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Unverified Commit 2c82e0c4 authored by Anton Lozhkov's avatar Anton Lozhkov Committed by GitHub
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Reorganize pipeline tests (#963) 

* Reorganize pipeline tests

* fix vq
parent 2d35f673
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Showing with 49 additions and 491 deletions
tests/pipelines/stable_diffusion/test_stable_diffusion_img2img.py
View file @ 2c82e0c4
......@@ -30,13 +30,16 @@ from diffusers import (
VQModel,
)
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from ...test_pipelines_common import PipelineTesterMixin
torch.backends.cuda.matmul.allow_tf32 = False
class PipelineFastTests(unittest.TestCase):
class StableDiffusionImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
......@@ -461,8 +464,8 @@ class PipelineFastTests(unittest.TestCase):
@slow
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
class PipelineIntegrationTests(unittest.TestCase):
@require_torch_gpu
class StableDiffusionImg2ImgPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
......
tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint.py
View file @ 2c82e0c4
......@@ -29,14 +29,17 @@ from diffusers import (
VQModel,
)
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from ...test_pipelines_common import PipelineTesterMixin
torch.backends.cuda.matmul.allow_tf32 = False
class PipelineFastTests(unittest.TestCase):
class StableDiffusionInpaintPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
......@@ -258,8 +261,8 @@ class PipelineFastTests(unittest.TestCase):
@slow
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
class PipelineIntegrationTests(unittest.TestCase):
@require_torch_gpu
class StableDiffusionInpaintPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
......
tests/pipelines/stable_diffusion/test_stable_diffusion_inpaint_legacy.py
View file @ 2c82e0c4
......@@ -31,14 +31,17 @@ from diffusers import (
VQModel,
)
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from ...test_pipelines_common import PipelineTesterMixin
torch.backends.cuda.matmul.allow_tf32 = False
class PipelineFastTests(unittest.TestCase):
class StableDiffusionInpaintLegacyPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
......@@ -338,8 +341,8 @@ class PipelineFastTests(unittest.TestCase):
@slow
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
class PipelineIntegrationTests(unittest.TestCase):
@require_torch_gpu
class StableDiffusionInpaintLegacyPipelineIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
......
tests/test_pipelines.py
View file @ 2c82e0c4
......@@ -32,17 +32,7 @@ from diffusers import (
DDIMScheduler,
DDPMPipeline,
DDPMScheduler,
KarrasVePipeline,
KarrasVeScheduler,
LDMPipeline,
LDMTextToImagePipeline,
OnnxStableDiffusionImg2ImgPipeline,
OnnxStableDiffusionInpaintPipeline,
OnnxStableDiffusionPipeline,
PNDMPipeline,
PNDMScheduler,
ScoreSdeVePipeline,
ScoreSdeVeScheduler,
StableDiffusionImg2ImgPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionPipeline,
......@@ -53,8 +43,8 @@ from diffusers import (
)
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
from diffusers.utils import CONFIG_NAME, WEIGHTS_NAME, floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import CaptureLogger, get_tests_dir
from diffusers.utils import CONFIG_NAME, WEIGHTS_NAME, floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import CaptureLogger, get_tests_dir, require_torch_gpu
from packaging import version
from PIL import Image
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextConfig, CLIPTextModel, CLIPTokenizer
......@@ -145,12 +135,6 @@ class CustomPipelineTests(unittest.TestCase):
class PipelineFastTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def dummy_image(self):
batch_size = 1
......@@ -261,174 +245,6 @@ class PipelineFastTests(unittest.TestCase):
return extract
def test_ddim(self):
unet = self.dummy_uncond_unet
scheduler = DDIMScheduler()
ddpm = DDIMPipeline(unet=unet, scheduler=scheduler)
ddpm.to(torch_device)
ddpm.set_progress_bar_config(disable=None)
# Warmup pass when using mps (see #372)
if torch_device == "mps":
_ = ddpm(num_inference_steps=1)
generator = torch.manual_seed(0)
image = ddpm(generator=generator, num_inference_steps=2, output_type="numpy").images
generator = torch.manual_seed(0)
image_from_tuple = ddpm(generator=generator, num_inference_steps=2, output_type="numpy", return_dict=False)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array(
[1.000e00, 5.717e-01, 4.717e-01, 1.000e00, 0.000e00, 1.000e00, 3.000e-04, 0.000e00, 9.000e-04]
)
tolerance = 1e-2 if torch_device != "mps" else 3e-2
assert np.abs(image_slice.flatten() - expected_slice).max() < tolerance
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < tolerance
def test_pndm_cifar10(self):
