John Pollock
e73c8bd5e3
- Adds HyVideoModelLoaderDiffSynthMultiGPU node implementing DiffSynth block-swapping - Introduces offload_device selection for secondary GPU utilization - Updates documentation with known behaviors and expected OOM patterns - Adds example workflow demonstrating higher resolution/longer duration video generation - Maintains backwards compatibility with existing MultiGPU workflows
837 lines
35 KiB
Python
837 lines
35 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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current_offload_device = comfy.model_management.get_torch_device()
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logging.info(f"MultiGPU: Initial device {current_device}")
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logging.info(f"MultiGPU: Initial offload device {current_offload_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 unet_offload_device_patched():
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if (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_offload_device).lower()):
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return torch.device("cpu")
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return torch.device(current_offload_device)
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comfy.model_management.unet_offload_device = unet_offload_device_patched
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def get_device_list():
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import torch
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return ["cpu"] + [f"cuda:{i}" for i in range(torch.cuda.device_count())]
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class DeviceSelectorMultiGPU:
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@classmethod
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def INPUT_TYPES(s):
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devices = get_device_list()
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return {
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"required": {
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"device": (devices, {"default": devices[1] if len(devices) > 1 else devices[0]})
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}
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}
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RETURN_TYPES = (get_device_list(),)
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RETURN_NAMES = ("device",)
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FUNCTION = "select_device"
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CATEGORY = "multigpu"
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def select_device(self, device):
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return (device,)
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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 = get_device_list()
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default_device = devices[1] if len(devices) > 1 else devices[0]
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inputs["optional"] = inputs.get("optional", {})
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inputs["optional"]["device"] = (devices, {"default": default_device})
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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=None, **kwargs):
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global current_device
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if device is not None:
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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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def override_class_with_offload(cls):
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class NodeOverrideDiffSynth(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 = get_device_list()
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default_device = devices[1] if len(devices) > 1 else devices[0]
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inputs["optional"] = inputs.get("optional", {})
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inputs["optional"]["device"] = (devices, {"default": default_device})
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inputs["optional"]["offload_device"] = (devices, {"default": "cpu"})
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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=None, offload_device=None, **kwargs):
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global current_device
