import nodes from typing import Set, Tuple, Dict, List from comfy_execution.graph_utils import is_link class DependencyCycleError(Exception): pass class NodeInputError(Exception): pass class NodeNotFoundError(Exception): pass class DynamicPrompt: def __init__(self, original_prompt): # The original prompt provided by the user self.original_prompt = original_prompt self.node_definitions = DynamicNodeDefinitionCache(self) # Any extra pieces of the graph created during execution self.ephemeral_prompt = {} self.ephemeral_parents = {} self.ephemeral_display = {} def get_node(self, node_id): if node_id in self.ephemeral_prompt: return self.ephemeral_prompt[node_id] if node_id in self.original_prompt: return self.original_prompt[node_id] raise NodeNotFoundError(f"Node {node_id} not found") def get_node_definition(self, node_id): return self.node_definitions.get_node_definition(node_id) def has_node(self, node_id): return node_id in self.original_prompt or node_id in self.ephemeral_prompt def add_ephemeral_node(self, node_id, node_info, parent_id, display_id): self.ephemeral_prompt[node_id] = node_info self.ephemeral_parents[node_id] = parent_id self.ephemeral_display[node_id] = display_id def get_real_node_id(self, node_id): while node_id in self.ephemeral_parents: node_id = self.ephemeral_parents[node_id] return node_id def get_parent_node_id(self, node_id): return self.ephemeral_parents.get(node_id, None) def get_display_node_id(self, node_id): while node_id in self.ephemeral_display: node_id = self.ephemeral_display[node_id] return node_id def all_node_ids(self): return set(self.original_prompt.keys()).union(set(self.ephemeral_prompt.keys())) def get_original_prompt(self): return self.original_prompt class DynamicNodeDefinitionCache: def __init__(self, dynprompt: DynamicPrompt): self.dynprompt = dynprompt self.definitions = {} self.inputs_from_output_slot = {} self.inputs_from_output_node = {} def get_node_definition(self, node_id): if node_id not in self.definitions: node = self.dynprompt.get_node(node_id) if node is None: return None class_type = node["class_type"] definition = node_class_info(class_type) 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] = {} for input_name, input_data in node["inputs"].items(): if is_link(input_data): from_node_id, from_socket = input_data if from_socket < len(self.definitions[from_node_id]["output_name"]): 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] if (node_id, index) not in self.inputs_from_output_slot: continue for (to_node_id, to_input_name) in self.inputs_from_output_slot[(node_id, index)]: if output_name not in output_types: output_types[output_name] = [] if to_input_name in self.definitions[to_node_id]["input"]["required"]: output_types[output_name].append(self.definitions[to_node_id]["input"]["required"][to_input_name][0]) elif to_input_name in self.definitions[to_node_id]["input"]["optional"]: output_types[output_name].append(self.definitions[to_node_id]["input"]["optional"][to_input_name][0]) else: output_types[output_name].append("*") return input_types, output_types def resolve_dynamic_definitions(self, node_id_set: Set[str]): entangled = {} # Pre-fill with class info. Also, build a lookup table for output nodes for node_id in node_id_set: node = self.dynprompt.get_node(node_id) class_type = node["class_type"] self.definitions[node_id] = node_class_info(class_type) for input_name, input_data in node["inputs"].items(): if is_link(input_data): input_tuple = tuple(input_data) if input_tuple not in self.inputs_from_output_slot: self.inputs_from_output_slot[input_tuple] = [] self.inputs_from_output_slot[input_tuple].append((node_id, input_name)) if input_tuple[0] not in self.inputs_from_output_node: self.inputs_from_output_node[input_tuple[0]] = [] self.inputs_from_output_node[input_tuple[0]].append((node_id, input_name)) _, _, extra_info = get_input_info(self.definitions[node_id], input_name) if extra_info is not None and extra_info.get("entangleTypes", False): from_node_id = input_data[0] if node_id not in entangled: entangled[node_id] = [] if from_node_id not in entangled: entangled[from_node_id] = [] entangled[node_id].append((from_node_id, input_name)) entangled[from_node_id].append((node_id, input_name)) # Evaluate node info to_resolve = node_id_set.copy() updated = {} while len(to_resolve) > 0: node_id = to_resolve.pop() node = self.dynprompt.get_node(node_id) class_type = node["class_type"] class_def = nodes.NODE_CLASS_MAPPINGS[class_type] if hasattr(class_def, "resolve_dynamic_types"): entangled_types = {} for (entangled_id, entangled_name) in entangled.get(node_id, []): entangled_def = self.get_node_definition(entangled_id) if entangled_def is None: continue input_types = {} output_types = {} for input_category, input_list in entangled_def["input"].items(): for input_name, input_info in input_list.items(): if isinstance(input_info, tuple) or input_category != "hidden": input_types[input_name] = input_info[0] for i in range(len(entangled_def["output"])): output_name = entangled_def["output_name"][i] output_types[output_name] = entangled_def["output"][i] if entangled_name not in entangled_types: entangled_types[entangled_name] = [] entangled_types[entangled_name].append({ "node_id": entangled_id, "input_types": input_types, "output_types": output_types }) input_types, output_types = self.get_input_output_types(node_id) dynamic_info = class_def.resolve_dynamic_types( input_types=input_types, output_types=output_types, entangled_types=entangled_types ) old_info = self.definitions[node_id].copy() self.definitions[node_id].update(dynamic_info) updated[node_id] = self.definitions[node_id] # We changed the info, so we potentially need to resolve adjacent and entangled nodes if old_info != self.definitions[node_id]: for (entangled_node_id, _) in entangled.get(node_id, []): if entangled_node_id in node_id_set: to_resolve.add(entangled_node_id) for i in range(len(self.definitions[node_id]["output"])): for (output_node_id, _) in self.inputs_from_output_slot.get((node_id, i), []): if output_node_id in node_id_set: to_resolve.add(output_node_id) for _, input_data in node["inputs"].items(): if is_link(input_data): if input_data[0] in node_id_set: to_resolve.add(input_data[0]) for (to_node_id, _) in self.inputs_from_output_node.get(node_id, []): if to_node_id in node_id_set: to_resolve.add(to_node_id) # Because this run may have changed the number of outputs, we may need to run it again # in order to get those outputs passed as output_types. to_resolve.add(node_id) return updated def node_class_info(node_class): if node_class not in nodes.NODE_CLASS_MAPPINGS: return None obj_class = nodes.NODE_CLASS_MAPPINGS[node_class] info = {} info['input'] = obj_class.INPUT_TYPES() info['input_order'] = {key: list(value.keys()) for (key, value) in obj_class.INPUT_TYPES().items()} info['output'] = obj_class.RETURN_TYPES info['output_is_list'] = obj_class.OUTPUT_IS_LIST if hasattr(obj_class, 'OUTPUT_IS_LIST') else [False] * len(obj_class.RETURN_TYPES) info['output_name'] = obj_class.RETURN_NAMES if hasattr(obj_class, 'RETURN_NAMES') else info['output'] info['name'] = node_class info['display_name'] = nodes.NODE_DISPLAY_NAME_MAPPINGS[node_class] if node_class in nodes.NODE_DISPLAY_NAME_MAPPINGS.keys() else node_class info['description'] = obj_class.DESCRIPTION if hasattr(obj_class,'DESCRIPTION') else '' info['python_module'] = getattr(obj_class, "RELATIVE_PYTHON_MODULE", "nodes") info['category'] = 'sd' if hasattr(obj_class, 'OUTPUT_NODE') and obj_class.OUTPUT_NODE == True: info['output_node'] = True else: info['output_node'] = False if hasattr(obj_class, 'CATEGORY'): info['category'] = obj_class.CATEGORY if hasattr(obj_class, 'OUTPUT_TOOLTIPS'): info['output_tooltips'] = obj_class.OUTPUT_TOOLTIPS if getattr(obj_class, "DEPRECATED", False): info['deprecated'] = True if getattr(obj_class, "EXPERIMENTAL", False): info['experimental'] = True return info def get_input_info(node_info, input_name): valid_inputs = node_info["input"] input_info = None input_category = None if "required" in valid_inputs and input_name in valid_inputs["required"]: input_category = "required" input_info = valid_inputs["required"][input_name] elif "optional" in valid_inputs and input_name in valid_inputs["optional"]: input_category = "optional" input_info = valid_inputs["optional"][input_name] elif "hidden" in valid_inputs and input_name in valid_inputs["hidden"]: input_category = "hidden" input_info = valid_inputs["hidden"][input_name] if input_info is None: return None, None, None input_type = input_info[0] if len(input_info) > 1: extra_info = input_info[1] else: extra_info = {} return input_type, input_category, extra_info class TopologicalSort: def __init__(self, dynprompt): self.dynprompt = dynprompt self.pendingNodes = {} self.blockCount = {} # Number of nodes this node is directly blocked by self.blocking = {} # Which nodes are blocked by this node def get_input_info(self, unique_id, input_name): return get_input_info(self.dynprompt.get_node_definition(unique_id), input_name) def make_input_strong_link(self, to_node_id, to_input): inputs = self.dynprompt.get_node(to_node_id)["inputs"] if to_input not in inputs: raise NodeInputError(f"Node {to_node_id} says it needs input {to_input}, but there is no input to that node at all") value = inputs[to_input] if not is_link(value): raise NodeInputError(f"Node {to_node_id} says it needs input {to_input}, but that value is a constant") from_node_id, from_socket = value self.add_strong_link(from_node_id, from_socket, to_node_id) def add_strong_link(self, from_node_id, from_socket, to_node_id): if not self.is_cached(from_node_id): self.add_node(from_node_id) if to_node_id not in self.blocking[from_node_id]: self.blocking[from_node_id][to_node_id] = {} self.blockCount[to_node_id] += 1 self.blocking[from_node_id][to_node_id][from_socket] = True def add_node(self, node_unique_id, include_lazy=False, subgraph_nodes=None): node_ids = [node_unique_id] links = [] while len(node_ids) > 0: unique_id = node_ids.pop() if unique_id in self.pendingNodes: continue self.pendingNodes[unique_id] = True self.blockCount[unique_id] = 0 self.blocking[unique_id] = {} inputs = self.dynprompt.get_node(unique_id)["inputs"] for input_name in inputs: value = inputs[input_name] if is_link(value): from_node_id, from_socket = value if subgraph_nodes is not None and from_node_id not in subgraph_nodes: continue