__init__.py

# __init__.py
import copy
import torch
import sys
import comfy.model_management as mm
import os
from pathlib import Path
import logging
import folder_paths
from collections import defaultdict
import hashlib

current_device = mm.get_torch_device()
current_offload_device = mm.get_torch_device()
model_allocation_store = {}

def get_torch_device_patched():
    device = None
    if (not torch.cuda.is_available() or mm.cpu_state == mm.CPUState.CPU or "cpu" in str(current_device).lower()):
        device = torch.device("cpu")
    else:
        device = torch.device(current_device)
    return device

def text_encoder_device_patched():
    device = None
    if (not torch.cuda.is_available() or mm.cpu_state == mm.CPUState.CPU or "cpu" in str(current_device).lower()):
        device = torch.device("cpu")
    else:
        device = torch.device(current_device)
    return device

def unet_offload_device_patched():
    device = None
    if (not torch.cuda.is_available() or mm.cpu_state == mm.CPUState.CPU or "cpu" in str(current_offload_device).lower()):
        device = torch.device("cpu")
    else:
        device = torch.device(current_offload_device)
    return device

def text_encoder_offload_device_patched():
    device = None
    if (not torch.cuda.is_available() or mm.cpu_state == mm.CPUState.CPU or "cpu" in str(current_offload_device).lower()):
        device = torch.device("cpu")
    else:
        device = torch.device(current_offload_device)
    return device

mm.get_torch_device = get_torch_device_patched
mm.unet_offload_device = unet_offload_device_patched
mm.text_encoder_device = text_encoder_device_patched
mm.text_encoder_offload_device = text_encoder_offload_device_patched


def create_model_hash(model, caller):

   model_type = type(model.model).__name__
   model_size = model.model_size()
   first_layers = str(list(model.model_state_dict().keys())[:3])
   identifier = f"{model_type}_{model_size}_{first_layers}"
   final_hash = hashlib.sha256(identifier.encode()).hexdigest()
 
   return final_hash

def register_patched_ggufmodelpatcher():
    from nodes import NODE_CLASS_MAPPINGS
    original_loader = NODE_CLASS_MAPPINGS["UnetLoaderGGUF"]
    module = sys.modules[original_loader.__module__]

    if not hasattr(module.GGUFModelPatcher, '_patched'):
        original_load = module.GGUFModelPatcher.load

    def new_load(self, *args, force_patch_weights=False, **kwargs):
        global model_allocation_store

        super(module.GGUFModelPatcher, self).load(*args, force_patch_weights=True, **kwargs)
        debug_hash = create_model_hash(self, "patcher")
        linked = []
        module_count = 0
        for n, m in self.model.named_modules():
            module_count += 1
            if hasattr(m, "weight"):
                device = getattr(m.weight, "device", None)
                if device is not None:
                    linked.append((n, m))
                    continue
            if hasattr(m, "bias"):
                device = getattr(m.bias, "device", None)
                if device is not None:
                    linked.append((n, m))
                    continue
        if linked:
            if hasattr(self, 'model'):
                debug_hash = create_model_hash(self, "patcher")
                debug_allocations = model_allocation_store.get(debug_hash)
                if debug_allocations:
                    device_assignments = analyze_ggml_loading(self.model, debug_allocations)['device_assignments']
                    for device, layers in device_assignments.items():
                        target_device = torch.device(device)
                        for n, m, _ in layers:
                            m.to(self.load_device).to(target_device)

                    self.mmap_released = True

    module.GGUFModelPatcher.load = new_load
    module.GGUFModelPatcher._patched = True

def analyze_ggml_loading(model, allocations_str):
    DEVICE_RATIOS_DISTORCH = {}
    device_table = {}

    for allocation in allocations_str.split(';'):
        dev_name, fraction = allocation.split(',')
        fraction = float(fraction)
        total_mem_bytes = mm.get_total_memory(torch.device(dev_name))
        alloc_gb = (total_mem_bytes * fraction) / (1024**3)
        DEVICE_RATIOS_DISTORCH[dev_name] = alloc_gb
        device_table[dev_name] = {
            "fraction": fraction,
            "total_gb": total_mem_bytes / (1024**3),
            "alloc_gb": alloc_gb
        }

