982 lines
38 KiB
Python
982 lines
38 KiB
Python
import sys
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import copy
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import logging
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import threading
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import heapq
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import time
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import traceback
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from enum import Enum
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import inspect
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from typing import List, Literal, NamedTuple, Optional
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import torch
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import nodes
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import comfy.model_management
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import comfy.graph_utils
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from comfy.graph import get_input_info, ExecutionList, DynamicPrompt, ExecutionBlocker
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from comfy.graph_utils import is_link, GraphBuilder
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from comfy.caching import HierarchicalCache, LRUCache, CacheKeySetInputSignature, CacheKeySetID
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from comfy.cli_args import args
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class ExecutionResult(Enum):
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SUCCESS = 0
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FAILURE = 1
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PENDING = 2
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class DuplicateNodeError(Exception):
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pass
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class IsChangedCache:
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def __init__(self, dynprompt, outputs_cache):
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self.dynprompt = dynprompt
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self.outputs_cache = outputs_cache
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self.is_changed = {}
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def get(self, node_id):
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if node_id in self.is_changed:
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return self.is_changed[node_id]
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node = self.dynprompt.get_node(node_id)
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class_type = node["class_type"]
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class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
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if not hasattr(class_def, "IS_CHANGED"):
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self.is_changed[node_id] = False
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return self.is_changed[node_id]
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if "is_changed" in node:
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self.is_changed[node_id] = node["is_changed"]
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return self.is_changed[node_id]
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input_data_all, _ = get_input_data(node["inputs"], class_def, node_id, self.outputs_cache)
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try:
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is_changed = map_node_over_list(class_def, input_data_all, "IS_CHANGED")
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node["is_changed"] = [None if isinstance(x, ExecutionBlocker) else x for x in is_changed]
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except:
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node["is_changed"] = float("NaN")
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finally:
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self.is_changed[node_id] = node["is_changed"]
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return self.is_changed[node_id]
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class CacheSet:
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def __init__(self, lru_size=None):
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if lru_size is None or lru_size == 0:
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self.init_classic_cache()
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else:
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self.init_lru_cache(lru_size)
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self.all = [self.outputs, self.ui, self.objects]
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# Useful for those with ample RAM/VRAM -- allows experimenting without
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# blowing away the cache every time
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def init_lru_cache(self, cache_size):
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self.outputs = LRUCache(CacheKeySetInputSignature, max_size=cache_size)
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self.ui = LRUCache(CacheKeySetInputSignature, max_size=cache_size)
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self.objects = HierarchicalCache(CacheKeySetID)
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# Performs like the old cache -- dump data ASAP
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def init_classic_cache(self):
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self.outputs = HierarchicalCache(CacheKeySetInputSignature)
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self.ui = HierarchicalCache(CacheKeySetInputSignature)
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self.objects = HierarchicalCache(CacheKeySetID)
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def recursive_debug_dump(self):
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result = {
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"outputs": self.outputs.recursive_debug_dump(),
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"ui": self.ui.recursive_debug_dump(),
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}
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return result
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def get_input_data(inputs, class_def, unique_id, outputs=None, dynprompt=None, extra_data={}):
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valid_inputs = class_def.INPUT_TYPES()
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input_data_all = {}
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missing_keys = {}
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for x in inputs:
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input_data = inputs[x]
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input_type, input_category, input_info = get_input_info(class_def, x)
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def mark_missing():
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missing_keys[x] = True
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input_data_all[x] = (None,)
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if is_link(input_data) and (not input_info or not input_info.get("rawLink", False)):
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input_unique_id = input_data[0]
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output_index = input_data[1]
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if outputs is None:
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mark_missing()
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continue # This might be a lazily-evaluated input
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cached_output = outputs.get(input_unique_id)
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if cached_output is None:
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mark_missing()
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continue
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if output_index >= len(cached_output):
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mark_missing()
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continue
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obj = cached_output[output_index]
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input_data_all[x] = obj
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elif input_category is not None:
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input_data_all[x] = [input_data]
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if "hidden" in valid_inputs:
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h = valid_inputs["hidden"]
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for x in h:
