import psutil import logging from enum import Enum from comfy.cli_args import args import torch import sys import platform class VRAMState(Enum): DISABLED = 0 #No vram present: no need to move models to vram NO_VRAM = 1 #Very low vram: enable all the options to save vram LOW_VRAM = 2 NORMAL_VRAM = 3 HIGH_VRAM = 4 SHARED = 5 #No dedicated vram: memory shared between CPU and GPU but models still need to be moved between both. class CPUState(Enum): GPU = 0 CPU = 1 MPS = 2 # Determine VRAM State vram_state = VRAMState.NORMAL_VRAM set_vram_to = VRAMState.NORMAL_VRAM cpu_state = CPUState.GPU total_vram = 0 lowvram_available = True xpu_available = False 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: 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: import intel_extension_for_pytorch as ipex if torch.xpu.is_available(): xpu_available = True except: pass try: if torch.backends.mps.is_available(): cpu_state = CPUState.MPS import torch.mps except: pass 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 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()) else: return torch.device(torch.cuda.current_device()) 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_torch = mem_reserved mem_total = torch.xpu.get_device_properties(dev).total_memory else: stats = torch.cuda.memory_stats(dev) mem_reserved = stats['reserved_bytes.all.current'] _, mem_total_cuda = torch.cuda.mem_get_info(dev) mem_total_torch = mem_reserved mem_total = mem_total_cuda if torch_total_too: return (mem_total, mem_total_torch) else: return mem_total total_vram = get_total_memory(get_torch_device()) / (1024 * 1024) total_ram = psutil.virtual_memory().total / (1024 * 1024) 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.__version__)) except: pass try: OOM_EXCEPTION = torch.cuda.OutOfMemoryError except: OOM_EXCEPTION = Exception XFORMERS_VERSION = "" XFORMERS_ENABLED_VAE = True if args.disable_xformers: XFORMERS_IS_AVAILABLE = False else: try: import xformers import xformers.ops XFORMERS_IS_AVAILABLE = True try: XFORMERS_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 ENABLE_PYTORCH_ATTENTION = False if args.use_pytorch_cross_attention: ENABLE_PYTORCH_ATTENTION = True XFORMERS_IS_AVAILABLE = False VAE_DTYPES = [torch.float32] try: if is_nvidia(): torch_version = torch.version.__version__ if int(torch_version[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 torch.cuda.is_bf16_supported() and torch.cuda.get_device_properties(torch.cuda.current_device()).major >= 8: VAE_DTYPES = [torch.bfloat16] + VAE_DTYPES if is_intel_xpu(): if args.use_split_cross_attention == False and args.use_quad_cross_attention == False: ENABLE_PYTORCH_ATTENTION = True except: pass if is_intel_xpu(): VAE_DTYPES = [torch.bfloat16] + VAE_DTYPES if args.cpu_vae: VAE_DTYPES = [torch.float32] 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) 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 if args.force_fp32: logging.info("Forcing FP32, if this improves things please report it.") FORCE_FP32 = True if args.force_fp16: logging.info("Forcing FP16.") FORCE_FP16 = 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) else: return "{}".format(device.type) elif is_intel_xpu(): return "{} {}".format(device, torch.xpu.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.") current_loaded_models = [] 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): self.model = model self.device = model.load_device self.weights_loaded = False self.real_model = None self.currently_used = True def model_memory(self): return self.model.model_size() def model_memory_required(self, device): if device == self.model.current_loaded_device(): return 0 else: return self.model_memory() def model_load(self, lowvram_model_memory=0, force_patch_weights=False): patch_model_to = self.device self.model.model_patches_to(self.device) self.model.model_patches_to(self.model.model_dtype()) load_weights = not self.weights_loaded try: if lowvram_model_memory > 0 and load_weights: self.real_model = self.model.patch_model_lowvram(device_to=patch_model_to, lowvram_model_memory=lowvram_model_memory, force_patch_weights=force_patch_weights) else: self.real_model = self.model.patch_model(device_to=patch_model_to, patch_weights=load_weights) except Exception as e: self.model.unpatch_model(self.model.offload_device) self.model_unload() raise e if is_intel_xpu() and not args.disable_ipex_optimize: self.real_model = ipex.optimize(self.real_model.eval(), graph_mode=True, concat_linear=True) self.weights_loaded = True return self.