mirror of
https://github.com/openvinotoolkit/stable-diffusion-webui.git
synced 2024-12-14 22:53:25 +03:00
207 lines
6.8 KiB
Python
207 lines
6.8 KiB
Python
import sys, os, shlex
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import contextlib
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import torch
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from modules import errors
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from packaging import version
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# has_mps is only available in nightly pytorch (for now) and macOS 12.3+.
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# check `getattr` and try it for compatibility
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def has_mps() -> bool:
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if not getattr(torch, 'has_mps', False):
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return False
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try:
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torch.zeros(1).to(torch.device("mps"))
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return True
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except Exception:
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return False
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def extract_device_id(args, name):
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for x in range(len(args)):
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if name in args[x]:
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return args[x + 1]
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return None
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def get_cuda_device_string():
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from modules import shared
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if shared.cmd_opts.device_id is not None:
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return f"cuda:{shared.cmd_opts.device_id}"
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return "cuda"
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def get_optimal_device():
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if torch.cuda.is_available():
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return torch.device(get_cuda_device_string())
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if has_mps():
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return torch.device("mps")
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return cpu
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def get_device_for(task):
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from modules import shared
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if task in shared.cmd_opts.use_cpu:
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return cpu
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return get_optimal_device()
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def torch_gc():
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if torch.cuda.is_available():
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with torch.cuda.device(get_cuda_device_string()):
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torch.cuda.empty_cache()
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torch.cuda.ipc_collect()
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def enable_tf32():
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if torch.cuda.is_available():
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# enabling benchmark option seems to enable a range of cards to do fp16 when they otherwise can't
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# see https://github.com/AUTOMATIC1111/stable-diffusion-webui/pull/4407
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if any([torch.cuda.get_device_capability(devid) == (7, 5) for devid in range(0, torch.cuda.device_count())]):
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torch.backends.cudnn.benchmark = True
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torch.backends.cuda.matmul.allow_tf32 = True
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torch.backends.cudnn.allow_tf32 = True
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errors.run(enable_tf32, "Enabling TF32")
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cpu = torch.device("cpu")
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device = device_interrogate = device_gfpgan = device_esrgan = device_codeformer = None
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dtype = torch.float16
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dtype_vae = torch.float16
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dtype_unet = torch.float16
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unet_needs_upcast = False
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def cond_cast_unet(input):
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return input.to(dtype_unet) if unet_needs_upcast else input
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def cond_cast_float(input):
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return input.float() if unet_needs_upcast else input
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def randn(seed, shape):
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torch.manual_seed(seed)
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if device.type == 'mps':
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return torch.randn(shape, device=cpu).to(device)
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return torch.randn(shape, device=device)
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def randn_without_seed(shape):
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if device.type == 'mps':
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return torch.randn(shape, device=cpu).to(device)
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return torch.randn(shape, device=device)
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def autocast(disable=False):
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from modules import shared
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if disable:
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return contextlib.nullcontext()
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if dtype == torch.float32 or shared.cmd_opts.precision == "full":
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return contextlib.nullcontext()
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return torch.autocast("cuda")
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def without_autocast(disable=False):
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return torch.autocast("cuda", enabled=False) if torch.is_autocast_enabled() and not disable else contextlib.nullcontext()
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class NansException(Exception):
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pass
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def test_for_nans(x, where):
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from modules import shared
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if shared.cmd_opts.disable_nan_check:
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return
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if not torch.all(torch.isnan(x)).item():
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return
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if where == "unet":
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message = "A tensor with all NaNs was produced in Unet."
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if not shared.cmd_opts.no_half:
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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."
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elif where == "vae":
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message = "A tensor with all NaNs was produced in VAE."
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if not shared.cmd_opts.no_half and not shared.cmd_opts.no_half_vae:
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message += " This could be because there's not enough precision to represent the picture. Try adding --no-half-vae commandline argument to fix this."
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else:
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message = "A tensor with all NaNs was produced."
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raise NansException(message)
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# MPS workaround for https://github.com/pytorch/pytorch/issues/79383
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orig_tensor_to = torch.Tensor.to
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def tensor_to_fix(self, *args, **kwargs):
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if self.device.type != 'mps' and \
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((len(args) > 0 and isinstance(args[0], torch.device) and args[0].type == 'mps') or \
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(isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps')):
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self = self.contiguous()
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return orig_tensor_to(self, *args, **kwargs)
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# MPS workaround for https://github.com/pytorch/pytorch/issues/80800
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orig_layer_norm = torch.nn.functional.layer_norm
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def layer_norm_fix(*args, **kwargs):
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if len(args) > 0 and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps':
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args = list(args)
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args[0] = args[0].contiguous()
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return orig_layer_norm(*args, **kwargs)
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# MPS workaround for https://github.com/pytorch/pytorch/issues/90532
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orig_tensor_numpy = torch.Tensor.numpy
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def numpy_fix(self, *args, **kwargs):
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if self.requires_grad:
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self = self.detach()
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return orig_tensor_numpy(self, *args, **kwargs)
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# MPS workaround for https://github.com/pytorch/pytorch/issues/89784
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orig_cumsum = torch.cumsum
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orig_Tensor_cumsum = torch.Tensor.cumsum
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def cumsum_fix(input, cumsum_func, *args, **kwargs):
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if input.device.type == 'mps':
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output_dtype = kwargs.get('dtype', input.dtype)
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if output_dtype == torch.int64:
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return cumsum_func(input.cpu(), *args, **kwargs).to(input.device)
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elif cumsum_needs_bool_fix and output_dtype == torch.bool or cumsum_needs_int_fix and (output_dtype == torch.int8 or output_dtype == torch.int16):
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return cumsum_func(input.to(torch.int32), *args, **kwargs).to(torch.int64)
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return cumsum_func(input, *args, **kwargs)
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if has_mps():
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if version.parse(torch.__version__) < version.parse("1.13"):
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# PyTorch 1.13 doesn't need these fixes but unfortunately is slower and has regressions that prevent training from working
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torch.Tensor.to = tensor_to_fix
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torch.nn.functional.layer_norm = layer_norm_fix
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torch.Tensor.numpy = numpy_fix
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elif version.parse(torch.__version__) > version.parse("1.13.1"):
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cumsum_needs_int_fix = not torch.Tensor([1,2]).to(torch.device("mps")).equal(torch.ShortTensor([1,1]).to(torch.device("mps")).cumsum(0))
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cumsum_needs_bool_fix = not torch.BoolTensor([True,True]).to(device=torch.device("mps"), dtype=torch.int64).equal(torch.BoolTensor([True,False]).to(torch.device("mps")).cumsum(0))
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torch.cumsum = lambda input, *args, **kwargs: ( cumsum_fix(input, orig_cumsum, *args, **kwargs) )
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torch.Tensor.cumsum = lambda self, *args, **kwargs: ( cumsum_fix(self, orig_Tensor_cumsum, *args, **kwargs) )
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orig_narrow = torch.narrow
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torch.narrow = lambda *args, **kwargs: ( orig_narrow(*args, **kwargs).clone() )
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