648 lines
29 KiB
Python
648 lines
29 KiB
Python
import copy
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import torch
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import sys
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import comfy.model_management as mm
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import os
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from pathlib import Path
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import logging
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import folder_paths
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import shutil
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from collections import defaultdict
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import hashlib
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import tempfile
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import subprocess
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import gc
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from safetensors.torch import save_file, load_file
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import comfy.utils
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from typing import Dict, List
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from nodes import NODE_CLASS_MAPPINGS as GLOBAL_NODE_CLASS_MAPPINGS
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from .nodes import (
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UnetLoaderGGUF, UnetLoaderGGUFAdvanced,
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CLIPLoaderGGUF, DualCLIPLoaderGGUF, TripleCLIPLoaderGGUF,
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LTXVLoader,
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Florence2ModelLoader, DownloadAndLoadFlorence2Model,
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CheckpointLoaderNF4,
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LoadFluxControlNet,
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MMAudioModelLoader, MMAudioFeatureUtilsLoader, MMAudioSampler,
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PulidModelLoader, PulidInsightFaceLoader, PulidEvaClipLoader,
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HyVideoModelLoader, HyVideoVAELoader, DownloadAndLoadHyVideoTextEncoder
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)
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current_device = mm.get_torch_device()
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model_allocation_store = {}
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def get_torch_device_patched():
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device = None
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if (not torch.cuda.is_available() or mm.cpu_state == mm.CPUState.CPU or "cpu" in str(current_device).lower()):
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device = torch.device("cpu")
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else:
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device = torch.device(current_device)
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return device
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mm.get_torch_device = get_torch_device_patched
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def create_model_hash(model, caller):
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model_type = type(model.model).__name__
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model_size = model.model_size()
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first_layers = str(list(model.model_state_dict().keys())[:3])
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identifier = f"{model_type}_{model_size}_{first_layers}"
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final_hash = hashlib.sha256(identifier.encode()).hexdigest()
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return final_hash
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def register_patched_ggufmodelpatcher():
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from nodes import NODE_CLASS_MAPPINGS
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original_loader = NODE_CLASS_MAPPINGS["UnetLoaderGGUF"]
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module = sys.modules[original_loader.__module__]
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if not hasattr(module.GGUFModelPatcher, '_patched'):
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original_load = module.GGUFModelPatcher.load
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def new_load(self, *args, force_patch_weights=False, **kwargs):
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global model_allocation_store
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super(module.GGUFModelPatcher, self).load(*args, force_patch_weights=True, **kwargs)
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debug_hash = create_model_hash(self, "patcher")
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linked = []
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module_count = 0
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for n, m in self.model.named_modules():
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module_count += 1
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if hasattr(m, "weight"):
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device = getattr(m.weight, "device", None)
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if device is not None:
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linked.append((n, m))
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continue
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if hasattr(m, "bias"):
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device = getattr(m.bias, "device", None)
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if device is not None:
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linked.append((n, m))
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continue
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if linked:
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if hasattr(self, 'model'):
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debug_hash = create_model_hash(self, "patcher")
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debug_allocations = model_allocation_store.get(debug_hash)
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if debug_allocations:
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device_assignments = analyze_ggml_loading(self.model, debug_allocations)['device_assignments']
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for device, layers in device_assignments.items():
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target_device = torch.device(device)
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for n, m, _ in layers:
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m.to(self.load_device).to(target_device)
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self.mmap_released = True
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module.GGUFModelPatcher.load = new_load
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module.GGUFModelPatcher._patched = True
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def analyze_ggml_loading(model, allocations_str):
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DEVICE_RATIOS_DISTORCH = {}
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device_table = {}
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distorch_alloc = allocations_str
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virtual_vram_gb = 0.0
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if '#' in allocations_str:
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distorch_alloc, virtual_vram_str = allocations_str.split('#')
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if not distorch_alloc:
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distorch_alloc = calculate_vvram_allocation_string(model, virtual_vram_str)
