mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-01-10 06:10:50 +08:00
6ab1aa1e8a
- fix #29 str(model) no longer raises exceptions like with HyVideoModelLoader - don't try to format CUDA tensors because that can sometimes raise exceptions - cudaAllocAsync has been disabled for now due to 2.6.0 bugs - improve florence2 support - add support for paligemma 2. This requires the fix for transformers that is currently staged in another repo, install with `uv pip install --no-deps "transformers@git+https://github.com/zucchini-nlp/transformers.git#branch=paligemma-fix-kwargs"` - triton has been updated - fix missing __init__.py files
1151 lines
48 KiB
Python
1151 lines
48 KiB
Python
from __future__ import annotations
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import copy
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import heapq
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import inspect
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import logging
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import sys
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import threading
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import time
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import traceback
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import typing
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from contextlib import nullcontext
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from os import PathLike
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from typing import List, Optional, Tuple
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import lazy_object_proxy
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import torch
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from opentelemetry.trace import get_current_span, StatusCode, Status
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from .main_pre import tracer
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from .. import interruption
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from .. import model_management
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from ..component_model.abstract_prompt_queue import AbstractPromptQueue
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from ..component_model.executor_types import ExecutorToClientProgress, ValidationTuple, ValidateInputsTuple, \
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ValidationErrorDict, NodeErrorsDictValue, ValidationErrorExtraInfoDict, FormattedValue, RecursiveExecutionTuple, \
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RecursiveExecutionErrorDetails, RecursiveExecutionErrorDetailsInterrupted, ExecutionResult, DuplicateNodeError, \
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HistoryResultDict, ExecutionErrorMessage, ExecutionInterruptedMessage
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from ..component_model.files import canonicalize_path
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from ..component_model.queue_types import QueueTuple, HistoryEntry, QueueItem, MAXIMUM_HISTORY_SIZE, ExecutionStatus
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from ..execution_context import context_execute_node, context_execute_prompt
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from ..nodes.package import import_all_nodes_in_workspace
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from ..nodes.package_typing import ExportedNodes, InputTypeSpec, FloatSpecOptions, IntSpecOptions, CustomNode
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# ideally this would be passed in from main, but the way this is authored, we can't easily pass nodes down to the
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# various functions that are declared here. It should have been a context in the first place.
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nodes: ExportedNodes = lazy_object_proxy.Proxy(import_all_nodes_in_workspace)
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# order matters
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from ..graph import get_input_info, ExecutionList, DynamicPrompt, ExecutionBlocker
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from ..graph_utils import is_link, GraphBuilder
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from ..caching import HierarchicalCache, LRUCache, CacheKeySetInputSignature, CacheKeySetID
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from ..validation import validate_node_input
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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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# Intentionally do not use cached outputs here. We only want constants in IS_CHANGED
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input_data_all, _ = get_input_data(node["inputs"], class_def, node_id, None)
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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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# Performs like the old cache -- dump data ASAP
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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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else:
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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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self.outputs = LRUCache(CacheKeySetInputSignature, max_size=lru_size)
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self.ui = LRUCache(CacheKeySetInputSignature, max_size=lru_size)
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self.objects = HierarchicalCache(CacheKeySetID)
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self.all = [self.outputs, self.ui, self.objects]
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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=None):
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if extra_data is None:
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extra_data = {}
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if outputs is None:
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outputs = {}
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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, valid_inputs)
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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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# todo: this should be retrieved from the execution context
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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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@tracer.start_as_current_span("Execute Node")
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def map_node_over_list(obj, input_data_all: typing.Dict[str, typing.Any], func: str, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None):
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span = get_current_span()
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class_type = obj.__class__.__name__
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span.set_attribute("class_type", class_type)
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if input_data_all is not None:
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for kwarg_name, kwarg_value in input_data_all.items():
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if isinstance(kwarg_value, str) or isinstance(kwarg_value, bool) or isinstance(kwarg_value, int) or isinstance(kwarg_value, float):
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span.set_attribute(f"input_data_all.{kwarg_name}", kwarg_value)
