Dynamic typing is working!

comfy_execution/graph.py

@@ -76,6 +76,16 @@ def get_node_definition(self, node_id):
             self.definitions[node_id] = definition
         return self.definitions[node_id]
 
+    def get_constant_type(self, value):
+        if isinstance(value, (int, float)):
+            return "INT,FLOAT"
+        elif isinstance(value, str):
+            return "STRING"
+        elif isinstance(value, bool):
+            return "BOOL"
+        else:
+            return None
+
     def get_input_output_types(self, node_id) -> Tuple[Dict[str, str], Dict[str, List[str]]]:
         node = self.dynprompt.get_node(node_id)
         input_types: Dict[str, str] = {}
@@ -86,6 +96,10 @@ def get_input_output_types(self, node_id) -> Tuple[Dict[str, str], Dict[str, Lis
                     input_types[input_name] = self.definitions[from_node_id]["output"][from_socket]
                 else:
                     input_types[input_name] = "*"
+            else:
+                constant_type = self.get_constant_type(input_data)
+                if constant_type is not None:
+                    input_types[input_name] = constant_type
         output_types: Dict[str, List[str]] = {}
         for index in range(len(self.definitions[node_id]["output_name"])):
             output_name = self.definitions[node_id]["output_name"][index]
@@ -103,7 +117,6 @@ def get_input_output_types(self, node_id) -> Tuple[Dict[str, str], Dict[str, Lis
         return input_types, output_types
 
     def resolve_dynamic_definitions(self, node_id_set: Set[str]):
-        print("Resolving dynamic definitions", node_id_set)
         entangled = {}
         # Pre-fill with class info. Also, build a lookup table for output nodes
         for node_id in node_id_set:

execution.py

@@ -124,6 +124,8 @@ def mark_missing():
                 input_data_all[x] = [extra_data.get('extra_pnginfo', None)]
             if h[x] == "UNIQUE_ID":
                 input_data_all[x] = [unique_id]
+            if h[x] == "NODE_DEFINITION":
+                input_data_all[x] = [node_def]
     return input_data_all, missing_keys
 
 def map_node_over_list(obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None):
@@ -169,6 +171,8 @@ def merge_result_data(results, node_def):
     # check which outputs need concatenating
     output = []
     output_is_list = node_def['output_is_list']
+    if len(output_is_list) < len(results[0]):
+        output_is_list = output_is_list + [False] * (len(results[0]) - len(output_is_list))
 
     # merge node execution results
     for i, is_list in zip(range(len(results[0])), output_is_list):
@@ -461,6 +465,7 @@ def execute(self, prompt, prompt_id, extra_data={}, execute_outputs=[]):
 
         with torch.inference_mode():
             dynamic_prompt = DynamicPrompt(prompt)
+            dynamic_prompt.node_definitions.resolve_dynamic_definitions(set(dynamic_prompt.all_node_ids()))
             is_changed_cache = IsChangedCache(dynamic_prompt, self.caches.outputs)
             for cache in self.caches.all:
                 cache.set_prompt(dynamic_prompt, prompt.keys(), is_changed_cache)