Peyton DeNiro
08ad731038
Add session-mode code generation that produces a reusable WorkflowSession class instead of a simple oneshot script. The session mode generates code with an in-class run/close lifecycle for warm ComfyUI reuse. Also refactors render.py to extract shared code sections into common methods, reducing duplication between oneshot and session renderers. Adds WorkflowSessionRuntime with cleanup policies (per_run, session, manual) and reset_every_n_runs support, plus 72 new tests covering session rendering, runtime session, and session export pipeline.
265 lines
10 KiB
Python
265 lines
10 KiB
Python
import inspect
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import json
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import keyword
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import random
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import re
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from typing import Any, Callable
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from .model import GenerationPlan
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class WorkflowPlanner:
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"""Convert ordered workflow nodes into a renderer-ready generation plan."""
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def __init__(self, node_class_mappings: dict, base_node_class_mappings: dict):
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self.node_class_mappings = node_class_mappings
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self.base_node_class_mappings = base_node_class_mappings
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@staticmethod
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def sanitize_node_id(node_id: str) -> str:
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sanitized = re.sub(r"[^a-z0-9_]", "_", str(node_id).lower().strip())
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sanitized = re.sub(r"_+", "_", sanitized).strip("_")
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if not sanitized:
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sanitized = "node"
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return sanitized
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@staticmethod
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def clean_variable_name(class_type: str) -> str:
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clean_name = class_type.lower().strip().replace("-", "_").replace(" ", "_")
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clean_name = re.sub(r"[^a-z0-9_]", "", clean_name)
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if clean_name[0].isdigit():
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clean_name = "_" + clean_name
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return clean_name
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def build_plan(
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self,
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load_order: list[tuple[str, dict, bool]],
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workflow_data: dict,
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metadata_workflow_data: dict | None = None,
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queue_size: int = 10,
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execution_mode: str = "oneshot",
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) -> GenerationPlan:
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import_statements = {"nodes": {"NODE_CLASS_MAPPINGS"}}
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executed_variables = {}
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special_functions_code = []
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code = []
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initialized_objects = {}
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custom_nodes = False
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for idx, data, is_special_function in load_order:
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inputs, class_type = data["inputs"], data["class_type"]
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input_types = self.node_class_mappings[class_type].INPUT_TYPES()
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input_value_types = self.get_input_value_types(input_types)
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class_def = self.node_class_mappings[class_type]()
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missing_required_variable = False
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if "required" in input_types.keys():
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for required in input_types["required"]:
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if required not in inputs.keys():
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missing_required_variable = True
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if missing_required_variable:
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continue
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if class_type not in initialized_objects:
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if class_type == "PreviewImage":
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continue
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class_type, import_statement, class_code = self.get_class_info(
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class_type
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)
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initialized_objects[class_type] = self.clean_variable_name(class_type)
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if class_type in self.base_node_class_mappings.keys():
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module_name, import_name = import_statement
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import_statements.setdefault(module_name, set()).add(import_name)
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if 'NODE_CLASS_MAPPINGS["' in class_code:
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custom_nodes = True
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special_functions_code.append(class_code)
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class_def_params = self.get_function_parameters(
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getattr(class_def, class_def.FUNCTION)
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)
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no_params = class_def_params is None
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inputs = {
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key: value
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for key, value in inputs.items()
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if no_params or key in class_def_params
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}
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hidden_inputs = input_types.get("hidden", {})
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if (
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"unique_id" in hidden_inputs
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and (no_params or "unique_id" in class_def_params)
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):
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inputs["unique_id"] = random.randint(1, 2**64)
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if "prompt" in hidden_inputs and (no_params or "prompt" in class_def_params):
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inputs["prompt"] = {"variable_name": "prompt"}
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if "extra_pnginfo" in hidden_inputs and (
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no_params or "extra_pnginfo" in class_def_params
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):
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inputs["extra_pnginfo"] = {"variable_name": "extra_pnginfo"}
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if "hidden" not in input_types and class_def_params is not None:
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if "unique_id" in class_def_params:
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inputs["unique_id"] = random.randint(1, 2**64)
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executed_variables[idx] = (
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f"{self.clean_variable_name(class_type)}_"
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f"{self.sanitize_node_id(str(idx))}"
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)
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inputs = self.update_inputs(inputs, executed_variables)
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seed_sync_code = self.create_prompt_seed_sync_code(
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idx, inputs, input_value_types, is_special_function
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)
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target_lines = special_functions_code if is_special_function else code
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if seed_sync_code:
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target_lines.extend(seed_sync_code)
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target_lines.append(
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self.create_function_call_code(
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initialized_objects[class_type],
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class_def.FUNCTION,
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executed_variables[idx],
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is_special_function,
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input_value_types=input_value_types,
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**inputs,
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)
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)
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return GenerationPlan(
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import_statements=import_statements,
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special_functions_code=special_functions_code,
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loop_code=code,
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workflow_data=workflow_data,