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if h[x] == "PROMPT":
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input_data_all[x] = [dynprompt.get_original_prompt() if dynprompt is not None else {}]
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if h[x] == "DYNPROMPT":
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input_data_all[x] = [dynprompt]
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if h[x] == "EXTRA_PNGINFO":
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input_data_all[x] = [extra_data.get('extra_pnginfo', None)]
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if h[x] == "UNIQUE_ID":
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input_data_all[x] = [unique_id]
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return input_data_all, missing_keys
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def map_node_over_list(obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None):
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# check if node wants the lists
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input_is_list = getattr(obj, "INPUT_IS_LIST", False)
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if len(input_data_all) == 0:
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max_len_input = 0
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else:
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max_len_input = max(len(x) for x in input_data_all.values())
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# get a slice of inputs, repeat last input when list isn't long enough
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def slice_dict(d, i):
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return {k: v[i if len(v) > i else -1] for k, v in d.items()}
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results = []
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def process_inputs(inputs, index=None):
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if allow_interrupt:
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nodes.before_node_execution()
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execution_block = None
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for k, v in inputs.items():
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if isinstance(v, ExecutionBlocker):
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execution_block = execution_block_cb(v) if execution_block_cb else v
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break
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if execution_block is None:
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if pre_execute_cb is not None and index is not None:
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pre_execute_cb(index)
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results.append(getattr(obj, func)(**inputs))
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else:
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results.append(execution_block)
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if input_is_list:
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process_inputs(input_data_all, 0)
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elif max_len_input == 0:
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process_inputs({})
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else:
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for i in range(max_len_input):
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input_dict = slice_dict(input_data_all, i)
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process_inputs(input_dict, i)
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return results
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def merge_result_data(results, obj):
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# check which outputs need concatenating
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output = []
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output_is_list = [False] * len(results[0])
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if hasattr(obj, "OUTPUT_IS_LIST"):
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output_is_list = obj.OUTPUT_IS_LIST
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# merge node execution results
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for i, is_list in zip(range(len(results[0])), output_is_list):
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if is_list:
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output.append([x for o in results for x in o[i]])
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else:
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output.append([o[i] for o in results])
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return output
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def get_output_data(obj, input_data_all, execution_block_cb=None, pre_execute_cb=None):
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results = []
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uis = []
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subgraph_results = []
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return_values = map_node_over_list(obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb)
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has_subgraph = False
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for i in range(len(return_values)):
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r = return_values[i]
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if isinstance(r, dict):
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if 'ui' in r:
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uis.append(r['ui'])
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if 'expand' in r:
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# Perform an expansion, but do not append results
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has_subgraph = True
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new_graph = r['expand']
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result = r.get("result", None)
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if isinstance(result, ExecutionBlocker):
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result = tuple([result] * len(obj.RETURN_TYPES))
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subgraph_results.append((new_graph, result))
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elif 'result' in r:
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result = r.get("result", None)
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if isinstance(result, ExecutionBlocker):
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result = tuple([result] * len(obj.RETURN_TYPES))
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results.append(result)
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subgraph_results.append((None, result))
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else:
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if isinstance(r, ExecutionBlocker):
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r = tuple([r] * len(obj.RETURN_TYPES))
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results.append(r)
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subgraph_results.append((None, r))
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if has_subgraph:
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output = subgraph_results
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elif len(results) > 0:
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output = merge_result_data(results, obj)
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else:
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output = []
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ui = dict()
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if len(uis) > 0:
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ui = {k: [y for x in uis for y in x[k]] for k in uis[0].keys()}
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return output, ui, has_subgraph
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def format_value(x):
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if x is None:
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return None
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elif isinstance(x, (int, float, bool, str)):
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return x
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else:
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return str(x)
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def execute(server, dynprompt, caches, current_item, extra_data, executed, prompt_id, execution_list, pending_subgraph_results):
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unique_id = current_item
