diff --git a/src/spatialdata_plot/pl/basic.py b/src/spatialdata_plot/pl/basic.py
index d34d9fe4..d6216341 100644
--- a/src/spatialdata_plot/pl/basic.py
+++ b/src/spatialdata_plot/pl/basic.py
@@ -1,10 +1,11 @@
 from __future__ import annotations
 
+import contextlib
 import sys
 from collections import OrderedDict
 from copy import deepcopy
 from pathlib import Path
-from typing import Any, Literal
+from typing import Any, Literal, cast
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -15,8 +16,10 @@
 from dask.dataframe import DataFrame as DaskDataFrame
 from geopandas import GeoDataFrame
 from matplotlib.axes import Axes
+from matplotlib.backend_bases import RendererBase
 from matplotlib.colors import Colormap, Normalize
 from matplotlib.figure import Figure
+from mpl_toolkits.axes_grid1.inset_locator import inset_axes
 from spatialdata import get_extent
 from spatialdata._utils import _deprecation_alias
 from xarray import DataArray, DataTree
@@ -28,9 +31,14 @@
     _render_labels,
     _render_points,
     _render_shapes,
+    _split_colorbar_params,
 )
 from spatialdata_plot.pl.render_params import (
+    CBAR_DEFAULT_FRACTION,
+    CBAR_DEFAULT_LOCATION,
+    CBAR_DEFAULT_PAD,
     CmapParams,
+    ColorbarSpec,
     ImageRenderParams,
     LabelsRenderParams,
     LegendParams,
@@ -172,6 +180,8 @@ def render_shapes(
         table_name: str | None = None,
         table_layer: str | None = None,
         shape: Literal["circle", "hex", "visium_hex", "square"] | None = None,
+        colorbar: bool | str | None = "auto",
+        colorbar_params: dict[str, object] | None = None,
         **kwargs: Any,
     ) -> sd.SpatialData:
         """
@@ -237,6 +247,11 @@ def render_shapes(
         method : str | None, optional
             Whether to use 'matplotlib' and 'datashader'. When None, the method is
             chosen based on the size of the data.
+        colorbar :
+            Whether to request a colorbar for continuous colors. Use "auto" (default) for automatic selection.
+        colorbar_params :
+            Parameters forwarded to Matplotlib's colorbar alongside layout hints such as ``loc``, ``width``, ``pad``,
+            and ``label``.
         table_name: str | None
             Name of the table containing the color(s) columns. If one name is given than the table is used for each
             spatial element to be plotted if the table annotates it. If you want to use different tables for particular
@@ -292,6 +307,8 @@ def render_shapes(
             shape=shape,
             method=method,
             ds_reduction=kwargs.get("datashader_reduction"),
+            colorbar=colorbar,
+            colorbar_params=colorbar_params,
         )
 
         sdata = self._copy()
@@ -326,6 +343,8 @@ def render_shapes(
                 zorder=n_steps,
                 method=param_values["method"],
                 ds_reduction=param_values["ds_reduction"],
+                colorbar=param_values["colorbar"],
+                colorbar_params=param_values["colorbar_params"],
             )
             n_steps += 1
 
@@ -347,6 +366,8 @@ def render_points(
         method: str | None = None,
         table_name: str | None = None,
         table_layer: str | None = None,
+        colorbar: bool | str | None = "auto",
+        colorbar_params: dict[str, object] | None = None,
         **kwargs: Any,
     ) -> sd.SpatialData:
         """
@@ -396,6 +417,11 @@ def render_points(
         method : str | None, optional
             Whether to use 'matplotlib' and 'datashader'. When None, the method is
             chosen based on the size of the data.
+        colorbar :
+            Whether to request a colorbar for continuous colors. Use "auto" (default) for automatic selection.
+        colorbar_params :
+            Parameters forwarded to Matplotlib's colorbar alongside layout hints such as ``loc``, ``width``, ``pad``,
+            and ``label``.
         table_name: str | None
             Name of the table containing the color(s) columns. If one name is given than the table is used for each
             spatial element to be plotted if the table annotates it. If you want to use different tables for particular
@@ -434,6 +460,8 @@ def render_points(
             table_name=table_name,
             table_layer=table_layer,
             ds_reduction=kwargs.get("datashader_reduction"),
+            colorbar=colorbar,
+            colorbar_params=colorbar_params,
         )
 
         if method is not None:
@@ -467,6 +495,8 @@ def render_points(
                 zorder=n_steps,
                 method=method,
                 ds_reduction=param_values["ds_reduction"],
+                colorbar=param_values["colorbar"],
+                colorbar_params=param_values["colorbar_params"],
             )
             n_steps += 1
 
@@ -484,6 +514,8 @@ def render_images(
         palette: list[str] | str | None = None,
         alpha: float | int = 1.0,
         scale: str | None = None,
+        colorbar: bool | str | None = "auto",
+        colorbar_params: dict[str, object] | None = None,
         **kwargs: Any,
     ) -> sd.SpatialData:
         """
@@ -526,6 +558,11 @@ def render_images(
                 3) "full": Renders the full image without rasterization. In the case of
                 multiscale images, the highest resolution scale is selected. Note that
                 this may result in long computing times for large images.
+        colorbar :
+            Whether to request a colorbar for continuous colors. Use "auto" (default) for automatic selection.
+        colorbar_params :
+            Parameters forwarded to Matplotlib's colorbar alongside layout hints such as ``loc``, ``width``, ``pad``,
+            and ``label``.
         kwargs
             Additional arguments to be passed to cmap, norm, and other rendering functions.
 
@@ -547,6 +584,8 @@ def render_images(
             cmap=cmap,
             norm=norm,
             scale=scale,
+            colorbar=colorbar,
+            colorbar_params=colorbar_params,
         )
 
         sdata = self._copy()
@@ -580,6 +619,8 @@ def render_images(
                 alpha=param_values["alpha"],
                 scale=param_values["scale"],
                 zorder=n_steps,
+                colorbar=param_values["colorbar"],
+                colorbar_params=param_values["colorbar_params"],
             )
             n_steps += 1
 
@@ -600,6 +641,8 @@ def render_labels(
         outline_alpha: float | int = 0.0,
         fill_alpha: float | int = 0.4,
         scale: str | None = None,
+        colorbar: bool | str | None = "auto",
+        colorbar_params: dict[str, object] | None = None,
         table_name: str | None = None,
         table_layer: str | None = None,
         **kwargs: Any,
@@ -653,6 +696,11 @@ def render_labels(
                 (exception: a dpi is specified in `show()`. Then the image is rasterized to fit the canvas and dpi).
                 3) "full": render the full image without rasterization. In the case of a multiscale image, the scale
                 with the highest resolution is selected. This can lead to long computing times for large images!
+        colorbar :
+            Whether to request a colorbar for continuous colors. Use "auto" (default) for automatic selection.
+        colorbar_params :
+            Parameters forwarded to Matplotlib's colorbar alongside layout hints such as ``loc``, ``width``, ``pad``,
+            and ``label``.
         table_name: str | None
             Name of the table containing the color columns.
         table_layer: str | None
@@ -681,6 +729,8 @@ def render_labels(
             outline_alpha=outline_alpha,
             palette=palette,
             scale=scale,
+            colorbar=colorbar,
+            colorbar_params=colorbar_params,
             table_name=table_name,
             table_layer=table_layer,
         )
@@ -709,6 +759,8 @@ def render_labels(
                 table_name=param_values["table_name"],
                 table_layer=param_values["table_layer"],
                 zorder=n_steps,
+                colorbar=param_values["colorbar"],
+                colorbar_params=param_values["colorbar_params"],
             )
             n_steps += 1
         return sdata
@@ -723,6 +775,7 @@ def show(
         legend_fontoutline: int | None = None,
         na_in_legend: bool = True,
         colorbar: bool = True,
+        colorbar_params: dict[str, object] | None = None,
         wspace: float | None = None,
         hspace: float = 0.25,
         ncols: int = 4,
@@ -761,7 +814,10 @@ def show(
         return_ax :
             Whether to return the axes object created. False by default.
         colorbar :
-            Whether to plot the colorbar. True by default.
+            Global switch to enable/disable all colorbars. Per-layer settings are ignored when this is False.
+        colorbar_params :
+            Global overrides passed to colorbars for all axes. Accepts the same keys as per-layer ``colorbar_params``
+            (e.g., ``loc``, ``width``, ``pad``, ``label``).
         title :
             The title of the plot. If not provided the plot will have the name of the coordinate system as title.
 
