The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.
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import os
import sys
import copy
import json
import logging
import threading
import heapq
import traceback
import gc
import inspect
from typing import List, Literal, NamedTuple, Optional
import torch
import nodes
import comfy.model_management
def get_input_data(inputs, class_def, unique_id, outputs={}, prompt={}, extra_data={}):
valid_inputs = class_def.INPUT_TYPES()
input_data_all = {}
for x in inputs:
input_data = inputs[x]
if isinstance(input_data, list):
input_unique_id = input_data[0]
output_index = input_data[1]
if input_unique_id not in outputs:
input_data_all[x] = (None,)
continue
obj = outputs[input_unique_id][output_index]
input_data_all[x] = obj
else:
if ("required" in valid_inputs and x in valid_inputs["required"]) or ("optional" in valid_inputs and x in valid_inputs["optional"]):
input_data_all[x] = [input_data]
if "hidden" in valid_inputs:
h = valid_inputs["hidden"]
for x in h:
if h[x] == "PROMPT":
input_data_all[x] = [prompt]
if h[x] == "EXTRA_PNGINFO":
if "extra_pnginfo" in extra_data:
input_data_all[x] = [extra_data['extra_pnginfo']]
if h[x] == "UNIQUE_ID":
input_data_all[x] = [unique_id]
return input_data_all
def map_node_over_list(obj, input_data_all, func, allow_interrupt=False):
# check if node wants the lists
input_is_list = False
if hasattr(obj, "INPUT_IS_LIST"):
input_is_list = obj.INPUT_IS_LIST
if len(input_data_all) == 0:
max_len_input = 0
else:
max_len_input = max([len(x) for x in input_data_all.values()])
# get a slice of inputs, repeat last input when list isn't long enough
def slice_dict(d, i):
d_new = dict()
for k,v in d.items():
d_new[k] = v[i if len(v) > i else -1]
return d_new
results = []
if input_is_list:
if allow_interrupt:
nodes.before_node_execution()
results.append(getattr(obj, func)(**input_data_all))
elif max_len_input == 0:
if allow_interrupt:
nodes.before_node_execution()
results.append(getattr(obj, func)())
else:
for i in range(max_len_input):
if allow_interrupt:
nodes.before_node_execution()
results.append(getattr(obj, func)(**slice_dict(input_data_all, i)))
return results
def get_output_data(obj, input_data_all):
results = []
uis = []
return_values = map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True)
for r in return_values:
if isinstance(r, dict):
if 'ui' in r:
uis.append(r['ui'])
if 'result' in r:
results.append(r['result'])
else:
results.append(r)
output = []
if len(results) > 0:
# check which outputs need concatenating
output_is_list = [False] * len(results[0])
if hasattr(obj, "OUTPUT_IS_LIST"):
output_is_list = obj.OUTPUT_IS_LIST
# merge node execution results
for i, is_list in zip(range(len(results[0])), output_is_list):
if is_list:
output.append([x for o in results for x in o[i]])
else:
output.append([o[i] for o in results])
ui = dict()
if len(uis) > 0:
ui = {k: [y for x in uis for y in x[k]] for k in uis[0].keys()}
return output, ui
def format_value(x):
if x is None:
return None
elif isinstance(x, (int, float, bool, str)):
return x
else:
return str(x)
def recursive_execute(server, prompt, outputs, current_item, extra_data, executed, prompt_id, outputs_ui, object_storage):
unique_id = current_item
inputs = prompt[unique_id]['inputs']
class_type = prompt[unique_id]['class_type']
class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
if unique_id in outputs:
return (True, None, None)
for x in inputs:
input_data = inputs[x]
if isinstance(input_data, list):
input_unique_id = input_data[0]
output_index = input_data[1]
if input_unique_id not in outputs:
result = recursive_execute(server, prompt, outputs, input_unique_id, extra_data, executed, prompt_id, outputs_ui, object_storage)
if result[0] is not True:
# Another node failed further upstream
return result
input_data_all = None
try:
input_data_all = get_input_data(inputs, class_def, unique_id, outputs, prompt, extra_data)
if server.client_id is not None:
server.last_node_id = unique_id
server.send_sync("executing", { "node": unique_id, "prompt_id": prompt_id }, server.client_id)
obj = object_storage.get((unique_id, class_type), None)
if obj is None:
obj = class_def()
object_storage[(unique_id, class_type)] = obj
output_data, output_ui = get_output_data(obj, input_data_all)
outputs[unique_id] = output_data
if len(output_ui) > 0:
outputs_ui[unique_id] = output_ui
if server.client_id is not None:
server.send_sync("executed", { "node": unique_id, "output": output_ui, "prompt_id": prompt_id }, server.client_id)
except comfy.model_management.InterruptProcessingException as iex:
logging.info("Processing interrupted")
# skip formatting inputs/outputs
error_details = {
"node_id": unique_id,
}
return (False, error_details, iex)
except Exception as ex:
typ, _, tb = sys.exc_info()
exception_type = full_type_name(typ)
input_data_formatted = {}
if input_data_all is not None:
input_data_formatted = {}
for name, inputs in input_data_all.items():
input_data_formatted[name] = [format_value(x) for x in inputs]
output_data_formatted = {}
for node_id, node_outputs in outputs.items():
output_data_formatted[node_id] = [[format_value(x) for x in l] for l in node_outputs]
logging.error("!!! Exception during processing !!!")
