mirror of
https://github.com/comfyanonymous/ComfyUI.git
synced 2026-06-21 07:19:42 +08:00
4e716f7c57
* Add jobs-namespace cancel endpoints
Add two cancel endpoints under the jobs namespace so a job can be
cancelled by id without the caller needing to know whether the job is
running or pending, or branching between /interrupt and /queue.
- POST /api/jobs/{job_id}/cancel cancels one job by id. Idempotent: an
already-finished or unknown id returns 200 {"cancelled": false} rather
than an error.
- POST /api/jobs/cancel takes {"job_ids": [...]} and cancels a batch.
Fail-fast: if any id is unknown the request returns 404 listing the
unknown ids and cancels nothing (no partial side effects).
Both are state-agnostic and map onto the existing queue mechanics: a
running job is interrupted (same path as /interrupt), a pending job is
dequeued (same path as /queue {"delete": [...]}). The cancel logic lives
in comfy_execution.jobs as pure, unit-tested helpers; the server handlers
are thin wrappers. openapi.yaml documents both routes.
* fix: resolve review feedback on cancel endpoints
- Guard cancel_job() against TOCTOU: when dequeue() returns False the
pending job left the queue between snapshot and delete; return
CANCEL_UNKNOWN so callers never report cancelled=True for a remove
that did not happen.
- Validate each job_ids element in the batch cancel endpoint before
any queue access; unhashable or non-UUID values now return 400
instead of raising TypeError (500).
- Update batch HTTP tests to use canonical UUID ids (required now that
the endpoint validates id format) and add tests for the new guards.
* fix: make job cancel atomic and best-effort
Addresses two cancel races/edges raised in review.
Targeted, atomic interrupt. cancel_job's interrupt callback now takes the
prompt id and returns whether it fired; the single-cancel route backs it
with the new PromptQueue.interrupt_if_running, which checks the running set
and signals the interrupt under the queue mutex. This closes the TOCTOU
where a pending job that starts executing between the snapshot and dequeue
(or a running job that finishes between the snapshot and interrupt) could be
missed or, worse, cause an unrelated prompt to be interrupted. The per-prompt
interrupt-flag reset in execute_async keeps a finished job from leaking the
interrupt onto its successor.
Best-effort batch cancel. POST /api/jobs/cancel no longer fails the whole
batch with 404 when one id is unknown/finished; such ids are treated as
no-ops, so "cancel all" still cancels the in-progress jobs even if some
finished between the client's snapshot and the request. Malformed ids are
still rejected with 400.
1400 lines
59 KiB
Python
1400 lines
59 KiB
Python
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 psutil
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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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from enum import Enum
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from typing import List, Literal, NamedTuple, Optional, Union
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import asyncio
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import torch
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from comfy.cli_args import args
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import comfy.memory_management
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import comfy.model_management
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import comfy.model_prefetch
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import comfy_aimdo.model_vbar
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from latent_preview import set_preview_method
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import nodes
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from comfy_execution.caching import (
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BasicCache,
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CacheKeySetID,
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CacheKeySetInputSignature,
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NullCache,
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HierarchicalCache,
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LRUCache,
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RAMPressureCache,
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)
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from comfy_execution.graph import (
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DynamicPrompt,
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ExecutionBlocker,
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ExecutionList,
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get_input_info,
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)
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from comfy_execution.graph_utils import GraphBuilder, is_link
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from comfy_execution.validation import validate_node_input
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from comfy_execution.progress import get_progress_state, reset_progress_state, add_progress_handler, WebUIProgressHandler
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from comfy_execution.utils import CurrentNodeContext
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from comfy_execution.asset_enrichment import enrich_output_with_assets
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from comfy_api.internal import _ComfyNodeInternal, _NodeOutputInternal, first_real_override, is_class, make_locked_method_func
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from comfy_api.latest import io, _io
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from comfy_execution.cache_provider import _has_cache_providers, _get_cache_providers, _logger as _cache_logger
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class ExecutionResult(Enum):
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SUCCESS = 0
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FAILURE = 1
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PENDING = 2
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class DuplicateNodeError(Exception):
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pass
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class IsChangedCache:
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def __init__(self, prompt_id: str, dynprompt: DynamicPrompt, outputs_cache: BasicCache):
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self.prompt_id = prompt_id
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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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async 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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has_is_changed = False
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is_changed_name = None
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if issubclass(class_def, _ComfyNodeInternal) and first_real_override(class_def, "fingerprint_inputs") is not None:
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has_is_changed = True
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is_changed_name = "fingerprint_inputs"
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elif hasattr(class_def, "IS_CHANGED"):
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has_is_changed = True
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is_changed_name = "IS_CHANGED"
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if not has_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, _, v3_data = get_input_data(node["inputs"], class_def, node_id, None)
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try:
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is_changed = await _async_map_node_over_list(self.prompt_id, node_id, class_def, input_data_all, is_changed_name, v3_data=v3_data)
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is_changed = await resolve_map_node_over_list_results(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 Exception as e:
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logging.warning("WARNING: {}".format(e))
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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 CacheEntry(NamedTuple):
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ui: dict
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outputs: list
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class CacheType(Enum):
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CLASSIC = 0
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LRU = 1
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NONE = 2
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RAM_PRESSURE = 3
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class CacheSet:
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def __init__(self, cache_type=None, cache_args={}):
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if cache_type == CacheType.NONE:
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self.init_null_cache()
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logging.info("Disabling intermediate node cache.")
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elif cache_type == CacheType.RAM_PRESSURE:
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cache_ram = cache_args.get("ram", 16.0)
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self.init_ram_cache(cache_ram)
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logging.info("Using RAM pressure cache.")
