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execution.py
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execution.py
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import sys
import copy
import logging
import threading
import heapq
import time
import traceback
from enum import Enum
import inspect
from typing import List, Literal, NamedTuple, Optional
import torch
import nodes
import comfy.model_management
from comfy_execution.graph import get_input_info, ExecutionList, DynamicPrompt, ExecutionBlocker
from comfy_execution.graph_utils import is_link, GraphBuilder
from comfy_execution.caching import HierarchicalCache, LRUCache, CacheKeySetInputSignature, CacheKeySetID
from comfy.cli_args import args
class ExecutionResult(Enum):
SUCCESS = 0
FAILURE = 1
PENDING = 2
class DuplicateNodeError(Exception):
pass
class IsChangedCache:
def __init__(self, dynprompt, outputs_cache):
self.dynprompt = dynprompt
self.outputs_cache = outputs_cache
self.is_changed = {}
def get(self, node_id):
if node_id in self.is_changed:
return self.is_changed[node_id]
node = self.dynprompt.get_node(node_id)
class_type = node["class_type"]
class_def = nodes.NODE_CLASS_MAPPINGS[class_type]
if not hasattr(class_def, "IS_CHANGED"):
self.is_changed[node_id] = False
return self.is_changed[node_id]
if "is_changed" in node:
self.is_changed[node_id] = node["is_changed"]
return self.is_changed[node_id]
# Intentionally do not use cached outputs here. We only want constants in IS_CHANGED
input_data_all, _ = get_input_data(node["inputs"], class_def, node_id, None)
try:
is_changed = _map_node_over_list(class_def, input_data_all, "IS_CHANGED")
node["is_changed"] = [None if isinstance(x, ExecutionBlocker) else x for x in is_changed]
except Exception as e:
logging.warning("WARNING: {}".format(e))
node["is_changed"] = float("NaN")
finally:
self.is_changed[node_id] = node["is_changed"]
return self.is_changed[node_id]
class CacheSet:
def __init__(self, lru_size=None):
if lru_size is None or lru_size == 0:
self.init_classic_cache()
else:
self.init_lru_cache(lru_size)
self.all = [self.outputs, self.ui, self.objects]
# Useful for those with ample RAM/VRAM -- allows experimenting without
# blowing away the cache every time
def init_lru_cache(self, cache_size):
self.outputs = LRUCache(CacheKeySetInputSignature, max_size=cache_size)
self.ui = LRUCache(CacheKeySetInputSignature, max_size=cache_size)
self.objects = HierarchicalCache(CacheKeySetID)
# Performs like the old cache -- dump data ASAP
def init_classic_cache(self):
self.outputs = HierarchicalCache(CacheKeySetInputSignature)
self.ui = HierarchicalCache(CacheKeySetInputSignature)
self.objects = HierarchicalCache(CacheKeySetID)
def recursive_debug_dump(self):
result = {
"outputs": self.outputs.recursive_debug_dump(),
"ui": self.ui.recursive_debug_dump(),
}
return result
def get_input_data(inputs, class_def, unique_id, outputs=None, dynprompt=None, extra_data={}):
valid_inputs = class_def.INPUT_TYPES()
input_data_all = {}
missing_keys = {}
for x in inputs:
input_data = inputs[x]
input_type, input_category, input_info = get_input_info(class_def, x)
def mark_missing():
missing_keys[x] = True
input_data_all[x] = (None,)
if is_link(input_data) and (not input_info or not input_info.get("rawLink", False)):
input_unique_id = input_data[0]
output_index = input_data[1]
if outputs is None:
mark_missing()
continue # This might be a lazily-evaluated input
cached_output = outputs.get(input_unique_id)
if cached_output is None:
mark_missing()
continue
if output_index >= len(cached_output):
mark_missing()
continue
obj = cached_output[output_index]
input_data_all[x] = obj
elif input_category is not None:
input_data_all[x] = [input_data]
if "hidden" in valid_inputs:
h = valid_inputs["hidden"]
for x in h:
if h[x] == "PROMPT":
input_data_all[x] = [dynprompt.get_original_prompt() if dynprompt is not None else {}]
if h[x] == "DYNPROMPT":
input_data_all[x] = [dynprompt]
if h[x] == "EXTRA_PNGINFO":
input_data_all[x] = [extra_data.get('extra_pnginfo', None)]
if h[x] == "UNIQUE_ID":
input_data_all[x] = [unique_id]
return input_data_all, missing_keys
map_node_over_list = None #Don't hook this please
def _map_node_over_list(obj, input_data_all, func, allow_interrupt=False, execution_block_cb=None, pre_execute_cb=None):
