ComfyUI/execution.py

import sys
import copy
import logging
import threading
import heapq
import time
import traceback
import inspect
from typing import List, Literal, NamedTuple, Optional

import torch
import nodes

import comfy.model_management

from comfy.cli_args import args


def validate_image_shape(image):
    transforms = {
        "HW": lambda t: t.unsqueeze(0).unsqueeze(-1).expand(-1, -1, -1, 3),
        "BHW": lambda t: t.unsqueeze(-1).expand(-1, -1, -1, 3),
        "HWC": lambda t: t.unsqueeze(0),
    }
    
    image_len = len(image.shape)
    
    if image_len == 2:
        #HW -> add Batch and Channel dimensions
        image = transforms["HW"](image)
        return image
    
    if image_len == 3:
        if image.shape[-1] > 4 or image.shape[-1] < 3:
            #BHW -> add Channel dimension
            image = transforms["BHW"](image)
            return image
            
        if image.shape[-1] == 3 or image.shape[-1] == 4:
            #HW3 or HW4 -> add Batch dimension --- can misbehave in edge cases where image is BHW but W is 3 or 4 px
            image = transforms["HWC"](image)
            return image
        
    return image


def generate_type_validator(valid_types, expected_type=None): 
    def validate_type(node_type, output_name, value):
        
        if not isinstance(value, valid_types) and expected_type is not "IMAGE":
            print(f"The value is not of the valid types. Expected one of {[t.__name__ for t in valid_types]}, got {[type(value)]}")
            
            if "all" == args.type_conformance:
                return valid_types[0](value)
            
            if "error" == args.type_conformance:     
                raise TypeError(f"The value is not of the valid types. Expected output: {output_name}, to be of type(s): {[vtype.__name__ for vtype in valid_types]}, got {[type(value).__name__]} instead")

            return value
        
        if isinstance(value, valid_types) and expected_type is "IMAGE":
            if args.type_conformance in ["all", "images"]:
                return validate_image_shape(value)
            
            if len(value.shape) != 4 or value.shape[-1] > 4 or value.shape[-1] < 3:
                    print(f"Image Shape Error: Node: {node_type}, Output: {output_name}, [{value.shape}] does not match the expected RGB format: torch.Size[B, H, W, 3] or the RGBA format: torch.Size[B, H, W, 4]")
                    
                    if "error" == args.type_conformance:
                        raise ValueError(f"Image Shape Error: Node: {node_type}, Output: {output_name}, [{value.shape}] does not match the expected RGB format: torch.Size[B, H, W, 3] or the RGBA format: torch.Size[B, H, W, 4]")

                    return value
            
            return value
        return value
        
    return validate_type 


def input_validation(input_data_all, obj):
    validation_funcs = { 
        "IMAGE": generate_type_validator((torch.Tensor,), "IMAGE"),
        "INT": generate_type_validator((int,)), 
        "FLOAT": generate_type_validator((float,)), 
        "STRING": generate_type_validator((str,)), 
    }
    input_types = obj.INPUT_TYPES()
    for _, v in input_types.items():
        if isinstance(v, dict):
            for k2, v2 in v.items():
                if tuple(v2[0]) in validation_funcs.keys():
                    input_data_all[k2] = [validation_funcs[v2[0]](obj.__class__.__name__, k2, x) for x in input_data_all[k2]]
                
    return input_data_all


def output_validation(results, obj):
    validation_funcs = { 
        "IMAGE": generate_type_validator((torch.Tensor,), "IMAGE"),
        "INT": generate_type_validator((int,)), 
        "FLOAT": generate_type_validator((float,)), 
        "STRING": generate_type_validator((str,)), 
    }
    if hasattr(obj, "RETURN_NAMES") and hasattr(obj, "RETURN_TYPES"):      
        return_indexs = {}
        formatted_results = []
        
        for i, return_type in enumerate(obj.RETURN_TYPES):
            return_indexs[i] = return_type
        
        for i, result in enumerate(results[0]):
            return_type = return_indexs[i]
            formatted_results.append(validation_funcs[return_type](obj.__class__.__name__, obj.RETURN_NAMES[i], result) if return_type in validation_funcs.keys() else result)
        
        results = [tuple(formatted_results)]  
        del formatted_results     
    return results
    