@@ -786,6 +842,7 @@ def show(
             legend_fontoutline,
             na_in_legend,
             colorbar,
+            colorbar_params,
             wspace,
             hspace,
             ncols,
@@ -852,6 +909,7 @@ def show(
 
         # Check if user specified only certain elements to be plotted
         cs_contents = _get_cs_contents(sdata)
+        pending_colorbars: list[tuple[Axes, list[ColorbarSpec]]] = []
 
         elements_to_be_rendered = _get_elements_to_be_rendered(render_cmds, cs_contents, cs)
 
@@ -888,15 +946,94 @@ def show(
             ncols=ncols,
             frameon=frameon,
         )
+        legend_colorbar = colorbar
         legend_params = LegendParams(
             legend_fontsize=legend_fontsize,
             legend_fontweight=legend_fontweight,
             legend_loc=legend_loc,
             legend_fontoutline=legend_fontoutline,
             na_in_legend=na_in_legend,
-            colorbar=colorbar,
+            colorbar=legend_colorbar,
         )
 
+        def _draw_colorbar(
+            spec: ColorbarSpec,
+            fig: Figure,
+            renderer: RendererBase,
+            base_offsets_axes: dict[str, float],
+            trackers_axes: dict[str, float],
+        ) -> None:
+            base_layout = {
+                "location": CBAR_DEFAULT_LOCATION,
+                "fraction": CBAR_DEFAULT_FRACTION,
+                "pad": CBAR_DEFAULT_PAD,
+            }
+            layer_layout, layer_kwargs, layer_label_override = _split_colorbar_params(spec.params)
+            global_layout, global_kwargs, global_label_override = _split_colorbar_params(colorbar_params)
+            layout = {**base_layout, **layer_layout, **global_layout}
+            cbar_kwargs = {**layer_kwargs, **global_kwargs}
+
+            location = cast(str, layout.get("location", base_layout["location"]))
+            if location not in {"left", "right", "top", "bottom"}:
+                location = CBAR_DEFAULT_LOCATION
+            default_orientation = "vertical" if location in {"right", "left"} else "horizontal"
+            cbar_kwargs.setdefault("orientation", default_orientation)
+
+            fraction = float(cast(float | int, layout.get("fraction", base_layout["fraction"])))
+            pad = float(cast(float | int, layout.get("pad", base_layout["pad"])))
+
+            if location in {"left", "right"}:
+                pad_axes = pad + trackers_axes[location]
+                x0 = -pad_axes - fraction if location == "left" else 1 + pad_axes
+                bbox = (float(x0), 0.0, float(fraction), 1.0)
+            else:
+                pad_axes = pad + trackers_axes[location]
+                y0 = -pad_axes - fraction if location == "bottom" else 1 + pad_axes
+                bbox = (0.0, float(y0), 1.0, float(fraction))
+            cax = inset_axes(
+                spec.ax,
+                width="100%",
+                height="100%",
+                loc="center",
+                bbox_to_anchor=bbox,
+                bbox_transform=spec.ax.transAxes,
+                borderpad=0.0,
+            )
+
+            cb = fig.colorbar(spec.mappable, cax=cax, **cbar_kwargs)
+            if location == "left":
+                cb.ax.yaxis.set_ticks_position("left")
+                cb.ax.yaxis.set_label_position("left")
+                cb.ax.tick_params(labelleft=True, labelright=False)
+            elif location == "top":
+                cb.ax.xaxis.set_ticks_position("top")
+                cb.ax.xaxis.set_label_position("top")
+                cb.ax.tick_params(labeltop=True, labelbottom=False)
+            elif location == "right":
+                cb.ax.yaxis.set_ticks_position("right")
+                cb.ax.yaxis.set_label_position("right")
+                cb.ax.tick_params(labelright=True, labelleft=False)
+            elif location == "bottom":
+                cb.ax.xaxis.set_ticks_position("bottom")
+                cb.ax.xaxis.set_label_position("bottom")
+                cb.ax.tick_params(labelbottom=True, labeltop=False)
+
+            final_label = global_label_override or layer_label_override or spec.label
+            if final_label:
+                cb.set_label(final_label)
+            if spec.alpha is not None:
+                with contextlib.suppress(Exception):
+                    cb.solids.set_alpha(spec.alpha)
+            bbox_axes = cb.ax.get_tightbbox(renderer).transformed(spec.ax.transAxes.inverted())
+            if location == "left":
+                trackers_axes["left"] = pad_axes + bbox_axes.width
+            elif location == "right":
+                trackers_axes["right"] = pad_axes + bbox_axes.width
+            elif location == "bottom":
+                trackers_axes["bottom"] = pad_axes + bbox_axes.height
+            elif location == "top":
+                trackers_axes["top"] = pad_axes + bbox_axes.height
+
         cs_contents = _get_cs_contents(sdata)
 
         # go through tree
@@ -908,6 +1045,7 @@ def show(
             )
             ax = fig_params.ax if fig_params.axs is None else fig_params.axs[i]
             assert isinstance(ax, Axes)
+            axis_colorbar_requests: list[ColorbarSpec] | None = [] if legend_params.colorbar else None
 
             wants_images = False
             wants_labels = False
@@ -937,6 +1075,7 @@ def show(
                             fig_params=fig_params,
                             scalebar_params=scalebar_params,
                             legend_params=legend_params,
+                            colorbar_requests=axis_colorbar_requests,
                             rasterize=rasterize,
                         )
 
@@ -954,6 +1093,7 @@ def show(
                             fig_params=fig_params,
                             scalebar_params=scalebar_params,
                             legend_params=legend_params,
+                            colorbar_requests=axis_colorbar_requests,
                         )
 
                 elif cmd == "render_points" and has_points:
@@ -970,6 +1110,7 @@ def show(
                             fig_params=fig_params,
                             scalebar_params=scalebar_params,
                             legend_params=legend_params,
+                            colorbar_requests=axis_colorbar_requests,
                         )
 
                 elif cmd == "render_labels" and has_labels:
@@ -978,7 +1119,8 @@ def show(
                     )
 
                     if wanted_labels_on_this_cs:
-                        if (table := params_copy.table_name) is not None:
+                        table = params_copy.table_name
+                        if table is not None:
                             assert isinstance(params_copy.color, str)
                             colors = sc.get.obs_df(sdata[table], [params_copy.color])
                             if isinstance(colors[params_copy.color].dtype, pd.CategoricalDtype):
@@ -1002,6 +1144,7 @@ def show(
                             fig_params=fig_params,
                             scalebar_params=scalebar_params,
                             legend_params=legend_params,
+                            colorbar_requests=axis_colorbar_requests,
                             rasterize=rasterize,
                         )
 
@@ -1038,6 +1181,33 @@ def show(
                 ax.set_xlim(x_min, x_max)
                 ax.set_ylim(y_max, y_min)  # (0, 0) is top-left
 
+            if legend_params.colorbar and axis_colorbar_requests:
+                pending_colorbars.append((ax, axis_colorbar_requests))
+
+        if pending_colorbars and fig_params.fig is not None:
+            fig = fig_params.fig
+            fig.canvas.draw()
+            renderer = fig.canvas.get_renderer()
+            for axis, requests in pending_colorbars:
+                unique_specs: list[ColorbarSpec] = []
+                seen_mappables: set[int] = set()
+                for spec in requests:
+                    mappable_id = id(spec.mappable)
+                    if mappable_id in seen_mappables:
+                        continue
+                    seen_mappables.add(mappable_id)
+                    unique_specs.append(spec)
+                tight_bbox = axis.get_tightbbox(renderer).transformed(axis.transAxes.inverted())
+                base_offsets_axes = {
+                    "left": max(0.0, -tight_bbox.x0),
+                    "right": max(0.0, tight_bbox.x1 - 1),
+                    "bottom": max(0.0, -tight_bbox.y0),
+                    "top": max(0.0, tight_bbox.y1 - 1),
+                }
+                trackers_axes = {k: base_offsets_axes[k] for k in base_offsets_axes}
+                for spec in unique_specs:
+                    _draw_colorbar(spec, fig, renderer, base_offsets_axes, trackers_axes)
+
         if fig_params.fig is not None and save is not None:
             save_fig(fig_params.fig, path=save)
 
diff --git a/src/spatialdata_plot/pl/render.py b/src/spatialdata_plot/pl/render.py
index 53c1033c..2d607fe9 100644
--- a/src/spatialdata_plot/pl/render.py
+++ b/src/spatialdata_plot/pl/render.py
@@ -26,6 +26,7 @@
 from spatialdata_plot._logging import logger
 from spatialdata_plot.pl.render_params import (
     Color,
+    ColorbarSpec,
     FigParams,
     ImageRenderParams,
     LabelsRenderParams,
@@ -61,6 +62,55 @@
 _Normalize = Normalize | abc.Sequence[Normalize]
 