    eq_line = "=" * 47
    dash_line = "-" * 47
    fmt_alloc = "{:<12}{:>10}{:>14}{:>10}"
    logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
    logging.info(eq_line)
    logging.info("               DisTorch Analysis")
    logging.info(eq_line)
    logging.info(dash_line)
    logging.info("          DisTorch Device Allocations")
    logging.info(dash_line)
    logging.info(fmt_alloc.format("Device", "Alloc %", "Total (GB)", " Alloc (GB)"))
    logging.info(dash_line)

    sorted_devices = sorted(device_table.keys(), key=lambda d: (d == "cpu", d))

    for dev in sorted_devices:
        frac = device_table[dev]["fraction"]
        tot_gb = device_table[dev]["total_gb"]
        alloc_gb = device_table[dev]["alloc_gb"]
        logging.info(fmt_alloc.format(dev,f"{int(frac * 100)}%",f"{tot_gb:.2f}",f"{alloc_gb:.2f}"))

    logging.info(dash_line)

    layer_summary = {}
    layer_list = []
    memory_by_type = defaultdict(int)
    total_memory = 0

    for name, module in model.named_modules():
        if hasattr(module, "weight"):
            layer_type = type(module).__name__
            layer_summary[layer_type] = layer_summary.get(layer_type, 0) + 1
            layer_list.append((name, module, layer_type))
            layer_memory = 0
            if module.weight is not None:
                layer_memory += module.weight.numel() * module.weight.element_size()
            if hasattr(module, "bias") and module.bias is not None:
                layer_memory += module.bias.numel() * module.bias.element_size()
            memory_by_type[layer_type] += layer_memory
            total_memory += layer_memory

    logging.info("     DisTorch GGML Layer Distribution")
    logging.info(dash_line)
    fmt_layer = "{:<12}{:>10}{:>14}{:>10}"
    logging.info(fmt_layer.format("Layer Type", "Layers", "Memory (MB)", "% Total"))
    logging.info(dash_line)
    for layer_type, count in layer_summary.items():
        mem_mb = memory_by_type[layer_type] / (1024 * 1024)
        mem_percent = (memory_by_type[layer_type] / total_memory) * 100 if total_memory > 0 else 0
        logging.info(fmt_layer.format(layer_type,str(count),f"{mem_mb:.2f}",f"{mem_percent:.1f}%"))
    logging.info(dash_line)

    nonzero_devices = [d for d, r in DEVICE_RATIOS_DISTORCH.items() if r > 0]
    nonzero_total_ratio = sum(DEVICE_RATIOS_DISTORCH[d] for d in nonzero_devices)
    device_assignments = {device: [] for device in DEVICE_RATIOS_DISTORCH.keys()}
    total_layers = len(layer_list)
    current_layer = 0

    for idx, device in enumerate(nonzero_devices):
        ratio = DEVICE_RATIOS_DISTORCH[device]
        if idx == len(nonzero_devices) - 1:
            device_layer_count = total_layers - current_layer
        else:
            device_layer_count = int((ratio / nonzero_total_ratio) * total_layers)
        start_idx = current_layer
        end_idx = current_layer + device_layer_count
        device_assignments[device] = layer_list[start_idx:end_idx]
        current_layer += device_layer_count

    logging.info("    DisTorch Final Device/Layer Assignments")
    logging.info(dash_line)
    fmt_assign = "{:<12}{:>10}{:>14}{:>10}"
    logging.info(fmt_assign.format("Device", "Layers", "Memory (MB)", "% Total"))
    logging.info(dash_line)
    total_assigned_memory = 0
    device_memories = {}
    for device, layers in device_assignments.items():
        device_memory = 0
        for layer_type in layer_summary:
            type_layers = sum(1 for _, _, lt in layers if lt == layer_type)
            if layer_summary[layer_type] > 0:
                mem_per_layer = memory_by_type[layer_type] / layer_summary[layer_type]
                device_memory += mem_per_layer * type_layers
        device_memories[device] = device_memory
        total_assigned_memory += device_memory

