import copy import torch import sys import comfy.model_management as mm import os from pathlib import Path import logging import folder_paths import shutil from collections import defaultdict import hashlib import tempfile import subprocess import gc from safetensors.torch import save_file, load_file import comfy.utils from typing import Dict, List from nodes import NODE_CLASS_MAPPINGS as GLOBAL_NODE_CLASS_MAPPINGS from .nodes import ( UnetLoaderGGUF, UnetLoaderGGUFAdvanced, CLIPLoaderGGUF, DualCLIPLoaderGGUF, TripleCLIPLoaderGGUF, LTXVLoader, Florence2ModelLoader, DownloadAndLoadFlorence2Model, CheckpointLoaderNF4, LoadFluxControlNet, MMAudioModelLoader, MMAudioFeatureUtilsLoader, MMAudioSampler, PulidModelLoader, PulidInsightFaceLoader, PulidEvaClipLoader, HyVideoModelLoader, HyVideoVAELoader, DownloadAndLoadHyVideoTextEncoder ) current_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 mm.get_torch_device = get_torch_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 = {} distorch_alloc = allocations_str virtual_vram_gb = 0.0 if '#' in allocations_str: distorch_alloc, virtual_vram_str = allocations_str.split('#') if not distorch_alloc: distorch_alloc = calculate_vvram_allocation_string(model, virtual_vram_str) eq_line = "=" * 47 dash_line = "-" * 47 fmt_assign = "{:<12}{:>10}{:>14}{:>10}" for allocation in distorch_alloc.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 } logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') logging.info(eq_line) logging.info(" DisTorch Device Allocations") logging.info(eq_line) logging.info(fmt_assign.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_assign.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 calculate_vvram_allocation_string(model, virtual_vram_str): recipient_device, vram_amount, donors = virtual_vram_str.split(';') virtual_vram_gb = float(vram_amount) eq_line = "=" * 47 dash_line = "-" * 47 fmt_assign = "{:<8} {:<6} {:>11} {:>9} {:>9}" logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') logging.info(eq_line) logging.info(" DisTorch Virtual VRAM Analysis") logging.info(eq_line) logging.info(fmt_assign.format("Object", "Role", "Original(GB)", "Total(GB)", "Virt(GB)")) logging.info(dash_line) recipient_vram = mm.get_total_memory(torch.device(recipient_device)) / (1024**3) recipient_virtual = recipient_vram + virtual_vram_gb logging.info(fmt_assign.format(recipient_device, 'recip', f"{recipient_vram:.2f}GB",f"{recipient_virtual:.2f}GB", f"+{virtual_vram_gb:.2f}GB")) ram_donors = [d for d in donors.split(',') if d != 'cpu'] remaining_vram_needed = virtual_vram_gb donor_device_info = {} donor_allocations = {} for donor in ram_donors: donor_vram = mm.get_total_memory(torch.device(donor)) / (1024**3) max_donor_capacity = donor_vram * 0.9 donation = min(remaining_vram_needed, max_donor_capacity) donor_virtual = donor_vram - donation remaining_vram_needed -= donation donor_allocations[donor] = donation donor_device_info[donor] = (donor_vram, donor_virtual) logging.info(fmt_assign.format(donor, 'donor', f"{donor_vram:.2f}GB", f"{donor_virtual:.2f}GB", f"-{donation:.2f}GB")) system_dram_gb = mm.get_total_memory(torch.device('cpu')) / (1024**3) cpu_donation = remaining_vram_needed cpu_virtual = system_dram_gb - cpu_donation donor_allocations['cpu'] = cpu_donation logging.info(fmt_assign.format('cpu', 'donor', f"{system_dram_gb:.2f}GB", f"{cpu_virtual:.2f}GB", f"-{cpu_donation:.2f}GB")) 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 model_size_gb = total_memory / (1024**3) new_model_size_gb = max(0, model_size_gb - virtual_vram_gb) logging.info(fmt_assign.format('model', 'model', f"{model_size_gb:.2f}GB",f"{new_model_size_gb:.2f}GB", f"-{virtual_vram_gb:.2f}GB")) if model_size_gb > (recipient_vram * 0.9): on_recipient = recipient_vram * 0.9 on_virtuals = model_size_gb - on_recipient logging.info(f"\nWarning: Model size is greater than 90% of recipient VRAM. {on_virtuals:.2f} GB of GGML Layers Offloaded Automatically to Virtual VRAM.