import copy import heapq import inspect import logging import sys import threading import time import traceback from enum import Enum from typing import List, Literal, NamedTuple, Optional, Union import asyncio import torch from comfy.cli_args import args import comfy.memory_management import comfy.model_management import comfy_aimdo.model_vbar from latent_preview import set_preview_method import nodes from comfy_execution.caching import ( BasicCache, CacheKeySetID, CacheKeySetInputSignature, NullCache, HierarchicalCache, LRUCache, RAMPressureCache, ) from comfy_execution.graph import ( DynamicPrompt, ExecutionBlocker, ExecutionList, get_input_info, ) from comfy_execution.graph_utils import GraphBuilder, is_link from comfy_execution.validation import validate_node_input from comfy_execution.progress import get_progress_state, reset_progress_state, add_progress_handler, WebUIProgressHandler from comfy_execution.utils import CurrentNodeContext from comfy_api.internal import _ComfyNodeInternal, _NodeOutputInternal, first_real_override, is_class, make_locked_method_func from comfy_api.latest import io, _io from comfy_execution.cache_provider import _has_cache_providers, _get_cache_providers, _logger as _cache_logger class ExecutionResult(Enum): SUCCESS = 0 FAILURE = 1 PENDING = 2 class DuplicateNodeError(Exception): pass class IsChangedCache: def __init__(self, prompt_id: str, dynprompt: DynamicPrompt, outputs_cache: BasicCache): self.prompt_id = prompt_id self.dynprompt = dynprompt self.outputs_cache = outputs_cache self.is_changed = {} async def get(self, node_id): if node_id in self.is_changed: return self.is_changed[node_id] node = self.dynprompt.get_node(node_id) class_type = node["class_type"] class_def = nodes.NODE_CLASS_MAPPINGS[class_type] has_is_changed = False is_changed_name = None if issubclass(class_def, _ComfyNodeInternal) and first_real_override(class_def, "fingerprint_inputs") is not None: has_is_changed = True is_changed_name = "fingerprint_inputs" elif hasattr(class_def, "IS_CHANGED"): has_is_changed = True is_changed_name = "IS_CHANGED" if not has_is_changed: self.is_changed[node_id] = False return self.is_changed[node_id] if "is_changed" in node: self.is_changed[node_id] = node["is_changed"] return self.is_changed[node_id] # Intentionally do not use cached outputs here. We only want constants in IS_CHANGED input_data_all, _, v3_data = get_input_data(node["inputs"], class_def, node_id, None) try: is_changed = await _async_map_node_over_list(self.prompt_id, node_id, class_def, input_data_all, is_changed_name, v3_data=v3_data) is_changed = await resolve_map_node_over_list_results(is_changed) node["is_changed"] = [None if isinstance(x, ExecutionBlocker) else x for x in is_changed] except Exception as e: logging.warning("WARNING: {}".format(e)) node["is_changed"] = float("NaN") finally: self.is_changed[node_id] = node["is_changed"] return self.is_changed[node_id] class CacheEntry(NamedTuple): ui: dict outputs: list class CacheType(Enum): CLASSIC = 0 LRU = 1 NONE = 2 RAM_PRESSURE = 3 class CacheSet: def __init__(self, cache_type=None, cache_args={}): if cache_type == CacheType.NONE: self.init_null_cache() logging.info("Disabling intermediate node cache.") elif cache_type == CacheType.RAM_PRESSURE: cache_ram = cache_args.get("ram", 16.0) self.init_ram_cache(cache_ram) logging.info("Using RAM pressure cache.") elif cache_type == CacheType.LRU: cache_size = cache_args.get("lru", 0) self.init_lru_cache(cache_size) logging.info("Using LRU cache") else: self.init_classic_cache() self.all = [self.outputs, self.objects] # Performs like the old cache -- dump data ASAP def init_classic_cache(self): self.outputs = HierarchicalCache(CacheKeySetInputSignature, enable_providers=True) self.objects = HierarchicalCache(CacheKeySetID) def init_lru_cache(self, cache_size): self.outputs = LRUCache(CacheKeySetInputSignature, max_size=cache_size, enable_providers=True) self.objects = HierarchicalCache(CacheKeySetID) def init_ram_cache(self, min_headroom): self.outputs = RAMPressureCache(CacheKeySetInputSignature, enable_providers=True) self.objects = HierarchicalCache(CacheKeySetID) def init_null_cache(self): self.outputs = NullCache() self.objects = NullCache() def recursive_debug_dump(self): result = { "outputs": self.outputs.recursive_debug_dump(), } return result SENSITIVE_EXTRA_DATA_KEYS = ("auth_token_comfy_org", "api_key_comfy_org") def get_input_data(inputs, class_def, unique_id, execution_list=None, dynprompt=None, extra_data={}): is_v3 = issubclass(class_def, _ComfyNodeInternal) v3_data: io.V3Data = {} hidden_inputs_v3 = {} valid_inputs = class_def.INPUT_TYPES() if is_v3: valid_inputs, hidden, v3_data = _io.get_finalized_class_inputs(valid_inputs, inputs) input_data_all = {} missing_keys = {} for x in inputs: input_data = inputs[x] _, input_category, input_info = get_input_info(class_def, x, valid_inputs) def mark_missing(): missing_keys[x] = True input_data_all[x] = (None,) if is_link(input_data) and (not input_info or not input_info.get("rawLink", False)): input_unique_id = input_data[0] output_index = input_data[1] if execution_list is None: mark_missing() continue # This might be a lazily-evaluated input cached = execution_list.get_cache(input_unique_id, unique_id) if cached is None or cached.outputs is None: mark_missing() continue if output_index >= len(cached.outputs): mark_missing() continue obj = cached.outputs[output_index] input_data_all[x] = obj elif input_category is not None or (is_v3 and class_def.ACCEPT_ALL_INPUTS): input_data_all[x] = [input_data] if is_v3: if hidden is not None: if io.Hidden.prompt.name in hidden: hidden_inputs_v3[io.Hidden.prompt] = dynprompt.get_original_prompt() if dynprompt is not None else {} if io.Hidden.dynprompt.name in hidden: hidden_inputs_v3[io.Hidden.dynprompt] = dynprompt if io.Hidden.extra_pnginfo.name in hidden: hidden_inputs_v3[io.Hidden.extra_pnginfo] = extra_data.get('extra_pnginfo', None) if io.Hidden.unique_id.name in hidden: hidden_inputs_v3[io.Hidden.unique_id] = unique_id if io.Hidden.auth_token_comfy_org.name in hidden: hidden_inputs_v3[io.Hidden.auth_token_comfy_org] = extra_data.get("auth_token_comfy_org", None) if io.Hidden.api_key_comfy_org.name in hidden: hidden_inputs_v3[io.Hidden.api_key_comfy_org] = extra_data.get("api_key_comfy_org", None) else: if "hidden" in valid_inputs: h = valid_inputs["hidden"] for x in h: if h[x] == "PROMPT": input_data_all[x] = [dynprompt.get_original_prompt() if dynprompt is not None else {}] if h[x] == "DYNPROMPT": input_data_all[x] = [dynprompt] 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] if h[x] == "AUTH_TOKEN_COMFY_ORG": input_data_all[x] = [extra_data.get("auth_token_comfy_org", None)] if h[x] == "API_KEY_COMFY_ORG": input_data_all[x] = [extra_data.get("api_key_comfy_org", None)] v3_data["hidden_inputs"] = hidden_inputs_v3 return input_data_all, missing_keys, v3_data map_node_over_list = None #Don't hook this please async def resolve_map_node_over_list_results(results): remaining = [x for x in results if isinstance(x, asyncio.Task) and not x.done()] if len(remaining) == 0: return [x.result() if isinstance(x, asyncio.Task) else x for x in results] else: done, pending = await asyncio.wait(remaining) for task in done: exc = task.exception() if exc is not None: raise exc return [x.result() if isinstance(x, asyncio.Task) else x for x in results] async def _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None, v3_data=None): # check if node wants the lists input_is_list = getattr(obj, "INPUT_IS_LIST", False) 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): return {k: v[i if len(v) > i else -1] for k, v in d.items()} results = [] async def process_inputs(inputs, index=None, input_is_list=False): if allow_interrupt: nodes.before_node_execution() execution_block = None for k, v in inputs.items(): if input_is_list: for e in v: if isinstance(e, ExecutionBlocker): v = e break if isinstance(v, ExecutionBlocker): execution_block = execution_block_cb(v) if execution_block_cb else v break if execution_block is None: if pre_execute_cb is not None and index is not None: pre_execute_cb(index) # V3 if isinstance(obj, _ComfyNodeInternal) or (is_class(obj) and issubclass(obj, _ComfyNodeInternal)): # if is just a class, then assign no state, just create clone if is_class(obj): type_obj = obj obj.VALIDATE_CLASS() class_clone = obj.PREPARE_CLASS_CLONE(v3_data) # otherwise, use class instance to populate/reuse some fields else: type_obj = type(obj) type_obj.VALIDATE_CLASS() class_clone = type_obj.PREPARE_CLASS_CLONE(v3_data) f = make_locked_method_func(type_obj, func, class_clone) # in case of dynamic inputs, restructure inputs to expected nested dict if v3_data is not None: inputs = _io.build_nested_inputs(inputs, v3_data) # V1 else: f = getattr(obj, func) if inspect.iscoroutinefunction(f): async def async_wrapper(f, prompt_id, unique_id, list_index, args): with CurrentNodeContext(prompt_id, unique_id, list_index): return await f(**args) task = asyncio.create_task(async_wrapper(f, prompt_id, unique_id, index, args=inputs)) # Give the task a chance to execute without yielding await asyncio.sleep(0) if task.done(): result = task.result() results.append(result) else: results.append(task) else: with CurrentNodeContext(prompt_id, unique_id, index): result = f(**inputs) results.append(result) else: results.append(execution_block) if input_is_list: await process_inputs(input_data_all, 0, input_is_list=input_is_list) elif max_len_input == 0: await process_inputs({}) else: for i in range(max_len_input): input_dict = slice_dict(input_data_all, i) await process_inputs(input_dict, i) return results def merge_result_data(results, obj): # check which outputs need concatenating output = [] 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: value = [] for o in results: if isinstance(o[i], ExecutionBlocker): value.append(o[i]) else: value.extend(o[i]) output.append(value) else: output.append([o[i] for o in results]) return output async def get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=None, pre_execute_cb=None, v3_data=None): return_values = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data) has_pending_task = any(isinstance(r, asyncio.Task) and not r.done() for r in return_values) if has_pending_task: return return_values, {}, False, has_pending_task output, ui, has_subgraph = get_output_from_returns(return_values, obj) return output, ui, has_subgraph, False def get_output_from_returns(return_values, obj): results = [] uis = [] subgraph_results = [] has_subgraph = False for i in range(len(return_values)): r = return_values[i] if isinstance(r, dict): if 'ui' in r: uis.append(r['ui']) if 'expand' in r: # Perform an expansion, but do not append results has_subgraph = True new_graph = r['expand'] result = r.get("result", None) if isinstance(result, ExecutionBlocker): result = tuple([result] * len(obj.RETURN_TYPES)) subgraph_results.append((new_graph, result)) elif 'result' in r: result = r.get("result", None) if isinstance(result, ExecutionBlocker): result = tuple([result] * len(obj.RETURN_TYPES)) results.append(result) subgraph_results.append((None, result)) elif isinstance(r, _NodeOutputInternal): # V3 if r.ui is not None: if isinstance(r.ui, dict): uis.append(r.ui) else: uis.append(r.ui.as_dict()) if r.expand is not None: has_subgraph = True new_graph = r.expand result = r.result if r.block_execution is not None: result = tuple([ExecutionBlocker(r.block_execution)] * len(obj.RETURN_TYPES)) subgraph_results.append((new_graph, result)) elif r.result is not None: result = r.result if r.block_execution is not None: result = tuple([ExecutionBlocker(r.block_execution)] * len(obj.RETURN_TYPES)) results.append(result) subgraph_results.append((None, result)) else: if isinstance(r, ExecutionBlocker): r = tuple([r] * len(obj.RETURN_TYPES)) results.append(r) subgraph_results.append((None, r)) if has_subgraph: output = subgraph_results elif len(results) > 0: output = merge_result_data(results, obj) else: output = [] ui = dict() # TODO: Think there's an existing bug here # If we're performing a subgraph expansion, we probably shouldn't be returning UI values yet. # They'll get cached without the completed subgraphs. It's an edge case and I'm not aware of # any nodes that use both subgraph expansion and custom UI outputs, but might be a problem in the future. 