diff --git a/jdaviz/configs/default/plugins/data_quality/data_quality.py b/jdaviz/configs/default/plugins/data_quality/data_quality.py
index 04a8a6f4f6..f58052de7f 100644
--- a/jdaviz/configs/default/plugins/data_quality/data_quality.py
+++ b/jdaviz/configs/default/plugins/data_quality/data_quality.py
@@ -147,18 +147,19 @@ def init_decoding(self, event={}):
         ]
         dq_layer.composite._allow_bad_alpha = True
 
-        flag_bits = np.float32([flag['flag'] for flag in self.decoded_flags])
+        flag_bits = np.array([flag['flag'] for flag in self.decoded_flags])
 
-        with delay_callback(dq_layer.state, 'alpha', 'cmap', 'v_min', 'v_max', 'stretch'):
-            dq_layer.state.cmap = cmap
+        dq_layer.state.stretch = 'lookup'
+        stretch_object = dq_layer.state.stretch_object
+        stretch_object.flags = flag_bits
 
-            dq_layer.state.stretch = 'lookup'
-            stretch_object = dq_layer.state.stretch_object
-            stretch_object.flags = flag_bits
+        with delay_callback(dq_layer.state, 'alpha', 'cmap', 'v_min', 'v_max'):
+            if len(flag_bits):
+                dq_layer.state.v_min = min(flag_bits)
+                dq_layer.state.v_max = max(flag_bits)
 
-            dq_layer.state.v_min = min(flag_bits)
-            dq_layer.state.v_max = max(flag_bits)
             dq_layer.state.alpha = 0.9
+            dq_layer.state.cmap = cmap
 
     @observe('decoded_flags')
     def update_cmap(self, event={}):
@@ -167,34 +168,73 @@ def update_cmap(self, event={}):
             layer for layer in viewer.layers if
             layer.layer.label == self.dq_layer_selected
         ]
-        flag_bits = np.float32([flag['flag'] for flag in self.decoded_flags])
+        flag_bits = np.array([flag['flag'] for flag in self.decoded_flags])
         rgb_colors = [hex2color(flag['color']) for flag in self.decoded_flags]
+        hidden_flags = np.array([flag['flag'] for flag in self.decoded_flags if not flag['show']])
 
-        # update the colors of the listed colormap without
-        # reassigning the layer.state.cmap object
-        cmap = dq_layer.state.cmap
-        cmap.colors = rgb_colors
-        cmap._init()
+        with delay_callback(dq_layer.state, 'v_min', 'v_max', 'alpha', 'stretch', 'cmap'):
+            # set correct stretch and limits:
+            # dq_layer.state.stretch = 'lookup'
+            stretch_object = dq_layer.state.stretch_object
+            stretch_object.flags = flag_bits
+            stretch_object.dq_array = dq_layer.get_image_data()
+            stretch_object.hidden_flags = hidden_flags
+
+            # update the colors of the listed colormap without
+            # reassigning the layer.state.cmap object
+            cmap = dq_layer.state.cmap
+            cmap.colors = rgb_colors
+            cmap._init()
 
-        with delay_callback(dq_layer.state, 'v_min', 'v_max', 'alpha'):
             # trigger updates to cmap in viewer:
             dq_layer.update()
 
-            # set correct stretch and limits:
-            dq_layer.state.stretch = 'lookup'
-            dq_layer.state.v_min = min(flag_bits)
-            dq_layer.state.v_max = max(flag_bits)
+            if len(flag_bits):
+                dq_layer.state.v_min = min(flag_bits)
+                dq_layer.state.v_max = max(flag_bits)
+
             dq_layer.state.alpha = 0.9
 
+    def update_visibility(self, index):
+        self.decoded_flags[index]['show'] = not self.decoded_flags[index]['show']
+        self.send_state('decoded_flags')
+        self.update_cmap()
+
+    def vue_update_visibility(self, index):
+        self.update_visibility(index)
+
+    def update_color(self, index, color):
+        self.decoded_flags[index]['color'] = color
+        self.send_state('decoded_flags')
+        self.update_cmap()
+
+    def vue_update_color(self, args):
+        index, color = args
+        self.update_color(index, color)
+
     @observe('science_layer_selected')
     def mission_or_instrument_from_meta(self, event):
         if not hasattr(self, 'science_layer'):
             return
 
