"""
    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

import psutil
import logging
from enum import Enum
from comfy.cli_args import args, PerformanceFeature
import threading
import torch
import sys
import platform
import weakref
import gc
import os
from contextlib import contextmanager, nullcontext
import comfy.memory_management
import comfy.utils
import comfy.quant_ops
import comfy_aimdo.host_buffer
import comfy_aimdo.vram_buffer
from comfy.internal_logging import detail

from typing import TYPE_CHECKING
if TYPE_CHECKING:
    from comfy.model_patcher import ModelPatcher


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


# Training Related State
in_training = False
training_fp8_bwd = False


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:
    logging.info("Using deterministic algorithms for pytorch")
    torch.use_deterministic_algorithms(True, warn_only=True)

directml_enabled = False
if args.directml is not None:
    logging.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
    directml_enabled = True
    device_index = args.directml
    if device_index < 0:
        directml_device = torch_directml.device()
    else:
        directml_device = torch_directml.device(device_index)
    logging.info("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:
    _ = 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  # noqa: F401
    _ = torch.npu.device_count()
    npu_available = torch.npu.is_available()
except:
    npu_available = False

try:
    import torch_mlu  # 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 is_wsl():
    version = platform.uname().release
    if version.endswith("-Microsoft"):
        return True
    elif version.endswith("microsoft-standard-WSL2"):
        return True
    return False

def get_torch_device():
    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 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:
            return torch.device(torch.cuda.current_device())

def get_all_torch_devices(exclude_current=False):
    global cpu_state
    devices = []
    if cpu_state == CPUState.GPU:
        # NVIDIA + AMD/ROCm both expose their GPUs through torch.cuda.*;
        # without the AMD arm, single-GPU ROCm users get an empty list
        # which silently turns unload_all_models() into a no-op.
        if is_nvidia() or is_amd():
            for i in range(torch.cuda.device_count()):
                devices.append(torch.device("cuda", i))
        elif is_intel_xpu():
            for i in range(torch.xpu.device_count()):
                devices.append(torch.device("xpu", i))
        elif is_ascend_npu():
            for i in range(torch.npu.device_count()):
                devices.append(torch.device("npu", i))
        elif is_mlu():
            for i in range(torch.mlu.device_count()):
                devices.append(torch.device("mlu", i))
        else:
            # Fallback for unhandled GPU backends (e.g. DirectML): at least
            # report the current device so callers like unload_all_models()
            # do not silently no-op.
            devices.append(get_torch_device())
    else:
        devices.append(get_torch_device())
    if exclude_current:
        current = get_torch_device()
        if current in devices:
            devices.remove(current)
    return devices

def get_gpu_device_options():
    """Return list of device option strings for node widgets.

    Always includes "default" and "cpu". When multiple GPUs are present,
    adds "gpu:0", "gpu:1", etc. (vendor-agnostic labels).
    """
    options = ["default", "cpu"]
    devices = get_all_torch_devices()
    if len(devices) > 1:
        for i in range(len(devices)):
            options.append(f"gpu:{i}")
    return options

def get_gpu_device_options_no_cpu():
    """Variant of get_gpu_device_options that omits "cpu".

    Intended for components like the VAE selector where running on CPU
    is impractical and should not be offered as a choice.
    """
    return [o for o in get_gpu_device_options() if o != "cpu"]

def resolve_gpu_device_option(option: str):
    """Resolve a device option string to a torch.device.

    Returns None for "default" (let the caller use its normal default).
    Returns torch.device("cpu") for "cpu".
    For "gpu:N", returns the Nth torch device. Returns None if the
    index is out of range, the option string is malformed, or
    unrecognized (callers are expected to log their own context-rich
    message before falling back to the default device).
    """
    if option is None or option == "default":
        return None
    if option == "cpu":
        return torch.device("cpu")
    if option.startswith("gpu:"):
        try:
            idx = int(option[4:])
        except ValueError:
            return None
        devices = get_all_torch_devices()
        if 0 <= idx < len(devices):
            return devices[idx]
    return None

@contextmanager
def cuda_device_context(device):
    """Context manager that sets torch.cuda.current_device to match *device*.

    Used when running operations on a non-default CUDA device so that custom
    CUDA kernels (e.g. comfy_kitchen fp8 quantization) pick up the correct
    device index.  The previous device is restored on exit.