\n") else: on_recipient = model_size_gb on_virtuals = 0 new_on_recipient = max(0, on_recipient - virtual_vram_gb) allocation_parts = [] recipient_percent = new_on_recipient / recipient_vram allocation_parts.append(f"{recipient_device},{recipient_percent:.4f}") for donor in ram_donors: donor_vram = donor_device_info[donor][0] donor_percent = donor_allocations[donor] / donor_vram allocation_parts.append(f"{donor},{donor_percent:.4f}") cpu_percent = donor_allocations['cpu'] / system_dram_gb allocation_parts.append(f"cpu,{cpu_percent:.4f}") allocation_string = ";".join(allocation_parts) fmt_mem = "{:<20}{:>20}" logging.info(fmt_mem.format("\nAllocation String", allocation_string)) return allocation_string 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,) class HunyuanVideoEmbeddingsAdapter: @classmethod def INPUT_TYPES(s): return { "required": { "hyvid_embeds": ("HYVIDEMBEDS",), } } RETURN_TYPES = ("CONDITIONING",) FUNCTION = "adapt_embeddings" CATEGORY = "multigpu" def adapt_embeddings(self, hyvid_embeds): cond = hyvid_embeds["prompt_embeds"] pooled_dict = { "pooled_output": hyvid_embeds["prompt_embeds_2"], "cross_attn": hyvid_embeds["prompt_embeds"], "attention_mask": hyvid_embeds["attention_mask"], } if hyvid_embeds["attention_mask_2"] is not None: pooled_dict["attention_mask_controlnet"] = hyvid_embeds["attention_mask_2"] if hyvid_embeds["cfg"] is not None: pooled_dict["guidance"] = float(hyvid_embeds["cfg"]) pooled_dict["start_percent"] = float(hyvid_embeds["start_percent"]) if hyvid_embeds["start_percent"] is not None else 0.0 pooled_dict["end_percent"] = float(hyvid_embeds["end_percent"]) if hyvid_embeds["end_percent"] is not None else 1.0 return ([[cond, pooled_dict]],) class MergeFluxLoRAsQuantizeAndLoad: @classmethod def INPUT_TYPES(cls): unet_name = folder_paths.get_filename_list("diffusion_models") loras = ["None"] + folder_paths.get_filename_list("loras") inputs = { "required": { "unet_name": (unet_name,), "switch_1": (["Off", "On"],), "lora_name_1": (loras,), "lora_weight_1": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "switch_2": (["Off", "On"],), "lora_name_2": (loras,), "lora_weight_2": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "switch_3": (["Off", "On"],), "lora_name_3": (loras,), "lora_weight_3": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "switch_4": (["Off", "On"],), "lora_name_4": (loras,), "lora_weight_4": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}), "quantization": (["Q2_K", "Q3_K_S", "Q4_0", "Q4_1", "Q4_K_S", "Q5_0", "Q5_1", "Q5_K_S", "Q6_K", "Q8_0", "FP16"], {"default": "Q4_K_S"}), "delete_final_gguf": ("BOOLEAN", {"default": False}), "new_model_name": ("STRING", {"default": "merged_model"}), } } return inputs RETURN_TYPES = ("MODEL",) FUNCTION = "load_and_quantize" CATEGORY = "loaders" def merge_flux_loras(self, model_sd: dict, lora_paths: list, weights: list, device="cuda") -> dict: for lora_path, weight in