         layer = self.science_layer.selected_obj
-        if len(layer):
-            # this is defined for JWST and ROMAN, should be upper case:
-            telescope = layer[0].layer.meta.get('telescope', None)
+        if not len(layer):
+            return
+
+        # this is defined for JWST and ROMAN, should be upper case:
+        telescope = layer[0].layer.meta.get('telescope', None)
+
+        if telescope is not None:
+            self.flag_map_selected = telescope_names[telescope.lower()]
+
+    def vue_hide_all_flags(self, event):
+        for flag in self.decoded_flags:
+            flag['show'] = False
+        self.send_state('decoded_flags')
+        self.update_cmap()
 
-            if telescope is not None:
-                self.flag_map_selected = telescope_names[telescope.lower()]
+    def vue_show_all_flags(self, event):
+        for flag in self.decoded_flags:
+            flag['show'] = True
+        self.send_state('decoded_flags')
+        self.update_cmap()
diff --git a/jdaviz/configs/default/plugins/data_quality/data_quality.vue b/jdaviz/configs/default/plugins/data_quality/data_quality.vue
index 4cfa4064cf..3a0d9a0f6f 100644
--- a/jdaviz/configs/default/plugins/data_quality/data_quality.vue
+++ b/jdaviz/configs/default/plugins/data_quality/data_quality.vue
@@ -49,17 +49,50 @@
     </v-row>
 
     <j-plugin-section-header>Quality Flags</j-plugin-section-header>
+    <v-row class="row-no-padding">
+      <v-col cols=6>
+        <j-tooltip tipid='plugin-line-lists-erase-all'>
+          <v-btn
+            tile
+            :elevation=0
+            x-small
+            dense
+            color="turquoise"
+            dark
+            style="padding-left: 8px; padding-right: 6px;"
+            @click="show_all_flags">
+            <v-icon left small dense style="margin-right: 2px">mdi-eye</v-icon>
+            Show All
+          </v-btn>
+        </j-tooltip>
+      </v-col>
+      <v-col cols=6 style="text-align: right">
+        <j-tooltip tipid='plugin-line-lists-plot-all'>
+          <v-btn
+            tile
+            :elevation=0
+            x-small
+            dense
+            color="turquoise"
+            dark
+            style="padding-left: 8px; padding-right: 6px;"
+            @click="hide_all_flags">
+            <v-icon left small dense style="margin-right: 2px">mdi-eye-off</v-icon>
+            Hide All
+          </v-btn>
+        </j-tooltip>
+      </v-col>
+    </v-row>
+
     <v-row style="max-width: calc(100% - 80px)">
       <v-col>
         Color
       </v-col>
-      <v-col>
-        <strong>Flag</strong>
-      </v-col>
-      <v-col>
-        (Decomposed)
+      <v-col cols=8>
+        <strong>Flag</strong> (Decomposed)
       </v-col>
     </v-row>
+
     <v-row>
       <v-expansion-panels accordion>
         <v-expansion-panel v-for="(item, index) in decoded_flags" key=":item">
@@ -78,21 +111,30 @@
                     </template>
                     <div @click.stop="" style="text-align: end; background-color: white">
                         <v-color-picker v-model="decoded_flags[index].color"
-                                        @update:color="throttledSetColor($event.hexa)">
+                                        @update:color="throttledSetColor(index, $event.hexa)">
                         ></v-color-picker>
                     </div>
                   </v-menu>
                 </j-tooltip>
               </v-col>
-              <v-col>
-                <div> <strong>{{item.flag}}</strong> ({{Object.keys(item.decomposed).join(', ')}})</div>
+              <v-col cols=8>
+                <div><strong>{{item.flag}}</strong> ({{Object.keys(item.decomposed).join(', ')}})</div>
               </v-col>
           </v-row>
           </v-expansion-panel-header>
           <v-expansion-panel-content>
-              <v-col v-for="(item, key, index) in item.decomposed">
-                <span>{{item.name}} ({{key}}): {{item.description}}</span>
+            <v-row no-gutters style="..." align="center">
+              <v-col cols=2 align="left">
+                <v-btn :color="item.show ? 'accent' : 'default'" icon @click="toggle_visibility(index)">
+                  <v-icon>{{item.show ? "mdi-eye" : "mdi-eye-off"}}</v-icon>
+                </v-btn>
               </v-col>
+            <v-col cols=8 align="left" style="...">
+              <v-row v-for="(item, key, index) in item.decomposed">
+                <span><strong>{{item.name}}</strong> ({{key}}): {{item.description}}</span>
+              </v-row>
+            </v-col>
+            </v-row>
           </v-expansion-panel-content>
         <v-expansion-panel>
       </v-expansion-panels>
@@ -103,12 +145,19 @@
 