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elif cache_type == CacheType.LRU:
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cache_size = cache_args.get("lru", 0)
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self.init_lru_cache(cache_size)
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logging.info("Using LRU cache")
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else:
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self.init_classic_cache()
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self.all = [self.outputs, self.objects]
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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, enable_providers=True)
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self.objects = HierarchicalCache(CacheKeySetID)
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def init_lru_cache(self, cache_size):
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self.outputs = LRUCache(CacheKeySetInputSignature, max_size=cache_size, enable_providers=True)
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self.objects = HierarchicalCache(CacheKeySetID)
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def init_ram_cache(self, min_headroom):
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self.outputs = RAMPressureCache(CacheKeySetInputSignature, enable_providers=True)
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self.objects = HierarchicalCache(CacheKeySetID)
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def init_null_cache(self):
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self.outputs = NullCache()
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self.objects = NullCache()
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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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}
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return result
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SENSITIVE_EXTRA_DATA_KEYS = ("auth_token_comfy_org", "api_key_comfy_org")
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def get_input_data(inputs, class_def, unique_id, execution_list=None, dynprompt=None, extra_data={}):
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is_v3 = issubclass(class_def, _ComfyNodeInternal)
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v3_data: io.V3Data = {}
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hidden_inputs_v3 = {}
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valid_inputs = class_def.INPUT_TYPES()
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if is_v3:
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valid_inputs, hidden, v3_data = _io.get_finalized_class_inputs(valid_inputs, inputs)
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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_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 execution_list 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 = execution_list.get_cache(input_unique_id, unique_id)
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if cached is None or cached.outputs is None:
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mark_missing()
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continue
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if output_index >= len(cached.outputs):
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mark_missing()
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continue
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obj = cached.outputs[output_index]
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input_data_all[x] = obj
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elif input_category is not None or (is_v3 and class_def.ACCEPT_ALL_INPUTS):
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input_data_all[x] = [input_data]
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if is_v3:
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if hidden is not None:
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if io.Hidden.prompt.name in hidden:
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hidden_inputs_v3[io.Hidden.prompt] = dynprompt.get_original_prompt() if dynprompt is not None else {}
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if io.Hidden.dynprompt.name in hidden:
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hidden_inputs_v3[io.Hidden.dynprompt] = dynprompt
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if io.Hidden.extra_pnginfo.name in hidden:
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hidden_inputs_v3[io.Hidden.extra_pnginfo] = extra_data.get('extra_pnginfo', None)
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if io.Hidden.unique_id.name in hidden:
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hidden_inputs_v3[io.Hidden.unique_id] = unique_id
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if io.Hidden.auth_token_comfy_org.name in hidden:
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hidden_inputs_v3[io.Hidden.auth_token_comfy_org] = extra_data.get("auth_token_comfy_org", None)
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if io.Hidden.api_key_comfy_org.name in hidden:
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hidden_inputs_v3[io.Hidden.api_key_comfy_org] = extra_data.get("api_key_comfy_org", None)
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if io.Hidden.comfy_usage_source.name in hidden:
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hidden_inputs_v3[io.Hidden.comfy_usage_source] = extra_data.get("comfy_usage_source", None)
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else:
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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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if h[x] == "AUTH_TOKEN_COMFY_ORG":
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input_data_all[x] = [extra_data.get("auth_token_comfy_org", None)]
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if h[x] == "API_KEY_COMFY_ORG":
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input_data_all[x] = [extra_data.get("api_key_comfy_org", None)]
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if h[x] == "COMFY_USAGE_SOURCE":
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input_data_all[x] = [extra_data.get("comfy_usage_source", None)]
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v3_data["hidden_inputs"] = hidden_inputs_v3
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return input_data_all, missing_keys, v3_data
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map_node_over_list = None #Don't hook this please
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async def resolve_map_node_over_list_results(results):
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remaining = [x for x in results if isinstance(x, asyncio.Task) and not x.done()]
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if len(remaining) == 0:
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return [x.result() if isinstance(x, asyncio.Task) else x for x in results]
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else:
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done, pending = await asyncio.wait(remaining)
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for task in done:
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exc = task.exception()
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if exc is not None:
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raise exc
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return [x.result() if isinstance(x, asyncio.Task) else x for x in results]
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async def _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None, v3_data=None):
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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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async def process_inputs(inputs, index=None, input_is_list=False):
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if allow_interrupt:
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nodes.before_node_execution()
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execution_block = None
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for k, v in inputs.items():
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if 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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# V3
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if isinstance(obj, _ComfyNodeInternal) or (is_class(obj) and issubclass(obj, _ComfyNodeInternal)):
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# if is just a class, then assign no state, just create clone
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if is_class(obj):
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type_obj = obj
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obj.VALIDATE_CLASS()
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class_clone = obj.PREPARE_CLASS_CLONE(v3_data)
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# otherwise, use class instance to populate/reuse some fields
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else:
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type_obj = type(obj)
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type_obj.VALIDATE_CLASS()
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class_clone = type_obj.PREPARE_CLASS_CLONE(v3_data)
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f = make_locked_method_func(type_obj, func, class_clone)
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# in case of dynamic inputs, restructure inputs to expected nested dict
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if v3_data is not None:
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inputs = _io.build_nested_inputs(inputs, v3_data)
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# V1
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else:
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f = getattr(obj, func)
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if inspect.iscoroutinefunction(f):
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async def async_wrapper(f, prompt_id, unique_id, list_index, args):
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with CurrentNodeContext(prompt_id, unique_id, list_index):
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return await f(**args)
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task = asyncio.create_task(async_wrapper(f, prompt_id, unique_id, index, args=inputs))
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# Give the task a chance to execute without yielding
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await asyncio.sleep(0)
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if task.done():
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result = task.result()
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results.append(result)
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else:
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results.append(task)
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else:
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with CurrentNodeContext(prompt_id, unique_id, index):
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result = f(**inputs)
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results.append(result)
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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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await 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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await 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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await 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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async def get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=None, pre_execute_cb=None, v3_data=None):
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return_values = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, obj.FUNCTION, allow_interrupt=True, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)
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has_pending_task = any(isinstance(r, asyncio.Task) and not r.done() for r in return_values)
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if has_pending_task:
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return return_values, {}, False, has_pending_task
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output, ui, has_subgraph = get_output_from_returns(return_values, obj)
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return output, ui, has_subgraph, False
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|
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def get_output_from_returns(return_values, obj):
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results = []
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uis = []
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subgraph_results = []
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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))
|
|
elif 'result' in r:
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result = r.get("result", None)
|
|
if isinstance(result, ExecutionBlocker):
|
|
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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elif isinstance(r, _NodeOutputInternal):
|
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# V3
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if r.ui is not None:
|
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if isinstance(r.ui, dict):
|
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uis.append(r.ui)
|
|
else:
|
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uis.append(r.ui.as_dict())
|
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if r.expand is not None:
|
|
has_subgraph = True
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new_graph = r.expand
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|
result = r.result
|
|
if r.block_execution is not None:
|
|
result = tuple([ExecutionBlocker(r.block_execution)] * len(obj.RETURN_TYPES))
|
|
subgraph_results.append((new_graph, result))
|
|
elif r.result is not None:
|
|
result = r.result
|
|
if r.block_execution is not None:
|
|
result = tuple([ExecutionBlocker(r.block_execution)] * len(obj.RETURN_TYPES))
|
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results.append(result)
|
|
subgraph_results.append((None, result))
|
|
else:
|
|
if isinstance(r, ExecutionBlocker):
|
|
r = tuple([r] * len(obj.RETURN_TYPES))
|
|
results.append(r)
|
|
subgraph_results.append((None, r))
|
|
|
|
if has_subgraph:
|
|
output = subgraph_results
|
|
elif len(results) > 0:
|
|
output = merge_result_data(results, obj)
|
|
else:
|
|
output = []
|
|
ui = dict()
