diff --git a/fastplotlib/widgets/histogram_lut.py b/fastplotlib/widgets/histogram_lut.py
index 872cf4319..a3b36eb45 100644
--- a/fastplotlib/widgets/histogram_lut.py
+++ b/fastplotlib/widgets/histogram_lut.py
@@ -25,9 +25,10 @@ def __init__(
         ----------
         data
         image_graphic
-        nbins
-        flank_divisor: float, default 5.0
-            set `np.inf` for no flanks
+        nbins: int, defaut 100.
+            Total number of bins used in the histogram
+        flank_divisor: float, default 5.0.
+            Fraction of empty histogram bins on the tails of the distribution set `np.inf` for no flanks
         kwargs
         """
         super().__init__(**kwargs)
@@ -161,9 +162,10 @@ def _calculate_histogram(self, data):
             hist, edges = np.histogram(data_ss, bins=self._nbins)
 
         # used if data ptp <= 10 because event things get weird
-        # with tiny world objects due to  floating point error
+        # with tiny world objects due to floating point error
         # so if ptp <= 10, scale up by a factor
-        self._scale_factor: int = max(1, 100 * int(10 / np.ptp(data_ss)))
+        data_interval = edges[-1] - edges[0]
+        self._scale_factor: int = max(1, 100 * int(10 / data_interval))
 
         edges = edges * self._scale_factor
 
@@ -178,7 +180,6 @@ def _calculate_histogram(self, data):
         )
 
         edges_flanked = np.concatenate((flank_left, edges, flank_right))
-        np.unique(np.diff(edges_flanked))
 
         hist_flanked = np.concatenate(
             (np.zeros(flank_nbins), hist, np.zeros(flank_nbins))
@@ -186,7 +187,10 @@ def _calculate_histogram(self, data):
 
         # scale 0-100 to make it easier to see
         # float32 data can produce unnecessarily high values
-        hist_scaled = hist_flanked / (hist_flanked.max() / 100)
+        hist_scale_value = hist_flanked.max()
+        if np.allclose(hist_scale_value, 0):
+            hist_scale_value = 1
+        hist_scaled = hist_flanked / (hist_scale_value / 100)
 
         if edges_flanked.size > hist_scaled.size:
             # we don't care about accuracy here so if it's off by 1-2 bins that's fine
diff --git a/fastplotlib/widgets/image.py b/fastplotlib/widgets/image.py
index df9b46b55..749403781 100644
--- a/fastplotlib/widgets/image.py
+++ b/fastplotlib/widgets/image.py
@@ -357,6 +357,9 @@ def __init__(
         """
         self._names = None
 
+        if figure_kwargs is None:
+            figure_kwargs = dict()
+
         # output context
         self._output = None
 
@@ -368,13 +371,20 @@ def __init__(
             if all([_is_arraylike(d) for d in data]):
                 # Grid computations
                 if figure_shape is None:
-                    figure_shape = calculate_figure_shape(len(data))
+                    if "shape" in figure_kwargs:
+                        figure_shape = figure_kwargs["shape"]
+                    else:
+                        figure_shape = calculate_figure_shape(len(data))
 
-                # verify that user-specified figure shape is large enough for the number of image arrays passed
-                elif figure_shape[0] * figure_shape[1] < len(data):
+                # Regardless of how figure_shape is computed, below code
+                # verifies that figure shape is large enough for the number of image arrays passed
+                if figure_shape[0] * figure_shape[1] < len(data):
+                    original_shape = (figure_shape[0], figure_shape[1])
                     figure_shape = calculate_figure_shape(len(data))
                     warn(
-                        f"Invalid `figure_shape` passed, setting figure shape to: {figure_shape}"
+                        f"Original `figure_shape` was: {original_shape} "
+                        f" but data length is {len(data)}"
+                        f" Resetting figure shape to: {figure_shape}"
                     )
 
                 self._data: list[np.ndarray] = data
@@ -500,19 +510,18 @@ def __init__(
                         )
 
         figure_kwargs_default = {"controller_ids": "sync"}
-        if figure_kwargs is None:
-            figure_kwargs = dict()
 
         # update the default kwargs with any user-specified kwargs
         # user specified kwargs will overwrite the defaults
         figure_kwargs_default.update(figure_kwargs)
+        figure_kwargs_default["shape"] = figure_shape
 
         if graphic_kwargs is None:
             graphic_kwargs = dict()
 
         graphic_kwargs.update({"cmap": cmap})
 
-        self._figure: Figure = Figure(shape=figure_shape, **figure_kwargs_default)
+        self._figure: Figure = Figure(**figure_kwargs_default)
 
         self._histogram_widget = histogram_widget
         for data_ix, (d, subplot) in enumerate(zip(self.data, self.figure)):