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Unverified Commit 135df5be authored by Aryan's avatar Aryan Committed by GitHub
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[tests] Add inference test slices for SD3 and remove unnecessary tests (#12106) 

* update

* nuke LoC for inference slices
parent 4a9dbd56
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Showing with 46 additions and 217 deletions
tests/pipelines/stable_diffusion_3/test_pipeline_stable_diffusion_3.py
View file @ 135df5be
......@@ -124,37 +124,22 @@ class StableDiffusion3PipelineFastTests(unittest.TestCase, PipelineTesterMixin):
}
return inputs
def test_stable_diffusion_3_different_prompts(self):
pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)
inputs = self.get_dummy_inputs(torch_device)
output_same_prompt = pipe(**inputs).images[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt_2"] = "a different prompt"
inputs["prompt_3"] = "another different prompt"
output_different_prompts = pipe(**inputs).images[0]
max_diff = np.abs(output_same_prompt - output_different_prompts).max()
# Outputs should be different here
assert max_diff > 1e-2
def test_stable_diffusion_3_different_negative_prompts(self):
pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)
inputs = self.get_dummy_inputs(torch_device)
output_same_prompt = pipe(**inputs).images[0]
def test_inference(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
inputs = self.get_dummy_inputs(torch_device)
inputs["negative_prompt_2"] = "deformed"
inputs["negative_prompt_3"] = "blurry"
output_different_prompts = pipe(**inputs).images[0]
image = pipe(**inputs).images[0]
generated_slice = image.flatten()
generated_slice = np.concatenate([generated_slice[:8], generated_slice[-8:]])
max_diff = np.abs(output_same_prompt - output_different_prompts).max()
# fmt: off
expected_slice = np.array([0.5112, 0.5228, 0.5235, 0.5524, 0.3188, 0.5017, 0.5574, 0.4899, 0.6812, 0.5991, 0.3908, 0.5213, 0.5582, 0.4457, 0.4204, 0.5616])
# fmt: on
# Outputs should be different here
assert max_diff > 1e-2
self.assertTrue(
np.allclose(generated_slice, expected_slice, atol=1e-3), "Output does not match expected slice."
)
def test_fused_qkv_projections(self):
device = "cpu" # ensure determinism for the device-dependent torch.Generator
......@@ -268,40 +253,9 @@ class StableDiffusion3PipelineSlowTests(unittest.TestCase):
image = pipe(**inputs).images[0]
image_slice = image[0, :10, :10]
expected_slice = np.array(
[
0.4648,
0.4404,
0.4177,
0.5063,
0.4800,
0.4287,
0.5425,
0.5190,
0.4717,
0.5430,
0.5195,
0.4766,
0.5361,
0.5122,
0.4612,
0.4871,
0.4749,
0.4058,
0.4756,
0.4678,
0.3804,
0.4832,
0.4822,
0.3799,
0.5103,
0.5034,
0.3953,
0.5073,
0.4839,
0.3884,
]
)
# fmt: off
expected_slice = np.array([0.4648, 0.4404, 0.4177, 0.5063, 0.4800, 0.4287, 0.5425, 0.5190, 0.4717, 0.5430, 0.5195, 0.4766, 0.5361, 0.5122, 0.4612, 0.4871, 0.4749, 0.4058, 0.4756, 0.4678, 0.3804, 0.4832, 0.4822, 0.3799, 0.5103, 0.5034, 0.3953, 0.5073, 0.4839, 0.3884])
# fmt: on
max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), image_slice.flatten())
......
tests/pipelines/stable_diffusion_3/test_pipeline_stable_diffusion_3_img2img.py
View file @ 135df5be
......@@ -128,37 +128,22 @@ class StableDiffusion3Img2ImgPipelineFastTests(PipelineLatentTesterMixin, unitte
}
return inputs
def test_stable_diffusion_3_img2img_different_prompts(self):
pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)
def test_inference(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
inputs = self.get_dummy_inputs(torch_device)
output_same_prompt = pipe(**inputs).images[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt_2"] = "a different prompt"
inputs["prompt_3"] = "another different prompt"
output_different_prompts = pipe(**inputs).images[0]
max_diff = np.abs(output_same_prompt - output_different_prompts).max()
# Outputs should be different here
assert max_diff > 1e-2
def test_stable_diffusion_3_img2img_different_negative_prompts(self):
pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)
inputs = self.get_dummy_inputs(torch_device)
output_same_prompt = pipe(**inputs).images[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["negative_prompt_2"] = "deformed"
inputs["negative_prompt_3"] = "blurry"
output_different_prompts = pipe(**inputs).images[0]
image = pipe(**inputs).images[0]
generated_slice = image.flatten()
generated_slice = np.concatenate([generated_slice[:8], generated_slice[-8:]])
max_diff = np.abs(output_same_prompt - output_different_prompts).max()
# fmt: off
expected_slice = np.array([0.4564, 0.5486, 0.4868, 0.5923, 0.3775, 0.5543, 0.4807, 0.4177, 0.3778, 0.5957, 0.5726, 0.4333, 0.6312, 0.5062, 0.4838, 0.5984])
# fmt: on
# Outputs should be different here
assert max_diff > 1e-2
self.assertTrue(
np.allclose(generated_slice, expected_slice, atol=1e-3), "Output does not match expected slice."
