diff --git a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py
index 37e61e2aaf..43615dc664 100644
--- a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py
+++ b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py
@@ -671,6 +671,8 @@ class StableDiffusionXLPipeline(
         return add_time_ids
 
     def upcast_vae(self):
+        from ...models.attention_processor import FusedAttnProcessor2_0
+
         dtype = self.vae.dtype
         self.vae.to(dtype=torch.float32)
         use_torch_2_0_or_xformers = isinstance(
@@ -680,6 +682,7 @@ class StableDiffusionXLPipeline(
                 XFormersAttnProcessor,
                 LoRAXFormersAttnProcessor,
                 LoRAAttnProcessor2_0,
+                FusedAttnProcessor2_0,
             ),
         )
         # if xformers or torch_2_0 is used attention block does not need
@@ -838,25 +841,25 @@ class StableDiffusionXLPipeline(
     def num_timesteps(self):
         return self._num_timesteps
 
-    def _enable_bfloat16_for_vae(self):
-        self.vae = self.vae.to(torch.bfloat16)
-        self.is_vae_in_blfoat16 = True
+    # def _enable_bfloat16_for_vae(self):
+    #     self.vae = self.vae.to(torch.bfloat16)
+    #     self.is_vae_in_blfoat16 = True
 
-    def _change_to_group_norm_32(self):
-        class GroupNorm32(torch.nn.GroupNorm):
-            def forward(self, x):
-                return super().forward(x.float()).type(x.dtype)
+    # def _change_to_group_norm_32(self):
+    #     class GroupNorm32(torch.nn.GroupNorm):
+    #         def forward(self, x):
+    #             return super().forward(x.float()).type(x.dtype)
 
-        def recursive_fn(model):
-            for name, module in model.named_children():
-                if isinstance(module, torch.nn.GroupNorm):
-                    new_gn = GroupNorm32(module.num_groups, module.num_channels, module.eps, module.affine)
-                    setattr(model, name, new_gn)
-                elif len(list(module.children())) > 0:
-                    recursive_fn(module)
+    #     def recursive_fn(model):
+    #         for name, module in model.named_children():
+    #             if isinstance(module, torch.nn.GroupNorm):
+    #                 new_gn = GroupNorm32(module.num_groups, module.num_channels, module.eps, module.affine)
+    #                 setattr(model, name, new_gn)
+    #             elif len(list(module.children())) > 0:
+    #                 recursive_fn(module)
 
-        recursive_fn(self.unet)
-        recursive_fn(self.vae)
+    #     recursive_fn(self.unet)
+    #     recursive_fn(self.vae)
 
     @torch.no_grad()
     @replace_example_docstring(EXAMPLE_DOC_STRING)
@@ -1267,8 +1270,8 @@ class StableDiffusionXLPipeline(
                 self.upcast_vae()
                 latents = latents.to(next(iter(self.vae.post_quant_conv.parameters())).dtype)
 
-            if hasattr(self, "is_vae_in_blfoat16") and self.is_vae_in_blfoat16:
-                latents = latents.to(self.vae.dtype)
+            # if hasattr(self, "is_vae_in_blfoat16") and self.is_vae_in_blfoat16:
+            #     latents = latents.to(self.vae.dtype)
             image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]
 
             # cast back to fp16 if needed