def get_input_data(inputs, class_def, unique_id, outputs={}, prompt={}, extra_data={}):
    valid_inputs = class_def.INPUT_TYPES()
    input_data_all = {}
    for x in inputs:
        input_data = inputs[x]
        if isinstance(input_data, list):
            input_unique_id = input_data[0]
            output_index = input_data[1]
            if input_unique_id not in outputs:
                input_data_all[x] = (None,)
                continue
            obj = outputs[input_unique_id][output_index]
            input_data_all[x] = obj
        else:
            if ("required" in valid_inputs and x in valid_inputs["required"]) or ("optional" in valid_inputs and x in valid_inputs["optional"]):
                    input_data_all[x] = [input_data]

    if "hidden" in valid_inputs:
        h = valid_inputs["hidden"]
        for x in h:
            if h[x] == "PROMPT":
                input_data_all[x] = [prompt]
            if h[x] == "EXTRA_PNGINFO":
                input_data_all[x] = [extra_data.get('extra_pnginfo', None)]
            if h[x] == "UNIQUE_ID":
                input_data_all[x] = [unique_id]
    
    return input_data_all


def map_node_over_list(obj, input_data_all, func, allow_interrupt=False):
    input_data_all = input_validation(input_data_all, obj)
    
    # check if node wants the lists
    input_is_list = False
    if hasattr(obj, "INPUT_IS_LIST"):
        input_is_list = obj.INPUT_IS_LIST

    if len(input_data_all) == 0:
        max_len_input = 0
    else:
        max_len_input = max([len(x) for x in input_data_all.values()])
     
    # get a slice of inputs, repeat last input when list isn't long enough
    def slice_dict(d, i):
        d_new = dict()
        for k,v in d.items():
            d_new[k] = v[i if len(v) > i else -1]
        return d_new
    
    results = []
    if input_is_list:
        if allow_interrupt:
            nodes.before_node_execution()
        results.append(getattr(obj, func)(**input_data_all))
    elif max_len_input == 0:
        if allow_interrupt:
            nodes.before_node_execution()
        results.append(getattr(obj, func)())
    else:
        for i in range(max_len_input):
            if allow_interrupt:
                nodes.before_node_execution()
            results.append(getattr(obj, func)(**slice_dict(input_data_all, i)))

    results = output_validation(results, obj)           
    return results


def get_output_data(obj, input_data_all):
    results = []
    uis = []
    return_values = map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True)
    
    for r in return_values:
        if isinstance(r, dict):
            if 'ui' in r:
                uis.append(r['ui'])
            if 'result' in r:
                results.append(r['result'])
        else:
            results.append(r)
    
    output = []
    if len(results) > 0:
        # check which outputs need concatenating
        output_is_list = [False] * len(results[0])
        if hasattr(obj, "OUTPUT_IS_LIST"):
            output_is_list = obj.OUTPUT_IS_LIST

        # merge node execution results
        for i, is_list in zip(range(len(results[0])), output_is_list):
            if is_list:
                output.append([x for o in results for x in o[i]])
            else:
                output.append([o[i] for o in results])

    ui = dict()    
    if len(uis) > 0:
        ui = {k: [y for x in uis for y in x[k]] for k in uis[0].keys()}
    return output, ui

def format_value(x):
    if x is None:
        return None
    elif isinstance(x, (int, float, bool, str)):
        return x
    else:
        return str(x)

def recursive_execute(server, prompt, outputs, current_item, extra_data, executed, prompt_id, outputs_ui, object_storage):
    unique_id = current_item
    inputs = prompt[unique_id]['inputs']
    class_type = prompt[unique_id]['class_type']
    class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
    if unique_id in outputs:
        return (True, None, None)

    for x in inputs:
        input_data = inputs[x]

        if isinstance(input_data, list):
            input_unique_id = input_data[0]
            output_index = input_data[1]
            if input_unique_id not in outputs:
                result = recursive_execute(server, prompt, outputs, input_unique_id, extra_data, executed, prompt_id, outputs_ui, object_storage)
                if result[0] is not True:
                    # Another node failed further upstream
                    return result

    input_data_all = None
    try:
        input_data_all = get_input_data(inputs, class_def, unique_id, outputs, prompt, extra_data)
        if server.client_id is not None:
            server.last_node_id = unique_id
            server.send_sync("executing", { "node": unique_id, "prompt_id": prompt_id }, server.client_id)

