The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.
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import nodes
from comfy.graph_utils import is_link
class DynamicPrompt:
def __init__(self, original_prompt):
# The original prompt provided by the user
self.original_prompt = original_prompt
# 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]
return None
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_input_info(class_def, input_name):
valid_inputs = class_def.INPUT_TYPES()
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):
class_type = self.dynprompt.get_node(unique_id)["class_type"]
class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
return get_input_info(class_def, 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 Exception("Node %s says it needs input %s, but there is no input to that node at all" % (to_node_id, to_input))
value = inputs[to_input]
if not is_link(value):
raise Exception("Node %s says it needs input %s, but that value is a constant" % (to_node_id, to_input))
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):
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, unique_id, include_lazy=False, subgraph_nodes=None):
if unique_id in self.pendingNodes:
return
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 = "lazy" in input_info and input_info["lazy"]
if include_lazy or not is_lazy:
self.add_strong_link(from_node_id, from_socket, unique_id)
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
# 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.
class ExecutionList(TopologicalSort):
def __init__(self, dynprompt, output_cache):
super().__init__(dynprompt)
self.output_cache = output_cache
self.staged_node_id = None
def add_strong_link(self, from_node_id, from_socket, to_node_id):
if self.output_cache.get(from_node_id) is not None:
# Nothing to do
return
super().add_strong_link(from_node_id, from_socket, to_node_id)
def stage_node_execution(self):
assert self.staged_node_id is None
if self.is_empty():
return None
available = self.get_ready_nodes()
if len(available) == 0:
raise Exception("Dependency cycle detected")
next_node = available[0]
# 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.
for node_id in available:
class_type = self.dynprompt.get_node(node_id)["class_type"]
class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
if hasattr(class_def, 'OUTPUT_NODE') and class_def.OUTPUT_NODE == True:
next_node = node_id
break
self.staged_node_id = next_node
return self.staged_node_id
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
# Return this from a node and any users will be blocked with the given error message.
class ExecutionBlocker:
def __init__(self, message):
self.message = message