Refactor Deepfloyd IF tests. (#6855)

* update

* update

* update

tests/pipelines/deepfloyd_if/test_if.py

@@ -14,22 +14,16 @@
 # limitations under the License.
 
 import gc
-import random
 import unittest
 
 import torch
 
 from diffusers import (
-    IFImg2ImgPipeline,
-    IFImg2ImgSuperResolutionPipeline,
-    IFInpaintingPipeline,
-    IFInpaintingSuperResolutionPipeline,
     IFPipeline,
-    IFSuperResolutionPipeline,
 )
 from diffusers.models.attention_processor import AttnAddedKVProcessor
 from diffusers.utils.import_utils import is_xformers_available
-from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
+from diffusers.utils.testing_utils import load_numpy, require_torch_gpu, skip_mps, slow, torch_device
 
 from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
 from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
@@ -97,77 +91,18 @@ class IFPipelineSlowTests(unittest.TestCase):
         gc.collect()
         torch.cuda.empty_cache()
 
-    def test_all(self):
-        # if
+    def test_if_text_to_image(self):
+        pipe = IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
+        pipe.unet.set_attn_processor(AttnAddedKVProcessor())
+        pipe.enable_model_cpu_offload()
 
-        pipe_1 = IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16)
-
-        pipe_2 = IFSuperResolutionPipeline.from_pretrained(
-            "DeepFloyd/IF-II-L-v1.0", variant="fp16", torch_dtype=torch.float16, text_encoder=None, tokenizer=None
-        )
-
-        # pre compute text embeddings and remove T5 to save memory
-
-        pipe_1.text_encoder.to("cuda")
-
-        prompt_embeds, negative_prompt_embeds = pipe_1.encode_prompt("anime turtle", device="cuda")
-
-        del pipe_1.tokenizer
-        del pipe_1.text_encoder
-        gc.collect()
-
-        pipe_1.tokenizer = None
-        pipe_1.text_encoder = None
-
-        pipe_1.enable_model_cpu_offload()
-        pipe_2.enable_model_cpu_offload()
-
-        pipe_1.unet.set_attn_processor(AttnAddedKVProcessor())
-        pipe_2.unet.set_attn_processor(AttnAddedKVProcessor())
-
-        self._test_if(pipe_1, pipe_2, prompt_embeds, negative_prompt_embeds)
-
-        pipe_1.remove_all_hooks()
-        pipe_2.remove_all_hooks()
-
-        # img2img
-
-        pipe_1 = IFImg2ImgPipeline(**pipe_1.components)
-        pipe_2 = IFImg2ImgSuperResolutionPipeline(**pipe_2.components)
-
-        pipe_1.enable_model_cpu_offload()
-        pipe_2.enable_model_cpu_offload()
-
-        pipe_1.unet.set_attn_processor(AttnAddedKVProcessor())
-        pipe_2.unet.set_attn_processor(AttnAddedKVProcessor())
-
-        self._test_if_img2img(pipe_1, pipe_2, prompt_embeds, negative_prompt_embeds)
-
-        pipe_1.remove_all_hooks()
-        pipe_2.remove_all_hooks()
-
-        # inpainting
-
-        pipe_1 = IFInpaintingPipeline(**pipe_1.components)
-        pipe_2 = IFInpaintingSuperResolutionPipeline(**pipe_2.components)
-
-        pipe_1.enable_model_cpu_offload()
-        pipe_2.enable_model_cpu_offload()
-
-        pipe_1.unet.set_attn_processor(AttnAddedKVProcessor())
-        pipe_2.unet.set_attn_processor(AttnAddedKVProcessor())
-
-        self._test_if_inpainting(pipe_1, pipe_2, prompt_embeds, negative_prompt_embeds)
-
-    def _test_if(self, pipe_1, pipe_2, prompt_embeds, negative_prompt_embeds):
-        # pipeline 1
-
-        _start_torch_memory_measurement()
+        torch.cuda.reset_max_memory_allocated()
+        torch.cuda.empty_cache()
+        torch.cuda.reset_peak_memory_stats()
 
         generator = torch.Generator(device="cpu").manual_seed(0)
-        output = pipe_1(
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
+        output = pipe(
+            prompt="anime turtle",
             num_inference_steps=2,
             generator=generator,
             output_type="np",
@@ -175,172 +110,11 @@ class IFPipelineSlowTests(unittest.TestCase):
 
         image = output.images[0]
 