        obj = object_storage.get((unique_id, class_type), None)
        if obj is None:
            obj = class_def()
            object_storage[(unique_id, class_type)] = obj

        output_data, output_ui = get_output_data(obj, input_data_all)
        outputs[unique_id] = output_data
        if len(output_ui) > 0:
            outputs_ui[unique_id] = output_ui
            if server.client_id is not None:
                server.send_sync("executed", { "node": unique_id, "output": output_ui, "prompt_id": prompt_id }, server.client_id)
    except comfy.model_management.InterruptProcessingException as iex:
        logging.info("Processing interrupted")

        # skip formatting inputs/outputs
        error_details = {
            "node_id": unique_id,
        }

        return (False, error_details, iex)
    except Exception as ex:
        typ, _, tb = sys.exc_info()
        exception_type = full_type_name(typ)
        input_data_formatted = {}
        if input_data_all is not None:
            input_data_formatted = {}
            for name, inputs in input_data_all.items():
                input_data_formatted[name] = [format_value(x) for x in inputs]

        output_data_formatted = {}
        for node_id, node_outputs in outputs.items():
            output_data_formatted[node_id] = [[format_value(x) for x in l] for l in node_outputs]

        logging.error(f"!!! Exception during processing!!! {ex}")
        logging.error(traceback.format_exc())

        error_details = {
            "node_id": unique_id,
            "exception_message": str(ex),
            "exception_type": exception_type,
            "traceback": traceback.format_tb(tb),
            "current_inputs": input_data_formatted,
            "current_outputs": output_data_formatted
        }
        return (False, error_details, ex)

    executed.add(unique_id)

    return (True, None, None)

def recursive_will_execute(prompt, outputs, current_item, memo={}):
    unique_id = current_item

    if unique_id in memo:
        return memo[unique_id]

    inputs = prompt[unique_id]['inputs']
    will_execute = []
    if unique_id in outputs:
        return []

    for x in inputs:
        input_data = inputs[x]
        if isinstance(input_data, list):
            input_unique_id = input_data[0]
            output_index = input_data[1]
            if input_unique_id not in outputs:
                will_execute += recursive_will_execute(prompt, outputs, input_unique_id, memo)

    memo[unique_id] = will_execute + [unique_id]
    return memo[unique_id]

def recursive_output_delete_if_changed(prompt, old_prompt, outputs, current_item):
    unique_id = current_item
    inputs = prompt[unique_id]['inputs']
    class_type = prompt[unique_id]['class_type']
    class_def = nodes.NODE_CLASS_MAPPINGS[class_type]

    is_changed_old = ''
    is_changed = ''
    to_delete = False
    if hasattr(class_def, 'IS_CHANGED'):
        if unique_id in old_prompt and 'is_changed' in old_prompt[unique_id]:
            is_changed_old = old_prompt[unique_id]['is_changed']
        if 'is_changed' not in prompt[unique_id]:
            input_data_all = get_input_data(inputs, class_def, unique_id, outputs)
            if input_data_all is not None:
                try:
                    #is_changed = class_def.IS_CHANGED(**input_data_all)
                    is_changed = map_node_over_list(class_def, input_data_all, "IS_CHANGED")
                    prompt[unique_id]['is_changed'] = is_changed
                except:
                    to_delete = True
        else:
            is_changed = prompt[unique_id]['is_changed']

    if unique_id not in outputs:
        return True

    if not to_delete:
        if is_changed != is_changed_old:
            to_delete = True
        elif unique_id not in old_prompt:
            to_delete = True
        elif class_type != old_prompt[unique_id]['class_type']:
            to_delete = True
        elif inputs == old_prompt[unique_id]['inputs']:
            for x in inputs:
                input_data = inputs[x]

                if isinstance(input_data, list):
                    input_unique_id = input_data[0]
                    output_index = input_data[1]
                    if input_unique_id in outputs:
                        to_delete = recursive_output_delete_if_changed(prompt, old_prompt, outputs, input_unique_id)
                    else:
                        to_delete = True
                    if to_delete:
                        break
        else:
            to_delete = True

    if to_delete:
        d = outputs.pop(unique_id)
        del d
    return to_delete

