238 lines
10 KiB
Python
238 lines
10 KiB
Python
import nodes
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from comfy_execution.graph_utils import is_link
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class DependencyCycleError(Exception):
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pass
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class NodeInputError(Exception):
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pass
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class NodeNotFoundError(Exception):
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pass
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class DynamicPrompt:
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def __init__(self, original_prompt):
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# The original prompt provided by the user
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self.original_prompt = original_prompt
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# Any extra pieces of the graph created during execution
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self.ephemeral_prompt = {}
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self.ephemeral_parents = {}
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self.ephemeral_display = {}
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def get_node(self, node_id):
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if node_id in self.ephemeral_prompt:
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return self.ephemeral_prompt[node_id]
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if node_id in self.original_prompt:
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return self.original_prompt[node_id]
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raise NodeNotFoundError(f"Node {node_id} not found")
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def has_node(self, node_id):
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return node_id in self.original_prompt or node_id in self.ephemeral_prompt
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def add_ephemeral_node(self, node_id, node_info, parent_id, display_id):
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self.ephemeral_prompt[node_id] = node_info
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self.ephemeral_parents[node_id] = parent_id
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self.ephemeral_display[node_id] = display_id
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def get_real_node_id(self, node_id):
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while node_id in self.ephemeral_parents:
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node_id = self.ephemeral_parents[node_id]
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return node_id
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def get_parent_node_id(self, node_id):
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return self.ephemeral_parents.get(node_id, None)
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def get_display_node_id(self, node_id):
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while node_id in self.ephemeral_display:
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node_id = self.ephemeral_display[node_id]
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return node_id
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def all_node_ids(self):
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return set(self.original_prompt.keys()).union(set(self.ephemeral_prompt.keys()))
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def get_original_prompt(self):
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return self.original_prompt
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def get_input_info(class_def, input_name):
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valid_inputs = class_def.INPUT_TYPES()
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input_info = None
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input_category = None
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if "required" in valid_inputs and input_name in valid_inputs["required"]:
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input_category = "required"
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input_info = valid_inputs["required"][input_name]
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elif "optional" in valid_inputs and input_name in valid_inputs["optional"]:
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input_category = "optional"
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input_info = valid_inputs["optional"][input_name]
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elif "hidden" in valid_inputs and input_name in valid_inputs["hidden"]:
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input_category = "hidden"
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input_info = valid_inputs["hidden"][input_name]
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if input_info is None:
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return None, None, None
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input_type = input_info[0]
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if len(input_info) > 1:
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extra_info = input_info[1]
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else:
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extra_info = {}
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return input_type, input_category, extra_info
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class TopologicalSort:
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def __init__(self, dynprompt):
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self.dynprompt = dynprompt
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self.pendingNodes = {}
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self.blockCount = {} # Number of nodes this node is directly blocked by
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self.blocking = {} # Which nodes are blocked by this node
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def get_input_info(self, unique_id, input_name):
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class_type = self.dynprompt.get_node(unique_id)["class_type"]
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class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
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return get_input_info(class_def, input_name)
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def make_input_strong_link(self, to_node_id, to_input):
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inputs = self.dynprompt.get_node(to_node_id)["inputs"]
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if to_input not in inputs:
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raise NodeInputError(f"Node {to_node_id} says it needs input {to_input}, but there is no input to that node at all")
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value = inputs[to_input]
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if not is_link(value):
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raise NodeInputError(f"Node {to_node_id} says it needs input {to_input}, but that value is a constant")
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from_node_id, from_socket = value
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self.add_strong_link(from_node_id, from_socket, to_node_id)
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def add_strong_link(self, from_node_id, from_socket, to_node_id):
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self.add_node(from_node_id)
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if to_node_id not in self.blocking[from_node_id]:
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self.blocking[from_node_id][to_node_id] = {}
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self.blockCount[to_node_id] += 1
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self.blocking[from_node_id][to_node_id][from_socket] = True
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def add_node(self, unique_id, include_lazy=False, subgraph_nodes=None):
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if unique_id in self.pendingNodes:
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return
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self.pendingNodes[unique_id] = True
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self.blockCount[unique_id] = 0
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self.blocking[unique_id] = {}
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inputs = self.dynprompt.get_node(unique_id)["inputs"]
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for input_name in inputs:
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value = inputs[input_name]
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if is_link(value):
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from_node_id, from_socket = value
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if subgraph_nodes is not None and from_node_id not in subgraph_nodes:
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continue
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input_type, input_category, input_info = self.get_input_info(unique_id, input_name)
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is_lazy = input_info is not None and "lazy" in input_info and input_info["lazy"]
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if include_lazy or not is_lazy:
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self.add_strong_link(from_node_id, from_socket, unique_id)
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def get_ready_nodes(self):
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return [node_id for node_id in self.pendingNodes if self.blockCount[node_id] == 0]
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def pop_node(self, unique_id):
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del self.pendingNodes[unique_id]
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for blocked_node_id in self.blocking[unique_id]:
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self.blockCount[blocked_node_id] -= 1
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del self.blocking[unique_id]
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def is_empty(self):
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return len(self.pendingNodes) == 0
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class ExecutionList(TopologicalSort):
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"""
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ExecutionList implements a topological dissolve of the graph. After a node is staged for execution,
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it can still be returned to the graph after having further dependencies added.
