"""
    This file is part of ComfyUI.
    Copyright (C) 2024 Comfy

    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.

    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU General Public License for more details.

    You should have received a copy of the GNU General Public License
    along with this program.  If not, see <https://www.gnu.org/licenses/>.
"""
from __future__ import annotations

from .cmd.main_pre import tracer

import gc
import logging
import platform
import sys
import importlib.util
import warnings
import weakref

from enum import Enum
from threading import RLock
from typing import List, Sequence, Final, Optional

import psutil
import torch
from opentelemetry.trace import get_current_span
from . import interruption
from .cli_args import args, PerformanceFeature
from .component_model.deprecation import _deprecate_method
from .model_management_types import ModelManageable

model_management_lock = RLock()

# https://github.com/sayakpaul/diffusers-torchao/blob/bade7a6abb1cab9ef44782e6bcfab76d0237ae1f/inference/benchmark_image.py#L3
# This setting optimizes performance on NVIDIA GPUs with Ampere architecture (e.g., A100, RTX 30 series) or newer.
torch.set_float32_matmul_precision("high")

logger = logging.getLogger(__name__)


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


def get_supported_float8_types():
    float8_types = []
    try:
        float8_types.append(torch.float8_e4m3fn)
    except:
        pass
    try:
        float8_types.append(torch.float8_e4m3fnuz)
    except:
        pass
    try:
        float8_types.append(torch.float8_e5m2)
    except:
        pass
    try:
        float8_types.append(torch.float8_e5m2fnuz)
    except:
        pass
    try:
        float8_types.append(torch.float8_e8m0fnu)
    except:
        pass
    return float8_types


FLOAT8_TYPES = get_supported_float8_types()

xpu_available = False
torch_version = ""
try:
    torch_version = torch.version.__version__
    temp = torch_version.split(".")
    torch_version_numeric = (int(temp[0]), int(temp[1]))
except:
    pass

lowvram_available = True
if args.deterministic:
    logger.debug("Using deterministic algorithms for pytorch")
    torch.use_deterministic_algorithms(True, warn_only=True)

directml_device = None
if args.directml is not None:
    logger.warning("WARNING: torch-directml barely works, is very slow, has not been updated in over 1 year and might be removed soon, please don't use it, there are better options.")
    import torch_directml  # pylint: disable=import-error

    device_index = args.directml
    if device_index < 0:
        directml_device = torch_directml.device()
    else:
        directml_device = torch_directml.device(device_index)
    logger.debug("Using directml with device: {}".format(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  # pylint: disable=import-error, noqa: F401
except:
    pass

try:
    _ = torch.xpu.device_count()
    xpu_available = torch.xpu.is_available()
except:
    xpu_available = False

try:
    if torch.backends.mps.is_available():
        cpu_state = CPUState.MPS
        import torch.mps
except:
    pass

try:
    import torch_npu  # pylint: disable=import-error, noqa: F401

    _ = torch.npu.device_count()
    npu_available = torch.npu.is_available()
except:
    npu_available = False

try:
    import torch_mlu  # pylint: disable=import-error, noqa: F401

    _ = torch.mlu.device_count()
    mlu_available = torch.mlu.is_available()
except:
    mlu_available = False

try:
    ixuca_available = hasattr(torch, "corex")
except:
    ixuca_available = False

if args.cpu:
    cpu_state = CPUState.CPU


def is_intel_xpu():
    global cpu_state
    global xpu_available
    if cpu_state == CPUState.GPU:
        if xpu_available:
            return True
    return False


def is_ascend_npu():
    global npu_available
    if npu_available:
        return True
    return False


def is_mlu():
    global mlu_available
    if mlu_available:
        return True
    return False


def is_ixuca():
    global ixuca_available
    if ixuca_available:
        return True
    return False


def get_torch_device():
    global directml_device
    global cpu_state
    if 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 is_intel_xpu():
            return torch.device("xpu", torch.xpu.current_device())
        elif is_ascend_npu():
            return torch.device("npu", torch.npu.current_device())
        elif is_mlu():
            return torch.device("mlu", torch.mlu.current_device())
        else:
            try:
                return torch.device(f"cuda:{torch.cuda.current_device()}")
            except:
                warnings.warn("torch.cuda.current_device() did not return a device, returning a CPU torch device")
                return torch.device("cpu")


def get_total_memory(dev=None, torch_total_too=False):
    global directml_device
    if dev is None:
        dev = get_torch_device()

    mem_total_torch = 0
    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_device:
            mem_total = 1024 * 1024 * 1024  # TODO
            mem_total_torch = mem_total
        elif is_intel_xpu():
            stats = torch.xpu.memory_stats(dev)  # pylint: disable=no-member
            mem_reserved = stats['reserved_bytes.all.current']
            mem_total_xpu = torch.xpu.get_device_properties(dev).total_memory
            mem_total_torch = mem_reserved
            mem_total = mem_total_xpu
        elif is_ascend_npu():
            stats = torch.npu.memory_stats(dev)
            mem_reserved = stats['reserved_bytes.all.current']
            _, mem_total_npu = torch.npu.mem_get_info(dev)
            mem_total_torch = mem_reserved
            mem_total = mem_total_npu
        elif is_mlu():
            stats = torch.mlu.memory_stats(dev)
            mem_reserved = stats['reserved_bytes.all.current']
            _, mem_total_mlu = torch.mlu.mem_get_info(dev)
            mem_total_torch = mem_reserved
            mem_total = mem_total_mlu
        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


def mac_version():
    try:
        return tuple(int(n) for n in platform.mac_ver()[0].split("."))
    except:
        return None


# we're required to call get_device_name early on to initialize the methods get_total_memory will call
if torch.cuda.is_available() and hasattr(torch.version, "hip") and torch.version.hip is not None:
    logger.debug(f"Detected HIP device: {torch.cuda.get_device_name(torch.cuda.current_device())}")
total_vram = get_total_memory(get_torch_device()) / (1024 * 1024)
total_ram = psutil.virtual_memory().total / (1024 * 1024)
logger.debug("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram))

try:
    logger.debug("pytorch version: {}".format(torch_version))
    mac_ver = mac_version()
    if mac_ver is not None:
        logger.debug("Mac Version {}".format(mac_ver))
except:
    pass


class _ComfyOutOfMemoryException(RuntimeError):
    pass


try:
    OOM_EXCEPTION = torch.cuda.OutOfMemoryError
except:
    OOM_EXCEPTION = _ComfyOutOfMemoryException

