Make --fast work on pytorch nightly.

comfy/ops.py

@@ -254,16 +254,33 @@ def fp8_linear(self, input):
         non_blocking = comfy.model_management.device_supports_non_blocking(input.device)
         w = cast_to(self.weight, device=input.device, non_blocking=non_blocking).t()
 
+        scale_weight = self.scale_weight
+        scale_input = self.scale_input
+        if scale_weight is None:
+            scale_weight = torch.ones((1), device=input.device, dtype=torch.float32)
+            if scale_input is None:
+                scale_input = scale_weight
+        if scale_input is None:
+            scale_input = torch.ones((1), device=input.device, dtype=torch.float32)
+
         if self.bias is not None:
-            o, _ = torch._scaled_mm(inn, w, out_dtype=input.dtype, bias=cast_to_input(self.bias, input, non_blocking=non_blocking))
+            o = torch._scaled_mm(inn, w, out_dtype=input.dtype, bias=cast_to_input(self.bias, input, non_blocking=non_blocking), scale_a=scale_input, scale_b=scale_weight)
         else:
-            o, _ = torch._scaled_mm(inn, w, out_dtype=input.dtype)
+            o = torch._scaled_mm(inn, w, out_dtype=input.dtype, scale_a=scale_input, scale_b=scale_weight)
+
+        if isinstance(o, tuple):
+            o = o[0]
 
         return o.reshape((-1, input.shape[1], self.weight.shape[0]))
     return None
 
 class fp8_ops(manual_cast):
     class Linear(manual_cast.Linear):
+        def reset_parameters(self):
+            self.scale_weight = None
+            self.scale_input = None
+            return None
+
         def forward_comfy_cast_weights(self, input):
             out = fp8_linear(self, input)
             if out is not None: