305 lines
8.4 KiB
Python
305 lines
8.4 KiB
Python
import psutil
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from enum import Enum
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from cli_args import args
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class VRAMState(Enum):
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CPU = 0
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NO_VRAM = 1
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LOW_VRAM = 2
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NORMAL_VRAM = 3
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HIGH_VRAM = 4
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MPS = 5
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# Determine VRAM State
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vram_state = VRAMState.NORMAL_VRAM
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set_vram_to = VRAMState.NORMAL_VRAM
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total_vram = 0
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total_vram_available_mb = -1
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accelerate_enabled = False
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try:
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import torch
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total_vram = torch.cuda.mem_get_info(torch.cuda.current_device())[1] / (1024 * 1024)
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total_ram = psutil.virtual_memory().total / (1024 * 1024)
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if not args.normalvram and not args.cpu:
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if total_vram <= 4096:
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print("Trying to enable lowvram mode because your GPU seems to have 4GB or less. If you don't want this use: --normalvram")
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set_vram_to = VRAMState.LOW_VRAM
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elif total_vram > total_ram * 1.1 and total_vram > 14336:
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print("Enabling highvram mode because your GPU has more vram than your computer has ram. If you don't want this use: --normalvram")
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vram_state = VRAMState.HIGH_VRAM
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except:
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pass
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try:
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OOM_EXCEPTION = torch.cuda.OutOfMemoryError
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except:
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OOM_EXCEPTION = Exception
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if args.disable_xformers:
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XFORMERS_IS_AVAILABLE = False
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else:
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try:
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import xformers
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import xformers.ops
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XFORMERS_IS_AVAILABLE = True
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except:
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XFORMERS_IS_AVAILABLE = False
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ENABLE_PYTORCH_ATTENTION = args.use_pytorch_cross_attention
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if ENABLE_PYTORCH_ATTENTION:
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torch.backends.cuda.enable_math_sdp(True)
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torch.backends.cuda.enable_flash_sdp(True)
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torch.backends.cuda.enable_mem_efficient_sdp(True)
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XFORMERS_IS_AVAILABLE = False
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if args.lowvram:
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set_vram_to = VRAMState.LOW_VRAM
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elif args.novram:
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set_vram_to = VRAMState.NO_VRAM
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elif args.highvram:
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vram_state = VRAMState.HIGH_VRAM
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if set_vram_to in (VRAMState.LOW_VRAM, VRAMState.NO_VRAM):
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try:
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import accelerate
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accelerate_enabled = True
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vram_state = set_vram_to
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except Exception as e:
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import traceback
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print(traceback.format_exc())
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print("ERROR: COULD NOT ENABLE LOW VRAM MODE.")
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total_vram_available_mb = (total_vram - 1024) // 2
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total_vram_available_mb = int(max(256, total_vram_available_mb))
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try:
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if torch.backends.mps.is_available():
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vram_state = VRAMState.MPS
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except:
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pass
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if args.cpu:
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vram_state = VRAMState.CPU
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print(f"Set vram state to: {vram_state.name}")
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current_loaded_model = None
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current_gpu_controlnets = []
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model_accelerated = False
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def unload_model():
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global current_loaded_model
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global model_accelerated
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global current_gpu_controlnets
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global vram_state
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if current_loaded_model is not None:
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if model_accelerated:
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accelerate.hooks.remove_hook_from_submodules(current_loaded_model.model)
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model_accelerated = False
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#never unload models from GPU on high vram
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if vram_state != VRAMState.HIGH_VRAM:
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current_loaded_model.model.cpu()
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current_loaded_model.unpatch_model()
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current_loaded_model = None
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if vram_state != VRAMState.HIGH_VRAM:
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if len(current_gpu_controlnets) > 0:
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for n in current_gpu_controlnets:
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n.cpu()
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current_gpu_controlnets = []
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def load_model_gpu(model):
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global current_loaded_model
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global vram_state
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global model_accelerated
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if model is current_loaded_model:
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return
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unload_model()
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try:
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real_model = model.patch_model()
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except Exception as e:
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model.unpatch_model()
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raise e
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current_loaded_model = model
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if vram_state == VRAMState.CPU:
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pass
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elif vram_state == VRAMState.MPS:
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mps_device = torch.device("mps")
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real_model.to(mps_device)
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pass
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elif vram_state == VRAMState.NORMAL_VRAM or vram_state == VRAMState.HIGH_VRAM:
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model_accelerated = False
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real_model.cuda()
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else:
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if vram_state == VRAMState.NO_VRAM:
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device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "256MiB", "cpu": "16GiB"})
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elif vram_state == VRAMState.LOW_VRAM:
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device_map = accelerate.infer_auto_device_map(real_model, max_memory={0: "{}MiB".format(total_vram_available_mb), "cpu": "16GiB"})
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accelerate.dispatch_model(real_model, device_map=device_map, main_device="cuda")
