Correctly keep vae in `float16` when using PyTorch 2 or xFormers (#4019)

* Update pipeline_stable_diffusion_xl.py

fix a bug

* Update pipeline_stable_diffusion_xl_img2img.py

* Update pipeline_stable_diffusion_xl_img2img.py

* Update pipeline_stable_diffusion_upscale.py

* style

---------
Co-authored-by: Hu Ye <xiaohuzc@gmail.com>

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_upscale.py

@@ -748,12 +748,16 @@ class StableDiffusionUpscalePipeline(DiffusionPipeline, TextualInversionLoaderMi
         # 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 [
+        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:

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py

@@ -786,15 +786,18 @@ class StableDiffusionXLPipeline(DiffusionPipeline, FromSingleFileMixin):
         # 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 [
+        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)

src/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl_img2img.py

@@ -860,15 +860,18 @@ class StableDiffusionXLImg2ImgPipeline(DiffusionPipeline, FromSingleFileMixin):
         # 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 [
+        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)