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global current_offload_device
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if device is not None:
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current_device = device
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if offload_device is not None:
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current_offload_device = offload_device
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fn = getattr(super(), cls.FUNCTION)
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return fn(*args, **kwargs)
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return NodeOverrideDiffSynth
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NODE_CLASS_MAPPINGS = {
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"DeviceSelectorMultiGPU": DeviceSelectorMultiGPU
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}
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def check_module_exists(module_path):
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full_path = os.path.join("custom_nodes", module_path)
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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}, creating compatible MultiGPU nodes")
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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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except Exception as e:
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logging.info(f"MultiGPU: Error processing {module_path}: {str(e)}")
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def register_LTXVLoaderMultiGPU():
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global NODE_CLASS_MAPPINGS
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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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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["LTXVLoader"]()
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return original_loader.load(ckpt_name, dtype)
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def _load_unet(self, load_device, offload_device, weights, num_latent_channels, dtype, config=None ):
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["LTXVLoader"]()
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return original_loader._load_unet(load_device, offload_device, weights, num_latent_channels, dtype, config=None )
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def _load_vae(self, weights, config=None):
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["LTXVLoader"]()
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return original_loader._load_vae(weights, config=None)
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NODE_CLASS_MAPPINGS["LTXVLoaderMultiGPU"] = override_class(LTXVLoader)
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logging.info(f"MultiGPU: Registered LTXVLoaderMultiGPU")
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def register_Florence2ModelLoaderMultiGPU():
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global NODE_CLASS_MAPPINGS
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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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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["Florence2ModelLoader"]()
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return original_loader.loadmodel(model, precision, attention, lora)
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NODE_CLASS_MAPPINGS["Florence2ModelLoaderMultiGPU"] = override_class(Florence2ModelLoader)
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logging.info(f"MultiGPU: Registered Florence2ModelLoaderMultiGPU")
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def register_DownloadAndLoadFlorence2ModelMultiGPU():
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global NODE_CLASS_MAPPINGS
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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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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["DownloadAndLoadFlorence2Model"]()
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return original_loader.loadmodel(model, precision, attention, lora)
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NODE_CLASS_MAPPINGS["DownloadAndLoadFlorence2ModelMultiGPU"] = override_class(DownloadAndLoadFlorence2Model)
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logging.info(f"MultiGPU: Registered DownloadAndLoadFlorence2ModelMultiGPU")
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def register_CheckpointLoaderNF4():