)
@unittest.skip("Skip for now.")
def test_multi_vae(self):
......@@ -207,112 +192,16 @@ class StableDiffusion3Img2ImgPipelineSlowTests(unittest.TestCase):
inputs = self.get_inputs(torch_device)
image = pipe(**inputs).images[0]
image_slice = image[0, :10, :10]
# fmt: off
expected_slices = Expectations(
{
("xpu", 3): np.array(
[
0.5117,
0.4421,
0.3852,
0.5044,
0.4219,
0.3262,
0.5024,
0.4329,
0.3276,
0.4978,
0.4412,
0.3355,
0.4983,
0.4338,
0.3279,
0.4893,
0.4241,
0.3129,
0.4875,
0.4253,
0.3030,
0.4961,
0.4267,
0.2988,
0.5029,
0.4255,
0.3054,
0.5132,
0.4248,
0.3222,
]
),
("cuda", 7): np.array(
[
0.5435,
0.4673,
0.5732,
0.4438,
0.3557,
0.4912,
0.4331,
0.3491,
0.4915,
0.4287,
0.347,
0.4849,
0.4355,
0.3469,
0.4871,
0.4431,
0.3538,
0.4912,
0.4521,
0.3643,
0.5059,
0.4587,
0.373,
0.5166,
0.4685,
0.3845,
0.5264,
0.4746,
0.3914,
0.5342,
]
),
("cuda", 8): np.array(
[
0.5146,
0.4385,
0.3826,
0.5098,
0.4150,
0.3218,
0.5142,
0.4312,
0.3298,
0.5127,
0.4431,
0.3411,
0.5171,
0.4424,
0.3374,
0.5088,
0.4348,
0.3242,
0.5073,
0.4380,
0.3174,
0.5132,
0.4397,
0.3115,
0.5132,
0.4343,
0.3118,
0.5219,
0.4328,
0.3256,
]
),
("xpu", 3): np.array([0.5117, 0.4421, 0.3852, 0.5044, 0.4219, 0.3262, 0.5024, 0.4329, 0.3276, 0.4978, 0.4412, 0.3355, 0.4983, 0.4338, 0.3279, 0.4893, 0.4241, 0.3129, 0.4875, 0.4253, 0.3030, 0.4961, 0.4267, 0.2988, 0.5029, 0.4255, 0.3054, 0.5132, 0.4248, 0.3222]),
("cuda", 7): np.array([0.5435, 0.4673, 0.5732, 0.4438, 0.3557, 0.4912, 0.4331, 0.3491, 0.4915, 0.4287, 0.347, 0.4849, 0.4355, 0.3469, 0.4871, 0.4431, 0.3538, 0.4912, 0.4521, 0.3643, 0.5059, 0.4587, 0.373, 0.5166, 0.4685, 0.3845, 0.5264, 0.4746, 0.3914, 0.5342]),
("cuda", 8): np.array([0.5146, 0.4385, 0.3826, 0.5098, 0.4150, 0.3218, 0.5142, 0.4312, 0.3298, 0.5127, 0.4431, 0.3411, 0.5171, 0.4424, 0.3374, 0.5088, 0.4348, 0.3242, 0.5073, 0.4380, 0.3174, 0.5132, 0.4397, 0.3115, 0.5132, 0.4343, 0.3118, 0.5219, 0.4328, 0.3256]),
}
)
# fmt: on
expected_slice = expected_slices.get_expectation()
......
tests/pipelines/stable_diffusion_3/test_pipeline_stable_diffusion_3_inpaint.py
View file @ 135df5be
......@@ -132,37 +132,23 @@ class StableDiffusion3InpaintPipelineFastTests(PipelineLatentTesterMixin, unitte
}
return inputs
def test_stable_diffusion_3_inpaint_different_prompts(self):
pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)
def test_inference(self):
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
inputs = self.get_dummy_inputs(torch_device)
output_same_prompt = pipe(**inputs).images[0]
image = pipe(**inputs).images[0]
generated_slice = image.flatten()
generated_slice = np.concatenate([generated_slice[:8], generated_slice[-8:]])
inputs = self.get_dummy_inputs(torch_device)
inputs["prompt_2"] = "a different prompt"
inputs["prompt_3"] = "another different prompt"
output_different_prompts = pipe(**inputs).images[0]
max_diff = np.abs(output_same_prompt - output_different_prompts).max()
# Outputs should be different here
assert max_diff > 1e-2
def test_stable_diffusion_3_inpaint_different_negative_prompts(self):
pipe = self.pipeline_class(**self.get_dummy_components()).to(torch_device)
inputs = self.get_dummy_inputs(torch_device)
output_same_prompt = pipe(**inputs).images[0]
inputs = self.get_dummy_inputs(torch_device)
inputs["negative_prompt_2"] = "deformed"
inputs["negative_prompt_3"] = "blurry"
output_different_prompts = pipe(**inputs).images[0]
# fmt: off
expected_slice = np.array([0.5035, 0.6661, 0.5859, 0.413, 0.4224, 0.4234, 0.7181, 0.5062, 0.5183, 0.6877, 0.5074, 0.585, 0.6111, 0.5422, 0.5306, 0.5891])
# fmt: on
max_diff = np.abs(output_same_prompt - output_different_prompts).max()
# Outputs should be different here
assert max_diff > 1e-2
self.assertTrue(
np.allclose(generated_slice, expected_slice, atol=1e-3), "Output does not match expected slice."
)
@unittest.skip("Skip for now.")
def test_multi_vae(self):
pass
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