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eq_line = "=" * 47
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dash_line = "-" * 47
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fmt_assign = "{:<12}{:>10}{:>14}{:>10}"
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for allocation in distorch_alloc.split(';'):
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dev_name, fraction = allocation.split(',')
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fraction = float(fraction)
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total_mem_bytes = mm.get_total_memory(torch.device(dev_name))
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alloc_gb = (total_mem_bytes * fraction) / (1024**3)
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DEVICE_RATIOS_DISTORCH[dev_name] = alloc_gb
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device_table[dev_name] = {
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"fraction": fraction,
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"total_gb": total_mem_bytes / (1024**3),
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"alloc_gb": alloc_gb
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}
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logging.info(eq_line)
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logging.info(" DisTorch Device Allocations")
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logging.info(eq_line)
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logging.info(fmt_assign.format("Device", "Alloc %", "Total (GB)", " Alloc (GB)"))
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logging.info(dash_line)
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sorted_devices = sorted(device_table.keys(), key=lambda d: (d == "cpu", d))
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for dev in sorted_devices:
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frac = device_table[dev]["fraction"]
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tot_gb = device_table[dev]["total_gb"]
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alloc_gb = device_table[dev]["alloc_gb"]
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logging.info(fmt_assign.format(dev,f"{int(frac * 100)}%",f"{tot_gb:.2f}",f"{alloc_gb:.2f}"))
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logging.info(dash_line)
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layer_summary = {}
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layer_list = []
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memory_by_type = defaultdict(int)
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total_memory = 0
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for name, module in model.named_modules():
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if hasattr(module, "weight"):
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layer_type = type(module).__name__
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layer_summary[layer_type] = layer_summary.get(layer_type, 0) + 1
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layer_list.append((name, module, layer_type))
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layer_memory = 0
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if module.weight is not None:
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layer_memory += module.weight.numel() * module.weight.element_size()
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if hasattr(module, "bias") and module.bias is not None:
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layer_memory += module.bias.numel() * module.bias.element_size()
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memory_by_type[layer_type] += layer_memory
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total_memory += layer_memory
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logging.info(" DisTorch GGML Layer Distribution")
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logging.info(dash_line)
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fmt_layer = "{:<12}{:>10}{:>14}{:>10}"
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logging.info(fmt_layer.format("Layer Type", "Layers", "Memory (MB)", "% Total"))
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logging.info(dash_line)
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for layer_type, count in layer_summary.items():
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mem_mb = memory_by_type[layer_type] / (1024 * 1024)
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mem_percent = (memory_by_type[layer_type] / total_memory) * 100 if total_memory > 0 else 0
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logging.info(fmt_layer.format(layer_type,str(count),f"{mem_mb:.2f}",f"{mem_percent:.1f}%"))
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logging.info(dash_line)
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nonzero_devices = [d for d, r in DEVICE_RATIOS_DISTORCH.items() if r > 0]
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nonzero_total_ratio = sum(DEVICE_RATIOS_DISTORCH[d] for d in nonzero_devices)
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device_assignments = {device: [] for device in DEVICE_RATIOS_DISTORCH.keys()}
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total_layers = len(layer_list)
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current_layer = 0
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for idx, device in enumerate(nonzero_devices):
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ratio = DEVICE_RATIOS_DISTORCH[device]
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if idx == len(nonzero_devices) - 1:
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device_layer_count = total_layers - current_layer
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else:
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device_layer_count = int((ratio / nonzero_total_ratio) * total_layers)
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start_idx = current_layer
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end_idx = current_layer + device_layer_count
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device_assignments[device] = layer_list[start_idx:end_idx]
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current_layer += device_layer_count
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logging.info(" DisTorch Final Device/Layer Assignments")
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logging.info(dash_line)
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fmt_assign = "{:<12}{:>10}{:>14}{:>10}"
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logging.info(fmt_assign.format("Device", "Layers", "Memory (MB)", "% Total"))
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logging.info(dash_line)
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total_assigned_memory = 0
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device_memories = {}
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for device, layers in device_assignments.items():
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device_memory = 0
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for layer_type in layer_summary:
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type_layers = sum(1 for _, _, lt in layers if lt == layer_type)
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if layer_summary[layer_type] > 0:
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mem_per_layer = memory_by_type[layer_type] / layer_summary[layer_type]
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device_memory += mem_per_layer * type_layers
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device_memories[device] = device_memory
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total_assigned_memory += device_memory
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sorted_assignments = sorted(device_assignments.keys(), key=lambda d: (d == "cpu", d))