unet = self.dummy_uncond_unet
scheduler = PNDMScheduler()
pndm = PNDMPipeline(unet=unet, scheduler=scheduler)
pndm.to(torch_device)
pndm.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = pndm(generator=generator, num_inference_steps=20, output_type="numpy").images
generator = torch.manual_seed(0)
image_from_tuple = pndm(generator=generator, num_inference_steps=20, output_type="numpy", return_dict=False)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
def test_ldm_text2img(self):
unet = self.dummy_cond_unet
scheduler = DDIMScheduler()
vae = self.dummy_vae
bert = self.dummy_text_encoder
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
ldm = LDMTextToImagePipeline(vqvae=vae, bert=bert, tokenizer=tokenizer, unet=unet, scheduler=scheduler)
ldm.to(torch_device)
ldm.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
# Warmup pass when using mps (see #372)
if torch_device == "mps":
generator = torch.manual_seed(0)
_ = ldm(
[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=1, output_type="numpy"
).images
generator = torch.manual_seed(0)
image = ldm(
[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=2, output_type="numpy"
).images
generator = torch.manual_seed(0)
image_from_tuple = ldm(
[prompt],
generator=generator,
guidance_scale=6.0,
num_inference_steps=2,
output_type="numpy",
return_dict=False,
)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.5074, 0.5026, 0.4998, 0.4056, 0.3523, 0.4649, 0.5289, 0.5299, 0.4897])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
def test_score_sde_ve_pipeline(self):
unet = self.dummy_uncond_unet
scheduler = ScoreSdeVeScheduler()
sde_ve = ScoreSdeVePipeline(unet=unet, scheduler=scheduler)
sde_ve.to(torch_device)
sde_ve.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = sde_ve(num_inference_steps=2, output_type="numpy", generator=generator).images
generator = torch.manual_seed(0)
image_from_tuple = sde_ve(num_inference_steps=2, output_type="numpy", generator=generator, return_dict=False)[
0
]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
def test_ldm_uncond(self):
unet = self.dummy_uncond_unet
scheduler = DDIMScheduler()
vae = self.dummy_vq_model
ldm = LDMPipeline(unet=unet, vqvae=vae, scheduler=scheduler)
ldm.to(torch_device)
ldm.set_progress_bar_config(disable=None)
# Warmup pass when using mps (see #372)
if torch_device == "mps":
generator = torch.manual_seed(0)
_ = ldm(generator=generator, num_inference_steps=1, output_type="numpy").images
generator = torch.manual_seed(0)
image = ldm(generator=generator, num_inference_steps=2, output_type="numpy").images
generator = torch.manual_seed(0)
image_from_tuple = ldm(generator=generator, num_inference_steps=2, output_type="numpy", return_dict=False)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
expected_slice = np.array([0.8512, 0.818, 0.6411, 0.6808, 0.4465, 0.5618, 0.46, 0.6231, 0.5172])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
def test_karras_ve_pipeline(self):
unet = self.dummy_uncond_unet
scheduler = KarrasVeScheduler()
pipe = KarrasVePipeline(unet=unet, scheduler=scheduler)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = pipe(num_inference_steps=2, generator=generator, output_type="numpy").images
generator = torch.manual_seed(0)
image_from_tuple = pipe(num_inference_steps=2, generator=generator, output_type="numpy", return_dict=False)[0]
image_slice = image[0, -3:, -3:, -1]
image_from_tuple_slice = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
def test_components(self):
"""Test that components property works correctly"""
unet = self.dummy_cond_unet
......@@ -489,7 +305,8 @@ class PipelineFastTests(unittest.TestCase):
assert image_text2img.shape == (1, 128, 128, 3)
class PipelineTesterMixin(unittest.TestCase):
@slow
class PipelineSlowTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
......@@ -556,7 +373,6 @@ class PipelineTesterMixin(unittest.TestCase):
assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
@slow
def test_from_pretrained_hub(self):
model_path = "google/ddpm-cifar10-32"
......@@ -577,7 +393,6 @@ class PipelineTesterMixin(unittest.TestCase):
assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
@slow
def test_from_pretrained_hub_pass_model(self):
model_path = "google/ddpm-cifar10-32"
......@@ -601,7 +416,6 @@ class PipelineTesterMixin(unittest.TestCase):
assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
@slow
def test_output_format(self):
model_path = "google/ddpm-cifar10-32"
......@@ -624,156 +438,6 @@ class PipelineTesterMixin(unittest.TestCase):
assert isinstance(images, list)
assert isinstance(images[0], PIL.Image.Image)
@slow
def test_ddpm_cifar10(self):
model_id = "google/ddpm-cifar10-32"
unet = UNet2DModel.from_pretrained(model_id)
scheduler = DDPMScheduler.from_config(model_id)
ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)
ddpm.to(torch_device)
ddpm.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = ddpm(generator=generator, output_type="numpy").images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.41995, 0.35885, 0.19385, 0.38475, 0.3382, 0.2647, 0.41545, 0.3582, 0.33845])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
def test_ddim_lsun(self):
model_id = "google/ddpm-ema-bedroom-256"
unet = UNet2DModel.from_pretrained(model_id)
scheduler = DDIMScheduler.from_config(model_id)
ddpm = DDIMPipeline(unet=unet, scheduler=scheduler)
ddpm.to(torch_device)
ddpm.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = ddpm(generator=generator, output_type="numpy").images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.00605, 0.0201, 0.0344, 0.00235, 0.00185, 0.00025, 0.00215, 0.0, 0.00685])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
def test_ddim_cifar10(self):
model_id = "google/ddpm-cifar10-32"
unet = UNet2DModel.from_pretrained(model_id)
scheduler = DDIMScheduler()
ddim = DDIMPipeline(unet=unet, scheduler=scheduler)
ddim.to(torch_device)
ddim.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = ddim(generator=generator, eta=0.0, output_type="numpy").images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.17235, 0.16175, 0.16005, 0.16255, 0.1497, 0.1513, 0.15045, 0.1442, 0.1453])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
def test_pndm_cifar10(self):
model_id = "google/ddpm-cifar10-32"
unet = UNet2DModel.from_pretrained(model_id)
scheduler = PNDMScheduler()
pndm = PNDMPipeline(unet=unet, scheduler=scheduler)
pndm.to(torch_device)
pndm.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = pndm(generator=generator, output_type="numpy").images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
expected_slice = np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
def test_ldm_text2img(self):
ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
ldm.to(torch_device)
ldm.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.manual_seed(0)
image = ldm(
[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="numpy"
).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.9256, 0.9340, 0.8933, 0.9361, 0.9113, 0.8727, 0.9122, 0.8745, 0.8099])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
def test_ldm_text2img_fast(self):
ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
ldm.to(torch_device)
ldm.set_progress_bar_config(disable=None)
prompt = "A painting of a squirrel eating a burger"
generator = torch.manual_seed(0)
image = ldm(prompt, generator=generator, num_inference_steps=1, output_type="numpy").images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.3163, 0.8670, 0.6465, 0.1865, 0.6291, 0.5139, 0.2824, 0.3723, 0.4344])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
def test_score_sde_ve_pipeline(self):
model_id = "google/ncsnpp-church-256"
model = UNet2DModel.from_pretrained(model_id)
scheduler = ScoreSdeVeScheduler.from_config(model_id)
sde_ve = ScoreSdeVePipeline(unet=model, scheduler=scheduler)
sde_ve.to(torch_device)
sde_ve.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = sde_ve(num_inference_steps=10, output_type="numpy", generator=generator).images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
def test_ldm_uncond(self):
ldm = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256")
ldm.to(torch_device)
ldm.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = ldm(generator=generator, num_inference_steps=5, output_type="numpy").images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
def test_ddpm_ddim_equality(self):
model_id = "google/ddpm-cifar10-32"
......@@ -824,145 +488,7 @@ class PipelineTesterMixin(unittest.TestCase):
# the values aren't exactly equal, but the images look the same visually
assert np.abs(ddpm_images - ddim_images).max() < 1e-1
@slow
def test_karras_ve_pipeline(self):
model_id = "google/ncsnpp-celebahq-256"
model = UNet2DModel.from_pretrained(model_id)
scheduler = KarrasVeScheduler()
pipe = KarrasVePipeline(unet=model, scheduler=scheduler)
pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
generator = torch.manual_seed(0)
image = pipe(num_inference_steps=20, generator=generator, output_type="numpy").images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
expected_slice = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
@slow
def test_stable_diffusion_onnx(self):
sd_pipe = OnnxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", revision="onnx", provider="CPUExecutionProvider"
)
prompt = "A painting of a squirrel eating a burger"
np.random.seed(0)
output = sd_pipe([prompt], guidance_scale=6.0, num_inference_steps=5, output_type="np")
image = output.images
image_slice = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
expected_slice = np.array([0.3602, 0.3688, 0.3652, 0.3895, 0.3782, 0.3747, 0.3927, 0.4241, 0.4327])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
@slow
def test_stable_diffusion_img2img_onnx(self):
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg"
)
init_image = init_image.resize((768, 512))
pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", revision="onnx", provider="CPUExecutionProvider"
)
pipe.set_progress_bar_config(disable=None)
prompt = "A fantasy landscape, trending on artstation"
np.random.seed(0)
output = pipe(
prompt=prompt,
init_image=init_image,
strength=0.75,
guidance_scale=7.5,
num_inference_steps=8,
output_type="np",
)
images = output.images
image_slice = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 768, 3)
expected_slice = np.array([0.4830, 0.5242, 0.5603, 0.5016, 0.5131, 0.5111, 0.4928, 0.5025, 0.5055])
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice).max() < 2e-2
@slow
def test_stable_diffusion_inpaint_onnx(self):
init_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo.png"
)
mask_image = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/in_paint/overture-creations-5sI6fQgYIuo_mask.png"
)
pipe = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"runwayml/stable-diffusion-inpainting", revision="onnx", provider="CPUExecutionProvider"
)
pipe.set_progress_bar_config(disable=None)
prompt = "A red cat sitting on a park bench"
np.random.seed(0)
output = pipe(
prompt=prompt,
image=init_image,
mask_image=mask_image,
guidance_scale=7.5,
num_inference_steps=8,
output_type="np",
)
images = output.images
image_slice = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
expected_slice = np.array([0.2951, 0.2955, 0.2922, 0.2036, 0.1977, 0.2279, 0.1716, 0.1641, 0.1799])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
@slow
def test_stable_diffusion_onnx_intermediate_state(self):
number_of_steps = 0
def test_callback_fn(step: int, timestep: int, latents: np.ndarray) -> None:
test_callback_fn.has_been_called = True
nonlocal number_of_steps
number_of_steps += 1
if step == 0:
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array(
[-0.5950, -0.3039, -1.1672, 0.1594, -1.1572, 0.6719, -1.9712, -0.0403, 0.9592]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
elif step == 5:
assert latents.shape == (1, 4, 64, 64)
latents_slice = latents[0, -3:, -3:, -1]
expected_slice = np.array(
[-0.4776, -0.0119, -0.8519, -0.0275, -0.9764, 0.9820, -0.3843, 0.3788, 1.2264]
)
assert np.abs(latents_slice.flatten() - expected_slice).max() < 1e-3
test_callback_fn.has_been_called = False
pipe = OnnxStableDiffusionPipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4", revision="onnx", provider="CPUExecutionProvider"
)
pipe.set_progress_bar_config(disable=None)
prompt = "Andromeda galaxy in a bottle"
np.random.seed(0)
pipe(prompt=prompt, num_inference_steps=5, guidance_scale=7.5, callback=test_callback_fn, callback_steps=1)
assert test_callback_fn.has_been_called
assert number_of_steps == 6
@slow
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
@require_torch_gpu
def test_stable_diffusion_accelerate_load_works(self):
if version.parse(version.parse(transformers.__version__).base_version) < version.parse("4.23"):
return
......@@ -975,8 +501,7 @@ class PipelineTesterMixin(unittest.TestCase):
model_id, revision="fp16", torch_dtype=torch.float16, use_auth_token=True, device_map="auto"
).to(torch_device)
@slow
@unittest.skipIf(torch_device == "cpu", "This test is supposed to run on GPU")
@require_torch_gpu
def test_stable_diffusion_accelerate_load_reduces_memory_footprint(self):
if version.parse(version.parse(transformers.__version__).base_version) < version.parse("4.23"):
return
......
tests/test_pipelines_common.py 0 → 100644
View file @ 2c82e0c4
from diffusers.utils.testing_utils import require_torch
@require_torch
class PipelineTesterMixin:
"""
This mixin is designed to be used with unittest.TestCase classes.
It provides a set of common tests for each PyTorch pipeline, e.g. saving and loading the pipeline,
equivalence of dict and tuple outputs, etc.
"""
pass
tests/test_pipelines_flax.py
View file @ 2c82e0c4
......@@ -30,8 +30,8 @@ if is_flax_available():
from jax import pmap
@require_flax
@slow
@require_flax
class FlaxPipelineTests(unittest.TestCase):
def test_dummy_all_tpus(self):
pipeline, params = FlaxStableDiffusionPipeline.from_pretrained(
......
tests/test_pipelines_onnx_common.py 0 → 100644
View file @ 2c82e0c4
from diffusers.utils.testing_utils import require_onnxruntime
@require_onnxruntime
class OnnxPipelineTesterMixin:
"""
This mixin is designed to be used with unittest.TestCase classes.
It provides a set of common tests for each ONNXRuntime pipeline, e.g. saving and loading the pipeline,
equivalence of dict and tuple outputs, etc.
"""
pass
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