         image_list = self._clone_without_tensor()
-        image_list._tensor = self.convert_to_grayscale()._as_single_size_image_list()._tensor * (
-            1.0 - factor * 1.0
-        ) + self._tensor * (factor * 1.0)
+        factor *= 1.0
+        if factor == 0:
+            image_list._tensor = _SingleSizeImageList._convert_tensor_to_grayscale(self._tensor)
+        else:
+            adjusted_factor = (1 - factor) / factor
+            image_list._tensor = torch.empty(self._tensor.size(), dtype=torch.uint8)
+            adjusted_tensor = _SingleSizeImageList._convert_tensor_to_grayscale(self._tensor) * torch.tensor(
+                adjusted_factor,
+                dtype=torch.float16,
+            )
+            adjusted_tensor += self._tensor
+            adjusted_tensor *= factor
+            torch.clamp(adjusted_tensor, 0, 255, out=adjusted_tensor)
+            image_list._tensor[:] = adjusted_tensor[:]
         return image_list
 
     def blur(self, radius: int) -> ImageList:
-        from torchvision.transforms.v2 import functional as func2
+        import torch
 
         _init_default_device()
 
+        float_dtype = torch.float32 if _get_device() != torch.device("cuda") else torch.float16
+
         _check_blur_errors_and_warnings(radius, min(self.widths[0], self.heights[0]), plural=True)
         image_list = self._clone_without_tensor()
-        image_list._tensor = func2.gaussian_blur(self._tensor, [radius * 2 + 1, radius * 2 + 1])
+
+        image_list._tensor = torch.empty(self._tensor.size(), dtype=torch.uint8)
+
+        kernel = torch.full(
+            (self._tensor.size(dim=-3), 1, radius * 2 + 1, radius * 2 + 1),
+            1 / (radius * 2 + 1) ** 2,
+            dtype=float_dtype,
+        )
+        image_tensor_size = (
+            self._tensor.size(dim=1) * (self._tensor.size(dim=2) + radius * 2) * (self._tensor.size(dim=3) + radius * 2)
+        )
+        number_of_executions = math.ceil(self._tensor.size(dim=0) / (2**31 / image_tensor_size))
+        number_of_images_per_execution = math.ceil(self._tensor.size(dim=0) / number_of_executions)
+        start = 0
+        for i in range(number_of_executions):
+            end = min((i + 1) * number_of_images_per_execution, self._tensor.size(dim=0)) + 1
+            image_list._tensor[start:end] = torch.nn.functional.conv2d(
+                torch.nn.functional.pad(
+                    self._tensor[start:end].to(float_dtype),
+                    (radius, radius, radius, radius),
+                    mode="replicate",
+                ),
+                kernel,
+                padding="valid",
+                groups=self._tensor.size(dim=-3),
+            )[:]
+            start = end
         return image_list
 
     def sharpen(self, factor: float) -> ImageList:
@@ -836,13 +1118,26 @@ def sharpen(self, factor: float) -> ImageList:
 
         _check_sharpen_errors_and_warnings(factor, plural=True)
         image_list = self._clone_without_tensor()
+        image_list._tensor = torch.empty(self._tensor.size(), dtype=torch.uint8)
+
+        image_tensor_size = self._tensor.size(dim=1) * self._tensor.size(dim=2) * self._tensor.size(dim=3)
+        number_of_executions = math.ceil(self._tensor.size(dim=0) / (2**31 / image_tensor_size))
+        number_of_images_per_execution = math.ceil(self._tensor.size(dim=0) / number_of_executions)
+        start = 0
+
         if self.channel == 4:
-            image_list._tensor = torch.cat(
-                [func2.adjust_sharpness(self._tensor[:, 0:3], factor * 1.0), self._tensor[:, 3].unsqueeze(dim=1)],
-                dim=1,
-            )
+            for i in range(number_of_executions):
+                end = min((i + 1) * number_of_images_per_execution, self._tensor.size(dim=0)) + 1
+                image_list._tensor[start:end] = torch.cat(
+                    [func2.adjust_sharpness(self._tensor[start:end, 0:3], factor * 1.0), self._tensor[start:end, 3:4]],
+                    dim=1,
+                )
+                start = end
         else:
-            image_list._tensor = func2.adjust_sharpness(self._tensor, factor * 1.0)
+            for i in range(number_of_executions):
+                end = min((i + 1) * number_of_images_per_execution, self._tensor.size(dim=0)) + 1
+                image_list._tensor[start:end] = func2.adjust_sharpness(self._tensor[start:end], factor * 1.0)
+                start = end
         return image_list
 
