import torch import logging import os import copy from pathlib import Path import folder_paths import comfy.model_management as mm from nodes import NODE_CLASS_MAPPINGS as GLOBAL_NODE_CLASS_MAPPINGS from .device_utils import get_device_list, is_accelerator_available, soft_empty_cache_multigpu # --- DisTorch V2 Logging Configuration --- # Set to "E" for Engineering (DEBUG) or "P" for Production (INFO) LOG_LEVEL = "P" # Configure logger logger = logging.getLogger("MultiGPU") logger.propagate = False if not logger.handlers: log_level = logging.DEBUG if LOG_LEVEL == "E" else logging.INFO handler = logging.StreamHandler() formatter = logging.Formatter('%(message)s') handler.setFormatter(formatter) logger.addHandler(handler) logger.setLevel(log_level) MGPU_MM_LOG = False def mgpu_mm_log_method(self, msg): if MGPU_MM_LOG: self.info(f"[MultiGPU Model Management] {msg}") logger.mgpu_mm_log = mgpu_mm_log_method.__get__(logger, type(logger)) # Global device state management current_device = mm.get_torch_device() current_text_encoder_device = mm.text_encoder_device() def set_current_device(device): global current_device current_device = device logger.debug(f"[MultiGPU Initialization] current_device set to: {device}") def set_current_text_encoder_device(device): global current_text_encoder_device current_text_encoder_device = device logger.debug(f"[MultiGPU Initialization] current_text_encoder_device set to: {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): if device is not None: set_current_device(device) fn = getattr(super(), cls.FUNCTION) out = fn(*args, **kwargs) return out return NodeOverride def override_class_clip(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): if device is not None: set_current_text_encoder_device(device) kwargs['device'] = 'default' fn = getattr(super(), cls.FUNCTION) out = fn(*args, **kwargs) return out return NodeOverride def override_class_clip_no_device(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): if device is not None: set_current_text_encoder_device(device) fn = getattr(super(), cls.FUNCTION) out = fn(*args, **kwargs) return out return NodeOverride def get_torch_device_patched(): device = None if (not is_accelerator_available() or mm.cpu_state == mm.CPUState.CPU or "cpu" in str(current_device).lower()): device = torch.device("cpu") else: devs = set(get_device_list()) device = torch.device(current_device) if str(current_device) in devs else torch.device("cpu") logger.debug(f"[MultiGPU Core Patching] get_torch_device_patched returning device: {device} (current_device={current_device})") return device def text_encoder_device_patched(): device = None if (not is_accelerator_available() or mm.cpu_state == mm.CPUState.CPU or "cpu" in str(current_text_encoder_device).lower()): device = torch.device("cpu") else: devs = set(get_device_list()) device = torch.device(current_text_encoder_device) if str(current_text_encoder_device) in devs else torch.device("cpu") logger.debug(f"[MultiGPU Core Patching] text_encoder_device_patched returning device: {device} (current_text_encoder_device={current_text_encoder_device})") return device logger.info(f"[MultiGPU Core Patching] Patching mm.get_torch_device and mm.text_encoder_device") logger.debug(f"[MultiGPU DEBUG] Initial current_device: {current_device}") logger.debug(f"[MultiGPU DEBUG] Initial current_text_encoder_device: {current_text_encoder_device}") mm.get_torch_device = get_torch_device_patched mm.text_encoder_device = text_encoder_device_patched def check_module_exists(module_path): full_path = os.path.join(folder_paths.get_folder_paths("custom_nodes")[0], module_path) logger.debug(f"[MultiGPU] Checking for module at {full_path}") if not os.path.exists(full_path): logger.debug(f"[MultiGPU] Module {module_path} not found - skipping") return False logger.debug(f"[MultiGPU] Found {module_path}, creating compatible MultiGPU nodes") return True # Import from nodes.py from .nodes import ( DeviceSelectorMultiGPU, HunyuanVideoEmbeddingsAdapter, UnetLoaderGGUF, UnetLoaderGGUFAdvanced, CLIPLoaderGGUF, DualCLIPLoaderGGUF, TripleCLIPLoaderGGUF, QuadrupleCLIPLoaderGGUF, LTXVLoader, Florence2ModelLoader, DownloadAndLoadFlorence2Model, CheckpointLoaderNF4, LoadFluxControlNet, MMAudioModelLoader, MMAudioFeatureUtilsLoader, MMAudioSampler, PulidModelLoader, PulidInsightFaceLoader, PulidEvaClipLoader, HyVideoModelLoader, HyVideoVAELoader, DownloadAndLoadHyVideoTextEncoder, ) # Import from wanvideo.py from .wanvideo import ( WanVideoModelLoader, WanVideoModelLoader_2, WanVideoVAELoader, LoadWanVideoT5TextEncoder, LoadWanVideoClipTextEncoder, WanVideoTextEncode, WanVideoBlockSwap, WanVideoSampler ) # Import from distorch.py from .distorch import ( model_allocation_store, create_model_hash, register_patched_ggufmodelpatcher, analyze_ggml_loading, calculate_vvram_allocation_string, override_class_with_distorch_gguf, override_class_with_distorch_gguf_v2, override_class_with_distorch_clip, override_class_with_distorch_clip_no_device, override_class_with_distorch ) # Import from distorch_2.py for DisTorch v2 SafeTensor support from .distorch_2 import ( safetensor_allocation_store, create_safetensor_model_hash, register_patched_safetensor_modelpatcher, analyze_safetensor_loading, calculate_safetensor_vvram_allocation, override_class_with_distorch_safetensor_v2, override_class_with_distorch_safetensor_v2_clip, override_class_with_distorch_safetensor_v2_clip_no_device ) logger.info("[MultiGPU Core Patching] Patching mm.soft_empty_cache for DisTorch2 Multi-Device Allocation/Clearing") original_soft_empty_cache = mm.soft_empty_cache def soft_empty_cache_distorch2_patched(force=False): """ Patched mm.soft_empty_cache. If DisTorch2 models are active, clear cache on ALL devices. Otherwise, execute original ComfyUI behavior. """ is_distorch_active = False # Check if any loaded model is managed by DisTorch2 using the allocation store for lm in mm.current_loaded_models: mp = lm.model # weakref call to ModelPatcher if mp is not None: model_hash = create_safetensor_model_hash(mp, "cache_patch_check") if model_hash in safetensor_allocation_store and safetensor_allocation_store[model_hash]: is_distorch_active = True break if is_distorch_active: logger.mgpu_mm_log("DisTorch2 active: clearing caches on all devices") soft_empty_cache_multigpu() else: logger.mgpu_mm_log("DisTorch2 not active: delegating to original mm.soft_empty_cache") original_soft_empty_cache(force) mm.soft_empty_cache = soft_empty_cache_distorch2_patched LARGE_MODEL_THRESHOLD = 2 * (1024**3) # 2 GB threshold for "large" models # Patch only once (handles reloads) if hasattr(mm, 'load_models_gpu') and not hasattr(mm.load_models_gpu, "_distorch2_proactive_patched"): logger.info("[MultiGPU Core Patching] Patching mm.load_models_gpu for DisTorch2 proactive unloading") original_load_models_gpu = mm.load_models_gpu def patched_load_models_gpu(models, memory_required=0, force_patch_weights=False, minimum_memory_required=None, force_full_load=False): """ Proactively unload large models that are not needed when loading a large DisTorch2 model. This frees both compute and donor device memory ahead of ComfyUI's compute-only check. """ # Validate models argument loudly if not isinstance(models, (list, tuple, set)): logger.error("[MultiGPU Core Patching] CRITICAL: mm.load_models_gpu 'models' is not a list/tuple/set. Bypassing proactive patch.") return original_load_models_gpu(models, memory_required, force_patch_weights, minimum_memory_required, force_full_load) # Detect incoming DisTorch2 request incoming_is_distorch = False incoming_distorch_nonzero = False incoming_patchers = set() incoming_loaded_names = [] incoming_allowed_devices = None incoming_compute_device = None incoming_required_bytes = 0 incoming_compute_planned_bytes = 0 for lm in models: # Identify ModelPatcher (prefer direct; fall back to .patcher) if hasattr(lm, "load_device"): patcher = lm elif hasattr(lm, "patcher"): patcher = lm.patcher else: patcher = None model_for_hash = patcher if patcher is not None else getattr(lm, "model", lm) if patcher is not None: incoming_patchers.add(patcher) # Determine required memory directly from ModelPatcher (no wrapper; no side effects) device_str = str(patcher.load_device) if patcher.current_loaded_device() == patcher.load_device: required_bytes = patcher.model_size() - patcher.loaded_size() else: required_bytes = patcher.model_size() else: device_str = "n/a" required_bytes = 0 # Check DisTorch2 management via allocation store (unchanged trigger) model_hash = create_safetensor_model_hash(model_for_hash, "load_patch_check") if model_hash in safetensor_allocation_store and safetensor_allocation_store.get(model_hash): incoming_is_distorch = True if required_bytes > 0: incoming_distorch_nonzero = True if incoming_allowed_devices is None: # Derive compute/donor devices from allocation string alloc_str = safetensor_allocation_store.get(model_hash, "") allowed = set() if alloc_str: parts = alloc_str.split("#", 1) if len(parts) == 2 and parts[1]: vram = parts[1] segs = vram.split(";") # compute device if len(segs) >= 1 and segs[0]: allowed.add(segs[0].strip()) # donors list (comma-separated) if len(segs) >= 3 and segs[2]: for d in segs[2].split(","): d = d.strip() if d: allowed.add(d) else: # Expert fraction string: "dev,fraction;dev2,fraction2;..." for token in alloc_str.split(";"): if "," in token: dev, frac = token.split(",", 1) fs = frac.strip() numlike = fs.replace(".", "", 1).isdigit() if numlike and float(fs) > 0.0: allowed.add(dev.strip()) if not allowed: allowed = {str(patcher.load_device), "cpu"} incoming_allowed_devices = allowed # Determine compute device and planned bytes from allocation string alloc = safetensor_allocation_store.get(model_hash, "") if "#" in alloc: vram = alloc.split("#", 1)[1] segs = vram.split(";") if len(segs) >= 2 and segs[0]: incoming_compute_device = segs[0].strip() try: vvram_gb = float(segs[1]) incoming_compute_planned_bytes = int(vvram_gb * (1024**3)) except Exception: incoming_compute_planned_bytes = 0 else: # Expert fractions: "dev,fraction;dev2,fraction2;..." tokens = [t for t in alloc.split(";") if "," in t] frac_map = {} for t in tokens: dev, frac = t.split(",", 1) try: frac_val = float(frac.strip()) except Exception: continue frac_map[dev.strip()] = frac_val if frac_map: ld = str(patcher.load_device) # Prefer the explicit load_device if present and > 0 target_dev = ld if (ld in frac_map and frac_map[ld] > 0.0) else None if target_dev is None: # Otherwise pick highest positive fraction target_dev = max((d for d,v in frac_map.items() if v > 0.0), key=lambda d: frac_map[d], default=None) if target_dev is not None: incoming_compute_device = target_dev total = mm.get_total_memory(torch.device(target_dev)) incoming_compute_planned_bytes = int(frac_map[target_dev] * (total or 0)) if incoming_compute_device is None: incoming_compute_device = str(patcher.load_device) if incoming_compute_planned_bytes <= 0: incoming_compute_planned_bytes = required_bytes # Log informational context with required bytes and device try: model_name = type(getattr(model_for_hash, "model", model_for_hash)).