Merge branch 'dev' into release_candidate

1 files changed, 12 insertions, 30 deletions

diff --git a/modules/devices.py b/modules/devices.py
index 57e51da3..c01f0602 100644
--- a/modules/devices.py
+++ b/modules/devices.py
@@ -3,7 +3,7 @@ import contextlib
 from functools import lru_cache
 
 import torch
-from modules import errors
+from modules import errors, shared
 
 if sys.platform == "darwin":
     from modules import mac_specific
@@ -17,8 +17,6 @@ def has_mps() -> bool:
 
 
 def get_cuda_device_string():
-    from modules import shared
-
     if shared.cmd_opts.device_id is not None:
         return f"cuda:{shared.cmd_opts.device_id}"
 
@@ -40,8 +38,6 @@ def get_optimal_device():
 
 
 def get_device_for(task):
-    from modules import shared
-
     if task in shared.cmd_opts.use_cpu:
         return cpu
 
@@ -71,14 +67,17 @@ def enable_tf32():
         torch.backends.cudnn.allow_tf32 = True
 
 
-
 errors.run(enable_tf32, "Enabling TF32")
 
-cpu = torch.device("cpu")
-device = device_interrogate = device_gfpgan = device_esrgan = device_codeformer = None
-dtype = torch.float16
-dtype_vae = torch.float16
-dtype_unet = torch.float16
+cpu: torch.device = torch.device("cpu")
+device: torch.device = None
+device_interrogate: torch.device = None
+device_gfpgan: torch.device = None
+device_esrgan: torch.device = None
+device_codeformer: torch.device = None
+dtype: torch.dtype = torch.float16
+dtype_vae: torch.dtype = torch.float16
+dtype_unet: torch.dtype = torch.float16
 unet_needs_upcast = False
 
 
@@ -90,26 +89,10 @@ def cond_cast_float(input):
     return input.float() if unet_needs_upcast else input
 
 
-def randn(seed, shape):
-    from modules.shared import opts
-
-    torch.manual_seed(seed)
-    if opts.randn_source == "CPU" or device.type == 'mps':
-        return torch.randn(shape, device=cpu).to(device)
-    return torch.randn(shape, device=device)
-
-
-def randn_without_seed(shape):
-    from modules.shared import opts
-
-    if opts.randn_source == "CPU" or device.type == 'mps':
-        return torch.randn(shape, device=cpu).to(device)
-    return torch.randn(shape, device=device)
+nv_rng = None
 
 
 def autocast(disable=False):
-    from modules import shared
-
     if disable:
         return contextlib.nullcontext()
 
@@ -128,8 +111,6 @@ class NansException(Exception):
 
 
 def test_for_nans(x, where):
-    from modules import shared
-
     if shared.cmd_opts.disable_nan_check:
         return
 
@@ -169,3 +150,4 @@ def first_time_calculation():
     x = torch.zeros((1, 1, 3, 3)).to(device, dtype)
     conv2d = torch.nn.Conv2d(1, 1, (3, 3)).to(device, dtype)
     conv2d(x)
+