XFORMERS_VERSION = ""
XFORMERS_ENABLED_VAE = True
if args.disable_xformers:
    XFORMERS_IS_AVAILABLE = False
else:
    try:
        import xformers  # pylint: disable=import-error
        import xformers.ops  # pylint: disable=import-error

        XFORMERS_IS_AVAILABLE = True
        try:
            XFORMERS_IS_AVAILABLE = xformers._has_cpp_library
        except:
            pass
        try:
            XFORMERS_VERSION = xformers.version.__version__
            logger.debug("xformers version: {}".format(XFORMERS_VERSION))
            if XFORMERS_VERSION.startswith("0.0.18"):
                logger.warning("\nWARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.")
                logger.warning("Please downgrade or upgrade xformers to a different version.\n")
                XFORMERS_ENABLED_VAE = False
        except:
            pass
    except:
        XFORMERS_IS_AVAILABLE = False


def is_nvidia():
    global cpu_state
    if cpu_state == CPUState.GPU:
        if torch.version.cuda:
            return True
    return False


def is_amd():
    global cpu_state
    if cpu_state == CPUState.GPU:
        if torch.version.hip:
            return True
    return False


def amd_min_version(device=None, min_rdna_version=0):
    if not is_amd():
        return False

    if is_device_cpu(device):
        return False

    arch = torch.cuda.get_device_properties(device).gcnArchName
    if arch.startswith('gfx') and len(arch) == 7:
        try:
            cmp_rdna_version = int(arch[4]) + 2
        except:
            cmp_rdna_version = 0
        if cmp_rdna_version >= min_rdna_version:
            return True

    return False


MIN_WEIGHT_MEMORY_RATIO = 0.4
if is_nvidia():
    MIN_WEIGHT_MEMORY_RATIO = 0.0

ENABLE_PYTORCH_ATTENTION = False
if args.use_pytorch_cross_attention:
    ENABLE_PYTORCH_ATTENTION = True
    XFORMERS_IS_AVAILABLE = False

try:
    if is_nvidia() or is_amd():
        if torch_version_numeric[0] >= 2:
            if ENABLE_PYTORCH_ATTENTION == False and args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
                ENABLE_PYTORCH_ATTENTION = True
    if is_intel_xpu() or is_ascend_npu() or is_mlu() or is_ixuca():
        if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
            ENABLE_PYTORCH_ATTENTION = True
except:
    pass

SUPPORT_FP8_OPS = args.supports_fp8_compute

AMD_RDNA2_AND_OLDER_ARCH = ["gfx1030", "gfx1031", "gfx1010", "gfx1011", "gfx1012", "gfx906", "gfx900", "gfx803"]

try:
    if is_amd():
        arch = torch.cuda.get_device_properties(get_torch_device()).gcnArchName
        if not (any((a in arch) for a in AMD_RDNA2_AND_OLDER_ARCH)):
            torch.backends.cudnn.enabled = False  # Seems to improve things a lot on AMD
            logger.info("Set: torch.backends.cudnn.enabled = False for better AMD performance.")
        try:
            rocm_version = tuple(map(int, str(torch.version.hip).split(".")[:2]))
        except:
            rocm_version = (6, -1)

        logger.debug("AMD arch: {}".format(arch))
        logger.debug("ROCm version: {}".format(rocm_version))
        if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
            if importlib.util.find_spec('triton') is not None:  # AMD efficient attention implementation depends on triton. TODO: better way of detecting if it's compiled in or not.
                if torch_version_numeric >= (2, 7):  # works on 2.6 but doesn't actually seem to improve much
                    if any((a in arch) for a in ["gfx90a", "gfx942", "gfx1100", "gfx1101", "gfx1102", "gfx1151"]):  # TODO: more arches, TODO: gfx950
                        ENABLE_PYTORCH_ATTENTION = True
                        if rocm_version >= (7, 0):
                            if any((a in arch) for a in ["gfx1201"]):
                                ENABLE_PYTORCH_ATTENTION = True
        if torch_version_numeric >= (2, 7) and rocm_version >= (6, 4):
            if any((a in arch) for a in ["gfx1200", "gfx1201", "gfx950"]):  # TODO: more arches, "gfx942" gives error on pytorch nightly 2.10 1013 rocm7.0
                SUPPORT_FP8_OPS = True

except:
    pass

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)

PRIORITIZE_FP16 = False  # TODO: remove and replace with something that shows exactly which dtype is faster than the other
try:
    if (is_nvidia() or is_amd()) and PerformanceFeature.Fp16Accumulation in args.fast:
        torch.backends.cuda.matmul.allow_fp16_accumulation = True
        PRIORITIZE_FP16 = True  # TODO: limit to cards where it actually boosts performance
        logger.info("Enabled fp16 accumulation.")
except:
    pass

if torch.cuda.is_available() and torch.backends.cudnn.is_available() and PerformanceFeature.AutoTune in args.fast:
    torch.backends.cudnn.benchmark = True

try:
    if torch_version_numeric >= (2, 5):
        torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp(True)  # pylint: disable=no-member
except:
    logger.warning("Warning, could not set allow_fp16_bf16_reduction_math_sdp")

if args.lowvram:
    set_vram_to = VRAMState.LOW_VRAM
    lowvram_available = True
elif args.novram:
    set_vram_to = VRAMState.NO_VRAM
elif args.highvram or args.gpu_only:
    vram_state = VRAMState.HIGH_VRAM

FORCE_FP32 = False
FORCE_FP16 = False
FORCE_BF16 = False
if args.force_fp32:
    logger.info("Forcing FP32, if this improves things please report it.")
    FORCE_FP32 = True

if args.force_fp16:
    logger.debug("Forcing FP16.")
    FORCE_FP16 = True

if args.force_bf16:
    logger.info("Force BF16")
    FORCE_BF16 = True

if lowvram_available:
    if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM):
        vram_state = set_vram_to

if cpu_state != CPUState.GPU:
    vram_state = VRAMState.DISABLED

if cpu_state == CPUState.MPS:
    vram_state = VRAMState.SHARED

logger.debug(f"Set vram state to: {vram_state.name}")