    sorted_assignments = sorted(device_assignments.keys(), key=lambda d: (d == "cpu", d))

    for dev in sorted_assignments:
        layers = device_assignments[dev]
        mem_mb = device_memories[dev] / (1024 * 1024)
        mem_percent = (device_memories[dev] / total_memory) * 100 if total_memory > 0 else 0
        logging.info(fmt_assign.format(dev,str(len(layers)),f"{mem_mb:.2f}",f"{mem_percent:.1f}%"))
    logging.info(dash_line)

    return {"device_assignments": device_assignments}





def log_comfy_states(label=""):

    global current_device
    global current_offload_device

    logger=logging.getLogger(__name__)
    main_dev=mm.get_torch_device()
    if torch.device(current_device) != main_dev:
        logging.info(f"MultiGPU: get_torch_device() {main_dev} = current_device {current_device}: False")

    unet_dev=mm.unet_offload_device()
    if (unet_dev != current_offload_device):
        logging.info(f"MultiGPU: unet_offload_device() {unet_dev} = current_offload_device {current_offload_device}: False")

    textenc_dev=mm.text_encoder_device()
    if torch.device(current_device) != textenc_dev:
        logging.info(f"MultiGPU: text_encoder_device() {textenc_dev} = current_device {current_device}: False")


    textenc_off_dev=mm.text_encoder_offload_device()
    if (textenc_off_dev != current_offload_device):
        logging.info(f"MultiGPU: text_encoder_offload_device() {textenc_off_dev} = current_offload_device {current_offload_device}: False")



def get_device_list():
    import torch
    return ["cpu"] + [f"cuda:{i}" for i in range(torch.cuda.device_count())]

class DeviceSelectorMultiGPU:
    @classmethod
    def INPUT_TYPES(s):
        devices = get_device_list()
        return {
            "required": {
                "device": (devices, {"default": devices[1] if len(devices) > 1 else devices[0]})
            }
        }

    RETURN_TYPES = (get_device_list(),)
    RETURN_NAMES = ("device",)
    FUNCTION = "select_device"
    CATEGORY = "multigpu"

    def select_device(self, device):
        return (device,)

def override_class(cls):
    class NodeOverride(cls):
        @classmethod
        def INPUT_TYPES(s):
            inputs = copy.deepcopy(cls.INPUT_TYPES())
            devices = get_device_list()
            default_device = devices[1] if len(devices) > 1 else devices[0]
            inputs["optional"] = inputs.get("optional", {})
            inputs["optional"]["device"] = (devices, {"default": default_device})
            return inputs

        CATEGORY = "multigpu"
        FUNCTION = "override"

        def override(self, *args, device=None, **kwargs):
            global current_device
            log_comfy_states(label=f"{cls.__name__}_override_pre")
            if device is not None:
                current_device = device
                log_comfy_states(label=f"{cls.__name__}_override_device_set_{device}")
            log_comfy_states(label=f"{cls.__name__}_override_post")
            fn = getattr(super(), cls.FUNCTION)
            out = fn(*args, **kwargs)
            log_comfy_states(label=f"{cls.__name__}_override_post_fn_call")
            return out

    return NodeOverride

def override_class_with_offload(cls):
    class NodeOverrideDiffSynth(cls):
        @classmethod
        def INPUT_TYPES(s):
            inputs = copy.deepcopy(cls.INPUT_TYPES())
            devices = get_device_list()
            default_device = devices[1] if len(devices) > 1 else devices[0]
            inputs["optional"] = inputs.get("optional", {})
            inputs["optional"]["device"] = (devices, {"default": default_device})
            inputs["optional"]["offload_device"] = (devices, {"default": "cpu"})
            return inputs