    No-op when *device* is not CUDA, has no explicit index, or already matches
    the current device.
    """
    prev = None
    if device.type == "cuda" and device.index is not None:
        prev = torch.cuda.current_device()
        if prev != device.index:
            torch.cuda.set_device(device)
        else:
            prev = None
    try:
        yield
    finally:
        if prev is not None:
            torch.cuda.set_device(prev)

def get_total_memory(dev=None, torch_total_too=False):
    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 is_intel_xpu():
            stats = torch.xpu.memory_stats(dev)
            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

total_vram = get_total_memory(get_torch_device()) / (1024 * 1024)
total_ram = psutil.virtual_memory().total / (1024 * 1024)
logging.info("Total VRAM {:0.0f} MB, total RAM {:0.0f} MB".format(total_vram, total_ram))

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

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

try:
    ACCELERATOR_ERROR = torch.AcceleratorError
except AttributeError:
    ACCELERATOR_ERROR = RuntimeError

def is_oom(e):
    if isinstance(e, OOM_EXCEPTION):
        return True
    if isinstance(e, ACCELERATOR_ERROR) and (getattr(e, 'error_code', None) == 2 or "out of memory" in str(e).lower()):
        discard_cuda_async_error()
        return True
    return False

def raise_non_oom(e):
    if not is_oom(e):
        raise e

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_IS_AVAILABLE = xformers._has_cpp_library
        except:
            pass
        try:
            XFORMERS_VERSION = xformers.version.__version__
            logging.info("xformers version: {}".format(XFORMERS_VERSION))
            if XFORMERS_VERSION.startswith("0.0.18"):
                logging.warning("\nWARNING: This version of xformers has a major bug where you will get black images when generating high resolution images.")
                logging.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():
        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", "gfx1035", "gfx1010", "gfx1011", "gfx1012", "gfx906", "gfx900", "gfx803"]
AMD_ENABLE_MIOPEN_ENV = 'COMFYUI_ENABLE_MIOPEN'

try:
    if is_amd():
        arch = torch.cuda.get_device_properties(get_torch_device()).gcnArchName.split(':')[0]
        if not (any((a in arch) for a in AMD_RDNA2_AND_OLDER_ARCH)):
            if os.getenv(AMD_ENABLE_MIOPEN_ENV) != '1':
                torch.backends.cudnn.enabled = False  # Seems to improve things a lot on AMD
                logging.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)

        def aotriton_supported(gpu_arch):
            path = torch.__path__[0]
            path = os.path.join(os.path.join(path, "lib"), "aotriton.images")
            gfx = set(map(lambda a: a[4:], filter(lambda a: a.startswith("amd-gfx"), os.listdir(path))))
            if gpu_arch in gfx:
                return True
            if "{}x".format(gpu_arch[:-1]) in gfx:
                return True
            if "{}xx".format(gpu_arch[:-2]) in gfx:
                return True
            return False

        logging.info("AMD arch: {}".format(arch))
        logging.info("ROCm version: {}".format(rocm_version))
        if args.use_split_cross_attention == False and args.use_quad_cross_attention == False:
            if aotriton_supported(arch):  # AMD efficient attention implementation depends on aotriton.
                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", "gfx950", "gfx1100", "gfx1101", "gfx1150", "gfx1151"]):  # TODO: more arches, TODO: gfx950
                        ENABLE_PYTORCH_ATTENTION = True
                if rocm_version >= (7, 0):
                   if any((a in arch) for a in ["gfx1200", "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
        logging.info("Enabled fp16 accumulation.")
except:
    pass


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

try:
    if torch_version_numeric >= (2, 5):
        torch.backends.cuda.allow_fp16_bf16_reduction_math_sdp(True)
except:
    logging.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
if args.force_fp32:
    logging.info("Forcing FP32, if this improves things please report it.")
    FORCE_FP32 = 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

logging.info(f"Set vram state to: {vram_state.name}")

DISABLE_SMART_MEMORY = args.disable_smart_memory

if DISABLE_SMART_MEMORY:
    logging.info("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:
    logging.info("Device: {}".format(get_torch_device_name(get_torch_device())))
except:
    logging.warning("Could not pick default device.")
try:
    for device in get_all_torch_devices(exclude_current=True):
        logging.info("Device: {}".format(get_torch_device_name(device)))
except:
    pass

current_loaded_models: list[LoadedModel] = []