class PromptExecutor:
    def __init__(self, server):
        self.server = server
        self.reset()

    def reset(self):
        self.outputs = {}
        self.object_storage = {}
        self.outputs_ui = {}
        self.status_messages = []
        self.success = True
        self.old_prompt = {}

    def add_message(self, event, data: dict, broadcast: bool):
        data = {
            **data,
            "timestamp": int(time.time() * 1000),
        }
        self.status_messages.append((event, data))
        if self.server.client_id is not None or broadcast:
            self.server.send_sync(event, data, self.server.client_id)

    def handle_execution_error(self, prompt_id, prompt, current_outputs, executed, error, ex):
        node_id = error["node_id"]
        class_type = prompt[node_id]["class_type"]

        # First, send back the status to the frontend depending
        # on the exception type
        if isinstance(ex, comfy.model_management.InterruptProcessingException):
            mes = {
                "prompt_id": prompt_id,
                "node_id": node_id,
                "node_type": class_type,
                "executed": list(executed),
            }
            self.add_message("execution_interrupted", mes, broadcast=True)
        else:
            mes = {
                "prompt_id": prompt_id,
                "node_id": node_id,
                "node_type": class_type,
                "executed": list(executed),

                "exception_message": error["exception_message"],
                "exception_type": error["exception_type"],
                "traceback": error["traceback"],
                "current_inputs": error["current_inputs"],
                "current_outputs": error["current_outputs"],
            }
            self.add_message("execution_error", mes, broadcast=False)
        
        # Next, remove the subsequent outputs since they will not be executed
        to_delete = []
        for o in self.outputs:
            if (o not in current_outputs) and (o not in executed):
                to_delete += [o]
                if o in self.old_prompt:
                    d = self.old_prompt.pop(o)
                    del d
        for o in to_delete:
            d = self.outputs.pop(o)
            del d

    def execute(self, prompt, prompt_id, extra_data={}, execute_outputs=[]):
        nodes.interrupt_processing(False)

        if "client_id" in extra_data:
            self.server.client_id = extra_data["client_id"]
        else:
            self.server.client_id = None

        self.status_messages = []
        self.add_message("execution_start", { "prompt_id": prompt_id}, broadcast=False)

        with torch.inference_mode():
            #delete cached outputs if nodes don't exist for them
            to_delete = []
            for o in self.outputs:
                if o not in prompt:
                    to_delete += [o]
            for o in to_delete:
                d = self.outputs.pop(o)
                del d
            to_delete = []
            for o in self.object_storage:
                if o[0] not in prompt:
                    to_delete += [o]
                else:
                    p = prompt[o[0]]
                    if o[1] != p['class_type']:
                        to_delete += [o]
            for o in to_delete:
                d = self.object_storage.pop(o)
                del d

            for x in prompt:
                recursive_output_delete_if_changed(prompt, self.old_prompt, self.outputs, x)

            current_outputs = set(self.outputs.keys())
            for x in list(self.outputs_ui.keys()):
                if x not in current_outputs:
                    d = self.outputs_ui.pop(x)
                    del d

            comfy.model_management.cleanup_models(keep_clone_weights_loaded=True)
            self.add_message("execution_cached",
                          { "nodes": list(current_outputs) , "prompt_id": prompt_id},
                          broadcast=False)
            executed = set()
            output_node_id = None
            to_execute = []

            for node_id in list(execute_outputs):
                to_execute += [(0, node_id)]

            while len(to_execute) > 0:
                #always execute the output that depends on the least amount of unexecuted nodes first
                memo = {}
                to_execute = sorted(list(map(lambda a: (len(recursive_will_execute(prompt, self.outputs, a[-1], memo)), a[-1]), to_execute)))
                output_node_id = to_execute.pop(0)[-1]

