489 lines
15 KiB
Python
489 lines
15 KiB
Python
import psutil
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from enum import Enum
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from comfy.cli_args import args
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import torch
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class VRAMState(Enum):
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DISABLED = 0 #No vram present: no need to move models to vram
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NO_VRAM = 1 #Very low vram: enable all the options to save vram
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LOW_VRAM = 2
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NORMAL_VRAM = 3
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HIGH_VRAM = 4
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SHARED = 5 #No dedicated vram: memory shared between CPU and GPU but models still need to be moved between both.
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class CPUState(Enum):
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GPU = 0
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CPU = 1
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MPS = 2
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# Determine VRAM State
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vram_state = VRAMState.NORMAL_VRAM
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set_vram_to = VRAMState.NORMAL_VRAM
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cpu_state = CPUState.GPU
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total_vram = 0
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lowvram_available = True
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xpu_available = False
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directml_enabled = False
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if args.directml is not None:
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import torch_directml
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directml_enabled = True
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device_index = args.directml
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if device_index < 0:
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directml_device = torch_directml.device()
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else:
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directml_device = torch_directml.device(device_index)
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print("Using directml with device:", torch_directml.device_name(device_index))
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# torch_directml.disable_tiled_resources(True)
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lowvram_available = False #TODO: need to find a way to get free memory in directml before this can be enabled by default.
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try:
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import intel_extension_for_pytorch as ipex
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if torch.xpu.is_available():
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xpu_available = True
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except:
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pass
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try:
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if torch.backends.mps.is_available():
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cpu_state = CPUState.MPS
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except:
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pass
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if args.cpu:
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cpu_state = CPUState.CPU
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def get_torch_device():
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global xpu_available
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global directml_enabled
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global cpu_state
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if directml_enabled:
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global directml_device
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return directml_device
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if cpu_state == CPUState.MPS:
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return torch.device("mps")
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if cpu_state == CPUState.CPU:
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return torch.device("cpu")
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else:
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if xpu_available:
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return torch.device("xpu")
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else:
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return torch.device(torch.cuda.current_device())
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def get_total_memory(dev=None, torch_total_too=False):
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global xpu_available
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global directml_enabled
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if dev is None:
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dev = get_torch_device()
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if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'):
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mem_total = psutil.virtual_memory().total
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mem_total_torch = mem_total
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else:
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if directml_enabled:
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mem_total = 1024 * 1024 * 1024 #TODO
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mem_total_torch = mem_total
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elif xpu_available:
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mem_total = torch.xpu.get_device_properties(dev).total_memory
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mem_total_torch = mem_total
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else:
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stats = torch.cuda.memory_stats(dev)
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mem_reserved = stats['reserved_bytes.all.current']
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_, mem_total_cuda = torch.cuda.mem_get_info(dev)
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mem_total_torch = mem_reserved
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mem_total = mem_total_cuda
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if torch_total_too:
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return (mem_total, mem_total_torch)
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else:
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return mem_total
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total_vram = get_total_memory(get_torch_device()) / (1024 * 1024)
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total_ram = psutil.virtual_memory().total / (1024 * 1024)
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print("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram))
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if not args.normalvram and not args.cpu:
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if lowvram_available and total_vram <= 4096:
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print("Trying to enable lowvram mode because your GPU seems to have 4GB or less. If you don't want this use: --normalvram")
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set_vram_to = VRAMState.LOW_VRAM
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elif total_vram > total_ram * 1.1 and total_vram > 14336:
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print("Enabling highvram mode because your GPU has more vram than your computer has ram. If you don't want this use: --normalvram")
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vram_state = VRAMState.HIGH_VRAM
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try:
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OOM_EXCEPTION = torch.cuda.OutOfMemoryError
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except:
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OOM_EXCEPTION = Exception
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XFORMERS_VERSION = ""
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XFORMERS_ENABLED_VAE = True
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if args.disable_xformers:
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XFORMERS_IS_AVAILABLE = False
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else:
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try:
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import xformers
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import xformers.ops
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XFORMERS_IS_AVAILABLE = True
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try:
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XFORMERS_VERSION = xformers.version.__version__
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print("xformers version:", XFORMERS_VERSION)
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if XFORMERS_VERSION.startswith("0.0.18"):
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print()
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print("WARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.")
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print("Please downgrade or upgrade xformers to a different version.")
