[docs] Add controlnet example to marigold (#8289)

* initial doc

* fix wrong LCM sentence

* implement binary colormap without requiring matplotlib
update section about Marigold for ControlNet
update formatting of marigold_usage.md

* fix indentation

---------
Co-authored-by: anton <anton.obukhov@gmail.com>

src/diffusers/pipelines/marigold/marigold_image_processing.py

@@ -245,9 +245,9 @@ class MarigoldImageProcessor(ConfigMixin):
     ) -> Union[np.ndarray, torch.Tensor]:
         """
         Converts a monochrome image into an RGB image by applying the specified colormap. This function mimics the
-        behavior of matplotlib.colormaps, but allows the user to use the most discriminative color map "Spectral"
-        without having to install or import matplotlib. For all other cases, the function will attempt to use the
-        native implementation.
+        behavior of matplotlib.colormaps, but allows the user to use the most discriminative color maps ("Spectral",
+        "binary") without having to install or import matplotlib. For all other cases, the function will attempt to use
+        the native implementation.
 
         Args:
             image: 2D tensor of values between 0 and 1, either as np.ndarray or torch.Tensor.
@@ -255,7 +255,7 @@ class MarigoldImageProcessor(ConfigMixin):
             bytes: Whether to return the output as uint8 or floating point image.
             _force_method:
                 Can be used to specify whether to use the native implementation (`"matplotlib"`), the efficient custom
-                implementation of the "Spectral" color map (`"custom"`), or rely on autodetection (`None`, default).
+                implementation of the select color maps (`"custom"`), or rely on autodetection (`None`, default).
 
         Returns:
             An RGB-colorized tensor corresponding to the input image.
@@ -265,6 +265,26 @@ class MarigoldImageProcessor(ConfigMixin):
         if _force_method not in (None, "matplotlib", "custom"):
             raise ValueError("_force_method must be either `None`, `'matplotlib'` or `'custom'`.")
 
+        supported_cmaps = {
+            "binary": [
+                (1.0, 1.0, 1.0),
+                (0.0, 0.0, 0.0),
+            ],
+            "Spectral": [  # Taken from matplotlib/_cm.py
+                (0.61960784313725492, 0.003921568627450980, 0.25882352941176473),  # 0.0 -> [0]
+                (0.83529411764705885, 0.24313725490196078, 0.30980392156862746),
+                (0.95686274509803926, 0.42745098039215684, 0.2627450980392157),
+                (0.99215686274509807, 0.68235294117647061, 0.38039215686274508),
+                (0.99607843137254903, 0.8784313725490196, 0.54509803921568623),
+                (1.0, 1.0, 0.74901960784313726),
+                (0.90196078431372551, 0.96078431372549022, 0.59607843137254901),
+                (0.6705882352941176, 0.8666666666666667, 0.64313725490196083),
+                (0.4, 0.76078431372549016, 0.6470588235294118),
+                (0.19607843137254902, 0.53333333333333333, 0.74117647058823533),
+                (0.36862745098039218, 0.30980392156862746, 0.63529411764705879),  # 1.0 -> [K-1]
+            ],
+        }
+
         def method_matplotlib(image, cmap, bytes=False):
             if is_matplotlib_available():
                 import matplotlib
@@ -298,24 +318,19 @@ class MarigoldImageProcessor(ConfigMixin):
             else:
                 image = image.float()
 
-            if cmap != "Spectral":
-                raise ValueError("Only 'Spectral' color map is available without installing matplotlib.")
+            is_cmap_reversed = cmap.endswith("_r")
+            if is_cmap_reversed:
+                cmap = cmap[:-2]
 
-            _Spectral_data = (  # Taken from matplotlib/_cm.py
-                (0.61960784313725492, 0.003921568627450980, 0.25882352941176473),  # 0.0 -> [0]
-                (0.83529411764705885, 0.24313725490196078, 0.30980392156862746),
-                (0.95686274509803926, 0.42745098039215684, 0.2627450980392157),
-                (0.99215686274509807, 0.68235294117647061, 0.38039215686274508),
-                (0.99607843137254903, 0.8784313725490196, 0.54509803921568623),
-                (1.0, 1.0, 0.74901960784313726),
-                (0.90196078431372551, 0.96078431372549022, 0.59607843137254901),
-                (0.6705882352941176, 0.8666666666666667, 0.64313725490196083),
-                (0.4, 0.76078431372549016, 0.6470588235294118),
-                (0.19607843137254902, 0.53333333333333333, 0.74117647058823533),
-                (0.36862745098039218, 0.30980392156862746, 0.63529411764705879),  # 1.0 -> [K-1]
-            )
+            if cmap not in supported_cmaps:
+                raise ValueError(
+                    f"Only {list(supported_cmaps.keys())} color maps are available without installing matplotlib."
+                )
 
-            cmap = torch.tensor(_Spectral_data, dtype=torch.float, device=image.device)  # [K,3]
+            cmap = supported_cmaps[cmap]
+            if is_cmap_reversed:
+                cmap = cmap[::-1]
+            cmap = torch.tensor(cmap, dtype=torch.float, device=image.device)  # [K,3]
             K = cmap.shape[0]
 
             pos = image.clamp(min=0, max=1) * (K - 1)