        CATEGORY = "multigpu"
        FUNCTION = "override"

        def override(self, *args, device=None, offload_device=None, **kwargs):
            global current_device
            global current_offload_device
            if device is not None:
                current_device = device
            if offload_device is not None:
                current_offload_device = offload_device
            fn = getattr(super(), cls.FUNCTION)
            return fn(*args, **kwargs)

    return NodeOverrideDiffSynth


def override_class_with_distorch(cls):
    class NodeOverrideDisTorch(cls):
        @classmethod
        def INPUT_TYPES(s):
            inputs = copy.deepcopy(cls.INPUT_TYPES())
            devices = get_device_list()
            default_device = devices[1] if len(devices) > 1 else devices[0]
            inputs["optional"] = inputs.get("optional", {})
            inputs["optional"]["device"] = (devices, {"default": default_device})
            inputs["optional"]["allocations"] = ("STRING", {"multiline": False, "default": "{cuda:0,0.15;cpu,0.5}"})
            return inputs

        CATEGORY = "multigpu"
        FUNCTION = "override"

        def override(self, *args, device=None, allocations=None, **kwargs):
            global current_device
            log_comfy_states(label=f"{cls.__name__}_override_pre")
            if device is not None:
                current_device = device
                log_comfy_states(label=f"{cls.__name__}_override_device_set_{device}")
            log_comfy_states(label=f"{cls.__name__}_override_post")

            register_patched_ggufmodelpatcher()

            fn = getattr(super(), cls.FUNCTION)
            out = fn(*args, **kwargs)
            log_comfy_states(label=f"{cls.__name__}_override_post_fn_call")

            if hasattr(out[0], 'model'):
                model_hash = create_model_hash(out[0], "override")
                model_allocation_store[model_hash] = allocations
            elif hasattr(out[0], 'patcher') and hasattr(out[0].patcher, 'model'):
                model_hash = create_model_hash(out[0].patcher, "override")
                model_allocation_store[model_hash] = allocations

            return out

    return NodeOverrideDisTorch


NODE_CLASS_MAPPINGS = {"DeviceSelectorMultiGPU": DeviceSelectorMultiGPU}

def check_module_exists(module_path):
    full_path = os.path.join(folder_paths.get_folder_paths("custom_nodes")[0], module_path)
    logging.info(f"MultiGPU: Checking for module at {full_path}")

    if not os.path.exists(full_path):
        logging.info(f"MultiGPU: Module {module_path} not found - skipping")
        return False

    logging.info(f"MultiGPU: Found {module_path}, creating compatible MultiGPU nodes")
    return True

def register_module(target_nodes):
    from nodes import NODE_CLASS_MAPPINGS as GLOBAL_NODE_CLASS_MAPPINGS

    for node in target_nodes:
            NODE_CLASS_MAPPINGS[f"{node}MultiGPU"] = override_class(GLOBAL_NODE_CLASS_MAPPINGS[node])
    return

def register_UnetLoaderGGUFMultiGPU():
    global NODE_CLASS_MAPPINGS

    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 both MultiGPU versions of the base class
    UnetLoaderGGUFMultiGPU = override_class(UnetLoaderGGUF)
    NODE_CLASS_MAPPINGS["UnetLoaderGGUFMultiGPU"] = UnetLoaderGGUFMultiGPU
    logging.info(f"MultiGPU: Registered UnetLoaderGGUFMultiGPU")

    UnetLoaderGGUFDisTorchMultiGPU = override_class_with_distorch(UnetLoaderGGUF)
    NODE_CLASS_MAPPINGS["UnetLoaderGGUFDisTorchMultiGPU"] = UnetLoaderGGUFDisTorchMultiGPU
    logging.info(f"MultiGPU: Registered UnetLoaderGGUFDisTorchMultiGPU")

    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 both MultiGPU versions of the advanced class
    UnetLoaderGGUFAdvancedMultiGPU = override_class(UnetLoaderGGUFAdvanced)
    NODE_CLASS_MAPPINGS["UnetLoaderGGUFAdvancedMultiGPU"] = UnetLoaderGGUFAdvancedMultiGPU
    logging.info(f"MultiGPU: Registered UnetLoaderGGUFAdvancedMultiGPU")

    UnetLoaderGGUFAdvancedDisTorchMultiGPU = override_class_with_distorch(UnetLoaderGGUFAdvanced)
    NODE_CLASS_MAPPINGS["UnetLoaderGGUFAdvancedDisTorchMultiGPU"] = UnetLoaderGGUFAdvancedDisTorchMultiGPU
    logging.info(f"MultiGPU: Registered UnetLoaderGGUFAdvancedDisTorchMultiGPU")

