a few fix for kandinsky combined pipeline

src/diffusers/pipelines/kandinsky/pipeline_kandinsky_combined.py

@@ -188,6 +188,9 @@ def __init__(
             movq=movq,
         )
 
+    def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):
+        self.decoder_pipe.enable_xformers_memory_efficient_attention(attention_op)
+
     def enable_model_cpu_offload(self, gpu_id=0):
         r"""
         Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
@@ -198,6 +201,16 @@ def enable_model_cpu_offload(self, gpu_id=0):
         self.prior_pipe.enable_model_cpu_offload()
         self.decoder_pipe.enable_model_cpu_offload()
 
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models (`unet`, `text_encoder`, `vae`, and `safety checker` state dicts) to CPU using 🤗 Accelerate, significantly reducing memory usage. Models are moved to a
+        `torch.device('meta')` and loaded on a GPU only when their specific submodule's `forward` method is called.
+        Offloading happens on a submodule basis. Memory savings are higher than using
+        `enable_model_cpu_offload`, but performance is lower.
+        """
+        self.prior_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id)
+        self.decoder_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id)
+
     def progress_bar(self, iterable=None, total=None):
         self.prior_pipe.progress_bar(iterable=iterable, total=total)
         self.decoder_pipe.progress_bar(iterable=iterable, total=total)
@@ -398,6 +411,9 @@ def __init__(
             movq=movq,
         )
 
+    def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):
+        self.decoder_pipe.enable_xformers_memory_efficient_attention(attention_op)
+
     def enable_model_cpu_offload(self, gpu_id=0):
         r"""
         Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
@@ -408,6 +424,17 @@ def enable_model_cpu_offload(self, gpu_id=0):
         self.prior_pipe.enable_model_cpu_offload()
         self.decoder_pipe.enable_model_cpu_offload()
 
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
+        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
+        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
+        Note that offloading happens on a submodule basis. Memory savings are higher than with
+        `enable_model_cpu_offload`, but performance is lower.
+        """
+        self.prior_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id)
+        self.decoder_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id)
+
     def progress_bar(self, iterable=None, total=None):
         self.prior_pipe.progress_bar(iterable=iterable, total=total)
         self.decoder_pipe.progress_bar(iterable=iterable, total=total)
@@ -630,6 +657,9 @@ def __init__(
             movq=movq,
         )
 
+    def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):
+        self.decoder_pipe.enable_xformers_memory_efficient_attention(attention_op)
+
     def enable_model_cpu_offload(self, gpu_id=0):
         r"""
         Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
@@ -640,6 +670,17 @@ def enable_model_cpu_offload(self, gpu_id=0):
         self.prior_pipe.enable_model_cpu_offload()
         self.decoder_pipe.enable_model_cpu_offload()
 
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
+        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
+        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
+        Note that offloading happens on a submodule basis. Memory savings are higher than with
+        `enable_model_cpu_offload`, but performance is lower.
+        """
+        self.prior_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id)
+        self.decoder_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id)
+
     def progress_bar(self, iterable=None, total=None):
         self.prior_pipe.progress_bar(iterable=iterable, total=total)
         self.decoder_pipe.progress_bar(iterable=iterable, total=total)

src/diffusers/pipelines/kandinsky2_2/pipeline_kandinsky2_2_combined.py

@@ -177,6 +177,9 @@ def __init__(
             movq=movq,
         )
 
+    def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):
+        self.decoder_pipe.enable_xformers_memory_efficient_attention(attention_op)
+
     def enable_model_cpu_offload(self, gpu_id=0):
         r"""
         Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
@@ -187,6 +190,17 @@ def enable_model_cpu_offload(self, gpu_id=0):
         self.prior_pipe.enable_model_cpu_offload()
         self.decoder_pipe.enable_model_cpu_offload()
 
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
+        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
+        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
+        Note that offloading happens on a submodule basis. Memory savings are higher than with
+        `enable_model_cpu_offload`, but performance is lower.
+        """
+        self.prior_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id)
+        self.decoder_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id)
+
     def progress_bar(self, iterable=None, total=None):
         self.prior_pipe.progress_bar(iterable=iterable, total=total)
         self.decoder_pipe.progress_bar(iterable=iterable, total=total)
@@ -378,6 +392,9 @@ def __init__(
             movq=movq,
         )
 
+    def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):
+        self.decoder_pipe.enable_xformers_memory_efficient_attention(attention_op)
+
     def enable_model_cpu_offload(self, gpu_id=0):
         r"""
         Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
@@ -388,6 +405,17 @@ def enable_model_cpu_offload(self, gpu_id=0):
         self.prior_pipe.enable_model_cpu_offload()
         self.decoder_pipe.enable_model_cpu_offload()
 
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
+        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
+        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
+        Note that offloading happens on a submodule basis. Memory savings are higher than with
+        `enable_model_cpu_offload`, but performance is lower.
+        """
+        self.prior_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id)
+        self.decoder_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id)
+
     def progress_bar(self, iterable=None, total=None):
         self.prior_pipe.progress_bar(iterable=iterable, total=total)
         self.decoder_pipe.progress_bar(iterable=iterable, total=total)
@@ -601,6 +629,9 @@ def __init__(
             movq=movq,
         )
 
+    def enable_xformers_memory_efficient_attention(self, attention_op: Optional[Callable] = None):
+        self.decoder_pipe.enable_xformers_memory_efficient_attention(attention_op)
+
     def enable_model_cpu_offload(self, gpu_id=0):
         r"""
         Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared
@@ -611,6 +642,17 @@ def enable_model_cpu_offload(self, gpu_id=0):
         self.prior_pipe.enable_model_cpu_offload()
         self.decoder_pipe.enable_model_cpu_offload()
 
+    def enable_sequential_cpu_offload(self, gpu_id=0):
+        r"""
+        Offloads all models to CPU using accelerate, significantly reducing memory usage. When called, unet,
+        text_encoder, vae and safety checker have their state dicts saved to CPU and then are moved to a
+        `torch.device('meta') and loaded to GPU only when their specific submodule has its `forward` method called.
+        Note that offloading happens on a submodule basis. Memory savings are higher than with
+        `enable_model_cpu_offload`, but performance is lower.
+        """
+        self.prior_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id)
+        self.decoder_pipe.enable_sequential_cpu_offload(gpu_id=gpu_id)
+
     def progress_bar(self, iterable=None, total=None):
         self.prior_pipe.progress_bar(iterable=iterable, total=total)
         self.decoder_pipe.progress_bar(iterable=iterable, total=total)

tests/pipelines/kandinsky/test_kandinsky_combined.py

@@ -51,7 +51,7 @@ class KandinskyPipelineCombinedFastTests(PipelineTesterMixin, unittest.TestCase)
         "output_type",
         "return_dict",
     ]
-    test_xformers_attention = False
+    test_xformers_attention = True
 
     def get_dummy_components(self):
         dummy = Dummies()

tests/pipelines/kandinsky_v22/test_kandinsky_combined.py

@@ -55,7 +55,7 @@ class KandinskyV22PipelineCombinedFastTests(PipelineTesterMixin, unittest.TestCa
         "output_type",
         "return_dict",
     ]
-    test_xformers_attention = False
+    test_xformers_attention = True
 
     def get_dummy_components(self):
         dummy = Dummies()