update

tests/pipelines/deepfloyd_if/test_if_img2img.py

@@ -115,10 +115,6 @@ class IFImg2ImgPipelineSlowTests(unittest.TestCase):
         pipe.unet.set_attn_processor(AttnAddedKVProcessor())
         pipe.enable_model_cpu_offload()
 
-        torch.cuda.reset_max_memory_allocated()
-        torch.cuda.empty_cache()
-        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(

tests/pipelines/deepfloyd_if/test_if_img2img_superresolution.py

@@ -113,10 +113,6 @@ class IFImg2ImgSuperResolutionPipelineSlowTests(unittest.TestCase):
         pipe.unet.set_attn_processor(AttnAddedKVProcessor())
         pipe.enable_model_cpu_offload()
 
-        torch.cuda.reset_max_memory_allocated()
-        torch.cuda.empty_cache()
-        torch.cuda.reset_peak_memory_stats()
-
         generator = torch.Generator(device="cpu").manual_seed(0)
 
         original_image = floats_tensor((1, 3, 256, 256), rng=random.Random(0)).to(torch_device)

tests/pipelines/deepfloyd_if/test_if_inpainting.py

@@ -111,6 +111,7 @@ class IFInpaintingPipelineSlowTests(unittest.TestCase):
         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()

tests/pipelines/kandinsky2_2/test_kandinsky.py

@@ -27,6 +27,7 @@ from diffusers.utils.testing_utils import (
     load_numpy,
     require_torch_gpu,
     slow,
+    torch_device,
 )
 
 from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
@@ -257,7 +258,7 @@ class KandinskyV22PipelineIntegrationTests(unittest.TestCase):
         image_emb, zero_image_emb = pipe_prior(
             prompt,
             generator=generator,
-            num_inference_steps=3,
+            num_inference_steps=5,
             negative_prompt="",
         ).to_tuple()
 
@@ -266,7 +267,7 @@ class KandinskyV22PipelineIntegrationTests(unittest.TestCase):
             image_embeds=image_emb,
             negative_image_embeds=zero_image_emb,
             generator=generator,
-            num_inference_steps=3,
+            num_inference_steps=100,
             output_type="np",
         )
 

tests/pipelines/kandinsky2_2/test_kandinsky_controlnet.py

@@ -34,6 +34,7 @@ from diffusers.utils.testing_utils import (
     load_numpy,
     nightly,
     require_torch_gpu,
+    torch_device,
 )
 
 from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
@@ -269,7 +270,7 @@ class KandinskyV22ControlnetPipelineIntegrationTests(unittest.TestCase):
         image_emb, zero_image_emb = pipe_prior(
             prompt,
             generator=generator,
-            num_inference_steps=2,
+            num_inference_steps=5,
             negative_prompt="",
         ).to_tuple()
 
@@ -279,7 +280,7 @@ class KandinskyV22ControlnetPipelineIntegrationTests(unittest.TestCase):
             negative_image_embeds=zero_image_emb,
             hint=hint,
             generator=generator,
-            num_inference_steps=2,
+            num_inference_steps=100,
             output_type="np",
         )
 

tests/pipelines/kandinsky2_2/test_kandinsky_controlnet_img2img.py

@@ -35,6 +35,7 @@ from diffusers.utils.testing_utils import (
     load_numpy,
     nightly,
     require_torch_gpu,
+    torch_device,
 )
 
 from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
@@ -286,7 +287,6 @@ class KandinskyV22ControlnetImg2ImgPipelineIntegrationTests(unittest.TestCase):
             strength=0.85,
             generator=generator,
             negative_prompt="",
-            num_inference_steps=5,
         ).to_tuple()
 
         output = pipeline(
@@ -295,7 +295,7 @@ class KandinskyV22ControlnetImg2ImgPipelineIntegrationTests(unittest.TestCase):
             negative_image_embeds=zero_image_emb,
             hint=hint,
             generator=generator,
-            num_inference_steps=5,
+            num_inference_steps=100,
             height=512,
             width=512,
             strength=0.5,

tests/pipelines/kandinsky2_2/test_kandinsky_img2img.py

@@ -35,6 +35,7 @@ from diffusers.utils.testing_utils import (
     load_numpy,
     require_torch_gpu,
     slow,
+    torch_device,
 )
 
 from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
@@ -287,7 +288,7 @@ class KandinskyV22Img2ImgPipelineIntegrationTests(unittest.TestCase):
             image_embeds=image_emb,
             negative_image_embeds=zero_image_emb,
             generator=generator,
-            num_inference_steps=5,
+            num_inference_steps=100,
             height=768,
             width=768,
             strength=0.2,

tests/pipelines/kandinsky2_2/test_kandinsky_inpaint.py

@@ -334,7 +334,7 @@ class KandinskyV22InpaintPipelineIntegrationTests(unittest.TestCase):
         image_emb, zero_image_emb = pipe_prior(
             prompt,
             generator=generator,
-            num_inference_steps=2,
+            num_inference_steps=5,
             negative_prompt="",
         ).to_tuple()
 
@@ -344,7 +344,7 @@ class KandinskyV22InpaintPipelineIntegrationTests(unittest.TestCase):
             image_embeds=image_emb,
             negative_image_embeds=zero_image_emb,
             generator=generator,
-            num_inference_steps=2,
+            num_inference_steps=100,
             height=768,
             width=768,
             output_type="np",

tests/pipelines/kandinsky3/test_kandinsky3.py

@@ -192,7 +192,7 @@ class Kandinsky3PipelineIntegrationTests(unittest.TestCase):
 
         generator = torch.Generator(device="cpu").manual_seed(0)
 
-        image = pipe(prompt, num_inference_steps=5, generator=generator).images[0]
+        image = pipe(prompt, num_inference_steps=25, generator=generator).images[0]
 
         assert image.size == (1024, 1024)
 
@@ -223,7 +223,7 @@ class Kandinsky3PipelineIntegrationTests(unittest.TestCase):
         image = image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)
         prompt = "A painting of the inside of a subway train with tiny raccoons."
 
-        image = pipe(prompt, image=image, strength=0.75, num_inference_steps=5, generator=generator).images[0]
+        image = pipe(prompt, image=image, strength=0.75, num_inference_steps=25, generator=generator).images[0]
 
         assert image.size == (512, 512)
 

tests/pipelines/kandinsky3/test_kandinsky3_img2img.py

@@ -215,7 +215,7 @@ class Kandinsky3Img2ImgPipelineIntegrationTests(unittest.TestCase):
         image = image.resize((w, h), resample=Image.BICUBIC, reducing_gap=1)
         prompt = "A painting of the inside of a subway train with tiny raccoons."
 
-        image = pipe(prompt, image=image, strength=0.75, num_inference_steps=5, generator=generator).images[0]
+        image = pipe(prompt, image=image, strength=0.75, num_inference_steps=25, generator=generator).images[0]
 
         assert image.size == (512, 512)
 

tests/pipelines/test_pipelines_common.py

@@ -639,12 +639,6 @@ class PipelineTesterMixin:
             "`callback_cfg_params = TEXT_TO_IMAGE_CFG_PARAMS.union({'mask', 'masked_image_latents'})`"
         )
 
-    def setUp(self):
-        # clean up the VRAM before each test
-        super().setUp()
-        gc.collect()
-        torch.cuda.empty_cache()
-
     def tearDown(self):
         # clean up the VRAM after each test in case of CUDA runtime errors
         super().tearDown()