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real_node_id = dynprompt.get_real_node_id(unique_id)
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display_node_id = dynprompt.get_display_node_id(unique_id)
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parent_node_id = dynprompt.get_parent_node_id(unique_id)
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inputs = dynprompt.get_node(unique_id)['inputs']
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class_type = dynprompt.get_node(unique_id)['class_type']
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class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
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if caches.outputs.get(unique_id) is not None:
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if server.client_id is not None:
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cached_output = caches.ui.get(unique_id) or {}
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server.send_sync("executed", { "node": unique_id, "display_node": display_node_id, "output": cached_output.get("output",None), "prompt_id": prompt_id }, server.client_id)
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return (ExecutionResult.SUCCESS, None, None)
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input_data_all = None
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try:
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if unique_id in pending_subgraph_results:
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cached_results = pending_subgraph_results[unique_id]
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resolved_outputs = []
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for is_subgraph, result in cached_results:
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if not is_subgraph:
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resolved_outputs.append(result)
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else:
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resolved_output = []
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for r in result:
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if is_link(r):
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source_node, source_output = r[0], r[1]
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node_output = caches.outputs.get(source_node)[source_output]
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for o in node_output:
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resolved_output.append(o)
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else:
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resolved_output.append(r)
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resolved_outputs.append(tuple(resolved_output))
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output_data = merge_result_data(resolved_outputs, class_def)
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output_ui = []
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has_subgraph = False
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else:
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input_data_all, missing_keys = get_input_data(inputs, class_def, unique_id, caches.outputs, dynprompt, extra_data)
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if server.client_id is not None:
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server.last_node_id = display_node_id
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server.send_sync("executing", { "node": unique_id, "display_node": display_node_id, "prompt_id": prompt_id }, server.client_id)
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obj = caches.objects.get(unique_id)
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if obj is None:
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obj = class_def()
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caches.objects.set(unique_id, obj)
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if hasattr(obj, "check_lazy_status"):
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required_inputs = map_node_over_list(obj, input_data_all, "check_lazy_status", allow_interrupt=True)
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required_inputs = set(sum([r for r in required_inputs if isinstance(r,list)], []))
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required_inputs = [x for x in required_inputs if isinstance(x,str) and (
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x not in input_data_all or x in missing_keys
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)]
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if len(required_inputs) > 0:
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for i in required_inputs:
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execution_list.make_input_strong_link(unique_id, i)
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return (ExecutionResult.PENDING, None, None)
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def execution_block_cb(block):
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if block.message is not None:
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mes = {
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"prompt_id": prompt_id,
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"node_id": unique_id,
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"node_type": class_type,
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"executed": list(executed),
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"exception_message": f"Execution Blocked: {block.message}",
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"exception_type": "ExecutionBlocked",
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"traceback": [],
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"current_inputs": [],
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"current_outputs": [],
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}
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server.send_sync("execution_error", mes, server.client_id)
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return ExecutionBlocker(None)
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else:
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return block
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def pre_execute_cb(call_index):
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GraphBuilder.set_default_prefix(unique_id, call_index, 0)
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output_data, output_ui, has_subgraph = get_output_data(obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb)
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if len(output_ui) > 0:
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caches.ui.set(unique_id, {
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"meta": {
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"node_id": unique_id,
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"display_node": display_node_id,
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"parent_node": parent_node_id,
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"real_node_id": real_node_id,
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},
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"output": output_ui
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})
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if server.client_id is not None:
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server.send_sync("executed", { "node": unique_id, "display_node": display_node_id, "output": output_ui, "prompt_id": prompt_id }, server.client_id)
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if has_subgraph:
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cached_outputs = []
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new_node_ids = []
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new_output_ids = []
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new_output_links = []
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for i in range(len(output_data)):
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new_graph, node_outputs = output_data[i]
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if new_graph is None:
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cached_outputs.append((False, node_outputs))
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else:
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# Check for conflicts
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for node_id in new_graph.keys():
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if dynprompt.has_node(node_id):
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raise DuplicateNodeError(f"Attempt to add duplicate node {node_id}. Ensure node ids are unique and deterministic or use graph_utils.GraphBuilder.")