DIRTY_MMAPS = set()

PIN_PRESSURE_HYSTERESIS = 256 * 1024 * 1024

#Freeing registerables on pressure does imply a GPU sync, so go big on
#the hysteresis so each expensive sync gives us back a good chunk.
REGISTERABLE_PIN_HYSTERESIS = 2048 * 1024 * 1024
WINDOWS_PIN_EVICTION_SWAP_PERCENT = 5.0
WINDOWS_PIN_EVICTION_EMERGENCY_AVAILABLE = 512 * 1024 ** 2

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

def mark_mmap_dirty(storage):
    mmap_refs = getattr(storage, "_comfy_tensor_mmap_refs", None)
    if mmap_refs is not None:
        DIRTY_MMAPS.add(mmap_refs[0])

PIN_SUBSETS = [ "weights", "patches" ]
LOADED_PIN_SUBSETS = [ "weights-loaded", "patches-loaded" ]

def models_for_pin_eviction(active, current_prompt=None):
    for loaded_model in current_loaded_models:
        model = loaded_model.model
        if model is None or not model.is_dynamic():
            continue
        pin_state = model.model.dynamic_pins[model.load_device]
        if ((active is None or pin_state["active"] == active) and
            (current_prompt is None or pin_state["current_prompt"] == current_prompt)):
            yield model

def free_model_pins(size, subsets, current_prompt, active, registrations=False):
    freed_total = 0
    for model in models_for_pin_eviction(active, current_prompt=current_prompt):
        if size <= 0:
            return freed_total
        if registrations:
            freed = model.unregister_inactive_pins(size, subsets=subsets)
        else:
            freed = model.partially_unload_ram(size, subsets=subsets)
        freed_total += freed
        size -= freed
    return freed_total

def pin_eviction_tiers(loaded, evict_active):
    tiers = [
        (PIN_SUBSETS, False, None),
        (LOADED_PIN_SUBSETS, False, None),
        (LOADED_PIN_SUBSETS, True, None),
    ]
    if not loaded:
        tiers.append((PIN_SUBSETS, True, False))
        if evict_active:
            tiers.append((PIN_SUBSETS, True, True))
    return tiers

def registration_eviction_tiers(evict_active):
    subsets = PIN_SUBSETS + LOADED_PIN_SUBSETS
    tiers = [
        (subsets, False, False),
        (subsets, True, False),
    ]
    if evict_active:
        tiers.extend([
            (subsets, False, True),
            (subsets, True, True),
        ])
    return tiers

def free_pins(size, evict_active=False, loaded=False):
    freed = 0
    for subsets, current_prompt, active in pin_eviction_tiers(loaded, evict_active):
        freed += free_model_pins(size - freed, subsets, current_prompt, active)
    return freed

def should_free_pins_for_ram_pressure(shortfall):
    if shortfall <= 0:
        return False
    if not WINDOWS:
        return True
    if psutil.virtual_memory().available < WINDOWS_PIN_EVICTION_EMERGENCY_AVAILABLE:
        return True
    try:
        return psutil.swap_memory().percent >= WINDOWS_PIN_EVICTION_SWAP_PERCENT
    except RuntimeError as err:
        logging.warning("Could not read Windows swap usage; falling back to RAM-pressure pin eviction: %s", err)
        return True

def ensure_pin_budget(size, evict_active=False, loaded=False):
    if args.high_ram:
        return True
    if args.fast_disk:
        shortfall = TOTAL_PINNED_MEMORY + size - MAX_PINNED_MEMORY
    else:
        shortfall = size + max(comfy.memory_management.RAM_CACHE_HEADROOM / 2, 2048 * 1024 ** 2) - psutil.virtual_memory().available
    if shortfall <= 0:
        return True

    to_free = shortfall + PIN_PRESSURE_HYSTERESIS
    return free_pins(to_free, evict_active=evict_active, loaded=loaded) >= shortfall

def free_registrations(shortfall, evict_active=True):
    if MAX_PINNED_MEMORY <= 0:
        return False
    if shortfall <= 0:
        return True