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global NODE_CLASS_MAPPINGS
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class CheckpointLoaderNF4:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
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}}
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RETURN_TYPES = ("MODEL", "CLIP", "VAE")
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FUNCTION = "load_checkpoint"
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CATEGORY = "loaders"
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def load_checkpoint(self, ckpt_name):
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["CheckpointLoaderNF4"]()
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return original_loader.load_checkpoint(ckpt_name)
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NODE_CLASS_MAPPINGS["CheckpointLoaderNF4MultiGPU"] = override_class(CheckpointLoaderNF4)
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logging.info(f"MultiGPU: Registered CheckpointLoaderNF4MultiGPU")
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def register_CheckpointLoaderNF4():
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global NODE_CLASS_MAPPINGS
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class CheckpointLoaderNF4:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
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}}
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RETURN_TYPES = ("MODEL", "CLIP", "VAE")
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FUNCTION = "load_checkpoint"
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CATEGORY = "loaders"
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def load_checkpoint(self, ckpt_name):
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["CheckpointLoaderNF4"]()
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return original_loader.load_checkpoint(ckpt_name)
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NODE_CLASS_MAPPINGS["CheckpointLoaderNF4MultiGPU"] = override_class(CheckpointLoaderNF4)
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logging.info(f"MultiGPU: Registered CheckpointLoaderNF4MultiGPU")
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def register_LoadFluxControlNetMultiGPU():
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global NODE_CLASS_MAPPINGS
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class LoadFluxControlNet:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {"model_name": (["flux-dev", "flux-dev-fp8", "flux-schnell"],),
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"controlnet_path": (folder_paths.get_filename_list("xlabs_controlnets"), ),
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}}
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RETURN_TYPES = ("FluxControlNet",)
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RETURN_NAMES = ("ControlNet",)
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FUNCTION = "loadmodel"
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CATEGORY = "XLabsNodes"
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def loadmodel(self, model_name, controlnet_path):
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["LoadFluxControlNet"]()
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return original_loader.loadmodel(model_name, controlnet_path)
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NODE_CLASS_MAPPINGS["LoadFluxControlNetMultiGPU"] = override_class(LoadFluxControlNet)
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logging.info(f"MultiGPU: Registered LoadFluxControlNetMultiGPU")
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def register_MMAudioModelLoaderMultiGPU():
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global NODE_CLASS_MAPPINGS
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class MMAudioModelLoader:
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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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"mmaudio_model": (folder_paths.get_filename_list("mmaudio"), {"tooltip": "These models are loaded from the 'ComfyUI/models/mmaudio' -folder",}),
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"base_precision": (["fp16", "fp32", "bf16"], {"default": "fp16"}),
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},
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}
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RETURN_TYPES = ("MMAUDIO_MODEL",)
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RETURN_NAMES = ("mmaudio_model", )
