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 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]],) 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_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 if device is not None: current_device = device register_patched_ggufmodelpatcher() fn = getattr(super(), cls.FUNCTION) out = fn(*args, **kwargs) 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 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 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, HyVideoModelLoaderDiffSynth, HyVideoVAELoader, DownloadAndLoadHyVideoTextEncoder ) NODE_CLASS_MAPPINGS = { "DeviceSelectorMultiGPU": DeviceSelectorMultiGPU, "HunyuanVideoEmbeddingsAdapter": HunyuanVideoEmbeddingsAdapter } 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())}")