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, has_subgraph def format_value(x): if x is None: return None elif isinstance(x, (int, float, bool, str)): return x else: return str(x) def _is_intermediate_output(dynprompt, node_id): class_type = dynprompt.get_node(node_id)["class_type"] class_def = nodes.NODE_CLASS_MAPPINGS[class_type] return getattr(class_def, 'HAS_INTERMEDIATE_OUTPUT', False) def _send_cached_ui(server, node_id, display_node_id, cached, prompt_id, ui_outputs): if server.client_id is None: return cached_ui = cached.ui or {} server.send_sync("executed", { "node": node_id, "display_node": display_node_id, "output": cached_ui.get("output", None), "prompt_id": prompt_id }, server.client_id) if cached.ui is not None: ui_outputs[node_id] = cached.ui async def execute(server, dynprompt, caches, current_item, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes, ui_outputs): unique_id = current_item real_node_id = dynprompt.get_real_node_id(unique_id) display_node_id = dynprompt.get_display_node_id(unique_id) parent_node_id = dynprompt.get_parent_node_id(unique_id) inputs = dynprompt.get_node(unique_id)['inputs'] class_type = dynprompt.get_node(unique_id)['class_type'] class_def = nodes.NODE_CLASS_MAPPINGS[class_type] cached = await caches.outputs.get(unique_id) if cached is not None: _send_cached_ui(server, unique_id, display_node_id, cached, prompt_id, ui_outputs) get_progress_state().finish_progress(unique_id) execution_list.cache_update(unique_id, cached) return (ExecutionResult.SUCCESS, None, None) input_data_all = None try: if unique_id in pending_async_nodes: results = [] for r in pending_async_nodes[unique_id]: if isinstance(r, asyncio.Task): try: results.append(r.result()) except Exception as ex: # An async task failed - propagate the exception up del pending_async_nodes[unique_id] raise ex else: results.append(r) del pending_async_nodes[unique_id] output_data, output_ui, has_subgraph = get_output_from_returns(results, class_def) elif unique_id in pending_subgraph_results: cached_results = pending_subgraph_results[unique_id] resolved_outputs = [] for is_subgraph, result in cached_results: if not is_subgraph: resolved_outputs.append(result) else: resolved_output = [] for r in result: if is_link(r): source_node, source_output = r[0], r[1] node_cached = execution_list.get_cache(source_node, unique_id) for o in node_cached.outputs[source_output]: resolved_output.append(o) else: resolved_output.append(r) resolved_outputs.append(tuple(resolved_output)) output_data = merge_result_data(resolved_outputs, class_def) output_ui = [] del pending_subgraph_results[unique_id] has_subgraph = False else: get_progress_state().start_progress(unique_id) input_data_all, missing_keys, v3_data = get_input_data(inputs, class_def, unique_id, execution_list, dynprompt, extra_data) if server.client_id is not None: server.last_node_id = display_node_id server.send_sync("executing", { "node": unique_id, "display_node": display_node_id, "prompt_id": prompt_id }, server.client_id) obj = await caches.objects.get(unique_id) if obj is None: obj = class_def() await caches.objects.set(unique_id, obj) if issubclass(class_def, _ComfyNodeInternal): lazy_status_present = first_real_override(class_def, "check_lazy_status") is not None else: lazy_status_present = getattr(obj, "check_lazy_status", None) is not None if lazy_status_present: # for check_lazy_status, the returned data should include the original key of the input v3_data_lazy = v3_data.copy() v3_data_lazy["create_dynamic_tuple"] = True required_inputs = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, "check_lazy_status", allow_interrupt=True, v3_data=v3_data_lazy) required_inputs = await resolve_map_node_over_list_results(required_inputs) required_inputs = set(sum([r for r in required_inputs if isinstance(r,list)], [])) required_inputs = [x for x in required_inputs if isinstance(x,str) and ( x not in input_data_all or x in missing_keys )] if len(required_inputs) > 0: for