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FUNCTION = "loadmodel"
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CATEGORY = "MMAudio"
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def loadmodel(self, mmaudio_model, base_precision):
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["MMAudioModelLoader"]()
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return original_loader.loadmodel(mmaudio_model, base_precision)
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NODE_CLASS_MAPPINGS["MMAudioModelLoaderMultiGPU"] = override_class(MMAudioModelLoader)
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logging.info(f"MultiGPU: Registered MMAudioModelLoaderMultiGPU")
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def register_MMAudioModelLoaderMultiGPU():
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global NODE_CLASS_MAPPINGS
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class MMAudioModelLoader:
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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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"mmaudio_model": (folder_paths.get_filename_list("mmaudio"), {"tooltip": "These models are loaded from the 'ComfyUI/models/mmaudio' -folder",}),
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"base_precision": (["fp16", "fp32", "bf16"], {"default": "fp16"}),
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},
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}
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RETURN_TYPES = ("MMAUDIO_MODEL",)
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RETURN_NAMES = ("mmaudio_model", )
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FUNCTION = "loadmodel"
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CATEGORY = "MMAudio"
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def loadmodel(self, mmaudio_model, base_precision):
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["MMAudioModelLoader"]()
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return original_loader.loadmodel(mmaudio_model, base_precision)
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NODE_CLASS_MAPPINGS["MMAudioModelLoaderMultiGPU"] = override_class(MMAudioModelLoader)
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logging.info(f"MultiGPU: Registered MMAudioModelLoaderMultiGPU")
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def register_MMAudioFeatureUtilsLoaderMultiGPU():
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global NODE_CLASS_MAPPINGS
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class MMAudioFeatureUtilsLoader:
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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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"vae_model": (folder_paths.get_filename_list("mmaudio"), {"tooltip": "These models are loaded from 'ComfyUI/models/mmaudio'"}),
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"synchformer_model": (folder_paths.get_filename_list("mmaudio"), {"tooltip": "These models are loaded from 'ComfyUI/models/mmaudio'"}),
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"clip_model": (folder_paths.get_filename_list("mmaudio"), {"tooltip": "These models are loaded from 'ComfyUI/models/mmaudio'"}),
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},
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"optional": {
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"bigvgan_vocoder_model": ("VOCODER_MODEL", {"tooltip": "These models are loaded from 'ComfyUI/models/mmaudio'"}),
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"mode": (["16k", "44k"], {"default": "44k"}),
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"precision": (["fp16", "fp32", "bf16"],
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{"default": "fp16"}
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),
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}
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}
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RETURN_TYPES = ("MMAUDIO_FEATUREUTILS",)
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RETURN_NAMES = ("mmaudio_featureutils", )
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FUNCTION = "loadmodel"
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CATEGORY = "MMAudio"
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def loadmodel(self, vae_model, precision, synchformer_model, clip_model, mode, bigvgan_vocoder_model=None):
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["MMAudioFeatureUtilsLoader"]()
