import sys import copy import logging import threading import heapq import time import traceback 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": input_data_all[x] = [extra_data.get('extra_pnginfo', None)] 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(f"!!! Exception during processing!!! {ex}") 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 } if isinstance(ex, comfy.model_management.OOM_EXCEPTION): logging.error("Got an OOM, unloading all loaded models.") comfy.model_management.unload_all_models() return (False, error_details, ex) executed.add(unique_id) return (True, None, None) def recursive_will_execute(prompt, outputs, current_item, memo={}): unique_id = current_item if unique_id in memo: return memo[unique_id] 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, memo) memo[unique_id] = will_execute + [unique_id] return memo[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 class_type != old_prompt[unique_id]['class_type']: 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: dict, broadcast: bool): data = { **data, "timestamp": int(time.time() * 1000), } 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(keep_clone_weights_loaded=True) 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 memo = {} to_execute = sorted(list(map(lambda a: (len(recursive_will_execute(prompt, self.outputs, a[-1], memo)), 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 else: # Only execute when the while-loop ends without break self.add_message("execution_success", { "prompt_id": prompt_id }, broadcast=False) 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: if 'class_type' not in prompt[x]: error = { "type": "invalid_prompt", "message": f"Cannot execute because a node is missing the class_type property.", "details": f"Node ID '#{x}'", "extra_info": {} } return (False, error, [], []) class_type = prompt[x]['class_type'] class_ = nodes.NODE_CLASS_MAPPINGS.get(class_type, None) if class_ is None: error = { "type": "invalid_prompt", "message": f"Cannot execute because node {class_type} does not exist.", "details": f"Node ID '#{x}'", "extra_info": {} } return (False, error, [], []) if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE is 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: Optional[dict] = 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()