 <script>
   module.exports = {
-    created() {
-      this.throttledSetColor = _.throttle(
-        (v) => { this.color = v },
-        100);
+  created() {
+    this.throttledSetColor = _.throttle(
+      (index, color) => {
+        this.update_color([index, color])
+      },
+      100);
+  },
+  methods: {
+    toggle_visibility(index) {
+      this.update_visibility(index)
     }
   }
+}
 </script>
 
 <style>
diff --git a/jdaviz/configs/default/plugins/data_quality/dq_utils.py b/jdaviz/configs/default/plugins/data_quality/dq_utils.py
index 940071295a..e17ae14e92 100644
--- a/jdaviz/configs/default/plugins/data_quality/dq_utils.py
+++ b/jdaviz/configs/default/plugins/data_quality/dq_utils.py
@@ -3,6 +3,7 @@
 
 import numpy as np
 from matplotlib.colors import ListedColormap, rgb2hex
+from glue.config import stretches
 from astropy.table import Table
 
 # paths to CSV files with DQ flag mappings:
@@ -12,6 +13,85 @@
 }
 
 
+class LookupStretch:
+    """
+    Stretch class specific to DQ arrays.
+
+    Attributes
+    ----------
+    flags : array-like
+        DQ flags.
+    """
+
+    def __init__(self, flags=None, hidden_flags=None):
+        # Default x, y values(0-1) range chosen for a typical initial spline shape.
+        # Can be modified if required.
+        print('initializing stretch')
+        if flags is None:
+            flags = np.linspace(0, 1, 5)
+        if hidden_flags is None:
+            hidden_flags = []
+
+        self.flags = np.asarray(flags)
+        self.hidden_flags = np.asarray(hidden_flags).astype(int)
+
+    @property
+    def flag_range(self):
+        return np.max(self.flags) - np.min(self.flags)
+
+    @property
+    def scaled_flags(self):
+        # renormalize the flags on range (0, 1):
+        return (self.flags - np.min(self.flags)) / self.flag_range
+
+    def dq_array_to_flag_index(self, values):
+        # Find the index of the closest entry in `scaled_flags`
+        # for each of `values` using array broadcasting:
+        return np.argmin(
+            np.abs(
+                np.nan_to_num(values, nan=-10).flatten()[None, :] -
+                self.scaled_flags[:, None]
+            ), axis=0
+        ).reshape(values.shape)
+
+    def __call__(self, values, out=None, clip=False):
+        # For our uses, we can ignore `out` and `clip`, but those would need
+        # to be implemented before contributing this class upstream.
+
+        # find closest index in `self.flags` for each value in `values`:
+        if hasattr(values, 'squeeze'):
+            values = values.squeeze()
+
+        # `values` will have already been passed through
+        # astropy.visualization.ManualInterval and normalized on (0, 1)
+        # before they arrive here. First, remove that interval and get
+        # back the integer values:
+        values_integer = np.round(values * self.flag_range + np.min(self.flags))
+
+        # normalize by the number of flags, onto interval (0, 1):
+        renormed = self.dq_array_to_flag_index(values) / len(self.flags)
+
+        if len(self.hidden_flags):
+            # mask that is True for `values` in the hidden flags list:
+            value_is_hidden = np.isin(
+                np.nan_to_num(values_integer, nan=-10),
+                self.hidden_flags
+            )
+        else:
+            value_is_hidden = False
+
+        # preserve NaNs in values, and make hidden flags NaNs:
+        return np.where(
+            np.isnan(values) | value_is_hidden,
+            np.nan,
+            renormed
+        )
+
+
+if "lookup" not in stretches:
+    stretches.add("lookup", LookupStretch, display="DQ")
+
+
 def load_flag_map(mission_or_instrument=None, path=None):
     """
     Load a flag map from disk.
@@ -77,7 +157,7 @@ def write_flag_map(flag_mapping, csv_path, **kwargs):
     table.write(csv_path, format='ascii.csv', **kwargs)
 
 
-def generate_listed_colormap(n_flags=None, seed=3):
+def generate_listed_colormap(n_flags=None, rgba_colors=None, seed=3):
     """
     Generate a list of random "light" colors of length ``n_flags``.
 