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else:
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try:
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items_to_display = []
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if hasattr(kwarg_value, "shape"):
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# if the object has a shape attribute (likely a NumPy array or similar), get up to the first ten elements
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flat_values = kwarg_value.flatten() if hasattr(kwarg_value, "flatten") else kwarg_value
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items_to_display = [flat_values[i] for i in range(min(10, flat_values.size))]
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elif hasattr(kwarg_value, "__getitem__") and hasattr(kwarg_value, "__len__"):
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# If the object is indexable and has a length, get the first ten items
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items_to_display = [kwarg_value[i] for i in range(min(10, len(kwarg_value)))]
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filtered_items = [
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item for item in items_to_display if isinstance(item, (str, bool, int, float))
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]
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if filtered_items:
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span.set_attribute(f"input_data_all.{kwarg_name}", filtered_items)
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except TypeError:
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pass
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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, input_is_list=False):
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if allow_interrupt:
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interruption.throw_exception_if_processing_interrupted()
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execution_block = None
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for k, v in inputs.items():
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if input_is_list:
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for e in v:
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if isinstance(e, ExecutionBlocker):
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v = e
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break
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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, input_is_list=input_is_list)
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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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value = []
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for o in results:
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if isinstance(o[i], ExecutionBlocker):
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value.append(o[i])
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else:
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value.extend(o[i])
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output.append(value)
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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) -> FormattedValue:
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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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elif isinstance(x, dict) and not any(isinstance(v, torch.Tensor) for v in x.values()):
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return str(x)
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else:
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return str(x.__class__)
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def execute(server: ExecutorToClientProgress, dynprompt: DynamicPrompt, caches, _node_id: str, extra_data: dict, executed, prompt_id, execution_list, pending_subgraph_results) -> RecursiveExecutionTuple:
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"""
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:param server:
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:param dynprompt:
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:param caches:
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:param node_id: the node id
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:param extra_data:
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:param executed:
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:param prompt_id:
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:param execution_list:
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:param pending_subgraph_results:
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:return:
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"""
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with context_execute_node(_node_id):
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return _execute(server, dynprompt, caches, _node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results)
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def _execute(server, dynprompt, caches: CacheSet, current_item: str, extra_data, executed, prompt_id, execution_list, pending_subgraph_results) -> RecursiveExecutionTuple:
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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 RecursiveExecutionTuple(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 RecursiveExecutionTuple(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: ExecutionErrorMessage = {
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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},
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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]):
|
|
from_node_id, from_socket = node_outputs[i][0], node_outputs[i][1]
|
|
new_output_links.append((from_node_id, from_socket))
|
|
cached_outputs.append((True, node_outputs))
|
|
new_node_ids = set(new_node_ids)
|
|
for cache in caches.all:
|
|
cache.ensure_subcache_for(unique_id, new_node_ids).clean_unused()
|
|
for node_id in new_output_ids:
|
|
execution_list.add_node(node_id)
|
|
for link in new_output_links:
|
|
execution_list.add_strong_link(link[0], link[1], unique_id)
|
|
pending_subgraph_results[unique_id] = cached_outputs
|
|
return RecursiveExecutionTuple(ExecutionResult.PENDING, None, None)
|
|
caches.outputs.set(unique_id, output_data)
|
|
except interruption.InterruptProcessingException as iex:
|
|
logging.info("Processing interrupted")
|
|
|
|
# skip formatting inputs/outputs
|
|
error_details: RecursiveExecutionErrorDetailsInterrupted = {
|
|
"node_id": real_node_id,
|
|
}
|
|
|
|
return RecursiveExecutionTuple(ExecutionResult.FAILURE, error_details, iex)
|
|
except Exception as ex:
|
|
typ, _, tb = sys.exc_info()
|
|
exception_type = full_type_name(typ)
|
|
input_data_formatted = {}
|
|
if input_data_all is not None:
|
|
input_data_formatted = {}
|
|
for name, inputs in input_data_all.items():
|
|
input_data_formatted[name] = [format_value(x) for x in inputs]
|
|
|
|
logging.error("An error occurred while executing a workflow", exc_info=ex)
|
|
logging.error(traceback.format_exc())
|
|
|
|
error_details: RecursiveExecutionErrorDetails = {
|
|
"node_id": real_node_id,
|
|
"exception_message": str(ex),
|
|
"exception_type": exception_type,
|
|
"traceback": traceback.format_tb(tb),
|
|
"current_inputs": input_data_formatted
|
|
}
|
|
|
|
if isinstance(ex, model_management.OOM_EXCEPTION):
|
|
logging.error("Got an OOM, unloading all loaded models.")
