execution.py

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
from contextlib import nullcontext

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


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)
        self.objects = HierarchicalCache(CacheKeySetID)

    def init_lru_cache(self, cache_size):
        self.outputs = LRUCache(CacheKeySetInputSignature, max_size=cache_size)
        self.objects = HierarchicalCache(CacheKeySetID)

    def init_ram_cache(self, min_headroom):
        self.outputs = RAMPressureCache(CacheKeySetInputSignature)
        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)

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 = caches.outputs.get(unique_id)
    if cached is not None:
        if server.client_id is not None:
            cached_ui = cached.ui or {}
            server.send_sync("executed", { "node": unique_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[unique_id] = cached.ui
        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 = caches.objects.get(unique_id)
            if obj is None:
                obj = class_def()
                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)

            #Do comfy_aimdo mempool chunking here on the per-node level. Multi-model workflows
            #will cause all sorts of incompatible memory shapes to fragment the pytorch alloc
            #that we just want to cull out each model run.
            allocator = comfy.memory_management.aimdo_allocator
            with nullcontext() if allocator is None else torch.cuda.use_mem_pool(torch.cuda.MemPool(allocator.allocator())):
                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 allocator is not None:
                        if args.verbose == "DEBUG":
                            comfy_aimdo.model_vbar.vbars_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)
        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 isinstance(ex, comfy.model_management.OOM_EXCEPTION):
            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 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)

        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()

            cached_nodes = []
            for node_id in prompt:
                if self.caches.outputs.get(node_id) is not None:
                    cached_nodes.append(node_id)

            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()
                self.caches.outputs.poll(ram_headroom=self.cache_args["ram"])
            else:
                # Only execute when the while-loop ends without break
                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()


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. This is used to pass
                # list widget value to execution, as by default list value is
                # reserved to represent the connection between nodes.
                if isinstance(val, dict) and "__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()