 
+def _split_colorbar_params(params: dict[str, object] | None) -> tuple[dict[str, object], dict[str, object], str | None]:
+    """Split colorbar params into layout hints, Matplotlib kwargs, and label override."""
+    layout: dict[str, object] = {}
+    cbar_kwargs: dict[str, object] = {}
+    label_override: str | None = None
+    for key, value in (params or {}).items():
+        key_lower = key.lower()
+        if key_lower in {"loc", "location"}:
+            layout["location"] = value
+        elif key_lower == "width" or key_lower == "fraction":
+            layout["fraction"] = value
+        elif key_lower == "pad":
+            layout["pad"] = value
+        elif key_lower == "label":
+            label_override = None if value is None else str(value)
+        else:
+            cbar_kwargs[key] = value
+    return layout, cbar_kwargs, label_override
+
+
+def _resolve_colorbar_label(
+    colorbar_params: dict[str, object] | None, fallback: str | None, *, is_default_channel_name: bool = False
+) -> str | None:
+    """Pick a colorbar label from params or fall back to provided value."""
+    _, _, label = _split_colorbar_params(colorbar_params)
+    if label is not None:
+        return label
+    if is_default_channel_name:
+        return None
+    return fallback
+
+
+def _should_request_colorbar(
+    colorbar: bool | str | None,
+    *,
+    has_mappable: bool,
+    is_continuous: bool,
+    auto_condition: bool = True,
+) -> bool:
+    """Resolve colorbar setting to a final boolean request."""
+    if not has_mappable or not is_continuous:
+        return False
+    if colorbar is True:
+        return True
+    if colorbar in {False, None}:
+        return False
+    return bool(auto_condition)
+
+
 def _render_shapes(
     sdata: sd.SpatialData,
     render_params: ShapesRenderParams,
@@ -69,6 +119,7 @@ def _render_shapes(
     fig_params: FigParams,
     scalebar_params: ScalebarParams,
     legend_params: LegendParams,
+    colorbar_requests: list[ColorbarSpec] | None = None,
 ) -> None:
     element = render_params.element
     col_for_color = render_params.col_for_color
@@ -80,7 +131,8 @@ def _render_shapes(
         filter_tables=bool(render_params.table_name),
     )
 
-    if (table_name := render_params.table_name) is None:
+    table_name = render_params.table_name
+    if table_name is None:
         table = None
         shapes = sdata_filt[element]
     else:
@@ -159,16 +211,13 @@ def _render_shapes(
     else:
         palette = ListedColormap(dict.fromkeys(color_vector[~pd.Categorical(color_source_vector).isnull()]))
 
-    if (
+    has_valid_color = (
         len(set(color_vector)) != 1
         or list(set(color_vector))[0] != render_params.cmap_params.na_color.get_hex_with_alpha()
-    ):
+    )
+    if has_valid_color and color_source_vector is not None and col_for_color is not None:
         # necessary in case different shapes elements are annotated with one table
-        if color_source_vector is not None and col_for_color is not None:
-            color_source_vector = color_source_vector.remove_unused_categories()
-
-        # False if user specified color-like with 'color' parameter
-        colorbar = False if col_for_color is None else legend_params.colorbar
+        color_source_vector = color_source_vector.remove_unused_categories()
 
     # Apply the transformation to the PatchCollection's paths
     trans, trans_data = _prepare_transformation(sdata_filt.shapes[element], coordinate_system)
@@ -515,8 +564,11 @@ def _render_shapes(
         if color_source_vector is not None and render_params.col_for_color is not None:
             color_source_vector = color_source_vector.remove_unused_categories()
 
-        # False if user specified color-like with 'color' parameter
-        colorbar = False if render_params.col_for_color is None else legend_params.colorbar
+        wants_colorbar = _should_request_colorbar(
+            render_params.colorbar,
+            has_mappable=cax is not None,
+            is_continuous=render_params.col_for_color is not None and color_source_vector is None,
+        )
 
         _ = _decorate_axs(
             ax=ax,
@@ -534,7 +586,13 @@ def _render_shapes(
             legend_loc=legend_params.legend_loc,
             legend_fontoutline=legend_params.legend_fontoutline,
             na_in_legend=legend_params.na_in_legend,
-            colorbar=colorbar,
+            colorbar=wants_colorbar and legend_params.colorbar,
+            colorbar_params=render_params.colorbar_params,
+            colorbar_requests=colorbar_requests,
+            colorbar_label=_resolve_colorbar_label(
+                render_params.colorbar_params,
+                col_for_color if isinstance(col_for_color, str) else None,
+            ),
             scalebar_dx=scalebar_params.scalebar_dx,
             scalebar_units=scalebar_params.scalebar_units,
         )
@@ -548,6 +606,7 @@ def _render_points(
     fig_params: FigParams,
     scalebar_params: ScalebarParams,
     legend_params: LegendParams,
+    colorbar_requests: list[ColorbarSpec] | None = None,
 ) -> None:
     element = render_params.element
     col_for_color = render_params.col_for_color
@@ -894,6 +953,12 @@ def _render_points(
         else:
             palette = ListedColormap(dict.fromkeys(color_vector[~pd.Categorical(color_source_vector).isnull()]))
 
+        wants_colorbar = _should_request_colorbar(
+            render_params.colorbar,
+            has_mappable=cax is not None,
+            is_continuous=col_for_color is not None and color_source_vector is None,
+        )
+
         _ = _decorate_axs(
             ax=ax,
             cax=cax,
@@ -910,7 +975,13 @@ def _render_points(
             legend_loc=legend_params.legend_loc,
             legend_fontoutline=legend_params.legend_fontoutline,
             na_in_legend=legend_params.na_in_legend,
-            colorbar=legend_params.colorbar,
+            colorbar=wants_colorbar and legend_params.colorbar,
+            colorbar_params=render_params.colorbar_params,
+            colorbar_requests=colorbar_requests,
+            colorbar_label=_resolve_colorbar_label(
+                render_params.colorbar_params,
+                col_for_color if isinstance(col_for_color, str) else None,
+            ),
             scalebar_dx=scalebar_params.scalebar_dx,
             scalebar_units=scalebar_params.scalebar_units,
         )
@@ -925,6 +996,7 @@ def _render_images(
     scalebar_params: ScalebarParams,
     legend_params: LegendParams,
     rasterize: bool,
+    colorbar_requests: list[ColorbarSpec] | None = None,
 ) -> None:
     sdata_filt = sdata.filter_by_coordinate_system(
         coordinate_system=coordinate_system,
@@ -1003,9 +1075,26 @@ def _render_images(
             norm=render_params.cmap_params.norm,
         )
 
-        if legend_params.colorbar:
+        wants_colorbar = _should_request_colorbar(
+            render_params.colorbar,
+            has_mappable=n_channels == 1,
+            is_continuous=True,
+            auto_condition=n_channels == 1,
+        )
+        if wants_colorbar and legend_params.colorbar and colorbar_requests is not None:
             sm = plt.cm.ScalarMappable(cmap=cmap, norm=render_params.cmap_params.norm)
-            fig_params.fig.colorbar(sm, ax=ax)
+            colorbar_requests.append(
+                ColorbarSpec(
+                    ax=ax,
+                    mappable=sm,
+                    params=render_params.colorbar_params,
+                    label=_resolve_colorbar_label(
+                        render_params.colorbar_params,
+                        str(channels[0]),
+                        is_default_channel_name=isinstance(channels[0], (int, np.integer)),
+                    ),
+                )
+            )
 
     # 2) Image has any number of channels but 1
     else:
@@ -1165,6 +1254,7 @@ def _render_labels(
     scalebar_params: ScalebarParams,
     legend_params: LegendParams,
     rasterize: bool,
+    colorbar_requests: list[ColorbarSpec] | None = None,
 ) -> None:
     element = render_params.element
     table_name = render_params.table_name
@@ -1310,6 +1400,12 @@ def _draw_labels(seg_erosionpx: int | None, seg_boundaries: bool, alpha: float)
     else:
         raise ValueError("Parameters 'fill_alpha' and 'outline_alpha' cannot both be 0.")
 