|
|
model_management.unload_all_models()
|
|
|
|
return RecursiveExecutionTuple(ExecutionResult.FAILURE, error_details, ex)
|
|
|
|
executed.add(unique_id)
|
|
|
|
return RecursiveExecutionTuple(ExecutionResult.SUCCESS, None, None)
|
|
|
|
|
|
class PromptExecutor:
|
|
def __init__(self, server: ExecutorToClientProgress, lru_size=None):
|
|
self.success = None
|
|
self.lru_size = lru_size
|
|
self.server = server
|
|
self.raise_exceptions = False
|
|
self.reset()
|
|
self.history_result: HistoryResultDict | None = None
|
|
|
|
def reset(self):
|
|
self.success = True
|
|
self.caches = CacheSet(self.lru_size)
|
|
self.status_messages = []
|
|
|
|
def add_message(self, event, data: dict, broadcast: bool):
|
|
data = {
|
|
**data,
|
|
# todo: use a real time library
|
|
"timestamp": int(time.time() * 1000),
|
|
}
|
|
self.status_messages.append((event, data))
|
|
if self.server.client_id is not None or broadcast:
|
|
self.server.send_sync(event, data, self.server.client_id)
|
|
|
|
def handle_execution_error(self, prompt_id, prompt, current_outputs, executed, error, ex):
|
|
current_span = get_current_span()
|
|
current_span.set_status(Status(StatusCode.ERROR))
|
|
current_span.record_exception(ex)
|
|
|
|
node_id = error["node_id"]
|
|
class_type = prompt[node_id]["class_type"]
|
|
|
|
# First, send back the status to the frontend depending
|
|
# on the exception type
|
|
if isinstance(ex, interruption.InterruptProcessingException):
|
|
mes: ExecutionInterruptedMessage = {
|
|
"prompt_id": prompt_id,
|
|
"node_id": node_id,
|
|
"node_type": class_type,
|
|
"executed": list(executed),
|
|
}
|
|
self.add_message("execution_interrupted", mes, broadcast=True)
|
|
else:
|
|
mes: ExecutionErrorMessage = {
|
|
"prompt_id": prompt_id,
|
|
"node_id": node_id,
|
|
"node_type": class_type,
|
|
"executed": list(executed),
|
|
"exception_message": error["exception_message"],
|
|
"exception_type": error["exception_type"],
|
|
"traceback": error["traceback"],
|
|
"current_inputs": error["current_inputs"],
|
|
"current_outputs": list(current_outputs),
|
|
}
|
|
self.add_message("execution_error", mes, broadcast=False)
|
|
|
|
if ex is not None and self.raise_exceptions:
|
|
raise ex
|
|
|
|
def execute(self, prompt, prompt_id, extra_data=None, execute_outputs: List[str] = None):
|
|
# torchao and potentially other optimization approaches break when the models are created in inference mode
|
|
# todo: this should really be backpropagated to code which creates ModelPatchers via lazy evaluation rather than globally checked here
|
|
inference_mode = all(not hasattr(node_class, "INFERENCE_MODE") or node_class.INFERENCE_MODE for node_class in iterate_obj_classes(prompt))
|
|
with context_execute_prompt(self.server, prompt_id, inference_mode=inference_mode):
|
|
self._execute_inner(prompt, prompt_id, extra_data, execute_outputs)
|
|
|
|
def _execute_inner(self, prompt, prompt_id, extra_data=None, execute_outputs: List[str] = None, inference_mode: bool = True):
|
|
if execute_outputs is None:
|
|
execute_outputs = []
|
|
if extra_data is None:
|
|
extra_data = {}
|
|
interruption.interrupt_current_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() if inference_mode else nullcontext():
|
|
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)
|
|
|
|
model_management.cleanup_models_gc()
|
|
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()
|
|
node_id: str
|
|
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)
|
|
self.success = result != ExecutionResult.FAILURE
|
|
if result == ExecutionResult.FAILURE:
|
|
self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
|
|
break
|
|
elif result == ExecutionResult.PENDING:
|
|
execution_list.unstage_node_execution()
|
|
else: # result == ExecutionResult.SUCCESS:
|
|
execution_list.complete_node_execution()
|
|
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 model_management.DISABLE_SMART_MEMORY:
|
|
model_management.unload_all_models()
|
|
|
|
@property
|
|
def outputs_ui(self) -> dict | None:
|
|
return self.history_result["outputs"] if self.history_result is not None else None
|
|
|
|
|
|
def iterate_obj_classes(prompt: dict[str, typing.Any]) -> typing.Generator[typing.Type[CustomNode], None, None]:
|
|
for _, node in prompt.items():
|
|
yield nodes.NODE_CLASS_MAPPINGS[node['class_type']]
|
|
|
|
|
|
def validate_inputs(prompt, item, validated: typing.Dict[str, ValidateInputsTuple]) -> ValidateInputsTuple:
|
|
# todo: this should check if LoadImage / LoadImageMask paths exist
|
|
# todo: or, nodes should provide a way to validate their values
|
|
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', {})))
|
|
|
|
error: ValidationErrorDict
|
|
errors = []
|
|
valid = True
|
|
|
|
# todo: investigate if these are at the right indent level
|
|
info: Optional[InputTypeSpec] = None
|
|
val = None
|
|
|
|
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, class_inputs)
|
|
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: InputTypeSpec = (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
|
|
any_enum = received_type == [] and (isinstance(type_input, list) or isinstance(type_input, tuple))
|
|
|
|
if 'input_types' not in validate_function_inputs and not validate_node_input(received_type, type_input) and not any_enum:
|
|
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:
|
|
r2 = validate_inputs(prompt, o_id, validated)
|
|
if r2[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] = ValidateInputsTuple(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:
|
|
has_min_max: IntSpecOptions | FloatSpecOptions = info[1]
|
|
if "min" in has_min_max and val < has_min_max["min"]:
|
|
error = {
|
|
"type": "value_smaller_than_min",
|
|
"message": "Value {} smaller than min of {}".format(val, has_min_max["min"]),
|
|
"details": f"{x}",
|
|
"extra_info": {
|
|
"input_name": x,
|
|
"input_config": info,
|
|
"received_value": val,
|
|
}
|
|
}
|
|
errors.append(error)
|
|
continue
|
|
if "max" in has_min_max and val > has_min_max["max"]:
|
|
error = {
|
|
"type": "value_bigger_than_max",
|
|
"message": "Value {} bigger than max of {}".format(val, has_min_max["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 "\\" in val:
|
|
# try to normalize paths for comparison purposes
|
|
val = canonicalize_path(val)
|
|
if all(isinstance(item, (str, PathLike)) for item in type_input):
|
|
type_input = [canonicalize_path(item) for item in type_input]
|
|
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 = ValidateInputsTuple(False, errors, unique_id)
|
|
else:
|
|
ret = ValidateInputsTuple(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__
|
|
|
|
|
|
@tracer.start_as_current_span("Validate Prompt")
|
|
def validate_prompt(prompt: typing.Mapping[str, typing.Any]) -> ValidationTuple:
|
|
res = _validate_prompt(prompt)
|
|
if not res.valid:
|
|
span = get_current_span()
|
|
span.set_status(Status(StatusCode.ERROR))
|
|
if res.error is not None and len(res.error) > 0:
|
|
span.set_attributes({
|
|
f"error.{k}": v for k, v in res.error.items() if isinstance(v, (bool, str, bytes, int, float, list))
|
|
})
|
|
if "extra_info" in res.error and isinstance(res.error["extra_info"], dict):
|
|
extra_info: ValidationErrorExtraInfoDict = res.error["extra_info"]
|
|
span.set_attributes({
|
|
f"error.extra_info.{k}": v for k, v in extra_info.items() if isinstance(v, (str, list))
|
|
})
|
|
if len(res.node_errors) > 0:
|
|
for node_id, node_error in res.node_errors.items():
|
|
for node_error_field, node_error_value in node_error.items():
|
|
if isinstance(node_error_value, (str, bool, int, float)):
|
|
span.set_attribute(f"node_errors.{node_id}.{node_error_field}", node_error_value)
|
|
return res
|
|
|
|
|
|
def _validate_prompt(prompt: typing.Mapping[str, typing.Any]) -> ValidationTuple:
|
|
outputs = set()
|
|
for x in prompt:
|
|
if 'class_type' not in prompt[x]:
|
|
error = {
|
|
"type": "invalid_prompt",
|
|
"message": "Cannot execute because a node is missing the class_type property.",
|
|
"details": f"Node ID '#{x}'",
|
|
"extra_info": {}
|
|
}
|
|
return ValidationTuple(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 ValidationTuple(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 ValidationTuple(False, error, [], [])
|
|
|
|
good_outputs = set()
|
|
errors = []
|
|
node_errors: typing.Dict[str, NodeErrorsDictValue] = {}
|
|
validated: typing.Dict[str, ValidateInputsTuple] = {}
|
|
for o in outputs:
|
|
valid = False
|
|
reasons: List[ValidationErrorDict] = []
|
|
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] = ValidateInputsTuple(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 ValidationTuple(False, error, list(good_outputs), node_errors)
|
|
|
|
return ValidationTuple(True, None, list(good_outputs), node_errors)
|
|
|
|
|
|
class PromptQueue(AbstractPromptQueue):
|
|
def __init__(self, server: ExecutorToClientProgress):
|
|
self.server = server
|
|
self.mutex = threading.RLock()
|
|
self.not_empty = threading.Condition(self.mutex)
|
|
self.queue: typing.List[QueueItem] = []
|
|
self.currently_running: typing.Dict[str, QueueItem] = {}
|
|
# history maps the second integer prompt id in the queue tuple to a dictionary with keys "prompt" and "outputs
|
|
# todo: use the new History class for the sake of simplicity
|
|
self.history: typing.Dict[str, HistoryEntry] = {}
|
|
self.flags = {}
|
|
|
|
def size(self) -> int:
|
|
return len(self.queue)
|
|
|
|
def put(self, item: QueueItem):
|
|
with self.mutex:
|
|
heapq.heappush(self.queue, item)
|
|
self.server.queue_updated()
|
|
self.not_empty.notify()
|
|
|
|
def get(self, timeout=None) -> typing.Optional[typing.Tuple[QueueTuple, str]]:
|
|
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_with_future: QueueItem = heapq.heappop(self.queue)
|
|
assert item_with_future.prompt_id is not None
|
|
assert item_with_future.prompt_id != ""
|
|
assert item_with_future.prompt_id not in self.currently_running
|
|
assert isinstance(item_with_future.prompt_id, str)
|
|
task_id = item_with_future.prompt_id
|
|
self.currently_running[task_id] = item_with_future
|
|
self.server.queue_updated()
|
|
return copy.deepcopy(item_with_future.queue_tuple), task_id
|
|
|
|
def task_done(self, item_id: str, outputs: dict,
|
|
status: Optional[ExecutionStatus]):
|
|
history_result = outputs
|
|
with self.mutex:
|
|
queue_item = self.currently_running.pop(item_id)
|
|
prompt = queue_item.queue_tuple
|
|
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(ExecutionStatus(*status)._asdict())
|
|
|
|
outputs_ = history_result["outputs"]
|
|
self.history[prompt[1]] = {
|
|
"prompt": prompt,
|
|
"outputs": copy.deepcopy(outputs_),
|
|
'status': status_dict,
|
|
}
|
|
self.history[prompt[1]].update(history_result)
|
|
self.server.queue_updated()
|
|
if queue_item.completed:
|
|
queue_item.completed.set_result(outputs_)
|
|
|
|
def get_current_queue(self) -> Tuple[typing.List[QueueTuple], typing.List[QueueTuple]]:
|
|
with self.mutex:
|
|
out: typing.List[QueueTuple] = []
|
|
for x in self.currently_running.values():
|
|
out += [x.queue_tuple]
|
|
return out, copy.deepcopy([item.queue_tuple for item in 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:
|
|
for item in self.queue:
|
|
if item.completed:
|
|
item.completed.set_exception(Exception("queue cancelled"))
|
|
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].queue_tuple):
|
|
if len(self.queue) == 1:
|
|
self.wipe_queue()
|
|
else:
|
|
item = self.queue.pop(x)
|
|
if item.completed:
|
|
item.completed.set_exception(Exception("queue item deleted"))
|
|
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.clear()
|
|
|
|
def delete_history_item(self, id_to_delete: str):
|
|
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()
|