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, unpatch_weights=True): self.model.unpatch_model(self.model.offload_device, unpatch_weights=unpatch_weights) self.model.model_patches_to(self.model.offload_device) self.weights_loaded = self.weights_loaded and not unpatch_weights self.real_model = None def __eq__(self, other): return self.model is other.model def minimum_inference_memory(): return (1024 * 1024 * 1024) * 1.2 def unload_model_clones(model, unload_weights_only=True, force_unload=True): to_unload = [] for i in range(len(current_loaded_models)): if model.is_clone(current_loaded_models[i].model): to_unload = [i] + to_unload if len(to_unload) == 0: return True same_weights = 0 for i in to_unload: if model.clone_has_same_weights(current_loaded_models[i].model): same_weights += 1 if same_weights == len(to_unload): unload_weight = False else: unload_weight = True if not force_unload: if unload_weights_only and unload_weight == False: return None for i in to_unload: logging.debug("unload clone {} {}".format(i, unload_weight)) current_loaded_models.pop(i).model_unload(unpatch_weights=unload_weight) return unload_weight def free_memory(memory_required, device, keep_loaded=[]): 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: can_unload.append((sys.getrefcount(shift_model.model), shift_model.model_memory(), i)) shift_model.currently_used = False for x in sorted(can_unload): i = x[-1] if not DISABLE_SMART_MEMORY: if get_free_memory(device) > memory_required: break current_loaded_models[i].model_unload() 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 def load_models_gpu(models, memory_required=0, force_patch_weights=False, minimum_memory_required=None): global vram_state inference_memory = minimum_inference_memory() extra_mem = max(inference_memory, memory_required) if minimum_memory_required is None: minimum_memory_required = extra_mem else: minimum_memory_required = max(inference_memory, minimum_memory_required) models = set(models) models_to_load = [] models_already_loaded = [] for x in models: loaded_model = LoadedModel(x) loaded = None 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] if loaded.should_reload_model(force_patch_weights=force_patch_weights): #TODO: cleanup this model reload logic current_loaded_models.pop(loaded_model_index).model_unload(unpatch_weights=True) loaded = None else: loaded.currently_used = True models_already_loaded.append(loaded) if loaded is None: if hasattr(x, "model"): logging.info(f"Requested to load {x.model.__class__.__name__}") models_to_load.append(loaded_model) if len(models_to_load) == 0: devs = set(map(lambda a: a.device, models_already_loaded)) for d in devs: if d != torch.device("cpu"): free_memory(extra_mem, d, models_already_loaded) free_mem = get_free_memory(d) if free_mem < minimum_memory_required: logging.info("Unloading models for lowram load.") #TODO: partial model unloading when this case happens, also handle the opposite case where models can be unlowvramed. models_to_load = free_memory(minimum_memory_required, d) logging.info("{} models unloaded.".format(len(models_to_load))) if len(models_to_load) == 0: return logging.info(f"Loading {len(models_to_load)} new model{'s' if len(models_to_load) > 1 else ''}") total_memory_required = {} for loaded_model in models_to_load: if unload_model_clones(loaded_model.model, unload_weights_only=True, force_unload=False) == True:#unload clones where the weights are different 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"): free_memory(total_memory_required[device] * 1.3 + extra_mem, device, models_already_loaded) for loaded_model in models_to_load: weights_unloaded = unload_model_clones(loaded_model.model, unload_weights_only=False, force_unload=False) #unload the rest of the clones where the weights can stay loaded if weights_unloaded is not None: loaded_model.weights_loaded = not weights_unloaded 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): model_size = loaded_model.model_memory_required(torch_dev) current_free_mem = get_free_memory(torch_dev) lowvram_model_memory = max(64 * (1024 * 1024), (current_free_mem - minimum_memory_required), min(current_free_mem * 0.4, current_free_mem - minimum_inference_memory())) if model_size <= lowvram_model_memory: #only switch to lowvram if really necessary lowvram_model_memory = 0 if vram_set_state == VRAMState.NO_VRAM: lowvram_model_memory = 64 * 1024 * 1024 cur_loaded_model = loaded_model.model_load(lowvram_model_memory, force_patch_weights=force_patch_weights) 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(keep_clone_weights_loaded=False): to_delete = [] for i in range(len(current_loaded_models)): if sys.getrefcount(current_loaded_models[i].model) <= 2: if not keep_clone_weights_loaded: to_delete = [i] + to_delete #TODO: find a less fragile way to do this. elif sys.getrefcount(current_loaded_models[i].real_model) <= 3: #references from .real_model + the .model to_delete = [i] + to_delete for i in to_delete: x = current_loaded_models.pop(i) x.model_unload() 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): torch_dev = get_torch_device() if vram_state == VRAMState.HIGH_VRAM: return torch_dev cpu_dev = torch.device("cpu") if DISABLE_SMART_MEMORY: 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]): 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 fp8_dtype = None try: for dtype in [torch.float8_e4m3fn, torch.float8_e5m2]: if dtype in supported_dtypes: fp8_dtype = dtype break except: pass if fp8_dtype is not None: free_model_memory = maximum_vram_for_weights(device) if model_params * 2 > free_model_memory: return fp8_dtype 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: 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 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_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.