zip(lora_paths, weights): logging.info(f"[DEBUG] Merging LoRA file: {lora_path} with weight: {weight}") lora_sd = load_file(lora_path, device=device) for key in list(lora_sd.keys()): if "lora_down" not in key: continue base_name = key[: key.rfind(".lora_down")] up_key = key.replace("lora_down", "lora_up") module_name = base_name.replace("_", ".") alpha_key = f"{base_name}.alpha" if module_name not in model_sd: logging.info(f"[DEBUG] Module {module_name} not found in model_sd; skipping key {key}") continue down_weight = lora_sd[key].float() up_weight = lora_sd[up_key].float() alpha = float(lora_sd.get(alpha_key, up_weight.shape[0])) scale = weight * alpha / up_weight.shape[0] logging.info(f"[DEBUG] Merging module: {module_name} with alpha: {alpha}, scale: {scale}") target_weight = model_sd[module_name] if len(target_weight.shape) == 2: update = (up_weight @ down_weight) * scale else: if down_weight.shape[2:4] == (1, 1): update = (up_weight.squeeze(3).squeeze(2) @ down_weight.squeeze(3).squeeze(2)) update = update.unsqueeze(2).unsqueeze(3) * scale else: update = torch.nn.functional.conv2d( down_weight.permute(1, 0, 2, 3), up_weight ).permute(1, 0, 2, 3) * scale model_sd[module_name] = target_weight + update.to(target_weight.dtype) logging.info(f"[DEBUG] Updated module: {module_name}") del up_weight, down_weight, update del lora_sd torch.cuda.empty_cache() return model_sd def convert_to_gguf(self, model_path, working_dir): base_path = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) convert_script = os.path.join(base_path, "ComfyUI-GGUF", "tools", "convert.py") temp_gguf = os.path.join(working_dir, "temp_converted.gguf") logging.info("[DEBUG] Running conversion script: " + convert_script) subprocess.run([sys.executable, convert_script, "--src", model_path, "--dst", temp_gguf], check=True) logging.info("[DEBUG] Conversion complete.") return temp_gguf def load_and_quantize(self, unet_name, quantization, delete_final_gguf, new_model_name, **kwargs): mapping = {"FP16": "F16"} logging.info(f"[DEBUG] Starting load_and_quantize: {new_model_name} | Quantization: {quantization}") with tempfile.TemporaryDirectory() as merge_dir: merged_model_path = os.path.join(merge_dir, "merged_model.safetensors") model_path = folder_paths.get_full_path("diffusion_models", unet_name) lora_list = [] for i in range(1, 5): name = kwargs.get(f"lora_name_{i}", "None") switch = kwargs.get(f"switch_{i}", "Off") logging.info(f"[DEBUG] Processing LoRA slot {i}: name = {name}, switch = {switch}") if switch == "On" and name and name != "None": lora_file_path = folder_paths.get_full_path("loras", name) weight = kwargs.get(f"lora_weight_{i}", 1.0) lora_list.append((lora_file_path, weight)) logging.info(f"[DEBUG] Slot {i} active: path = {lora_file_path}, weight = {weight}") else: logging.info(f"[DEBUG] Slot {i} is inactive") logging.info(f"[DEBUG] Total active LoRAs: {len(lora_list)}") if lora_list: model_sd = load_file(model_path, device="cuda") model_sd = self.merge_flux_loras( model_sd, [lp for lp, _ in lora_list], [w for _, w in lora_list] ) save_file(model_sd, merged_model_path) del model_sd torch.cuda.empty_cache() else: shutil.copy2(model_path, merged_model_path) initial_gguf = self.convert_to_gguf(merged_model_path, merge_dir) logging.info("[DEBUG] Initial GGUF file created.") if quantization == "FP16": final_gguf = os.path.join(merge_dir, f"{new_model_name}-{mapping.get(quantization, quantization)}.gguf") shutil.copy2(initial_gguf, final_gguf) logging.info("[DEBUG] FP16 