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for node_id, node_info in new_graph.items():
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new_node_ids.append(node_id)
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display_id = node_info.get("override_display_id", unique_id)
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dynprompt.add_ephemeral_node(node_id, node_info, unique_id, display_id)
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# Figure out if the newly created node is an output node
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class_type = node_info["class_type"]
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class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
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if hasattr(class_def, 'OUTPUT_NODE') and class_def.OUTPUT_NODE == True:
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new_output_ids.append(node_id)
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for i in range(len(node_outputs)):
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if is_link(node_outputs[i]):
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from_node_id, from_socket = node_outputs[i][0], node_outputs[i][1]
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new_output_links.append((from_node_id, from_socket))
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cached_outputs.append((True, node_outputs))
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new_node_ids = set(new_node_ids)
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for cache in caches.all:
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cache.ensure_subcache_for(unique_id, new_node_ids).clean_unused()
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for node_id in new_output_ids:
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execution_list.add_node(node_id)
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for link in new_output_links:
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execution_list.add_strong_link(link[0], link[1], unique_id)
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pending_subgraph_results[unique_id] = cached_outputs
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return (ExecutionResult.PENDING, None, None)
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caches.outputs.set(unique_id, output_data)
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except comfy.model_management.InterruptProcessingException as iex:
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logging.info("Processing interrupted")
|
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|
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# skip formatting inputs/outputs
|
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error_details = {
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"node_id": real_node_id,
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}
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return (ExecutionResult.FAILURE, error_details, iex)
|
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except Exception as ex:
|
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typ, _, tb = sys.exc_info()
|
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exception_type = full_type_name(typ)
|
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input_data_formatted = {}
|
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if input_data_all is not None:
|
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input_data_formatted = {}
|
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for name, inputs in input_data_all.items():
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input_data_formatted[name] = [format_value(x) for x in inputs]
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|
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logging.error(f"!!! Exception during processing !!! {ex}")
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logging.error(traceback.format_exc())
|
|
|
|
error_details = {
|
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"node_id": real_node_id,
|
|
"exception_message": str(ex),
|
|
"exception_type": exception_type,
|
|
"traceback": traceback.format_tb(tb),
|
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"current_inputs": input_data_formatted
|
|
}
|
|
if isinstance(ex, comfy.model_management.OOM_EXCEPTION):
|
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logging.error("Got an OOM, unloading all loaded models.")
|
|
comfy.model_management.unload_all_models()
|
|
|
|
return (ExecutionResult.FAILURE, error_details, ex)
|
|
|
|
executed.add(unique_id)
|
|
|
|
return (ExecutionResult.SUCCESS, None, None)
|
|
|
|
class PromptExecutor:
|
|
def __init__(self, server, lru_size=None):
|
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self.lru_size = lru_size
|
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self.server = server
|
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self.reset()
|
|
|
|
def reset(self):
|
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self.caches = CacheSet(self.lru_size)
|
|
self.status_messages = []
|
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self.success = True
|
|
|
|
def add_message(self, event, data: dict, broadcast: bool):
|
|
data = {
|
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**data,
|
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"timestamp": int(time.time() * 1000),
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}
|
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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)
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|
|
|
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
|
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# 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),
|
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}
|
|
self.add_message("execution_interrupted", mes, broadcast=True)
|
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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"],
|
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"current_outputs": list(current_outputs),
|
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}
|
|
self.add_message("execution_error", mes, broadcast=False)
|
|
|
|
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():
|
|
dynamic_prompt = DynamicPrompt(prompt)
|
|
is_changed_cache = IsChangedCache(dynamic_prompt, self.caches.outputs)
|
|
for cache in self.caches.all:
|
|
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(keep_clone_weights_loaded=True)
|
|
self.add_message("execution_cached",
|
|
{ "nodes": cached_nodes, "prompt_id": prompt_id},
|
|
broadcast=False)
|
|
pending_subgraph_results = {}
|
|
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 = 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
|
|
|
|
result, error, ex = execute(self.server, dynamic_prompt, self.caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results)
|
|
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()
|
|
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 = {}
|
|
all_node_ids = self.caches.ui.all_node_ids()
|
|
for node_id in all_node_ids:
|
|
ui_info = self.caches.ui.get(node_id)
|
|
if ui_info is not None:
|
|
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()
|
|
|
|
|
|
|
|
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()
|
|
valid_inputs = set(class_inputs.get('required',{})).union(set(class_inputs.get('optional',{})))
|
|
|
|
errors = []
|
|
valid = True
|
|
|
|
validate_function_inputs = []
|
|
validate_has_kwargs = False
|
|
if hasattr(obj_class, "VALIDATE_INPUTS"):
|
|
argspec = inspect.getfullargspec(obj_class.VALIDATE_INPUTS)
|
|
validate_function_inputs = argspec.args
|
|
validate_has_kwargs = argspec.varkw is not None
|
|
received_types = {}
|
|
|
|
for x in valid_inputs:
|
|
type_input, input_category, extra_info = get_input_info(obj_class, x)
|
|
assert extra_info is not None
|
|
if x not in inputs:
|
|
if input_category == "required":
|
|
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 = (type_input, 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 received_type != type_input:
|
|
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
|
|
if type_input == "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 {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 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(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 or validate_has_kwargs:
|
|
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 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 = 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 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__
|
|
|
|
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, history_result,
|
|
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": {},
|
|
'status': status_dict,
|
|
}
|
|
self.history[prompt[1]].update(history_result)
|
|
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()
|