mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-01-11 23:00:51 +08:00
e60dbe3a44
Behavior should now match the master branch with regard to undeclared inputs. Undeclared inputs that are socket connections will be used while undeclared inputs that are literals will be ignored.
935 lines
37 KiB
Python
935 lines
37 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 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, CacheKeySetInputSignatureWithID, 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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SLEEPING = 2
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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 not in self.is_changed:
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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 hasattr(class_def, "IS_CHANGED"):
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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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else:
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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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self.is_changed[node_id] = node["is_changed"]
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except:
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node["is_changed"] = float("NaN")
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self.is_changed[node_id] = node["is_changed"]
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else:
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self.is_changed[node_id] = False
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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(CacheKeySetInputSignatureWithID, 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(CacheKeySetInputSignatureWithID)
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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, prompt={}, 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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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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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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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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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] = [prompt]
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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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if "extra_pnginfo" in extra_data:
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input_data_all[x] = [extra_data['extra_pnginfo']]
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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
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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 = False
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if hasattr(obj, "INPUT_IS_LIST"):
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input_is_list = obj.INPUT_IS_LIST
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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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d_new = dict()
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for k,v in d.items():
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d_new[k] = v[i if len(v) > i else -1]
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return d_new
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results = []
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if input_is_list:
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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 input_data_all.items():
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for input in v:
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if isinstance(v, ExecutionBlocker):
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execution_block = execution_block_cb(v) if execution_block_cb is not None 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:
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pre_execute_cb(0)
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results.append(getattr(obj, func)(**input_data_all))
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else:
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results.append(execution_block)
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elif max_len_input == 0:
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if allow_interrupt:
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nodes.before_node_execution()
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results.append(getattr(obj, func)())
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else:
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for i in range(max_len_input):
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if allow_interrupt:
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nodes.before_node_execution()
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input_dict = slice_dict(input_data_all, i)
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execution_block = None
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for k, v in input_dict.items():
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if isinstance(v, ExecutionBlocker):
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execution_block = execution_block_cb(v) if execution_block_cb is not None 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:
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pre_execute_cb(i)
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results.append(getattr(obj, func)(**input_dict))
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else:
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results.append(execution_block)
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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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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 = get_input_data(inputs, class_def, unique_id, caches.outputs, dynprompt.original_prompt, 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 x not in input_data_all]
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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.SLEEPING, 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": "Execution Blocked: %s" % 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.get_node(node_id) is not None:
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raise Exception("Attempt to add duplicate node %s. Ensure node ids are unique and deterministic or use graph_utils.GraphBuilder." % node_id)
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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.SLEEPING, 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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# 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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logging.error("!!! Exception during processing !!!")
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logging.error(traceback.format_exc())
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error_details = {
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"node_id": real_node_id,
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"exception_message": str(ex),
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"exception_type": exception_type,
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"traceback": traceback.format_tb(tb),
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"current_inputs": input_data_formatted
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}
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return (ExecutionResult.FAILURE, error_details, ex)
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executed.add(unique_id)
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return (ExecutionResult.SUCCESS, None, None)
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class PromptExecutor:
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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()
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def reset(self):
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self.caches = CacheSet(self.lru_size)
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self.status_messages = []
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self.success = True
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def add_message(self, event, data, broadcast: bool):
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self.status_messages.append((event, data))
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if self.server.client_id is not None or broadcast:
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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):
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node_id = error["node_id"]
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class_type = prompt[node_id]["class_type"]
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# First, send back the status to the frontend depending
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# on the exception type
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if isinstance(ex, comfy.model_management.InterruptProcessingException):
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mes = {
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"prompt_id": prompt_id,
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"node_id": node_id,
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"node_type": class_type,
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"executed": list(executed),
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}
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self.add_message("execution_interrupted", mes, broadcast=True)
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else:
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mes = {
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"prompt_id": prompt_id,
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"node_id": node_id,
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"node_type": class_type,
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"executed": list(executed),
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"exception_message": error["exception_message"],
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"exception_type": error["exception_type"],
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"traceback": error["traceback"],
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"current_inputs": error["current_inputs"],
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"current_outputs": list(current_outputs),
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}
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self.add_message("execution_error", mes, broadcast=False)
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def execute(self, prompt, prompt_id, extra_data={}, execute_outputs=[]):
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nodes.interrupt_processing(False)
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if "client_id" in extra_data:
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self.server.client_id = extra_data["client_id"]
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else:
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self.server.client_id = None
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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()
|
|
|
|
current_outputs = self.caches.outputs.all_node_ids()
|
|
|
|
comfy.model_management.cleanup_models()
|
|
self.add_message("execution_cached",
|
|
{ "nodes": list(current_outputs) , "prompt_id": prompt_id},
|
|
broadcast=False)
|
|
pending_subgraph_results = {}
|
|
executed = set()
|
|
execution_list = ExecutionList(dynamic_prompt, self.caches.outputs)
|
|
for node_id in list(execute_outputs):
|
|
execution_list.add_node(node_id)
|
|
|
|
while not execution_list.is_empty():
|
|
node_id = execution_list.stage_node_execution()
|
|
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.SLEEPING:
|
|
execution_list.unstage_node_execution()
|
|
else: # result == ExecutionResult.SUCCESS:
|
|
execution_list.complete_node_execution()
|
|
|
|
ui_outputs = {}
|
|
meta_outputs = {}
|
|
for ui_info in self.caches.ui.all_active_values():
|
|
node_id = ui_info["meta"]["node_id"]
|
|
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 = []
|
|
if hasattr(obj_class, "VALIDATE_INPUTS"):
|
|
validate_function_inputs = inspect.getfullargspec(obj_class.VALIDATE_INPUTS).args
|
|
|
|
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
|
|
is_variant = args.enable_variants and (r[val[1]] == "*" or type_input == "*")
|
|
if r[val[1]] != type_input and not is_variant:
|
|
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
|
|
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 "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 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 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:
|
|
class_ = nodes.NODE_CLASS_MAPPINGS[prompt[x]['class_type']]
|
|
if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE == 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()
|