                # This call shouldn't raise anything if there's an error deep in
                # the actual SD code, instead it will report the node where the
                # error was raised
                self.success, error, ex = recursive_execute(self.server, prompt, self.outputs, output_node_id, extra_data, executed, prompt_id, self.outputs_ui, self.object_storage)
                if self.success is not True:
                    self.handle_execution_error(prompt_id, prompt, current_outputs, executed, error, ex)
                    break
            else:
                # Only execute when the while-loop ends without break
                self.add_message("execution_success", { "prompt_id": prompt_id }, broadcast=False)

            for x in executed:
                self.old_prompt[x] = copy.deepcopy(prompt[x])
            self.server.last_node_id = None
            if comfy.model_management.DISABLE_SMART_MEMORY:
                comfy.model_management.unload_all_models()



def validate_inputs(prompt, item, validated):
    unique_id = item
    if unique_id in validated:
        return validated[unique_id]

    inputs = prompt[unique_id]['inputs']
    class_type = prompt[unique_id]['class_type']
    obj_class = nodes.NODE_CLASS_MAPPINGS[class_type]

    class_inputs = obj_class.INPUT_TYPES()
    required_inputs = class_inputs['required']

    errors = []
    valid = True

    validate_function_inputs = []
    if hasattr(obj_class, "VALIDATE_INPUTS"):
        validate_function_inputs = inspect.getfullargspec(obj_class.VALIDATE_INPUTS).args

    for x in required_inputs:
        if x not in inputs:
            error = {
                "type": "required_input_missing",
                "message": "Required input is missing",
                "details": f"{x}",
                "extra_info": {
                    "input_name": x
                }
            }
            errors.append(error)
            continue

        val = inputs[x]
        info = required_inputs[x]
        type_input = info[0]
        if isinstance(val, list):
            if len(val) != 2:
                error = {
                    "type": "bad_linked_input",
                    "message": "Bad linked input, must be a length-2 list of [node_id, slot_index]",
                    "details": f"{x}",
                    "extra_info": {
                        "input_name": x,
                        "input_config": info,
                        "received_value": val
                    }
                }
                errors.append(error)
                continue

            o_id = val[0]
            o_class_type = prompt[o_id]['class_type']
            r = nodes.NODE_CLASS_MAPPINGS[o_class_type].RETURN_TYPES
            if r[val[1]] != type_input:
                received_type = r[val[1]]
                details = f"{x}, {received_type} != {type_input}"
                error = {
                    "type": "return_type_mismatch",
                    "message": "Return type mismatch between linked nodes",
                    "details": details,
                    "extra_info": {
                        "input_name": x,
                        "input_config": info,
                        "received_type": received_type,
                        "linked_node": val
                    }
                }
                errors.append(error)
                continue
            try:
                r = validate_inputs(prompt, o_id, validated)
                if r[0] is False:
                    # `r` will be set in `validated[o_id]` already
                    valid = False
                    continue
            except Exception as ex:
                typ, _, tb = sys.exc_info()
                valid = False
                exception_type = full_type_name(typ)
                reasons = [{
                    "type": "exception_during_inner_validation",
                    "message": "Exception when validating inner node",
                    "details": str(ex),
                    "extra_info": {
                        "input_name": x,
                        "input_config": info,
                        "exception_message": str(ex),
                        "exception_type": exception_type,
                        "traceback": traceback.format_tb(tb),
                        "linked_node": val
                    }
                }]
                validated[o_id] = (False, reasons, o_id)
                continue
        else:
            try:
                if type_input == "INT":
                    val = int(val)
                    inputs[x] = val
                if type_input == "FLOAT":
                    val = float(val)
                    inputs[x] = val
                if type_input == "STRING":
                    val = str(val)
                    inputs[x] = val
            except Exception as ex:
                error = {
                    "type": "invalid_input_type",
                    "message": f"Failed to convert an input value to a {type_input} value",
                    "details": f"{x}, {val}, {ex}",
                    "extra_info": {
                        "input_name": x,
                        "input_config": info,
                        "received_value": val,
                        "exception_message": str(ex)
                    }
                }
                errors.append(error)
                continue

            if len(info) > 1:
                if "min" in info[1] and val < info[1]["min"]:
                    error = {
                        "type": "value_smaller_than_min",
                        "message": "Value {} smaller than min of {}".format(val, info[1]["min"]),
                        "details": f"{x}",
                        "extra_info": {
                            "input_name": x,
                            "input_config": info,
                            "received_value": val,
                        }
                    }
                    errors.append(error)
                    continue
                if "max" in info[1] and val > info[1]["max"]:
                    error = {
                        "type": "value_bigger_than_max",
                        "message": "Value {} bigger than max of {}".format(val, info[1]["max"]),
                        "details": f"{x}",
                        "extra_info": {
                            "input_name": x,
                            "input_config": info,
                            "received_value": val,
                        }
                    }
                    errors.append(error)
                    continue

            if x not in validate_function_inputs:
                if isinstance(type_input, list):
                    if val not in type_input:
                        input_config = info
                        list_info = ""

