DirectML update.

DirectML reuses GPU memory instead of returning it.
So prints "practical" GPU memory utilization too.

modules/dml/__init__.py

@@ -1,5 +1,10 @@
+import os
 import torch
 
+from modules.sd_hijack_utils import CondFunc
+
+do_nothing = lambda: None
+
 def directml_init():
     from modules.dml.backend import DirectML # pylint: disable=ungrouped-imports
     # Alternative of torch.cuda for DirectML.
@@ -7,18 +12,34 @@ def directml_init():
 
     torch.cuda.is_available = lambda: False
     torch.cuda.device = torch.dml.device
+    torch.cuda.device_count = torch.dml.device_count
     torch.cuda.current_device = torch.dml.current_device
     torch.cuda.get_device_name = torch.dml.get_device_name
     torch.cuda.get_device_properties = torch.dml.get_device_properties
 
+    torch.cuda.empty_cache = do_nothing
+    torch.cuda.ipc_collect = do_nothing
     torch.cuda.memory_stats = torch.dml.memory_stats
     torch.cuda.mem_get_info = torch.dml.mem_get_info
     torch.cuda.memory_allocated = torch.dml.memory_allocated
     torch.cuda.max_memory_allocated = torch.dml.max_memory_allocated
     torch.cuda.reset_peak_memory_stats = torch.dml.reset_peak_memory_stats
+    torch.cuda.utilization = lambda: 0
 
-def directml_hijack_init():
+    torch.Tensor.directml = lambda self: self.to(torch.dml.current_device())
+
+    mem_bound = os.environ.get("DML_GPU_MEMORY_BOUND", None)
+    if mem_bound is not None:
+        torch.dml.set_gpu_memory_bound(int(mem_bound))
+
+def directml_do_hijack():
     import modules.dml.hijack
+    from modules.devices import device
+
+    if not torch.dml.has_float64_support(device):
+        CondFunc('torch.from_numpy',
+            lambda orig_func, *args, **kwargs: orig_func(args[0].astype('float32')),
+            lambda *args, **kwargs: args[1].dtype == float)
 
 def directml_override_opts():
     from modules import shared