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")

    CLIPLoaderGGUFDisTorchMultiGPU = override_class_with_distorch(CLIPLoaderGGUF)
    NODE_CLASS_MAPPINGS["CLIPLoaderGGUFDisTorchMultiGPU"] = CLIPLoaderGGUFDisTorchMultiGPU
    logging.info(f"MultiGPU: Registered CLIPLoaderGGUFDisTorchMultiGPU")

    # 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"]()
            clip = original_loader.load_clip(clip_name1, clip_name2, type)
            clip[0].patcher.load(force_patch_weights=True)
            return clip
    # Create the MultiGPU version of the advanced class
    DualCLIPLoaderGGUFMultiGPU = override_class(DualCLIPLoaderGGUF)
    NODE_CLASS_MAPPINGS["DualCLIPLoaderGGUFMultiGPU"] = DualCLIPLoaderGGUFMultiGPU
    logging.info(f"MultiGPU: Registered DualCLIPLoaderGGUFMultiGPU")

    DualCLIPLoaderGGUFDisTorchMultiGPU = override_class_with_distorch(DualCLIPLoaderGGUF)
    NODE_CLASS_MAPPINGS["DualCLIPLoaderGGUFDisTorchMultiGPU"] = DualCLIPLoaderGGUFDisTorchMultiGPU
    logging.info(f"MultiGPU: Registered DualCLIPLoaderGGUFDisTorchMultiGPU")

    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")

    TripleCLIPLoaderGGUFDisTorchMultiGPU = override_class_with_distorch(TripleCLIPLoaderGGUF)
    NODE_CLASS_MAPPINGS["TripleCLIPLoaderGGUFDisTorchMultiGPU"] = TripleCLIPLoaderGGUFDisTorchMultiGPU
    logging.info(f"MultiGPU: Registered TripleCLIPLoaderGGUFDisTorchMultiGPU")

def register_LTXVLoaderMultiGPU():
    global NODE_CLASS_MAPPINGS

    class LTXVLoader:
        @classmethod
        def INPUT_TYPES(s):
            return {
                "required": {
                    "ckpt_name": (folder_paths.get_filename_list("checkpoints"),
                                {"tooltip": "The name of the checkpoint (model) to load."}),
                    "dtype": (["bfloat16", "float32"], {"default": "bfloat16"})
                }
            }

        RETURN_TYPES = ("MODEL", "VAE")
        RETURN_NAMES = ("model", "vae")
        FUNCTION = "load"
        CATEGORY = "lightricks/LTXV"
        TITLE = "LTXV Loader"
        OUTPUT_NODE = False

        def load(self, ckpt_name, dtype):
            from nodes import NODE_CLASS_MAPPINGS
            original_loader = NODE_CLASS_MAPPINGS["LTXVLoader"]()
            return original_loader.load(ckpt_name, dtype)
        def _load_unet(self, load_device, offload_device, weights, num_latent_channels, dtype, config=None ):
            from nodes import NODE_CLASS_MAPPINGS
            original_loader = NODE_CLASS_MAPPINGS["LTXVLoader"]()
            return original_loader._load_unet(load_device, offload_device, weights, num_latent_channels, dtype, config=None )
        def _load_vae(self, weights, config=None):
            from nodes import NODE_CLASS_MAPPINGS
            original_loader = NODE_CLASS_MAPPINGS["LTXVLoader"]()
            return original_loader._load_vae(weights, config=None)
    NODE_CLASS_MAPPINGS["LTXVLoaderMultiGPU"] = override_class(LTXVLoader)

    logging.info(f"MultiGPU: Registered LTXVLoaderMultiGPU")

def register_Florence2ModelLoaderMultiGPU():
    global NODE_CLASS_MAPPINGS

    class Florence2ModelLoader:
        @classmethod
        def INPUT_TYPES(s):
            return {"required": {
                "model": ([item.name for item in Path(folder_paths.models_dir, "LLM").iterdir() if item.is_dir()],
                         {"tooltip": "models are expected to be in Comfyui/models/LLM folder"}),
                "precision": (['fp16','bf16','fp32'],),
                "attention": (['flash_attention_2', 'sdpa', 'eager'], {"default": 'sdpa'}),
            },
            "optional": {
                "lora": ("PEFTLORA",),
            }}

