diff --git a/comfy_extras/nodes_mask.py b/comfy_extras/nodes_mask.py
new file mode 100644
index 00000000..ab17fc50
--- /dev/null
+++ b/comfy_extras/nodes_mask.py
@@ -0,0 +1,240 @@
+import torch
+
+from nodes import MAX_RESOLUTION
+
+class LatentCompositeMasked:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "destination": ("LATENT",),
+                "source": ("LATENT",),
+                "x": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 8}),
+                "y": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 8}),
+            },
+            "optional": {
+                "mask": ("MASK",),
+            }
+        }
+    RETURN_TYPES = ("LATENT",)
+    FUNCTION = "composite"
+
+    CATEGORY = "latent"
+
+    def composite(self, destination, source, x, y, mask = None):
+        output = destination.copy()
+        destination = destination["samples"].clone()
+        source = source["samples"]
+
+        x = max(-source.shape[3] * 8, min(x, destination.shape[3] * 8))
+        y = max(-source.shape[2] * 8, min(y, destination.shape[2] * 8))
+
+        left, top = (x // 8, y // 8)
+        right, bottom = (left + source.shape[3], top + source.shape[2],)
+
+
+        if mask is None:
+            mask = torch.ones_like(source)
+        else:
+            mask = mask.clone()
+            mask = torch.nn.functional.interpolate(mask[None, None], size=(source.shape[2], source.shape[3]), mode="bilinear")
+            mask = mask.repeat((source.shape[0], source.shape[1], 1, 1))
+
+        # calculate the bounds of the source that will be overlapping the destination
+        # this prevents the source trying to overwrite latent pixels that are out of bounds
+        # of the destination
+        visible_width, visible_height = (destination.shape[3] - left + min(0, x), destination.shape[2] - top + min(0, y),)
+
+        mask = mask[:, :, :visible_height, :visible_width]
+        inverse_mask = torch.ones_like(mask) - mask
+
+        source_portion = mask * source[:, :, :visible_height, :visible_width]
+        destination_portion = inverse_mask  * destination[:, :, top:bottom, left:right]
+
+        destination[:, :, top:bottom, left:right] = source_portion + destination_portion
+
+        output["samples"] = destination
+
+        return (output,)
+
+class MaskToImage:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "mask": ("MASK",),
+            }
+        }
+
+    CATEGORY = "mask"
+
+    RETURN_TYPES = ("IMAGE",)
+
+    FUNCTION = "convert"
+
+    def convert(self, mask):
+        image = torch.cat([torch.reshape(mask.clone(), [1, mask.shape[0], mask.shape[1], 1,])] * 3, 3)
+
+        return (image,)
+
+class SolidMask:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "value": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.01}),
+                "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
+                "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
+            }
+        }
+
+    CATEGORY = "mask"
+
+    RETURN_TYPES = ("MASK",)
+
+    FUNCTION = "solid"
+
+    def solid(self, value, width, height):
+        out = torch.full((height, width), value, dtype=torch.float32, device="cpu")
+        return (out,)
+
+class InvertMask:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "mask": ("MASK",),
+            }
+        }
+
+    CATEGORY = "mask"
+
+    RETURN_TYPES = ("MASK",)
+
+    FUNCTION = "invert"
+
+    def invert(self, mask):
+        out = 1.0 - mask
+        return (out,)
+
+class CropMask:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "mask": ("MASK",),
+                "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                "width": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
+                "height": ("INT", {"default": 512, "min": 1, "max": MAX_RESOLUTION, "step": 1}),
+            }
+        }
+
+    CATEGORY = "mask"
+
+    RETURN_TYPES = ("MASK",)
+
+    FUNCTION = "crop"
+
+    def crop(self, mask, x, y, width, height):
+        out = mask[y:y + height, x:x + width]
+        return (out,)
+
+class MaskComposite:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "destination": ("MASK",),
+                "source": ("MASK",),
+                "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                "operation": (["multiply", "add", "subtract"],),
+            }
+        }
+
+    CATEGORY = "mask"
+
+    RETURN_TYPES = ("MASK",)
+
+    FUNCTION = "combine"
+
+    def combine(self, destination, source, x, y, operation):
+        output = destination.clone()
+
+        left, top = (x, y,)
+        right, bottom = (min(left + source.shape[1], destination.shape[1]), min(top + source.shape[0], destination.shape[0]))
+        visible_width, visible_height = (right - left, bottom - top,)
+
+        source_portion = source[:visible_height, :visible_width]
+        destination_portion = destination[top:bottom, left:right]
+
+        match operation:
+            case "multiply":
+                output[top:bottom, left:right] = destination_portion * source_portion
+            case "add":
+                output[top:bottom, left:right] = destination_portion + source_portion
+            case "subtract":
+                output[top:bottom, left:right] = destination_portion - source_portion
+
+        output = torch.clamp(output, 0.0, 1.0)
+
+        return (output,)
+
+class FeatherMask:
+    @classmethod
+    def INPUT_TYPES(cls):
+        return {
+            "required": {
+                "mask": ("MASK",),
+                "left": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                "top": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                "right": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+                "bottom": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
+            }
+        }
+
+    CATEGORY = "mask"
+
+    RETURN_TYPES = ("MASK",)
+
+    FUNCTION = "feather"
+
+    def feather(self, mask, left, top, right, bottom):
+        output = mask.clone()
+
+        left = min(left, output.shape[1])
+        right = min(right, output.shape[1])
+        top = min(top, output.shape[0])
+        bottom = min(bottom, output.shape[0])
+
+        for x in range(left):
+            feather_rate = (x + 1.0) / left
+            output[:, x] *= feather_rate
+
+        for x in range(right):
+            feather_rate = (x + 1) / right
+            output[:, -x] *= feather_rate
+
+        for y in range(top):
+            feather_rate = (y + 1) / top
+            output[y, :] *= feather_rate
+
+        for y in range(bottom):
+            feather_rate = (y + 1) / bottom
+            output[-y, :] *= feather_rate
+
+        return (output,)
+
+
+
+NODE_CLASS_MAPPINGS = {
+    "LatentCompositeMasked": LatentCompositeMasked,
+    "MaskToImage": MaskToImage,
+    "SolidMask": SolidMask,
+    "InvertMask": InvertMask,
+    "CropMask": CropMask,
+    "MaskComposite": MaskComposite,
+    "FeatherMask": FeatherMask,
+}
+
diff --git a/nodes.py b/nodes.py
index f1362677..661f879a 100644
--- a/nodes.py
+++ b/nodes.py
@@ -578,44 +578,64 @@ class LatentFlip:
 class LatentComposite:
     @classmethod
     def INPUT_TYPES(s):
-        return {"required": { "samples_to": ("LATENT",),
-                              "samples_from": ("LATENT",),
-                              "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
-                              "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
-                              "feather": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
-                              }}
+        return {
+            "required": {
+                "samples_to": ("LATENT",),
+                "samples_from": ("LATENT",),
+                "x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                "y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+                "feather": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 8}),
+            }
+        }
     RETURN_TYPES = ("LATENT",)
     FUNCTION = "composite"
 