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for dev in sorted_assignments:
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layers = device_assignments[dev]
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mem_mb = device_memories[dev] / (1024 * 1024)
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mem_percent = (device_memories[dev] / total_memory) * 100 if total_memory > 0 else 0
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logging.info(fmt_assign.format(dev,str(len(layers)),f"{mem_mb:.2f}",f"{mem_percent:.1f}%"))
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logging.info(dash_line)
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return {"device_assignments": device_assignments}
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def calculate_vvram_allocation_string(model, virtual_vram_str):
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recipient_device, vram_amount, donors = virtual_vram_str.split(';')
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virtual_vram_gb = float(vram_amount)
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eq_line = "=" * 47
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dash_line = "-" * 47
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fmt_assign = "{:<8} {:<6} {:>11} {:>9} {:>9}"
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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logging.info(eq_line)
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logging.info(" DisTorch Virtual VRAM Analysis")
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logging.info(eq_line)
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logging.info(fmt_assign.format("Object", "Role", "Original(GB)", "Total(GB)", "Virt(GB)"))
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logging.info(dash_line)
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recipient_vram = mm.get_total_memory(torch.device(recipient_device)) / (1024**3)
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recipient_virtual = recipient_vram + virtual_vram_gb
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logging.info(fmt_assign.format(recipient_device, 'recip', f"{recipient_vram:.2f}GB",f"{recipient_virtual:.2f}GB", f"+{virtual_vram_gb:.2f}GB"))
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ram_donors = [d for d in donors.split(',') if d != 'cpu']
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remaining_vram_needed = virtual_vram_gb
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donor_device_info = {}
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donor_allocations = {}
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for donor in ram_donors:
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donor_vram = mm.get_total_memory(torch.device(donor)) / (1024**3)
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max_donor_capacity = donor_vram * 0.9
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donation = min(remaining_vram_needed, max_donor_capacity)
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donor_virtual = donor_vram - donation
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remaining_vram_needed -= donation
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donor_allocations[donor] = donation
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donor_device_info[donor] = (donor_vram, donor_virtual)
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logging.info(fmt_assign.format(donor, 'donor', f"{donor_vram:.2f}GB", f"{donor_virtual:.2f}GB", f"-{donation:.2f}GB"))
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system_dram_gb = mm.get_total_memory(torch.device('cpu')) / (1024**3)
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cpu_donation = remaining_vram_needed
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cpu_virtual = system_dram_gb - cpu_donation
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donor_allocations['cpu'] = cpu_donation
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logging.info(fmt_assign.format('cpu', 'donor', f"{system_dram_gb:.2f}GB", f"{cpu_virtual:.2f}GB", f"-{cpu_donation:.2f}GB"))
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logging.info(dash_line)
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layer_summary = {}
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layer_list = []
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memory_by_type = defaultdict(int)
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total_memory = 0
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for name, module in model.named_modules():
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if hasattr(module, "weight"):
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layer_type = type(module).__name__
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layer_summary[layer_type] = layer_summary.get(layer_type, 0) + 1
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layer_list.append((name, module, layer_type))
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layer_memory = 0
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if module.weight is not None:
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layer_memory += module.weight.numel() * module.weight.element_size()
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if hasattr(module, "bias") and module.bias is not None:
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layer_memory += module.bias.numel() * module.bias.element_size()
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memory_by_type[layer_type] += layer_memory
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total_memory += layer_memory
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model_size_gb = total_memory / (1024**3)
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new_model_size_gb = max(0, model_size_gb - virtual_vram_gb)
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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"))
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if model_size_gb > (recipient_vram * 0.9):
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on_recipient = recipient_vram * 0.9
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on_virtuals = model_size_gb - on_recipient
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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")
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else:
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on_recipient = model_size_gb
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on_virtuals = 0
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new_on_recipient = max(0, on_recipient - virtual_vram_gb)
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allocation_parts = []
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recipient_percent = new_on_recipient / recipient_vram
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allocation_parts.append(f"{recipient_device},{recipient_percent:.4f}")
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for donor in ram_donors:
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donor_vram = donor_device_info[donor][0]
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donor_percent = donor_allocations[donor] / donor_vram
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allocation_parts.append(f"{donor},{donor_percent:.4f}")
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cpu_percent = donor_allocations['cpu'] / system_dram_gb
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allocation_parts.append(f"cpu,{cpu_percent:.4f}")
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allocation_string = ";".join(allocation_parts)
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fmt_mem = "{:<20}{:>20}"
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logging.info(fmt_mem.format("\nAllocation String", allocation_string))
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return allocation_string