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return original_loader.loadmodel(vae_model, precision, synchformer_model, clip_model, mode, bigvgan_vocoder_model)
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NODE_CLASS_MAPPINGS["MMAudioFeatureUtilsLoaderMultiGPU"] = override_class(MMAudioFeatureUtilsLoader)
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logging.info(f"MultiGPU: Registered MMAudioFeatureUtilsLoaderMultiGPU")
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def register_MMAudioSamplerMultiGPU():
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global NODE_CLASS_MAPPINGS
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class MMAudioSampler:
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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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"mmaudio_model": ("MMAUDIO_MODEL",),
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"feature_utils": ("MMAUDIO_FEATUREUTILS",),
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"duration": ("FLOAT", {"default": 8, "step": 0.01, "tooltip": "Duration of the audio in seconds"}),
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"steps": ("INT", {"default": 25, "step": 1, "tooltip": "Number of steps to interpolate"}),
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"cfg": ("FLOAT", {"default": 4.5, "step": 0.1, "tooltip": "Strength of the conditioning"}),
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"seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
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"prompt": ("STRING", {"default": "", "multiline": True} ),
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"negative_prompt": ("STRING", {"default": "", "multiline": True} ),
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"mask_away_clip": ("BOOLEAN", {"default": False, "tooltip": "If true, the clip video will be masked away"}),
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"force_offload": ("BOOLEAN", {"default": True, "tooltip": "If true, the model will be offloaded to the offload device"}),
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},
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"optional": {
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"images": ("IMAGE",),
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},
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}
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RETURN_TYPES = ("AUDIO",)
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RETURN_NAMES = ("audio", )
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FUNCTION = "sample"
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CATEGORY = "MMAudio"
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def sample(self, mmaudio_model, seed, feature_utils, duration, steps, cfg, prompt, negative_prompt, mask_away_clip, force_offload, images=None):
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["MMAudioSampler"]()
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return original_loader.sample(mmaudio_model, seed, feature_utils, duration, steps, cfg, prompt, negative_prompt, mask_away_clip, force_offload, images)
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|
NODE_CLASS_MAPPINGS["MMAudioSamplerMultiGPU"] = override_class(MMAudioSampler)
|
|
logging.info(f"MultiGPU: Registered MMAudioSamplerMultiGPU")
|
|
|
|
def register_UnetLoaderGGUFMultiGPU():
|
|
global NODE_CLASS_MAPPINGS
|
|
|
|
# First define the base UnetLoaderGGUF class
|
|
class UnetLoaderGGUF:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
unet_names = [x for x in folder_paths.get_filename_list("unet_gguf")]
|
|
return {
|
|
"required": {
|
|
"unet_name": (unet_names,),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("MODEL",)
|
|
FUNCTION = "load_unet"
|
|
CATEGORY = "bootleg"
|
|
TITLE = "Unet Loader (GGUF)"
|
|
|
|
def load_unet(self, unet_name, dequant_dtype=None, patch_dtype=None, patch_on_device=None):
|
|
from nodes import NODE_CLASS_MAPPINGS
|
|
original_loader = NODE_CLASS_MAPPINGS["UnetLoaderGGUF"]()
|
|
return original_loader.load_unet(unet_name, dequant_dtype, patch_dtype, patch_on_device)
|
|
|
|
# Create the MultiGPU version of the base class
|
|
UnetLoaderGGUFMultiGPU = override_class(UnetLoaderGGUF)
|
|
NODE_CLASS_MAPPINGS["UnetLoaderGGUFMultiGPU"] = UnetLoaderGGUFMultiGPU
|
|
logging.info(f"MultiGPU: Registered UnetLoaderGGUFMultiGPU")
|
|
|
|
# Now create the advanced version that inherits from the MultiGPU base class
|
|
class UnetLoaderGGUFAdvanced(UnetLoaderGGUF):
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
