import sys import contextlib from functools import lru_cache import torch from modules import errors if sys.platform == "darwin": from modules import mac_specific def has_mps() -> bool: if sys.platform != "darwin": return False else: return mac_specific.has_mps def get_cuda_device_string(): from modules import shared if shared.cmd_opts.device_id is not None: return f"cuda:{shared.cmd_opts.device_id}" return "cuda" def get_optimal_device_name(): if torch.cuda.is_available(): return get_cuda_device_string() if has_mps(): return "mps" return "cpu" def get_optimal_device(): return torch.device(get_optimal_device_name()) def get_device_for(task): from modules import shared if task in shared.cmd_opts.use_cpu: return cpu return get_optimal_device() def torch_gc(): if torch.cuda.is_available(): with torch.cuda.device(get_cuda_device_string()): torch.cuda.empty_cache() torch.cuda.ipc_collect() elif has_mps() and hasattr(torch.mps, 'empty_cache'): torch.mps.empty_cache() def enable_tf32(): if torch.cuda.is_available(): # enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't # see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407 if any(torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())): torch.backends.cudnn.benchmark = True torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.allow_tf32 = True errors.run(enable_tf32, "Enabling TF32") cpu = torch.device("cpu") device = device_interrogate = device_gfpgan = device_esrgan = device_codeformer = None dtype = torch.float16 dtype_vae = torch.float16 dtype_unet = torch.float16 unet_needs_upcast = False def cond_cast_unet(input): return input.to(dtype_unet) if unet_needs_upcast else input def cond_cast_float(input): return input.float() if unet_needs_upcast else input def randn(seed, shape): from modules.shared import opts torch.manual_seed(seed) if opts.randn_source == "CPU" or device.type == 'mps': return torch.randn(shape, device=cpu).to(device) return torch.randn(shape, device=device) def randn_without_seed(shape): from modules.shared import opts if opts.randn_source == "CPU" or device.type == 'mps': return torch.randn(shape, device=cpu).to(device) return torch.randn(shape, device=device) def autocast(disable=False): from modules import shared if disable: return contextlib.nullcontext() if dtype == torch.float32 or shared.cmd_opts.precision == "full": return contextlib.nullcontext() return torch.autocast("cuda") def without_autocast(disable=False): return torch.autocast("cuda", enabled=False) if torch.is_autocast_enabled() and not disable else contextlib.nullcontext() class NansException(Exception): pass def test_for_nans(x, where): from modules import shared if shared.cmd_opts.disable_nan_check: return if not torch.all(torch.isnan(x)).item(): return if where == "unet": message = "A tensor with all NaNs was produced in Unet." if not shared.cmd_opts.no_half: message += " This could be either because there's not enough precision to represent the picture, or because your video card does not support half type. Try setting the \"Upcast cross attention layer to float32\" option in Settings > Stable Diffusion or using the --no-half commandline argument to fix this." elif where == "vae": message = "A tensor with all NaNs was produced in VAE." if not shared.cmd_opts.no_half and not shared.cmd_opts.no_half_vae: message += " This could be because there's not enough precision to represent the picture. Try adding --no-half-vae commandline argument to fix this." else: message = "A tensor with all NaNs was produced." message += " Use --disable-nan-check commandline argument to disable this check." raise NansException(message) @lru_cache def first_time_calculation(): """ just do any calculation with pytorch layers - the first time this is done it allocaltes about 700MB of memory and spends about 2.7 seconds doing that, at least wih NVidia. """ x = torch.zeros((1, 1)).to(device, dtype) linear = torch.nn.Linear(1, 1).to(device, dtype) linear(x) x = torch.zeros((1, 1, 3, 3)).to(device, dtype) conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype) conv2d(x)