    shortfall += REGISTERABLE_PIN_HYSTERESIS
    for subsets, current_prompt, active in registration_eviction_tiers(evict_active):
        shortfall -= free_model_pins(shortfall, subsets, current_prompt, active, registrations=True)
    return shortfall <= REGISTERABLE_PIN_HYSTERESIS

def ensure_pin_registerable(size, evict_active=True):
    return free_registrations(TOTAL_PINNED_MEMORY + size - MAX_PINNED_MEMORY, evict_active=evict_active)

class LoadedModel:
    def __init__(self, model: ModelPatcher):
        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: ModelPatcher):
        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)
            self._patcher_finalizer.atexit = False

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

    @property
    def model(self):
        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


        self.real_model = weakref.ref(real_model)
        self.model_finalizer = weakref.finalize(real_model, cleanup_models)
        self.model_finalizer.atexit = False
        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 __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
    logging.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 free_memory(memory_required, device, keep_loaded=[], for_dynamic=False, pins_required=0, ram_required=0):
    cleanup_models_gc()
    if not for_dynamic:
        detail("Non dynamic memory free called! memory_required=%s pins_required=%s ram_required=%s", memory_required, pins_required, ram_required)
    unloaded_model = []
    can_unload = []
    unloaded_models = []

    for i in range(len(current_loaded_models) -1, -1, -1):
        shift_model = current_loaded_models[i]
        if device is None or 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

    can_unload_sorted = sorted(can_unload)
    for x in can_unload_sorted:
        i = x[-1]
        memory_to_free = 1e32
        if not DISABLE_SMART_MEMORY or device is None:
            memory_to_free = 0 if device is None else memory_required - get_free_memory(device)
            if current_loaded_models[i].model.is_dynamic() and for_dynamic:
                #don't actually unload dynamic models for the sake of other dynamic models
                #as that works on-demand.
                memory_required -= current_loaded_models[i].model.loaded_size()
                memory_to_free = 0
        if memory_to_free > 0 and current_loaded_models[i].model_unload(memory_to_free):
            logging.debug(f"Unloading {current_loaded_models[i].model.model.__class__.__name__}")
            unloaded_model.append(i)

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

    if not for_dynamic and pins_required > 0:
        ensure_pin_budget(pins_required)
        ensure_pin_registerable(pins_required)

    if len(unloaded_model) > 0:
        soft_empty_cache()
    elif device is not None:
        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

def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimum_memory_required=None, force_full_load=False):
    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())

    # Order-preserving dedup. A plain set() would randomize iteration order across runs
    models_temp = {}
    for m in models:
        models_temp[m] = None
        for mm in m.model_patches_models():
            models_temp[mm] = None

    models = list(models_temp)
    models.reverse()

    models_to_load = []

    free_for_dynamic=True
    for x in models:
        if not x.is_dynamic():
            free_for_dynamic = False
        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:
            if hasattr(x, "model"):
                logging.info(f"Requested to load {x.model.__class__.__name__}")
            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 = {}
    total_pins_required = {}
    for loaded_model in models_to_load:
        device = loaded_model.device
        total_memory_required[device] = total_memory_required.get(device, 0) + loaded_model.model_memory_required(device)
        if not loaded_model.model.is_dynamic():
            total_pins_required[device] = total_pins_required.get(device, 0) + loaded_model.model_memory()

    for device in total_memory_required:
        if device != torch.device("cpu"):
            free_memory(total_memory_required[device] * 1.1 + extra_mem,
                        device,
                        for_dynamic=free_for_dynamic,
                        pins_required=total_pins_required.get(device, 0))

    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, for_dynamic=free_for_dynamic)
                logging.info("{} 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)
        vram_used = 0 if is_device_cpu(torch_dev) else loaded_model.model_loaded_memory()
        ram_used = model.loaded_ram_size() if model.is_dynamic() else loaded_model.model_memory() - vram_used
        detail("Model loaded: patcher=%s model=%s ram_mb=%.1f vram_mb=%.1f", model.__class__.__name__, model.model.__class__.__name__, ram_used / (1024 ** 2), vram_used / (1024 ** 2))
        current_loaded_models.insert(0, loaded_model)
    return

def load_model_gpu(model):
    return load_models_gpu([model])

def loaded_models(only_currently_used=False):
    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():
            logging.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():
                logging.warning("WARNING, memory leak with model {}. Please make sure it is not being referenced from somewhere.".format(cur.real_model().__class__.__name__))


def archive_model_dtypes(model):
    for name, module in model.named_modules():
        for param_name, param in module.named_parameters(recurse=False):
            setattr(module, f"{param_name}_comfy_model_dtype", param.dtype)
        for buf_name, buf in module.named_buffers(recurse=False):
            setattr(module, f"{buf_name}_comfy_model_dtype", buf.dtype)


