mirror of
https://github.com/comfyanonymous/ComfyUI.git
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Add a system where an input is marked as a cache barrier, deferring its evaluation. Once the node is executed, the barrier is released and everything behind the barrier is executed at increase priority.
372 lines
15 KiB
Python
372 lines
15 KiB
Python
from __future__ import annotations
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from typing import Type, Literal
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import nodes
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import asyncio
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import inspect
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from comfy_execution.graph_utils import is_link, ExecutionBlocker
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from comfy.comfy_types.node_typing import ComfyNodeABC, InputTypeDict, InputTypeOptions
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# NOTE: ExecutionBlocker code got moved to graph_utils.py to prevent torch being imported too soon during unit tests
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ExecutionBlocker = ExecutionBlocker
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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(
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class_def: Type[ComfyNodeABC],
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input_name: str,
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valid_inputs: InputTypeDict | None = None
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) -> tuple[str, Literal["required", "optional", "hidden"], InputTypeOptions] | tuple[None, None, None]:
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"""Get the input type, category, and extra info for a given input name.
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Arguments:
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class_def: The class definition of the node.
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input_name: The name of the input to get info for.
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valid_inputs: The valid inputs for the node, or None to use the class_def.INPUT_TYPES().
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Returns:
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tuple[str, str, dict] | tuple[None, None, None]: The input type, category, and extra info for the input name.
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"""
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valid_inputs = valid_inputs or 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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self.externalBlocks = 0
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self.unblockedEvent = asyncio.Event()
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self.priorities = {}
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self.barrierNodes = set()
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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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if not self.is_cached(from_node_id):
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self.add_node(from_node_id, priority=self.priorities.get(to_node_id, 0))
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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 is_barrier(self, node_id):
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return node_id in self.barrierNodes
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def unbarrier(self, node_id):
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if not node_id in self.barrierNodes:
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return
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self.barrierNodes.remove(node_id)
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self.priorities[node_id] = self.priorities.get(node_id, 0) + 1
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links = []
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inputs = self.dynprompt.get_node(node_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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_, _, input_info = self.get_input_info(node_id, input_name)
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is_barrier = input_info is not None and "cache-barrier" in input_info and input_info["cache-barrier"]
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if is_barrier:
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links.append((from_node_id, from_socket, node_id))
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for link in links:
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self.add_strong_link(*link)
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def add_node(self, node_unique_id, include_lazy=False, subgraph_nodes=None, priority=0):
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node_ids = [node_unique_id]
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links = []
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while len(node_ids) > 0:
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unique_id = node_ids.pop()
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if unique_id in self.pendingNodes:
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continue
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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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self.priorities[unique_id] = priority
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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_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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is_barrier = input_info is not None and "cache-barrier" in input_info and input_info["cache-barrier"]
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if (include_lazy or (not is_lazy and not is_barrier)):
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if not self.is_cached(from_node_id):
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node_ids.append(from_node_id)
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links.append((from_node_id, from_socket, unique_id))
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if is_barrier:
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self.barrierNodes.add(unique_id)
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for link in links:
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self.add_strong_link(*link)
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def add_external_block(self, node_id):
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assert node_id in self.blockCount, "Can't add external block to a node that isn't pending"
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self.externalBlocks += 1
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self.blockCount[node_id] += 1
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def unblock():
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self.externalBlocks -= 1
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self.blockCount[node_id] -= 1
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self.unblockedEvent.set()
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return unblock
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def is_cached(self, node_id):
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return False
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def get_ready_nodes(self):
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return [(self.priorities.get(node_id, 0), 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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self.execution_cache = {}
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self.execution_cache_listeners = {}
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def is_cached(self, node_id):
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return self.output_cache.get(node_id) is not None
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def cache_link(self, from_node_id, to_node_id):
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if not to_node_id in self.execution_cache:
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self.execution_cache[to_node_id] = {}
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self.execution_cache[to_node_id][from_node_id] = self.output_cache.get(from_node_id)
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if not from_node_id in self.execution_cache_listeners:
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self.execution_cache_listeners[from_node_id] = set()
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self.execution_cache_listeners[from_node_id].add(to_node_id)
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def get_cache(self, from_node_id, to_node_id):
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if not to_node_id in self.execution_cache:
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return None
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value = self.execution_cache[to_node_id].get(from_node_id)
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if value is None:
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return None
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#Write back to the main cache on touch.
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self.output_cache.set(from_node_id, value)
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return value
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def cache_update(self, node_id, value):
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if node_id in self.execution_cache_listeners:
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for to_node_id in self.execution_cache_listeners[node_id]:
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if to_node_id in self.execution_cache:
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self.execution_cache[to_node_id][node_id] = value
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def add_strong_link(self, from_node_id, from_socket, to_node_id):
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super().add_strong_link(from_node_id, from_socket, to_node_id)
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self.cache_link(from_node_id, to_node_id)
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async 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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while len(available) == 0 and self.externalBlocks > 0:
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# Wait for an external block to be released
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await self.unblockedEvent.wait()
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self.unblockedEvent.clear()
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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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self.staged_node_id = self.ux_friendly_pick_node(available)
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return self.staged_node_id, None, None
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def ux_friendly_pick_node(self, node_list):
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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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def is_output(node_id):
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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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return True
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return False
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# If an available node is async, do that first.
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# This will execute the asynchronous function earlier, reducing the overall time.
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def is_async(node_id):
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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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return inspect.iscoroutinefunction(getattr(class_def, class_def.FUNCTION))
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priority_level = 0
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priority_nodes = []
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for (priority, node_id) in node_list:
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if priority > priority_level:
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priority_level = priority
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priority_nodes = []
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if priority == priority_level:
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priority_nodes.append(node_id)
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for node_id in priority_nodes:
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if is_output(node_id) or is_async(node_id):
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return node_id
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#This should handle the VAEDecode -> preview case
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for node_id in priority_nodes:
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for blocked_node_id in self.blocking[node_id]:
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if is_output(blocked_node_id):
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return node_id
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#This should handle the VAELoader -> VAEDecode -> preview case
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for node_id in priority_nodes:
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for blocked_node_id in self.blocking[node_id]:
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for blocked_node_id1 in self.blocking[blocked_node_id]:
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if is_output(blocked_node_id1):
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return node_id
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#TODO: this function should be improved
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return priority_nodes[0]
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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.execution_cache.pop(node_id, None)
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self.execution_cache_listeners.pop(node_id, None)
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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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