|
|
# TODO: Think there's an existing bug here
|
|
# If we're performing a subgraph expansion, we probably shouldn't be returning UI values yet.
|
|
# They'll get cached without the completed subgraphs. It's an edge case and I'm not aware of
|
|
# any nodes that use both subgraph expansion and custom UI outputs, but might be a problem in the future.
|
|
if len(uis) > 0:
|
|
ui = {k: [y for x in uis for y in x[k]] for k in uis[0].keys()}
|
|
return output, ui, has_subgraph
|
|
|
|
def format_value(x):
|
|
if x is None:
|
|
return None
|
|
elif isinstance(x, (int, float, bool, str)):
|
|
return x
|
|
else:
|
|
return str(x)
|
|
|
|
def _is_intermediate_output(dynprompt, node_id):
|
|
class_type = dynprompt.get_node(node_id)["class_type"]
|
|
class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
|
|
return getattr(class_def, 'HAS_INTERMEDIATE_OUTPUT', False)
|
|
|
|
|
|
def _send_cached_ui(server, node_id, display_node_id, cached, prompt_id, ui_outputs):
|
|
if server.client_id is None:
|
|
return
|
|
cached_ui = cached.ui or {}
|
|
server.send_sync("executed", { "node": node_id, "display_node": display_node_id, "output": cached_ui.get("output", None), "prompt_id": prompt_id }, server.client_id)
|
|
if cached.ui is not None:
|
|
ui_outputs[node_id] = cached.ui
|
|
|
|
async def execute(server, dynprompt, caches, current_item, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes, ui_outputs):
|
|
unique_id = current_item
|
|
real_node_id = dynprompt.get_real_node_id(unique_id)
|
|
display_node_id = dynprompt.get_display_node_id(unique_id)
|
|
parent_node_id = dynprompt.get_parent_node_id(unique_id)
|
|
inputs = dynprompt.get_node(unique_id)['inputs']
|
|
class_type = dynprompt.get_node(unique_id)['class_type']
|
|
class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
|
|
cached = await caches.outputs.get(unique_id)
|
|
if cached is not None:
|
|
_send_cached_ui(server, unique_id, display_node_id, cached, prompt_id, ui_outputs)
|
|
get_progress_state().finish_progress(unique_id)
|
|
execution_list.cache_update(unique_id, cached)
|
|
return (ExecutionResult.SUCCESS, None, None)
|
|
|
|
input_data_all = None
|
|
try:
|
|
if unique_id in pending_async_nodes:
|
|
results = []
|
|
for r in pending_async_nodes[unique_id]:
|
|
if isinstance(r, asyncio.Task):
|
|
try:
|
|
results.append(r.result())
|
|
except Exception as ex:
|
|
# An async task failed - propagate the exception up
|
|
del pending_async_nodes[unique_id]
|
|
raise ex
|
|
else:
|
|
results.append(r)
|
|
del pending_async_nodes[unique_id]
|
|
output_data, output_ui, has_subgraph = get_output_from_returns(results, class_def)
|
|
elif unique_id in pending_subgraph_results:
|
|
cached_results = pending_subgraph_results[unique_id]
|
|
resolved_outputs = []
|
|
for is_subgraph, result in cached_results:
|
|
if not is_subgraph:
|
|
resolved_outputs.append(result)
|
|
else:
|
|
resolved_output = []
|
|
for r in result:
|
|
if is_link(r):
|
|
source_node, source_output = r[0], r[1]
|
|
node_cached = execution_list.get_cache(source_node, unique_id)
|
|
for o in node_cached.outputs[source_output]:
|
|
resolved_output.append(o)
|
|
|
|
else:
|
|
resolved_output.append(r)
|
|
resolved_outputs.append(tuple(resolved_output))
|
|
output_data = merge_result_data(resolved_outputs, class_def)
|
|
output_ui = []
|
|
del pending_subgraph_results[unique_id]
|
|
has_subgraph = False
|
|
else:
|
|
get_progress_state().start_progress(unique_id)
|
|
input_data_all, missing_keys, v3_data = get_input_data(inputs, class_def, unique_id, execution_list, dynprompt, extra_data)
|
|
if server.client_id is not None:
|
|
server.last_node_id = display_node_id
|
|
server.send_sync("executing", { "node": unique_id, "display_node": display_node_id, "prompt_id": prompt_id }, server.client_id)
|
|
|
|
obj = await caches.objects.get(unique_id)
|
|
if obj is None:
|
|
obj = class_def()
|
|
await caches.objects.set(unique_id, obj)
|
|
|
|
if issubclass(class_def, _ComfyNodeInternal):
|
|
lazy_status_present = first_real_override(class_def, "check_lazy_status") is not None
|
|
else:
|
|
lazy_status_present = getattr(obj, "check_lazy_status", None) is not None
|
|
if lazy_status_present:
|
|
# for check_lazy_status, the returned data should include the original key of the input
|
|
v3_data_lazy = v3_data.copy()
|
|
v3_data_lazy["create_dynamic_tuple"] = True
|
|
required_inputs = await _async_map_node_over_list(prompt_id, unique_id, obj, input_data_all, "check_lazy_status", allow_interrupt=True, v3_data=v3_data_lazy)
|
|
required_inputs = await resolve_map_node_over_list_results(required_inputs)
|
|
required_inputs = set(sum([r for r in required_inputs if isinstance(r,list)], []))
|
|
required_inputs = [x for x in required_inputs if isinstance(x,str) and (
|
|
x not in input_data_all or x in missing_keys
|
|
)]
|
|
if len(required_inputs) > 0:
|
|
for i in required_inputs:
|
|
execution_list.make_input_strong_link(unique_id, i)
|
|
return (ExecutionResult.PENDING, None, None)
|
|
|
|
def execution_block_cb(block):
|
|
if block.message is not None:
|
|
mes = {
|
|
"prompt_id": prompt_id,
|
|
"node_id": unique_id,
|
|
"node_type": class_type,
|
|
"executed": list(executed),
|
|
|
|
"exception_message": f"Execution Blocked: {block.message}",
|
|
"exception_type": "ExecutionBlocked",
|
|
"traceback": [],
|
|
"current_inputs": [],
|
|
"current_outputs": [],
|
|
}
|
|
server.send_sync("execution_error", mes, server.client_id)
|
|
return ExecutionBlocker(None)
|
|
else:
|
|
return block
|
|
def pre_execute_cb(call_index):
|
|
# TODO - How to handle this with async functions without contextvars (which requires Python 3.12)?
|
|
GraphBuilder.set_default_prefix(unique_id, call_index, 0)
|
|
|
|
try:
|
|
output_data, output_ui, has_subgraph, has_pending_tasks = await get_output_data(prompt_id, unique_id, obj, input_data_all, execution_block_cb=execution_block_cb, pre_execute_cb=pre_execute_cb, v3_data=v3_data)
|
|
finally:
|
|
if comfy.memory_management.aimdo_enabled:
|
|
if args.verbose == "DEBUG":
|
|
comfy_aimdo.control.analyze()
|
|
comfy.model_management.reset_cast_buffers()
|
|
comfy.model_prefetch.cleanup_prefetch_queues()
|
|
comfy_aimdo.model_vbar.vbars_reset_watermark_limits()
|
|
|
|
if has_pending_tasks:
|
|
pending_async_nodes[unique_id] = output_data
|
|
unblock = execution_list.add_external_block(unique_id)
|
|
async def await_completion():
|
|
tasks = [x for x in output_data if isinstance(x, asyncio.Task)]
|
|
await asyncio.gather(*tasks, return_exceptions=True)
|
|
unblock()
|
|
asyncio.create_task(await_completion())
|
|
return (ExecutionResult.PENDING, None, None)
|
|
if len(output_ui) > 0:
|
|
# Enrich at output-processing time (not in the send path) so assets
|
|
# are registered even when no client is connected, and the asset id
|
|
# flows into ui_outputs and the cache alongside the raw entries.
|
|
output_ui = enrich_output_with_assets(output_ui)
|
|
ui_outputs[unique_id] = {
|
|
"meta": {
|
|
"node_id": unique_id,
|
|
"display_node": display_node_id,
|
|
"parent_node": parent_node_id,
|
|
"real_node_id": real_node_id,
|
|
},
|
|
"output": output_ui
|
|
}
|
|
if server.client_id is not None:
|
|
server.send_sync("executed", { "node": unique_id, "display_node": display_node_id, "output": output_ui, "prompt_id": prompt_id }, server.client_id)
|
|
if has_subgraph:
|
|
cached_outputs = []
|
|
new_node_ids = []
|
|
new_output_ids = []
|
|
new_output_links = []
|
|
for i in range(len(output_data)):
|
|
new_graph, node_outputs = output_data[i]
|
|
if new_graph is None:
|
|
cached_outputs.append((False, node_outputs))
|
|
else:
|
|
for node_id, node_info in new_graph.items():
|
|
new_node_ids.append(node_id)
|
|
display_id = node_info.get("override_display_id", unique_id)
|
|
dynprompt.add_ephemeral_node(node_id, node_info, unique_id, display_id)
|
|
# Figure out if the newly created node is an output node
|
|
class_type = node_info["class_type"]
|
|
class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
|
|
if hasattr(class_def, 'OUTPUT_NODE') and class_def.OUTPUT_NODE == True:
|
|
new_output_ids.append(node_id)
|
|
for i in range(len(node_outputs)):
|
|
if is_link(node_outputs[i]):
|
|
from_node_id, from_socket = node_outputs[i][0], node_outputs[i][1]
|
|
new_output_links.append((from_node_id, from_socket))
|
|
cached_outputs.append((True, node_outputs))
|
|
new_node_ids = set(new_node_ids)
|
|
for cache in caches.all:
|
|
subcache = await cache.ensure_subcache_for(unique_id, new_node_ids)
|
|
subcache.clean_unused()
|
|
for node_id in new_output_ids:
|
|
execution_list.add_node(node_id)
|
|
execution_list.cache_link(node_id, unique_id)
|
|
for link in new_output_links:
|
|
execution_list.add_strong_link(link[0], link[1], unique_id)
|
|
pending_subgraph_results[unique_id] = cached_outputs
|
|
return (ExecutionResult.PENDING, None, None)
|
|
|
|
cache_entry = CacheEntry(ui=ui_outputs.get(unique_id), outputs=output_data)
|
|
execution_list.cache_update(unique_id, cache_entry)
|
|
await caches.outputs.set(unique_id, cache_entry)
|
|
|
|
except comfy.model_management.InterruptProcessingException as iex:
|
|
logging.info("Processing interrupted")
|
|
|
|
# skip formatting inputs/outputs
|
|
error_details = {
|
|
"node_id": real_node_id,
|
|
}
|
|
|
|
return (ExecutionResult.FAILURE, error_details, iex)
|
|
except Exception as ex:
|
|
typ, _, tb = sys.exc_info()
|
|
exception_type = full_type_name(typ)
|
|
input_data_formatted = {}
|
|
if input_data_all is not None:
|
|
input_data_formatted = {}
|
|
for name, inputs in input_data_all.items():
|
|
input_data_formatted[name] = [format_value(x) for x in inputs]
|
|
|
|
logging.error(f"!!! Exception during processing !!! {ex}")
|
|
logging.error(traceback.format_exc())
|
|
tips = ""
|
|
|
|
if comfy.model_management.is_oom(ex):
|
|
tips = "This error means you ran out of memory on your GPU.\n\nTIPS: If the workflow worked before you might have accidentally set the batch_size to a large number."
|
|
logging.info("Memory summary:\n{}".format(comfy.model_management.debug_memory_summary()))
|
|
logging.error("Got an OOM, unloading all loaded models.")
|
|
comfy.model_management.unload_all_models()
|
|
elif isinstance(ex, RuntimeError) and ("mat1 and mat2 shapes" in str(ex)) and "Sampler" in class_type:
|
|
tips = "\n\nTIPS: If you have any \"Load CLIP\" or \"*CLIP Loader\" nodes in your workflow connected to this sampler node make sure the correct file(s) and type is selected."