__name__ except Exception: model_name = "UnknownModel" incoming_loaded_names.append(f"{model_name}:{required_bytes/(1024**3):.2f}GB req on {device_str}") if incoming_loaded_names: logger.mgpu_mm_log(f"Incoming models summary: {', '.join(incoming_loaded_names)}") if incoming_distorch_nonzero: logger.mgpu_mm_log("Non-Zero incoming DisTorch2 model detected. Initiating proactive unload.") if not hasattr(mm, 'current_loaded_models'): raise AttributeError("comfy.model_management is missing 'current_loaded_models'. Proactive unload check failed.") needed_patchers = incoming_patchers # Need-based free on compute device only (scale-aware; core-aligned) dev_str = incoming_compute_device or (next(iter(incoming_allowed_devices)) if incoming_allowed_devices else None) freed_bytes = 0 to_unload_indices = [] unload_summaries = [] if dev_str is not None: dev_obj = torch.device(dev_str) free_now = mm.get_free_memory(dev_obj) try: free_now_val = free_now[0] if isinstance(free_now, tuple) else free_now except Exception: free_now_val = free_now # Use core-aligned immediate needs: planned vs. memory_required vs. minimum_memory_required effective_needed = max(incoming_compute_planned_bytes or 0, memory_required or 0, minimum_memory_required or 0) need_bytes = max(0, effective_needed - (free_now_val or 0)) logger.mgpu_mm_log(f"Need calc on {dev_str}: effective_needed={effective_needed/(1024**3):.2f}GB, free_now={((free_now_val or 0)/(1024**3)):.2f}GB, need_bytes={need_bytes/(1024**3):.2f}GB") if need_bytes > 0: logger.mgpu_mm_log(f"Need-based unload on {dev_str}: need ~{need_bytes/(1024**3):.2f}GB") # Build candidates on this device only, excluding needed patchers candidates = [] for idx, lm_cur in enumerate(mm.current_loaded_models): mp_cur = getattr(lm_cur, 'model', None) if mp_cur is None or mp_cur in needed_patchers: continue if str(getattr(lm_cur, "device", "")) != dev_str: continue size_cur = 0 if hasattr(lm_cur, 'model_memory'): try: size_cur = lm_cur.model_memory() except Exception: size_cur = 0 if size_cur <= 0 and hasattr(mp_cur, 'model_size'): size_cur = mp_cur.model_size() candidates.append((size_cur, idx, lm_cur, mp_cur)) # Sort by size descending candidates.sort(key=lambda x: x[0], reverse=True) for size_cur, idx, lm_cur, mp_cur in candidates: model_name = type(getattr(mp_cur, 'model', mp_cur)).__name__ logger.mgpu_mm_log(f"Unloading model on {dev_str}: {model_name} (~{size_cur/(1024**3):.2f}GB)") success = False if hasattr(lm_cur, 'model_unload'): success = lm_cur.model_unload(memory_to_free=None, unpatch_weights=True) if success: to_unload_indices.append(idx) unload_summaries.append(f"{model_name}:{size_cur/(1024**3):.2f}GB") freed_bytes += size_cur if freed_bytes >= need_bytes: break # Remove from management list and clear caches unloaded_count = 0 for idx in sorted(to_unload_indices, reverse=True): mm.current_loaded_models.pop(idx) unloaded_count += 1 if unloaded_count > 0: logger.mgpu_mm_log(f"Proactively unloaded {unloaded_count} large model(s): {', '.join(unload_summaries)}") logger.mgpu_mm_log("Performing