update

Author: DN6

tests/modular_pipelines/stable_diffusion_xl/test_modular_pipeline_stable_diffusion_xl.py

@@ -24,7 +24,6 @@
 
 from diffusers import (
     ClassifierFreeGuidance,
-    ComponentsManager,
     ModularPipeline,
     StableDiffusionXLAutoBlocks,
     StableDiffusionXLModularPipeline,
@@ -33,7 +32,6 @@
 from diffusers.utils.testing_utils import (
     enable_full_determinism,
     floats_tensor,
-    require_torch_accelerator,
     torch_device,
 )
 
@@ -99,9 +97,9 @@ def _test_stable_diffusion_xl_euler(self, expected_image_shape, expected_slice,
 
         assert image.shape == expected_image_shape
 
-        assert (
-            np.abs(image_slice.flatten() - expected_slice).max() < expected_max_diff
-        ), "Image Slice does not match expected slice"
+        assert np.abs(image_slice.flatten() - expected_slice).max() < expected_max_diff, (
+            "Image Slice does not match expected slice"
+        )
 
 
 class SDXLModularIPAdapterTests:
@@ -114,20 +112,20 @@ def test_pipeline_inputs_and_blocks(self):
         parameters = blocks.input_names
 
         assert issubclass(self.pipeline_class, ModularIPAdapterMixin)
-        assert (
-            "ip_adapter_image" in parameters
-        ), "`ip_adapter_image` argument must be supported by the `__call__` method"
+        assert "ip_adapter_image" in parameters, (
+            "`ip_adapter_image` argument must be supported by the `__call__` method"
+        )
         assert "ip_adapter" in blocks.sub_blocks, "pipeline must contain an IPAdapter block"
 
         _ = blocks.sub_blocks.pop("ip_adapter")
         parameters = blocks.input_names
         intermediate_parameters = blocks.intermediate_input_names
-        assert (
-            "ip_adapter_image" not in parameters
-        ), "`ip_adapter_image` argument must be removed from the `__call__` method"
-        assert (
-            "ip_adapter_image_embeds" not in intermediate_parameters
-        ), "`ip_adapter_image_embeds` argument must be supported by the `__call__` method"
+        assert "ip_adapter_image" not in parameters, (
+            "`ip_adapter_image` argument must be removed from the `__call__` method"
+        )
+        assert "ip_adapter_image_embeds" not in intermediate_parameters, (
+            "`ip_adapter_image_embeds` argument must be supported by the `__call__` method"
+        )
 
     def _get_dummy_image_embeds(self, cross_attention_dim: int = 32):
         return torch.randn((1, 1, cross_attention_dim), device=torch_device)
@@ -203,9 +201,9 @@ def test_ip_adapter(self, expected_max_diff: float = 1e-4, expected_pipe_slice=N
         max_diff_without_adapter_scale = np.abs(output_without_adapter_scale - output_without_adapter).max()
         max_diff_with_adapter_scale = np.abs(output_with_adapter_scale - output_without_adapter).max()
 
-        assert (
-            max_diff_without_adapter_scale < expected_max_diff
-        ), "Output without ip-adapter must be same as normal inference"
+        assert max_diff_without_adapter_scale < expected_max_diff, (
+            "Output without ip-adapter must be same as normal inference"
+        )
         assert max_diff_with_adapter_scale > 1e-2, "Output with ip-adapter must be different from normal inference"
 
         # 2. Multi IP-Adapter test cases
@@ -235,12 +233,12 @@ def test_ip_adapter(self, expected_max_diff: float = 1e-4, expected_pipe_slice=N
             output_without_multi_adapter_scale - output_without_adapter
         ).max()
         max_diff_with_multi_adapter_scale = np.abs(output_with_multi_adapter_scale - output_without_adapter).max()
-        assert (
-            max_diff_without_multi_adapter_scale < expected_max_diff
-        ), "Output without multi-ip-adapter must be same as normal inference"
-        assert (
-            max_diff_with_multi_adapter_scale > 1e-2
-        ), "Output with multi-ip-adapter scale must be different from normal inference"
+        assert max_diff_without_multi_adapter_scale < expected_max_diff, (
+            "Output without multi-ip-adapter must be same as normal inference"
+        )
+        assert max_diff_with_multi_adapter_scale > 1e-2, (
+            "Output with multi-ip-adapter scale must be different from normal inference"
+        )
 
