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