+    colorbar_requested = _should_request_colorbar(
+        render_params.colorbar,
+        has_mappable=cax is not None,
+        is_continuous=color is not None and color_source_vector is None and not categorical,
+    )
+
     _ = _decorate_axs(
         ax=ax,
         cax=cax,
@@ -1326,7 +1422,13 @@ def _draw_labels(seg_erosionpx: int | None, seg_boundaries: bool, alpha: float)
         legend_loc=legend_params.legend_loc,
         legend_fontoutline=legend_params.legend_fontoutline,
         na_in_legend=(legend_params.na_in_legend if groups is None else len(groups) == len(set(color_vector))),
-        colorbar=legend_params.colorbar,
+        colorbar=colorbar_requested and legend_params.colorbar,
+        colorbar_params=render_params.colorbar_params,
+        colorbar_requests=colorbar_requests,
+        colorbar_label=_resolve_colorbar_label(
+            render_params.colorbar_params,
+            color if isinstance(color, str) else None,
+        ),
         scalebar_dx=scalebar_params.scalebar_dx,
         scalebar_units=scalebar_params.scalebar_units,
         # scalebar_kwargs=scalebar_params.scalebar_kwargs,
diff --git a/src/spatialdata_plot/pl/render_params.py b/src/spatialdata_plot/pl/render_params.py
index a8382753..4936468f 100644
--- a/src/spatialdata_plot/pl/render_params.py
+++ b/src/spatialdata_plot/pl/render_params.py
@@ -6,6 +6,7 @@
 
 import numpy as np
 from matplotlib.axes import Axes
+from matplotlib.cm import ScalarMappable
 from matplotlib.colors import Colormap, ListedColormap, Normalize, rgb2hex, to_hex
 from matplotlib.figure import Figure
 
@@ -183,6 +184,22 @@ class LegendParams:
     colorbar: bool = True
 
 
+@dataclass
+class ColorbarSpec:
+    """Data required to create a colorbar."""
+
+    ax: Axes
+    mappable: ScalarMappable
+    params: dict[str, object] | None = None
+    label: str | None = None
+    alpha: float | None = None
+
+
+CBAR_DEFAULT_LOCATION = "right"
+CBAR_DEFAULT_FRACTION = 0.075
+CBAR_DEFAULT_PAD = 0.015
+
+
 @dataclass
 class ScalebarParams:
     """Scalebar params."""
@@ -213,6 +230,8 @@ class ShapesRenderParams:
     table_layer: str | None = None
     shape: Literal["circle", "hex", "visium_hex", "square"] | None = None
     ds_reduction: Literal["sum", "mean", "any", "count", "std", "var", "max", "min"] | None = None
+    colorbar: bool | str | None = "auto"
+    colorbar_params: dict[str, object] | None = None
 
 
 @dataclass
@@ -233,6 +252,8 @@ class PointsRenderParams:
     table_name: str | None = None
     table_layer: str | None = None
     ds_reduction: Literal["sum", "mean", "any", "count", "std", "var", "max", "min"] | None = None
+    colorbar: bool | str | None = "auto"
+    colorbar_params: dict[str, object] | None = None
 
 
 @dataclass
@@ -247,6 +268,8 @@ class ImageRenderParams:
     percentiles_for_norm: tuple[float | None, float | None] = (None, None)
     scale: str | None = None
     zorder: int = 0
+    colorbar: bool | str | None = "auto"
+    colorbar_params: dict[str, object] | None = None
 
 
 @dataclass
@@ -267,3 +290,5 @@ class LabelsRenderParams:
     table_name: str | None = None
     table_layer: str | None = None
     zorder: int = 0
+    colorbar: bool | str | None = "auto"
+    colorbar_params: dict[str, object] | None = None
diff --git a/src/spatialdata_plot/pl/utils.py b/src/spatialdata_plot/pl/utils.py
index 97ffac1d..11d5d10d 100644
--- a/src/spatialdata_plot/pl/utils.py
+++ b/src/spatialdata_plot/pl/utils.py
@@ -75,6 +75,7 @@
 from spatialdata_plot.pl.render_params import (
     CmapParams,
     Color,
+    ColorbarSpec,
     FigParams,
     ImageRenderParams,
     LabelsRenderParams,
@@ -1525,6 +1526,9 @@ def _decorate_axs(
     legend_fontoutline: int | None = None,
     na_in_legend: bool = True,
     colorbar: bool = True,
+    colorbar_params: dict[str, object] | None = None,
+    colorbar_requests: list[ColorbarSpec] | None = None,
+    colorbar_label: str | None = None,
     scalebar_dx: Sequence[float] | None = None,
     scalebar_units: Sequence[str] | None = None,
     scalebar_kwargs: Mapping[str, Any] = MappingProxyType({}),
@@ -1563,10 +1567,16 @@ def _decorate_axs(
                 na_in_legend=na_in_legend,
                 multi_panel=fig_params.axs is not None,
             )
-        elif colorbar:
-            # TODO: na_in_legend should have some effect here
-            cb = plt.colorbar(cax, ax=ax, pad=0.01, fraction=0.08, aspect=30)
-            cb.solids.set_alpha(alpha)
+        elif colorbar and colorbar_requests is not None and cax is not None:
+            colorbar_requests.append(
+                ColorbarSpec(
+                    ax=ax,
+                    mappable=cax,
+                    params=colorbar_params,
+                    label=colorbar_label,
+                    alpha=alpha,
+                )
+            )
 
     if isinstance(scalebar_dx, list) and isinstance(scalebar_units, list):
         scalebar = ScaleBar(scalebar_dx, units=scalebar_units, **scalebar_kwargs)
@@ -1976,6 +1986,7 @@ def _validate_show_parameters(
     legend_fontoutline: int | None,
     na_in_legend: bool,
     colorbar: bool,
+    colorbar_params: dict[str, object] | None,
     wspace: float | None,
     hspace: float,
     ncols: int,
@@ -2036,6 +2047,9 @@ def _validate_show_parameters(
     if not isinstance(colorbar, bool):
         raise TypeError("Parameter 'colorbar' must be a boolean.")
 
+    if colorbar_params is not None and not isinstance(colorbar_params, dict):
+        raise TypeError("Parameter 'colorbar_params' must be a dictionary or None.")
+
     if wspace is not None and not isinstance(wspace, float):
         raise TypeError("Parameter 'wspace' must be a float.")
 
@@ -2077,7 +2091,16 @@ def _validate_show_parameters(
 
 
 def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[str, Any]:
-    if (element := param_dict.get("element")) is not None and not isinstance(element, str):
+    colorbar = param_dict.get("colorbar", "auto")
+    if colorbar not in {True, False, None, "auto"}:
+        raise TypeError("Parameter 'colorbar' must be one of True, False or 'auto'.")
+
+    colorbar_params = param_dict.get("colorbar_params")
+    if colorbar_params is not None and not isinstance(colorbar_params, dict):
+        raise TypeError("Parameter 'colorbar_params' must be a dictionary or None.")
+
+    element = param_dict.get("element")
+    if element is not None and not isinstance(element, str):
         raise ValueError(
             "Parameter 'element' must be a string. If you want to display more elements, pass `element` "
             "as `None` or chain pl.render(...).pl.render(...).pl.show()"
@@ -2091,7 +2114,8 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
     elif element_type == "shapes":
         param_dict["element"] = [element] if element is not None else list(param_dict["sdata"].shapes.keys())
 
-    if (channel := param_dict.get("channel")) is not None and not isinstance(channel, list | str | int):
+    channel = param_dict.get("channel")
+    if channel is not None and not isinstance(channel, list | str | int):
         raise TypeError("Parameter 'channel' must be a string, an integer, or a list of strings or integers.")
     if isinstance(channel, list):
         if not all(isinstance(c, str | int) for c in channel):
@@ -2102,10 +2126,12 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
     elif "channel" in param_dict:
         param_dict["channel"] = [channel] if channel is not None else None
 
-    if (contour_px := param_dict.get("contour_px")) and not isinstance(contour_px, int):
+    contour_px = param_dict.get("contour_px")
+    if contour_px and not isinstance(contour_px, int):
         raise TypeError("Parameter 'contour_px' must be an integer.")
 