        RETURN_TYPES = ("FL2MODEL",)
        RETURN_NAMES = ("florence2_model",)
        FUNCTION = "loadmodel"
        CATEGORY = "Florence2"

        def loadmodel(self, model, precision, attention, lora=None):
            from nodes import NODE_CLASS_MAPPINGS
            original_loader = NODE_CLASS_MAPPINGS["Florence2ModelLoader"]()
            return original_loader.loadmodel(model, precision, attention, lora)
    NODE_CLASS_MAPPINGS["Florence2ModelLoaderMultiGPU"] = override_class(Florence2ModelLoader)
    logging.info(f"MultiGPU: Registered Florence2ModelLoaderMultiGPU")

def register_DownloadAndLoadFlorence2ModelMultiGPU():
    global NODE_CLASS_MAPPINGS

    class DownloadAndLoadFlorence2Model:
        @classmethod
        def INPUT_TYPES(s):
            return {"required": {
                "model": ([
                    'microsoft/Florence-2-base',
                    'microsoft/Florence-2-base-ft',
                    'microsoft/Florence-2-large',
                    'microsoft/Florence-2-large-ft',
                    'HuggingFaceM4/Florence-2-DocVQA',
                    'thwri/CogFlorence-2.1-Large',
                    'thwri/CogFlorence-2.2-Large',
                    'gokaygokay/Florence-2-SD3-Captioner',
                    'gokaygokay/Florence-2-Flux-Large',
                    'MiaoshouAI/Florence-2-base-PromptGen-v1.5',
                    'MiaoshouAI/Florence-2-large-PromptGen-v1.5',
                    'MiaoshouAI/Florence-2-base-PromptGen-v2.0',
                    'MiaoshouAI/Florence-2-large-PromptGen-v2.0'
                ], {"default": 'microsoft/Florence-2-base'}),
                "precision": (['fp16','bf16','fp32'], {"default": 'fp16'}),
                "attention": (['flash_attention_2', 'sdpa', 'eager'], {"default": 'sdpa'}),
            },
            "optional": {
                "lora": ("PEFTLORA",),
            }}

        RETURN_TYPES = ("FL2MODEL",)
        RETURN_NAMES = ("florence2_model",)
        FUNCTION = "loadmodel"
        CATEGORY = "Florence2"

        def loadmodel(self, model, precision, attention, lora=None):
            from nodes import NODE_CLASS_MAPPINGS
            original_loader = NODE_CLASS_MAPPINGS["DownloadAndLoadFlorence2Model"]()
            return original_loader.loadmodel(model, precision, attention, lora)
    NODE_CLASS_MAPPINGS["DownloadAndLoadFlorence2ModelMultiGPU"] = override_class(DownloadAndLoadFlorence2Model)
    logging.info(f"MultiGPU: Registered DownloadAndLoadFlorence2ModelMultiGPU")

def register_CheckpointLoaderNF4():
    global NODE_CLASS_MAPPINGS

    class CheckpointLoaderNF4:
        @classmethod
        def INPUT_TYPES(s):
            return {"required": { "ckpt_name": (folder_paths.get_filename_list("checkpoints"), ),
                                }}
        RETURN_TYPES = ("MODEL", "CLIP", "VAE")
        FUNCTION = "load_checkpoint"

        CATEGORY = "loaders"


        def load_checkpoint(self, ckpt_name):
            from nodes import NODE_CLASS_MAPPINGS
            original_loader = NODE_CLASS_MAPPINGS["CheckpointLoaderNF4"]()
            return original_loader.load_checkpoint(ckpt_name)

