68 lines
1.8 KiB
Python
68 lines
1.8 KiB
Python
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), `getattr` for compatibility
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has_mps = getattr(torch, 'has_mps', False)
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cpu = torch.device("cpu")
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def get_optimal_device():
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if torch.cuda.is_available():
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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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device = device_gfpgan = device_bsrgan = device_esrgan = device_scunet = device_codeformer = get_optimal_device()
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dtype = 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():
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from modules import shared
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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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