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metadata_workflow_data=metadata_workflow_data,
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queue_size=queue_size,
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custom_nodes=custom_nodes,
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execution_mode=execution_mode,
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executed_variables=executed_variables,
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)
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def create_function_call_code(
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self,
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obj_name: str,
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func: str,
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variable_name: str,
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is_special_function: bool,
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input_value_types: dict[str, str] | None = None,
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**kwargs,
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) -> str:
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args = ", ".join(
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self.format_arg(key, value, (input_value_types or {}).get(key))
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for key, value in kwargs.items()
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)
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code = f"{variable_name} = {obj_name}.{func}({args})\n"
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if not is_special_function:
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code = f"\t{code}"
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return code
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def create_prompt_seed_sync_code(
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self,
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node_id: str,
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inputs: dict,
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input_value_types: dict[str, str],
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is_special_function: bool,
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) -> list[str]:
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seed_sync_lines = []
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for key in ("seed", "noise_seed"):
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if key not in inputs:
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continue
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randomized_seed_variable = (
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f"node_{self.sanitize_node_id(str(node_id))}_{self.clean_variable_name(key)}"
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)
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randomized_seed_code = self.get_randomized_seed_code(
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input_value_types.get(key)
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)
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seed_sync_lines.append(
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f'{randomized_seed_variable} = prompt["{node_id}"]["inputs"]["{key}"] = {randomized_seed_code}'
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)
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inputs[key] = {"variable_name": randomized_seed_variable}
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if not seed_sync_lines:
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return []
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indentation = "" if is_special_function else "\t"
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return [f"{indentation}{line}\n" for line in seed_sync_lines]
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def format_arg(self, key: str, value: Any, input_value_type: str | None = None) -> str:
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value_code = self.format_arg_value(key, value, input_value_type)
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if key.isidentifier() and not keyword.iskeyword(key):
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return f"{key}={value_code}"
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return f"**{{{json.dumps(key)}: {value_code}}}"
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@staticmethod
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def format_arg_value(
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key: str, value: Any, input_value_type: str | None = None
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) -> str:
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if isinstance(value, dict) and "variable_name" in value:
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return value["variable_name"]
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if key == "noise_seed" or key == "seed":
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return WorkflowPlanner.get_randomized_seed_code(input_value_type)
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if isinstance(value, str):
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return json.dumps(value)
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return repr(value)
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@staticmethod
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def get_input_value_types(input_types: dict) -> dict[str, str]:
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value_types = {}
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for section in ("required", "optional", "hidden"):
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for key, value in input_types.get(section, {}).items():
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if isinstance(value, tuple) and value:
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value_types[key] = value[0]
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elif isinstance(value, str):
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value_types[key] = value
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return value_types
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@staticmethod
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def get_randomized_seed_code(input_value_type: str | None) -> str:
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randomized_seed_code = "random.randint(1, 2**64)"
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if input_value_type == "STRING":
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return f"str({randomized_seed_code})"
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return randomized_seed_code
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def get_class_info(self, class_type: str) -> tuple[str, tuple[str, str], str]:
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class_obj = self.base_node_class_mappings.get(class_type)
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module_name = "nodes"
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if class_obj is not None:
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module_name = class_obj.__module__
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variable_name = self.clean_variable_name(class_type)
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is_importable_module = bool(
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module_name
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and "/" not in module_name
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and "\\" not in module_name
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and all(part.isidentifier() for part in module_name.split("."))
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)
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if class_type in self.base_node_class_mappings.keys() and is_importable_module:
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import_statement = (module_name, class_type)
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class_code = f"{variable_name} = {class_type.strip()}()"
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else:
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import_statement = ("nodes", "NODE_CLASS_MAPPINGS")
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class_code = f'{variable_name} = NODE_CLASS_MAPPINGS["{class_type}"]()'
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return class_type, import_statement, class_code
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@staticmethod
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def get_function_parameters(func: Callable) -> list | None:
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signature = inspect.signature(func)
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parameters = {
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name: param.default if param.default != param.empty else None
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for name, param in signature.parameters.items()
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}
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catch_all = any(
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param.kind == inspect.Parameter.VAR_KEYWORD
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for param in signature.parameters.values()
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)
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return list(parameters.keys()) if not catch_all else None
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def update_inputs(self, inputs: dict, executed_variables: dict) -> dict:
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for key in inputs.keys():
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if (
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isinstance(inputs[key], list)
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and inputs[key][0] in executed_variables.keys()
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):
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inputs[key] = {
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"variable_name": f"get_value_at_index({executed_variables[inputs[key][0]]}, {inputs[key][1]})"
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}
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return inputs
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