Skip to content

[LoRA] Fix LoRA tests (side effects of RGB ordering) part ii #7932

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 3 commits into from
May 13, 2024
Merged

[LoRA] Fix LoRA tests (side effects of RGB ordering) part ii #7932

Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
491db57
check
sayakpaul May 13, 2024
cfaab24
check 2.
sayakpaul May 13, 2024
51a2044
update slices
sayakpaul May 13, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
5 changes: 2 additions & 3 deletions tests/lora/test_lora_layers_sdxl.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -224,7 +224,7 @@ def test_sdxl_1_0_blockwise_lora(self):
).images

images = images[0, -3:, -3:, -1].flatten()
expected = np.array([0.4468, 0.4087, 0.4134, 0.366, 0.3202, 0.3505, 0.3786, 0.387, 0.3535])
expected = np.array([00.4468, 0.4061, 0.4134, 0.3637, 0.3202, 0.365, 0.3786, 0.3725, 0.3535])

max_diff = numpy_cosine_similarity_distance(expected, images)
assert max_diff < 1e-4
Expand Down Expand Up @@ -507,13 +507,12 @@ def test_controlnet_canny_lora(self):
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
)

images = pipe(prompt, image=image, generator=generator, output_type="np", num_inference_steps=3).images

assert images[0].shape == (768, 512, 3)

original_image = images[0, -3:, -3:, -1].flatten()
expected_image = np.array([0.4574, 0.4461, 0.4435, 0.4462, 0.4396, 0.439, 0.4474, 0.4486, 0.4333])
expected_image = np.array([0.4574, 0.4487, 0.4435, 0.5163, 0.4396, 0.4411, 0.518, 0.4465, 0.4333])

max_diff = numpy_cosine_similarity_distance(expected_image, original_image)
assert max_diff < 1e-4
Expand Down