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_inital_load_device(parameters, dtype):
    cpu_dev = torch.device("cpu")
    if comfy.memory_management.aimdo_enabled:
        return cpu_dev

    torch_dev = get_torch_device()
    if vram_state == VRAMState.HIGH_VRAM or vram_state == VRAMState.SHARED:
        return torch_dev

    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):
    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=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 in (VRAMState.HIGH_VRAM, VRAMState.NORMAL_VRAM) or comfy.memory_management.aimdo_enabled:
        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 comfy.memory_management.aimdo_enabled:
        return offload_device

    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 intermediate_dtype():
    if args.fp16_intermediates:
        return torch.float16
    else:
        return torch.float32

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_enabled: #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 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_enabled:
        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 and AMD
    if is_nvidia() or is_amd():
        NUM_STREAMS = 2

if args.disable_async_offload:
    NUM_STREAMS = 0

if NUM_STREAMS > 0:
    logging.info("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 = {}

STREAM_CAST_BUFFERS = {}
LARGEST_CASTED_WEIGHT = (None, 0)
STREAM_AIMDO_CAST_BUFFERS = {}
LARGEST_AIMDO_CASTED_WEIGHT = (None, 0)

DEFAULT_AIMDO_CAST_BUFFER_RESERVATION_SIZE = 16 * 1024 ** 3

def get_cast_buffer(offload_stream, device, size, ref):
    global LARGEST_CASTED_WEIGHT

    if offload_stream is not None:
        wf_context = offload_stream
        if hasattr(wf_context, "as_context"):
            wf_context = wf_context.as_context(offload_stream)
    else:
        wf_context = nullcontext()

    cast_buffer = STREAM_CAST_BUFFERS.get(offload_stream, None)
    if cast_buffer is None or cast_buffer.numel() < size:
        if ref is LARGEST_CASTED_WEIGHT[0]:
            #If there is one giant weight we do not want both streams to
            #allocate a buffer for it. It's up to the caster to get the other
            #offload stream in this corner case
            return None
        if cast_buffer is not None and cast_buffer.numel() > 50 * (1024 ** 2):
            #I want my wrongly sized 50MB+ of VRAM back from the caching allocator right now
            synchronize()
            del STREAM_CAST_BUFFERS[offload_stream]
            del cast_buffer
            soft_empty_cache()
        with wf_context:
            cast_buffer = torch.empty((size), dtype=torch.int8, device=device)
            STREAM_CAST_BUFFERS[offload_stream] = cast_buffer

        if  size > LARGEST_CASTED_WEIGHT[1]:
            LARGEST_CASTED_WEIGHT = (ref, size)

    return cast_buffer

def get_aimdo_cast_buffer(offload_stream, device):
    cast_buffer = STREAM_AIMDO_CAST_BUFFERS.get(offload_stream, None)
    if cast_buffer is None:
        cast_buffer = comfy_aimdo.vram_buffer.VRAMBuffer(DEFAULT_AIMDO_CAST_BUFFER_RESERVATION_SIZE, device.index)
        STREAM_AIMDO_CAST_BUFFERS[offload_stream] = cast_buffer
    return cast_buffer

def reset_cast_buffers():
    global LARGEST_CASTED_WEIGHT
    global LARGEST_AIMDO_CASTED_WEIGHT

