Invert the expected denoise_mask parameter to the FLUX Denoise node

invokeai/app/invocations/create_gradient_mask.py

@@ -28,7 +28,10 @@
 class GradientMaskOutput(BaseInvocationOutput):
     """Outputs a denoise mask and an image representing the total gradient of the mask."""
 
-    denoise_mask: DenoiseMaskField = OutputField(description="Mask for denoise model run")
+    denoise_mask: DenoiseMaskField = OutputField(
+        description="Mask for denoise model run. Values of 0.0 represent the regions to be fully denoised, and 1.0 "
+        + "represent the regions to be preserved."
+    )
     expanded_mask_area: ImageField = OutputField(
         description="Image representing the total gradient area of the mask. For paste-back purposes."
     )

invokeai/app/invocations/fields.py

@@ -181,7 +181,7 @@ class FieldDescriptions:
     )
     num_1 = "The first number"
     num_2 = "The second number"
-    denoise_mask = "A mask of the region to apply the denoising process to."
+    denoise_mask = "A mask of the region to apply the denoising process to. Values of 0.0 represent the regions to be fully denoised, and 1.0 represent the regions to be preserved."
     board = "The board to save the image to"
     image = "The image to process"
     tile_size = "Tile size"

invokeai/app/invocations/flux_denoise.py

@@ -39,7 +39,7 @@
     title="FLUX Denoise",
     tags=["image", "flux"],
     category="image",
-    version="1.0.0",
+    version="2.0.0",
     classification=Classification.Prototype,
 )
 class FluxDenoiseInvocation(BaseInvocation, WithMetadata, WithBoard):
@@ -220,13 +220,19 @@ def _prep_inpaint_mask(self, context: InvocationContext, latents: torch.Tensor)
                 device, and dtype for the inpaint mask.
 
         Returns:
-            torch.Tensor | None: Inpaint mask.
+            torch.Tensor | None: Inpaint mask. Values of 0.0 represent the regions to be fully denoised, and 1.0
+                represent the regions to be preserved.
         """
         if self.denoise_mask is None:
             return None
 
         mask = context.tensors.load(self.denoise_mask.mask_name)
 
+        # The input denoise_mask contains values in [0, 1], where 0.0 represents the regions to be fully denoised, and
+        # 1.0 represents the regions to be preserved.
+        # We invert the mask so that the regions to be preserved are 0.0 and the regions to be denoised are 1.0.
+        mask = 1.0 - mask
+
         _, _, latent_height, latent_width = latents.shape
         mask = tv_resize(
             img=mask,