b6e5edd746
add support for adding upscalers in extensions move LDSR, ScuNET and SwinIR to built-in extensions
131 lines
3.6 KiB
Python
131 lines
3.6 KiB
Python
import sys, os, shlex
|
|
import contextlib
|
|
import torch
|
|
from modules import errors
|
|
from packaging import version
|
|
|
|
|
|
# has_mps is only available in nightly pytorch (for now) and macOS 12.3+.
|
|
# check `getattr` and try it for compatibility
|
|
def has_mps() -> bool:
|
|
if not getattr(torch, 'has_mps', False):
|
|
return False
|
|
try:
|
|
torch.zeros(1).to(torch.device("mps"))
|
|
return True
|
|
except Exception:
|
|
return False
|
|
|
|
|
|
def extract_device_id(args, name):
|
|
for x in range(len(args)):
|
|
if name in args[x]:
|
|
return args[x + 1]
|
|
|
|
return None
|
|
|
|
|
|
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():
|
|
if torch.cuda.is_available():
|
|
return torch.device(get_cuda_device_string())
|
|
|
|
if has_mps():
|
|
return torch.device("mps")
|
|
|
|
return cpu
|
|
|
|
|
|
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()
|
|
|
|
|
|
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
|
|
|
|
|
|
def randn(seed, shape):
|
|
torch.manual_seed(seed)
|
|
if device.type == 'mps':
|
|
return torch.randn(shape, device=cpu).to(device)
|
|
return torch.randn(shape, device=device)
|
|
|
|
|
|
def randn_without_seed(shape):
|
|
if 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")
|
|
|
|
|
|
# MPS workaround for https://github.com/pytorch/pytorch/issues/79383
|
|
orig_tensor_to = torch.Tensor.to
|
|
def tensor_to_fix(self, *args, **kwargs):
|
|
if self.device.type != 'mps' and \
|
|
((len(args) > 0 and isinstance(args[0], torch.device) and args[0].type == 'mps') or \
|
|
(isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps')):
|
|
self = self.contiguous()
|
|
return orig_tensor_to(self, *args, **kwargs)
|
|
|
|
|
|
# MPS workaround for https://github.com/pytorch/pytorch/issues/80800
|
|
orig_layer_norm = torch.nn.functional.layer_norm
|
|
def layer_norm_fix(*args, **kwargs):
|
|
if len(args) > 0 and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps':
|
|
args = list(args)
|
|
args[0] = args[0].contiguous()
|
|
return orig_layer_norm(*args, **kwargs)
|
|
|
|
|
|
# PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
|
|
if has_mps() and version.parse(torch.__version__) < version.parse("1.13"):
|
|
torch.Tensor.to = tensor_to_fix
|
|
torch.nn.functional.layer_norm = layer_norm_fix
|