-    if (color := param_dict.get("color")) and element_type in {
+    color = param_dict.get("color")
+    if color and element_type in {
         "shapes",
         "points",
         "labels",
@@ -2135,7 +2161,8 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
     elif "color" in param_dict and element_type != "labels":
         param_dict["col_for_color"] = None
 
-    if outline_width := param_dict.get("outline_width"):
+    outline_width = param_dict.get("outline_width")
+    if outline_width:
         # outline_width only exists for shapes at the moment
         if isinstance(outline_width, tuple):
             for ow in outline_width:
@@ -2149,7 +2176,8 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
         if isinstance(outline_width, float | int) and outline_width < 0:
             raise ValueError("Parameter 'outline_width' cannot be negative.")
 
-    if outline_alpha := param_dict.get("outline_alpha"):
+    outline_alpha = param_dict.get("outline_alpha")
+    if outline_alpha:
         if isinstance(outline_alpha, tuple):
             if element_type != "shapes":
                 raise ValueError("Parameter 'outline_alpha' must be a single numeric.")
@@ -2176,7 +2204,8 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
         elif not isinstance(outline_alpha, float | int) or not 0 <= outline_alpha <= 1:
             raise TypeError("Parameter 'outline_alpha' must be numeric and between 0 and 1.")
 
-    if outline_color := param_dict.get("outline_color"):
+    outline_color = param_dict.get("outline_color")
+    if outline_color:
         if not isinstance(outline_color, str | tuple | list):
             raise TypeError("Parameter 'color' must be a string or a tuple/list of floats or colors.")
         if isinstance(outline_color, tuple | list):
@@ -2201,7 +2230,8 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
     if contour_px is not None and contour_px <= 0:
         raise ValueError("Parameter 'contour_px' must be a positive number.")
 
-    if (alpha := param_dict.get("alpha")) is not None:
+    alpha = param_dict.get("alpha")
+    if alpha is not None:
         if not isinstance(alpha, float | int):
             raise TypeError("Parameter 'alpha' must be numeric.")
         if not 0 <= alpha <= 1:
@@ -2210,7 +2240,8 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
         # set default alpha for points if not given by user explicitly or implicitly (as part of color)
         param_dict["alpha"] = 1.0
 
-    if (fill_alpha := param_dict.get("fill_alpha")) is not None:
+    fill_alpha = param_dict.get("fill_alpha")
+    if fill_alpha is not None:
         if not isinstance(fill_alpha, float | int):
             raise TypeError("Parameter 'fill_alpha' must be numeric.")
         if fill_alpha < 0:
@@ -2219,11 +2250,14 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
         # set default fill_alpha for shapes if not given by user explicitly or implicitly (as part of color)
         param_dict["fill_alpha"] = 1.0
 
-    if (cmap := param_dict.get("cmap")) is not None and (palette := param_dict.get("palette")) is not None:
+    cmap = param_dict.get("cmap")
+    palette = param_dict.get("palette")
+    if cmap is not None and palette is not None:
         raise ValueError("Both `palette` and `cmap` are specified. Please specify only one of them.")
     param_dict["cmap"] = cmap
 
-    if (groups := param_dict.get("groups")) is not None:
+    groups = param_dict.get("groups")
+    if groups is not None:
         if not isinstance(groups, list | str):
             raise TypeError("Parameter 'groups' must be a string or a list of strings.")
         if isinstance(groups, str):
@@ -2233,19 +2267,20 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
 
     palette = param_dict["palette"]
 
-    if isinstance((palette := param_dict["palette"]), list):
+    if isinstance(palette, list):
         if not all(isinstance(p, str) for p in palette):
             raise ValueError("If specified, parameter 'palette' must contain only strings.")
     elif isinstance(palette, str | type(None)) and "palette" in param_dict:
         param_dict["palette"] = [palette] if palette is not None else None
 
-    if element_type in ["shapes", "points", "labels"] and (palette := param_dict.get("palette")) is not None:
+    palette_group = param_dict.get("palette")
+    if element_type in ["shapes", "points", "labels"] and palette_group is not None:
         groups = param_dict.get("groups")
         if groups is None:
             raise ValueError("When specifying 'palette', 'groups' must also be specified.")
-        if len(groups) != len(palette):
+        if len(groups) != len(palette_group):
             raise ValueError(
-                f"The length of 'palette' and 'groups' must be the same, length is {len(palette)} and"
+                f"The length of 'palette' and 'groups' must be the same, length is {len(palette_group)} and"
                 f"{len(groups)} respectively."
             )
 
@@ -2261,13 +2296,15 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
     # validation happens within Color constructor
     param_dict["na_color"] = Color(param_dict.get("na_color"))
 
-    if (norm := param_dict.get("norm")) is not None:
+    norm = param_dict.get("norm")
+    if norm is not None:
         if element_type in {"images", "labels"} and not isinstance(norm, Normalize):
             raise TypeError("Parameter 'norm' must be of type Normalize.")
         if element_type in {"shapes", "points"} and not isinstance(norm, bool | Normalize):
             raise TypeError("Parameter 'norm' must be a boolean or a mpl.Normalize.")
 
-    if (scale := param_dict.get("scale")) is not None:
+    scale = param_dict.get("scale")
+    if scale is not None:
         if element_type in {"images", "labels"} and not isinstance(scale, str):
             raise TypeError("Parameter 'scale' must be a string if specified.")
         if element_type == "shapes":
@@ -2276,13 +2313,15 @@ def _type_check_params(param_dict: dict[str, Any], element_type: str) -> dict[st
             if scale < 0:
                 raise ValueError("Parameter 'scale' must be a positive number.")
 
-    if size := param_dict.get("size"):
+    size = param_dict.get("size")
+    if size:
         if not isinstance(size, float | int):
             raise TypeError("Parameter 'size' must be numeric.")
         if size < 0:
             raise ValueError("Parameter 'size' must be a positive number.")
 
-    if element_type == "shapes" and (shape := param_dict.get("shape")) is not None:
+    shape = param_dict.get("shape")
+    if element_type == "shapes" and shape is not None:
         valid_shapes = {"circle", "hex", "visium_hex", "square"}
         if not isinstance(shape, str):
             raise TypeError(f"Parameter 'shape' must be a String from {valid_shapes} if not None.")
@@ -2336,7 +2375,8 @@ def _ensure_table_and_layer_exist_in_sdata(
 
     assert _ensure_table_and_layer_exist_in_sdata(param_dict.get("sdata"), table_name, table_layer)
 
-    if (method := param_dict.get("method")) not in ["matplotlib", "datashader", None]:
+    method = param_dict.get("method")
+    if method not in ["matplotlib", "datashader", None]:
         raise ValueError("If specified, parameter 'method' must be either 'matplotlib' or 'datashader'.")
 
     valid_ds_reduction_methods = [
@@ -2351,7 +2391,8 @@ def _ensure_table_and_layer_exist_in_sdata(
         "max",
         "min",
     ]
-    if (ds_reduction := param_dict.get("ds_reduction")) and (ds_reduction not in valid_ds_reduction_methods):
+    ds_reduction = param_dict.get("ds_reduction")
+    if ds_reduction and (ds_reduction not in valid_ds_reduction_methods):
         raise ValueError(f"Parameter 'ds_reduction' must be one of the following: {valid_ds_reduction_methods}.")
 
     if method == "datashader" and ds_reduction is None:
@@ -2375,6 +2416,8 @@ def _validate_label_render_params(
     scale: str | None,
     table_name: str | None,
     table_layer: str | None,
+    colorbar: bool | str | None,
+    colorbar_params: dict[str, object] | None,
 ) -> dict[str, dict[str, Any]]:
     param_dict: dict[str, Any] = {
         "sdata": sdata,
@@ -2391,6 +2434,8 @@ def _validate_label_render_params(
         "scale": scale,
         "table_name": table_name,
         "table_layer": table_layer,
+        "colorbar": colorbar,
+        "colorbar_params": colorbar_params,
     }
     param_dict = _type_check_params(param_dict, "labels")
 