                        # Don't send back gigantic lists like if they're lots of
                        # scanned model filepaths
                        if len(type_input) > 20:
                            list_info = f"(list of length {len(type_input)})"
                            input_config = None
                        else:
                            list_info = str(type_input)

                        error = {
                            "type": "value_not_in_list",
                            "message": "Value not in list",
                            "details": f"{x}: '{val}' not in {list_info}",
                            "extra_info": {
                                "input_name": x,
                                "input_config": input_config,
                                "received_value": val,
                            }
                        }
                        errors.append(error)
                        continue

    if len(validate_function_inputs) > 0:
        input_data_all = get_input_data(inputs, obj_class, unique_id)
        input_filtered = {}
        for x in input_data_all:
            if x in validate_function_inputs:
                input_filtered[x] = input_data_all[x]

        #ret = obj_class.VALIDATE_INPUTS(**input_filtered)
        ret = map_node_over_list(obj_class, input_filtered, "VALIDATE_INPUTS")
        for x in input_filtered:
            for i, r in enumerate(ret):
                if r is not True:
                    details = f"{x}"
                    if r is not False:
                        details += f" - {str(r)}"

                    error = {
                        "type": "custom_validation_failed",
                        "message": "Custom validation failed for node",
                        "details": details,
                        "extra_info": {
                            "input_name": x,
                            "input_config": info,
                            "received_value": val,
                        }
                    }
                    errors.append(error)
                    continue

    if len(errors) > 0 or valid is not True:
        ret = (False, errors, unique_id)
    else:
        ret = (True, [], unique_id)

    validated[unique_id] = ret
    return ret

def full_type_name(klass):
    module = klass.__module__
    if module == 'builtins':
        return klass.__qualname__
    return module + '.' + klass.__qualname__

def validate_prompt(prompt):
    outputs = set()
    for x in prompt:
        if 'class_type' not in prompt[x]:
            error = {
                "type": "invalid_prompt",
                "message": f"Cannot execute because a node is missing the class_type property.",
                "details": f"Node ID '#{x}'",
                "extra_info": {}
            }
            return (False, error, [], [])

        class_type = prompt[x]['class_type']
        class_ = nodes.NODE_CLASS_MAPPINGS.get(class_type, None)
        if class_ is None:
            error = {
                "type": "invalid_prompt",
                "message": f"Cannot execute because node {class_type} does not exist.",
                "details": f"Node ID '#{x}'",
                "extra_info": {}
            }
            return (False, error, [], [])

        if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE is True:
            outputs.add(x)

    if len(outputs) == 0:
        error = {
            "type": "prompt_no_outputs",
            "message": "Prompt has no outputs",
            "details": "",
            "extra_info": {}
        }
        return (False, error, [], [])

    good_outputs = set()
    errors = []
    node_errors = {}
    validated = {}
    for o in outputs:
        valid = False
        reasons = []
        try:
            m = validate_inputs(prompt, o, validated)
            valid = m[0]
            reasons = m[1]
        except Exception as ex:
            typ, _, tb = sys.exc_info()
            valid = False
            exception_type = full_type_name(typ)
            reasons = [{
                "type": "exception_during_validation",
                "message": "Exception when validating node",
                "details": str(ex),
                "extra_info": {
                    "exception_type": exception_type,
                    "traceback": traceback.format_tb(tb)
                }
            }]
            validated[o] = (False, reasons, o)

        if valid is True:
            good_outputs.add(o)
        else:
            logging.error(f"Failed to validate prompt for output {o}:")
            if len(reasons) > 0:
                logging.error("* (prompt):")
                for reason in reasons:
                    logging.error(f"  - {reason['message']}: {reason['details']}")
            errors += [(o, reasons)]
            for node_id, result in validated.items():
                valid = result[0]
                reasons = result[1]
                # If a node upstream has errors, the nodes downstream will also
                # be reported as invalid, but there will be no errors attached.
                # So don't return those nodes as having errors in the response.
                if valid is not True and len(reasons) > 0:
                    if node_id not in node_errors:
                        class_type = prompt[node_id]['class_type']
                        node_errors[node_id] = {
                            "errors": reasons,
                            "dependent_outputs": [],
                            "class_type": class_type
                        }
                        logging.error(f"* {class_type} {node_id}:")
                        for reason in reasons:
                            logging.error(f"  - {reason['message']}: {reason['details']}")
                    node_errors[node_id]["dependent_outputs"].append(o)
            logging.error("Output will be ignored")