DISABLE_SMART_MEMORY = args.disable_smart_memory

if DISABLE_SMART_MEMORY:
    logger.debug("Disabling smart memory management")


def get_torch_device_name(device):
    if hasattr(device, 'type'):
        if device.type == "cuda":
            try:
                allocator_backend = torch.cuda.get_allocator_backend()
            except:
                allocator_backend = ""
            return "{} {} : {}".format(device, torch.cuda.get_device_name(device), allocator_backend)
        elif device.type == "xpu":
            return "{} {}".format(device, torch.xpu.get_device_name(device))
        else:
            return "{}".format(device.type)
    elif is_intel_xpu():
        return "{} {}".format(device, torch.xpu.get_device_name(device))
    elif is_ascend_npu():
        return "{} {}".format(device, torch.npu.get_device_name(device))
    elif is_mlu():
        return "{} {}".format(device, torch.mlu.get_device_name(device))
    else:
        return "CUDA {}: {}".format(device, torch.cuda.get_device_name(device))


try:
    logger.debug("Device: {}".format(get_torch_device_name(get_torch_device())))
except:
    logger.warning("Could not pick default device.")

current_loaded_models: Final[List["LoadedModel"]] = []


def module_size(module):
    module_mem = 0
    sd = module.state_dict()
    for k in sd:
        t = sd[k]
        module_mem += t.nelement() * t.element_size()
    return module_mem


class LoadedModel:
    def __init__(self, model: ModelManageable):
        self._set_model(model)
        self.device = model.load_device
        self.real_model = None
        self.currently_used = True
        self.model_finalizer = None
        self._patcher_finalizer = None

    def _set_model(self, model):
        self._model = weakref.ref(model)
        if model.parent is not None:
            self._parent_model = weakref.ref(model.parent)
            self._patcher_finalizer = weakref.finalize(model, self._switch_parent)

    def _switch_parent(self):
        model = self._parent_model()
        if model is not None:
            self._set_model(model)

    @property
    def model(self) -> ModelManageable:
        return self._model()

    def model_memory(self):
        return self.model.model_size()

    def model_loaded_memory(self):
        return self.model.loaded_size()

    def model_offloaded_memory(self):
        return self.model.model_size() - self.model.loaded_size()

    def model_memory_required(self, device):
        if device == self.model.current_loaded_device():
            return self.model_offloaded_memory()
        else:
            return self.model_memory()

    def model_load(self, lowvram_model_memory=0, force_patch_weights=False):
        self.model.model_patches_to(self.device)
        self.model.model_patches_to(self.model.model_dtype())

        # if self.model.loaded_size() > 0:
        use_more_vram = lowvram_model_memory
        if use_more_vram == 0:
            use_more_vram = 1e32
        self.model_use_more_vram(use_more_vram, force_patch_weights=force_patch_weights)

        real_model = self.model.model

        if is_intel_xpu() and not args.disable_ipex_optimize and 'ipex' in globals() and real_model is not None:
            with torch.no_grad():
                real_model = ipex.optimize(real_model.eval(), inplace=True, graph_mode=True, concat_linear=True)

        self.real_model = weakref.ref(real_model)
        self.model_finalizer = weakref.finalize(real_model, cleanup_models)
        return real_model

    def should_reload_model(self, force_patch_weights=False):
        if force_patch_weights and self.model.lowvram_patch_counter() > 0:
            return True
        return False

    def model_unload(self, memory_to_free=None, unpatch_weights=True):
        if memory_to_free is not None:
            if memory_to_free < self.model.loaded_size():
                freed = self.model.partially_unload(self.model.offload_device, memory_to_free)
                if freed >= memory_to_free:
                    return False
        self.model.detach(unpatch_weights)
        self.model_finalizer.detach()
        self.model_finalizer = None
        self.real_model = None
        return True

    def model_use_more_vram(self, extra_memory, force_patch_weights=False):
        return self.model.partially_load(self.device, extra_memory, force_patch_weights=force_patch_weights)

    def __eq__(self, other):
        return self.model is other.model

    def __str__(self):
        if self.model is not None:
            return f"<LoadedModel {str(self.model)}>"
        else:
            return f"<LoadedModel>"

    def __del__(self):
        if self._patcher_finalizer is not None:
            self._patcher_finalizer.detach()

    def is_dead(self):
        return self.real_model() is not None and self.model is None


def use_more_memory(extra_memory, loaded_models, device):
    for m in loaded_models:
        if m.device == device:
            extra_memory -= m.model_use_more_vram(extra_memory)
            if extra_memory <= 0:
                break


def offloaded_memory(loaded_models, device):
    offloaded_mem = 0
    for m in loaded_models:
        if m.device == device:
            offloaded_mem += m.model_offloaded_memory()
    return offloaded_mem


WINDOWS = any(platform.win32_ver())

EXTRA_RESERVED_VRAM = 400 * 1024 * 1024
if WINDOWS:
    EXTRA_RESERVED_VRAM = 600 * 1024 * 1024  # Windows is higher because of the shared vram issue
    if total_vram > (15 * 1024):  # more extra reserved vram on 16GB+ cards
        EXTRA_RESERVED_VRAM += 100 * 1024 * 1024

if args.reserve_vram is not None:
    EXTRA_RESERVED_VRAM = args.reserve_vram * 1024 * 1024 * 1024
    logger.debug("Reserving {}MB vram for other applications.".format(EXTRA_RESERVED_VRAM / (1024 * 1024)))


def extra_reserved_memory():
    return EXTRA_RESERVED_VRAM


def minimum_inference_memory():
    return (1024 * 1024 * 1024) * 0.8 + extra_reserved_memory()


def trim_memory() -> bool:
    """
    Trims memory usage, returning reserved memory to the system