     CATEGORY = "latent"
 
-    def composite(self, samples_to, samples_from, x, y, composite_method="normal", feather=0):
-        x =  x // 8
-        y = y // 8
-        feather = feather // 8
-        samples_out = samples_to.copy()
-        s = samples_to["samples"].clone()
-        samples_to = samples_to["samples"]
-        samples_from = samples_from["samples"]
-        if feather == 0:
-            s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x]
-        else:
-            samples_from = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x]
-            mask = torch.ones_like(samples_from)
-            for t in range(feather):
-                if y != 0:
-                    mask[:,:,t:1+t,:] *= ((1.0/feather) * (t + 1))
+    def composite(self, samples_to, samples_from, x, y, feather):
+        output = samples_to.copy()
+        destination = samples_to["samples"].clone()
+        source = samples_from["samples"]
 
-                if y + samples_from.shape[2] < samples_to.shape[2]:
-                    mask[:,:,mask.shape[2] -1 -t: mask.shape[2]-t,:] *= ((1.0/feather) * (t + 1))
-                if x != 0:
-                    mask[:,:,:,t:1+t] *= ((1.0/feather) * (t + 1))
-                if x + samples_from.shape[3] < samples_to.shape[3]:
-                    mask[:,:,:,mask.shape[3]- 1 - t: mask.shape[3]- t] *= ((1.0/feather) * (t + 1))
-            rev_mask = torch.ones_like(mask) - mask
-            s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] = samples_from[:,:,:samples_to.shape[2] - y, :samples_to.shape[3] - x] * mask + s[:,:,y:y+samples_from.shape[2],x:x+samples_from.shape[3]] * rev_mask
-        samples_out["samples"] = s
-        return (samples_out,)
+        left, top = (x // 8, y // 8)
+        right, bottom = (left + source.shape[3], top + source.shape[2],)
+        feather = feather // 8
+
+
+
+        # calculate the bounds of the source that will be overlapping the destination
+        # this prevents the source trying to overwrite latent pixels that are out of bounds
+        # of the destination
+        visible_width, visible_height = (destination.shape[3] - left, destination.shape[2] - top,)
+
+        mask = torch.ones_like(source)
+
+        for f in range(feather):
+            feather_rate = (f + 1.0) / feather
+
+            if left > 0:
+                mask[:, :, :, f] *= feather_rate
+
+            if right < destination.shape[3] - 1:
+                mask[:, :, :, -f] *= feather_rate
+
+            if top > 0:
+                mask[:, :, f, :] *= feather_rate
+
+            if bottom < destination.shape[2] - 1:
+                mask[:, :, -f, :] *= feather_rate
+
+        mask = mask[:, :, :visible_height, :visible_width]
+        inverse_mask = torch.ones_like(mask) - mask
+
+        source_portion = mask * source[:, :, :visible_height, :visible_width]
+        destination_portion = inverse_mask  * destination[:, :, top:bottom, left:right]
+
+        destination[:, :, top:bottom, left:right] = source_portion + destination_portion
+
+        output["samples"] = destination
+
+        return (output,)
 
 class LatentCrop:
     @classmethod
@@ -932,7 +952,7 @@ class LoadImageMask:
                     "channel": (["alpha", "red", "green", "blue"], ),}
                 }
 
-    CATEGORY = "image"
+    CATEGORY = "mask"
 
     RETURN_TYPES = ("MASK",)
     FUNCTION = "load_image"
@@ -1192,3 +1212,4 @@ def init_custom_nodes():
     load_custom_nodes()
     load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_upscale_model.py"))
     load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_post_processing.py"))
+    load_custom_node(os.path.join(os.path.join(os.path.dirname(os.path.realpath(__file__)), "comfy_extras"), "nodes_mask.py"))