Mask strength should be separate from area strength.

comfy/samplers.py

@@ -26,10 +26,13 @@ def sampling_function(model_function, x, timestep, uncond, cond, cond_scale, con
             if 'mask' in cond[1]:
                 # Scale the mask to the size of the input
                 # The mask should have been resized as we began the sampling process
+                mask_strength = 1.0
+                if "mask_strength" in cond[1]:
+                    mask_strength = cond[1]["mask_strength"]
                 mask = cond[1]['mask']
                 assert(mask.shape[1] == x_in.shape[2])
                 assert(mask.shape[2] == x_in.shape[3])
-                mask = mask[:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]]
+                mask = mask[:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]] * mask_strength
                 mask = mask.unsqueeze(1).repeat(input_x.shape[0] // mask.shape[0], input_x.shape[1], 1, 1)
             else:
                 mask = torch.ones_like(input_x)

nodes.py

@@ -98,7 +98,7 @@ class ConditioningSetMask:
 
     CATEGORY = "conditioning"
 
-    def append(self, conditioning, mask, set_area_to_bounds, strength, min_sigma=0.0, max_sigma=99.0):
+    def append(self, conditioning, mask, set_area_to_bounds, strength):
         c = []
         if len(mask.shape) < 3:
             mask = mask.unsqueeze(0)
@@ -107,9 +107,7 @@ class ConditioningSetMask:
             _, h, w = mask.shape
             n[1]['mask'] = mask
             n[1]['set_area_to_bounds'] = set_area_to_bounds
-            n[1]['strength'] = strength
-            n[1]['min_sigma'] = min_sigma
-            n[1]['max_sigma'] = max_sigma
+            n[1]['mask_strength'] = strength
             c.append(n)
         return (c, )