fix always start with last output option

image_filter_nodes.py

@@ -25,17 +25,15 @@ class FilterNodeBase:
         return cls._preview_image.save_images(images, **kwargs)['ui']['images']
 
     @classmethod
-    def load_mask(cls, file:str, type:str="clipspace", append=" [input]") -> torch.Tensor:
+    def load_mask(cls, file:str|Path, type:str="clipspace", append=" [input]") -> torch.Tensor:
         f = os.path.join(type, file)+append
-        path = folder_paths.get_annotated_filepath(f)
-        print(f"Loading mask from {f}")
         return cls._load_image.load_image(f)[1]
     
     @classmethod
-    def newest_mask_file(cls) -> Path:
+    def newest_mask_file(cls) -> Path|None:
         dr = Path(folder_paths.get_input_directory()) / 'clipspace'
         masked_files = list(dr.glob("*masked*"))
-        return max([f for f in masked_files], key=lambda item: item.stat().st_ctime) if masked_files else None
+        return max([f for f in masked_files], key=lambda item: item.stat().st_birthtime) if masked_files else None
     
     @classmethod
     def fingerprint_inputs(cls, **kwargs): # type: ignore
@@ -279,8 +277,9 @@ class MaskImageFilter(FilterNodeBase, io.ComfyNode):
                 mask=None, audiofile="", extra1="", extra2="", extra3="", tip="", **kwargs): 
         iostore = InOutStore.get_store(f"{graph_id}_{cls.hidden.unique_id}")
 
-        if if_inputs_unchanged == "Always start with last output" and iostore.have_last_output:
-            if iostore.check_input_tensors_congruent(image):
+        if (if_inputs_unchanged == "Always start with last output" and 
+            iostore.have_last_output and 
+            iostore.check_input_image_congruent(image)):
                 image, mask, extra1, extra2, extra3 = iostore.get_last_outputs()
                 mask = 1.0 - mask if mask is not None else None  # The mask editor works in inverse
 
@@ -296,7 +295,6 @@ class MaskImageFilter(FilterNodeBase, io.ComfyNode):
 
         if unchanged_in:
             if if_inputs_unchanged == "Start with last output":
-                print("\n\nStarting with last output\n\n")
                 image, mask, extra1, extra2, extra3 = iostore.get_last_outputs()
                 mask = 1.0 - mask if mask is not None else None  # The mask editor works in inverse
             elif if_inputs_unchanged == "Resend last output": 
@@ -327,13 +325,11 @@ class MaskImageFilter(FilterNodeBase, io.ComfyNode):
             (time.monotonic()-started_waiting_at < 5)): time.sleep(1)
         
         if (mask_file==last_mask_file):
-            print("Didn't get a new mask file - using input mask or image")
             mask = mask if mask is not None else cls.load_mask(urls[0]['filename']+" [temp]")
-        else:
+        elif (mask_file is not None):
             mask = cls.load_mask(mask_file)
 
         if mask is None: 
-            print("No mask file - setting blank")
             mask = torch.zeros_like(image[...,0]) 
 
         if if_no_mask == 'cancel' and torch.all(mask==0): raise InterruptProcessingException() 

modules/InOutStore.py

@@ -3,8 +3,6 @@ from typing import Any
 
 outputs_type = tuple[torch.Tensor, torch.Tensor|None, str, str, str]
 
-def types(l:list[Any]) -> str: return ":".join(str(x.__class__) for x in l)
-
 def make_copy(x): return x.clone() if isinstance(x, torch.Tensor) else x
 
 class InOutStore:
@@ -32,40 +30,33 @@ class InOutStore:
         return self.last_output
     
     def update_last_outputs(self, outputs:outputs_type):
-        self.last_output = [ make_copy(x) for x in outputs ]
+        self.last_output = tuple( make_copy(x) for x in outputs ) # type: ignore
 
     def update_last_inputs(self, *args):
         self.last_inputs = [ make_copy(x) for x in args ]
 
     def compare_with_last_inputs(self, *args) -> bool:
         if self.last_inputs is None: return False   # first time we've been called
-        if not types(self.last_inputs) == types(args):
-            assert False, "Called compare_with_last_inputs with different classes"
 
         # compare the tensors
         for prev, new in zip(self.last_inputs, args):
-            if isinstance(prev, torch.Tensor) and not torch.equal(prev, new) and not torch.equal(1-prev, new): 
-                print("Tensor input changed")
+            if isinstance(prev, torch.Tensor) and isinstance(new, torch.Tensor) and not torch.equal(prev, new) and not torch.equal(1-prev, new): 
                 return False
-
+            if (isinstance(prev, torch.Tensor) and new is None) or (isinstance(new, torch.Tensor) and prev is None):
+                return False
+            
         # compare the non-tensors
         if self.tensor_free_hash(*args) != self.tensor_free_hash(self.last_inputs): 
-            print("Non tensor input changed")
             return False
 
         return True
 
-    def check_input_tensors_congruent(self, *args) -> bool:
+    def check_input_image_congruent(self, image:torch.Tensor) -> bool:
         if self.last_inputs is None: return False
-        for a,i in enumerate(args):
-            assert isinstance(a,torch.Tensor)
-            if self.last_input_tensors[i].shape != a.shape: return False
-        return True
+        return (image.shape == self.last_input_tensors[0].shape)
     
     def tensor_free_hash(self, *args):
-        tf = ",".join( str(v) for v in flatten(args) if not isinstance(v, torch.Tensor) and v )
-        print(f"Hashing {tf} ... ")
-        return hash( tf )
+        return hash( ",".join( str(v) for v in flatten(args) if not isinstance(v, torch.Tensor) ))
 
 def flatten(args) -> list[Any]:
     f = []