248 lines
10 KiB
Python
248 lines
10 KiB
Python
import time
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import copy
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import torch
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import comfy.model_management
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import os
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from pathlib import Path # Add this import
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import importlib.util
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import logging
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import folder_paths
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logging.info("MultiGPU: Initialization started")
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current_device = comfy.model_management.get_torch_device()
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logging.info(f"MultiGPU: Initial device {current_device}")
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def get_torch_device_patched():
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if (
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not torch.cuda.is_available()
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or comfy.model_management.cpu_state == comfy.model_management.CPUState.CPU
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or "cpu" in str(current_device).lower()
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):
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return torch.device("cpu")
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return torch.device(current_device)
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comfy.model_management.get_torch_device = get_torch_device_patched
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def override_class(cls):
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class NodeOverride(cls):
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@classmethod
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def INPUT_TYPES(s):
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inputs = copy.deepcopy(cls.INPUT_TYPES())
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devices = ["cpu"] + [f"cuda:{i}" for i in range(torch.cuda.device_count())]
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inputs["required"]["device"] = (devices,)
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return inputs
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CATEGORY = "multigpu"
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FUNCTION = "override"
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def override(self, *args, device, **kwargs):
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global current_device
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current_device = device
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fn = getattr(super(), cls.FUNCTION)
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return fn(*args, **kwargs)
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return NodeOverride
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NODE_CLASS_MAPPINGS = {}
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def check_module_exists(module_path):
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"""Utility function to check if module exists"""
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full_path = os.path.join("custom_nodes", module_path, "__init__.py")
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logging.info(f"MultiGPU: Checking for module at {full_path}")
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if not os.path.exists(full_path):
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logging.info(f"MultiGPU: Module {module_path} not found - skipping")
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return False
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logging.info(f"MultiGPU: Found {module_path}, attempting to load")
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return True
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def register_module(module_path, target_nodes):
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try:
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# For core nodes, skip module loading and just register from the global mappings
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if not module_path:
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logging.info("MultiGPU: Starting core node registration")
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from nodes import NODE_CLASS_MAPPINGS as GLOBAL_NODE_CLASS_MAPPINGS
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for node in target_nodes:
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if node in GLOBAL_NODE_CLASS_MAPPINGS:
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NODE_CLASS_MAPPINGS[f"{node}MultiGPU"] = override_class(GLOBAL_NODE_CLASS_MAPPINGS[node])
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logging.info(f"MultiGPU: Registered core node {node}")
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else:
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logging.info(f"MultiGPU: Core node {node} not found - this shouldn't happen!")
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return
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# For custom nodes, try to load the module first
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full_path = os.path.join("custom_nodes", module_path, "__init__.py")
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logging.info(f"MultiGPU: Checking for module at {full_path}")
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if not os.path.exists(full_path):
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logging.info(f"MultiGPU: Module {module_path} not found - skipping")
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return
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logging.info(f"MultiGPU: Found {module_path}, attempting to load")
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spec = importlib.util.spec_from_file_location(module_path, full_path)
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module = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(module)
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logging.info(f"MultiGPU: Executed {module_path} initialization")
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# Use the module's local dictionary instead of the global one
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local_map_name = "NODE_CLASS_MAPPINGS"
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local_map = getattr(module, local_map_name, None)
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if not local_map:
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logging.info(f"MultiGPU: {module_path} has no '{local_map_name}' dictionary, skipping override.")