    LARGEST_CASTED_WEIGHT = (None, 0)
    LARGEST_AIMDO_CASTED_WEIGHT = (None, 0)
    for offload_stream in set(STREAM_CAST_BUFFERS) | set(STREAM_AIMDO_CAST_BUFFERS):
        if offload_stream is not None:
            offload_stream.synchronize()
    synchronize()

    for mmap_obj in DIRTY_MMAPS:
        mmap_obj.bounce()
    DIRTY_MMAPS.clear()

    for loaded_model in current_loaded_models:
        model = loaded_model.model
        if model is not None and model.is_dynamic():
            pin_state = model.model.dynamic_pins[model.load_device]

            if pin_state["active"]:
                for subset in ("weights", "weights-loaded"):
                    *_, buckets = pin_state[subset]
                    for size, bucket in list(buckets.items()):
                        bucket[:] = [ entry for entry in bucket if entry[-1] is not None ]
                        if not bucket:
                            del buckets[size]

            pin_state["active"] = False
            model.partially_unload_ram(1e30, subsets=[ "patches", "patches-loaded" ])
            for subset in ("patches", "patches-loaded"):
                pin_state[subset] = (comfy_aimdo.host_buffer.HostBuffer(0, 8 * 1024 * 1024, pinned_hostbuf_size(model.model_size())), [], [-1], [0], [0], {})

    STREAM_CAST_BUFFERS.clear()
    STREAM_AIMDO_CAST_BUFFERS.clear()
    soft_empty_cache()

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

    if torch.compiler.is_compiling():
        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_gathered(tensors, r, non_blocking=False, stream=None, r2=None):
    wf_context = nullcontext()
    if stream is not None:
       wf_context = stream
       if hasattr(wf_context, "as_context"):
           wf_context = wf_context.as_context(stream)

    dest_views = comfy.memory_management.interpret_gathered_like(tensors, r) if r is not None else [None] * len(tensors)
    dest2_views = comfy.memory_management.interpret_gathered_like(tensors, r2) if r2 is not None else None
    with wf_context:
        for tensor in tensors:
            dest_view = dest_views.pop(0)
            dest2_view = dest2_views.pop(0) if dest2_views is not None else None
            if tensor is None:
                continue
            if comfy.memory_management.read_tensor_file_slice_into(tensor, dest_view, stream=stream, destination2=dest2_view):
                continue
            storage = tensor._qdata.untyped_storage() if isinstance(tensor, comfy.quant_ops.QuantizedTensor) else tensor.untyped_storage()
            mark_mmap_dirty(storage)
            if dest_view is not None:
                dest_view.copy_(tensor, non_blocking=non_blocking)
            if dest2_view is not None:
                dest2_view.copy_(tensor if dest_view is None else dest_view, non_blocking=non_blocking)


def cast_to(weight, dtype=None, device=None, non_blocking=False, copy=False, stream=None, r=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:
            if r is None:
                r = torch.empty_like(weight, dtype=dtype, device=device)
            r.copy_(weight, non_blocking=non_blocking)
    else:
        if r is None:
            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

def get_disk_swap_total():
    if not os.path.exists("/proc/swaps"):
        return 0

    total = 0
    try:
        with open("/proc/swaps", encoding="utf-8") as swaps:
            next(swaps, None)
            for line in swaps:
                filename, _, size, _, _ = line.rsplit(maxsplit=4)
                if os.path.basename(os.path.realpath(filename)).startswith("zram"):
                    continue
                total += int(size) * 1024
    except:
        logging.warning("Could not get amount of swap memory on system.")
    return total

if not args.disable_pinned_memory:
    if is_nvidia() or is_amd():
        ram = get_total_memory(torch.device("cpu"))
        if WINDOWS:
            MAX_PINNED_MEMORY = ram * 0.40  # Windows limit is apparently 50%
        else:
            MAX_PINNED_MEMORY = max(ram * 0.40, min(ram * 0.90, ram - 4 * 1024 ** 3, ram + get_disk_swap_total() - 16 * 1024 ** 3))
        logging.info("Enabled pinned memory {}".format(MAX_PINNED_MEMORY // (1024 * 1024)))

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

def pinned_hostbuf_size(size):
    if args.high_ram:
        return max(0, int(size * 2))
    return max(0, int(min(size, MAX_PINNED_MEMORY) * 2))

def discard_cuda_async_error():
    try:
        a = torch.tensor([1], dtype=torch.uint8, device=get_torch_device())
        b = torch.tensor([1], dtype=torch.uint8, device=get_torch_device())
        _ = a + b
        synchronize()
    except RuntimeError:
        #Dump it! We already know about it from the synchronous return
        pass