    Only supported on Windows and Linux
    :return:
    """
    try:
        if sys.platform.startswith('linux'):
            import ctypes
            import ctypes.util
            libc_path = ctypes.util.find_library('c')
            if not libc_path:
                return False

            libc = ctypes.CDLL(libc_path)

            if hasattr(libc, 'malloc_trim'):
                return libc.malloc_trim(0) == 1
            else:
                return False
        elif sys.platform == 'win32':
            import ctypes.wintypes
            kernel32 = ctypes.WinDLL("kernel32")
            EmptyProcessWorkingSet = kernel32.EmptyProcessWorkingSet
            EmptyProcessWorkingSet.argtypes = [ctypes.wintypes.HANDLE]
            EmptyProcessWorkingSet.restype = ctypes.wintypes.BOOL
            handle = -1
            success = EmptyProcessWorkingSet(handle)
            return bool(success)
        else:
            return False
    except Exception as exc_info:
        logger.warning("failed to trim", exc_info=exc_info)
        return False


@tracer.start_as_current_span("Free Memory")
def free_memory(memory_required, device, keep_loaded=[]) -> List[LoadedModel]:
    span = get_current_span()
    span.set_attribute("memory_required", memory_required)
    with model_management_lock:
        unloaded_models = _free_memory(memory_required, device, keep_loaded)
        span.set_attribute("unloaded_models", list(map(str, unloaded_models)))
        return unloaded_models


def _free_memory(memory_required, device, keep_loaded=[]):
    cleanup_models_gc()
    unloaded_model = []
    can_unload = []
    unloaded_models = []

    for i in range(len(current_loaded_models) - 1, -1, -1):
        shift_model = current_loaded_models[i]
        if shift_model.device == device:
            if shift_model not in keep_loaded and not shift_model.is_dead():
                can_unload.append((-shift_model.model_offloaded_memory(), sys.getrefcount(shift_model.model), shift_model.model_memory(), i))
                shift_model.currently_used = False

    for x in sorted(can_unload):
        i = x[-1]
        memory_to_free = None
        if not DISABLE_SMART_MEMORY:
            free_mem = get_free_memory(device)
            if free_mem > memory_required:
                break
            memory_to_free = memory_required - free_mem
        logger.debug(f"Unloading {current_loaded_models[i].model.model.__class__.__name__}")
        if current_loaded_models[i].model_unload(memory_to_free):
            unloaded_model.append(i)

    for i in sorted(unloaded_model, reverse=True):
        unloaded_models.append(current_loaded_models.pop(i))

    if len(unloaded_model) > 0:
        soft_empty_cache()
    else:
        if vram_state != VRAMState.HIGH_VRAM:
            mem_free_total, mem_free_torch = get_free_memory(device, torch_free_too=True)
            if mem_free_torch > mem_free_total * 0.25:
                soft_empty_cache()
    return unloaded_models


@tracer.start_as_current_span("Load Models GPU")
def load_models_gpu(models: Sequence[ModelManageable], memory_required: int = 0, force_patch_weights=False, minimum_memory_required=None, force_full_load=False) -> None:
    span = get_current_span()
    if memory_required != 0:
        span.set_attribute("memory_required", memory_required)
    with model_management_lock:
        _load_models_gpu(models, memory_required, force_patch_weights, minimum_memory_required, force_full_load)
        to_load = list(map(str, models))
        span.set_attribute("models", to_load)
        logger.debug(f"Loaded {to_load}")


def _load_models_gpu(models: Sequence[ModelManageable], memory_required: int = 0, force_patch_weights=False, minimum_memory_required=None, force_full_load=False) -> None:
    cleanup_models_gc()
    global vram_state

    inference_memory = minimum_inference_memory()
    extra_mem = max(inference_memory, memory_required + extra_reserved_memory())
    if minimum_memory_required is None:
        minimum_memory_required = extra_mem
    else:
        minimum_memory_required = max(inference_memory, minimum_memory_required + extra_reserved_memory())

    models_temp = set()
    for m in models:
        models_temp.add(m)
        for mm in m.model_patches_models():
            models_temp.add(mm)

    models = models_temp

    models_to_load = []
    models_freed = []
    for x in models:
        loaded_model = LoadedModel(x)
        try:
            loaded_model_index = current_loaded_models.index(loaded_model)
        except:
            loaded_model_index = None

        if loaded_model_index is not None:
            loaded = current_loaded_models[loaded_model_index]
            loaded.currently_used = True
            models_to_load.append(loaded)
        else:
            models_to_load.append(loaded_model)

    for loaded_model in models_to_load:
        to_unload = []
        for i in range(len(current_loaded_models)):
            if loaded_model.model.is_clone(current_loaded_models[i].model):
                to_unload = [i] + to_unload
        for i in to_unload:
            model_to_unload = current_loaded_models.pop(i)
            model_to_unload.model.detach(unpatch_all=False)
            model_to_unload.model_finalizer.detach()

    total_memory_required = {}
    for loaded_model in models_to_load:
        total_memory_required[loaded_model.device] = total_memory_required.get(loaded_model.device, 0) + loaded_model.model_memory_required(loaded_model.device)

    for device in total_memory_required:
        if device != torch.device("cpu"):
            models_freed += free_memory(total_memory_required[device] * 1.1 + extra_mem, device)

    for device in total_memory_required:
        if device != torch.device("cpu"):
            free_mem = get_free_memory(device)
            if free_mem < minimum_memory_required:
                models_l = free_memory(minimum_memory_required, device)
                models_freed += models_l
                logger.debug("{} models unloaded.".format(len(models_l)))

    for loaded_model in models_to_load:
        model = loaded_model.model
        torch_dev = model.load_device
        if is_device_cpu(torch_dev):
            vram_set_state = VRAMState.DISABLED
        else:
            vram_set_state = vram_state
        lowvram_model_memory = 0
        if lowvram_available and (vram_set_state == VRAMState.LOW_VRAM or vram_set_state == VRAMState.NORMAL_VRAM) and not force_full_load:
            loaded_memory = loaded_model.model_loaded_memory()
            current_free_mem = get_free_memory(torch_dev) + loaded_memory

            lowvram_model_memory = max(0, (current_free_mem - minimum_memory_required), min(current_free_mem * MIN_WEIGHT_MEMORY_RATIO, current_free_mem - minimum_inference_memory()))
            lowvram_model_memory = lowvram_model_memory - loaded_memory

            if lowvram_model_memory == 0:
                lowvram_model_memory = 0.1

        if vram_set_state == VRAMState.NO_VRAM:
            lowvram_model_memory = 0.1

        loaded_model.model_load(lowvram_model_memory, force_patch_weights=force_patch_weights)
        current_loaded_models.insert(0, loaded_model)

    span = get_current_span()
    span.set_attribute("models_to_load", list(map(str, models_to_load)))
    span.set_attribute("models_freed", list(map(str, models_freed)))