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return
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all_defined_nodes = list(local_map.keys())
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logging.info(f"MultiGPU: {module_path} local dict keys: {all_defined_nodes}")
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for node in target_nodes:
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if node in local_map:
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mgpu_class = override_class(local_map[node])
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NODE_CLASS_MAPPINGS[f"{node}MultiGPU"] = mgpu_class
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logging.info(f"MultiGPU: Successfully wrapped {node} from {module_path}")
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else:
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logging.info(f"MultiGPU: Node '{node}' not found in {module_path}'s local dictionary")
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except Exception as e:
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logging.info(f"MultiGPU: Error processing {module_path}: {str(e)}")
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def register_LTXmodule(module_path, node_list):
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"""Hard-coded registration for LTX Video nodes"""
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global NODE_CLASS_MAPPINGS
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if not check_module_exists(module_path):
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return
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class LTXVLoader:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"ckpt_name": (folder_paths.get_filename_list("checkpoints"),
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{"tooltip": "The name of the checkpoint (model) to load."}),
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"dtype": (["bfloat16", "float32"], {"default": "bfloat16"})
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}
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}
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RETURN_TYPES = ("MODEL", "VAE")
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RETURN_NAMES = ("model", "vae")
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FUNCTION = "load"
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CATEGORY = "lightricks/LTXV"
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TITLE = "LTXV Loader"
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OUTPUT_NODE = False
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def load(self, ckpt_name, dtype):
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# Get original node instance
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["LTXVLoader"]()
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# Use original node to load model and VAE
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model, vae = original_loader.load(ckpt_name, dtype)
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# Return original objects
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return (model, vae)
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ltx_nodes = {
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"LTXVLoader": LTXVLoader
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}
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for node_name in node_list:
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if node_name in ltx_nodes:
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NODE_CLASS_MAPPINGS[f"{node_name}MultiGPU"] = override_class(ltx_nodes[node_name])
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logging.info(f"MultiGPU: Registered hard-coded LTX node {node_name}")
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def register_Florence2module(module_path, node_list):
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"""Hard-coded registration for Florence2 nodes"""
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global NODE_CLASS_MAPPINGS
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if not check_module_exists(module_path):
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return
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class DownloadAndLoadFlorence2Model:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"model": ([
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'microsoft/Florence-2-base',
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'microsoft/Florence-2-base-ft',
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'microsoft/Florence-2-large',
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'microsoft/Florence-2-large-ft',
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'HuggingFaceM4/Florence-2-DocVQA',
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'thwri/CogFlorence-2.1-Large',
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'thwri/CogFlorence-2.2-Large',
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'gokaygokay/Florence-2-SD3-Captioner',
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'gokaygokay/Florence-2-Flux-Large',
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'MiaoshouAI/Florence-2-base-PromptGen-v1.5',
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'MiaoshouAI/Florence-2-large-PromptGen-v1.5',
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'MiaoshouAI/Florence-2-base-PromptGen-v2.0',
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'MiaoshouAI/Florence-2-large-PromptGen-v2.0'
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], {"default": 'microsoft/Florence-2-base'}),
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"precision": (['fp16','bf16','fp32'], {"default": 'fp16'}),
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"attention": (['flash_attention_2', 'sdpa', 'eager'], {"default": 'sdpa'}),
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},
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"optional": {
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"lora": ("PEFTLORA",),
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}}
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RETURN_TYPES = ("FL2MODEL",)
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RETURN_NAMES = ("florence2_model",)
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FUNCTION = "loadmodel"
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CATEGORY = "Florence2"
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def loadmodel(self, model, precision, attention, lora=None):
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# Get original node instance
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["DownloadAndLoadFlorence2Model"]()
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# Use original node to load model
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return original_loader.loadmodel(model, precision, attention, lora)
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class Florence2ModelLoader:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"model": ([item.name for item in Path(folder_paths.models_dir, "LLM").iterdir() if item.is_dir()],
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{"tooltip": "models are expected to be in Comfyui/models/LLM folder"}),
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"precision": (['fp16','bf16','fp32'],),
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"attention": (['flash_attention_2', 'sdpa', 'eager'], {"default": 'sdpa'}),
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},
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"optional": {
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"lora": ("PEFTLORA",),
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}}
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RETURN_TYPES = ("FL2MODEL",)
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RETURN_NAMES = ("florence2_model",)
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FUNCTION = "loadmodel"
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CATEGORY = "Florence2"
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def loadmodel(self, model, precision, attention, lora=None):
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# Get original node instance
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["Florence2ModelLoader"]()
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# Use original node to load model
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return original_loader.loadmodel(model, precision, attention, lora)
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florence2_nodes = {
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"Florence2ModelLoader": Florence2ModelLoader,
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"DownloadAndLoadFlorence2Model": DownloadAndLoadFlorence2Model
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}
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for node_name in node_list:
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if node_name in florence2_nodes:
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NODE_CLASS_MAPPINGS[f"{node_name}MultiGPU"] = override_class(florence2_nodes[node_name])
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logging.info(f"MultiGPU: Registered hard-coded Florence2 node {node_name}")
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# Register desired nodes
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register_module("", ["UNETLoader", "VAELoader", "CLIPLoader", "DualCLIPLoader", "TripleCLIPLoader", "CheckpointLoaderSimple", "ControlNetLoader"])
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register_module("ComfyUI-GGUF", ["UnetLoaderGGUF","UnetLoaderGGUFAdvanced","CLIPLoaderGGUF","DualCLIPLoaderGGUF","TripleCLIPLoaderGGUF"])
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register_module("x-flux-comfyui", ["LoadFluxControlNet"])
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register_Florence2module("ComfyUI-Florence2", ["Florence2ModelLoader", "DownloadAndLoadFlorence2Model"])
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register_LTXmodule("ComfyUI-LTXVideo", ["LTXVLoader"])
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register_module("ComfyUI-MMAudio", ["MMAudioFeatureUtilsLoader","MMAudioModelLoader","MMAudioSampler"])
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register_module("ComfyUI_bitsandbytes_NF4", ["CheckpointLoaderNF4",])
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logging.info(f"MultiGPU: Registration complete. Final mappings: {', '.join(NODE_CLASS_MAPPINGS.keys())}") |