logging.error(traceback.format_exc())
error_details = {
"node_id": unique_id,
"exception_message": str(ex),
"exception_type": exception_type,
"traceback": traceback.format_tb(tb),
"current_inputs": input_data_formatted,
"current_outputs": output_data_formatted
}
return (False, error_details, ex)
executed.add(unique_id)
return (True, None, None)
def recursive_will_execute(prompt, outputs, current_item):
unique_id = current_item
inputs = prompt[unique_id]['inputs']
will_execute = []
if unique_id in outputs:
return []
for x in inputs:
input_data = inputs[x]
if isinstance(input_data, list):
input_unique_id = input_data[0]
output_index = input_data[1]
if input_unique_id not in outputs:
will_execute += recursive_will_execute(prompt, outputs, input_unique_id)
return will_execute + [unique_id]
def recursive_output_delete_if_changed(prompt, old_prompt, outputs, current_item):
unique_id = current_item
inputs = prompt[unique_id]['inputs']
class_type = prompt[unique_id]['class_type']
class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
is_changed_old = ''
is_changed = ''
to_delete = False
if hasattr(class_def, 'IS_CHANGED'):
if unique_id in old_prompt and 'is_changed' in old_prompt[unique_id]:
is_changed_old = old_prompt[unique_id]['is_changed']
if 'is_changed' not in prompt[unique_id]:
input_data_all = get_input_data(inputs, class_def, unique_id, outputs)
if input_data_all is not None:
try:
#is_changed = class_def.IS_CHANGED(**input_data_all)
is_changed = map_node_over_list(class_def, input_data_all, "IS_CHANGED")
prompt[unique_id]['is_changed'] = is_changed
except:
to_delete = True
else:
is_changed = prompt[unique_id]['is_changed']
if unique_id not in outputs:
return True
if not to_delete:
if is_changed != is_changed_old:
to_delete = True
elif unique_id not in old_prompt:
to_delete = True
elif inputs == old_prompt[unique_id]['inputs']:
for x in inputs:
input_data = inputs[x]
if isinstance(input_data, list):
input_unique_id = input_data[0]
output_index = input_data[1]
if input_unique_id in outputs:
to_delete = recursive_output_delete_if_changed(prompt, old_prompt, outputs, input_unique_id)
else:
to_delete = True
if to_delete:
break
else:
to_delete = True
if to_delete:
d = outputs.pop(unique_id)
del d
return to_delete
class PromptExecutor:
def __init__(self, server):
self.server = server
self.reset()
def reset(self):
self.outputs = {}
self.object_storage = {}
self.outputs_ui = {}
self.status_messages = []
self.success = True
self.old_prompt = {}
def add_message(self, event, data, broadcast: bool):
self.status_messages.append((event, data))
if self.server.client_id is not None or broadcast:
self.server.send_sync(event, data, self.server.client_id)
def handle_execution_error(self, prompt_id, prompt, current_outputs, executed, error, ex):
node_id = error["node_id"]
class_type = prompt[node_id]["class_type"]
# First, send back the status to the frontend depending
# on the exception type
if isinstance(ex, comfy.model_management.InterruptProcessingException):
mes = {
"prompt_id": prompt_id,
"node_id": node_id,
"node_type": class_type,
"executed": list(executed),
}
self.add_message("execution_interrupted", mes, broadcast=True)
else:
mes = {
"prompt_id": prompt_id,
"node_id": node_id,
"node_type": class_type,
"executed": list(executed),
"exception_message": error["exception_message"],
"exception_type": error["exception_type"],
"traceback": error["traceback"],
"current_inputs": error["current_inputs"],
"current_outputs": error["current_outputs"],
}
self.add_message("execution_error", mes, broadcast=False)
# Next, remove the subsequent outputs since they will not be executed
to_delete = []
for o in self.outputs:
if (o not in current_outputs) and (o not in executed):
to_delete += [o]
if o in self.old_prompt:
d = self.old_prompt.pop(o)
del d
for o in to_delete:
d = self.outputs.pop(o)
del d
def execute(self, prompt, prompt_id, extra_data={}, execute_outputs=[]):
nodes.interrupt_processing(False)
if "client_id" in extra_data:
self.server.client_id = extra_data["client_id"]