@_deprecate_method(message="Use load_models_gpu instead", version="0.0.2")
def load_model_gpu(model):
    return load_models_gpu([model])


def loaded_models(only_currently_used=False):
    with model_management_lock:
        output = []
        for m in current_loaded_models:
            if only_currently_used:
                if not m.currently_used:
                    continue

            output.append(m.model)
        return output


def cleanup_models_gc():
    do_gc = False
    for i in range(len(current_loaded_models)):
        cur = current_loaded_models[i]
        if cur.is_dead():
            logger.info("Potential memory leak detected with model {}, doing a full garbage collect, for maximum performance avoid circular references in the model code.".format(cur.real_model().__class__.__name__))
            do_gc = True
            break

    if do_gc:
        gc.collect()
        soft_empty_cache()

        for i in range(len(current_loaded_models)):
            cur = current_loaded_models[i]
            if cur.is_dead():
                logger.warning("WARNING, memory leak with model {}. Please make sure it is not being referenced from somewhere.".format(cur.real_model().__class__.__name__))


def cleanup_models():
    to_delete = []
    for i in range(len(current_loaded_models)):
        if current_loaded_models[i].real_model() is None:
            to_delete = [i] + to_delete

    for i in to_delete:
        x = current_loaded_models.pop(i)
        del x


def dtype_size(dtype):
    dtype_size = 4
    if dtype == torch.float16 or dtype == torch.bfloat16:
        dtype_size = 2
    elif dtype == torch.float32:
        dtype_size = 4
    else:
        try:
            dtype_size = dtype.itemsize
        except:  # Old pytorch doesn't have .itemsize
            pass
    return dtype_size


def unet_offload_device():
    if vram_state == VRAMState.HIGH_VRAM:
        return get_torch_device()
    else:
        return torch.device("cpu")


def unet_initial_load_device(parameters, dtype):
    torch_dev = get_torch_device()
    if vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.SHARED:
        return torch_dev

    cpu_dev = torch.device("cpu")
    if DISABLE_SMART_MEMORY or vram_state == VRAMState.NO_VRAM:
        return cpu_dev

    model_size = dtype_size(dtype) * parameters

    mem_dev = get_free_memory(torch_dev)
    mem_cpu = get_free_memory(cpu_dev)
    if mem_dev > mem_cpu and model_size < mem_dev:
        return torch_dev
    else:
        return cpu_dev


def maximum_vram_for_weights(device=None) -> int:
    return get_total_memory(device) * 0.88 - minimum_inference_memory()


def unet_dtype(device=None, model_params=0, supported_dtypes=(torch.float16, torch.bfloat16, torch.float32), weight_dtype: Optional[torch.dtype] = None):
    if model_params < 0:
        model_params = 1000000000000000000000
    if args.fp32_unet:
        return torch.float32
    if args.fp64_unet:
        return torch.float64
    if args.bf16_unet:
        return torch.bfloat16
    if args.fp16_unet:
        return torch.float16
    if args.fp8_e4m3fn_unet:
        return torch.float8_e4m3fn
    if args.fp8_e5m2_unet:
        return torch.float8_e5m2
    if args.fp8_e8m0fnu_unet:
        return torch.float8_e8m0fnu

    fp8_dtype = None
    if weight_dtype in FLOAT8_TYPES:
        fp8_dtype = weight_dtype

    if fp8_dtype is not None:
        if supports_fp8_compute(device):  # if fp8 compute is supported the casting is most likely not expensive
            return fp8_dtype

        free_model_memory = maximum_vram_for_weights(device)
        if model_params * 2 > free_model_memory:
            return fp8_dtype

    if PRIORITIZE_FP16 or weight_dtype == torch.float16:
        if torch.float16 in supported_dtypes and should_use_fp16(device=device, model_params=model_params):
            return torch.float16

    for dt in supported_dtypes:
        if dt == torch.float16 and should_use_fp16(device=device, model_params=model_params):
            if torch.float16 in supported_dtypes:
                return torch.float16
        if dt == torch.bfloat16 and should_use_bf16(device, model_params=model_params):
            if torch.bfloat16 in supported_dtypes:
                return torch.bfloat16

    for dt in supported_dtypes:
        if dt == torch.float16 and should_use_fp16(device=device, model_params=model_params, manual_cast=True):
            if torch.float16 in supported_dtypes:
                return torch.float16
        if dt == torch.bfloat16 and should_use_bf16(device, model_params=model_params, manual_cast=True):
            if torch.bfloat16 in supported_dtypes:
                return torch.bfloat16

    return torch.float32


# None means no manual cast
def unet_manual_cast(weight_dtype, inference_device, supported_dtypes=(torch.float16, torch.bfloat16, torch.float32)):
    if weight_dtype == torch.float32 or weight_dtype == torch.float64:
        return None

    fp16_supported = should_use_fp16(inference_device, prioritize_performance=False)
    if fp16_supported and weight_dtype == torch.float16:
        return None

    bf16_supported = should_use_bf16(inference_device)
    if bf16_supported and weight_dtype == torch.bfloat16:
        return None

    fp16_supported = should_use_fp16(inference_device, prioritize_performance=True)
    if PRIORITIZE_FP16 and fp16_supported and torch.float16 in supported_dtypes:
        return torch.float16

    for dt in supported_dtypes:
        if dt == torch.float16 and fp16_supported:
            return torch.float16
        if dt == torch.bfloat16 and bf16_supported:
            return torch.bfloat16

    return torch.float32


def text_encoder_offload_device():
    if args.gpu_only:
        return get_torch_device()
    else:
        return torch.device("cpu")


def text_encoder_device():
    if args.gpu_only:
        return get_torch_device()
    elif vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.NORMAL_VRAM:
        if should_use_fp16(prioritize_performance=False):
            return get_torch_device()
        else:
            return torch.device("cpu")
    else:
        return torch.device("cpu")


def text_encoder_initial_device(load_device, offload_device, model_size=0):
    if load_device == offload_device or model_size <= 1024 * 1024 * 1024:
        return offload_device