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print()
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XFORMERS_ENABLED_VAE = False
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except:
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pass
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except:
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XFORMERS_IS_AVAILABLE = False
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def is_nvidia():
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global cpu_state
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if cpu_state == CPUState.GPU:
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if torch.version.cuda:
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return True
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ENABLE_PYTORCH_ATTENTION = args.use_pytorch_cross_attention
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if ENABLE_PYTORCH_ATTENTION == False and XFORMERS_IS_AVAILABLE == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
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try:
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if is_nvidia():
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torch_version = torch.version.__version__
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if int(torch_version[0]) >= 2:
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ENABLE_PYTORCH_ATTENTION = True
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except:
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pass
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if ENABLE_PYTORCH_ATTENTION:
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torch.backends.cuda.enable_math_sdp(True)
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torch.backends.cuda.enable_flash_sdp(True)
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torch.backends.cuda.enable_mem_efficient_sdp(True)
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XFORMERS_IS_AVAILABLE = False
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if args.lowvram:
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set_vram_to = VRAMState.LOW_VRAM
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lowvram_available = True
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elif args.novram:
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set_vram_to = VRAMState.NO_VRAM
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elif args.highvram or args.gpu_only:
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vram_state = VRAMState.HIGH_VRAM
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FORCE_FP32 = False
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if args.force_fp32:
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print("Forcing FP32, if this improves things please report it.")
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FORCE_FP32 = True
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if lowvram_available:
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try:
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import accelerate
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if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM):
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vram_state = set_vram_to
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except Exception as e:
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import traceback
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print(traceback.format_exc())
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print("ERROR: LOW VRAM MODE NEEDS accelerate.")
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lowvram_available = False
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if cpu_state != CPUState.GPU:
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vram_state = VRAMState.DISABLED
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if cpu_state == CPUState.MPS:
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vram_state = VRAMState.SHARED
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print(f"Set vram state to: {vram_state.name}")
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def get_torch_device_name(device):
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if hasattr(device, 'type'):
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if device.type == "cuda":
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return "{} {}".format(device, torch.cuda.get_device_name(device))
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else:
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return "{}".format(device.type)
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else:
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return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device))
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try:
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print("Device:", get_torch_device_name(get_torch_device()))
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except:
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print("Could not pick default device.")
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current_loaded_model = None
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current_gpu_controlnets = []
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model_accelerated = False
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def unload_model():
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global current_loaded_model
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global model_accelerated
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global current_gpu_controlnets
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global vram_state
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if current_loaded_model is not None:
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if model_accelerated:
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accelerate.hooks.remove_hook_from_submodules(current_loaded_model.model)
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model_accelerated = False
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#never unload models from GPU on high vram
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if vram_state != VRAMState.HIGH_VRAM:
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current_loaded_model.model.cpu()
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current_loaded_model.model_patches_to("cpu")
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current_loaded_model.unpatch_model()
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current_loaded_model = None
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if vram_state != VRAMState.HIGH_VRAM:
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if len(current_gpu_controlnets) > 0:
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for n in current_gpu_controlnets:
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n.cpu()
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current_gpu_controlnets = []
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def load_model_gpu(model):
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global current_loaded_model
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global vram_state
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global model_accelerated
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if model is current_loaded_model:
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return
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unload_model()
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try:
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real_model = model.patch_model()
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except Exception as e:
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model.unpatch_model()
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raise e
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torch_dev = get_torch_device()
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model.model_patches_to(torch_dev)
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vram_set_state = vram_state
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if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM):
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model_size = model.model_size()
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current_free_mem = get_free_memory(torch_dev)
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lowvram_model_memory = int(max(256 * (1024 * 1024), (current_free_mem - 1024 * (1024 * 1024)) / 1.3 ))
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if model_size > (current_free_mem - (512 * 1024 * 1024)): #only switch to lowvram if really necessary
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vram_set_state = VRAMState.LOW_VRAM
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current_loaded_model = model
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if vram_set_state == VRAMState.DISABLED:
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pass
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elif vram_set_state == VRAMState.NORMAL_VRAM or vram_set_state == VRAMState.HIGH_VRAM or vram_set_state == VRAMState.SHARED:
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model_accelerated = False
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real_model.to(get_torch_device())
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else:
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if vram_set_state == VRAMState.NO_VRAM:
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device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "256MiB", "cpu": "16GiB"})
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elif vram_set_state == VRAMState.LOW_VRAM:
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device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "{}MiB".format(lowvram_model_memory // (1024 * 1024)), "cpu": "16GiB"})
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accelerate.dispatch_model(real_model, device_map=device_map, main_device=get_torch_device())
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model_accelerated = True
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return current_loaded_model
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def load_controlnet_gpu(control_models):
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global current_gpu_controlnets
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global vram_state
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if vram_state == VRAMState.DISABLED:
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return
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if vram_state == VRAMState.LOW_VRAM or vram_state == VRAMState.NO_VRAM:
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for m in control_models:
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if hasattr(m, 'set_lowvram'):
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m.set_lowvram(True)
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#don't load controlnets like this if low vram because they will be loaded right before running and unloaded right after
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return
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models = []
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for m in control_models:
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models += m.get_models()
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for m in current_gpu_controlnets:
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if m not in models:
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m.cpu()
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device = get_torch_device()
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current_gpu_controlnets = []
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for m in models:
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current_gpu_controlnets.append(m.to(device))
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def load_if_low_vram(model):
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global vram_state
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if vram_state == VRAMState.LOW_VRAM or vram_state == VRAMState.NO_VRAM:
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return model.to(get_torch_device())
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return model
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def unload_if_low_vram(model):
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global vram_state
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if vram_state == VRAMState.LOW_VRAM or vram_state == VRAMState.NO_VRAM:
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return model.cpu()
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return model
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def text_encoder_device():
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if args.gpu_only:
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return get_torch_device()
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else:
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return torch.device("cpu")
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def get_autocast_device(dev):
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if hasattr(dev, 'type'):
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return dev.type
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return "cuda"
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def xformers_enabled():
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global xpu_available
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global directml_enabled
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global cpu_state
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if cpu_state != CPUState.GPU:
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return False
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if xpu_available:
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return False
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if directml_enabled:
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return False
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return XFORMERS_IS_AVAILABLE
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def xformers_enabled_vae():
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enabled = xformers_enabled()
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if not enabled:
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return False
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return XFORMERS_ENABLED_VAE
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def pytorch_attention_enabled():
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global ENABLE_PYTORCH_ATTENTION
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return ENABLE_PYTORCH_ATTENTION
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def pytorch_attention_flash_attention():
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global ENABLE_PYTORCH_ATTENTION
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if ENABLE_PYTORCH_ATTENTION:
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#TODO: more reliable way of checking for flash attention?
