mirror of
https://github.com/AUTOMATIC1111/stable-diffusion-webui.git
synced 2024-06-07 21:20:49 +00:00
106 lines
2.7 KiB
Python
106 lines
2.7 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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# has_mps is only available in nightly pytorch (for now) and MasOS 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_optimal_device():
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if torch.cuda.is_available():
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from modules import shared
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device_id = shared.cmd_opts.device_id
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if device_id is not None:
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cuda_device = f"cuda:{device_id}"
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return torch.device(cuda_device)
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else:
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return torch.device("cuda")
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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 torch_gc():
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if torch.cuda.is_available():
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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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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_swinir = device_esrgan = device_scunet = device_codeformer = None
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dtype = torch.float16
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dtype_vae = torch.float16
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def randn(seed, shape):
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# Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
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if device.type == 'mps':
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generator = torch.Generator(device=cpu)
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generator.manual_seed(seed)
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noise = torch.randn(shape, generator=generator, device=cpu).to(device)
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return noise
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torch.manual_seed(seed)
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return torch.randn(shape, device=device)
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def randn_without_seed(shape):
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# Pytorch currently doesn't handle setting randomness correctly when the metal backend is used.
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if device.type == 'mps':
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generator = torch.Generator(device=cpu)
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noise = torch.randn(shape, generator=generator, device=cpu).to(device)
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return noise
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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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# MPS workaround for https://github.com/pytorch/pytorch/issues/79383
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def mps_contiguous(input_tensor, device):
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return input_tensor.contiguous() if device.type == 'mps' else input_tensor
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def mps_contiguous_to(input_tensor, device):
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return mps_contiguous(input_tensor, device).to(device)
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