else:
self.server.client_id = None
self.status_messages = []
self.add_message("execution_start", { "prompt_id": prompt_id}, broadcast=False)
with torch.inference_mode():
#delete cached outputs if nodes don't exist for them
to_delete = []
for o in self.outputs:
if o not in prompt:
to_delete += [o]
for o in to_delete:
d = self.outputs.pop(o)
del d
to_delete = []
for o in self.object_storage:
if o[0] not in prompt:
to_delete += [o]
else:
p = prompt[o[0]]
if o[1] != p['class_type']:
to_delete += [o]
for o in to_delete:
d = self.object_storage.pop(o)
del d
for x in prompt:
recursive_output_delete_if_changed(prompt, self.old_prompt, self.outputs, x)
current_outputs = set(self.outputs.keys())
for x in list(self.outputs_ui.keys()):
if x not in current_outputs:
d = self.outputs_ui.pop(x)
del d
comfy.model_management.cleanup_models()
self.add_message("execution_cached",
{ "nodes": list(current_outputs) , "prompt_id": prompt_id},
broadcast=False)
executed = set()
output_node_id = None
to_execute = []
for node_id in list(execute_outputs):
to_execute += [(0, node_id)]
while len(to_execute) > 0:
#always execute the output that depends on the least amount of unexecuted nodes first
to_execute = sorted(list(map(lambda a: (len(recursive_will_execute(prompt, self.outputs, a[-1])), a[-1]), to_execute)))
output_node_id = to_execute.pop(0)[-1]
# This call shouldn't raise anything if there's an error deep in
# the actual SD code, instead it will report the node where the
# error was raised
self.success, error, ex = recursive_execute(self.server, prompt, self.outputs, output_node_id, extra_data, executed, prompt_id, self.outputs_ui, self.object_storage)
if self.success is not True:
self.handle_execution_error(prompt_id, prompt, current_outputs, executed, error, ex)
break
for x in executed:
self.old_prompt[x] = copy.deepcopy(prompt[x])
self.server.last_node_id = None
if comfy.model_management.DISABLE_SMART_MEMORY:
comfy.model_management.unload_all_models()
def validate_inputs(prompt, item, validated):
unique_id = item
if unique_id in validated:
return validated[unique_id]
inputs = prompt[unique_id]['inputs']
class_type = prompt[unique_id]['class_type']
obj_class = nodes.NODE_CLASS_MAPPINGS[class_type]
class_inputs = obj_class.INPUT_TYPES()
required_inputs = class_inputs['required']
errors = []
valid = True
validate_function_inputs = []
if hasattr(obj_class, "VALIDATE_INPUTS"):
validate_function_inputs = inspect.getfullargspec(obj_class.VALIDATE_INPUTS).args
for x in required_inputs:
if x not in inputs:
error = {
"type": "required_input_missing",
"message": "Required input is missing",
"details": f"{x}",
"extra_info": {
"input_name": x
}
}
errors.append(error)
continue
val = inputs[x]
info = required_inputs[x]
type_input = info[0]
if isinstance(val, list):
if len(val) != 2:
error = {
"type": "bad_linked_input",
"message": "Bad linked input, must be a length-2 list of [node_id, slot_index]",
"details": f"{x}",
"extra_info": {
"input_name": x,
"input_config": info,
"received_value": val
}
}
errors.append(error)
continue
o_id = val[0]
o_class_type = prompt[o_id]['class_type']
r = nodes.NODE_CLASS_MAPPINGS[o_class_type].RETURN_TYPES
if r[val[1]] != type_input:
received_type = r[val[1]]
details = f"{x}, {received_type} != {type_input}"
error = {
"type": "return_type_mismatch",
"message": "Return type mismatch between linked nodes",
"details": details,
"extra_info": {
"input_name": x,
"input_config": info,
"received_type": received_type,
"linked_node": val
}
}
errors.append(error)
continue
try:
r = validate_inputs(prompt, o_id, validated)
if r[0] is False:
# `r` will be set in `validated[o_id]` already
valid = False
continue
except Exception as ex:
typ, _, tb = sys.exc_info()
valid = False
exception_type = full_type_name(typ)
reasons = [{
"type": "exception_during_inner_validation",
"message": "Exception when validating inner node",
"details": str(ex),
"extra_info": {
"input_name": x,
"input_config": info,
"exception_message": str(ex),
"exception_type": exception_type,