    if is_device_mps(load_device):
        return load_device

    mem_l = get_free_memory(load_device)
    mem_o = get_free_memory(offload_device)
    if mem_l > (mem_o * 0.5) and model_size * 1.2 < mem_l:
        return load_device
    else:
        return offload_device


def text_encoder_dtype(device=None):
    if args.fp8_e4m3fn_text_enc:
        return torch.float8_e4m3fn
    elif args.fp8_e5m2_text_enc:
        return torch.float8_e5m2
    elif args.fp16_text_enc:
        return torch.float16
    elif args.bf16_text_enc:
        return torch.bfloat16
    elif args.fp32_text_enc:
        return torch.float32

    if is_device_cpu(device):
        return torch.float16

    return torch.float16


def intermediate_device():
    if args.gpu_only:
        return get_torch_device()
    else:
        return torch.device("cpu")


def vae_device():
    if args.cpu_vae:
        return torch.device("cpu")
    return get_torch_device()


def vae_offload_device():
    if args.gpu_only:
        return get_torch_device()
    else:
        return torch.device("cpu")


def vae_dtype(device=None, allowed_dtypes=[]):
    if args.fp16_vae:
        return torch.float16
    elif args.bf16_vae:
        return torch.bfloat16
    elif args.fp32_vae:
        return torch.float32

    for d in allowed_dtypes:
        if d == torch.float16 and should_use_fp16(device):
            return d

        if d == torch.bfloat16 and should_use_bf16(device):
            return d

    return torch.float32


def get_autocast_device(dev):
    if hasattr(dev, 'type'):
        return dev.type
    return "cuda"


def supports_dtype(device, dtype):  # TODO
    if dtype == torch.float32:
        return True
    if is_device_cpu(device):
        return False
    if dtype == torch.float16:
        return True
    if dtype == torch.bfloat16:
        return True
    return False


def supports_cast(device, dtype):  # TODO
    if dtype == torch.float32:
        return True
    if dtype == torch.float16:
        return True
    if directml_device:  # TODO: test this
        return False
    if dtype == torch.bfloat16:
        return True
    if is_device_mps(device):
        return False
    if dtype == torch.float8_e4m3fn:
        return True
    if dtype == torch.float8_e5m2:
        return True
    return False


def pick_weight_dtype(dtype, fallback_dtype, device=None):
    if dtype is None:
        dtype = fallback_dtype
    elif dtype_size(dtype) > dtype_size(fallback_dtype):
        dtype = fallback_dtype

    if not supports_cast(device, dtype):
        dtype = fallback_dtype

    return dtype


def device_supports_non_blocking(device):
    if torch.jit.is_tracing() or torch.jit.is_scripting():
        return True
    if args.force_non_blocking:
        return True
    if is_device_mps(device):
        return False  # pytorch bug? mps doesn't support non blocking
    if is_intel_xpu():  # xpu does support non blocking but it is slower on iGPUs for some reason so disable by default until situation changes
        return False
    if args.deterministic:  # TODO: figure out why deterministic breaks non blocking from gpu to cpu (previews)
        return False
    if directml_device:
        return False
    return True


def force_channels_last():
    if args.force_channels_last:
        return True

    # TODO
    return False


STREAMS = {}
NUM_STREAMS = 0
if args.async_offload is not None:
    NUM_STREAMS = args.async_offload
else:
    #  Enable by default on Nvidia
    if is_nvidia():
        NUM_STREAMS = 2

if args.disable_async_offload:
    NUM_STREAMS = 0

if NUM_STREAMS > 0:
    logger.debug("Using async weight offloading with {} streams".format(NUM_STREAMS))


def current_stream(device):
    if device is None:
        return None
    if is_device_cuda(device):
        return torch.cuda.current_stream()
    elif is_device_xpu(device):
        return torch.xpu.current_stream()
    else:
        return None


stream_counters = {}


def get_offload_stream(device):
    stream_counter = stream_counters.get(device, 0)
    if NUM_STREAMS == 0:
        return None

    if hasattr(torch.compiler, "is_compiling") and torch.compiler.is_compiling():  # pylint: disable=no-member
        return None

    if device in STREAMS:
        ss = STREAMS[device]
        # Sync the oldest stream in the queue with the current
        ss[stream_counter].wait_stream(current_stream(device))
        stream_counter = (stream_counter + 1) % len(ss)
        stream_counters[device] = stream_counter
        return ss[stream_counter]
    elif is_device_cuda(device):
        ss = []
        for k in range(NUM_STREAMS):
            s1 = torch.cuda.Stream(device=device, priority=0)
            s1.as_context = torch.cuda.stream
            ss.append(s1)
        STREAMS[device] = ss
        s = ss[stream_counter]
        stream_counters[device] = stream_counter
        return s
    elif is_device_xpu(device):
        ss = []
        for k in range(NUM_STREAMS):
            s1 = torch.xpu.Stream(device=device, priority=0)
            s1.as_context = torch.xpu.stream
            ss.append(s1)
        STREAMS[device] = ss
        s = ss[stream_counter]
        stream_counters[device] = stream_counter
        return s
    return None


def sync_stream(device, stream):
    if stream is None or current_stream(device) is None:
        return
    current_stream(device).wait_stream(stream)


def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, stream=None):
    if device is None or weight.device == device:
        if not copy:
            if dtype is None or weight.dtype == dtype:
                return weight
        if stream is not None:
            wf_context = stream
            if hasattr(wf_context, "as_context"):
                wf_context = wf_context.as_context(stream)
            with wf_context:
                return weight.to(dtype=dtype, copy=copy)
        return weight.to(dtype=dtype, copy=copy)

    if stream is not None:
        wf_context = stream
        if hasattr(wf_context, "as_context"):
            wf_context = wf_context.as_context(stream)
        with wf_context:
            r = torch.empty_like(weight, dtype=dtype, device=device)
            r.copy_(weight, non_blocking=non_blocking)
    else:
        r = torch.empty_like(weight, dtype=dtype, device=device)
        r.copy_(weight, non_blocking=non_blocking)
    return r


def cast_to_device(tensor, device, dtype, copy=False):
    non_blocking = device_supports_non_blocking(device)
    return cast_to(tensor, dtype=dtype, device=device, non_blocking=non_blocking, copy=copy)


