[Tests] Speed up fast pipelines part II (#7521)

* start printing the tensors. * print full throttle * set static slices for 7 tests. * remove printing. * flatten * disable test for controlnet * what happens when things are seeded properly? * set the right value * style./ * make pia test fail to check things * print. * fix pia. * checking for animatediff. * fix: animatediff. * video synthesis * final piece. * style. * print guess. * fix: assertion for control guess. --------- Co-authored-by: Dhruv Nair <dhruv.nair@gmail.com>
2026-01-27 17:22:53 +03:00 · 2024-04-02 13:24:56 +05:30
parent 000fa82a1e
commit 2b04ec2ff7
10 changed files with 76 additions and 11 deletions
--- a/tests/pipelines/animatediff/test_animatediff.py
+++ b/tests/pipelines/animatediff/test_animatediff.py
@@ -172,6 +172,12 @@ class AnimateDiffPipelineFastTests(
            )
        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)

+    def test_dict_tuple_outputs_equivalent(self):
+        expected_slice = None
+        if torch_device == "cpu":
+            expected_slice = np.array([0.4051, 0.4495, 0.4480, 0.5845, 0.4172, 0.6066, 0.4205, 0.3786, 0.5323])
+        return super().test_dict_tuple_outputs_equivalent(expected_slice=expected_slice)
+
    def test_inference_batch_single_identical(
        self,
        batch_size=2,
--- a/tests/pipelines/controlnet/test_controlnet_blip_diffusion.py
+++ b/tests/pipelines/controlnet/test_controlnet_blip_diffusion.py
@@ -28,7 +28,7 @@ from diffusers import (
    PNDMScheduler,
    UNet2DConditionModel,
 )
-from diffusers.utils.testing_utils import enable_full_determinism
+from diffusers.utils.testing_utils import enable_full_determinism, torch_device
 from src.diffusers.pipelines.blip_diffusion.blip_image_processing import BlipImageProcessor
 from src.diffusers.pipelines.blip_diffusion.modeling_blip2 import Blip2QFormerModel
 from src.diffusers.pipelines.blip_diffusion.modeling_ctx_clip import ContextCLIPTextModel
@@ -196,6 +196,12 @@ class BlipDiffusionControlNetPipelineFastTests(PipelineTesterMixin, unittest.Tes
        }
        return inputs

+    def test_dict_tuple_outputs_equivalent(self):
+        expected_slice = None
+        if torch_device == "cpu":
+            expected_slice = np.array([0.4803, 0.3865, 0.1422, 0.6119, 0.2283, 0.6365, 0.5453, 0.5205, 0.3581])
+        super().test_dict_tuple_outputs_equivalent(expected_slice=expected_slice)
+
    def test_blipdiffusion_controlnet(self):
        device = "cpu"
        components = self.get_dummy_components()
--- a/tests/pipelines/controlnet/test_controlnet_inpaint_sdxl.py
+++ b/tests/pipelines/controlnet/test_controlnet_inpaint_sdxl.py
@@ -57,6 +57,7 @@ class ControlNetPipelineSDXLFastTests(
    image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS

    def get_dummy_components(self):
+        torch.manual_seed(0)
        unet = UNet2DConditionModel(
            block_out_channels=(32, 64),
            layers_per_block=2,
@@ -74,6 +75,7 @@ class ControlNetPipelineSDXLFastTests(
            projection_class_embeddings_input_dim=80,  # 6 * 8 + 32
            cross_attention_dim=64,
        )
+        torch.manual_seed(0)
        controlnet = ControlNetModel(
            block_out_channels=(32, 64),
            layers_per_block=2,
@@ -123,6 +125,7 @@ class ControlNetPipelineSDXLFastTests(
        text_encoder = CLIPTextModel(text_encoder_config)
        tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

+        torch.manual_seed(0)
        text_encoder_2 = CLIPTextModelWithProjection(text_encoder_config)
        tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")

@@ -182,6 +185,12 @@ class ControlNetPipelineSDXLFastTests(
    def test_attention_slicing_forward_pass(self):
        return self._test_attention_slicing_forward_pass(expected_max_diff=2e-3)