# check if node wants the lists
input_is_list = getattr(obj, "INPUT_IS_LIST", False)
if len(input_data_all) == 0:
max_len_input = 0
else:
max_len_input = max(len(x) for x in input_data_all.values())
# get a slice of inputs, repeat last input when list isn't long enough
def slice_dict(d, i):
return {k: v[i if len(v) > i else -1] for k, v in d.items()}
results = []
def process_inputs(inputs, index=None):
if allow_interrupt:
nodes.before_node_execution()
execution_block = None
for k, v in inputs.items():
if isinstance(v, ExecutionBlocker):
execution_block = execution_block_cb(v) if execution_block_cb else v
break
if execution_block is None:
if pre_execute_cb is not None and index is not None:
pre_execute_cb(index)
results.append(getattr(obj, func)(**inputs))
else:
results.append(execution_block)
if input_is_list:
process_inputs(input_data_all, 0)
elif max_len_input == 0:
process_inputs({})
else:
for i in range(max_len_input):
input_dict = slice_dict(input_data_all, i)
process_inputs(input_dict, i)
return results
def merge_result_data(results, obj):
# check which outputs need concatenating
output = []
output_is_list = [False] * len(results[0])
if hasattr(obj, "OUTPUT_IS_LIST"):
output_is_list = obj.OUTPUT_IS_LIST
# merge node execution results
for i, is_list in zip(range(len(results[0])), output_is_list):
if is_list:
output.append([x for o in results for x in o[i]])
else:
output.append([o[i] for o in results])
return output
def get_output_data(obj, input_data_all, execution_block_cb=None, pre_execute_cb=None):
results = []
uis = []
subgraph_results = []
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)
has_subgraph = False
for i in range(len(return_values)):
r = return_values[i]
if isinstance(r, dict):
if 'ui' in r:
uis.append(r['ui'])
if 'expand' in r:
# Perform an expansion, but do not append results
has_subgraph = True
new_graph = r['expand']
result = r.get("result", None)
if isinstance(result, ExecutionBlocker):
result = tuple([result] * len(obj.RETURN_TYPES))
subgraph_results.append((new_graph, result))
elif 'result' in r:
result = r.get("result", None)
if isinstance(result, ExecutionBlocker):
result = tuple([result] * len(obj.RETURN_TYPES))
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()
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 execute(server, dynprompt, caches, current_item, extra_data, executed, prompt_id, execution_list, pending_subgraph_results):
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]
if caches.outputs.get(unique_id) is not None:
if server.client_id is not None:
cached_output = caches.ui.get(unique_id) or {}
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)
return (ExecutionResult.SUCCESS, None, None)
input_data_all = None
try:
if 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_output = caches.outputs.get(source_node)[source_output]
for o in node_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 = []
has_subgraph = False
else:
input_data_all, missing_keys = get_input_data(inputs, class_def, unique_id, caches.outputs, 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 = caches.objects.get(unique_id)
if obj is None:
obj = class_def()
caches.objects.set(unique_id, obj)
if hasattr(obj, "check_lazy_status"):
required_inputs = _map_node_over_list(obj, input_data_all, "check_lazy_status", allow_interrupt=True)
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):
GraphBuilder.set_default_prefix(unique_id, call_index, 0)
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)
if len(output_ui) > 0:
caches.ui.set(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:
# Check for conflicts
for node_id in new_graph.keys():
if dynprompt.has_node(node_id):
raise DuplicateNodeError(f"Attempt to add duplicate node {node_id}. Ensure node ids are unique and deterministic or use graph_utils.GraphBuilder.")
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:
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 (ExecutionResult.PENDING, None, None)
caches.outputs.set(unique_id, output_data)
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())
error_details = {
"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, comfy.model_management.OOM_EXCEPTION):
logging.error("Got an OOM, unloading all loaded models.")
