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3 changes: 1 addition & 2 deletions tests/pipelines/kandinsky2_2/test_kandinsky_controlnet.py
Original file line number Diff line number Diff line change
Expand Up @@ -289,6 +289,5 @@ def test_kandinsky_controlnet(self):
image = output.images[0]

assert image.shape == (512, 512, 3)

max_diff = numpy_cosine_similarity_distance(expected_image.flatten(), image.flatten())
assert max_diff < 1e-4
assert max_diff < 2e-4
33 changes: 31 additions & 2 deletions tests/pipelines/ledits_pp/test_ledits_pp_stable_diffusion.py
Original file line number Diff line number Diff line change
Expand Up @@ -29,6 +29,7 @@
UNet2DConditionModel,
)
from diffusers.utils.testing_utils import (
Expectations,
backend_empty_cache,
enable_full_determinism,
floats_tensor,
Expand Down Expand Up @@ -244,7 +245,35 @@ def test_ledits_pp_editing(self):

output_slice = reconstruction[150:153, 140:143, -1]
output_slice = output_slice.flatten()
expected_slice = np.array(
[0.9453125, 0.93310547, 0.84521484, 0.94628906, 0.9111328, 0.80859375, 0.93847656, 0.9042969, 0.8144531]
expected_slices = Expectations(
{
("xpu", 3): np.array(
[
0.9511719,
0.94140625,
0.87597656,
0.9472656,
0.9296875,
0.8378906,
0.94433594,
0.91503906,
0.8491211
]
),
("cuda", 7): np.array(
[
0.9453125,
0.93310547,
0.84521484,
0.94628906,
0.9111328,
0.80859375,
0.93847656,
0.9042969,
0.8144531,
]
),
}
)
expected_slice = expected_slices.get_expectation()
assert np.abs(output_slice - expected_slice).max() < 1e-2
6 changes: 3 additions & 3 deletions tests/pipelines/test_pipelines_common.py
Original file line number Diff line number Diff line change
Expand Up @@ -49,6 +49,7 @@
from diffusers.utils.testing_utils import (
CaptureLogger,
backend_empty_cache,
numpy_cosine_similarity_distance,
require_accelerate_version_greater,
require_accelerator,
require_hf_hub_version_greater,
Expand Down Expand Up @@ -1394,9 +1395,8 @@ def test_float16_inference(self, expected_max_diff=5e-2):
fp16_inputs["generator"] = self.get_generator(0)

output_fp16 = pipe_fp16(**fp16_inputs)[0]

max_diff = np.abs(to_np(output) - to_np(output_fp16)).max()
self.assertLess(max_diff, expected_max_diff, "The outputs of the fp16 and fp32 pipelines are too different.")
max_diff = numpy_cosine_similarity_distance(output.flatten(), output_fp16.flatten())
assert max_diff < 2e-4

@unittest.skipIf(torch_device not in ["cuda", "xpu"], reason="float16 requires CUDA or XPU")
@require_accelerator
Expand Down
50 changes: 25 additions & 25 deletions tests/quantization/gguf/test_gguf.py
Original file line number Diff line number Diff line change
Expand Up @@ -286,33 +286,33 @@ def test_pipeline_inference(self):
{
("xpu", 3): np.array(
[
0.19335938,
0.3125,
0.3203125,
0.1328125,
0.3046875,
0.296875,
0.11914062,
0.2890625,
0.2890625,
0.16796875,
0.30273438,
0.33203125,
0.14648438,
0.31640625,
0.33007812,
0.16210938,
0.2734375,
0.27734375,
0.109375,
0.27148438,
0.2578125,
0.1015625,
0.2578125,
0.2578125,
0.14453125,
0.26953125,
0.29492188,
0.12890625,
0.3046875,
0.30859375,
0.17773438,
0.33789062,
0.33203125,
0.16796875,
0.34570312,
0.32421875,
0.28710938,
0.30078125,
0.11132812,
0.27734375,
0.27929688,
0.15625,
0.33203125,
0.31445312,
0.31054688,
0.296875,
0.15234375,
0.3203125,
0.29492188,
0.140625,
0.3046875,
0.28515625
]
),
("cuda", 7): np.array(
Expand Down