improve efficiency and support more device

modules/devices.py

@@ -110,6 +110,7 @@ device_codeformer: torch.device = None
 dtype: torch.dtype = torch.float16
 dtype_vae: torch.dtype = torch.float16
 dtype_unet: torch.dtype = torch.float16
+dtype_inference: torch.dtype = torch.float16
 unet_needs_upcast = False
 
 
@@ -131,21 +132,49 @@ patch_module_list = [
 ]
 
 
-def manual_cast_forward(self, *args, **kwargs):
-    org_dtype = torch_utils.get_param(self).dtype
-    self.to(dtype)
-    args = [arg.to(dtype) if isinstance(arg, torch.Tensor) else arg for arg in args]
-    kwargs = {k: v.to(dtype) if isinstance(v, torch.Tensor) else v for k, v in kwargs.items()}
-    result = self.org_forward(*args, **kwargs)
-    self.to(org_dtype)
-    return result
+def manual_cast_forward(target_dtype):
+    def forward_wrapper(self, *args, **kwargs):
+        org_dtype = torch_utils.get_param(self).dtype
+        if not target_dtype == org_dtype == dtype_inference:
+            self.to(target_dtype)
+            args = [
+                arg.to(target_dtype) 
+                if isinstance(arg, torch.Tensor) 
+                else arg 
+                for arg in args
+            ]
+            kwargs = {
+                k: v.to(target_dtype) 
+                if isinstance(v, torch.Tensor) 
+                else v 
+                for k, v in kwargs.items()
+            }
+        
+        result = self.org_forward(*args, **kwargs)
+        self.to(org_dtype)
+        
+        if target_dtype != dtype_inference:
+            if isinstance(result, tuple):
+                result = tuple(
+                    i.to(dtype_inference) 
+                    if isinstance(i, torch.Tensor) 
+                    else i 
+                    for i in result
+                )
+            elif isinstance(result, torch.Tensor):
+                result = result.to(dtype_inference)
+        return result
+    return forward_wrapper
 
 
 @contextlib.contextmanager
-def manual_cast():
+def manual_cast(target_dtype):
     for module_type in patch_module_list:
         org_forward = module_type.forward
-        module_type.forward = manual_cast_forward
+        if module_type == torch.nn.MultiheadAttention and has_xpu():
+            module_type.forward = manual_cast_forward(torch.float32)
+        else:
+            module_type.forward = manual_cast_forward(target_dtype)
         module_type.org_forward = org_forward
     try:
         yield None
@@ -161,15 +190,12 @@ def autocast(disable=False):
     if fp8 and device==cpu:
         return torch.autocast("cpu", dtype=torch.bfloat16, enabled=True)
 
-    if fp8 and (dtype == torch.float32 or shared.cmd_opts.precision == "full" or cuda_no_autocast()):
-        return manual_cast()
-
-    if has_mps() and shared.cmd_opts.precision != "full":
-        return manual_cast()
-
-    if dtype == torch.float32 or shared.cmd_opts.precision == "full":
+    if dtype == torch.float32 and shared.cmd_opts.precision == "full":
         return contextlib.nullcontext()
 
+    if has_xpu() or has_mps() or cuda_no_autocast():
+        return manual_cast(dtype_inference)
+
     return torch.autocast("cuda")
 
 

modules/shared_init.py

@@ -29,6 +29,7 @@ def initialize():
 
     devices.dtype = torch.float32 if cmd_opts.no_half else torch.float16
     devices.dtype_vae = torch.float32 if cmd_opts.no_half or cmd_opts.no_half_vae else torch.float16
+    devices.dtype_inference = torch.float32 if cmd_opts.precision == 'full' else devices.dtype
 
     shared.device = devices.device
     shared.weight_load_location = None if cmd_opts.lowram else "cpu"