multi-device cache clear after proactive unload") # Force multi-device cache clear via patched soft_empty_cache (which detects DisTorch2) mm.soft_empty_cache(force=True) else: # Lineage-aligned cache clear when no unloads happened: apply core 25% rule, per DisTorch devices if incoming_allowed_devices is not None and mm.vram_state != mm.VRAMState.HIGH_VRAM: triggered = [] for dev_str in incoming_allowed_devices: try: dev_obj = torch.device(dev_str) except Exception: continue free_total, free_torch = mm.get_free_memory(dev_obj, torch_free_too=True) # free_total: system free; free_torch: torch reserved-but-free if free_torch > free_total * 0.25: triggered.append(dev_str) if triggered: logger.mgpu_mm_log(f"No unloads; 25% torch-cache rule triggered on: {', '.join(triggered)}. Calling soft_empty_cache()") mm.soft_empty_cache(force=True) else: logger.mgpu_mm_log("No unloads; 25% torch-cache rule not met on DisTorch devices; skipping cache clear") else: logger.mgpu_mm_log("No unload candidates matched criteria and either HIGH_VRAM or no DisTorch devices; skipping cache clear") elif incoming_is_distorch: logger.mgpu_mm_log("Incoming DisTorch2 model requires 0.00GB; skipping proactive unload") # Continue with original behavior return original_load_models_gpu(models, memory_required, force_patch_weights, minimum_memory_required, force_full_load) # Mark and apply the patch patched_load_models_gpu._distorch2_proactive_patched = True mm.load_models_gpu = patched_load_models_gpu else: if not hasattr(mm, 'load_models_gpu'): raise AttributeError("comfy.model_management is missing 'load_models_gpu'. Core patching failed.") else: logger.debug("[MultiGPU Core Patching] mm.load_models_gpu already patched; skipping") # Import advanced checkpoint loaders from .checkpoint_multigpu import ( CheckpointLoaderAdvancedMultiGPU, CheckpointLoaderAdvancedDisTorch2MultiGPU ) # Initialize NODE_CLASS_MAPPINGS NODE_CLASS_MAPPINGS = { "DeviceSelectorMultiGPU": DeviceSelectorMultiGPU, "HunyuanVideoEmbeddingsAdapter": HunyuanVideoEmbeddingsAdapter, "CheckpointLoaderAdvancedMultiGPU": CheckpointLoaderAdvancedMultiGPU, "CheckpointLoaderAdvancedDisTorch2MultiGPU": CheckpointLoaderAdvancedDisTorch2MultiGPU, } # Standard MultiGPU nodes 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_clip(GLOBAL_NODE_CLASS_MAPPINGS["CLIPLoader"]) NODE_CLASS_MAPPINGS["DualCLIPLoaderMultiGPU"] = override_class_clip(GLOBAL_NODE_CLASS_MAPPINGS["DualCLIPLoader"]) if "TripleCLIPLoader" in GLOBAL_NODE_CLASS_MAPPINGS: NODE_CLASS_MAPPINGS["TripleCLIPLoaderMultiGPU"] = override_class_clip_no_device(GLOBAL_NODE_CLASS_MAPPINGS["TripleCLIPLoader"]) if "QuadrupleCLIPLoader" in GLOBAL_NODE_CLASS_MAPPINGS: NODE_CLASS_MAPPINGS["QuadrupleCLIPLoaderMultiGPU"] = override_class_clip_no_device(GLOBAL_NODE_CLASS_MAPPINGS["QuadrupleCLIPLoader"]) NODE_CLASS_MAPPINGS["CLIPVisionLoaderMultiGPU"] = override_class_clip_no_device(GLOBAL_NODE_CLASS_MAPPINGS["CLIPVisionLoader"]) NODE_CLASS_MAPPINGS["CheckpointLoaderSimpleMultiGPU"] = override_class(GLOBAL_NODE_CLASS_MAPPINGS["CheckpointLoaderSimple"]) NODE_CLASS_MAPPINGS["ControlNetLoaderMultiGPU"] = override_class(GLOBAL_NODE_CLASS_MAPPINGS["ControlNetLoader"]) if "DiffusersLoader" in GLOBAL_NODE_CLASS_MAPPINGS: NODE_CLASS_MAPPINGS["DiffusersLoaderMultiGPU"] = override_class(GLOBAL_NODE_CLASS_MAPPINGS["DiffusersLoader"]) if "DiffControlNetLoader" in GLOBAL_NODE_CLASS_MAPPINGS: NODE_CLASS_MAPPINGS["DiffControlNetLoaderMultiGPU"] = override_class(GLOBAL_NODE_CLASS_MAPPINGS["DiffControlNetLoader"]) # DisTorch 