+    def test_dict_tuple_outputs_equivalent(self):
+        expected_slice = None
+        if torch_device == "cpu":
+            expected_slice = np.array([0.5490, 0.5053, 0.4676, 0.5816, 0.5364, 0.4830, 0.5937, 0.5719, 0.4318])
+        super().test_dict_tuple_outputs_equivalent(expected_slice=expected_slice)
+
    @unittest.skipIf(
        torch_device != "cuda" or not is_xformers_available(),
        reason="XFormers attention is only available with CUDA and `xformers` installed",
@@ -289,9 +298,7 @@ class ControlNetPipelineSDXLFastTests(

        output = sd_pipe(**inputs)
        image_slice = output.images[0, -3:, -3:, -1]
-        expected_slice = np.array(
-            [0.5381963, 0.4836803, 0.45821992, 0.5577731, 0.51210403, 0.4794795, 0.59282357, 0.5647199, 0.43100584]
-        )
+        expected_slice = np.array([0.549, 0.5053, 0.4676, 0.5816, 0.5364, 0.483, 0.5937, 0.5719, 0.4318])

        # make sure that it's equal
        assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-4
--- a/tests/pipelines/kandinsky3/test_kandinsky3_img2img.py
+++ b/tests/pipelines/kandinsky3/test_kandinsky3_img2img.py
@@ -36,6 +36,7 @@ from diffusers.utils.testing_utils import (
    load_image,
    require_torch_gpu,
    slow,
+    torch_device,
 )

 from ..pipeline_params import (
@@ -152,6 +153,12 @@ class Kandinsky3Img2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase)
        }
        return inputs

+    def test_dict_tuple_outputs_equivalent(self):
+        expected_slice = None
+        if torch_device == "cpu":
+            expected_slice = np.array([0.5762, 0.6112, 0.4150, 0.6018, 0.6167, 0.4626, 0.5426, 0.5641, 0.6536])
+        super().test_dict_tuple_outputs_equivalent(expected_slice=expected_slice)
+
    def test_kandinsky3_img2img(self):
        device = "cpu"

--- a/tests/pipelines/pia/test_pia.py
+++ b/tests/pipelines/pia/test_pia.py
@@ -175,6 +175,12 @@ class PIAPipelineFastTests(IPAdapterTesterMixin, PipelineTesterMixin, PipelineFr
            )
        return super().test_ip_adapter_single(expected_pipe_slice=expected_pipe_slice)

+    def test_dict_tuple_outputs_equivalent(self):
+        expected_slice = None
+        if torch_device == "cpu":
+            expected_slice = np.array([0.3740, 0.4284, 0.4038, 0.5417, 0.4405, 0.5521, 0.4273, 0.4124, 0.4997])
+        return super().test_dict_tuple_outputs_equivalent(expected_slice=expected_slice)
+
    @unittest.skip("Attention slicing is not enabled in this pipeline")
    def test_attention_slicing_forward_pass(self):
        pass
--- a/tests/pipelines/stable_diffusion_2/test_stable_diffusion_attend_and_excite.py
+++ b/tests/pipelines/stable_diffusion_2/test_stable_diffusion_attend_and_excite.py
@@ -32,6 +32,7 @@ from diffusers.utils.testing_utils import (
    numpy_cosine_similarity_distance,
    require_torch_gpu,
    skip_mps,
+    torch_device,
 )

 from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
@@ -153,6 +154,12 @@ class StableDiffusionAttendAndExcitePipelineFastTests(
        }
        return inputs

+    def test_dict_tuple_outputs_equivalent(self):
+        expected_slice = None
+        if torch_device == "cpu":
+            expected_slice = np.array([0.6391, 0.6290, 0.4860, 0.5134, 0.5550, 0.4577, 0.5033, 0.5023, 0.4538])
+        super().test_dict_tuple_outputs_equivalent(expected_slice=expected_slice, expected_max_difference=3e-3)
+
    def test_inference(self):
        device = "cpu"

@@ -182,9 +189,6 @@ class StableDiffusionAttendAndExcitePipelineFastTests(
    def test_inference_batch_single_identical(self):
        self._test_inference_batch_single_identical(batch_size=2, expected_max_diff=7e-4)

-    def test_dict_tuple_outputs_equivalent(self):
-        super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3)
-
    def test_pt_np_pil_outputs_equivalent(self):
        super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4)

