Handle None arg

Handle None arg

ipex_hijack/__init__.py

@@ -15,3 +15,11 @@ def wrapper(*args, **kwargs):
 
         return wrapper
     return decorator
+
+
+def asfp16(v):
+    return v.to(torch.half) if v is not None else None
+
+
+def astype(v, type):
+    return v.to(type) if v is not None else None

scripts/ipex_enhance.py

@@ -1,6 +1,6 @@
 from modules import script_callbacks, devices
 from modules.sd_hijack_utils import CondFunc
-from ipex_hijack import log
+from ipex_hijack import log, asfp16
 from ipex_hijack.controlnet import apply_controlnet_hijacks
 
 
@@ -34,7 +34,7 @@ def apply_general_hijacks():
     # IPEX: incorrect batch_norm result with XPU fp32, downcast to fp16 instead
     # TODO: file an issue to IPEX
     CondFunc('torch.nn.functional.batch_norm',
-        lambda orig_func, input, running_mean, running_var, weight=None, bias=None, training=False, momentum=0.1, eps=1e-05: orig_func(input.half(), running_mean.half(), running_var.half(), weight=weight.half() if weight is not None else None, bias=bias.half() if bias is not None else None, training=training, momentum=momentum, eps=eps).to(input.dtype),
+        lambda orig_func, input, running_mean, running_var, weight=None, bias=None, training=False, momentum=0.1, eps=1e-05: orig_func(input.half(), asfp16(running_mean), asfp16(running_var), weight=asfp16(weight), bias=asfp16(bias), training=training, momentum=momentum, eps=eps).to(input.dtype),
         lambda orig_func, input, running_mean, running_var, weight=None, bias=None, training=False, momentum=0.1, eps=1e-05: input.device.type == 'xpu' and input.dtype == torch.float)
 
     # IPEX: incorrect interpolate result with XPU when align_corner=True, move to cpu instead