-        assert image.shape == (64, 64, 3)
-
         mem_bytes = torch.cuda.max_memory_allocated()
-        assert mem_bytes < 13 * 10**9
+        assert mem_bytes < 12 * 10**9
 
         expected_image = load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"
         )
         assert_mean_pixel_difference(image, expected_image)
-
-        # pipeline 2
-
-        _start_torch_memory_measurement()
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-
-        image = floats_tensor((1, 3, 64, 64), rng=random.Random(0)).to(torch_device)
-
-        output = pipe_2(
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            image=image,
-            generator=generator,
-            num_inference_steps=2,
-            output_type="np",
-        )
-
-        image = output.images[0]
-
-        assert image.shape == (256, 256, 3)
-
-        mem_bytes = torch.cuda.max_memory_allocated()
-        assert mem_bytes < 4 * 10**9
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"
-        )
-        assert_mean_pixel_difference(image, expected_image)
-
-    def _test_if_img2img(self, pipe_1, pipe_2, prompt_embeds, negative_prompt_embeds):
-        # pipeline 1
-
-        _start_torch_memory_measurement()
-
-        image = floats_tensor((1, 3, 64, 64), rng=random.Random(0)).to(torch_device)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-
-        output = pipe_1(
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            image=image,
-            num_inference_steps=2,
-            generator=generator,
-            output_type="np",
-        )
-
-        image = output.images[0]
-
-        assert image.shape == (64, 64, 3)
-
-        mem_bytes = torch.cuda.max_memory_allocated()
-        assert mem_bytes < 10 * 10**9
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"
-        )
-        assert_mean_pixel_difference(image, expected_image)
-
-        # pipeline 2
-
-        _start_torch_memory_measurement()
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-
-        original_image = floats_tensor((1, 3, 256, 256), rng=random.Random(0)).to(torch_device)
-        image = floats_tensor((1, 3, 64, 64), rng=random.Random(0)).to(torch_device)
-
-        output = pipe_2(
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            image=image,
-            original_image=original_image,
-            generator=generator,
-            num_inference_steps=2,
-            output_type="np",
-        )
-
-        image = output.images[0]
-
-        assert image.shape == (256, 256, 3)
-
-        mem_bytes = torch.cuda.max_memory_allocated()
-        assert mem_bytes < 4 * 10**9
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"
-        )
-        assert_mean_pixel_difference(image, expected_image)
-
-    def _test_if_inpainting(self, pipe_1, pipe_2, prompt_embeds, negative_prompt_embeds):
-        # pipeline 1
-
-        _start_torch_memory_measurement()
-
-        image = floats_tensor((1, 3, 64, 64), rng=random.Random(0)).to(torch_device)
-        mask_image = floats_tensor((1, 3, 64, 64), rng=random.Random(1)).to(torch_device)
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-        output = pipe_1(
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            image=image,
-            mask_image=mask_image,
-            num_inference_steps=2,
-            generator=generator,
-            output_type="np",
-        )
-
-        image = output.images[0]
-
-        assert image.shape == (64, 64, 3)
-
-        mem_bytes = torch.cuda.max_memory_allocated()
-        assert mem_bytes < 10 * 10**9
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"
-        )
-        assert_mean_pixel_difference(image, expected_image)
-
-        # pipeline 2
-
-        _start_torch_memory_measurement()
-
-        generator = torch.Generator(device="cpu").manual_seed(0)
-
-        image = floats_tensor((1, 3, 64, 64), rng=random.Random(0)).to(torch_device)
-        original_image = floats_tensor((1, 3, 256, 256), rng=random.Random(0)).to(torch_device)
-        mask_image = floats_tensor((1, 3, 256, 256), rng=random.Random(1)).to(torch_device)
-
-        output = pipe_2(
-            prompt_embeds=prompt_embeds,
-            negative_prompt_embeds=negative_prompt_embeds,
-            image=image,
-            mask_image=mask_image,
-            original_image=original_image,
-            generator=generator,
-            num_inference_steps=2,
-            output_type="np",
-        )
-
-        image = output.images[0]
-
-        assert image.shape == (256, 256, 3)
-
-        mem_bytes = torch.cuda.max_memory_allocated()
-        assert mem_bytes < 4 * 10**9
-
-        expected_image = load_numpy(
-            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"
-        )
-        assert_mean_pixel_difference(image, expected_image)
-
-
-def _start_torch_memory_measurement():
-    torch.cuda.empty_cache()
-    torch.cuda.reset_max_memory_allocated()
-    torch.cuda.reset_peak_memory_stats()
+        pipe.remove_all_hooks()

tests/pipelines/deepfloyd_if/test_if_img2img.py

@@ -13,20 +13,22 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import gc
 import random
 import unittest
 
 import torch
 
 from diffusers import IFImg2ImgPipeline
+from diffusers.models.attention_processor import AttnAddedKVProcessor
 from diffusers.utils.import_utils import is_xformers_available
-from diffusers.utils.testing_utils import floats_tensor, skip_mps, torch_device
+from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
 
 from ..pipeline_params import (
     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
     TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
 )
-from ..test_pipelines_common import PipelineTesterMixin
+from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
 from . import IFPipelineTesterMixin
 
 
@@ -87,3 +89,43 @@ class IFImg2ImgPipelineFastTests(PipelineTesterMixin, IFPipelineTesterMixin, uni
         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2,
         )
+
+
+@slow
+@require_torch_gpu
+class IFImg2ImgPipelineSlowTests(unittest.TestCase):
+    def tearDown(self):
+        # clean up the VRAM after each test
+        super().tearDown()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def test_if_img2img(self):
+        pipe = IFImg2ImgPipeline.from_pretrained(
+            "DeepFloyd/IF-I-L-v1.0",
+            variant="fp16",
+            torch_dtype=torch.float16,
+        )
+        pipe.unet.set_attn_processor(AttnAddedKVProcessor())
+        pipe.enable_model_cpu_offload()
+
+        image = floats_tensor((1, 3, 64, 64), rng=random.Random(0)).to(torch_device)
+        generator = torch.Generator(device="cpu").manual_seed(0)
+        output = pipe(
+            prompt="anime turtle",
+            image=image,
+            num_inference_steps=2,
+            generator=generator,
+            output_type="np",
+        )
+        image = output.images[0]
+
+        mem_bytes = torch.cuda.max_memory_allocated()
+        assert mem_bytes < 12 * 10**9
+
+        expected_image = load_numpy(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"
+        )
+        assert_mean_pixel_difference(image, expected_image)
+
+        pipe.remove_all_hooks()

tests/pipelines/deepfloyd_if/test_if_img2img_superresolution.py

@@ -13,17 +13,22 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import gc
 import random
 import unittest
 
 import torch
 
 from diffusers import IFImg2ImgSuperResolutionPipeline
+from diffusers.models.attention_processor import AttnAddedKVProcessor
 from diffusers.utils.import_utils import is_xformers_available
-from diffusers.utils.testing_utils import floats_tensor, skip_mps, torch_device
+from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
 
-from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
-from ..test_pipelines_common import PipelineTesterMixin
+from ..pipeline_params import (
+    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
+    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
+)
+from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
 from . import IFPipelineTesterMixin
 
 
@@ -82,3 +87,50 @@ class IFImg2ImgSuperResolutionPipelineFastTests(PipelineTesterMixin, IFPipelineT
         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2,
         )
+
+
+@slow
+@require_torch_gpu
+class IFImg2ImgSuperResolutionPipelineSlowTests(unittest.TestCase):
+    def tearDown(self):
+        # clean up the VRAM after each test
+        super().tearDown()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def test_if_img2img_superresolution(self):
+        pipe = IFImg2ImgSuperResolutionPipeline.from_pretrained(
+            "DeepFloyd/IF-II-L-v1.0",
+            variant="fp16",
+            torch_dtype=torch.float16,
+        )
+        pipe.unet.set_attn_processor(AttnAddedKVProcessor())
+        pipe.enable_model_cpu_offload()
+
+        generator = torch.Generator(device="cpu").manual_seed(0)
+
+        original_image = floats_tensor((1, 3, 256, 256), rng=random.Random(0)).to(torch_device)
+        image = floats_tensor((1, 3, 64, 64), rng=random.Random(0)).to(torch_device)
+
+        output = pipe(
+            prompt="anime turtle",
+            image=image,
+            original_image=original_image,
+            generator=generator,
+            num_inference_steps=2,
+            output_type="np",
+        )
+
+        image = output.images[0]
+
+        assert image.shape == (256, 256, 3)
+
+        mem_bytes = torch.cuda.max_memory_allocated()
+        assert mem_bytes < 12 * 10**9
+
+        expected_image = load_numpy(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"
+        )
+        assert_mean_pixel_difference(image, expected_image)
+
+        pipe.remove_all_hooks()

tests/pipelines/deepfloyd_if/test_if_inpainting.py

@@ -13,20 +13,22 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import gc
 import random
 import unittest
 
 import torch
 
 from diffusers import IFInpaintingPipeline
+from diffusers.models.attention_processor import AttnAddedKVProcessor
 from diffusers.utils.import_utils import is_xformers_available
-from diffusers.utils.testing_utils import floats_tensor, skip_mps, torch_device
+from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
 
 from ..pipeline_params import (
     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
 )
-from ..test_pipelines_common import PipelineTesterMixin
+from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
 from . import IFPipelineTesterMixin
 
 
@@ -85,3 +87,48 @@ class IFInpaintingPipelineFastTests(PipelineTesterMixin, IFPipelineTesterMixin,
         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2,
         )
+
+
+@slow
+@require_torch_gpu
+class IFInpaintingPipelineSlowTests(unittest.TestCase):
+    def tearDown(self):
+        # clean up the VRAM after each test
+        super().tearDown()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def test_if_inpainting(self):
+        pipe = IFInpaintingPipeline.from_pretrained(
+            "DeepFloyd/IF-I-XL-v1.0", variant="fp16", torch_dtype=torch.float16
+        )
+        pipe.unet.set_attn_processor(AttnAddedKVProcessor())
+        pipe.enable_model_cpu_offload()
+
+        # Super resolution test
+        torch.cuda.empty_cache()
+        torch.cuda.reset_max_memory_allocated()
+        torch.cuda.reset_peak_memory_stats()
+
+        image = floats_tensor((1, 3, 64, 64), rng=random.Random(0)).to(torch_device)
+        mask_image = floats_tensor((1, 3, 64, 64), rng=random.Random(1)).to(torch_device)
+
+        generator = torch.Generator(device="cpu").manual_seed(0)
+        output = pipe(
+            prompt="anime prompts",
+            image=image,
+            mask_image=mask_image,
+            num_inference_steps=2,
+            generator=generator,
+            output_type="np",
+        )
+        image = output.images[0]
+
+        mem_bytes = torch.cuda.max_memory_allocated()
+        assert mem_bytes < 12 * 10**9
+
+        expected_image = load_numpy(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"
+        )
+        assert_mean_pixel_difference(image, expected_image)
+        pipe.remove_all_hooks()

tests/pipelines/deepfloyd_if/test_if_inpainting_superresolution.py

@@ -13,20 +13,22 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import gc
 import random
 import unittest
 
 import torch
 
 from diffusers import IFInpaintingSuperResolutionPipeline
+from diffusers.models.attention_processor import AttnAddedKVProcessor
 from diffusers.utils.import_utils import is_xformers_available
-from diffusers.utils.testing_utils import floats_tensor, skip_mps, torch_device
+from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
 
 from ..pipeline_params import (
     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
     TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
 )
-from ..test_pipelines_common import PipelineTesterMixin
+from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
 from . import IFPipelineTesterMixin
 
