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Author: sayakpaul

src/diffusers/pipelines/aura_flow/pipeline_aura_flow.py

@@ -410,11 +410,7 @@ def upcast_vae(self):
         self.vae.to(dtype=torch.float32)
         use_torch_2_0_or_xformers = isinstance(
             self.vae.decoder.mid_block.attentions[0].processor,
-            (
-                AttnProcessor2_0,
-                XFormersAttnProcessor,
-                FusedAttnProcessor2_0,
-            ),
+            (AttnProcessor2_0, XFormersAttnProcessor, FusedAttnProcessor2_0),
         )
         # if xformers or torch_2_0 is used attention block does not need
         # to be in float32 which can save lots of memory

src/diffusers/pipelines/kolors/pipeline_kolors.py

@@ -584,11 +584,7 @@ def upcast_vae(self):
         self.vae.to(dtype=torch.float32)
         use_torch_2_0_or_xformers = isinstance(
             self.vae.decoder.mid_block.attentions[0].processor,
-            (
-                AttnProcessor2_0,
-                XFormersAttnProcessor,
-                FusedAttnProcessor2_0,
-            ),
+            (AttnProcessor2_0, XFormersAttnProcessor, FusedAttnProcessor2_0),
         )
         # if xformers or torch_2_0 is used attention block does not need
         # to be in float32 which can save lots of memory

src/diffusers/pipelines/kolors/pipeline_kolors_img2img.py

@@ -712,11 +712,7 @@ def upcast_vae(self):
         self.vae.to(dtype=torch.float32)
         use_torch_2_0_or_xformers = isinstance(
             self.vae.decoder.mid_block.attentions[0].processor,
-            (
-                AttnProcessor2_0,
-                XFormersAttnProcessor,
-                FusedAttnProcessor2_0,
-            ),
+            (AttnProcessor2_0, XFormersAttnProcessor, FusedAttnProcessor2_0),
         )
         # if xformers or torch_2_0 is used attention block does not need
         # to be in float32 which can save lots of memory

src/diffusers/pipelines/pag/pipeline_pag_kolors.py

@@ -21,7 +21,7 @@
 from ...image_processor import PipelineImageInput, VaeImageProcessor
 from ...loaders import IPAdapterMixin, StableDiffusionXLLoraLoaderMixin
 from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel
-from ...models.attention_processor import AttnProcessor2_0, XFormersAttnProcessor
+from ...models.attention_processor import AttnProcessor2_0, FusedAttnProcessor2_0, XFormersAttnProcessor
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import is_torch_xla_available, logging, replace_example_docstring
 from ...utils.torch_utils import randn_tensor
@@ -602,10 +602,7 @@ def upcast_vae(self):
         self.vae.to(dtype=torch.float32)
         use_torch_2_0_or_xformers = isinstance(
             self.vae.decoder.mid_block.attentions[0].processor,
-            (
-                AttnProcessor2_0,
-                XFormersAttnProcessor,
-            ),
+            (AttnProcessor2_0, XFormersAttnProcessor, FusedAttnProcessor2_0),
         )
         # if xformers or torch_2_0 is used attention block does not need
         # to be in float32 which can save lots of memory

src/diffusers/pipelines/pag/pipeline_pag_sd_xl.py

@@ -32,10 +32,7 @@
     TextualInversionLoaderMixin,
 )
 from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel
-from ...models.attention_processor import (
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
-)
+from ...models.attention_processor import AttnProcessor2_0, FusedAttnProcessor2_0, XFormersAttnProcessor
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers
 from ...utils import (
@@ -765,10 +762,7 @@ def upcast_vae(self):
         self.vae.to(dtype=torch.float32)
         use_torch_2_0_or_xformers = isinstance(
             self.vae.decoder.mid_block.attentions[0].processor,
-            (
-                AttnProcessor2_0,
-                XFormersAttnProcessor,
-            ),
+            (AttnProcessor2_0, XFormersAttnProcessor, FusedAttnProcessor2_0),
         )
         # if xformers or torch_2_0 is used attention block does not need
         # to be in float32 which can save lots of memory

src/diffusers/pipelines/stable_diffusion_k_diffusion/pipeline_stable_diffusion_xl_k_diffusion.py

@@ -33,10 +33,7 @@
     TextualInversionLoaderMixin,
 )
 from ...models import AutoencoderKL, UNet2DConditionModel
-from ...models.attention_processor import (
-    AttnProcessor2_0,
-    XFormersAttnProcessor,
-)
+from ...models.attention_processor import AttnProcessor2_0, FusedAttnProcessor2_0, XFormersAttnProcessor
 from ...models.lora import adjust_lora_scale_text_encoder
 from ...schedulers import KarrasDiffusionSchedulers, LMSDiscreteScheduler
 from ...utils import (
@@ -548,10 +545,7 @@ def upcast_vae(self):
         self.vae.to(dtype=torch.float32)
         use_torch_2_0_or_xformers = isinstance(
             self.vae.decoder.mid_block.attentions[0].processor,
-            (
-                AttnProcessor2_0,
-                XFormersAttnProcessor,
-            ),
+            (AttnProcessor2_0, XFormersAttnProcessor, FusedAttnProcessor2_0),
         )
         # if xformers or torch_2_0 is used attention block does not need
         # to be in float32 which can save lots of memory

src/diffusers/pipelines/text_to_video_synthesis/pipeline_text_to_video_zero_sdxl.py

@@ -461,11 +461,7 @@ def upcast_vae(self):
         self.vae.to(dtype=torch.float32)
         use_torch_2_0_or_xformers = isinstance(
             self.vae.decoder.mid_block.attentions[0].processor,
-            (
-                AttnProcessor2_0,
-                XFormersAttnProcessor,
-                FusedAttnProcessor2_0,
-            ),
+            (AttnProcessor2_0, XFormersAttnProcessor, FusedAttnProcessor2_0),
         )
         # if xformers or torch_2_0 is used attention block does not need
         # to be in float32 which can save lots of memory