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Move controlnet load local tests to nightly (#4543)

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move controlnet load local tests to nihghtly
This commit is contained in:
Dhruv Nair
2023-08-09 19:30:42 +02:00
committed by GitHub
parent 16ad13b61d
commit 7b07f9812a
3 changed files with 66 additions and 39 deletions
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83
tests/pipelines/controlnet/test_controlnet.py
View File

@@ -31,7 +31,7 @@ from diffusers import (
UNet2DConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils import load_image, load_numpy, nightly, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
@@ -925,42 +925,6 @@ class ControlNetPipelineSlowTests(unittest.TestCase):
expected_slice = np.array([0.1338, 0.1597, 0.1202, 0.1687, 0.1377, 0.1017, 0.2070, 0.1574, 0.1348])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
def test_load_local(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny")
pipe_1 = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
)
controlnet = ControlNetModel.from_single_file(
"https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth"
)
pipe_2 = StableDiffusionControlNetPipeline.from_single_file(
"https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors",
safety_checker=None,
controlnet=controlnet,
)
pipes = [pipe_1, pipe_2]
images = []
for pipe in pipes:
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "bird"
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
)
output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
images.append(output.images[0])
del pipe
gc.collect()
torch.cuda.empty_cache()
assert np.abs(images[0] - images[1]).sum() < 1e-3
@slow
@require_torch_gpu
@@ -1000,3 +964,48 @@ class StableDiffusionMultiControlNetPipelineSlowTests(unittest.TestCase):
)
assert np.abs(expected_image - image).max() < 5e-2
@nightly
@require_torch_gpu
class StableDiffusionMultiControlNetPipelineNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_load_local(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny")
pipe_1 = StableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5", safety_checker=None, controlnet=controlnet
)
controlnet = ControlNetModel.from_single_file(
"https://huggingface.co/lllyasviel/ControlNet-v1-1/blob/main/control_v11p_sd15_canny.pth"
)
pipe_2 = StableDiffusionControlNetPipeline.from_single_file(
"https://huggingface.co/runwayml/stable-diffusion-v1-5/blob/main/v1-5-pruned-emaonly.safetensors",
safety_checker=None,
controlnet=controlnet,
)
pipes = [pipe_1, pipe_2]
images = []
for pipe in pipes:
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=None)
generator = torch.Generator(device="cpu").manual_seed(0)
prompt = "bird"
image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"
)
output = pipe(prompt, image, generator=generator, output_type="np", num_inference_steps=3)
images.append(output.images[0])
del pipe
gc.collect()
torch.cuda.empty_cache()
assert np.abs(images[0] - images[1]).sum() < 1e-3

11
tests/pipelines/controlnet/test_controlnet_img2img.py
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@@ -33,7 +33,7 @@ from diffusers import (
UNet2DConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
@@ -402,6 +402,15 @@ class ControlNetImg2ImgPipelineSlowTests(unittest.TestCase):
assert np.abs(expected_image - image).max() < 9e-2
@nightly
@require_torch_gpu
class ControlNetImg2ImgPipelineNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_load_local(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny")
pipe_1 = StableDiffusionControlNetImg2ImgPipeline.from_pretrained(

11
tests/pipelines/controlnet/test_controlnet_inpaint.py
View File

@@ -33,7 +33,7 @@ from diffusers import (
UNet2DConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils import floats_tensor, load_image, load_numpy, nightly, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
@@ -544,6 +544,15 @@ class ControlNetInpaintPipelineSlowTests(unittest.TestCase):
assert np.abs(expected_image - image).max() < 9e-2
@nightly
@require_torch_gpu
class ControlNetInpaintPipelineNightlyTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def test_load_local(self):
controlnet = ControlNetModel.from_pretrained("lllyasviel/control_v11p_sd15_canny")
pipe_1 = StableDiffusionControlNetInpaintPipeline.from_pretrained(