selected; conversion skipped.") else: binary = os.path.join(os.path.dirname(os.path.abspath(__file__)), "binaries", "linux", "llama-quantize") final_gguf = os.path.join(merge_dir, f"quantized_{quantization}.gguf") subprocess.run([binary, initial_gguf, final_gguf, quantization], check=True) logging.info("[DEBUG] Quantization completed.") models_dir = os.path.join(folder_paths.models_dir, "unet") os.makedirs(models_dir, exist_ok=True) final_name = f"{new_model_name}-{mapping.get(quantization, quantization)}.gguf" final_path = os.path.join(models_dir, final_name) shutil.copy2(final_gguf, final_path) logging.info("[DEBUG] Final model file copied to: " + final_path) logging.info("[DEBUG] Loading final model.") loader = UnetLoaderGGUF() result = loader.load_unet(final_name) logging.info("[DEBUG] Final model loaded.") if delete_final_gguf: os.unlink(final_path) return result 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 if device is not None: current_device = device fn = getattr(super(), cls.FUNCTION) out = fn(*args, **kwargs) return out return NodeOverride 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"]["virtual_vram_gb"] = ("FLOAT", {"default": 0.0, "min": 4.0, "max": 24.0, "step": 0.1}) inputs["optional"]["use_other_vram"] = ("BOOLEAN", {"default": False}) inputs["optional"]["expert_mode_allocations"] = ("STRING", { "multiline": False, "default": "", "tooltip": "Expert use only: Manual VRAM allocation string. Incorrect values can cause crashes. Do not modify unless you fully understand DisTorch memory management." }) return inputs CATEGORY = "multigpu" FUNCTION = "override" def override(self, *args, device=None, expert_mode_allocations=None, use_other_vram=None, virtual_vram_gb=0.0, **kwargs): global current_device if device is not None: current_device = device register_patched_ggufmodelpatcher() fn = getattr(super(), cls.FUNCTION) out = fn(*args, **kwargs) vram_string = "" if virtual_vram_gb > 0: if use_other_vram: available_devices = [d for d in get_device_list() if d.startswith('cuda')] other_devices = [d for d in available_devices if d != device] other_devices.sort(key=lambda x: int(x.split(':')[1] if ':' in x else x[-1]), reverse=False) device_string = ','.join(other_devices + ['cpu']) vram_string = f"{device};{virtual_vram_gb};{device_string}" else: vram_string = f"{device};{virtual_vram_gb};cpu" full_allocation = f"{expert_mode_allocations}#{vram_string}" if expert_mode_allocations or vram_string else "" logging.info(f"[DisTorch] Full allocation string: {full_allocation}") if hasattr(out[0], 'model'): model_hash = create_model_hash(out[0], "override") model_allocation_store[model_hash] = full_allocation 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] = full_allocation return out return NodeOverrideDisTorch 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 NODE_CLASS_MAPPINGS = { "DeviceSelectorMultiGPU": DeviceSelectorMultiGPU, "HunyuanVideoEmbeddingsAdapter": HunyuanVideoEmbeddingsAdapter, } NODE_CLASS_MAPPINGS["MergeFluxLoRAsQuantizeAndLoaddMultiGPU"] = override_class(MergeFluxLoRAsQuantizeAndLoad) NODE_CLASS_MAPPINGS["UNETLoaderMultiGPU"] = override_class(GLOBAL_NODE_CLASS_MAPPINGS["UNETLoader"]) NODE_CLASS_MAPPINGS["VAELoaderMultiGPU"] = override_class(GLOBAL_NODE_CLASS_MAPPINGS["VAELoader"]) NODE_CLASS_MAPPINGS["CLIPLoaderMultiGPU"] = override_class(GLOBAL_NODE_CLASS_MAPPINGS["CLIPLoader"]) NODE_CLASS_MAPPINGS["DualCLIPLoaderMultiGPU"] = override_class(GLOBAL_NODE_CLASS_MAPPINGS["DualCLIPLoader"]) NODE_CLASS_MAPPINGS["TripleCLIPLoaderMultiGPU"] = override_class(GLOBAL_NODE_CLASS_MAPPINGS["TripleCLIPLoader"]) NODE_CLASS_MAPPINGS["CheckpointLoaderSimpleMultiGPU"] = override_class(GLOBAL_NODE_CLASS_MAPPINGS["CheckpointLoaderSimple"]) NODE_CLASS_MAPPINGS["ControlNetLoaderMultiGPU"] = override_class(GLOBAL_NODE_CLASS_MAPPINGS["ControlNetLoader"]) if