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.nbytes
    comfy.memory_management.extra_ram_release(comfy.memory_management.RAM_CACHE_HEADROOM)
    ensure_pin_registerable(size)

    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
    else:
        logging.warning("Pin error.")
        discard_cuda_async_error()

    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.nbytes

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

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

    if torch.cuda.cudart().cudaHostUnregister(ptr) == 0:
        size = PINNED_MEMORY.pop(ptr)
        TOTAL_PINNED_MEMORY -= size
        return True
    else:
        logging.warning("Unpin error.")
        discard_cuda_async_error()

    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_enabled
    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_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_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_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 is_intel_xpu():
            stats = torch.xpu.memory_stats(dev)
            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 set_torch_device(device):
    """Set the current device for the given torch device. Supports CUDA and XPU."""
    if is_device_cuda(device):
        torch.cuda.set_device(device)
    elif is_device_xpu(device):
        torch.xpu.set_device(device)

def is_directml_enabled():
    global directml_enabled
    if directml_enabled:
        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 True

    if cpu_mode():
        return False

    if is_intel_xpu():
        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 torch.version.hip:
        return True

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

    if props.major < 6:
        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 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_enabled:
        return False

    if (device is not None and is_device_mps(device)) or mps_mode():
        if mac_version() < (14,):
            return False
        return True

    if cpu_mode():
        return False

    if is_intel_xpu():
        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

    props = torch.cuda.get_device_properties(device)

    if is_mlu():
        if props.major > 3:
            return True

    if props.major >= 8:
        return True

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

    if not is_nvidia():
        return False

    props = torch.cuda.get_device_properties(device)
    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 supports_nvfp4_compute(device=None):
    if not is_nvidia():
        return False

    props = torch.cuda.get_device_properties(device)
    if props.major < 10:
        return False

    return True

def supports_mxfp8_compute(device=None):
    if not is_nvidia():
        return False

    if torch_version_numeric < (2, 10):
        return False

    props = torch.cuda.get_device_properties(device)
    if props.major < 10:
        return False

    return True

def supports_fp64(device=None):
    if is_device_mps(device):
        return False

    if is_intel_xpu():
        return False

    if is_directml_enabled():
        return False

    if is_ixuca():
        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 synchronize():
    if cpu_mode():
        return
    if is_intel_xpu():
        torch.xpu.synchronize()
    elif torch.cuda.is_available():
        torch.cuda.synchronize()

def soft_empty_cache(force=False):
    if cpu_mode():
        return
    global cpu_state
    if cpu_state == CPUState.MPS:
        torch.mps.empty_cache()
    elif is_intel_xpu():
        torch.xpu.synchronize()
        torch.xpu.empty_cache()
    elif is_ascend_npu():
        torch.npu.empty_cache()
    elif is_mlu():
        torch.mlu.empty_cache()
    elif torch.cuda.is_available():
        torch.cuda.synchronize()
        torch.cuda.empty_cache()
        torch.cuda.ipc_collect()

def unload_all_models():
    for device in get_all_torch_devices():
        free_memory(1e30, device)

def unload_model_and_clones(model: ModelPatcher, unload_additional_models=True, all_devices=False):
    'Unload only model and its clones - primarily for multigpu cloning purposes.'
    initial_keep_loaded: list[LoadedModel] = current_loaded_models.copy()
    additional_models = []
    if unload_additional_models:
        additional_models = model.get_nested_additional_models()
    keep_loaded = []
    for loaded_model in initial_keep_loaded:
        if loaded_model.model is not None:
            if model.clone_base_uuid == loaded_model.model.clone_base_uuid:
                continue
            # check additional models if they are a match
            skip = False
            for add_model in additional_models:
                if add_model.clone_base_uuid == loaded_model.model.clone_base_uuid:
                    skip = True
                    break
            if skip:
                continue
        keep_loaded.append(loaded_model)
    if not all_devices:
        free_memory(1e30, get_torch_device(), keep_loaded)
    else:
        for device in get_all_torch_devices():
            free_memory(1e30, device, keep_loaded)

def debug_memory_summary():
    if is_amd() or is_nvidia():
        return torch.cuda.memory.memory_summary()
    return ""

class InterruptProcessingException(BaseException):
    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()