2 SafeTensor nodes for FLUX and other safetensor models NODE_CLASS_MAPPINGS["UNETLoaderDisTorch2MultiGPU"] = override_class_with_distorch_safetensor_v2(GLOBAL_NODE_CLASS_MAPPINGS["UNETLoader"]) NODE_CLASS_MAPPINGS["VAELoaderDisTorch2MultiGPU"] = override_class_with_distorch_safetensor_v2(GLOBAL_NODE_CLASS_MAPPINGS["VAELoader"]) NODE_CLASS_MAPPINGS["CLIPLoaderDisTorch2MultiGPU"] = override_class_with_distorch_safetensor_v2_clip(GLOBAL_NODE_CLASS_MAPPINGS["CLIPLoader"]) NODE_CLASS_MAPPINGS["DualCLIPLoaderDisTorch2MultiGPU"] = override_class_with_distorch_safetensor_v2_clip(GLOBAL_NODE_CLASS_MAPPINGS["DualCLIPLoader"]) if "TripleCLIPLoader" in GLOBAL_NODE_CLASS_MAPPINGS: NODE_CLASS_MAPPINGS["TripleCLIPLoaderDisTorch2MultiGPU"] = override_class_with_distorch_safetensor_v2_clip_no_device(GLOBAL_NODE_CLASS_MAPPINGS["TripleCLIPLoader"]) if "QuadrupleCLIPLoader" in GLOBAL_NODE_CLASS_MAPPINGS: NODE_CLASS_MAPPINGS["QuadrupleCLIPLoaderDisTorch2MultiGPU"] = override_class_with_distorch_safetensor_v2_clip_no_device(GLOBAL_NODE_CLASS_MAPPINGS["QuadrupleCLIPLoader"]) NODE_CLASS_MAPPINGS["CLIPVisionLoaderDisTorch2MultiGPU"] = override_class_with_distorch_safetensor_v2_clip_no_device(GLOBAL_NODE_CLASS_MAPPINGS["CLIPVisionLoader"]) NODE_CLASS_MAPPINGS["CheckpointLoaderSimpleDisTorch2MultiGPU"] = override_class_with_distorch_safetensor_v2(GLOBAL_NODE_CLASS_MAPPINGS["CheckpointLoaderSimple"]) NODE_CLASS_MAPPINGS["ControlNetLoaderDisTorch2MultiGPU"] = override_class_with_distorch_safetensor_v2(GLOBAL_NODE_CLASS_MAPPINGS["ControlNetLoader"]) if "DiffusersLoader" in GLOBAL_NODE_CLASS_MAPPINGS: NODE_CLASS_MAPPINGS["DiffusersLoaderDisTorch2MultiGPU"] = override_class_with_distorch_safetensor_v2(GLOBAL_NODE_CLASS_MAPPINGS["DiffusersLoader"]) if "DiffControlNetLoader" in GLOBAL_NODE_CLASS_MAPPINGS: NODE_CLASS_MAPPINGS["DiffControlNetLoaderDisTorch2MultiGPU"] = override_class_with_distorch_safetensor_v2(GLOBAL_NODE_CLASS_MAPPINGS["DiffControlNetLoader"]) # --- Registration Table --- logger.info("[MultiGPU] Initiating custom_node Registration. . .") dash_line = "-" * 47 fmt_reg = "{:<30}{:>5}{:>10}" logger.info(dash_line) logger.info(fmt_reg.format("custom_node", "Found", "Nodes")) logger.info(dash_line) registration_data = [] def register_and_count(module_names, node_map): found = False for name in module_names: if check_module_exists(name): found = True break count = 0 if found: initial_len = len(NODE_CLASS_MAPPINGS) for key, value in node_map.items(): NODE_CLASS_MAPPINGS[key] = value count = len(NODE_CLASS_MAPPINGS) - initial_len registration_data.append({"name": module_names[0], "found": "Y" if found else "N", "count": count}) return found # ComfyUI-LTXVideo ltx_nodes = {"LTXVLoaderMultiGPU": override_class(LTXVLoader)} register_and_count(["ComfyUI-LTXVideo", "comfyui-ltxvideo"], ltx_nodes) # ComfyUI-Florence2 florence_nodes = { "Florence2ModelLoaderMultiGPU": override_class(Florence2ModelLoader), "DownloadAndLoadFlorence2ModelMultiGPU": override_class(DownloadAndLoadFlorence2Model) } register_and_count(["ComfyUI-Florence2", "comfyui-florence2"], florence_nodes) # ComfyUI_bitsandbytes_NF4 nf4_nodes = {"CheckpointLoaderNF4MultiGPU": override_class(CheckpointLoaderNF4)} register_and_count(["ComfyUI_bitsandbytes_NF4", "comfyui_bitsandbytes_nf4"], nf4_nodes) # x-flux-comfyui flux_controlnet_nodes = {"LoadFluxControlNetMultiGPU": override_class(LoadFluxControlNet)} register_and_count(["x-flux-comfyui"], flux_controlnet_nodes) # ComfyUI-MMAudio mmaudio_nodes = { "MMAudioModelLoaderMultiGPU": override_class(MMAudioModelLoader), "MMAudioFeatureUtilsLoaderMultiGPU": override_class(MMAudioFeatureUtilsLoader), "MMAudioSamplerMultiGPU": override_class(MMAudioSampler) } register_and_count(["ComfyUI-MMAudio", "comfyui-mmaudio"], mmaudio_nodes) # ComfyUI-GGUF gguf_nodes = { "UnetLoaderGGUFDisTorchMultiGPU": override_class_with_distorch_gguf(UnetLoaderGGUF), "UnetLoaderGGUFAdvancedDisTorchMultiGPU": override_class_with_distorch_gguf(UnetLoaderGGUFAdvanced), "CLIPLoaderGGUFDisTorchMultiGPU": override_class_with_distorch_clip(CLIPLoaderGGUF), "DualCLIPLoaderGGUFDisTorchMultiGPU": override_class_with_distorch_clip(DualCLIPLoaderGGUF), "TripleCLIPLoaderGGUFDisTorchMultiGPU": override_class_with_distorch_clip_no_device(TripleCLIPLoaderGGUF), "QuadrupleCLIPLoaderGGUFDisTorchMultiGPU": override_class_with_distorch_clip_no_device(QuadrupleCLIPLoaderGGUF), "UnetLoaderGGUFDisTorch2MultiGPU": override_class_with_distorch_safetensor_v2(UnetLoaderGGUF), "UnetLoaderGGUFAdvancedDisTorch2MultiGPU": override_class_with_distorch_safetensor_v2(UnetLoaderGGUFAdvanced), "CLIPLoaderGGUFDisTorch2MultiGPU": override_class_with_distorch_safetensor_v2_clip(CLIPLoaderGGUF), "DualCLIPLoaderGGUFDisTorch2MultiGPU": override_class_with_distorch_safetensor_v2_clip(DualCLIPLoaderGGUF), "TripleCLIPLoaderGGUFDisTorch2MultiGPU": override_class_with_distorch_safetensor_v2_clip_no_device(TripleCLIPLoaderGGUF), "QuadrupleCLIPLoaderGGUFDisTorch2MultiGPU": override_class_with_distorch_safetensor_v2_clip_no_device(QuadrupleCLIPLoaderGGUF), "UnetLoaderGGUFMultiGPU": override_class(UnetLoaderGGUF), "UnetLoaderGGUFAdvancedMultiGPU": override_class(UnetLoaderGGUFAdvanced), "CLIPLoaderGGUFMultiGPU": override_class_clip(CLIPLoaderGGUF), "DualCLIPLoaderGGUFMultiGPU": override_class_clip(DualCLIPLoaderGGUF), "TripleCLIPLoaderGGUFMultiGPU": override_class_clip_no_device(TripleCLIPLoaderGGUF), "QuadrupleCLIPLoaderGGUFMultiGPU": override_class_clip_no_device(QuadrupleCLIPLoaderGGUF) } register_and_count(["ComfyUI-GGUF", "comfyui-gguf"], gguf_nodes) # PuLID_ComfyUI pulid_nodes = { "PulidModelLoaderMultiGPU": override_class(PulidModelLoader), "PulidInsightFaceLoaderMultiGPU": override_class(PulidInsightFaceLoader), "PulidEvaClipLoaderMultiGPU": override_class(PulidEvaClipLoader) } register_and_count(["PuLID_ComfyUI", "pulid_comfyui"], pulid_nodes) # ComfyUI-HunyuanVideoWrapper hunyuan_nodes = { "HyVideoModelLoaderMultiGPU": override_class(HyVideoModelLoader), "HyVideoVAELoaderMultiGPU": override_class(HyVideoVAELoader), "DownloadAndLoadHyVideoTextEncoderMultiGPU": override_class(DownloadAndLoadHyVideoTextEncoder) } register_and_count(["ComfyUI-HunyuanVideoWrapper", "comfyui-hunyuanvideowrapper"], hunyuan_nodes) # ComfyUI-WanVideoWrapper wanvideo_nodes = { "WanVideoModelLoaderMultiGPU": WanVideoModelLoader, "WanVideoModelLoaderMultiGPU_2": WanVideoModelLoader_2, "WanVideoVAELoaderMultiGPU": WanVideoVAELoader, "LoadWanVideoT5TextEncoderMultiGPU": LoadWanVideoT5TextEncoder, "LoadWanVideoClipTextEncoderMultiGPU": LoadWanVideoClipTextEncoder, "WanVideoTextEncodeMultiGPU": WanVideoTextEncode, "WanVideoBlockSwapMultiGPU": WanVideoBlockSwap, "WanVideoSamplerMultiGPU": WanVideoSampler } register_and_count(["ComfyUI-WanVideoWrapper", "comfyui-wanvideowrapper"], wanvideo_nodes) # Print the registration table for item in registration_data: logger.info(fmt_reg.format(item['name'], item['found'], str(item['count']))) logger.info(dash_line) logger.info(f"[MultiGPU] Registration complete. Final mappings: {', '.join(NODE_CLASS_MAPPINGS.keys())}")