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if is_nvidia(): #pytorch flash attention only works on Nvidia
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return True
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return False
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def get_free_memory(dev=None, torch_free_too=False):
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global xpu_available
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global directml_enabled
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if dev is None:
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dev = get_torch_device()
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if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'):
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mem_free_total = psutil.virtual_memory().available
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mem_free_torch = mem_free_total
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else:
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if directml_enabled:
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mem_free_total = 1024 * 1024 * 1024 #TODO
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mem_free_torch = mem_free_total
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elif xpu_available:
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mem_free_total = torch.xpu.get_device_properties(dev).total_memory - torch.xpu.memory_allocated(dev)
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mem_free_torch = mem_free_total
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else:
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stats = torch.cuda.memory_stats(dev)
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mem_active = stats['active_bytes.all.current']
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mem_reserved = stats['reserved_bytes.all.current']
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mem_free_cuda, _ = torch.cuda.mem_get_info(dev)
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mem_free_torch = mem_reserved - mem_active
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mem_free_total = mem_free_cuda + mem_free_torch
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if torch_free_too:
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return (mem_free_total, mem_free_torch)
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else:
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return mem_free_total
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def maximum_batch_area():
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global vram_state
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if vram_state == VRAMState.NO_VRAM:
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return 0
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memory_free = get_free_memory() / (1024 * 1024)
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if xformers_enabled() or pytorch_attention_flash_attention():
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#TODO: this needs to be tweaked
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area = 20 * memory_free
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else:
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#TODO: this formula is because AMD sucks and has memory management issues which might be fixed in the future
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area = ((memory_free - 1024) * 0.9) / (0.6)
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return int(max(area, 0))
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def cpu_mode():
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global cpu_state
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return cpu_state == CPUState.CPU
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def mps_mode():
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global cpu_state
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return cpu_state == CPUState.MPS
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def should_use_fp16():
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global xpu_available
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global directml_enabled
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if FORCE_FP32:
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return False
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if directml_enabled:
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return False
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if cpu_mode() or mps_mode() or xpu_available:
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return False #TODO ?
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if torch.cuda.is_bf16_supported():
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return True
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props = torch.cuda.get_device_properties("cuda")
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if props.major < 7:
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return False
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#FP32 is faster on those cards?
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nvidia_16_series = ["1660", "1650", "1630", "T500", "T550", "T600"]
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for x in nvidia_16_series:
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if x in props.name:
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return False
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return True
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def soft_empty_cache():
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global xpu_available
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global cpu_state
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if cpu_state == CPUState.MPS:
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torch.mps.empty_cache()
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elif xpu_available:
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torch.xpu.empty_cache()
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elif torch.cuda.is_available():
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if is_nvidia(): #This seems to make things worse on ROCm so I only do it for cuda
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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#TODO: might be cleaner to put this somewhere else
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import threading
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class InterruptProcessingException(Exception):
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pass
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interrupt_processing_mutex = threading.RLock()
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interrupt_processing = False
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def interrupt_current_processing(value=True):
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global interrupt_processing
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global interrupt_processing_mutex
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with interrupt_processing_mutex:
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interrupt_processing = value
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def processing_interrupted():
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global interrupt_processing
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global interrupt_processing_mutex
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with interrupt_processing_mutex:
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return interrupt_processing
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def throw_exception_if_processing_interrupted():
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global interrupt_processing
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global interrupt_processing_mutex
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with interrupt_processing_mutex:
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if interrupt_processing:
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interrupt_processing = False
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raise InterruptProcessingException()
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