PINNED_MEMORY = {}
TOTAL_PINNED_MEMORY = 0
MAX_PINNED_MEMORY = -1
if not args.disable_pinned_memory:
    if is_nvidia() or is_amd():
        if WINDOWS:
            MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.45  # Windows limit is apparently 50%
        else:
            MAX_PINNED_MEMORY = get_total_memory(torch.device("cpu")) * 0.95
        logger.debug("Enabled pinned memory {}".format(MAX_PINNED_MEMORY // (1024 * 1024)))

PINNING_ALLOWED_TYPES = set(["Parameter", "QuantizedTensor"])


def pin_memory(tensor):
    global TOTAL_PINNED_MEMORY
    if MAX_PINNED_MEMORY <= 0:
        return False

    if type(tensor).__name__ not in PINNING_ALLOWED_TYPES:
        return False

    if not is_device_cpu(tensor.device):
        return False

    if tensor.is_pinned():
        # NOTE: Cuda does detect when a tensor is already pinned and would
        # error below, but there are proven cases where this also queues an error
        # on the GPU async. So dont trust the CUDA API and guard here
        return False

    if not tensor.is_contiguous():
        return False

    size = tensor.numel() * tensor.element_size()
    if (TOTAL_PINNED_MEMORY + size) > MAX_PINNED_MEMORY:
        return False

    ptr = tensor.data_ptr()
    if ptr == 0:
        return False

    if torch.cuda.cudart().cudaHostRegister(ptr, size, 1) == 0:
        PINNED_MEMORY[ptr] = size
        TOTAL_PINNED_MEMORY += size
        return True

    return False


def unpin_memory(tensor):
    global TOTAL_PINNED_MEMORY
    if MAX_PINNED_MEMORY <= 0:
        return False

    if not is_device_cpu(tensor.device):
        return False

    ptr = tensor.data_ptr()
    size = tensor.numel() * tensor.element_size()

    size_stored = PINNED_MEMORY.get(ptr, None)
    if size_stored is None:
        logger.warning("Tried to unpin tensor not pinned by ComfyUI")
        return False

    if size != size_stored:
        logger.warning("Size of pinned tensor changed")
        return False

    if torch.cuda.cudart().cudaHostUnregister(ptr) == 0:
        TOTAL_PINNED_MEMORY -= PINNED_MEMORY.pop(ptr)
        if len(PINNED_MEMORY) == 0:
            TOTAL_PINNED_MEMORY = 0
        return True

    return False


def sage_attention_enabled():
    return args.use_sage_attention


def flash_attention_enabled():
    return args.use_flash_attention


def xformers_enabled():
    global directml_device
    global cpu_state
    if cpu_state != CPUState.GPU:
        return False
    if is_intel_xpu():
        return False
    if is_ascend_npu():
        return False
    if is_mlu():
        return False
    if is_ixuca():
        return False
    if directml_device:
        return False
    return XFORMERS_IS_AVAILABLE


def flash_attn_enabled():
    global directml_device
    global cpu_state
    if cpu_state != CPUState.GPU:
        return False
    if is_intel_xpu():
        return False
    if directml_device:
        return False
    return flash_attention_enabled()


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_enabled_vae():
    if is_amd():
        return False  # enabling pytorch attention on AMD currently causes crash when doing high res
    return pytorch_attention_enabled()


def pytorch_attention_flash_attention():
    global ENABLE_PYTORCH_ATTENTION
    if ENABLE_PYTORCH_ATTENTION:
        # TODO: more reliable way of checking for flash attention?
        if is_nvidia():
            return True
        if is_intel_xpu():
            return True
        if is_ascend_npu():
            return True
        if is_mlu():
            return True
        if is_amd():
            return True  # if you have pytorch attention enabled on AMD it probably supports at least mem efficient attention
        if is_ixuca():
            return True
    return False


def force_upcast_attention_dtype():
    upcast = args.force_upcast_attention

    macos_version = mac_version()
    if macos_version is not None and ((14, 5) <= macos_version):  # black image bug on recent versions of macOS, I don't think it's ever getting fixed
        upcast = True

    if upcast:
        return {torch.float16: torch.float32}
    else:
        return None


def get_free_memory(dev=None, torch_free_too=False):
    global directml_device
    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_device:
            mem_free_total = 1024 * 1024 * 1024  # TODO
            mem_free_torch = mem_free_total
        elif is_intel_xpu():
            stats = torch.xpu.memory_stats(dev)  # pylint: disable=no-member
            mem_active = stats['active_bytes.all.current']
            mem_reserved = stats['reserved_bytes.all.current']
            mem_free_xpu = torch.xpu.get_device_properties(dev).total_memory - mem_reserved
            mem_free_torch = mem_reserved - mem_active
            mem_free_total = mem_free_xpu + mem_free_torch
        elif is_ascend_npu():
            stats = torch.npu.memory_stats(dev)
            mem_active = stats['active_bytes.all.current']
            mem_reserved = stats['reserved_bytes.all.current']
            mem_free_npu, _ = torch.npu.mem_get_info(dev)
            mem_free_torch = mem_reserved - mem_active
            mem_free_total = mem_free_npu + mem_free_torch
        elif is_mlu():
            stats = torch.mlu.memory_stats(dev)
            mem_active = stats['active_bytes.all.current']
            mem_reserved = stats['reserved_bytes.all.current']
            mem_free_mlu, _ = torch.mlu.mem_get_info(dev)
            mem_free_torch = mem_reserved - mem_active
            mem_free_total = mem_free_mlu + mem_free_torch
        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 cpu_mode():
    global cpu_state
    return cpu_state == CPUState.CPU


def mps_mode():
    global cpu_state
    return cpu_state == CPUState.MPS


def is_device_type(device, type):
    if hasattr(device, 'type'):
        if (device.type == type):
            return True
    return False


def is_device_cpu(device):
    return is_device_type(device, 'cpu')


def is_device_mps(device):
    return is_device_type(device, 'mps')


def is_device_xpu(device):
    return is_device_type(device, 'xpu')


def is_device_cuda(device):
    return is_device_type(device, 'cuda')


def is_directml_enabled():
    global directml_device
    if directml_device is not None:
        return True