--- a/tests/pipelines/stable_diffusion_gligen_text_image/test_stable_diffusion_gligen_text_image.py
+++ b/tests/pipelines/stable_diffusion_gligen_text_image/test_stable_diffusion_gligen_text_image.py
@@ -35,7 +35,7 @@ from diffusers import (
 )
 from diffusers.pipelines.stable_diffusion import CLIPImageProjection
 from diffusers.utils import load_image
-from diffusers.utils.testing_utils import enable_full_determinism
+from diffusers.utils.testing_utils import enable_full_determinism, torch_device

 from ..pipeline_params import (
    TEXT_TO_IMAGE_BATCH_PARAMS,
@@ -160,6 +160,12 @@ class GligenTextImagePipelineFastTests(
        }
        return inputs

+    def test_dict_tuple_outputs_equivalent(self):
+        expected_slice = None
+        if torch_device == "cpu":
+            expected_slice = np.array([0.5052, 0.5546, 0.4567, 0.4770, 0.5195, 0.4085, 0.5026, 0.4909, 0.4495])
+        super().test_dict_tuple_outputs_equivalent(expected_slice=expected_slice)
+
    def test_stable_diffusion_gligen_text_image_default_case(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        components = self.get_dummy_components()
--- a/tests/pipelines/test_pipelines_common.py
+++ b/tests/pipelines/test_pipelines_common.py
@@ -1037,7 +1037,7 @@ class PipelineTesterMixin:
        max_diff = np.abs(to_np(output_batch[0][0]) - to_np(output[0][0])).max()
        assert max_diff < expected_max_diff

-    def test_dict_tuple_outputs_equivalent(self, expected_max_difference=1e-4):
+    def test_dict_tuple_outputs_equivalent(self, expected_slice=None, expected_max_difference=1e-4):
        components = self.get_dummy_components()
        pipe = self.pipeline_class(**components)
        for component in pipe.components.values():
@@ -1048,10 +1048,21 @@ class PipelineTesterMixin:
        pipe.set_progress_bar_config(disable=None)

        generator_device = "cpu"
-        output = pipe(**self.get_dummy_inputs(generator_device))[0]
+        if expected_slice is None:
+            output = pipe(**self.get_dummy_inputs(generator_device))[0]
+        else:
+            output = expected_slice
+
        output_tuple = pipe(**self.get_dummy_inputs(generator_device), return_dict=False)[0]

-        max_diff = np.abs(to_np(output) - to_np(output_tuple)).max()
+        if expected_slice is None:
+            max_diff = np.abs(to_np(output) - to_np(output_tuple)).max()
+        else:
+            if output_tuple.ndim != 5:
+                max_diff = np.abs(to_np(output) - to_np(output_tuple)[0, -3:, -3:, -1].flatten()).max()
+            else:
+                max_diff = np.abs(to_np(output) - to_np(output_tuple)[0, -3:, -3:, -1, -1].flatten()).max()
+
        self.assertLess(max_diff, expected_max_difference)

    def test_components_function(self):
--- a/tests/pipelines/text_to_video_synthesis/test_text_to_video.py
+++ b/tests/pipelines/text_to_video_synthesis/test_text_to_video.py
@@ -133,6 +133,12 @@ class TextToVideoSDPipelineFastTests(PipelineTesterMixin, SDFunctionTesterMixin,
        }
        return inputs

+    def test_dict_tuple_outputs_equivalent(self):
+        expected_slice = None
+        if torch_device == "cpu":
+            expected_slice = np.array([0.4903, 0.5649, 0.5504, 0.5179, 0.4821, 0.5466, 0.4131, 0.5052, 0.5077])
+        return super().test_dict_tuple_outputs_equivalent(expected_slice=expected_slice)
+
    def test_text_to_video_default_case(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        components = self.get_dummy_components()
--- a/tests/pipelines/unidiffuser/test_unidiffuser.py
+++ b/tests/pipelines/unidiffuser/test_unidiffuser.py
@@ -206,6 +206,12 @@ class UniDiffuserPipelineFastTests(
        }
        return inputs

+    def test_dict_tuple_outputs_equivalent(self):
+        expected_slice = None
+        if torch_device == "cpu":
+            expected_slice = np.array([0.7489, 0.3722, 0.4475, 0.5630, 0.5923, 0.4992, 0.3936, 0.5844, 0.4975])
+        super().test_dict_tuple_outputs_equivalent(expected_slice=expected_slice)
+
    def test_unidiffuser_default_joint_v0(self):
        device = "cpu"  # ensure determinism for the device-dependent torch.Generator
        components = self.get_dummy_components()