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import contextlib
import torch
import ldm_patched.modules.model_management as model_management
def has_xpu() -> bool:
return model_management.xpu_available
def has_mps() -> bool:
return model_management.mps_mode()
def cuda_no_autocast(device_id=None) -> bool:
return False
def get_cuda_device_id():
return model_management.get_torch_device().index
def get_cuda_device_string():
return str(model_management.get_torch_device())
def get_optimal_device_name():
return model_management.get_torch_device().type
def get_optimal_device():
return model_management.get_torch_device()
def get_device_for(task):
return model_management.get_torch_device()
def torch_gc():
model_management.soft_empty_cache()
def torch_npu_set_device():
return
def enable_tf32():
return
cpu: torch.device = torch.device("cpu")
fp8: bool = False
device: torch.device = model_management.get_torch_device()
device_interrogate: torch.device = model_management.text_encoder_device() # for backward compatibility, not used now
device_gfpgan: torch.device = model_management.get_torch_device() # will be managed by memory management system
device_esrgan: torch.device = model_management.get_torch_device() # will be managed by memory management system
device_codeformer: torch.device = model_management.get_torch_device() # will be managed by memory management system
dtype: torch.dtype = model_management.unet_dtype()
dtype_vae: torch.dtype = model_management.vae_dtype()
dtype_unet: torch.dtype = model_management.unet_dtype()
dtype_inference: torch.dtype = model_management.unet_dtype()
unet_needs_upcast = False
def cond_cast_unet(input):
return input
def cond_cast_float(input):
return input
nv_rng = None
patch_module_list = []
def manual_cast_forward(target_dtype):
return
@contextlib.contextmanager
def manual_cast(target_dtype):
return
def autocast(disable=False):
return contextlib.nullcontext()
def without_autocast(disable=False):
return contextlib.nullcontext()
class NansException(Exception):
pass
def test_for_nans(x, where):
return
def first_time_calculation():
return
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