    if len(good_outputs) == 0:
        errors_list = []
        for o, errors in errors:
            for error in errors:
                errors_list.append(f"{error['message']}: {error['details']}")
        errors_list = "\n".join(errors_list)

        error = {
            "type": "prompt_outputs_failed_validation",
            "message": "Prompt outputs failed validation",
            "details": errors_list,
            "extra_info": {}
        }

        return (False, error, list(good_outputs), node_errors)

    return (True, None, list(good_outputs), node_errors)

MAXIMUM_HISTORY_SIZE = 10000

class PromptQueue:
    def __init__(self, server):
        self.server = server
        self.mutex = threading.RLock()
        self.not_empty = threading.Condition(self.mutex)
        self.task_counter = 0
        self.queue = []
        self.currently_running = {}
        self.history = {}
        self.flags = {}
        server.prompt_queue = self

    def put(self, item):
        with self.mutex:
            heapq.heappush(self.queue, item)
            self.server.queue_updated()
            self.not_empty.notify()

    def get(self, timeout=None):
        with self.not_empty:
            while len(self.queue) == 0:
                self.not_empty.wait(timeout=timeout)
                if timeout is not None and len(self.queue) == 0:
                    return None
            item = heapq.heappop(self.queue)
            i = self.task_counter
            self.currently_running[i] = copy.deepcopy(item)
            self.task_counter += 1
            self.server.queue_updated()
            return (item, i)

    class ExecutionStatus(NamedTuple):
        status_str: Literal['success', 'error']
        completed: bool
        messages: List[str]

    def task_done(self, item_id, outputs,
                  status: Optional['PromptQueue.ExecutionStatus']):
        with self.mutex:
            prompt = self.currently_running.pop(item_id)
            if len(self.history) > MAXIMUM_HISTORY_SIZE:
                self.history.pop(next(iter(self.history)))

            status_dict: Optional[dict] = None
            if status is not None:
                status_dict = copy.deepcopy(status._asdict())

            self.history[prompt[1]] = {
                "prompt": prompt,
                "outputs": copy.deepcopy(outputs),
                'status': status_dict,
            }
            self.server.queue_updated()

    def get_current_queue(self):
        with self.mutex:
            out = []
            for x in self.currently_running.values():
                out += [x]
            return (out, copy.deepcopy(self.queue))

    def get_tasks_remaining(self):
        with self.mutex:
            return len(self.queue) + len(self.currently_running)

    def wipe_queue(self):
        with self.mutex:
            self.queue = []
            self.server.queue_updated()

    def delete_queue_item(self, function):
        with self.mutex:
            for x in range(len(self.queue)):
                if function(self.queue[x]):
                    if len(self.queue) == 1:
                        self.wipe_queue()
                    else:
                        self.queue.pop(x)
                        heapq.heapify(self.queue)
                    self.server.queue_updated()
                    return True
        return False

    def get_history(self, prompt_id=None, max_items=None, offset=-1):
        with self.mutex:
            if prompt_id is None:
                out = {}
                i = 0
                if offset < 0 and max_items is not None:
                    offset = len(self.history) - max_items
                for k in self.history:
                    if i >= offset:
                        out[k] = self.history[k]
                        if max_items is not None and len(out) >= max_items:
                            break
                    i += 1
                return out
            elif prompt_id in self.history:
                return {prompt_id: copy.deepcopy(self.history[prompt_id])}
            else:
                return {}

    def wipe_history(self):
        with self.mutex:
            self.history = {}

    def delete_history_item(self, id_to_delete):
        with self.mutex:
            self.history.pop(id_to_delete, None)

    def set_flag(self, name, data):
        with self.mutex:
            self.flags[name] = data
            self.not_empty.notify()

    def get_flags(self, reset=True):
        with self.mutex:
            if reset:
                ret = self.flags
                self.flags = {}
                return ret
            else:
                return self.flags.copy()