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update

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Dhruv Nair
2024-01-02 11:32:10 +00:00
parent fa3a0d6634
commit bbc60be3b6
2 changed files with 41 additions and 4 deletions
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8
tests/pipelines/controlnet/test_controlnet_sdxl.py
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@@ -827,7 +827,7 @@ class ControlNetSDXLPipelineSlowTests(unittest.TestCase):
assert np.allclose(original_image, expected_image, atol=1e-04)
def test_download_ckpt_diff_format_is_same(self):
controlnet = ControlNetModel.from_pretrained("diffusers/controlnet-depth-sdxl-1.0")
controlnet = ControlNetModel.from_pretrained("diffusers/controlnet-depth-sdxl-1.0", torch_dtype=torch.float16)
single_file_url = (
"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors"
)
@@ -844,7 +844,7 @@ class ControlNetSDXLPipelineSlowTests(unittest.TestCase):
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/stormtrooper_depth.png"
)
single_file_images = pipe_single_file(
prompt, image=image, generator=generator, output_type="np", num_inference_steps=3
prompt, image=image, generator=generator, output_type="np", num_inference_steps=2
).images
generator = torch.Generator(device="cpu").manual_seed(0)
@@ -853,13 +853,13 @@ class ControlNetSDXLPipelineSlowTests(unittest.TestCase):
)
pipe.unet.set_default_attn_processor()
pipe.enable_model_cpu_offload()
images = pipe(prompt, image=image, generator=generator, output_type="np", num_inference_steps=3).images
images = pipe(prompt, image=image, generator=generator, output_type="np", num_inference_steps=2).images
assert images[0].shape == (512, 512, 3)
assert single_file_images[0].shape == (512, 512, 3)
max_diff = numpy_cosine_similarity_distance(images[0].flatten(), single_file_images[0].flatten())
assert max_diff < 1e-4
assert max_diff < 5e-2
class StableDiffusionSSD1BControlNetPipelineFastTests(StableDiffusionXLControlNetPipelineFastTests):

37
tests/pipelines/stable_diffusion_xl/test_stable_diffusion_xl_adapter.py
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@@ -697,3 +697,40 @@ class AdapterSDXLPipelineSlowTests(unittest.TestCase):
image_slice = images[0, -3:, -3:, -1].flatten()
expected_slice = np.array([0.4284, 0.4337, 0.4319, 0.4255, 0.4329, 0.4280, 0.4338, 0.4420, 0.4226])
assert numpy_cosine_similarity_distance(image_slice, expected_slice) < 1e-4
def test_download_ckpt_diff_format_is_same(self):
ckpt_path = (
"https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/sd_xl_base_1.0.safetensors"
)
adapter = T2IAdapter.from_pretrained("TencentARC/t2i-adapter-lineart-sdxl-1.0", torch_dtype=torch.float16)
prompt = "toy"
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/t2i_adapter/toy_canny.png"
)
pipe_single_file = StableDiffusionXLAdapterPipeline.from_single_file(
ckpt_path,
adapter=adapter,
torch_dtype=torch.float16,
)
pipe_single_file.enable_model_cpu_offload()
pipe_single_file.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
images_single_file = pipe_single_file(
prompt, image=image, generator=generator, output_type="np", num_inference_steps=3
).images
generator = torch.Generator(device="cpu").manual_seed(0)
pipe = StableDiffusionXLAdapterPipeline.from_pretrained(
"stabilityai/stable-diffusion-xl-base-1.0",
adapter=adapter,
torch_dtype=torch.float16,
)
pipe.enable_model_cpu_offload()
images = pipe(prompt, image=image, generator=generator, output_type="np", num_inference_steps=3).images
assert images_single_file[0].shape == (768, 512, 3)
assert images[0].shape == (768, 512, 3)
max_diff = numpy_cosine_similarity_distance(images[0].flatten(), images_single_file[0].flatten())
assert max_diff < 5e-3