|
|
|
|
error_details = {
|
|
"node_id": real_node_id,
|
|
"exception_message": "{}\n{}".format(ex, tips),
|
|
"exception_type": exception_type,
|
|
"traceback": traceback.format_tb(tb),
|
|
"current_inputs": input_data_formatted
|
|
}
|
|
|
|
return (ExecutionResult.FAILURE, error_details, ex)
|
|
|
|
get_progress_state().finish_progress(unique_id)
|
|
executed.add(unique_id)
|
|
|
|
return (ExecutionResult.SUCCESS, None, None)
|
|
|
|
class PromptExecutor:
|
|
def __init__(self, server, cache_type=False, cache_args=None):
|
|
self.cache_args = cache_args
|
|
self.cache_type = cache_type
|
|
self.server = server
|
|
self.reset()
|
|
|
|
def reset(self):
|
|
self.caches = CacheSet(cache_type=self.cache_type, cache_args=self.cache_args)
|
|
self.status_messages = []
|
|
self.success = True
|
|
|
|
def add_message(self, event, data: dict, broadcast: bool):
|
|
data = {
|
|
**data,
|
|
"timestamp": int(time.time() * 1000),
|
|
}
|
|
self.status_messages.append((event, data))
|
|
if self.server.client_id is not None or broadcast:
|
|
self.server.send_sync(event, data, self.server.client_id)
|
|
|
|
def handle_execution_error(self, prompt_id, prompt, current_outputs, executed, error, ex):
|
|
node_id = error["node_id"]
|
|
class_type = prompt[node_id]["class_type"]
|
|
|
|
# First, send back the status to the frontend depending
|
|
# on the exception type
|
|
if isinstance(ex, comfy.model_management.InterruptProcessingException):
|
|
mes = {
|
|
"prompt_id": prompt_id,
|
|
"node_id": node_id,
|
|
"node_type": class_type,
|
|
"executed": list(executed),
|
|
}
|
|
self.add_message("execution_interrupted", mes, broadcast=True)
|
|
else:
|
|
mes = {
|
|
"prompt_id": prompt_id,
|
|
"node_id": node_id,
|
|
"node_type": class_type,
|
|
"executed": list(executed),
|
|
"exception_message": error["exception_message"],
|
|
"exception_type": error["exception_type"],
|
|
"traceback": error["traceback"],
|
|
"current_inputs": error["current_inputs"],
|
|
"current_outputs": list(current_outputs),
|
|
}
|
|
self.add_message("execution_error", mes, broadcast=False)
|
|
|
|
def _notify_prompt_lifecycle(self, event: str, prompt_id: str):
|
|
if not _has_cache_providers():
|
|
return
|
|
|
|
for provider in _get_cache_providers():
|
|
try:
|
|
if event == "start":
|
|
provider.on_prompt_start(prompt_id)
|
|
elif event == "end":
|
|
provider.on_prompt_end(prompt_id)
|
|
except Exception as e:
|
|
_cache_logger.warning(f"Cache provider {provider.__class__.__name__} error on {event}: {e}")
|
|
|
|
def execute(self, prompt, prompt_id, extra_data={}, execute_outputs=[]):
|
|
asyncio.run(self.execute_async(prompt, prompt_id, extra_data, execute_outputs))
|
|
|
|
async def execute_async(self, prompt, prompt_id, extra_data={}, execute_outputs=[]):
|
|
set_preview_method(extra_data.get("preview_method"))
|
|
|
|
nodes.interrupt_processing(False)
|
|
|
|
if "client_id" in extra_data:
|
|
self.server.client_id = extra_data["client_id"]
|
|
else:
|
|
self.server.client_id = None
|
|
|
|
self.status_messages = []
|
|
self.add_message("execution_start", { "prompt_id": prompt_id}, broadcast=False)
|
|
|
|
self._notify_prompt_lifecycle("start", prompt_id)
|
|
ram_headroom = int(self.cache_args["ram"] * (1024 ** 3))
|
|
ram_inactive_headroom = int(self.cache_args["ram_inactive"] * (1024 ** 3))
|
|
ram_release_callback = self.caches.outputs.ram_release if self.cache_type == CacheType.RAM_PRESSURE else None
|
|
comfy.memory_management.set_ram_cache_release_state(ram_release_callback, ram_headroom)
|
|
|
|
try:
|
|
with torch.inference_mode():
|
|
dynamic_prompt = DynamicPrompt(prompt)
|
|
reset_progress_state(prompt_id, dynamic_prompt)
|
|
add_progress_handler(WebUIProgressHandler(self.server))
|
|
is_changed_cache = IsChangedCache(prompt_id, dynamic_prompt, self.caches.outputs)
|
|
for cache in self.caches.all:
|
|
await cache.set_prompt(dynamic_prompt, prompt.keys(), is_changed_cache)
|
|
cache.clean_unused()
|
|
|
|
node_ids = list(prompt.keys())
|
|
cache_results = await asyncio.gather(
|
|
*(self.caches.outputs.get(node_id) for node_id in node_ids)
|
|
)
|
|
cached_nodes = [
|
|
node_id for node_id, result in zip(node_ids, cache_results)
|
|
if result is not None
|
|
]
|
|
|
|
comfy.model_management.cleanup_models_gc()
|
|
self.add_message("execution_cached",
|
|
{ "nodes": cached_nodes, "prompt_id": prompt_id},
|
|
broadcast=False)
|
|
pending_subgraph_results = {}
|
|
pending_async_nodes = {} # TODO - Unify this with pending_subgraph_results
|
|
ui_node_outputs = {}
|
|
executed = set()
|
|
execution_list = ExecutionList(dynamic_prompt, self.caches.outputs)
|
|
current_outputs = self.caches.outputs.all_node_ids()
|
|
for node_id in list(execute_outputs):
|
|
execution_list.add_node(node_id)
|
|
|
|
while not execution_list.is_empty():
|
|
node_id, error, ex = await execution_list.stage_node_execution()
|
|
if error is not None:
|
|
self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
|
|
break
|
|
|
|
assert node_id is not None, "Node ID should not be None at this point"
|
|
result, error, ex = await execute(self.server, dynamic_prompt, self.caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results, pending_async_nodes, ui_node_outputs)
|
|
self.success = result != ExecutionResult.FAILURE
|
|
if result == ExecutionResult.FAILURE:
|
|
self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
|
|
break
|
|
elif result == ExecutionResult.PENDING:
|
|
execution_list.unstage_node_execution()
|
|
else: # result == ExecutionResult.SUCCESS:
|
|
execution_list.complete_node_execution()
|
|
|
|
if self.cache_type == CacheType.RAM_PRESSURE:
|
|
ram_release_callback(ram_inactive_headroom)
|
|
ram_shortfall = ram_headroom - psutil.virtual_memory().available
|
|
freed = comfy.model_management.free_pins(ram_shortfall + 512 * (1024 ** 2))
|
|
if freed < ram_shortfall:
|
|
if freed > 64 * (1024 ** 2):