i in required_inputs: execution_list.make_input_strong_link(unique_id, i) return (ExecutionResult.PENDING, None, None) def execution_block_cb(block): if block.message is not None: mes = { "prompt_id": prompt_id, "node_id": unique_id, "node_type": class_type, "executed": list(executed), "exception_message": f"Execution Blocked: {block.message}", "exception_type": "ExecutionBlocked", "traceback": [], "current_inputs": [], "current_outputs": [], } server.send_sync("execution_error", mes, server.client_id) return ExecutionBlocker(None) else: return block def pre_execute_cb(call_index): # TODO - How to handle this with async functions without contextvars (which requires Python 3.12)? GraphBuilder.set_default_prefix(unique_id, call_index, 0) try: output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data) finally: if comfy.memory_management.aimdo_enabled: if args.verbose == "DEBUG": comfy_aimdo.control.analyze() comfy.model_management.reset_cast_buffers() comfy_aimdo.model_vbar.vbars_reset_watermark_limits() if has_pending_tasks: pending_async_nodes[unique_id] = output_data unblock = execution_list.add_external_block(unique_id) async def await_completion(): tasks = [x for x in output_data if isinstance(x, asyncio.Task)] await asyncio.gather(*tasks, return_exceptions=True) unblock() asyncio.create_task(await_completion()) return (ExecutionResult.PENDING, None, None) if len(output_ui) > 0: ui_outputs[unique_id] = { "meta": { "node_id": unique_id, "display_node": display_node_id, "parent_node": parent_node_id, "real_node_id": real_node_id, }, "output": output_ui } if server.client_id is not None: server.send_sync("executed", { "node": unique_id, "display_node": display_node_id, "output": output_ui, "prompt_id": prompt_id }, server.client_id) if has_subgraph: cached_outputs = [] new_node_ids = [] new_output_ids = [] new_output_links = [] for i in range(len(output_data)): new_graph, node_outputs = output_data[i] if new_graph is None: cached_outputs.append((False, node_outputs)) else: for node_id, node_info in new_graph.items(): new_node_ids.append(node_id) display_id = node_info.get("override_display_id", unique_id) dynprompt.add_ephemeral_node(node_id, node_info, unique_id, display_id) # Figure out if the newly created node is an output node class_type = node_info["class_type"] class_def = nodes.NODE_CLASS_MAPPINGS[class_type] if hasattr(class_def, 'OUTPUT_NODE') and class_def.OUTPUT_NODE == True: new_output_ids.append(node_id) for i in range(len(node_outputs)): if is_link(node_outputs[i]): from_node_id, from_socket = node_outputs[i][0], node_outputs[i][1] new_output_links.append((from_node_id, from_socket)) cached_outputs.append((True, node_outputs)) new_node_ids = set(new_node_ids) for cache in caches.all: subcache = await cache.ensure_subcache_for(unique_id, new_node_ids) subcache.clean_unused() for node_id in new_output_ids: execution_list.add_node(node_id) execution_list.cache_link(node_id, unique_id) for link in new_output_links: execution_list.add_strong_link(link[0], link[1], unique_id) pending_subgraph_results[unique_id] = cached_outputs return (ExecutionResult.PENDING, None, None) cache_entry = CacheEntry(ui=ui_outputs.get(unique_id), outputs=output_data) execution_list.cache_update(unique_id, cache_entry) await caches.outputs.set(unique_id, cache_entry) except comfy.model_management.InterruptProcessingException as iex: logging.info("Processing interrupted") # skip formatting inputs/outputs error_details = { "node_id": real_node_id, } return (ExecutionResult.FAILURE, 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] logging.error(f"!!! Exception during processing !!! {ex}") logging.error(traceback.format_exc()) tips = "" if comfy.model_management.is_oom(ex): tips = "This error means you ran out of memory on your GPU.