"traceback": traceback.format_tb(tb),
"linked_node": val
}
}]
validated[o_id] = (False, reasons, o_id)
continue
else:
try:
if type_input == "INT":
val = int(val)
inputs[x] = val
if type_input == "FLOAT":
val = float(val)
inputs[x] = val
if type_input == "STRING":
val = str(val)
inputs[x] = val
except Exception as ex:
error = {
"type": "invalid_input_type",
"message": f"Failed to convert an input value to a {type_input} value",
"details": f"{x}, {val}, {ex}",
"extra_info": {
"input_name": x,
"input_config": info,
"received_value": val,
"exception_message": str(ex)
}
}
errors.append(error)
continue
if len(info) > 1:
if "min" in info[1] and val < info[1]["min"]:
error = {
"type": "value_smaller_than_min",
"message": "Value {} smaller than min of {}".format(val, info[1]["min"]),
"details": f"{x}",
"extra_info": {
"input_name": x,
"input_config": info,
"received_value": val,
}
}
errors.append(error)
continue
if "max" in info[1] and val > info[1]["max"]:
error = {
"type": "value_bigger_than_max",
"message": "Value {} bigger than max of {}".format(val, info[1]["max"]),
"details": f"{x}",
"extra_info": {
"input_name": x,
"input_config": info,
"received_value": val,
}
}
errors.append(error)
continue
if x not in validate_function_inputs:
if isinstance(type_input, list):
if val not in type_input:
input_config = info
list_info = ""
# Don't send back gigantic lists like if they're lots of
# scanned model filepaths
if len(type_input) > 20:
list_info = f"(list of length {len(type_input)})"
input_config = None
else:
list_info = str(type_input)
error = {
"type": "value_not_in_list",
"message": "Value not in list",
"details": f"{x}: '{val}' not in {list_info}",
"extra_info": {
"input_name": x,
"input_config": input_config,
"received_value": val,
}
}
errors.append(error)
continue
if len(validate_function_inputs) > 0:
input_data_all = get_input_data(inputs, obj_class, unique_id)
input_filtered = {}
for x in input_data_all:
if x in validate_function_inputs:
input_filtered[x] = input_data_all[x]
#ret = obj_class.VALIDATE_INPUTS(**input_filtered)
ret = map_node_over_list(obj_class, input_filtered, "VALIDATE_INPUTS")
for x in input_filtered:
for i, r in enumerate(ret):
if r is not True:
details = f"{x}"
if r is not False:
details += f" - {str(r)}"
error = {
"type": "custom_validation_failed",
"message": "Custom validation failed for node",
"details": details,
"extra_info": {
"input_name": x,
"input_config": info,
"received_value": val,
}
}
errors.append(error)
continue
if len(errors) > 0 or valid is not True:
ret = (False, errors, unique_id)
else:
ret = (True, [], unique_id)
validated[unique_id] = ret
return ret
def full_type_name(klass):
module = klass.__module__
if module == 'builtins':
return klass.__qualname__
return module + '.' + klass.__qualname__
def validate_prompt(prompt):
outputs = set()
for x in prompt:
class_ = nodes.NODE_CLASS_MAPPINGS[prompt[x]['class_type']]
if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE == True:
outputs.add(x)
if len(outputs) == 0:
error = {
"type": "prompt_no_outputs",
"message": "Prompt has no outputs",
"details": "",
"extra_info": {}
}
return (False, error, [], [])
good_outputs = set()
errors = []
node_errors = {}
validated = {}
for o in outputs:
valid = False
reasons = []
try:
m = validate_inputs(prompt, o, validated)
valid = m[0]
reasons = m[1]
except Exception as ex:
typ, _, tb = sys.exc_info()
valid = False
exception_type = full_type_name(typ)
reasons = [{
"type": "exception_during_validation",
"message": "Exception when validating node",
"details": str(ex),
"extra_info": {
"exception_type": exception_type,
"traceback": traceback.format_tb(tb)
}
}]
validated[o] = (False, reasons, o)
if valid is True:
good_outputs.add(o)
else:
logging.error(f"Failed to validate prompt for output {o}:")
if len(reasons) > 0:
logging.error("* (prompt):")
for reason in reasons:
logging.error(f" - {reason['message']}: {reason['details']}")
errors += [(o, reasons)]
for node_id, result in validated.items():
valid = result[0]
reasons = result[1]