|
|
# AIMDO MEM_DECOMMIT can outrun psutil.available catching up.
|
|
time.sleep(0.05)
|
|
ram_release_callback(ram_headroom, free_active=True)
|
|
else:
|
|
# Only execute when the while-loop ends without break
|
|
# Send cached UI for intermediate output nodes that weren't executed
|
|
for node_id in dynamic_prompt.all_node_ids():
|
|
if node_id in executed:
|
|
continue
|
|
if not _is_intermediate_output(dynamic_prompt, node_id):
|
|
continue
|
|
cached = await self.caches.outputs.get(node_id)
|
|
if cached is not None:
|
|
display_node_id = dynamic_prompt.get_display_node_id(node_id)
|
|
_send_cached_ui(self.server, node_id, display_node_id, cached, prompt_id, ui_node_outputs)
|
|
self.add_message("execution_success", { "prompt_id": prompt_id }, broadcast=False)
|
|
|
|
ui_outputs = {}
|
|
meta_outputs = {}
|
|
for node_id, ui_info in ui_node_outputs.items():
|
|
ui_outputs[node_id] = ui_info["output"]
|
|
meta_outputs[node_id] = ui_info["meta"]
|
|
self.history_result = {
|
|
"outputs": ui_outputs,
|
|
"meta": meta_outputs,
|
|
}
|
|
self.server.last_node_id = None
|
|
if comfy.model_management.DISABLE_SMART_MEMORY:
|
|
comfy.model_management.unload_all_models()
|
|
finally:
|
|
comfy.memory_management.set_ram_cache_release_state(None, 0)
|
|
self._notify_prompt_lifecycle("end", prompt_id)
|
|
|
|
|
|
async def validate_inputs(prompt_id, prompt, item, validated, visiting=None):
|
|
if visiting is None:
|
|
visiting = []
|
|
|
|
unique_id = item
|
|
if unique_id in validated:
|
|
return validated[unique_id]
|
|
|
|
if unique_id in visiting:
|
|
cycle_path_nodes = visiting[visiting.index(unique_id):] + [unique_id]
|
|
cycle_nodes = list(dict.fromkeys(cycle_path_nodes))
|
|
cycle_path = " -> ".join(f"{node_id} ({prompt[node_id]['class_type']})" for node_id in cycle_path_nodes)
|
|
for node_id in cycle_nodes:
|
|
validated[node_id] = (False, [{
|
|
"type": "dependency_cycle",
|
|
"message": "Dependency cycle detected",
|
|
"details": cycle_path,
|
|
"extra_info": {
|
|
"node_id": node_id,
|
|
"cycle_nodes": cycle_nodes,
|
|
}
|
|
}], node_id)
|
|
return validated[unique_id]
|
|
|
|
inputs = prompt[unique_id]['inputs']
|
|
class_type = prompt[unique_id]['class_type']
|
|
obj_class = nodes.NODE_CLASS_MAPPINGS[class_type]
|
|
|
|
errors = []
|
|
valid = True
|
|
|
|
v3_data = None
|
|
validate_function_inputs = []
|
|
validate_has_kwargs = False
|
|
if issubclass(obj_class, _ComfyNodeInternal):
|
|
obj_class: _io._ComfyNodeBaseInternal
|
|
class_inputs = obj_class.INPUT_TYPES()
|
|
class_inputs, _, v3_data = _io.get_finalized_class_inputs(class_inputs, inputs)
|
|
validate_function_name = "validate_inputs"
|
|
validate_function = first_real_override(obj_class, validate_function_name)
|
|
else:
|
|
class_inputs = obj_class.INPUT_TYPES()
|
|
validate_function_name = "VALIDATE_INPUTS"
|
|
validate_function = getattr(obj_class, validate_function_name, None)
|
|
if validate_function is not None:
|
|
argspec = inspect.getfullargspec(validate_function)
|
|
validate_function_inputs = argspec.args
|
|
validate_has_kwargs = argspec.varkw is not None
|
|
received_types = {}
|
|
|
|
valid_inputs = set(class_inputs.get('required',{})).union(set(class_inputs.get('optional',{})))
|
|
|
|
for x in valid_inputs:
|
|
input_type, input_category, extra_info = get_input_info(obj_class, x, class_inputs)
|
|
assert extra_info is not None
|
|
if x not in inputs:
|
|
if input_category == "required":
|
|
details = f"{x}" if not v3_data else x.split(".")[-1]
|
|
error = {
|
|
"type": "required_input_missing",
|
|
"message": "Required input is missing",
|
|
"details": details,
|
|
"extra_info": {
|
|
"input_name": x
|
|
}
|
|
}
|
|
errors.append(error)
|
|
continue
|
|
|
|
val = inputs[x]
|
|
info = (input_type, extra_info)
|
|
if isinstance(val, list):
|
|
if len(val) != 2:
|
|
error = {
|
|
"type": "bad_linked_input",
|
|
"message": "Bad linked input, must be a length-2 list of [node_id, slot_index]",
|
|
"details": f"{x}",
|
|
"extra_info": {
|
|
"input_name": x,
|
|
"input_config": info,
|
|
"received_value": val
|
|
}
|
|
}
|
|
errors.append(error)
|
|
continue
|
|
|
|
o_id = val[0]
|
|
o_class_type = prompt[o_id]['class_type']
|
|
r = nodes.NODE_CLASS_MAPPINGS[o_class_type].RETURN_TYPES
|
|
received_type = r[val[1]]
|
|
received_types[x] = received_type
|
|
if 'input_types' not in validate_function_inputs and not validate_node_input(received_type, input_type):
|
|
details = f"{x}, received_type({received_type}) mismatch input_type({input_type})"
|
|
error = {
|
|
"type": "return_type_mismatch",
|
|
"message": "Return type mismatch between linked nodes",
|
|
"details": details,
|
|
"extra_info": {
|
|
"input_name": x,
|
|
"input_config": info,
|
|
"received_type": received_type,
|
|
"linked_node": val
|
|
}
|
|
}
|
|
errors.append(error)
|
|
continue
|
|
try:
|
|
visiting.append(unique_id)
|
|
try:
|
|
r = await validate_inputs(prompt_id, prompt, o_id, validated, visiting)
|
|
finally:
|
|
visiting.pop()
|
|
if r[0] is False:
|
|
# `r` will be set in `validated[o_id]` already
|
|
valid = False
|
|
continue
|
|
except Exception as ex:
|
|
typ, _, tb = sys.exc_info()
|
|
valid = False
|
|
exception_type = full_type_name(typ)
|
|
reasons = [{
|
|
"type": "exception_during_inner_validation",
|
|
"message": "Exception when validating inner node",
|
|
"details": str(ex),
|
|
"extra_info": {
|
|
"input_name": x,
|
|
"input_config": info,
|
|
"exception_message": str(ex),
|
|
"exception_type": exception_type,
|
|
"traceback": traceback.format_tb(tb),
|
|
"linked_node": val
|
|
}
|
|
}]
|
|
validated[o_id] = (False, reasons, o_id)
|
|
continue
|
|
else:
|
|
try:
|
|
# Unwraps values wrapped in __value__ key or typed wrapper.
|
|
# This is used to pass list widget values to execution,
|
|
# as by default list value is reserved to represent the
|
|
# connection between nodes.
|
|
if isinstance(val, dict):
|
|
if "__value__" in val:
|
|
val = val["__value__"]
|
|
inputs[x] = val
|
|
|
|
if input_type == "INT":
|
|
val = int(val)
|
|
inputs[x] = val
|
|
if input_type == "FLOAT":
|
|
val = float(val)
|
|
inputs[x] = val
|
|
if input_type == "STRING":
|
|
val = str(val)
|
|
inputs[x] = val
|
|
if input_type == "BOOLEAN":
|
|
val = bool(val)
|
|
inputs[x] = val
|
|
except Exception as ex:
|
|
error = {
|
|
"type": "invalid_input_type",
|
|
"message": f"Failed to convert an input value to a {input_type} value",
|
|
"details": f"{x}, {val}, {ex}",
|
|
"extra_info": {
|
|
"input_name": x,
|
|
"input_config": info,
|
|
"received_value": val,
|
|
"exception_message": str(ex)
|
|
}
|
|
}
|
|
errors.append(error)
|
|
continue
|
|
|
|
if x not in validate_function_inputs and not validate_has_kwargs:
|
|
if "min" in extra_info and val < extra_info["min"]:
|
|
error = {
|
|
"type": "value_smaller_than_min",
|
|
"message": "Value {} smaller than min of {}".format(val, extra_info["min"]),
|
|
"details": f"{x}",
|
|
"extra_info": {
|
|
"input_name": x,
|
|
"input_config": info,
|
|
"received_value": val,
|
|
}
|
|
}
|
|
errors.append(error)
|
|
continue
|
|
if "max" in extra_info and val > extra_info["max"]:
|
|
error = {
|
|
"type": "value_bigger_than_max",
|
|
"message": "Value {} bigger than max of {}".format(val, extra_info["max"]),
|
|
"details": f"{x}",
|
|
"extra_info": {
|
|
"input_name": x,
|
|
"input_config": info,
|
|
"received_value": val,
|
|
}
|
|
}
|
|
errors.append(error)
|
|
continue
|
|
|
|
if isinstance(input_type, list) or input_type == io.Combo.io_type:
|
|
if input_type == io.Combo.io_type:
|
|
combo_options = extra_info.get("options", [])
|
|
else:
|
|
combo_options = input_type
|
|
is_multiselect = extra_info.get("multiselect", False)
|
|
if is_multiselect and isinstance(val, list):
|
|
invalid_vals = [v for v in val if v not in combo_options]
|
|
else:
|
|
invalid_vals = [val] if val not in combo_options else []
|
|
if invalid_vals:
|
|
input_config = info
|
|
list_info = ""
|
|
|
|
# Don't send back gigantic lists like if they're lots of
|
|
# scanned model filepaths
|
|
if len(combo_options) > 20:
|
|
list_info = f"(list of length {len(combo_options)})"
|
|
input_config = None
|
|
else:
|
|
list_info = str(combo_options)
|
|
|
|
error = {
|
|
"type": "value_not_in_list",
|
|
"message": "Value not in list",
|
|
"details": f"{x}: {', '.join(repr(v) for v in invalid_vals)} not in {list_info}",
|
|
"extra_info": {
|
|
"input_name": x,
|
|
"input_config": input_config,
|
|
"received_value": val,
|
|
}
|
|
}
|
|
errors.append(error)
|
|
continue
|
|
|
|
if len(validate_function_inputs) > 0 or validate_has_kwargs:
|
|
input_data_all, _, v3_data = get_input_data(inputs, obj_class, unique_id)
|
|
input_filtered = {}
|
|
for x in input_data_all:
|
|
if x in validate_function_inputs or validate_has_kwargs:
|
|
input_filtered[x] = input_data_all[x]
|
|
if 'input_types' in validate_function_inputs:
|
|
input_filtered['input_types'] = [received_types]
|
|
|
|
ret = await _async_map_node_over_list(prompt_id, unique_id, obj_class, input_filtered, validate_function_name, v3_data=v3_data)
|
|
ret = await resolve_map_node_over_list_results(ret)
|
|
for x in input_filtered:
|
|
for i, r in enumerate(ret):
|
|
if r is not True and not isinstance(r, ExecutionBlocker):
|
|
details = f"{x}"
|
|
if r is not False:
|
|
details += f" - {str(r)}"
|
|
|
|
error = {
|
|
"type": "custom_validation_failed",
|
|
"message": "Custom validation failed for node",
|
|
"details": details,
|
|
"extra_info": {
|
|
"input_name": x,
|
|
}
|
|
}
|
|
errors.append(error)
|
|
continue
|
|
|
|
ret = validated.get(unique_id, (True, [], unique_id))
|
|
# Recursive cycle detection may have already populated an error on us. Join it.
|
|
ret = (
|
|
ret[0] and valid is True and not errors,
|
|
ret[1] + [error for error in errors if error not in ret[1]],
|
|
unique_id,
|
|
)
|
|
|
|
validated[unique_id] = ret
|
|
return ret
|
|
|
|
def full_type_name(klass):
|
|
module = klass.__module__
|
|
if module == 'builtins':
|
|
return klass.__qualname__
|
|
return module + '.' + klass.__qualname__
|
|
|
|
async def validate_prompt(prompt_id, prompt, partial_execution_list: Union[list[str], None]):
|
|
outputs = set()
|
|
for x in prompt:
|
|
if 'class_type' not in prompt[x]:
|
|
node_data = prompt[x]
|
|
node_title = node_data.get('_meta', {}).get('title')
|
|
error = {
|
|
"type": "missing_node_type",
|
|
"message": f"Node '{node_title or f'ID #{x}'}' has no class_type. The workflow may be corrupted or a custom node is missing.",
|
|
"details": f"Node ID '#{x}'",
|
|
"extra_info": {
|
|
"node_id": x,
|
|
"class_type": None,
|
|
"node_title": node_title
|
|
}
|
|
}
|
|
return (False, error, [], {})
|
|
|
|
class_type = prompt[x]['class_type']
|
|
class_ = nodes.NODE_CLASS_MAPPINGS.get(class_type, None)
|
|
if class_ is None:
|
|
node_data = prompt[x]
|
|
node_title = node_data.get('_meta', {}).get('title', class_type)
|
|
error = {
|
|
"type": "missing_node_type",
|
|
"message": f"Node '{node_title}' not found. The custom node may not be installed.",
|
|
"details": f"Node ID '#{x}'",
|
|
"extra_info": {
|
|
"node_id": x,
|
|
"class_type": class_type,
|
|
"node_title": node_title
|
|
}
|
|
}
|
|
return (False, error, [], {})
|
|
|
|
if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE is True:
|
|
if partial_execution_list is None or x in partial_execution_list:
|
|
outputs.add(x)
|
|
|
|
if len(outputs) == 0:
|
|
error = {
|
|
"type": "prompt_no_outputs",
|
|
"message": "Prompt has no outputs",
|
|
"details": "",
|
|
"extra_info": {}
|
|
}
|
|
return (False, error, [], {})
|
|
|
|
good_outputs = set()
|
|
errors = []
|
|
node_errors = {}
|
|
validated = {}
|
|
for o in outputs:
|
|
valid = False
|
|
reasons = []
|
|
try:
|
|
m = await validate_inputs(prompt_id, prompt, o, validated)
|
|
valid = m[0]
|
|
reasons = m[1]
|
|
except Exception as ex:
|
|
typ, _, tb = sys.exc_info()
|
|
valid = False
|
|
exception_type = full_type_name(typ)
|
|
reasons = [{
|
|
"type": "exception_during_validation",
|
|
"message": "Exception when validating node",
|
|
"details": str(ex),
|
|
"extra_info": {
|
|
"exception_type": exception_type,
|
|
"traceback": traceback.format_tb(tb)
|
|
}
|
|
}]
|
|
validated[o] = (False, reasons, o)
|
|
|
|
if valid is True:
|
|
good_outputs.add(o)
|
|
else:
|
|
logging.error(f"Failed to validate prompt for output {o}:")
|
|
if len(reasons) > 0:
|
|
logging.error("* (prompt):")
|
|
for reason in reasons:
|
|
logging.error(f" - {reason['message']}: {reason['details']}")
|
|
errors += [(o, reasons)]
|
|
for node_id, result in validated.items():
|
|
valid = result[0]
|
|
reasons = result[1]