@@ -2419,6 +2464,8 @@ def _validate_label_render_params(
 
         element_params[el]["palette"] = param_dict["palette"] if element_params[el]["table_name"] is not None else None
         element_params[el]["groups"] = param_dict["groups"] if element_params[el]["table_name"] is not None else None
+        element_params[el]["colorbar"] = param_dict["colorbar"]
+        element_params[el]["colorbar_params"] = param_dict["colorbar_params"]
 
     return element_params
 
@@ -2437,6 +2484,8 @@ def _validate_points_render_params(
     table_name: str | None,
     table_layer: str | None,
     ds_reduction: str | None,
+    colorbar: bool | str | None,
+    colorbar_params: dict[str, object] | None,
 ) -> dict[str, dict[str, Any]]:
     param_dict: dict[str, Any] = {
         "sdata": sdata,
@@ -2452,6 +2501,8 @@ def _validate_points_render_params(
         "table_name": table_name,
         "table_layer": table_layer,
         "ds_reduction": ds_reduction,
+        "colorbar": colorbar,
+        "colorbar_params": colorbar_params,
     }
     param_dict = _type_check_params(param_dict, "points")
 
@@ -2471,7 +2522,8 @@ def _validate_points_render_params(
 
         element_params[el]["table_name"] = None
         element_params[el]["col_for_color"] = None
-        if (col_for_color := param_dict["col_for_color"]) is not None:
+        col_for_color = param_dict["col_for_color"]
+        if col_for_color is not None:
             col_for_color, table_name = _validate_col_for_column_table(
                 sdata, el, col_for_color, param_dict["table_name"]
             )
@@ -2481,6 +2533,8 @@ def _validate_points_render_params(
         element_params[el]["palette"] = param_dict["palette"] if param_dict["col_for_color"] is not None else None
         element_params[el]["groups"] = param_dict["groups"] if param_dict["col_for_color"] is not None else None
         element_params[el]["ds_reduction"] = param_dict["ds_reduction"]
+        element_params[el]["colorbar"] = param_dict["colorbar"]
+        element_params[el]["colorbar_params"] = param_dict["colorbar_params"]
 
     return element_params
 
@@ -2504,6 +2558,8 @@ def _validate_shape_render_params(
     shape: Literal["circle", "hex", "visium_hex", "square"] | None,
     method: str | None,
     ds_reduction: str | None,
+    colorbar: bool | str | None,
+    colorbar_params: dict[str, object] | None,
 ) -> dict[str, dict[str, Any]]:
     param_dict: dict[str, Any] = {
         "sdata": sdata,
@@ -2524,6 +2580,8 @@ def _validate_shape_render_params(
         "shape": shape,
         "method": method,
         "ds_reduction": ds_reduction,
+        "colorbar": colorbar,
+        "colorbar_params": colorbar_params,
     }
     param_dict = _type_check_params(param_dict, "shapes")
 
@@ -2548,7 +2606,8 @@ def _validate_shape_render_params(
 
         element_params[el]["table_name"] = None
         element_params[el]["col_for_color"] = None
-        if (col_for_color := param_dict["col_for_color"]) is not None:
+        col_for_color = param_dict["col_for_color"]
+        if col_for_color is not None:
             col_for_color, table_name = _validate_col_for_column_table(
                 sdata, el, col_for_color, param_dict["table_name"]
             )
@@ -2559,6 +2618,8 @@ def _validate_shape_render_params(
         element_params[el]["groups"] = param_dict["groups"] if param_dict["col_for_color"] is not None else None
         element_params[el]["method"] = param_dict["method"]
         element_params[el]["ds_reduction"] = param_dict["ds_reduction"]
+        element_params[el]["colorbar"] = param_dict["colorbar"]
+        element_params[el]["colorbar_params"] = param_dict["colorbar_params"]
 
     return element_params
 
@@ -2616,6 +2677,8 @@ def _validate_image_render_params(
     cmap: list[Colormap | str] | Colormap | str | None,
     norm: Normalize | None,
     scale: str | None,
+    colorbar: bool | str | None,
+    colorbar_params: dict[str, object] | None,
 ) -> dict[str, dict[str, Any]]:
     param_dict: dict[str, Any] = {
         "sdata": sdata,
@@ -2627,6 +2690,8 @@ def _validate_image_render_params(
         "cmap": cmap,
         "norm": norm,
         "scale": scale,
+        "colorbar": colorbar,
+        "colorbar_params": colorbar_params,
     }
     param_dict = _type_check_params(param_dict, "images")
 
@@ -2681,7 +2746,8 @@ def _validate_image_render_params(
         element_params[el]["palette"] = palette
         element_params[el]["na_color"] = param_dict["na_color"]
 
-        if (cmap := param_dict["cmap"]) is not None:
+        cmap = param_dict["cmap"]
+        if cmap is not None:
             if len(cmap) == 1:
                 cmap_length = len(channel) if channel is not None else len(spatial_element_ch)
                 cmap = cmap * cmap_length
@@ -2689,13 +2755,16 @@ def _validate_image_render_params(
                 cmap = None
         element_params[el]["cmap"] = cmap
         element_params[el]["norm"] = param_dict["norm"]
-        if (scale := param_dict["scale"]) and isinstance(sdata[el], DataTree):
-            if scale not in list(sdata[el].keys()) and scale != "full":
+        scale = param_dict["scale"]
+        if scale and isinstance(param_dict["sdata"][el], DataTree):
+            if scale not in list(param_dict["sdata"][el].keys()) and scale != "full":
                 element_params[el]["scale"] = None
             else:
                 element_params[el]["scale"] = scale
         else:
             element_params[el]["scale"] = scale
+        element_params[el]["colorbar"] = param_dict["colorbar"]
+        element_params[el]["colorbar_params"] = param_dict["colorbar_params"]
 
     return element_params
 
diff --git a/tests/_images/ColorbarControls_can_globally_turn_off_colorbars.png b/tests/_images/ColorbarControls_can_globally_turn_off_colorbars.png
new file mode 100644
index 00000000..6128bea9
Binary files /dev/null and b/tests/_images/ColorbarControls_can_globally_turn_off_colorbars.png differ
diff --git a/tests/_images/ColorbarControls_colorbar_can_adjust_pad.png b/tests/_images/ColorbarControls_colorbar_can_adjust_pad.png
new file mode 100644
index 00000000..ed34c034
Binary files /dev/null and b/tests/_images/ColorbarControls_colorbar_can_adjust_pad.png differ
diff --git a/tests/_images/ColorbarControls_colorbar_can_adjust_title.png b/tests/_images/ColorbarControls_colorbar_can_adjust_title.png
new file mode 100644
index 00000000..d05e5b33
Binary files /dev/null and b/tests/_images/ColorbarControls_colorbar_can_adjust_title.png differ
diff --git a/tests/_images/ColorbarControls_colorbar_can_adjust_width.png b/tests/_images/ColorbarControls_colorbar_can_adjust_width.png
new file mode 100644
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diff --git a/tests/conftest.py b/tests/conftest.py
index b44d6d8e..2299f126 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -14,6 +14,7 @@
 from anndata import AnnData
 from geopandas import GeoDataFrame
 from matplotlib.testing.compare import compare_images
+from PIL import Image
 from shapely.geometry import MultiPolygon, Polygon
 from spatialdata import SpatialData
 from spatialdata.datasets import blobs, raccoon
@@ -36,6 +37,9 @@
 ACTUAL = HERE / "figures"
 TOL = 15
 DPI = 80
+CANVAS_WIDTH = 400
+CANVAS_HEIGHT = 300
+_RESAMPLE = Image.Resampling.LANCZOS if hasattr(Image, "Resampling") else Image.LANCZOS
 
 
 def get_standard_RNG():
@@ -43,6 +47,23 @@ def get_standard_RNG():
     return np.random.default_rng(seed=42)
 
 
+def _resize_and_pad_image(path: Path, canvas_size: tuple[int, int] = (CANVAS_WIDTH, CANVAS_HEIGHT)) -> None:
+    """Scale image to fit canvas while keeping aspect ratio, then pad."""
+    with Image.open(path) as img:
+        img = img.convert("RGBA")
+        target_w, target_h = canvas_size
+        if img.width == 0 or img.height == 0:
+            raise ValueError("Cannot resize image with zero dimension.")
+        scale = min(target_w / img.width, target_h / img.height)
+        new_w = max(1, int(round(img.width * scale)))
+        new_h = max(1, int(round(img.height * scale)))
+        resized = img.resize((new_w, new_h), resample=_RESAMPLE)
+        canvas = Image.new("RGBA", canvas_size, (255, 255, 255, 255))
+        offset = ((target_w - new_w) // 2, (target_h - new_h) // 2)
+        canvas.paste(resized, offset, resized)
+        canvas.convert("RGB").save(path)
+
+
 @pytest.fixture()
 def full_sdata() -> SpatialData:
     return SpatialData(
@@ -77,8 +98,7 @@ def test_sdata_single_image():
             np.zeros((1, 10, 10)), dims=("c", "y", "x"), transformations={"data1": sd.transformations.Identity()}
         )
     }
-    sdata = sd.SpatialData(images=images)
-    return sdata
+    return sd.SpatialData(images=images)
 