     def invert_colors(self) -> ImageList:
@@ -854,7 +1149,7 @@ def invert_colors(self) -> ImageList:
         image_list = self._clone_without_tensor()
         if self.channel == 4:
             image_list._tensor = torch.cat(
-                [func2.invert(self._tensor[:, 0:3]), self._tensor[:, 3].unsqueeze(dim=1)],
+                [func2.invert(self._tensor[:, 0:3]), self._tensor[:, 3:4]],
                 dim=1,
             )
         else:
@@ -885,23 +1180,25 @@ def find_edges(self) -> ImageList:
         _init_default_device()
 
         kernel = Image._filter_edges_kernel()
-        edges_tensor = torch.clamp(
-            torch.nn.functional.conv2d(
-                self.convert_to_grayscale()._as_single_size_image_list()._tensor.float()[:, 0].unsqueeze(dim=1),
-                kernel,
-                padding="same",
-            ),
-            0,
-            255,
-        ).to(torch.uint8)
+
         image_list = self._clone_without_tensor()
+
+        edges_tensor_float16 = torch.nn.functional.conv2d(
+            _SingleSizeImageList._convert_tensor_to_grayscale(self._tensor).to(torch.float16)[:, 0:1],
+            kernel,
+            padding="same",
+        )
+        torch.clamp(edges_tensor_float16, 0, 255, out=edges_tensor_float16)
+        if self.channel == 1:
+            image_list._tensor = edges_tensor_float16.to(torch.uint8)
+            return image_list
+        edges_tensor = edges_tensor_float16.to(torch.uint8)
+        del edges_tensor_float16
         if self.channel == 3:
             image_list._tensor = edges_tensor.repeat(1, 3, 1, 1)
-        elif self.channel == 4:
+        else:  # self.channel == 4
             image_list._tensor = torch.cat(
-                [edges_tensor.repeat(1, 3, 1, 1), self._tensor[:, 3].unsqueeze(dim=1)],
+                [edges_tensor.repeat(1, 3, 1, 1), self._tensor[:, 3:4]],
                 dim=1,
             )
-        else:
-            image_list._tensor = edges_tensor
         return image_list
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index 5e67a2173..d8eceb418 100644
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diff --git a/tests/safeds/data/image/containers/test_image.py b/tests/safeds/data/image/containers/test_image.py
index ad3ae820b..d1f145046 100644
--- a/tests/safeds/data/image/containers/test_image.py
+++ b/tests/safeds/data/image/containers/test_image.py
@@ -612,7 +612,7 @@ def test_should_be_original(self, resource_path: str, device: Device) -> None:
 
 @pytest.mark.parametrize("device", get_devices(), ids=get_devices_ids())
 class TestBrightness:
-    @pytest.mark.parametrize("factor", [0.5, 10], ids=["small factor", "large factor"])
+    @pytest.mark.parametrize("factor", [0, 0.5, 10], ids=["zero factor", "small factor", "large factor"])
     @pytest.mark.parametrize(
         "resource_path",
         [plane_jpg_path, plane_png_path, grayscale_png_path, grayscale_jpg_path],
@@ -762,7 +762,7 @@ def test_should_raise_negative_contrast(self, resource_path: str, device: Device
 