    NODE_CLASS_MAPPINGS["CheckpointLoaderNF4MultiGPU"] = override_class(CheckpointLoaderNF4)
    logging.info(f"MultiGPU: Registered CheckpointLoaderNF4MultiGPU")

def register_LoadFluxControlNetMultiGPU():
    global NODE_CLASS_MAPPINGS

    class LoadFluxControlNet:
        @classmethod
        def INPUT_TYPES(s):
            return {"required": {"model_name": (["flux-dev", "flux-dev-fp8", "flux-schnell"],),
                                "controlnet_path": (folder_paths.get_filename_list("xlabs_controlnets"), ),
                                }}

        RETURN_TYPES = ("FluxControlNet",)
        RETURN_NAMES = ("ControlNet",)
        FUNCTION = "loadmodel"
        CATEGORY = "XLabsNodes"

        def loadmodel(self, model_name, controlnet_path):
            from nodes import NODE_CLASS_MAPPINGS
            original_loader = NODE_CLASS_MAPPINGS["LoadFluxControlNet"]()
            return original_loader.loadmodel(model_name, controlnet_path)

    NODE_CLASS_MAPPINGS["LoadFluxControlNetMultiGPU"] = override_class(LoadFluxControlNet)
    logging.info(f"MultiGPU: Registered LoadFluxControlNetMultiGPU")

def register_MMAudioModelLoaderMultiGPU():

    global NODE_CLASS_MAPPINGS

    class MMAudioModelLoader:
        @classmethod
        def INPUT_TYPES(s):
            return {
                "required": {
                    "mmaudio_model": (folder_paths.get_filename_list("mmaudio"), {"tooltip": "These models are loaded from the 'ComfyUI/models/mmaudio' -folder",}),

                "base_precision": (["fp16", "fp32", "bf16"], {"default": "fp16"}),
                },
            }

        RETURN_TYPES = ("MMAUDIO_MODEL",)
        RETURN_NAMES = ("mmaudio_model", )
        FUNCTION = "loadmodel"
        CATEGORY = "MMAudio"

        def loadmodel(self, mmaudio_model, base_precision):
            from nodes import NODE_CLASS_MAPPINGS
            original_loader = NODE_CLASS_MAPPINGS["MMAudioModelLoader"]()
            return original_loader.loadmodel(mmaudio_model, base_precision)

    NODE_CLASS_MAPPINGS["MMAudioModelLoaderMultiGPU"] = override_class(MMAudioModelLoader)
    logging.info(f"MultiGPU: Registered MMAudioModelLoaderMultiGPU")

def register_MMAudioFeatureUtilsLoaderMultiGPU():

    global NODE_CLASS_MAPPINGS


    class MMAudioFeatureUtilsLoader:
        @classmethod
        def INPUT_TYPES(s):
            return {
                "required": {
                    "vae_model": (folder_paths.get_filename_list("mmaudio"), {"tooltip": "These models are loaded from 'ComfyUI/models/mmaudio'"}),
                    "synchformer_model": (folder_paths.get_filename_list("mmaudio"), {"tooltip": "These models are loaded from 'ComfyUI/models/mmaudio'"}),
                    "clip_model": (folder_paths.get_filename_list("mmaudio"), {"tooltip": "These models are loaded from 'ComfyUI/models/mmaudio'"}),
                },
                "optional": {
                "bigvgan_vocoder_model": ("VOCODER_MODEL", {"tooltip": "These models are loaded from 'ComfyUI/models/mmaudio'"}),
                    "mode": (["16k", "44k"], {"default": "44k"}),
                    "precision": (["fp16", "fp32", "bf16"],
                        {"default": "fp16"}
                    ),
                }
            }

        RETURN_TYPES = ("MMAUDIO_FEATUREUTILS",)
        RETURN_NAMES = ("mmaudio_featureutils", )
        FUNCTION = "loadmodel"
        CATEGORY = "MMAudio"

        def loadmodel(self, vae_model, precision, synchformer_model, clip_model, mode, bigvgan_vocoder_model=None):
            from nodes import NODE_CLASS_MAPPINGS
            original_loader = NODE_CLASS_MAPPINGS["MMAudioFeatureUtilsLoader"]()
            return original_loader.loadmodel(vae_model, precision, synchformer_model, clip_model, mode, bigvgan_vocoder_model)