 
 @pytest.fixture
@@ -86,8 +106,7 @@ def test_sdata_single_image_with_label():
     """Creates a simple sdata object."""
     images = {"data1": sd.models.Image2DModel.parse(np.zeros((1, 10, 10)), dims=("c", "y", "x"))}
     labels = {"label1": sd.models.Labels2DModel.parse(np.zeros((10, 10)), dims=("y", "x"))}
-    sdata = sd.SpatialData(images=images, labels=labels)
-    return sdata
+    return sd.SpatialData(images=images, labels=labels)
 
 
 @pytest.fixture
@@ -104,8 +123,7 @@ def test_sdata_multiple_images():
             np.zeros((1, 10, 10)), dims=("c", "y", "x"), transformations={"data1": sd.transformations.Identity()}
         ),
     }
-    sdata = sd.SpatialData(images=images)
-    return sdata
+    return sd.SpatialData(images=images)
 
 
 @pytest.fixture
@@ -141,8 +159,7 @@ def test_sdata_multiple_images_dims():
         "data2": sd.models.Image2DModel.parse(np.zeros((3, 10, 10)), dims=("c", "y", "x")),
         "data3": sd.models.Image2DModel.parse(np.zeros((3, 10, 10)), dims=("c", "y", "x")),
     }
-    sdata = sd.SpatialData(images=images)
-    return sdata
+    return sd.SpatialData(images=images)
 
 
 @pytest.fixture
@@ -153,8 +170,7 @@ def test_sdata_multiple_images_diverging_dims():
         "data2": sd.models.Image2DModel.parse(np.zeros((6, 10, 10)), dims=("c", "y", "x")),
         "data3": sd.models.Image2DModel.parse(np.zeros((3, 10, 10)), dims=("c", "y", "x")),
     }
-    sdata = sd.SpatialData(images=images)
-    return sdata
+    return sd.SpatialData(images=images)
 
 
 @pytest.fixture
@@ -226,27 +242,28 @@ def empty_table() -> SpatialData:
 )
 def sdata(request) -> SpatialData:
     if request.param == "full":
-        s = SpatialData(
+        return SpatialData(
             images=_get_images(),
             labels=_get_labels(),
             shapes=_get_shapes(),
             points=_get_points(),
             table=_get_table("sample1"),
         )
-    elif request.param == "empty":
-        s = SpatialData()
-    else:
-        s = request.getfixturevalue(request.param)
-    return s
+    if request.param == "empty":
+        return SpatialData()
+    return request.getfixturevalue(request.param)
 
 
 def _get_images() -> dict[str, DataArray | DataTree]:
-    out = {}
     dims_2d = ("c", "y", "x")
     dims_3d = ("z", "y", "x", "c")
-    out["image2d"] = Image2DModel.parse(
-        get_standard_RNG().normal(size=(3, 64, 64)), dims=dims_2d, c_coords=["r", "g", "b"]
-    )
+    out = {
+        "image2d": Image2DModel.parse(
+            get_standard_RNG().normal(size=(3, 64, 64)),
+            dims=dims_2d,
+            c_coords=["r", "g", "b"],
+        )
+    }
     out["image2d_multiscale"] = Image2DModel.parse(
         get_standard_RNG().normal(size=(3, 64, 64)), scale_factors=[2, 2], dims=dims_2d, c_coords=["r", "g", "b"]
     )
@@ -274,11 +291,10 @@ def _get_images() -> dict[str, DataArray | DataTree]:
 
 
 def _get_labels() -> dict[str, DataArray | DataTree]:
-    out = {}
     dims_2d = ("y", "x")
     dims_3d = ("z", "y", "x")
 
-    out["labels2d"] = Labels2DModel.parse(get_standard_RNG().integers(0, 100, size=(64, 64)), dims=dims_2d)
+    out = {"labels2d": Labels2DModel.parse(get_standard_RNG().integers(0, 100, size=(64, 64)), dims=dims_2d)}
     out["labels2d_multiscale"] = Labels2DModel.parse(
         get_standard_RNG().integers(0, 100, size=(64, 64)), scale_factors=[2, 4], dims=dims_2d
     )
@@ -307,7 +323,6 @@ def _get_labels() -> dict[str, DataArray | DataTree]:
 
 def _get_polygons() -> dict[str, GeoDataFrame]:
     # TODO: add polygons from geojson and from ragged arrays since now only the GeoDataFrame initializer is tested.
-    out = {}
     poly = GeoDataFrame(
         {
             "geometry": [
@@ -340,19 +355,19 @@ def _get_polygons() -> dict[str, GeoDataFrame]:
         }
     )
 
-    out["poly"] = ShapesModel.parse(poly, name="poly")
-    out["multipoly"] = ShapesModel.parse(multipoly, name="multipoly")
-
-    return out
+    return {
+        "poly": ShapesModel.parse(poly, name="poly"),
+        "multipoly": ShapesModel.parse(multipoly, name="multipoly"),
+    }
 
 
 def _get_shapes() -> dict[str, AnnData]:
-    out = {}
     arr = get_standard_RNG().normal(size=(100, 2))
-    out["shapes_0"] = ShapesModel.parse(arr, shape_type="Square", shape_size=3)
-    out["shapes_1"] = ShapesModel.parse(arr, shape_type="Circle", shape_size=np.repeat(1, len(arr)))
 
-    return out
+    return {
+        "shapes_0": ShapesModel.parse(arr, shape_type="Square", shape_size=3),
+        "shapes_1": ShapesModel.parse(arr, shape_type="Circle", shape_size=np.repeat(1, len(arr))),
+    }
 
 
 def _get_points() -> dict[str, pa.Table]:
@@ -386,15 +401,22 @@ def _get_table(
     )
     adata.obs[instance_key] = np.arange(adata.n_obs)
     if isinstance(region, str):
-        table = TableModel.parse(adata=adata, region=region, instance_key=instance_key)
-    elif isinstance(region, list):
+        return TableModel.parse(adata=adata, region=region, instance_key=instance_key)
+    if isinstance(region, list):
         adata.obs[region_key] = get_standard_RNG().choice(region, size=adata.n_obs)
         adata.obs[instance_key] = get_standard_RNG().integers(0, 10, size=(100,))
-        table = TableModel.parse(adata=adata, region=region, region_key=region_key, instance_key=instance_key)
-    else:
-        table = TableModel.parse(adata=adata, region=region, region_key=region_key, instance_key=instance_key)
-
-    return table
+        return TableModel.parse(
+            adata=adata,
+            region=region,
+            region_key=region_key,
+            instance_key=instance_key,
+        )
+    return TableModel.parse(
+        adata=adata,
+        region=region,
+        region_key=region_key,
+        instance_key=instance_key,
+    )
 
 
 class PlotTesterMeta(ABCMeta):
@@ -411,15 +433,35 @@ def compare(cls, basename: str, tolerance: float | None = None):
         ACTUAL.mkdir(parents=True, exist_ok=True)
         out_path = ACTUAL / f"{basename}.png"
 
-        width, height = 400, 300  # fixed dimensions so runners don't change
+        width, height = CANVAS_WIDTH, CANVAS_HEIGHT  # base dimensions; actual PNG may grow/shrink
         fig = plt.gcf()
         fig.set_size_inches(width / DPI, height / DPI)
         fig.set_dpi(DPI)
 