input_type, input_category, input_info = self.get_input_info(unique_id, input_name) is_lazy = input_info is not None and "lazy" in input_info and input_info["lazy"] if (include_lazy or not is_lazy) and not self.is_cached(from_node_id): node_ids.append(from_node_id) links.append((from_node_id, from_socket, unique_id)) for link in links: self.add_strong_link(*link) def is_cached(self, node_id): return False def get_ready_nodes(self): return [node_id for node_id in self.pendingNodes if self.blockCount[node_id] == 0] def pop_node(self, unique_id): del self.pendingNodes[unique_id] for blocked_node_id in self.blocking[unique_id]: self.blockCount[blocked_node_id] -= 1 del self.blocking[unique_id] def is_empty(self): return len(self.pendingNodes) == 0 class ExecutionList(TopologicalSort): """ ExecutionList implements a topological dissolve of the graph. After a node is staged for execution, it can still be returned to the graph after having further dependencies added. """ def __init__(self, dynprompt, output_cache): super().__init__(dynprompt) self.output_cache = output_cache self.staged_node_id = None def is_cached(self, node_id): return self.output_cache.get(node_id) is not None def stage_node_execution(self): assert self.staged_node_id is None if self.is_empty(): return None, None, None available = self.get_ready_nodes() if len(available) == 0: cycled_nodes = self.get_nodes_in_cycle() # Because cycles composed entirely of static nodes are caught during initial validation, # we will 'blame' the first node in the cycle that is not a static node. blamed_node = cycled_nodes[0] for node_id in cycled_nodes: display_node_id = self.dynprompt.get_display_node_id(node_id) if display_node_id != node_id: blamed_node = display_node_id break ex = DependencyCycleError("Dependency cycle detected") error_details = { "node_id": blamed_node, "exception_message": str(ex), "exception_type": "graph.DependencyCycleError", "traceback": [], "current_inputs": [] } return None, error_details, ex self.staged_node_id = self.ux_friendly_pick_node(available) return self.staged_node_id, None, None def ux_friendly_pick_node(self, node_list): # If an output node is available, do that first. # Technically this has no effect on the overall length of execution, but it feels better as a user # for a PreviewImage to display a result as soon as it can # Some other heuristics could probably be used here to improve the UX further. def is_output(node_id): node_def = self.dynprompt.get_node_definition(node_id) return node_def['output_node'] for node_id in node_list: if is_output(node_id): return node_id #This should handle the VAEDecode -> preview case for node_id in node_list: for blocked_node_id in self.blocking[node_id]: if is_output(blocked_node_id): return node_id #This should handle the VAELoader -> VAEDecode -> preview case for node_id in node_list: for blocked_node_id in self.blocking[node_id]: for blocked_node_id1 in self.blocking[blocked_node_id]: if is_output(blocked_node_id1): return node_id #TODO: this function should be improved return node_list[0] def unstage_node_execution(self): assert self.staged_node_id is not None self.staged_node_id = None def complete_node_execution(self): node_id = self.staged_node_id self.pop_node(node_id) self.staged_node_id = None def get_nodes_in_cycle(self): # We'll dissolve the graph in reverse topological order to leave only the nodes in the cycle. # We're skipping some of the performance optimizations from the original TopologicalSort to keep # the code simple (and because having a cycle in the first place is a catastrophic error) blocked_by = { node_id: {} for node_id in self.pendingNodes } for from_node_id in self.blocking: for to_node_id in self.blocking[from_node_id]: if True in self.blocking[from_node_id][to_node_id].values(): blocked_by[to_node_id][from_node_id] = True to_remove = [node_id for node_id in blocked_by if len(blocked_by[node_id]) == 0] while len(to_remove) > 0: for node_id in to_remove: for to_node_id in blocked_by: if node_id in blocked_by[to_node_id]: del blocked_by[to_node_id][node_id] del blocked_by[node_id] to_remove = [node_id for node_id in blocked_by if len(blocked_by[node_id]) == 0] return list(blocked_by.keys()) class ExecutionBlocker: """ Return this from a node and any users will be blocked with the given error message. If the message is None, execution will be blocked silently instead. Generally, you should avoid using this functionality unless absolutely necessary. Whenever it's possible, a lazy input will be more efficient and have a better user experience. This functionality is useful in two cases: 1. You want to conditionally prevent an output node from executing. (Particularly a built-in node like SaveImage. For your own output nodes, I would recommend just adding a BOOL input and using lazy evaluation to let it conditionally disable itself.) 2. You have a node with multiple possible outputs, some of which are invalid and should not be used. (I would recommend not making nodes like this in the future -- instead, make multiple nodes with different outputs. Unfortunately, there are several popular existing nodes using this pattern.) """ def __init__(self, message): self.message = message