# If a node upstream has errors, the nodes downstream will also
# be reported as invalid, but there will be no errors attached.
# So don't return those nodes as having errors in the response.
if valid is not True and len(reasons) > 0:
if node_id not in node_errors:
class_type = prompt[node_id]['class_type']
node_errors[node_id] = {
"errors": reasons,
"dependent_outputs": [],
"class_type": class_type
}
logging.error(f"* {class_type} {node_id}:")
for reason in reasons:
logging.error(f" - {reason['message']}: {reason['details']}")
node_errors[node_id]["dependent_outputs"].append(o)
logging.error("Output will be ignored")
if len(good_outputs) == 0:
errors_list = []
for o, errors in errors:
for error in errors:
errors_list.append(f"{error['message']}: {error['details']}")
errors_list = "\n".join(errors_list)
error = {
"type": "prompt_outputs_failed_validation",
"message": "Prompt outputs failed validation",
"details": errors_list,
"extra_info": {}
}
return (False, error, list(good_outputs), node_errors)
return (True, None, list(good_outputs), node_errors)
MAXIMUM_HISTORY_SIZE = 10000
class PromptQueue:
def __init__(self, server):
self.server = server
self.mutex = threading.RLock()
self.not_empty = threading.Condition(self.mutex)
self.task_counter = 0
self.queue = []
self.currently_running = {}
self.history = {}
self.flags = {}
server.prompt_queue = self
def put(self, item):
with self.mutex:
heapq.heappush(self.queue, item)
self.server.queue_updated()
self.not_empty.notify()
def get(self, timeout=None):
with self.not_empty:
while len(self.queue) == 0:
self.not_empty.wait(timeout=timeout)
if timeout is not None and len(self.queue) == 0:
return None
item = heapq.heappop(self.queue)
i = self.task_counter
self.currently_running[i] = copy.deepcopy(item)
self.task_counter += 1
self.server.queue_updated()
return (item, i)
class ExecutionStatus(NamedTuple):
status_str: Literal['success', 'error']
completed: bool
messages: List[str]
def task_done(self, item_id, outputs,
status: Optional['PromptQueue.ExecutionStatus']):
with self.mutex:
prompt = self.currently_running.pop(item_id)
if len(self.history) > MAXIMUM_HISTORY_SIZE:
self.history.pop(next(iter(self.history)))
status_dict: dict|None = None
if status is not None:
status_dict = copy.deepcopy(status._asdict())
self.history[prompt[1]] = {
"prompt": prompt,
"outputs": copy.deepcopy(outputs),
'status': status_dict,
}
self.server.queue_updated()
def get_current_queue(self):
with self.mutex:
out = []
for x in self.currently_running.values():
out += [x]
return (out, copy.deepcopy(self.queue))
def get_tasks_remaining(self):
with self.mutex:
return len(self.queue) + len(self.currently_running)
def wipe_queue(self):
with self.mutex:
self.queue = []
self.server.queue_updated()
def delete_queue_item(self, function):
with self.mutex:
for x in range(len(self.queue)):
if function(self.queue[x]):
if len(self.queue) == 1:
self.wipe_queue()
else:
self.queue.pop(x)
heapq.heapify(self.queue)
self.server.queue_updated()
return True
return False
def get_history(self, prompt_id=None, max_items=None, offset=-1):
with self.mutex:
if prompt_id is None:
out = {}
i = 0
if offset < 0 and max_items is not None:
offset = len(self.history) - max_items
for k in self.history:
if i >= offset:
out[k] = self.history[k]
if max_items is not None and len(out) >= max_items:
break
i += 1
return out
elif prompt_id in self.history:
return {prompt_id: copy.deepcopy(self.history[prompt_id])}
else:
return {}
def wipe_history(self):
with self.mutex:
self.history = {}
def delete_history_item(self, id_to_delete):
with self.mutex:
self.history.pop(id_to_delete, None)
def set_flag(self, name, data):
with self.mutex:
self.flags[name] = data
self.not_empty.notify()
def get_flags(self, reset=True):
with self.mutex:
if reset:
ret = self.flags
self.flags = {}
return ret
else:
return self.flags.copy()