    NODE_CLASS_MAPPINGS["MMAudioFeatureUtilsLoaderMultiGPU"] = override_class(MMAudioFeatureUtilsLoader)
    logging.info(f"MultiGPU: Registered MMAudioFeatureUtilsLoaderMultiGPU")

def register_MMAudioSamplerMultiGPU():

    global NODE_CLASS_MAPPINGS

    class MMAudioSampler:
        @classmethod
        def INPUT_TYPES(s):
            return {
                "required": {
                    "mmaudio_model": ("MMAUDIO_MODEL",),
                    "feature_utils": ("MMAUDIO_FEATUREUTILS",),
                    "duration": ("FLOAT", {"default": 8, "step": 0.01, "tooltip": "Duration of the audio in seconds"}),
                    "steps": ("INT", {"default": 25, "step": 1, "tooltip": "Number of steps to interpolate"}),
                    "cfg": ("FLOAT", {"default": 4.5, "step": 0.1, "tooltip": "Strength of the conditioning"}),
                    "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff}),
                    "prompt": ("STRING", {"default": "", "multiline": True} ),
                    "negative_prompt": ("STRING", {"default": "", "multiline": True} ),
                    "mask_away_clip": ("BOOLEAN", {"default": False, "tooltip": "If true, the clip video will be masked away"}),
                    "force_offload": ("BOOLEAN", {"default": True, "tooltip": "If true, the model will be offloaded to the offload device"}),
                },
                "optional": {
                    "images": ("IMAGE",),
                },
            }

        RETURN_TYPES = ("AUDIO",)
        RETURN_NAMES = ("audio", )
        FUNCTION = "sample"
        CATEGORY = "MMAudio"

        def sample(self, mmaudio_model, seed, feature_utils, duration, steps, cfg, prompt, negative_prompt, mask_away_clip, force_offload, images=None):
            from nodes import NODE_CLASS_MAPPINGS
            original_loader = NODE_CLASS_MAPPINGS["MMAudioSampler"]()
            return original_loader.sample(mmaudio_model, seed, feature_utils, duration, steps, cfg, prompt, negative_prompt, mask_away_clip, force_offload, images)

    NODE_CLASS_MAPPINGS["MMAudioSamplerMultiGPU"] = override_class(MMAudioSampler)
    logging.info(f"MultiGPU: Registered MMAudioSamplerMultiGPU")

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") or check_module_exists("comfyui-ltxvideo"):
    register_LTXVLoaderMultiGPU()
if check_module_exists("ComfyUI-Florence2") or check_module_exists("comfyui-florence2"):
    register_Florence2ModelLoaderMultiGPU()
    register_DownloadAndLoadFlorence2ModelMultiGPU()
if check_module_exists("ComfyUI_bitsandbytes_NF4") or check_module_exists("comfyui_bitsandbytes_nf4"):
    register_CheckpointLoaderNF4()
if check_module_exists("x-flux-comfyui") or check_module_exists("x-flux-comfyui"):
    register_LoadFluxControlNetMultiGPU()
if check_module_exists("ComfyUI-MMAudio") or check_module_exists("comfyui-mmaudio"):
    register_MMAudioModelLoaderMultiGPU()
    register_MMAudioFeatureUtilsLoaderMultiGPU()
    register_MMAudioSamplerMultiGPU()
if check_module_exists("ComfyUI-GGUF") or check_module_exists("comfyui-gguf"):
    register_UnetLoaderGGUFMultiGPU()
    register_CLIPLoaderGGUFMultiGPU()
if check_module_exists("PuLID_ComfyUI") or check_module_exists("pulid_comfyui"):
    register_PulidModelLoader()
    register_PulidInsightFaceLoader()
    register_PulidEvaClipLoader()
if check_module_exists("ComfyUI-HunyuanVideoWrapper") or check_module_exists("comfyui-hunyuanvideowrapper"):
    register_HyVideoModelLoader()
    register_HyVideoVAELoader()
    register_DownloadAndLoadHyVideoTextEncoder()


logging.info(f"MultiGPU: Registration complete. Final mappings: {', '.join(NODE_CLASS_MAPPINGS.keys())}")