unet_names = [x for x in folder_paths.get_filename_list("unet_gguf")]
|
|
return {
|
|
"required": {
|
|
"unet_name": (unet_names,),
|
|
"dequant_dtype": (["default", "target", "float32", "float16", "bfloat16"], {"default": "default"}),
|
|
"patch_dtype": (["default", "target", "float32", "float16", "bfloat16"], {"default": "default"}),
|
|
"patch_on_device": ("BOOLEAN", {"default": False}),
|
|
}
|
|
}
|
|
TITLE = "Unet Loader (GGUF/Advanced)"
|
|
|
|
# Create the MultiGPU version of the advanced class
|
|
UnetLoaderGGUFAdvancedMultiGPU = override_class(UnetLoaderGGUFAdvanced)
|
|
NODE_CLASS_MAPPINGS["UnetLoaderGGUFAdvancedMultiGPU"] = UnetLoaderGGUFAdvancedMultiGPU
|
|
logging.info(f"MultiGPU: Registered UnetLoaderGGUFAdvancedMultiGPU")
|
|
|
|
def register_CLIPLoaderGGUFMultiGPU():
|
|
global NODE_CLASS_MAPPINGS
|
|
|
|
class CLIPLoaderGGUF:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"clip_name": (s.get_filename_list(),),
|
|
"type": (["stable_diffusion", "stable_cascade", "sd3", "stable_audio", "mochi", "ltxv"],),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("CLIP",)
|
|
FUNCTION = "load_clip"
|
|
CATEGORY = "bootleg"
|
|
TITLE = "CLIPLoader (GGUF)"
|
|
|
|
@classmethod
|
|
def get_filename_list(s):
|
|
files = []
|
|
files += folder_paths.get_filename_list("clip")
|
|
files += folder_paths.get_filename_list("clip_gguf")
|
|
return sorted(files)
|
|
|
|
def load_data(self, ckpt_paths):
|
|
from nodes import NODE_CLASS_MAPPINGS
|
|
original_loader = NODE_CLASS_MAPPINGS["CLIPLoaderGGUF"]()
|
|
return original_loader.load_data(ckpt_paths)
|
|
|
|
def load_patcher(self, clip_paths, clip_type, clip_data):
|
|
from nodes import NODE_CLASS_MAPPINGS
|
|
original_loader = NODE_CLASS_MAPPINGS["CLIPLoaderGGUF"]()
|
|
return original_loader.load_patcher(clip_paths, clip_type, clip_data)
|
|
|
|
def load_clip(self, clip_name, type="stable_diffusion"):
|
|
from nodes import NODE_CLASS_MAPPINGS
|
|
original_loader = NODE_CLASS_MAPPINGS["CLIPLoaderGGUF"]()
|
|
return original_loader.load_clip(clip_name, type)
|
|
|
|
# Create the MultiGPU version of the base class
|
|
CLIPLoaderGGUFMultiGPU = override_class(CLIPLoaderGGUF)
|
|
NODE_CLASS_MAPPINGS["CLIPLoaderGGUFMultiGPU"] = CLIPLoaderGGUFMultiGPU
|
|
logging.info(f"MultiGPU: Registered CLIPLoaderGGUFMultiGPU")
|
|
|
|
# Now create the advanced version that inherits from the MultiGPU base class
|
|
|
|
class DualCLIPLoaderGGUF(CLIPLoaderGGUF):
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
file_options = (s.get_filename_list(), )
|
|
return {
|
|
"required": {
|
|
"clip_name1": file_options,
|
|
"clip_name2": file_options,
|
|
"type": (("sdxl", "sd3", "flux", "hunyuan_video"),),
|
|
}
|
|
}
|
|
|
|
TITLE = "DualCLIPLoader (GGUF)"
|
|
|
|
def load_clip(self, clip_name1, clip_name2, type):
|
|
from nodes import NODE_CLASS_MAPPINGS
|
|
original_loader = NODE_CLASS_MAPPINGS["DualCLIPLoaderGGUF"]()
|
|
return original_loader.load_clip(clip_name1, clip_name2, type)
|
|
# Create the MultiGPU version of the advanced class
|
|
DualCLIPLoaderGGUFMultiGPU = override_class(DualCLIPLoaderGGUF)
|
|
NODE_CLASS_MAPPINGS["DualCLIPLoaderGGUFMultiGPU"] = DualCLIPLoaderGGUFMultiGPU
|
|
logging.info(f"MultiGPU: Registered DualCLIPLoaderGGUFMultiGPU")
|
|
|
|
class TripleCLIPLoaderGGUF(CLIPLoaderGGUF):
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
file_options = (s.get_filename_list(), )
|
|
return {
|
|
"required": {
|
|
"clip_name1": file_options,
|
|
"clip_name2": file_options,
|
|
"clip_name3": file_options,
|
|
}
|
|
}
|
|
|
|
TITLE = "TripleCLIPLoader (GGUF)"
|
|
|
|
def load_clip(self, clip_name1, clip_name2, clip_name3, type="sd3"):
|
|
from nodes import NODE_CLASS_MAPPINGS
|
|
original_loader = NODE_CLASS_MAPPINGS["TripleCLIPLoaderGGUF"]()
|
|
return original_loader.load_clip(clip_name1, clip_name2, clip_name3, type)
|
|
# Create the MultiGPU version of the advanced class
|
|
TripleCLIPLoaderGGUFMultiGPU = override_class(TripleCLIPLoaderGGUF)
|
|
NODE_CLASS_MAPPINGS["TripleCLIPLoaderGGUFMultiGPU"] = TripleCLIPLoaderGGUFMultiGPU
|
|
logging.info(f"MultiGPU: Registered TripleCLIPLoaderGGUFMultiGPU")
|
|
|
|
def register_PulidModelLoader():
|
|
|
|
global NODE_CLASS_MAPPINGS
|
|
|
|
class PulidModelLoader:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {"required": { "pulid_file": (folder_paths.get_filename_list("pulid"), )}}
|
|
|
|
RETURN_TYPES = ("PULID",)
|
|
FUNCTION = "load_model"
|
|
CATEGORY = "pulid"
|
|
|
|
def load_model(self, pulid_file):
|
|
from nodes import NODE_CLASS_MAPPINGS
|
|
original_loader = NODE_CLASS_MAPPINGS["PulidModelLoader"]()
