diff --git a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py
index 48283bf31156..68d13014c388 100644
--- a/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py
+++ b/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py
@@ -748,12 +748,16 @@ def __call__(
         # make sure the VAE is in float32 mode, as it overflows in float16
         self.vae.to(dtype=torch.float32)
 
-        use_torch_2_0_or_xformers = self.vae.decoder.mid_block.attentions[0].processor in [
-            AttnProcessor2_0,
-            XFormersAttnProcessor,
-            LoRAXFormersAttnProcessor,
-            LoRAAttnProcessor2_0,
-        ]
+        use_torch_2_0_or_xformers = isinstance(
+            self.vae.decoder.mid_block.attentions[0].processor,
+            (
+                AttnProcessor2_0,
+                XFormersAttnProcessor,
+                LoRAXFormersAttnProcessor,
+                LoRAAttnProcessor2_0,
+            ),
+        )
+
         # if xformers or torch_2_0 is used attention block does not need
         # to be in float32 which can save lots of memory
         if not use_torch_2_0_or_xformers:
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 0afc07e6b2e8..8171b7efbfc0 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
@@ -785,15 +785,18 @@ def __call__(
         # make sure the VAE is in float32 mode, as it overflows in float16
         self.vae.to(dtype=torch.float32)
 
-        use_torch_2_0_or_xformers = self.vae.decoder.mid_block.attentions[0].processor in [
-            AttnProcessor2_0,
-            XFormersAttnProcessor,
-            LoRAXFormersAttnProcessor,
-            LoRAAttnProcessor2_0,
-        ]
+        use_torch_2_0_or_xformers = isinstance(
+            self.vae.decoder.mid_block.attentions[0].processor,
+            (
+                AttnProcessor2_0,
+                XFormersAttnProcessor,
+                LoRAXFormersAttnProcessor,
+                LoRAAttnProcessor2_0,
+            ),
+        )
         # if xformers or torch_2_0 is used attention block does not need
         # to be in float32 which can save lots of memory
-        if not use_torch_2_0_or_xformers:
+        if use_torch_2_0_or_xformers:
             self.vae.post_quant_conv.to(latents.dtype)
             self.vae.decoder.conv_in.to(latents.dtype)
             self.vae.decoder.mid_block.to(latents.dtype)
diff --git a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py
index 1618d9d04a74..f11ff330da85 100644
--- a/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py
+++ b/src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py
@@ -859,15 +859,18 @@ def __call__(
         # make sure the VAE is in float32 mode, as it overflows in float16
         self.vae.to(dtype=torch.float32)
 
-        use_torch_2_0_or_xformers = self.vae.decoder.mid_block.attentions[0].processor in [
-            AttnProcessor2_0,
-            XFormersAttnProcessor,
-            LoRAXFormersAttnProcessor,
-            LoRAAttnProcessor2_0,
-        ]
+        use_torch_2_0_or_xformers = isinstance(
+            self.vae.decoder.mid_block.attentions[0].processor,
+            (
+                AttnProcessor2_0,
+                XFormersAttnProcessor,
+                LoRAXFormersAttnProcessor,
+                LoRAAttnProcessor2_0,
+            ),
+        )
         # if xformers or torch_2_0 is used attention block does not need
         # to be in float32 which can save lots of memory
-        if not use_torch_2_0_or_xformers:
+        if use_torch_2_0_or_xformers:
             self.vae.post_quant_conv.to(latents.dtype)
             self.vae.decoder.conv_in.to(latents.dtype)
             self.vae.decoder.mid_block.to(latents.dtype)