|
|
# If a node upstream has errors, the nodes downstream will also
|
|
# be reported as invalid, but there will be no errors attached.
|
|
# So don't return those nodes as having errors in the response.
|
|
if valid is not True and len(reasons) > 0:
|
|
if node_id not in node_errors:
|
|
class_type = prompt[node_id]['class_type']
|
|
node_errors[node_id] = {
|
|
"errors": reasons,
|
|
"dependent_outputs": [],
|
|
"class_type": class_type
|
|
}
|
|
logging.error(f"* {class_type} {node_id}:")
|
|
for reason in reasons:
|
|
logging.error(f" - {reason['message']}: {reason['details']}")
|
|
node_errors[node_id]["dependent_outputs"].append(o)
|
|
logging.error("Output will be ignored")
|
|
|
|
if len(good_outputs) == 0:
|
|
errors_list = []
|
|
for o, errors in errors:
|
|
for error in errors:
|
|
errors_list.append(f"{error['message']}: {error['details']}")
|
|
errors_list = "\n".join(errors_list)
|
|
|
|
error = {
|
|
"type": "prompt_outputs_failed_validation",
|
|
"message": "Prompt outputs failed validation",
|
|
"details": errors_list,
|
|
"extra_info": {}
|
|
}
|
|
|
|
return (False, error, list(good_outputs), node_errors)
|
|
|
|
return (True, None, list(good_outputs), node_errors)
|
|
|
|
MAXIMUM_HISTORY_SIZE = 10000
|
|
|
|
class PromptQueue:
|
|
def __init__(self, server):
|
|
self.server = server
|
|
self.mutex = threading.RLock()
|
|
self.not_empty = threading.Condition(self.mutex)
|
|
self.task_counter = 0
|
|
self.queue = []
|
|
self.currently_running = {}
|
|
self.history = {}
|
|
self.flags = {}
|
|
|
|
def put(self, item):
|
|
with self.mutex:
|
|
heapq.heappush(self.queue, item)
|
|
self.server.queue_updated()
|
|
self.not_empty.notify()
|
|
|
|
def get(self, timeout=None):
|
|
with self.not_empty:
|
|
while len(self.queue) == 0:
|
|
self.not_empty.wait(timeout=timeout)
|
|
if timeout is not None and len(self.queue) == 0:
|
|
return None
|
|
item = heapq.heappop(self.queue)
|
|
i = self.task_counter
|
|
self.currently_running[i] = copy.deepcopy(item)
|
|
self.task_counter += 1
|
|
self.server.queue_updated()
|
|
return (item, i)
|
|
|
|
class ExecutionStatus(NamedTuple):
|
|
status_str: Literal['success', 'error']
|
|
completed: bool
|
|
messages: List[str]
|
|
|
|
def task_done(self, item_id, history_result,
|
|
status: Optional['PromptQueue.ExecutionStatus'], process_item=None):
|
|
with self.mutex:
|
|
prompt = self.currently_running.pop(item_id)
|
|
if len(self.history) > MAXIMUM_HISTORY_SIZE:
|
|
self.history.pop(next(iter(self.history)))
|
|
|
|
status_dict: Optional[dict] = None
|
|
if status is not None:
|
|
status_dict = copy.deepcopy(status._asdict())
|
|
|
|
if process_item is not None:
|
|
prompt = process_item(prompt)
|
|
|
|
self.history[prompt[1]] = {
|
|
"prompt": prompt,
|
|
"outputs": {},
|
|
'status': status_dict,
|
|
}
|
|
self.history[prompt[1]].update(history_result)
|
|
self.server.queue_updated()
|
|
|
|
# Note: slow
|
|
def get_current_queue(self):
|
|
with self.mutex:
|
|
out = []
|
|
for x in self.currently_running.values():
|
|
out += [x]
|
|
return (out, copy.deepcopy(self.queue))
|
|
|
|
# read-safe as long as queue items are immutable
|
|
def get_current_queue_volatile(self):
|
|
with self.mutex:
|
|
running = [x for x in self.currently_running.values()]
|
|
queued = copy.copy(self.queue)
|
|
return (running, queued)
|
|
|
|
def interrupt_if_running(self, prompt_id):
|
|
"""Interrupt the running prompt with this id, atomically.
|
|
|
|
Checks the live running set and signals the interrupt under the queue
|
|
mutex, so the worker cannot move the job to done (and start the next
|
|
prompt) in between. Returns True if a matching job was running and an
|
|
interrupt was signalled, False otherwise. The atomicity is what keeps a
|
|
cancel from landing on an unrelated prompt that started after a separate
|
|
is-running check: the global interrupt flag is reset at the start of
|
|
every prompt (execute_async), so a job that finishes before consuming
|
|
the flag cannot leak the interrupt onto its successor.
|
|
"""
|
|
with self.mutex:
|
|
for item in self.currently_running.values():
|
|
if item[1] == prompt_id:
|
|
nodes.interrupt_processing()
|
|
return True
|
|
return False
|
|
|
|
def get_tasks_remaining(self):
|
|
with self.mutex:
|
|
return len(self.queue) + len(self.currently_running)
|
|
|
|
def wipe_queue(self):
|
|
with self.mutex:
|
|
self.queue = []
|
|
self.server.queue_updated()
|
|
|
|
def delete_queue_item(self, function):
|
|
with self.mutex:
|
|
for x in range(len(self.queue)):
|
|
if function(self.queue[x]):
|
|
if len(self.queue) == 1:
|
|
self.wipe_queue()
|
|
else:
|
|
self.queue.pop(x)
|
|
heapq.heapify(self.queue)
|
|
self.server.queue_updated()
|
|
return True
|
|
return False
|
|
|
|
def get_history(self, prompt_id=None, max_items=None, offset=-1, map_function=None):
|
|
with self.mutex:
|
|
if prompt_id is None:
|
|
out = {}
|
|
i = 0
|
|
if offset < 0 and max_items is not None:
|
|
offset = len(self.history) - max_items
|
|
for k in self.history:
|
|
if i >= offset:
|
|
p = self.history[k]
|
|
if map_function is not None:
|
|
p = map_function(p)
|
|
out[k] = p
|
|
if max_items is not None and len(out) >= max_items:
|
|
break
|
|
i += 1
|
|
return out
|
|
elif prompt_id in self.history:
|
|
p = self.history[prompt_id]
|
|
if map_function is None:
|
|
p = copy.deepcopy(p)
|
|
else:
|
|
p = map_function(p)
|
|
return {prompt_id: p}
|
|
else:
|
|
return {}
|
|
|
|
def wipe_history(self):
|
|
with self.mutex:
|
|
self.history = {}
|
|
|
|
def delete_history_item(self, id_to_delete):
|
|
with self.mutex:
|
|
self.history.pop(id_to_delete, None)
|
|
|
|
def set_flag(self, name, data):
|
|
with self.mutex:
|
|
self.flags[name] = data
|
|
self.not_empty.notify()
|
|
|
|
def get_flags(self, reset=True):
|
|
with self.mutex:
|
|
if reset:
|
|
ret = self.flags
|
|
self.flags = {}
|
|
return ret
|
|
else:
|
|
return self.flags.copy()
|