Marcel Hofer
da97ff6544
Decision Router memory and explanation controls Add keep_model_loaded to the image and text routers. It defaults to releasing the llama.cpp model and MTMD handler after routing, including errors and empty batches; users can retain the model for faster queue reuse. Add an opt-in include_reason pass that records a bounded post-hoc explanation in decision_json. The native decision head remains authoritative for route selection and probabilities. Explanation errors are reported without failing routing. Document both controls and bump package, node registry, and runtime metadata to 3.0.1. Validation: 227 tests passed in the bundled ComfyUI Python runtime (17.62 seconds), including 29 Decision Router tests. Version metadata parsing and git diff --check passed. Limitation: explanation generation has not been smoke-tested with downloaded model weights; model-specific chat compatibility and added latency may vary.
118 lines
4.1 KiB
Python
118 lines
4.1 KiB
Python
"""
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{
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"name": "ComfyUI-ThinkingLLM",
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"description": "A multimodal ComfyUI AI node pack with Qwen, Gemma 4, GGUF, Whisper, image and text decision routing, and live thinking output.",
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"author": "goodguy1963",
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"version": "3.0.1",
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"url": "https://github.com/goodguy1963/ComfyUI-ThinkingLLM",
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"category": "image"
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}
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"""
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import importlib.util
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import os
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import sys
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# Get the directory of the current script
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current_dir = os.path.dirname(__file__)
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sys.path.insert(0, current_dir)
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NODE_CLASS_MAPPINGS = {}
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NODE_DISPLAY_NAME_MAPPINGS = {}
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COMMERCIAL_RELEASE = os.environ.get("THINKINGLLM_COMMERCIAL_RELEASE") == "1"
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WEB_DIRECTORY = "./web_commercial" if COMMERCIAL_RELEASE else "./web"
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COMMERCIAL_MODULES = {
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"AILab_OutputCleaner",
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"AILab_QwenVL",
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"AILab_QwenVL_PromptEnhancer",
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"AILab_StreamDisplay",
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}
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COMMERCIAL_NODE_MODULES = {
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"decision_router",
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"story_split_node",
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"system_prompt_preset",
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"vram_cleanup",
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}
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LEGACY_NODE_REPLACEMENTS = {
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"AILab_QwenVL": "ThinkingLLM_QwenVL",
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"AILab_QwenVL_Advanced": "ThinkingLLM_QwenVL_Advanced",
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"AILab_QwenVL_GGUF": "ThinkingLLM_QwenVL_GGUF",
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"AILab_QwenVL_GGUF_Advanced": "ThinkingLLM_QwenVL_GGUF_Advanced",
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"AILab_QwenVL_PromptEnhancer": "ThinkingLLM_QwenVL_PromptEnhancer",
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"AILab_QwenVL_GGUF_PromptEnhancer": "ThinkingLLM_QwenVL_GGUF_PromptEnhancer",
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}
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if COMMERCIAL_RELEASE:
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LEGACY_NODE_REPLACEMENTS = {
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old: new for old, new in LEGACY_NODE_REPLACEMENTS.items() if "GGUF" not in old
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}
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def load_modules_from_directory(directory):
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for file in os.listdir(directory):
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if file.endswith(".py"):
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file_path = os.path.join(directory, file)
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module_name = os.path.basename(file)[:-3]
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if module_name == os.path.basename(__file__)[:-3]:
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continue
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if COMMERCIAL_RELEASE:
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allowed = (
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COMMERCIAL_MODULES
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if os.path.abspath(directory) == os.path.abspath(current_dir)
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else COMMERCIAL_NODE_MODULES
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)
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if module_name not in allowed:
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continue
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try:
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spec = importlib.util.spec_from_file_location(module_name, file_path)
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if spec is None or spec.loader is None:
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raise ImportError(f"Unable to create import spec for {module_name}")
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module = importlib.util.module_from_spec(spec)
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sys.modules[module_name] = module
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spec.loader.exec_module(module)
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if hasattr(module, "NODE_CLASS_MAPPINGS"):
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NODE_CLASS_MAPPINGS.update(module.NODE_CLASS_MAPPINGS)
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if hasattr(module, "NODE_DISPLAY_NAME_MAPPINGS"):
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NODE_DISPLAY_NAME_MAPPINGS.update(module.NODE_DISPLAY_NAME_MAPPINGS)
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except Exception as e:
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print(f"Error loading module {module_name}: {e}")
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load_modules_from_directory(current_dir)
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# Also load from nodes subdirectory
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nodes_dir = os.path.join(current_dir, "nodes")
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if os.path.exists(nodes_dir):
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load_modules_from_directory(nodes_dir)
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for legacy_node_name in LEGACY_NODE_REPLACEMENTS:
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NODE_CLASS_MAPPINGS.pop(legacy_node_name, None)
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NODE_DISPLAY_NAME_MAPPINGS.pop(legacy_node_name, None)
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def _register_legacy_node_replacements():
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try:
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from comfy_api.latest._io import NodeReplace
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from server import PromptServer
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except Exception:
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return
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manager = getattr(getattr(PromptServer, "instance", None), "node_replace_manager", None)
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if manager is None:
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return
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for old_node_id, new_node_id in LEGACY_NODE_REPLACEMENTS.items():
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manager.register(NodeReplace(new_node_id=new_node_id, old_node_id=old_node_id))
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_register_legacy_node_replacements()
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NODE_CLASS_MAPPINGS = dict(sorted(NODE_CLASS_MAPPINGS.items(), key=lambda x: NODE_DISPLAY_NAME_MAPPINGS.get(x[0], x[0])))
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NODE_DISPLAY_NAME_MAPPINGS = dict(sorted(NODE_DISPLAY_NAME_MAPPINGS.items(), key=lambda x: x[1]))
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__all__ = [
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"NODE_CLASS_MAPPINGS",
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"NODE_DISPLAY_NAME_MAPPINGS",
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"LEGACY_NODE_REPLACEMENTS",
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]
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