comfy.model_management.unload_all_models()
return (ExecutionResult.FAILURE, error_details, ex)
executed.add(unique_id)
return (ExecutionResult.SUCCESS, None, None)
class PromptExecutor:
def __init__(self, server, lru_size=None):
self.lru_size = lru_size
self.server = server
self.reset()
def reset(self):
self.caches = CacheSet(self.lru_size)
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 execute(self, prompt, prompt_id, extra_data={}, execute_outputs=[]):
nodes.interrupt_processing(False)
if "client_id" in extra_data:
self.server.client_id = extra_data["client_id"]
else:
self.server.client_id = None
self.status_messages = []
self.add_message("execution_start", { "prompt_id": prompt_id}, broadcast=False)
with torch.inference_mode():
dynamic_prompt = DynamicPrompt(prompt)
is_changed_cache = IsChangedCache(dynamic_prompt, self.caches.outputs)
for cache in self.caches.all:
cache.set_prompt(dynamic_prompt, prompt.keys(), is_changed_cache)
cache.clean_unused()
cached_nodes = []
for node_id in prompt:
if self.caches.outputs.get(node_id) is not None:
cached_nodes.append(node_id)
comfy.model_management.cleanup_models(keep_clone_weights_loaded=True)
self.add_message("execution_cached",
{ "nodes": cached_nodes, "prompt_id": prompt_id},
broadcast=False)
pending_subgraph_results = {}
executed = set()
execution_list = ExecutionList(dynamic_prompt, self.caches.outputs)
current_outputs = self.caches.outputs.all_node_ids()
for node_id in list(execute_outputs):
execution_list.add_node(node_id)
while not execution_list.is_empty():
node_id, error, ex = execution_list.stage_node_execution()
if error is not None:
self.handle_execution_error(prompt_id, dynamic_prompt.original_prompt, current_outputs, executed, error, ex)
break
result, error, ex = execute(self.server, dynamic_prompt, self.caches, node_id, extra_data, executed, prompt_id, execution_list, pending_subgraph_results)
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 comfy.model_management.DISABLE_SMART_MEMORY:
comfy.model_management.unload_all_models()
def validate_inputs(prompt, item, validated):
unique_id = item
if unique_id in validated:
return validated[unique_id]
inputs = prompt[unique_id]['inputs']
class_type = prompt[unique_id]['class_type']
obj_class = nodes.NODE_CLASS_MAPPINGS[class_type]
class_inputs = obj_class.INPUT_TYPES()
valid_inputs = set(class_inputs.get('required',{})).union(set(class_inputs.get('optional',{})))
errors = []
valid = True
validate_function_inputs = []
validate_has_kwargs = False
if hasattr(obj_class, "VALIDATE_INPUTS"):
argspec = inspect.getfullargspec(obj_class.VALIDATE_INPUTS)
validate_function_inputs = argspec.args
validate_has_kwargs = argspec.varkw is not None
received_types = {}
for x in valid_inputs:
type_input, input_category, extra_info = get_input_info(obj_class, x)
assert extra_info is not None
if x not in inputs:
if input_category == "required":
error = {
"type": "required_input_missing",
"message": "Required input is missing",
"details": f"{x}",
"extra_info": {
"input_name": x
}
}
errors.append(error)
continue
val = inputs[x]
info = (type_input, extra_info)
if isinstance(val, list):
if len(val) != 2:
error = {
"type": "bad_linked_input",
"message": "Bad linked input, must be a length-2 list of [node_id, slot_index]",
"details": f"{x}",
"extra_info": {
"input_name": x,
"input_config": info,
"received_value": val
}
}
errors.append(error)
continue
o_id = val[0]
o_class_type = prompt[o_id]['class_type']
r = nodes.NODE_CLASS_MAPPINGS[o_class_type].RETURN_TYPES
received_type = r[val[1]]
received_types[x] = received_type
if 'input_types' not in validate_function_inputs and received_type != type_input:
details = f"{x}, {received_type} != {type_input}"
error = {
"type": "return_type_mismatch",
"message": "Return type mismatch between linked nodes",
"details": details,
"extra_info": {
"input_name": x,
"input_config": info,
"received_type": received_type,
"linked_node": val
}
}
errors.append(error)
continue
try:
r = validate_inputs(prompt, o_id, validated)
if r[0] is False:
# `r` will be set in `validated[o_id]` already
valid = False
continue
except Exception as ex:
typ, _, tb = sys.exc_info()
valid = False
exception_type = full_type_name(typ)
reasons = [{
"type": "exception_during_inner_validation",
"message": "Exception when validating inner node",
"details": str(ex),
"extra_info": {
"input_name": x,
"input_config": info,
"exception_message": str(ex),
"exception_type": exception_type,
"traceback": traceback.format_tb(tb),
"linked_node": val
}
}]
validated[o_id] = (False, reasons, o_id)
continue
else:
try:
if type_input == "INT":
val = int(val)
inputs[x] = val
if type_input == "FLOAT":
val = float(val)
inputs[x] = val
if type_input == "STRING":
val = str(val)
inputs[x] = val
if type_input == "BOOLEAN":
val = bool(val)
inputs[x] = val
except Exception as ex:
error = {
"type": "invalid_input_type",
"message": f"Failed to convert an input value to a {type_input} value",
"details": f"{x}, {val}, {ex}",
"extra_info": {
"input_name": x,
"input_config": info,
"received_value": val,
"exception_message": str(ex)
}
}
errors.append(error)
continue
if x not in validate_function_inputs and not validate_has_kwargs:
if "min" in extra_info and val < extra_info["min"]:
error = {
"type": "value_smaller_than_min",
"message": "Value {} smaller than min of {}".format(val, extra_info["min"]),
"details": f"{x}",
"extra_info": {
"input_name": x,
"input_config": info,
"received_value": val,
}
}
errors.append(error)
continue
if "max" in extra_info and val > extra_info["max"]:
error = {
"type": "value_bigger_than_max",
"message": "Value {} bigger than max of {}".format(val, extra_info["max"]),
"details": f"{x}",
"extra_info": {
"input_name": x,
"input_config": info,
"received_value": val,
}
}
errors.append(error)
continue
if isinstance(type_input, list):
if val not in type_input:
input_config = info
list_info = ""
# Don't send back gigantic lists like if they're lots of
# scanned model filepaths
if len(type_input) > 20:
list_info = f"(list of length {len(type_input)})"
input_config = None
else:
list_info = str(type_input)
error = {
"type": "value_not_in_list",
"message": "Value not in list",
"details": f"{x}: '{val}' not in {list_info}",
"extra_info": {
"input_name": x,
"input_config": input_config,
"received_value": val,
}
}
errors.append(error)
continue
if len(validate_function_inputs) > 0 or validate_has_kwargs:
input_data_all, _ = get_input_data(inputs, obj_class, unique_id)
input_filtered = {}
for x in input_data_all:
if x in validate_function_inputs or validate_has_kwargs:
input_filtered[x] = input_data_all[x]
if 'input_types' in validate_function_inputs:
input_filtered['input_types'] = [received_types]
#ret = obj_class.VALIDATE_INPUTS(**input_filtered)
ret = _map_node_over_list(obj_class, input_filtered, "VALIDATE_INPUTS")
for x in input_filtered:
for i, r in enumerate(ret):
if r is not True and not isinstance(r, ExecutionBlocker):
details = f"{x}"
if r is not False:
details += f" - {str(r)}"
error = {
"type": "custom_validation_failed",
"message": "Custom validation failed for node",
"details": details,
"extra_info": {
"input_name": x,
}
}
errors.append(error)
continue
if len(errors) > 0 or valid is not True:
ret = (False, errors, unique_id)
else:
ret = (True, [], unique_id)
validated[unique_id] = ret
return ret
def full_type_name(klass):
module = klass.__module__
if module == 'builtins':
return klass.__qualname__
return module + '.' + klass.__qualname__
def validate_prompt(prompt):
outputs = set()
for x in prompt:
if 'class_type' not in prompt[x]:
error = {
"type": "invalid_prompt",
"message": f"Cannot execute because a node is missing the class_type property.",
"details": f"Node ID '#{x}'",
"extra_info": {}
}
return (False, error, [], [])
class_type = prompt[x]['class_type']
class_ = nodes.NODE_CLASS_MAPPINGS.get(class_type, None)
if class_ is None:
error = {
"type": "invalid_prompt",
"message": f"Cannot execute because node {class_type} does not exist.",
"details": f"Node ID '#{x}'",
"extra_info": {}
}
return (False, error, [], [])
if hasattr(class_, 'OUTPUT_NODE') and class_.OUTPUT_NODE is True:
outputs.add(x)
if len(outputs) == 0:
error = {
"type": "prompt_no_outputs",
"message": "Prompt has no outputs",
"details": "",
"extra_info": {}
}
return (False, error, [], [])
good_outputs = set()
errors = []
node_errors = {}
validated = {}
for o in outputs:
valid = False
reasons = []
try:
m = validate_inputs(prompt, o, validated)
valid = m[0]
reasons = m[1]
except Exception as ex:
typ, _, tb = sys.exc_info()
valid = False
exception_type = full_type_name(typ)
reasons = [{
"type": "exception_during_validation",
"message": "Exception when validating node",
"details": str(ex),
"extra_info": {
"exception_type": exception_type,
"traceback": traceback.format_tb(tb)
}
}]
validated[o] = (False, reasons, o)
if valid is True:
good_outputs.add(o)
else:
logging.error(f"Failed to validate prompt for output {o}:")
if len(reasons) > 0:
logging.error("* (prompt):")
for reason in reasons:
logging.error(f" - {reason['message']}: {reason['details']}")
errors += [(o, reasons)]
for node_id, result in validated.items():
valid = result[0]
reasons = result[1]