check_module_exists("ComfyUI-LTXVideo") or check_module_exists("comfyui-ltxvideo"): NODE_CLASS_MAPPINGS["LTXVLoaderMultiGPU"] = override_class(LTXVLoader) if check_module_exists("ComfyUI-Florence2") or check_module_exists("comfyui-florence2"): NODE_CLASS_MAPPINGS["Florence2ModelLoaderMultiGPU"] = override_class(Florence2ModelLoader) NODE_CLASS_MAPPINGS["DownloadAndLoadFlorence2ModelMultiGPU"] = override_class(DownloadAndLoadFlorence2Model) if check_module_exists("ComfyUI_bitsandbytes_NF4") or check_module_exists("comfyui_bitsandbytes_nf4"): NODE_CLASS_MAPPINGS["CheckpointLoaderNF4MultiGPU"] = override_class(CheckpointLoaderNF4) if check_module_exists("x-flux-comfyui") or check_module_exists("x-flux-comfyui"): NODE_CLASS_MAPPINGS["LoadFluxControlNetMultiGPU"] = override_class(LoadFluxControlNet) if check_module_exists("ComfyUI-MMAudio") or check_module_exists("comfyui-mmaudio"): NODE_CLASS_MAPPINGS["MMAudioModelLoaderMultiGPU"] = override_class(MMAudioModelLoader) NODE_CLASS_MAPPINGS["MMAudioFeatureUtilsLoaderMultiGPU"] = override_class(MMAudioFeatureUtilsLoader) NODE_CLASS_MAPPINGS["MMAudioSamplerMultiGPU"] = override_class(MMAudioSampler) if check_module_exists("ComfyUI-GGUF") or check_module_exists("comfyui-gguf"): NODE_CLASS_MAPPINGS["UnetLoaderGGUFMultiGPU"] = override_class(UnetLoaderGGUF) NODE_CLASS_MAPPINGS["UnetLoaderGGUFDisTorchMultiGPU"] = override_class_with_distorch(UnetLoaderGGUF) NODE_CLASS_MAPPINGS["UnetLoaderGGUFAdvancedMultiGPU"] = override_class(UnetLoaderGGUFAdvanced) NODE_CLASS_MAPPINGS["UnetLoaderGGUFAdvancedDisTorchMultiGPU"] = override_class_with_distorch(UnetLoaderGGUFAdvanced) NODE_CLASS_MAPPINGS["CLIPLoaderGGUFMultiGPU"] = override_class(CLIPLoaderGGUF) NODE_CLASS_MAPPINGS["CLIPLoaderGGUFDisTorchMultiGPU"] = override_class_with_distorch(CLIPLoaderGGUF) NODE_CLASS_MAPPINGS["DualCLIPLoaderGGUFMultiGPU"] = override_class(DualCLIPLoaderGGUF) NODE_CLASS_MAPPINGS["DualCLIPLoaderGGUFDisTorchMultiGPU"] = override_class_with_distorch(DualCLIPLoaderGGUF) NODE_CLASS_MAPPINGS["TripleCLIPLoaderGGUFMultiGPU"] = override_class(TripleCLIPLoaderGGUF) NODE_CLASS_MAPPINGS["TripleCLIPLoaderGGUFDisTorchMultiGPU"] = override_class_with_distorch(TripleCLIPLoaderGGUF) if check_module_exists("PuLID_ComfyUI") or check_module_exists("pulid_comfyui"): NODE_CLASS_MAPPINGS["PulidModelLoaderMultiGPU"] = override_class(PulidModelLoader) NODE_CLASS_MAPPINGS["PulidInsightFaceLoaderMultiGPU"] = override_class(PulidInsightFaceLoader) NODE_CLASS_MAPPINGS["PulidEvaClipLoaderMultiGPU"] = override_class(PulidEvaClipLoader) if check_module_exists("ComfyUI-HunyuanVideoWrapper") or check_module_exists("comfyui-hunyuanvideowrapper"): NODE_CLASS_MAPPINGS["HyVideoModelLoaderMultiGPU"] = override_class(HyVideoModelLoader) NODE_CLASS_MAPPINGS["HyVideoVAELoaderMultiGPU"] = override_class(HyVideoVAELoader) NODE_CLASS_MAPPINGS["DownloadAndLoadHyVideoTextEncoderMultiGPU"] = override_class(DownloadAndLoadHyVideoTextEncoder) logging.info(f"MultiGPU: Registration complete. Final mappings: {', '.join(NODE_CLASS_MAPPINGS.keys())}")