| import contextlib |
| import torch |
| from ldm_patched.modules import 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 = model_management.unet_dtype() == torch.float8_e4m3fn |
| device: torch.device = model_management.get_torch_device() |
| device_gfpgan = device_esrgan = device_codeformer = device |
| dtype: torch.dtype = torch.float32 if model_management.unet_dtype() is torch.float32 else torch.float16 |
| dtype_vae: torch.dtype = model_management.vae_dtype() |
| dtype_unet: torch.dtype = model_management.unet_dtype() |
| dtype_inference = dtype_unet |
| 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 |
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|
|
| def first_time_calculation(): |
| return |
|
|
