172 lines
4.6 KiB
Python
172 lines
4.6 KiB
Python
import sys
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import contextlib
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from functools import lru_cache
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import torch
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from modules import errors
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if sys.platform == "darwin":
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from modules import mac_specific
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def has_mps() -> bool:
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if sys.platform != "darwin":
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return False
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else:
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return mac_specific.has_mps
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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_name():
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if torch.cuda.is_available():
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return get_cuda_device_string()
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if has_mps():
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return "mps"
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return "cpu"
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def get_optimal_device():
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return torch.device(get_optimal_device_name())
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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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if has_mps():
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mac_specific.torch_mps_gc()
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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://ghproxy.com/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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from modules.shared import opts
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torch.manual_seed(seed)
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if opts.randn_source == "CPU" or 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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from modules.shared import opts
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if opts.randn_source == "CPU" or 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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message += " Use --disable-nan-check commandline argument to disable this check."
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raise NansException(message)
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@lru_cache
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def first_time_calculation():
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"""
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just do any calculation with pytorch layers - the first time this is done it allocaltes about 700MB of memory and
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spends about 2.7 seconds doing that, at least wih NVidia.
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"""
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x = torch.zeros((1, 1)).to(device, dtype)
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linear = torch.nn.Linear(1, 1).to(device, dtype)
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linear(x)
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x = torch.zeros((1, 1, 3, 3)).to(device, dtype)
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conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype)
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conv2d(x)
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