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"""
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def __init__(self, dynprompt, output_cache):
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super().__init__(dynprompt)
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self.output_cache = output_cache
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self.staged_node_id = None
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def add_strong_link(self, from_node_id, from_socket, to_node_id):
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if self.output_cache.get(from_node_id) is not None:
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# Nothing to do
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return
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super().add_strong_link(from_node_id, from_socket, to_node_id)
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def stage_node_execution(self):
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assert self.staged_node_id is None
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if self.is_empty():
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return None, None, None
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available = self.get_ready_nodes()
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if len(available) == 0:
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cycled_nodes = self.get_nodes_in_cycle()
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# Because cycles composed entirely of static nodes are caught during initial validation,
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# we will 'blame' the first node in the cycle that is not a static node.
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blamed_node = cycled_nodes[0]
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for node_id in cycled_nodes:
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display_node_id = self.dynprompt.get_display_node_id(node_id)
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if display_node_id != node_id:
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blamed_node = display_node_id
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break
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ex = DependencyCycleError("Dependency cycle detected")
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error_details = {
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"node_id": blamed_node,
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"exception_message": str(ex),
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"exception_type": "graph.DependencyCycleError",
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"traceback": [],
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"current_inputs": []
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}
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return None, error_details, ex
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next_node = available[0]
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# If an output node is available, do that first.
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# Technically this has no effect on the overall length of execution, but it feels better as a user
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# for a PreviewImage to display a result as soon as it can
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# Some other heuristics could probably be used here to improve the UX further.
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for node_id in available:
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class_type = self.dynprompt.get_node(node_id)["class_type"]
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class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
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if hasattr(class_def, 'OUTPUT_NODE') and class_def.OUTPUT_NODE == True:
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next_node = node_id
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break
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self.staged_node_id = next_node
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return self.staged_node_id, None, None
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def unstage_node_execution(self):
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assert self.staged_node_id is not None
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self.staged_node_id = None
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def complete_node_execution(self):
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node_id = self.staged_node_id
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self.pop_node(node_id)
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self.staged_node_id = None
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def get_nodes_in_cycle(self):
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# We'll dissolve the graph in reverse topological order to leave only the nodes in the cycle.
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# We're skipping some of the performance optimizations from the original TopologicalSort to keep
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# the code simple (and because having a cycle in the first place is a catastrophic error)
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blocked_by = { node_id: {} for node_id in self.pendingNodes }
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for from_node_id in self.blocking:
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for to_node_id in self.blocking[from_node_id]:
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if True in self.blocking[from_node_id][to_node_id].values():
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blocked_by[to_node_id][from_node_id] = True
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to_remove = [node_id for node_id in blocked_by if len(blocked_by[node_id]) == 0]
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while len(to_remove) > 0:
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for node_id in to_remove:
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for to_node_id in blocked_by:
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if node_id in blocked_by[to_node_id]:
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del blocked_by[to_node_id][node_id]
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del blocked_by[node_id]
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to_remove = [node_id for node_id in blocked_by if len(blocked_by[node_id]) == 0]
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return list(blocked_by.keys())
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class ExecutionBlocker:
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"""
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Return this from a node and any users will be blocked with the given error message.
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If the message is None, execution will be blocked silently instead.
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Generally, you should avoid using this functionality unless absolutely necessary. Whenever it's
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possible, a lazy input will be more efficient and have a better user experience.
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This functionality is useful in two cases:
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1. You want to conditionally prevent an output node from executing. (Particularly a built-in node
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like SaveImage. For your own output nodes, I would recommend just adding a BOOL input and using
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lazy evaluation to let it conditionally disable itself.)
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2. You have a node with multiple possible outputs, some of which are invalid and should not be used.
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(I would recommend not making nodes like this in the future -- instead, make multiple nodes with
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different outputs. Unfortunately, there are several popular existing nodes using this pattern.)
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"""
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def __init__(self, message):
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self.message = message
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