    return False


def should_use_fp16(device=None, model_params=0, prioritize_performance=True, manual_cast=False):
    if device is not None:
        if is_device_cpu(device):
            return False

    if args.force_fp16:
        return True

    if FORCE_FP32:
        return False

    if is_directml_enabled():
        return True

    if (device is not None and is_device_mps(device)) or mps_mode():
        return not bfloat16_support_mps(device)

    if cpu_mode():
        return False

    if is_intel_xpu():
        if torch_version_numeric < (2, 3):
            return True
        else:
            return torch.xpu.get_device_properties(device).has_fp16

    if is_ascend_npu():
        return True

    if is_mlu():
        return True

    if is_ixuca():
        return True

    if is_amd():
        return True
    try:
        props = torch.cuda.get_device_properties(device)
        if props.major >= 8:
            return True

        if props.major < 6:
            return False
    except (ValueError, RuntimeError):
        logger.warning("No CUDA devices were present, even though CUDA is available in this torch installation. This assumes the CPU device will be selected for computation")
        return False
    except AssertionError:
        logger.warning("Torch was not compiled with cuda support")
        return False

    # FP16 is confirmed working on a 1080 (GP104) and on latest pytorch actually seems faster than fp32
    nvidia_10_series = ["1080", "1070", "titan x", "p3000", "p3200", "p4000", "p4200", "p5000", "p5200", "p6000", "1060", "1050", "p40", "p100", "p6", "p4"]
    for x in nvidia_10_series:
        if x in props.name.lower():
            if WINDOWS or manual_cast:
                return True
            else:
                return False  # weird linux behavior where fp32 is faster

    if manual_cast:
        free_model_memory = maximum_vram_for_weights(device)
        if (not prioritize_performance) or model_params * 4 > free_model_memory:
            return True

    if props.major < 7:
        return False

    # FP16 is just broken on these cards
    nvidia_16_series = ["1660", "1650", "1630", "T500", "T550", "T600", "MX550", "MX450", "CMP 30HX", "T2000", "T1000", "T1200"]
    for x in nvidia_16_series:
        if x in props.name:
            return False

    return True


def should_use_bf16(device=None, model_params=0, prioritize_performance=True, manual_cast=False):
    if FORCE_BF16:
        return True

    if device is not None:
        if is_device_cpu(device):  # TODO ? bf16 works on CPU but is extremely slow
            return False

    if FORCE_FP32:
        return False

    if directml_device:
        return False

    if (device is not None and is_device_mps(device)) or mps_mode():
        return bfloat16_support_mps(device)

    if cpu_mode():
        return False

    if is_intel_xpu():
        if torch_version_numeric < (2, 3):
            return True
        else:
            return torch.xpu.is_bf16_supported()

    if is_ascend_npu():
        return True

    if is_ixuca():
        return True

    if is_amd():
        arch = torch.cuda.get_device_properties(device).gcnArchName
        if any((a in arch) for a in AMD_RDNA2_AND_OLDER_ARCH):  # RDNA2 and older don't support bf16
            if manual_cast:
                return True
            return False

    try:
        props_major = min(torch.cuda.get_device_properties(torch.device(f"cuda:{i}")).major for i in range(torch.cuda.device_count()))
        if props_major >= 8:
            return True
    except (ValueError, RuntimeError):
        logger.warning("No CUDA devices were present, even though CUDA is available in this torch installation. This assumes the CPU device will be selected for computation")
        return False
    except AssertionError:
        logger.warning("Torch was not compiled with CUDA support")
        return False

    bf16_works = torch.cuda.is_bf16_supported()

    if bf16_works and manual_cast:
        free_model_memory = maximum_vram_for_weights(device)
        if (not prioritize_performance) or model_params * 4 > free_model_memory:
            return True

    return False


def bfloat16_support_mps(device):
    # test bfloat 16
    try:
        x = torch.ones(1, dtype=torch.bfloat16, device=device)
        x = x + 1.0
        _ = repr(x)
        supported = True
        del x
    except:
        supported = False
    return supported


def supports_fp8_compute(device=None):
    if SUPPORT_FP8_OPS:
        return True

    if not is_nvidia():
        return False

    try:
        props = torch.cuda.get_device_properties(device)
    except (RuntimeError, ValueError, AssertionError):
        return False

    if props.major >= 9:
        return True
    if props.major < 8:
        return False
    if props.minor < 9:
        return False

    if torch_version_numeric < (2, 3):
        return False

    if WINDOWS:
        if torch_version_numeric < (2, 4):
            return False

    return True


def extended_fp16_support():
    # TODO: check why some models work with fp16 on newer torch versions but not on older
    if torch_version_numeric < (2, 7):
        return False

    return True


LORA_COMPUTE_DTYPES = {}


def lora_compute_dtype(device):
    dtype = LORA_COMPUTE_DTYPES.get(device, None)
    if dtype is not None:
        return dtype

    if should_use_fp16(device):
        dtype = torch.float16
    else:
        dtype = torch.float32

    LORA_COMPUTE_DTYPES[device] = dtype
    return dtype


def soft_empty_cache(force=False):
    with model_management_lock:
        _soft_empty_cache(force=force)


def _soft_empty_cache(force=False):
    global cpu_state
    if cpu_state == CPUState.MPS:
        torch.mps.empty_cache()  # pylint: disable=no-member
    elif is_intel_xpu():
        torch.xpu.empty_cache()  # pylint: disable=no-member
    elif is_ascend_npu():
        torch.npu.empty_cache()  # pylint: disable=no-member
    elif is_mlu():
        torch.mlu.empty_cache()  # pylint: disable=no-member
    elif torch.cuda.is_available():
        torch.cuda.empty_cache()
        torch.cuda.ipc_collect()


def unload_all_models():
    with model_management_lock:
        free_memory(1e30, get_torch_device())
        trim_memory()


@_deprecate_method(version="*", message="The comfy.model_management.resolve_lowvram_weight function will be removed soon, please stop using it.")
def resolve_lowvram_weight(weight, model, key):
    return weight


def interrupt_current_processing(value=True):
    interruption.interrupt_current_processing(value)


def processing_interrupted():
    interruption.processing_interrupted()


def throw_exception_if_processing_interrupted():
    interruption.throw_exception_if_processing_interrupted()