|
|
return original_loader.load_model(pulid_file)
|
|
|
|
NODE_CLASS_MAPPINGS["PulidModelLoaderMultiGPU"] = override_class(PulidModelLoader)
|
|
logging.info(f"MultiGPU: Registered PulidModelLoaderMultiGPU")
|
|
|
|
def register_PulidInsightFaceLoader():
|
|
|
|
global NODE_CLASS_MAPPINGS
|
|
|
|
class PulidInsightFaceLoader:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"provider": (["CPU", "CUDA", "ROCM", "CoreML"], ),
|
|
},
|
|
}
|
|
|
|
RETURN_TYPES = ("FACEANALYSIS",)
|
|
FUNCTION = "load_insightface"
|
|
CATEGORY = "pulid"
|
|
|
|
def load_insightface(self, provider):
|
|
from nodes import NODE_CLASS_MAPPINGS
|
|
original_loader = NODE_CLASS_MAPPINGS["PulidInsightFaceLoader"]()
|
|
return original_loader.load_insightface(provider)
|
|
|
|
NODE_CLASS_MAPPINGS["PulidInsightFaceLoaderMultiGPU"] = override_class(PulidInsightFaceLoader)
|
|
logging.info(f"MultiGPU: Registered PulidInsightFaceLoaderMultiGPU")
|
|
|
|
def register_PulidEvaClipLoader():
|
|
|
|
global NODE_CLASS_MAPPINGS
|
|
|
|
class PulidEvaClipLoader:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {},
|
|
}
|
|
|
|
RETURN_TYPES = ("EVA_CLIP",)
|
|
FUNCTION = "load_eva_clip"
|
|
CATEGORY = "pulid"
|
|
|
|
def load_eva_clip(self):
|
|
from nodes import NODE_CLASS_MAPPINGS
|
|
original_loader = NODE_CLASS_MAPPINGS["PulidEvaClipLoader"]()
|
|
return original_loader.load_eva_clip()
|
|
|
|
NODE_CLASS_MAPPINGS["PulidEvaClipLoaderMultiGPU"] = override_class(PulidEvaClipLoader)
|
|
logging.info(f"MultiGPU: Registered PulidEvaClipLoaderMultiGPU")
|
|
|
|
def register_HyVideoModelLoader():
|
|
global NODE_CLASS_MAPPINGS
|
|
|
|
# Keep original MultiGPU wrapper unchanged
|
|
class HyVideoModelLoader:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"model": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "These models are loaded from the 'ComfyUI/models/diffusion_models' -folder",}),
|
|
"base_precision": (["fp32", "bf16"], {"default": "bf16"}),
|
|
"quantization": (['disabled', 'fp8_e4m3fn', 'fp8_e4m3fn_fast', 'fp8_scaled', 'torchao_fp8dq', "torchao_fp8dqrow", "torchao_int8dq", "torchao_fp6", "torchao_int4", "torchao_int8"], {"default": 'disabled', "tooltip": "optional quantization method"}),
|
|
"load_device": (["main_device"], {"default": "main_device"}),
|
|
},
|
|
"optional": {
|
|
"attention_mode": ([
|
|
"sdpa",
|
|
"flash_attn_varlen",
|
|
"sageattn_varlen",
|
|
"comfy",
|
|
], {"default": "flash_attn"}),
|
|
"compile_args": ("COMPILEARGS", ),
|
|
"block_swap_args": ("BLOCKSWAPARGS", ),
|
|
"lora": ("HYVIDLORA", {"default": None}),
|
|
"auto_cpu_offload": ("BOOLEAN", {"default": False, "tooltip": "Enable auto offloading for reduced VRAM usage, implementation from DiffSynth-Studio, slightly different from block swapping and uses even less VRAM, but can be slower as you can't define how much VRAM to use"}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("HYVIDEOMODEL",)
|
|
RETURN_NAMES = ("model", )
|
|
FUNCTION = "loadmodel"
|
|
CATEGORY = "HunyuanVideoWrapper"
|
|
|
|
def loadmodel(self, model, base_precision, load_device, quantization, compile_args=None, attention_mode="sdpa", block_swap_args=None, lora=None, auto_cpu_offload=False):
|
|
from nodes import NODE_CLASS_MAPPINGS
|
|
original_loader = NODE_CLASS_MAPPINGS["HyVideoModelLoader"]()
|
|
return original_loader.loadmodel(model, base_precision, load_device, quantization, compile_args, attention_mode, block_swap_args, lora, auto_cpu_offload)
|
|
|
|
# Add new DiffSynth-style node
|
|
class HyVideoModelLoaderDiffSynth:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"model": (folder_paths.get_filename_list("diffusion_models"), {"tooltip": "These models are loaded from the 'ComfyUI/models/diffusion_models' -folder",}),
|
|
"base_precision": (["fp32", "bf16"], {"default": "bf16"}),
|
|
"quantization": (['disabled', 'fp8_e4m3fn', 'fp8_e4m3fn_fast', 'fp8_scaled', 'torchao_fp8dq', "torchao_fp8dqrow", "torchao_int8dq", "torchao_fp6", "torchao_int4", "torchao_int8"],
|
|
{"default": 'disabled', "tooltip": "optional quantization method"}),
|
|
},
|
|
"optional": {
|
|
"attention_mode": ([
|
|
"sdpa",
|
|
"flash_attn_varlen",
|
|
"sageattn_varlen",
|
|
"comfy",
|
|
], {"default": "flash_attn"}),
|
|
"compile_args": ("COMPILEARGS", ),
|
|
"block_swap_args": ("BLOCKSWAPARGS", ),
|
|
"lora": ("HYVIDLORA", {"default": None}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("HYVIDEOMODEL",)
|
|
RETURN_NAMES = ("model", )
|
|
FUNCTION = "loadmodel"
|
|
CATEGORY = "HunyuanVideoWrapper"
|
|
|
|
def loadmodel(self, model, base_precision, quantization, compile_args=None, attention_mode="sdpa", block_swap_args=None, lora=None):
