Add IS_CHANGED method to nodes to check if nodes should be executed again.

LoadImage.IS_CHANGED returns the hash of the image so it will execute again
if the image changed on the disk.

main.py

@@ -10,27 +10,8 @@ import torch
 
 import nodes
 
-
-def recursive_execute(prompt, outputs, current_item, extra_data={}):
-    unique_id = current_item
-    inputs = prompt[unique_id]['inputs']
-    class_type = prompt[unique_id]['class_type']
-    c_obj = nodes.NODE_CLASS_MAPPINGS[class_type]
-    valid_inputs = c_obj.INPUT_TYPES()
-    if unique_id in outputs:
-        return []
-
-    executed = []
-
-    for x in inputs:
-        input_data = inputs[x]
-
-        if isinstance(input_data, list):
-            input_unique_id = input_data[0]
-            output_index = input_data[1]
-            if input_unique_id not in outputs:
-                executed += recursive_execute(prompt, outputs, input_unique_id, extra_data)
-
+def get_input_data(inputs, class_def, outputs={}, prompt={}, extra_data={}):
+    valid_inputs = class_def.INPUT_TYPES()
     input_data_all = {}
     for x in inputs:
         input_data = inputs[x]
@@ -43,8 +24,6 @@ def recursive_execute(prompt, outputs, current_item, extra_data={}):
             if ("required" in valid_inputs and x in valid_inputs["required"]) or ("optional" in valid_inputs and x in valid_inputs["optional"]):
                 input_data_all[x] = input_data
 
-
-    obj = c_obj()
     if "hidden" in valid_inputs:
         h = valid_inputs["hidden"]
         for x in h:
@@ -53,6 +32,29 @@ def recursive_execute(prompt, outputs, current_item, extra_data={}):
             if h[x] == "EXTRA_PNGINFO":
                 if "extra_pnginfo" in extra_data:
                     input_data_all[x] = extra_data['extra_pnginfo']
+    return input_data_all
+
+def recursive_execute(prompt, outputs, current_item, extra_data={}):
+    unique_id = current_item
+    inputs = prompt[unique_id]['inputs']
+    class_type = prompt[unique_id]['class_type']
+    class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+    if unique_id in outputs:
+        return []
+
+    executed = []
+
+    for x in inputs:
+        input_data = inputs[x]
+
+        if isinstance(input_data, list):
+            input_unique_id = input_data[0]
+            output_index = input_data[1]
+            if input_unique_id not in outputs:
+                executed += recursive_execute(prompt, outputs, input_unique_id, extra_data)
+
+    input_data_all = get_input_data(inputs, class_def, outputs, prompt, extra_data)
+    obj = class_def()
 
     outputs[unique_id] = getattr(obj, obj.FUNCTION)(**input_data_all)
     return executed + [unique_id]
@@ -61,12 +63,27 @@ def recursive_output_delete_if_changed(prompt, old_prompt, outputs, current_item
     unique_id = current_item
     inputs = prompt[unique_id]['inputs']
     class_type = prompt[unique_id]['class_type']
+    class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
+
+    is_changed_old = ''
+    is_changed = ''
+    if hasattr(class_def, 'IS_CHANGED'):
+        if 'is_changed' not in prompt[unique_id]:
+            if unique_id in old_prompt and 'is_changed' in old_prompt[unique_id]:
+                is_changed_old = old_prompt[unique_id]['is_changed']
+            input_data_all = get_input_data(inputs, class_def)
+            is_changed = class_def.IS_CHANGED(**input_data_all)
+            prompt[unique_id]['is_changed'] = is_changed
+        else:
+            is_changed = prompt[unique_id]['is_changed']
 
     if unique_id not in outputs:
         return True
 
     to_delete = False
-    if unique_id not in old_prompt:
+    if is_changed != is_changed_old:
+        to_delete = True
+    elif unique_id not in old_prompt:
         to_delete = True
     elif inputs == old_prompt[unique_id]['inputs']:
         for x in inputs:

nodes.py

@@ -3,6 +3,7 @@ import torch
 import os
 import sys
 import json
+import hashlib
 
 from PIL import Image
 from PIL.PngImagePlugin import PngInfo
@@ -226,6 +227,14 @@ class LoadImage:
         image = torch.from_numpy(image[None])[None,]
         return image
 
+    @classmethod
+    def IS_CHANGED(s, image):
+        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()
+
 
 
 NODE_CLASS_MAPPINGS = {