 @pytest.mark.parametrize("device", get_devices(), ids=get_devices_ids())
 class TestAdjustColor:
-    @pytest.mark.parametrize("factor", [2, 0.5, 0], ids=["add color", "remove color", "gray"])
+    @pytest.mark.parametrize("factor", [0.5, 0], ids=["remove color", "gray"])
     @pytest.mark.parametrize(
         "resource_path",
         [plane_jpg_path, plane_png_path, rgba_png_path, white_square_jpg_path, white_square_png_path],
@@ -781,6 +781,23 @@ def test_should_adjust_colors(
         assert image != image_adjusted_color_balance
         assert image_adjusted_color_balance == snapshot_png_image
 
+    @pytest.mark.parametrize(
+        "resource_path",
+        [plane_jpg_path, plane_png_path],
+        ids=[plane_jpg_id, plane_png_id],
+    )
+    def test_should_adjust_colors_add_color(
+        self,
+        resource_path: str,
+        snapshot_png_image: SnapshotAssertion,
+        device: Device,
+    ) -> None:
+        configure_test_with_device(device)
+        image = Image.from_file(resolve_resource_path(resource_path))
+        image_adjusted_color_balance = image.adjust_color_balance(2)
+        assert image != image_adjusted_color_balance
+        assert image_adjusted_color_balance == snapshot_png_image
+
     @pytest.mark.parametrize(
         "resource_path",
         images_all(),
@@ -811,6 +828,17 @@ def test_should_not_adjust_colors_channel_1(self, resource_path: str, device: De
             image_adjusted_color_balance = image.adjust_color_balance(0.5)
             assert image == image_adjusted_color_balance
 
+    @pytest.mark.parametrize(
+        "resource_path",
+        [rgba_png_path, white_square_png_path, white_square_jpg_path],
+        ids=[rgba_png_id, white_square_png_id, white_square_jpg_id],
+    )
+    def test_should_not_adjust_colors_value_255(self, resource_path: str, device: Device) -> None:
+        configure_test_with_device(device)
+        image = Image.from_file(resolve_resource_path(resource_path))
+        image_adjusted_color_balance = image.adjust_color_balance(2)
+        assert image == image_adjusted_color_balance
+
     @pytest.mark.parametrize(
         "resource_path",
         images_all(),
diff --git a/tests/safeds/data/image/containers/test_image_list.py b/tests/safeds/data/image/containers/test_image_list.py
index 06bf5fdaa..d8da7f6b6 100644
--- a/tests/safeds/data/image/containers/test_image_list.py
+++ b/tests/safeds/data/image/containers/test_image_list.py
@@ -893,6 +893,7 @@ class TestTransformsEqualImageTransforms:
             ("crop", [0, 0, 100, 100]),
             ("flip_vertically", None),
             ("flip_horizontally", None),
+            ("adjust_brightness", [0]),
             ("adjust_brightness", [0.5]),
             ("adjust_brightness", [10]),
             ("adjust_contrast", [0.75]),
@@ -917,6 +918,7 @@ class TestTransformsEqualImageTransforms:
             "crop-(0, 0, 100, 100)",
             "flip_vertically",
             "flip_horizontally",
+            "adjust_brightness-zero factor",
             "adjust_brightness-small factor",
             "adjust_brightness-large factor",
             "adjust_contrast-small factor",
@@ -1629,6 +1631,7 @@ def test_remove_image_by_index(self, device: Device) -> None:
             ("crop", [0, 0, 100, 100]),
             ("flip_vertically", None),
             ("flip_horizontally", None),
+            ("adjust_brightness", [0]),
             ("adjust_brightness", [0.5]),
             ("adjust_brightness", [10]),
             ("add_noise", [10]),
@@ -1660,6 +1663,7 @@ def test_remove_image_by_index(self, device: Device) -> None:
             "crop-(0, 0, 100, 100)",
             "flip_vertically",
             "flip_horizontally",
+            "adjust_brightness-zero factor",
             "adjust_brightness-small factor",
             "adjust_brightness-large factor",
             "add_noise",