@@ -86,6 +166,8 @@ def generate_listed_colormap(n_flags=None, seed=3):
     n_flags : int
         Number of colors in the listed colormap, should match the
         number of unique DQ flags (before they're decomposed).
+    rgba_colors : list of tuples
+        List of RGBA tuples for each color in the colormap.
     seed : int
         Seed for the random number generator used to
         draw random colors.
@@ -100,12 +182,13 @@ def generate_listed_colormap(n_flags=None, seed=3):
     rng = np.random.default_rng(seed)
     default_alpha = 1
 
-    # Generate random colors that are generally "light", i.e. with
-    # RGB values in the upper half of the interval (0, 1):
-    rgba_colors = [
-        tuple(np.insert(rng.uniform(size=2), rng.integers(0, 3), 1).tolist() + [default_alpha])
-        for _ in range(n_flags)
-    ]
+    if rgba_colors is None:
+        # Generate random colors that are generally "light", i.e. with
+        # RGB values in the upper half of the interval (0, 1):
+        rgba_colors = [
+            tuple(np.insert(rng.uniform(size=2), rng.integers(0, 3), 1).tolist() + [default_alpha])
+            for _ in range(n_flags)
+        ]
 
     cmap = ListedColormap(rgba_colors)
 
@@ -162,6 +245,7 @@ def decode_flags(flag_map, unique_flags, rgba_colors):
             'flag': int(bit),
             'decomposed': {bit: flag_map[bit] for bit in decoded_bits},
             'color': rgb2hex(color),
+            'show': True,
         })
 
     return decoded_flags
diff --git a/jdaviz/configs/default/plugins/plot_options/plot_options.py b/jdaviz/configs/default/plugins/plot_options/plot_options.py
index 77dfa3600e..ef78823fdf 100644
--- a/jdaviz/configs/default/plugins/plot_options/plot_options.py
+++ b/jdaviz/configs/default/plugins/plot_options/plot_options.py
@@ -115,61 +115,10 @@ def update_knots(self, x, y):
         self.spline = PchipInterpolator(self._x, self._y)
 
 
-class LookupStretch:
-    """
-    Stretch class specific to DQ arrays.
-
-    Attributes
-    ----------
-    flags : array-like
-        DQ flags.
-    """
-
-    def __init__(self, flags=None):
-        # Default x, y values(0-1) range chosen for a typical initial spline shape.
-        # Can be modified if required.
-        if flags is None:
-            flags = np.linspace(0, 1, 5)
-        self.flags = np.asarray(flags)
-
-    def __call__(self, values, out=None, clip=False):
-        # For our uses, we can ignore `out` and `clip`, but those would need
-        # to be implemented before contributing this class upstream.
-
-        # find closest index in `self.flags` for each value in `values`:
-        if hasattr(values, 'squeeze'):
-            values = values.squeeze()
-
-        # renormalize the flags on range (0, 1):
-        scaled_flags = self.flags / np.max(self.flags)
-
-        # `values` will have already been passed through
-        # astropy.visualization.ManualInterval and normalized on (0, 1)
-        # before they arrive here. Now find the index of the closest entry in
-        # `scaled_flags` for each of `values` using array broadcasting.
-        min_indices = np.argmin(np.abs(
-            np.nan_to_num(values, nan=-10).flatten()[None, :] - scaled_flags[:, None]
-        ), axis=0).reshape(values.shape)
-
-        # normalize by the number of flags, onto interval (0, 1):
-        renormed = min_indices / (len(self.flags) - 1)
-
-        # preserve nans in the result:
-        renormed = np.where(
-            np.isnan(values),
-            np.nan,
-            renormed
-        )
-        return renormed
-
-
 # Add the spline stretch to the glue stretch registry if not registered
 if "spline" not in stretches:
     stretches.add("spline", SplineStretch, display="Spline")
 
-if "lookup" not in stretches:
-    stretches.add("lookup", LookupStretch, display="DQ")
-
 
 def _round_step(step):
     # round the step for a float input