Update README.md

README.md

@@ -76,7 +76,165 @@ Experimentally, the checkpoint can be used with other diffusion models such as d
 $ pip install diffusers transformers accelerate
 ```
 
-2. Run code:
+2. We'll need to make use of a color palette here as described in [semantic_segmentation](https://huggingface.co/docs/transformers/tasks/semantic_segmentation):
+
+```py
+palette = np.asarray([
+    [0, 0, 0],
+    [120, 120, 120],
+    [180, 120, 120],
+    [6, 230, 230],
+    [80, 50, 50],
+    [4, 200, 3],
+    [120, 120, 80],
+    [140, 140, 140],
+    [204, 5, 255],
+    [230, 230, 230],
+    [4, 250, 7],
+    [224, 5, 255],
+    [235, 255, 7],
+    [150, 5, 61],
+    [120, 120, 70],
+    [8, 255, 51],
+    [255, 6, 82],
+    [143, 255, 140],
+    [204, 255, 4],
+    [255, 51, 7],
+    [204, 70, 3],
+    [0, 102, 200],
+    [61, 230, 250],
+    [255, 6, 51],
+    [11, 102, 255],
+    [255, 7, 71],
+    [255, 9, 224],
+    [9, 7, 230],
+    [220, 220, 220],
+    [255, 9, 92],
+    [112, 9, 255],
+    [8, 255, 214],
+    [7, 255, 224],
+    [255, 184, 6],
+    [10, 255, 71],
+    [255, 41, 10],
+    [7, 255, 255],
+    [224, 255, 8],
+    [102, 8, 255],
+    [255, 61, 6],
+    [255, 194, 7],
+    [255, 122, 8],
+    [0, 255, 20],
+    [255, 8, 41],
+    [255, 5, 153],
+    [6, 51, 255],
+    [235, 12, 255],
+    [160, 150, 20],
+    [0, 163, 255],
+    [140, 140, 140],
+    [250, 10, 15],
+    [20, 255, 0],
+    [31, 255, 0],
+    [255, 31, 0],
+    [255, 224, 0],
+    [153, 255, 0],
+    [0, 0, 255],
+    [255, 71, 0],
+    [0, 235, 255],
+    [0, 173, 255],
+    [31, 0, 255],
+    [11, 200, 200],
+    [255, 82, 0],
+    [0, 255, 245],
+    [0, 61, 255],
+    [0, 255, 112],
+    [0, 255, 133],
+    [255, 0, 0],
+    [255, 163, 0],
+    [255, 102, 0],
+    [194, 255, 0],
+    [0, 143, 255],
+    [51, 255, 0],
+    [0, 82, 255],
+    [0, 255, 41],
+    [0, 255, 173],
+    [10, 0, 255],
+    [173, 255, 0],
+    [0, 255, 153],
+    [255, 92, 0],
+    [255, 0, 255],
+    [255, 0, 245],
+    [255, 0, 102],
+    [255, 173, 0],
+    [255, 0, 20],
+    [255, 184, 184],
+    [0, 31, 255],
+    [0, 255, 61],
+    [0, 71, 255],
+    [255, 0, 204],
+    [0, 255, 194],
+    [0, 255, 82],
+    [0, 10, 255],
+    [0, 112, 255],
+    [51, 0, 255],
+    [0, 194, 255],
+    [0, 122, 255],
+    [0, 255, 163],
+    [255, 153, 0],
+    [0, 255, 10],
+    [255, 112, 0],
+    [143, 255, 0],
+    [82, 0, 255],
+    [163, 255, 0],
+    [255, 235, 0],
+    [8, 184, 170],
+    [133, 0, 255],
+    [0, 255, 92],
+    [184, 0, 255],
+    [255, 0, 31],
+    [0, 184, 255],
+    [0, 214, 255],
+    [255, 0, 112],
+    [92, 255, 0],
+    [0, 224, 255],
+    [112, 224, 255],
+    [70, 184, 160],
+    [163, 0, 255],
+    [153, 0, 255],
+    [71, 255, 0],
+    [255, 0, 163],
+    [255, 204, 0],
+    [255, 0, 143],
+    [0, 255, 235],
+    [133, 255, 0],
+    [255, 0, 235],
+    [245, 0, 255],
+    [255, 0, 122],
+    [255, 245, 0],
+    [10, 190, 212],
+    [214, 255, 0],
+    [0, 204, 255],
+    [20, 0, 255],
+    [255, 255, 0],
+    [0, 153, 255],
+    [0, 41, 255],
+    [0, 255, 204],
+    [41, 0, 255],
+    [41, 255, 0],
+    [173, 0, 255],
+    [0, 245, 255],
+    [71, 0, 255],
+    [122, 0, 255],
+    [0, 255, 184],
+    [0, 92, 255],
+    [184, 255, 0],
+    [0, 133, 255],
+    [255, 214, 0],
+    [25, 194, 194],
+    [102, 255, 0],
+    [92, 0, 255],
+])
+```
+
+3. Having defined the color palette we can now run the whole segmentation + controlnet generation code:
 
 ```py
 from transformers import AutoImageProcessor, UperNetForSemanticSegmentation
@@ -101,8 +259,6 @@ seg = image_processor.post_process_semantic_segmentation(outputs, target_sizes=[
 
 color_seg = np.zeros((seg.shape[0], seg.shape[1], 3), dtype=np.uint8) # height, width, 3
 
-palette = np.array(ade_palette())
-
 for label, color in enumerate(palette):
     color_seg[seg == label, :] = color