|
|
from nodes import NODE_CLASS_MAPPINGS
|
|
original_loader = NODE_CLASS_MAPPINGS["HyVideoModelLoader"]()
|
|
# Use DiffSynth's auto offloading approach
|
|
return original_loader.loadmodel(model, base_precision, "main_device", quantization,
|
|
compile_args, attention_mode, block_swap_args, lora,
|
|
auto_cpu_offload=True)
|
|
|
|
# Register both with MultiGPU wrapper
|
|
NODE_CLASS_MAPPINGS["HyVideoModelLoaderMultiGPU"] = override_class(HyVideoModelLoader)
|
|
NODE_CLASS_MAPPINGS["HyVideoModelLoaderDiffSynthMultiGPU"] = override_class_with_offload(HyVideoModelLoaderDiffSynth)
|
|
|
|
logging.info(f"MultiGPU: Registered HyVideoModelLoader nodes")
|
|
|
|
def register_HyVideoVAELoader():
|
|
|
|
global NODE_CLASS_MAPPINGS
|
|
|
|
class HyVideoVAELoader:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"model_name": (folder_paths.get_filename_list("vae"), {"tooltip": "These models are loaded from 'ComfyUI/models/vae'"}),
|
|
},
|
|
"optional": {
|
|
"precision": (["fp16", "fp32", "bf16"],
|
|
{"default": "bf16"}
|
|
),
|
|
"compile_args":("COMPILEARGS", ),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("VAE",)
|
|
RETURN_NAMES = ("vae", )
|
|
FUNCTION = "loadmodel"
|
|
CATEGORY = "HunyuanVideoWrapper"
|
|
DESCRIPTION = "Loads Hunyuan VAE model from 'ComfyUI/models/vae'"
|
|
|
|
def loadmodel(self, model_name, precision, compile_args=None):
|
|
from nodes import NODE_CLASS_MAPPINGS
|
|
original_loader = NODE_CLASS_MAPPINGS["HyVideoVAELoader"]()
|
|
return original_loader.loadmodel(model_name, precision, compile_args)
|
|
|
|
NODE_CLASS_MAPPINGS["HyVideoVAELoaderMultiGPU"] = override_class(HyVideoVAELoader)
|
|
logging.info(f"MultiGPU: Registered HyVideoVAELoaderMultiGPU")
|
|
|
|
def register_DownloadAndLoadHyVideoTextEncoder():
|
|
|
|
global NODE_CLASS_MAPPINGS
|
|
|
|
class DownloadAndLoadHyVideoTextEncoder:
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {
|
|
"required": {
|
|
"llm_model": (["Kijai/llava-llama-3-8b-text-encoder-tokenizer","xtuner/llava-llama-3-8b-v1_1-transformers"],),
|
|
"clip_model": (["disabled","openai/clip-vit-large-patch14",],),
|
|
"precision": (["fp16", "fp32", "bf16"],
|
|
{"default": "bf16"}
|
|
),
|
|
},
|
|
"optional": {
|
|
"apply_final_norm": ("BOOLEAN", {"default": False}),
|
|
"hidden_state_skip_layer": ("INT", {"default": 2}),
|
|
"quantization": (['disabled', 'bnb_nf4', "fp8_e4m3fn"], {"default": 'disabled'}),
|
|
}
|
|
}
|
|
|
|
RETURN_TYPES = ("HYVIDTEXTENCODER",)
|
|
RETURN_NAMES = ("hyvid_text_encoder", )
|
|
FUNCTION = "loadmodel"
|
|
CATEGORY = "HunyuanVideoWrapper"
|
|
DESCRIPTION = "Loads Hunyuan text_encoder model from 'ComfyUI/models/LLM'"
|
|
|
|
def loadmodel(self, llm_model, clip_model, precision, apply_final_norm=False, hidden_state_skip_layer=2, quantization="disabled"):
|
|
from nodes import NODE_CLASS_MAPPINGS
|
|
original_loader = NODE_CLASS_MAPPINGS["DownloadAndLoadHyVideoTextEncoder"]()
|
|
return original_loader.loadmodel(llm_model, clip_model, precision, apply_final_norm, hidden_state_skip_layer, quantization)
|
|
|
|
NODE_CLASS_MAPPINGS["DownloadAndLoadHyVideoTextEncoderMultiGPU"] = override_class(DownloadAndLoadHyVideoTextEncoder)
|
|
logging.info(f"MultiGPU: Registered DownloadAndLoadHyVideoTextEncoderMultiGPU")
|
|
|
|
# Register desired nodes
|
|
register_module("", ["UNETLoader", "VAELoader", "CLIPLoader", "DualCLIPLoader", "TripleCLIPLoader", "CheckpointLoaderSimple", "ControlNetLoader"])
|
|
|
|
if check_module_exists("ComfyUI-LTXVideo"):
|
|
register_LTXVLoaderMultiGPU()
|
|
if check_module_exists("ComfyUI-Florence2"):
|
|
register_Florence2ModelLoaderMultiGPU()
|
|
register_DownloadAndLoadFlorence2ModelMultiGPU()
|
|
if check_module_exists("ComfyUI_bitsandbytes_NF4"):
|
|
register_CheckpointLoaderNF4()
|
|
if check_module_exists("x-flux-comfyui"):
|
|
register_LoadFluxControlNetMultiGPU()
|
|
if check_module_exists("ComfyUI-MMAudio"):
|
|
register_MMAudioModelLoaderMultiGPU()
|
|
register_MMAudioFeatureUtilsLoaderMultiGPU()
|
|
register_MMAudioSamplerMultiGPU()
|
|
if check_module_exists("ComfyUI-GGUF"):
|
|
register_UnetLoaderGGUFMultiGPU()
|
|
register_CLIPLoaderGGUFMultiGPU()
|
|
if check_module_exists("PuLID_ComfyUI"):
|
|
register_PulidModelLoader()
|
|
register_PulidInsightFaceLoader()
|
|
register_PulidEvaClipLoader()
|
|
if check_module_exists("ComfyUI-HunyuanVideoWrapper"):
|
|
register_HyVideoModelLoader()
|
|
register_HyVideoVAELoader()
|
|
register_DownloadAndLoadHyVideoTextEncoder()
|
|
|
|
logging.info(f"MultiGPU: Registration complete. Final mappings: {', '.join(NODE_CLASS_MAPPINGS.keys())}")
|