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def get_device_list():
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import torch
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return ["cpu"] + [f"cuda:{i}" for i in range(torch.cuda.device_count())]
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class DeviceSelectorMultiGPU:
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@classmethod
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def INPUT_TYPES(s):
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devices = get_device_list()
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return {
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"required": {
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"device": (devices, {"default": devices[1] if len(devices) > 1 else devices[0]})
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}
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}
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RETURN_TYPES = (get_device_list(),)
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RETURN_NAMES = ("device",)
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FUNCTION = "select_device"
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CATEGORY = "multigpu"
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def select_device(self, device):
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return (device,)
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class HunyuanVideoEmbeddingsAdapter:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"hyvid_embeds": ("HYVIDEMBEDS",),
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}
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}
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RETURN_TYPES = ("CONDITIONING",)
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FUNCTION = "adapt_embeddings"
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CATEGORY = "multigpu"
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def adapt_embeddings(self, hyvid_embeds):
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cond = hyvid_embeds["prompt_embeds"]
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pooled_dict = {
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"pooled_output": hyvid_embeds["prompt_embeds_2"],
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"cross_attn": hyvid_embeds["prompt_embeds"],
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"attention_mask": hyvid_embeds["attention_mask"],
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}
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if hyvid_embeds["attention_mask_2"] is not None:
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pooled_dict["attention_mask_controlnet"] = hyvid_embeds["attention_mask_2"]
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if hyvid_embeds["cfg"] is not None:
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pooled_dict["guidance"] = float(hyvid_embeds["cfg"])
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pooled_dict["start_percent"] = float(hyvid_embeds["start_percent"]) if hyvid_embeds["start_percent"] is not None else 0.0
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pooled_dict["end_percent"] = float(hyvid_embeds["end_percent"]) if hyvid_embeds["end_percent"] is not None else 1.0
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return ([[cond, pooled_dict]],)
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class MergeFluxLoRAsQuantizeAndLoad:
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@classmethod
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def INPUT_TYPES(cls):
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unet_name = folder_paths.get_filename_list("diffusion_models")
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loras = ["None"] + folder_paths.get_filename_list("loras")
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inputs = {
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"required": {
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"unet_name": (unet_name,),
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"switch_1": (["Off", "On"],),
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"lora_name_1": (loras,),
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"lora_weight_1": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"switch_2": (["Off", "On"],),
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"lora_name_2": (loras,),
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"lora_weight_2": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"switch_3": (["Off", "On"],),
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"lora_name_3": (loras,),
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"lora_weight_3": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"switch_4": (["Off", "On"],),
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"lora_name_4": (loras,),
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"lora_weight_4": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
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"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"}),
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"delete_final_gguf": ("BOOLEAN", {"default": False}),
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"new_model_name": ("STRING", {"default": "merged_model"}),
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}
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}
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return inputs
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RETURN_TYPES = ("MODEL",)
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FUNCTION = "load_and_quantize"
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CATEGORY = "loaders"
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def merge_flux_loras(self, model_sd: dict, lora_paths: list, weights: list, device="cuda") -> dict:
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for lora_path, weight in zip(lora_paths, weights):
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logging.info(f"[DEBUG] Merging LoRA file: {lora_path} with weight: {weight}")
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lora_sd = load_file(lora_path, device=device)
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for key in list(lora_sd.keys()):
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if "lora_down" not in key:
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continue
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base_name = key[: key.rfind(".lora_down")]
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up_key = key.replace("lora_down", "lora_up")
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module_name = base_name.replace("_", ".")
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alpha_key = f"{base_name}.alpha"
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if module_name not in model_sd:
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logging.info(f"[DEBUG] Module {module_name} not found in model_sd; skipping key {key}")
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continue
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down_weight = lora_sd[key].float()
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up_weight = lora_sd[up_key].float()
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alpha = float(lora_sd.get(alpha_key, up_weight.shape[0]))
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scale = weight * alpha / up_weight.shape[0]
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logging.info(f"[DEBUG] Merging module: {module_name} with alpha: {alpha}, scale: {scale}")
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target_weight = model_sd[module_name]
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if len(target_weight.shape) == 2:
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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": 4.0, "min": 0.0, "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())}")
|