 
@@ -87,3 +89,55 @@ class IFInpaintingSuperResolutionPipelineFastTests(PipelineTesterMixin, IFPipeli
         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2,
         )
+
+
+@slow
+@require_torch_gpu
+class IFInpaintingSuperResolutionPipelineSlowTests(unittest.TestCase):
+    def tearDown(self):
+        # clean up the VRAM after each test
+        super().tearDown()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def test_if_inpainting_superresolution(self):
+        pipe = IFInpaintingSuperResolutionPipeline.from_pretrained(
+            "DeepFloyd/IF-II-L-v1.0", variant="fp16", torch_dtype=torch.float16
+        )
+        pipe.unet.set_attn_processor(AttnAddedKVProcessor())
+        pipe.enable_model_cpu_offload()
+
+        # Super resolution test
+        torch.cuda.empty_cache()
+        torch.cuda.reset_max_memory_allocated()
+        torch.cuda.reset_peak_memory_stats()
+
+        generator = torch.Generator(device="cpu").manual_seed(0)
+
+        image = floats_tensor((1, 3, 64, 64), rng=random.Random(0)).to(torch_device)
+        original_image = floats_tensor((1, 3, 256, 256), rng=random.Random(0)).to(torch_device)
+        mask_image = floats_tensor((1, 3, 256, 256), rng=random.Random(1)).to(torch_device)
+
+        output = pipe(
+            prompt="anime turtle",
+            image=image,
+            original_image=original_image,
+            mask_image=mask_image,
+            generator=generator,
+            num_inference_steps=2,
+            output_type="np",
+        )
+
+        image = output.images[0]
+
+        assert image.shape == (256, 256, 3)
+
+        mem_bytes = torch.cuda.max_memory_allocated()
+        assert mem_bytes < 12 * 10**9
+
+        expected_image = load_numpy(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"
+        )
+        assert_mean_pixel_difference(image, expected_image)
+
+        pipe.remove_all_hooks()

tests/pipelines/deepfloyd_if/test_if_superresolution.py

@@ -13,17 +13,19 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import gc
 import random
 import unittest
 
 import torch
 
 from diffusers import IFSuperResolutionPipeline
+from diffusers.models.attention_processor import AttnAddedKVProcessor
 from diffusers.utils.import_utils import is_xformers_available
-from diffusers.utils.testing_utils import floats_tensor, skip_mps, torch_device
+from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
 
 from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
-from ..test_pipelines_common import PipelineTesterMixin
+from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
 from . import IFPipelineTesterMixin
 
 
@@ -80,3 +82,49 @@ class IFSuperResolutionPipelineFastTests(PipelineTesterMixin, IFPipelineTesterMi
         self._test_inference_batch_single_identical(
             expected_max_diff=1e-2,
         )
+
+
+@slow
+@require_torch_gpu
+class IFSuperResolutionPipelineSlowTests(unittest.TestCase):
+    def tearDown(self):
+        # clean up the VRAM after each test
+        super().tearDown()
+        gc.collect()
+        torch.cuda.empty_cache()
+
+    def test_if_superresolution(self):
+        pipe = IFSuperResolutionPipeline.from_pretrained(
+            "DeepFloyd/IF-II-L-v1.0", variant="fp16", torch_dtype=torch.float16
+        )
+        pipe.unet.set_attn_processor(AttnAddedKVProcessor())
+        pipe.enable_model_cpu_offload()
+
+        # Super resolution test
+        torch.cuda.empty_cache()
+        torch.cuda.reset_max_memory_allocated()
+        torch.cuda.reset_peak_memory_stats()
+
+        image = floats_tensor((1, 3, 64, 64), rng=random.Random(0)).to(torch_device)
+        generator = torch.Generator(device="cpu").manual_seed(0)
+        output = pipe(
+            prompt="anime turtle",
+            image=image,
+            generator=generator,
+            num_inference_steps=2,
+            output_type="np",
+        )
+
+        image = output.images[0]
+
+        assert image.shape == (256, 256, 3)
+
+        mem_bytes = torch.cuda.max_memory_allocated()
+        assert mem_bytes < 12 * 10**9
+
+        expected_image = load_numpy(
+            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"
+        )
+        assert_mean_pixel_difference(image, expected_image)
+
+        pipe.remove_all_hooks()