\n\nTIPS: If the workflow worked before you might have accidentally set the batch_size to a large number." logging.info("Memory summary: {}".format(comfy.model_management.debug_memory_summary())) logging.error("Got an OOM, unloading all loaded models.") comfy.model_management.unload_all_models() elif isinstance(ex, RuntimeError) and ("mat1 and mat2 shapes" in str(ex)) and "Sampler" in class_type: tips = "\n\nTIPS: If you have any \"Load CLIP\" or \"*CLIP Loader\" nodes in your workflow connected to this sampler node make sure the correct file(s) and type is selected." error_details = { "node_id": real_node_id, "exception_message": "{}\n{}".format(ex, tips), "exception_type": exception_type, "traceback": traceback.format_tb(tb), "current_inputs": input_data_formatted } return (ExecutionResult.FAILURE, error_details, ex) get_progress_state().finish_progress(unique_id) executed.add(unique_id) return (ExecutionResult.SUCCESS, None, None) class PromptExecutor: def __init__(self, server, cache_type=False, cache_args=None): self.cache_args = cache_args self.cache_type = cache_type self.server = server self.reset() def reset(self): self.caches = CacheSet(cache_type=self.cache_type, cache_args=self.cache_args) self.status_messages = [] self.success = True 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": list(current_outputs), } self.add_message("execution_error", mes, broadcast=False) def _notify_prompt_lifecycle(self, event: str, prompt_id: str): if not _has_cache_providers(): return for provider in _get_cache_providers(): try: if event == "start": provider.on_prompt_start(prompt_id) elif event == "end": provider.on_prompt_end(prompt_id) except Exception as e: _cache_logger.warning(f"Cache provider {provider.__class__.__name__} error on {event}: {e}") def execute(self, prompt, prompt_id, extra_data={}, execute_outputs=[]): asyncio.run(self.execute_async(prompt, prompt_id, extra_data, execute_outputs)) async def execute_async(self, prompt, prompt_id, extra_data={}, execute_outputs=[]): set_preview_method(extra_data.get("preview_method")) 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) self._notify_prompt_lifecycle("start", prompt_id) ram_headroom = int(self.cache_args["ram"] * (1024 ** 3)) ram_release_callback = self.caches.outputs.ram_release if self.cache_type == CacheType.RAM_PRESSURE else None comfy.memory_management.set_ram_cache_release_state(ram_release_callback, ram_headroom) try: with torch.inference_mode(): dynamic_prompt = DynamicPrompt(prompt) reset_progress_state(prompt_id, dynamic_prompt) add_progress_handler(WebUIProgressHandler(self.server)) is_changed_cache = IsChangedCache(prompt_id, dynamic_prompt, self.caches.outputs) for cache in self.caches.all: await cache.set_prompt(dynamic_prompt, prompt.keys(), is_changed_cache) cache.clean_unused() node_ids = list(prompt.keys()) cache_results = await asyncio.gather( *(self.caches.outputs.get(node_id) for node_id in node_ids) ) cached_nodes = [ node_id for node_id, result in zip(node_ids, cache_results) if result is not None ] comfy.model_management.cleanup_models_gc() self.add_message("execution_cached", { "nodes": cached_nodes, "prompt_id": prompt_id}, broadcast=False) pending_subgraph_results = {} pending_async_nodes = {} # TODO - Unify this with pending_subgraph_results ui_node_outputs = {} executed = set() execution_list = ExecutionList(dynamic_prompt, self.caches.outputs) current_outputs = self.caches.outputs.all_node_ids() for node_id in list(execute_outputs): execution_list.add_node(node_id) while not execution_list.is_empty(): node_id, error, ex = await execution_list.stage_node_execution() if error is not None: self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex) break assert node_id is not None, "Node ID should not be None at this point" result, error, ex = await execute(self.server, dynamic_prompt, self.caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes, ui_node_outputs) self.success = result != ExecutionResult.FAILURE if result == ExecutionResult.FAILURE: self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex) break elif result == ExecutionResult.PENDING: execution_list.unstage_node_execution() else: # result == ExecutionResult.SUCCESS: execution_list.complete_node_execution() if self.cache_type == CacheType.RAM_PRESSURE: comfy.model_management.free_memory(0, None, pins_required=ram_headroom, ram_required=ram_headroom) comfy.memory_management.extra_ram_release(ram_headroom) else: # Only execute when the while-loop ends without break # Send cached UI for intermediate output nodes that weren't executed for node_id in dynamic_prompt.all_node_ids(): if node_id in executed: continue if not _is_intermediate_output(dynamic_prompt, node_id): continue cached = await self.caches.outputs.get(node_id) if cached is not None: display_node_id = dynamic_prompt.get_display_node_id(node_id) _send_cached_ui(self.server, node_id, display_node_id, cached, prompt_id, ui_node_outputs) self.add_message("execution_success", { "prompt_id": prompt_id }, broadcast=False) ui_outputs = {} meta_outputs = {} for node_id, ui_info in ui_node_outputs.items(): ui_outputs[node_id] = ui_info["output"] meta_outputs[node_id] = ui_info["meta"] self.history_result = { "outputs": ui_outputs, "meta": meta_outputs, } self.server.last_node_id = None if comfy.model_management.DISABLE_SMART_MEMORY: comfy.model_management.unload_all_models() finally: comfy.memory_management.set_ram_cache_release_state(None, 0) self._notify_prompt_lifecycle("end", prompt_id) async def validate_inputs(prompt_id, 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] errors = [] valid = True v3_data = None validate_function_inputs = [] validate_has_kwargs = False if issubclass(obj_class, _ComfyNodeInternal): obj_class: _io._ComfyNodeBaseInternal class_inputs = obj_class.INPUT_TYPES() class_inputs, _, v3_data = _io.get_finalized_class_inputs(class_inputs, inputs) validate_function_name = "validate_inputs" validate_function = first_real_override(obj_class, validate_function_name) else: class_inputs = obj_class.INPUT_TYPES() validate_function_name = "VALIDATE_INPUTS" validate_function = getattr(obj_class, validate_function_name, None) if validate_function is not None: argspec = inspect.getfullargspec(validate_function) validate_function_inputs = argspec.args validate_has_kwargs = argspec.varkw is not None received_types = {} valid_inputs = set(class_inputs.get('required',{})).union(set(class_inputs.get('optional',{}))) for x in valid_inputs: input_type, input_category, extra_info = get_input_info(obj_class, x, class_inputs) assert extra_info is not None if x not in inputs: if input_category == "required": details = f"{x}" if not v3_data else x.split(".")[-1] error = { "type": "required_input_missing", "message": "Required input is missing", "details": details, "extra_info": { "input_name": x } } errors.append(error) continue val = inputs[x] info = (input_type, extra_info) 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 received_type = r[val[1]] received_types[x] = received_type if 'input_types' not in validate_function_inputs and not validate_node_input(received_type, input_type): details = f"{x}, received_type({received_type}) mismatch input_type({input_type})" 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 = await validate_inputs(prompt_id, 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: # Unwraps values wrapped in __value__ key or typed wrapper. # This is used to pass list widget values to execution, # as by default list value is reserved to represent the # connection between nodes. if isinstance(val, dict): if "__value__" in val: val = val["__value__"] inputs[x] = val if input_type == "INT": val = int(val) inputs[x] = val if input_type == "FLOAT": val = float(val) inputs[x] = val if input_type == "STRING": val = str(val) inputs[x] = val if input_type == "BOOLEAN": val = bool(val) inputs[x] = val except Exception as ex: error = { "type": "invalid_input_type", "message": f"Failed to convert an input value to a {input_type} 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 x not in validate_function_inputs and not validate_has_kwargs: if "min" in extra_info and val < extra_info["min"]: error = { "type": "value_smaller_than_min", "message": "Value {} smaller than min of {}".format(val, extra_info["min"]), "details": f"{x}", "extra_info": { "input_name": x, "input_config": info, "received_value": val, } } errors.append(error) continue if "max" in extra_info and val > extra_info["max"]: error = { "type": "value_bigger_than_max", "message": "Value {} bigger than max of {}".format(val, extra_info["max"]), "details": f"{x}", "extra_info": { "input_name": x, "input_config": info, "received_value": val, } } errors.append(error) continue if isinstance(input_type, list) or input_type == io.Combo.io_type: if input_type == io.Combo.io_type: combo_options = extra_info.get("options", []) else: combo_options = input_type if val not in combo_options: input_config = info list_info = "" # Don't send back gigantic lists like if they're lots of # scanned model filepaths if len(combo_options) > 20: list_info = f"(list of length {len(combo_options)})" input_config = None else: list_info = str(combo_options) 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 or validate_has_kwargs: input_data_all, _, v3_data = get_input_data(inputs, obj_class, unique_id) input_filtered = {} for x in input_data_all: if x in validate_function_inputs or validate_has_kwargs: input_filtered[x] = input_data_all[x] if 'input_types' in validate_function_inputs: input_filtered['input_types'] = [received_types] ret = await _async_map_node_over_list(prompt_id, unique_id, obj_class, input_filtered, validate_function_name, v3_data=v3_data) ret = await resolve_map_node_over_list_results(ret) for x in input_filtered: for i, r in enumerate(ret): if r is not True and not isinstance(r, ExecutionBlocker): 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, } } 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__ async def validate_prompt(prompt_id, prompt, partial_execution_list: Union[list[str], None]): outputs = set() for x in prompt: if 'class_type' not in prompt[x]: node_data = prompt[x] node_title = node_data.get('_meta', {}).get('title') error = { "type": "missing_node_type", "message": f"Node '{node_title or f'ID #{x}'}' has no class_type. The workflow may be corrupted or a custom node is missing.", "details": f"Node ID '#{x}'", "extra_info": { "node_id": x, "class_type": None, "node_title": node_title } } return (False, error, [], {}) class_type = prompt[x]['class_type'] class_ = nodes.NODE_CLASS_MAPPINGS.get(class_type, None) if class_ is None: node_data = prompt[x] node_title = node_data.get('_meta', {}).get('title', class_type) error = { "type": "missing_node_type", "message": f"Node '{node_title}' not found. The custom node may not be installed.", "details": f"Node ID '#{x}'", "extra_info": { "node_id": x, "class_type": class_type, "node_title": node_title } } return (False, error, [], {}) if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE is True: if partial_execution_list is None or x in partial_execution_list: 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 = await validate_inputs(prompt_id, 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 = {} 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, history_result, status: Optional['PromptQueue.ExecutionStatus'], process_item=None): 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()) if process_item is not None: prompt = process_item(prompt) self.history[prompt[1]] = { "prompt": prompt, "outputs": {}, 'status': status_dict, } self.history[prompt[1]].update(history_result) self.server.queue_updated() # Note: slow def get_current_queue(self): with self.mutex: out = [] for x in self.currently_running.values(): out += [x] return (out, copy.deepcopy(self.queue)) # read-safe as long as queue items are immutable def get_current_queue_volatile(self): with self.mutex: running = [x for x in self.currently_running.values()] queued = copy.copy(self.queue) return (running, queued) 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, map_function=None): 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: p = self.history[k] if map_function is not None: p = map_function(p) out[k] = p if max_items is not None and len(out) >= max_items: break i += 1 return out elif prompt_id in self.history: p = self.history[prompt_id] if map_function is None: p = copy.deepcopy(p) else: p = map_function(p) return {prompt_id: p} 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()