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import contextlib
import torch
from modules import errors
# has_mps is only available in nightly pytorch (for now), `getattr` for compatibility
has_mps = getattr(torch, 'has_mps', False)
cpu = torch.device("cpu")
def get_optimal_device():
if torch.cuda.is_available():
return torch.device("cuda")
if has_mps:
return torch.device("mps")
return cpu
def torch_gc():
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.ipc_collect()
def enable_tf32():
if torch.cuda.is_available():
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
errors.run(enable_tf32, "Enabling TF32")
device = device_interrogate = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
dtype = torch.float16
dtype_vae = torch.float16
def randn(seed, shape):
# Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
if device.type == 'mps':
generator = torch.Generator(device=cpu)
generator.manual_seed(seed)
noise = torch.randn(shape, generator=generator, device=cpu).to(device)
return noise
torch.manual_seed(seed)
return torch.randn(shape, device=device)
def randn_without_seed(shape):
# Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
if device.type == 'mps':
generator = torch.Generator(device=cpu)
noise = torch.randn(shape, generator=generator, device=cpu).to(device)
return noise
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")
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