-        # Apply constrained layout and save the plot
-        fig.set_constrained_layout(True)
-        plt.savefig(out_path, dpi=DPI)
-        plt.close()
+        # Try to get a reasonable layout first (helps with axes/labels)
+        if not fig.get_constrained_layout():
+            try:
+                fig.set_constrained_layout(True)
+            except (ValueError, RuntimeError):
+                try:
+                    fig.tight_layout(pad=2.0, rect=[0.02, 0.02, 0.98, 0.98])
+                except (ValueError, RuntimeError):
+                    fig.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1)
+
+        plt.figure(fig.number)  # ensure this figure is current
+
+        # Force a draw so that tight bbox "sees" all artists (including colorbars)
+        fig.canvas.draw()
+
+        # Let matplotlib adjust the output size so that all artists are included
+        fig.savefig(
+            out_path,
+            dpi=DPI,
+            bbox_inches="tight",
+            pad_inches=0.02,  # small margin around everything
+        )
+        _resize_and_pad_image(out_path, (width, height))
+        plt.close(fig)
 
         if tolerance is None:
             # see https://github.com/scverse/squidpy/pull/302
@@ -433,7 +475,28 @@ def compare(cls, basename: str, tolerance: float | None = None):
 def _decorate(fn: Callable, clsname: str, name: str | None = None) -> Callable:
     @wraps(fn)
     def save_and_compare(self, *args, **kwargs):
+        # Get all figures before the test runs
+        figures_before = set(plt.get_fignums())
+
         fn(self, *args, **kwargs)
+
+        # Get all figures after the test runs
+        figures_after = set(plt.get_fignums())
+
+        # Find the figure(s) created during the test
+        new_figures = figures_after - figures_before
+
+        if new_figures:
+            # Use the most recently created figure (highest number)
+            fig_num = max(new_figures)
+            plt.figure(fig_num)
+        elif figures_after:
+            # If no new figures were created, use the current figure
+            # but ensure it's set as current
+            current_fig = plt.gcf()
+            plt.figure(current_fig.number)
+        # If no figures exist, plt.gcf() will create one, which is fine
+
         self.compare(fig_name)
 
     if not callable(fn):
diff --git a/tests/pl/test_colorbar.py b/tests/pl/test_colorbar.py
new file mode 100644
index 00000000..6fef5035
--- /dev/null
+++ b/tests/pl/test_colorbar.py
@@ -0,0 +1,104 @@
+import matplotlib
+import numpy as np
+import scanpy as sc
+from spatialdata import SpatialData
+from spatialdata.models import Image2DModel, Labels2DModel
+
+import spatialdata_plot  # noqa: F401
+from tests.conftest import DPI, PlotTester, PlotTesterMeta
+
+sc.pl.set_rcParams_defaults()
+sc.set_figure_params(dpi=DPI, color_map="viridis")
+matplotlib.use("agg")  # same as GitHub action runner
+_ = spatialdata_plot
+
+# WARNING:
+# 1. all classes must both subclass PlotTester and use metaclass=PlotTesterMeta
+# 2. tests which produce a plot must be prefixed with `test_plot_`
+# 3. if the tolerance needs to be changed, don't prefix the function with `test_plot_`, but with something else
+#    the comp. function can be accessed as `self.compare(<your_filename>, tolerance=<your_tolerance>)`
+#    ".png" is appended to <your_filename>, no need to set it
+
+
+class TestColorbarControls(PlotTester, metaclass=PlotTesterMeta):
+    def _make_multi_image_single_channel_sdata(self) -> SpatialData:
+        img1 = Image2DModel.parse(np.linspace(0, 1, 2500, dtype=float).reshape(1, 50, 50), dims=("c", "y", "x"))
+        img2 = Image2DModel.parse(np.linspace(2, 0, 2500, dtype=float).reshape(1, 50, 50), dims=("c", "y", "x"))
+        labels = Labels2DModel.parse(np.arange(2500, dtype=int).reshape(50, 50), dims=("y", "x"))
+        return SpatialData(images={"img1": img1, "img2": img2}, labels={"lab": labels})
+
+    def test_plot_image_auto_colorbar_for_single_channel(self, sdata_blobs: SpatialData):
+        # Create a single-channel image by selecting only channel 0
+        original_img = sdata_blobs["blobs_image"]
+        single_channel_img = original_img.isel(c=0).expand_dims("c")
+        sdata_blobs["blobs_image_1c"] = single_channel_img
+        sdata_blobs.pl.render_images(element="blobs_image_1c").pl.show()
+
+    def test_plot_colorbar_img_default_location(self, sdata_blobs: SpatialData):
+        sdata_blobs.pl.render_images("blobs_image", channel=0, cmap="Reds").pl.show()
+
+    def test_plot_colorbar_img_bottom(self, sdata_blobs: SpatialData):
+        sdata_blobs.pl.render_images("blobs_image", channel=0, cmap="Reds", colorbar_params={"loc": "bottom"}).pl.show()
+
+    def test_plot_colorbar_img_left(self, sdata_blobs: SpatialData):
+        sdata_blobs.pl.render_images("blobs_image", channel=0, cmap="Reds", colorbar_params={"loc": "left"}).pl.show()
+
+    def test_plot_colorbar_img_top(self, sdata_blobs: SpatialData):
+        sdata_blobs.pl.render_images("blobs_image", channel=0, cmap="Reds", colorbar_params={"loc": "top"}).pl.show()
+
+    def test_plot_colorbar_can_adjust_width(self, sdata_blobs: SpatialData):
+        sdata_blobs.pl.render_images("blobs_image", channel=0, cmap="Reds", colorbar_params={"width": 0.4}).pl.show()
+
+    def test_plot_colorbar_can_adjust_title(self, sdata_blobs: SpatialData):
+        sdata_blobs.pl.render_images(
+            "blobs_image", channel=0, cmap="Reds", colorbar_params={"label": "Intensity"}
+        ).pl.show()
+
+    def test_plot_colorbar_can_adjust_pad(self, sdata_blobs: SpatialData):
+        sdata_blobs.pl.render_images("blobs_image", channel=0, cmap="Reds", colorbar_params={"pad": 0.4}).pl.show()
+
+    def test_plot_colorbar_can_have_colorbars_on_different_sides(self, sdata_blobs: SpatialData):
+        (
+            sdata_blobs.pl.render_images("blobs_image", channel=0, cmap="Reds", colorbar_params={"loc": "top"})
+            .pl.render_labels("blobs_labels", color="instance_id", colorbar_params={"loc": "bottom"})
+            .pl.show()
+        )
+
+    def test_plot_colorbar_can_have_two_colorbars_on_same_side(self, sdata_blobs: SpatialData):
+        (
+            sdata_blobs.pl.render_images("blobs_image", channel=0, cmap="Reds")
+            .pl.render_labels("blobs_labels", color="instance_id")
+            .pl.show()
+        )
+
+    def test_plot_colorbar_can_have_colorbars_on_all_sides(self, sdata_blobs: SpatialData):
+        # primarily shows that spacing is correct between colorbars and plot elements
+        shared_params = {
+            "element": "blobs_image",
+            "channel": 0,
+            "cmap": "Reds",
+        }
+        (
+            sdata_blobs.pl.render_images(**shared_params, colorbar_params={"loc": "top", "label": "top_1"})
+            .pl.render_images(**shared_params, colorbar_params={"loc": "right", "label": "right_1"})
+            .pl.render_images(**shared_params, colorbar_params={"loc": "left", "label": "left_1"})
+            .pl.render_images(**shared_params, colorbar_params={"loc": "bottom", "label": "bottom_1"})
+            .pl.render_images(**shared_params, colorbar_params={"loc": "top", "label": "top_2"})
+            .pl.render_images(**shared_params, colorbar_params={"loc": "right", "label": "right_2"})
+            .pl.render_images(**shared_params, colorbar_params={"loc": "left", "label": "left_2"})
+            .pl.render_images(**shared_params, colorbar_params={"loc": "bottom", "label": "bottom_2"})
+            .pl.show()
+        )
+
+    def test_plot_multiple_images_in_one_cs_result_in_multiple_colorbars(self):
+        sdata = self._make_multi_image_single_channel_sdata()
+        sdata.pl.render_images(channel=0, cmap="Reds").pl.show()
+
+    def test_plot_can_globally_turn_off_colorbars(self):
+        # adresses https://github.com/scverse/spatialdata-plot/issues/431
+        sdata = self._make_multi_image_single_channel_sdata()
+        sdata.pl.render_images(channel=0, cmap="Reds").pl.show(colorbar=False)
+
+    def test_plot_single_channel_default_channel_name_omits_label(self):
+        sdata = self._make_multi_image_single_channel_sdata()
+        sdata.pl.render_images(element="img1", channel=0, cmap="Reds").pl.show()