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model_accelerated = True
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return current_loaded_model
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def load_controlnet_gpu(models):
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global current_gpu_controlnets
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global vram_state
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if vram_state == VRAMState.CPU:
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return
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if vram_state == VRAMState.LOW_VRAM or vram_state == VRAMState.NO_VRAM:
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#don't load controlnets like this if low vram because they will be loaded right before running and unloaded right after
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return
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for m in current_gpu_controlnets:
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if m not in models:
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m.cpu()
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device = get_torch_device()
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current_gpu_controlnets = []
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for m in models:
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current_gpu_controlnets.append(m.to(device))
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def load_if_low_vram(model):
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global vram_state
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if vram_state == VRAMState.LOW_VRAM or vram_state == VRAMState.NO_VRAM:
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return model.cuda()
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return model
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def unload_if_low_vram(model):
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global vram_state
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if vram_state == VRAMState.LOW_VRAM or vram_state == VRAMState.NO_VRAM:
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return model.cpu()
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return model
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def get_torch_device():
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if vram_state == VRAMState.MPS:
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return torch.device("mps")
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if vram_state == VRAMState.CPU:
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return torch.device("cpu")
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else:
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return torch.cuda.current_device()
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def get_autocast_device(dev):
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if hasattr(dev, 'type'):
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return dev.type
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return "cuda"
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def xformers_enabled():
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if vram_state == VRAMState.CPU:
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return False
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return XFORMERS_IS_AVAILABLE
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def xformers_enabled_vae():
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enabled = xformers_enabled()
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if not enabled:
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return False
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try:
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#0.0.18 has a bug where Nan is returned when inputs are too big (1152x1920 res images and above)
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if xformers.version.__version__ == "0.0.18":
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return False
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except:
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pass
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return enabled
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def pytorch_attention_enabled():
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return ENABLE_PYTORCH_ATTENTION
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def get_free_memory(dev=None, torch_free_too=False):
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if dev is None:
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dev = get_torch_device()
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if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'):
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mem_free_total = psutil.virtual_memory().available
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mem_free_torch = mem_free_total
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else:
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stats = torch.cuda.memory_stats(dev)
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mem_active = stats['active_bytes.all.current']
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mem_reserved = stats['reserved_bytes.all.current']
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mem_free_cuda, _ = torch.cuda.mem_get_info(dev)
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mem_free_torch = mem_reserved - mem_active
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mem_free_total = mem_free_cuda + mem_free_torch
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if torch_free_too:
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return (mem_free_total, mem_free_torch)
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else:
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return mem_free_total
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def maximum_batch_area():
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global vram_state
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if vram_state == VRAMState.NO_VRAM:
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return 0
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memory_free = get_free_memory() / (1024 * 1024)
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area = ((memory_free - 1024) * 0.9) / (0.6)
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return int(max(area, 0))
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def cpu_mode():
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global vram_state
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return vram_state == VRAMState.CPU
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def mps_mode():
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global vram_state
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return vram_state == VRAMState.MPS
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def should_use_fp16():
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if cpu_mode() or mps_mode():
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return False #TODO ?
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if torch.cuda.is_bf16_supported():
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return True
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props = torch.cuda.get_device_properties("cuda")
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if props.major < 7:
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return False
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#FP32 is faster on those cards?
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nvidia_16_series = ["1660", "1650", "1630", "T500", "T550", "T600"]
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for x in nvidia_16_series:
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if x in props.name:
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return False
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return True
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#TODO: might be cleaner to put this somewhere else
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import threading
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class InterruptProcessingException(Exception):
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pass
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interrupt_processing_mutex = threading.RLock()
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interrupt_processing = False
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def interrupt_current_processing(value=True):
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global interrupt_processing
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global interrupt_processing_mutex
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with interrupt_processing_mutex:
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interrupt_processing = value
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def processing_interrupted():
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global interrupt_processing
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global interrupt_processing_mutex
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with interrupt_processing_mutex:
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return interrupt_processing
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def throw_exception_if_processing_interrupted():
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global interrupt_processing
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global interrupt_processing_mutex
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with interrupt_processing_mutex:
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if interrupt_processing:
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interrupt_processing = False
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raise InterruptProcessingException()
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