Basic torch_directml support. Use --directml to use it.

comfy/cli_args.py

@@ -10,6 +10,7 @@
 parser.add_argument("--cuda-device", type=int, default=None, metavar="DEVICE_ID", help="Set the id of the cuda device this instance will use.")
 parser.add_argument("--dont-upcast-attention", action="store_true", help="Disable upcasting of attention. Can boost speed but increase the chances of black images.")
 parser.add_argument("--force-fp32", action="store_true", help="Force fp32 (If this makes your GPU work better please report it).")
+parser.add_argument("--directml", action="store_true", help="Use torch-directml.")
 
 attn_group = parser.add_mutually_exclusive_group()
 attn_group.add_argument("--use-split-cross-attention", action="store_true", help="Use the split cross attention optimization instead of the sub-quadratic one. Ignored when xformers is used.")

comfy/model_management.py

@@ -20,6 +20,13 @@ class VRAMState(Enum):
 accelerate_enabled = False
 xpu_available = False
 
+directml_enabled = False
+if args.directml:
+    import torch_directml
+    print("Using directml")
+    directml_enabled = True
+    # torch_directml.disable_tiled_resources(True)
+
 try:
     import torch
     try:
@@ -217,6 +224,9 @@ def unload_if_low_vram(model):
 
 def get_torch_device():
     global xpu_available
+    global directml_enabled
+    if directml_enabled:
+        return torch_directml.device()
     if vram_state == VRAMState.MPS:
         return torch.device("mps")
     if vram_state == VRAMState.CPU:
@@ -234,8 +244,14 @@ def get_autocast_device(dev):
 
 
 def xformers_enabled():
+    global xpu_available
+    global directml_enabled
     if vram_state == VRAMState.CPU:
         return False
+    if xpu_available:
+        return False
+    if directml_enabled:
+        return False
     return XFORMERS_IS_AVAILABLE
 
 
@@ -251,14 +267,18 @@ def pytorch_attention_enabled():
 
 def get_free_memory(dev=None, torch_free_too=False):
     global xpu_available
+    global directml_enabled
     if dev is None:
         dev = get_torch_device()
 
     if hasattr(dev, 'type') and (dev.type == 'cpu' or dev.type == 'mps'):
         mem_free_total = psutil.virtual_memory().available
         mem_free_torch = mem_free_total
     else:
-        if xpu_available:
+        if directml_enabled:
+            mem_free_total = 1024 * 1024 * 1024 #TODO
+            mem_free_torch = mem_free_total
+        elif xpu_available:
             mem_free_total = torch.xpu.get_device_properties(dev).total_memory - torch.xpu.memory_allocated(dev)
             mem_free_torch = mem_free_total
         else:
@@ -293,9 +313,14 @@ def mps_mode():
 
 def should_use_fp16():
     global xpu_available
+    global directml_enabled
+
     if FORCE_FP32:
         return False
 
+    if directml_enabled:
+        return False
+
     if cpu_mode() or mps_mode() or xpu_available:
         return False #TODO ?