Slow Test for Pipelines minor fixes (#6221)

update

src/diffusers/pipelines/shap_e/pipeline_shap_e_img2img.py

@@ -283,6 +283,9 @@ class ShapEImg2ImgPipeline(DiffusionPipeline):
                 f"Only the output types `pil`, `np`, `latent` and `mesh` are supported not output_type={output_type}"
             )
 
+        # Offload all models
+        self.maybe_free_model_hooks()
+
         if output_type == "latent":
             return ShapEPipelineOutput(images=latents)
 
@@ -312,9 +315,6 @@ class ShapEImg2ImgPipeline(DiffusionPipeline):
             if output_type == "pil":
                 images = [self.numpy_to_pil(image) for image in images]
 
-        # Offload all models
-        self.maybe_free_model_hooks()
-
         if not return_dict:
             return (images,)
 

src/diffusers/pipelines/unclip/pipeline_unclip.py

@@ -477,8 +477,9 @@ class UnCLIPPipeline(DiffusionPipeline):
         image = super_res_latents
         # done super res
 
-        # post processing
+        self.maybe_free_model_hooks()
 
+        # post processing
         image = image * 0.5 + 0.5
         image = image.clamp(0, 1)
         image = image.cpu().permute(0, 2, 3, 1).float().numpy()

src/diffusers/pipelines/unclip/pipeline_unclip_image_variation.py

@@ -403,6 +403,7 @@ class UnCLIPImageVariationPipeline(DiffusionPipeline):
         image = super_res_latents
 
         # done super res
+        self.maybe_free_model_hooks()
 
         # post processing
 

tests/pipelines/animatediff/test_animatediff.py

@@ -14,7 +14,7 @@ from diffusers import (
     UNet2DConditionModel,
     UNetMotionModel,
 )
-from diffusers.utils import logging
+from diffusers.utils import is_xformers_available, logging
 from diffusers.utils.testing_utils import numpy_cosine_similarity_distance, require_torch_gpu, slow, torch_device
 
 from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
@@ -233,6 +233,35 @@ class AnimateDiffPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
         inputs["prompt_embeds"] = torch.randn((1, 4, 32), device=torch_device)
         pipe(**inputs)
 
+    @unittest.skipIf(
+        torch_device != "cuda" or not is_xformers_available(),
+        reason="XFormers attention is only available with CUDA and `xformers` installed",
+    )
+    def test_xformers_attention_forwardGenerator_pass(self):
+        components = self.get_dummy_components()
+        pipe = self.pipeline_class(**components)
+        for component in pipe.components.values():
+            if hasattr(component, "set_default_attn_processor"):
+                component.set_default_attn_processor()
+        pipe.to(torch_device)
+        pipe.set_progress_bar_config(disable=None)
+
+        inputs = self.get_dummy_inputs(torch_device)
+        output_without_offload = pipe(**inputs).frames[0]
+        output_without_offload = (
+            output_without_offload.cpu() if torch.is_tensor(output_without_offload) else output_without_offload
+        )
+
+        pipe.enable_xformers_memory_efficient_attention()
+        inputs = self.get_dummy_inputs(torch_device)
+        output_with_offload = pipe(**inputs).frames[0]
+        output_with_offload = (
+            output_with_offload.cpu() if torch.is_tensor(output_with_offload) else output_without_offload
+        )
+
+        max_diff = np.abs(to_np(output_with_offload) - to_np(output_without_offload)).max()
+        self.assertLess(max_diff, 1e-4, "XFormers attention should not affect the inference results")
+
 
 @slow
 @require_torch_gpu

tests/pipelines/stable_diffusion/test_stable_diffusion_adapter.py

@@ -804,8 +804,7 @@ class StableDiffusionAdapterPipelineSlowTests(unittest.TestCase):
         pipe = StableDiffusionAdapterPipeline.from_pretrained(sd_model, adapter=adapter, safety_checker=None)
         pipe.to(torch_device)
         pipe.set_progress_bar_config(disable=None)
-        pipe.enable_attention_slicing()
-
+        pipe.enable_model_cpu_offload()
         generator = torch.Generator(device="cpu").manual_seed(0)
         out = pipe(prompt=prompt, image=image, generator=generator, num_inference_steps=2, output_type="np").images
 

tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_adapter.py

@@ -681,7 +681,7 @@ class AdapterSDXLPipelineSlowTests(unittest.TestCase):
             variant="fp16",
         )
         pipe.load_lora_weights("CiroN2022/toy-face", weight_name="toy_face_sdxl.safetensors")
-        pipe.enable_sequential_cpu_offload()
+        pipe.enable_model_cpu_offload()
         pipe.set_progress_bar_config(disable=None)
 
         generator = torch.Generator(device="cpu").manual_seed(0)
@@ -694,8 +694,6 @@ class AdapterSDXLPipelineSlowTests(unittest.TestCase):
 
         assert images[0].shape == (768, 512, 3)
 
-        original_image = images[0, -3:, -3:, -1].flatten()
-        expected_image = np.array(
-            [0.50346327, 0.50708383, 0.50719553, 0.5135172, 0.5155377, 0.5066059, 0.49680984, 0.5005894, 0.48509413]
-        )
-        assert numpy_cosine_similarity_distance(original_image, expected_image) < 1e-4
+        image_slice = images[0, -3:, -3:, -1].flatten()
+        expected_slice = np.array([0.4284, 0.4337, 0.4319, 0.4255, 0.4329, 0.4280, 0.4338, 0.4420, 0.4226])
+        assert numpy_cosine_similarity_distance(image_slice, expected_slice) < 1e-4