Add a LoadImageMask node to load one colour channel in an image as a mask.

nodes.py

@@ -410,11 +410,8 @@ def common_ksampler(device, model, seed, steps, cfg, sampler_name, scheduler, po
 
     if "noise_mask" in latent:
         noise_mask = latent['noise_mask']
-        print(noise_mask.shape, noise.shape)
-
         noise_mask = torch.nn.functional.interpolate(noise_mask[None,None,], size=(noise.shape[2], noise.shape[3]), mode="bilinear")
-        noise_mask = noise_mask.floor()
+        noise_mask = noise_mask.round()
-        noise_mask = torch.ones_like(noise_mask) - noise_mask
         noise_mask = torch.cat([noise_mask] * noise.shape[1], dim=1)
         noise_mask = torch.cat([noise_mask] * noise.shape[0])
         noise_mask = noise_mask.to(device)
@@ -581,10 +578,11 @@ class LoadImage:
     FUNCTION = "load_image"
     def load_image(self, image):
         image_path = os.path.join(self.input_dir, image)
-        image = Image.open(image_path).convert("RGB")
+        i = Image.open(image_path)
+        image = i.convert("RGB")
         image = np.array(image).astype(np.float32) / 255.0
-        image = torch.from_numpy(image[None])[None,]
+        image = torch.from_numpy(image)[None,]
-        return image
+        return (image,)
 
     @classmethod
     def IS_CHANGED(s, image):
@@ -594,6 +592,41 @@ class LoadImage:
             m.update(f.read())
         return m.digest().hex()
 
+class LoadImageMask:
+    input_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), "input")
+    @classmethod
+    def INPUT_TYPES(s):
+        return {"required":
+                    {"image": (os.listdir(s.input_dir), ),
+                    "channel": (["alpha", "red", "green", "blue"], ),}
+                }
+
+    CATEGORY = "image"
+
+    RETURN_TYPES = ("MASK",)
+    FUNCTION = "load_image"
+    def load_image(self, image, channel):
+        image_path = os.path.join(self.input_dir, image)
+        i = Image.open(image_path)
+        mask = None
+        c = channel[0].upper()
+        if c in i.getbands():
+            mask = np.array(i.getchannel(c)).astype(np.float32) / 255.0
+            mask = torch.from_numpy(mask)
+            if c == 'A':
+                mask = 1. - mask
+        else:
+            mask = torch.zeros((64,64), dtype=torch.float32, device="cpu")
+        return (mask,)
+
+    @classmethod
+    def IS_CHANGED(s, image, channel):
+        image_path = os.path.join(s.input_dir, image)
+        m = hashlib.sha256()
+        with open(image_path, 'rb') as f:
+            m.update(f.read())
+        return m.digest().hex()
+
 class ImageScale:
     upscale_methods = ["nearest-exact", "bilinear", "area"]
     crop_methods = ["disabled", "center"]
@@ -626,6 +659,7 @@ NODE_CLASS_MAPPINGS = {
     "LatentUpscale": LatentUpscale,
     "SaveImage": SaveImage,
     "LoadImage": LoadImage,
+    "LoadImageMask": LoadImageMask,
     "ImageScale": ImageScale,
     "ConditioningCombine": ConditioningCombine,
     "ConditioningSetArea": ConditioningSetArea,