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=[]): global VAE_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, prioritize_performance=False): return d if d in VAE_DTYPES: return d return VAE_DTYPES[0] 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 is_device_mps(device): return False #pytorch bug? mps doesn't support non blocking if is_intel_xpu(): 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 device_should_use_non_blocking(device): if not device_supports_non_blocking(device): return False return False # return True #TODO: figure out why this causes memory issues on Nvidia and possibly others def force_channels_last(): if args.force_channels_last: return True #TODO return False def cast_to_device(tensor, device, dtype, copy=False): device_supports_cast = False if tensor.dtype == torch.float32 or tensor.dtype == torch.float16: device_supports_cast = True elif tensor.dtype == torch.bfloat16: if hasattr(device, 'type') and device.type.startswith("cuda"): device_supports_cast = True elif is_intel_xpu(): device_supports_cast = True non_blocking = device_should_use_non_blocking(device) if device_supports_cast: if copy: if tensor.device == device: return tensor.to(dtype, copy=copy, non_blocking=non_blocking) return tensor.to(device, copy=copy, non_blocking=non_blocking).to(dtype, non_blocking=non_blocking) else: return tensor.to(device, non_blocking=non_blocking).to(dtype, non_blocking=non_blocking) else: return tensor.to(device, dtype, copy=copy, non_blocking=non_blocking) def xformers_enabled(): global directml_enabled global cpu_state if cpu_state != CPUState.GPU: return False if is_intel_xpu(): return False if directml_enabled: return False return XFORMERS_IS_AVAILABLE def xformers_enabled_vae(): enabled = xformers_enabled() if not enabled: return False return XFORMERS_ENABLED_VAE def pytorch_attention_enabled(): global ENABLE_PYTORCH_ATTENTION return ENABLE_PYTORCH_ATTENTION def pytorch_attention_flash_attention(): global ENABLE_PYTORCH_ATTENTION if ENABLE_PYTORCH_ATTENTION: #TODO: more reliable way of checking for flash attention? if is_nvidia(): #pytorch flash attention only works on Nvidia return True if is_intel_xpu(): return True return False def force_upcast_attention_dtype(): upcast = args.force_upcast_attention try: if platform.mac_ver()[0] in ['14.5']: #black image bug on OSX Sonoma 14.5 upcast = True except: pass if upcast: return 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_torch = mem_reserved - mem_active mem_free_xpu = torch.xpu.get_device_properties(dev).total_memory - mem_reserved mem_free_total = mem_free_xpu + 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_cuda(device): return is_device_type(device, 'cuda') def should_use_fp16(device=None, model_params=0, prioritize_performance=True, manual_cast=False): global directml_enabled if device is not None: if is_device_cpu(device): return False if FORCE_FP16: return True if device is not None: if is_device_mps(device): return True if FORCE_FP32: return False if directml_enabled: return False if mps_mode(): return True if cpu_mode(): return False if is_intel_xpu(): return True if torch.version.hip: return True props = torch.cuda.get_device_properties("cuda") if props.major >= 8: return True if props.major < 6: return False fp16_works = False #FP16 is confirmed working on a 1080 (GP104) but it's a bit slower than FP32 so it should only be enabled #when the model doesn't actually fit on the card #TODO: actually test if GP106 and others have the same type of behavior 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(): fp16_works = True if fp16_works or 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 device is not None: if is_device_mps(device): return True if FORCE_FP32: return False if directml_enabled: return False if mps_mode(): return True if cpu_mode(): return False if is_intel_xpu(): return True props = torch.cuda.get_device_properties("cuda") if props.major >= 8: return True bf16_works = torch.cuda.is_bf16_supported() if bf16_works or 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 soft_empty_cache(force=False): global cpu_state if cpu_state == CPUState.MPS: torch.mps.empty_cache() elif is_intel_xpu(): torch.xpu.empty_cache() elif torch.cuda.is_available(): if force or is_nvidia(): #This seems to make things worse on ROCm so I only do it for cuda torch.cuda.empty_cache() torch.cuda.ipc_collect() def unload_all_models(): free_memory(1e30, get_torch_device()) def resolve_lowvram_weight(weight, model, key): #TODO: remove print("WARNING: The comfy.model_management.resolve_lowvram_weight function will be removed soon, please stop using it.") return weight #TODO: might be cleaner to put this somewhere else import threading class InterruptProcessingException(Exception): pass interrupt_processing_mutex = threading.RLock() interrupt_processing = False def interrupt_current_processing(value=True): global interrupt_processing global interrupt_processing_mutex with interrupt_processing_mutex: interrupt_processing = value def processing_interrupted(): global interrupt_processing global interrupt_processing_mutex with interrupt_processing_mutex: return interrupt_processing def throw_exception_if_processing_interrupted(): global interrupt_processing global interrupt_processing_mutex with interrupt_processing_mutex: if interrupt_processing: interrupt_processing = False raise InterruptProcessingException()