 
 class SDXLModularControlNetTests:
@@ -253,9 +251,9 @@ def test_pipeline_inputs(self):
         parameters = blocks.input_names
 
         assert "control_image" in parameters, "`control_image` argument must be supported by the `__call__` method"
-        assert (
-            "controlnet_conditioning_scale" in parameters
-        ), "`controlnet_conditioning_scale` argument must be supported by the `__call__` method"
+        assert "controlnet_conditioning_scale" in parameters, (
+            "`controlnet_conditioning_scale` argument must be supported by the `__call__` method"
+        )
 
     def _modify_inputs_for_controlnet_test(self, inputs: Dict[str, Any]):
         controlnet_embedder_scale_factor = 2
@@ -301,9 +299,9 @@ def test_controlnet(self, expected_max_diff: float = 1e-4, expected_pipe_slice=N
         max_diff_without_controlnet_scale = np.abs(output_without_controlnet_scale - output_without_controlnet).max()
         max_diff_with_controlnet_scale = np.abs(output_with_controlnet_scale - output_without_controlnet).max()
 
-        assert (
-            max_diff_without_controlnet_scale < expected_max_diff
-        ), "Output without controlnet must be same as normal inference"
+        assert max_diff_without_controlnet_scale < expected_max_diff, (
+            "Output without controlnet must be same as normal inference"
+        )
         assert max_diff_with_controlnet_scale > 1e-2, "Output with controlnet must be different from normal inference"
 
     def test_controlnet_cfg(self):

tests/modular_pipelines/test_modular_pipelines_common.py

@@ -1,13 +1,14 @@
 import gc
+import tempfile
 import unittest
 from typing import Callable, Union
 
 import numpy as np
 import torch
 
 import diffusers
+from diffusers import ComponentsManager, ModularPipeline, ModularPipelineBlocks
 from diffusers.utils import logging
-from diffusers.utils.dummy_pt_objects import ModularPipeline, ModularPipelineBlocks
 from diffusers.utils.testing_utils import (
     backend_empty_cache,
     numpy_cosine_similarity_distance,
@@ -143,9 +144,9 @@ def test_pipeline_call_signature(self):
 
         def _check_for_parameters(parameters, expected_parameters, param_type):
             remaining_parameters = {param for param in parameters if param not in expected_parameters}
-            assert (
-                len(remaining_parameters) == 0
-            ), f"Required {param_type} parameters not present: {remaining_parameters}"
+            assert len(remaining_parameters) == 0, (
+                f"Required {param_type} parameters not present: {remaining_parameters}"
+            )
 
         _check_for_parameters(self.params, input_parameters, "input")
         _check_for_parameters(self.intermediate_params, intermediate_parameters, "intermediate")
@@ -274,9 +275,9 @@ def test_to_device(self):
         model_devices = [
             component.device.type for component in pipe.components.values() if hasattr(component, "device")
         ]
-        assert all(
-            device == torch_device for device in model_devices
-        ), "All pipeline components are not on accelerator device"
+        assert all(device == torch_device for device in model_devices), (
+            "All pipeline components are not on accelerator device"
+        )
 
     def test_inference_is_not_nan_cpu(self):
         pipe = self.get_pipeline()
@@ -344,7 +345,7 @@ def test_save_from_pretrained(self):
         with tempfile.TemporaryDirectory() as tmpdirname:
             base_pipe.save_pretrained(tmpdirname)
             pipe = ModularPipeline.from_pretrained(tmpdirname).to(torch_device)
-            pipe.load_default_components(torch_dtype=torch.float32)
+            pipe.load_default_components(torch_dtype=torch.float16)
             pipe.to(torch_device)
 
         pipes.append(pipe)