# If a node upstream has errors, the nodes downstream will also
# be reported as invalid, but there will be no errors attached.
# So don't return those nodes as having errors in the response.
if valid is not True and len(reasons) > 0:
if node_id not in node_errors:
class_type = prompt[node_id]['class_type']
node_errors[node_id] = {
"errors": reasons,
"dependent_outputs": [],
"class_type": class_type
}
logging.error(f"* {class_type} {node_id}:")
for reason in reasons:
logging.error(f" - {reason['message']}: {reason['details']}")
node_errors[node_id]["dependent_outputs"].append(o)
logging.error("Output will be ignored")
if len(good_outputs) == 0:
errors_list = []
for o, errors in errors:
for error in errors:
errors_list.append(f"{error['message']}: {error['details']}")
errors_list = "\n".join(errors_list)
error = {
"type": "prompt_outputs_failed_validation",
"message": "Prompt outputs failed validation",
"details": errors_list,
"extra_info": {}
}
return (False, error, list(good_outputs), node_errors)
return (True, None, list(good_outputs), node_errors)
MAXIMUM_HISTORY_SIZE = 10000
class PromptQueue:
def __init__(self, server):
self.server = server
self.mutex = threading.RLock()
self.not_empty = threading.Condition(self.mutex)
self.task_counter = 0
self.queue = []
self.currently_running = {}
self.history = {}
self.flags = {}
server.prompt_queue = self
def put(self, item):
with self.mutex:
heapq.heappush(self.queue, item)
self.server.queue_updated()
self.not_empty.notify()
def get(self, timeout=None):
with self.not_empty:
while len(self.queue) == 0:
self.not_empty.wait(timeout=timeout)
if timeout is not None and len(self.queue) == 0:
return None
item = heapq.heappop(self.queue)
i = self.task_counter
self.currently_running[i] = copy.deepcopy(item)
self.task_counter += 1
self.server.queue_updated()
return (item, i)
class ExecutionStatus(NamedTuple):
status_str: Literal['success', 'error']
completed: bool
messages: List[str]
def task_done(self, item_id, history_result,
status: Optional['PromptQueue.ExecutionStatus']):
with self.mutex:
prompt = self.currently_running.pop(item_id)
if len(self.history) > MAXIMUM_HISTORY_SIZE:
self.history.pop(next(iter(self.history)))
status_dict: Optional[dict] = None
if status is not None:
status_dict = copy.deepcopy(status._asdict())
self.history[prompt[1]] = {
"prompt": prompt,
"outputs": {},
'status': status_dict,
}
self.history[prompt[1]].update(history_result)
self.server.queue_updated()
def get_current_queue(self):
with self.mutex:
out = []
for x in self.currently_running.values():
out += [x]
return (out, copy.deepcopy(self.queue))
def get_tasks_remaining(self):
with self.mutex:
return len(self.queue) + len(self.currently_running)
def wipe_queue(self):
with self.mutex:
self.queue = []
self.server.queue_updated()
def delete_queue_item(self, function):
with self.mutex:
for x in range(len(self.queue)):
if function(self.queue[x]):
if len(self.queue) == 1:
self.wipe_queue()
else:
self.queue.pop(x)
heapq.heapify(self.queue)
self.server.queue_updated()
return True
return False
def get_history(self, prompt_id=None, max_items=None, offset=-1):
with self.mutex:
if prompt_id is None:
out = {}
i = 0
if offset < 0 and max_items is not None:
offset = len(self.history) - max_items
for k in self.history:
if i >= offset:
out[k] = self.history[k]
if max_items is not None and len(out) >= max_items:
break
i += 1
return out
elif prompt_id in self.history:
return {prompt_id: copy.deepcopy(self.history[prompt_id])}
else:
return {}
def wipe_history(self):
with self.mutex:
self.history = {}
def delete_history_item(self, id_to_delete):
with self.mutex:
self.history.pop(id_to_delete, None)
def set_flag(self, name, data):
with self.mutex:
self.flags[name] = data
self.not_empty.notify()
def get_flags(self, reset=True):
with self.mutex:
if reset:
ret = self.flags
self.flags = {}
return ret
else:
return self.flags.copy()