[Tests] Improve unet / vae tests (#1018)
* improve tests * up * finish * upload * add init * up * finish vae * finish * reduce loading time with device_map * remove device_map from CPU * uP
This commit is contained in:
Patrick von Platen
GitHub
parent
ab079f27cf
commit
a80480f0f2
2
setup.py
2
setup.py
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@ -94,6 +94,7 @@ _deps = [
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"modelcards>=0.1.4",
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"numpy",
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"onnxruntime",
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"parameterized",
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"pytest",
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"pytest-timeout",
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"pytest-xdist",
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@ -181,6 +182,7 @@ extras["test"] = deps_list(
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"accelerate",
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"datasets",
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"onnxruntime",
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"parameterized",
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"pytest",
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"pytest-timeout",
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"pytest-xdist",
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@ -18,6 +18,7 @@ deps = {
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"modelcards": "modelcards>=0.1.4",
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"numpy": "numpy",
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"onnxruntime": "onnxruntime",
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"parameterized": "parameterized",
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"pytest": "pytest",
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"pytest-timeout": "pytest-timeout",
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"pytest-xdist": "pytest-xdist",
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@ -40,7 +40,7 @@ from .outputs import BaseOutput
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if is_torch_available():
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from .testing_utils import floats_tensor, load_image, parse_flag_from_env, slow, torch_device
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from .testing_utils import floats_tensor, load_image, parse_flag_from_env, require_torch_gpu, slow, torch_device
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logger = get_logger(__name__)
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@ -28,7 +28,7 @@ class UnetModel1DTests(unittest.TestCase):
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@slow
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def test_unet_1d_maestro(self):
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model_id = "harmonai/maestro-150k"
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model = UNet1DModel.from_pretrained(model_id, subfolder="unet")
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model = UNet1DModel.from_pretrained(model_id, subfolder="unet", device_map="auto")
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model.to(torch_device)
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sample_size = 65536
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@ -21,9 +21,10 @@ import unittest
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import torch
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from diffusers import UNet2DConditionModel, UNet2DModel
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from diffusers.utils import floats_tensor, slow, torch_device
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from diffusers.utils import floats_tensor, require_torch_gpu, slow, torch_device
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from parameterized import parameterized
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from .test_modeling_common import ModelTesterMixin
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from ..test_modeling_common import ModelTesterMixin
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torch.backends.cuda.matmul.allow_tf32 = False
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@ -66,28 +67,6 @@ class Unet2DModelTests(ModelTesterMixin, unittest.TestCase):
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return init_dict, inputs_dict
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# TODO(Patrick) - Re-add this test after having correctly added the final VE checkpoints
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# def test_output_pretrained(self):
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# model = UNet2DModel.from_pretrained("fusing/ddpm_dummy_update", subfolder="unet")
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# model.eval()
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#
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# torch.manual_seed(0)
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# if torch.cuda.is_available():
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# torch.cuda.manual_seed_all(0)
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#
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# noise = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
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# time_step = torch.tensor([10])
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#
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# with torch.no_grad():
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# output = model(noise, time_step).sample
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#
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# output_slice = output[0, -1, -3:, -3:].flatten()
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# fmt: off
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# expected_output_slice = torch.tensor([0.2891, -0.1899, 0.2595, -0.6214, 0.0968, -0.2622, 0.4688, 0.1311, 0.0053])
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# fmt: on
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# self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
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class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
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model_class = UNet2DModel
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@ -170,7 +149,9 @@ class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
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torch.cuda.empty_cache()
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gc.collect()
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model_normal_load, _ = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)
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model_normal_load, _ = UNet2DModel.from_pretrained(
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"fusing/unet-ldm-dummy-update", output_loading_info=True, device_map="auto"
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)
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model_normal_load.to(torch_device)
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model_normal_load.eval()
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arr_normal_load = model_normal_load(noise, time_step)["sample"]
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@ -309,31 +290,6 @@ class UNet2DConditionModelTests(ModelTesterMixin, unittest.TestCase):
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self.assertTrue(torch.allclose(param.grad.data, named_params_2[name].grad.data, atol=5e-5))
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# TODO(Patrick) - Re-add this test after having cleaned up LDM
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# def test_output_pretrained_spatial_transformer(self):
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# model = UNetLDMModel.from_pretrained("fusing/unet-ldm-dummy-spatial")
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# model.eval()
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#
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# torch.manual_seed(0)
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# if torch.cuda.is_available():
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# torch.cuda.manual_seed_all(0)
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#
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# noise = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
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# context = torch.ones((1, 16, 64), dtype=torch.float32)
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# time_step = torch.tensor([10] * noise.shape[0])
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#
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# with torch.no_grad():
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# output = model(noise, time_step, context=context)
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#
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# output_slice = output[0, -1, -3:, -3:].flatten()
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# fmt: off
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# expected_output_slice = torch.tensor([61.3445, 56.9005, 29.4339, 59.5497, 60.7375, 34.1719, 48.1951, 42.6569, 25.0890])
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# fmt: on
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#
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# self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
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#
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class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
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model_class = UNet2DModel
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@ -383,7 +339,9 @@ class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
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@slow
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def test_from_pretrained_hub(self):
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model, loading_info = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256", output_loading_info=True)
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model, loading_info = UNet2DModel.from_pretrained(
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"google/ncsnpp-celebahq-256", output_loading_info=True, device_map="auto"
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)
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self.assertIsNotNone(model)
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self.assertEqual(len(loading_info["missing_keys"]), 0)
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@ -397,7 +355,7 @@ class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
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@slow
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def test_output_pretrained_ve_mid(self):
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model = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256")
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model = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256", device_map="auto")
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model.to(torch_device)
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torch.manual_seed(0)
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@ -449,3 +407,189 @@ class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
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def test_forward_with_norm_groups(self):
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# not required for this model
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pass
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@slow
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class UNet2DConditionModelIntegrationTests(unittest.TestCase):
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def tearDown(self):
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# clean up the VRAM after each test
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super().tearDown()
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gc.collect()
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torch.cuda.empty_cache()
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def get_latents(self, seed=0, shape=(4, 4, 64, 64), fp16=False):
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batch_size, channels, height, width = shape
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generator = torch.Generator(device=torch_device).manual_seed(seed)
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dtype = torch.float16 if fp16 else torch.float32
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image = torch.randn(batch_size, channels, height, width, device=torch_device, generator=generator, dtype=dtype)
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return image
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def get_unet_model(self, fp16=False, model_id="CompVis/stable-diffusion-v1-4"):
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revision = "fp16" if fp16 else None
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torch_dtype = torch.float16 if fp16 else torch.float32
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model = UNet2DConditionModel.from_pretrained(
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model_id, subfolder="unet", torch_dtype=torch_dtype, revision=revision, device_map="auto"
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)
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model.to(torch_device).eval()
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return model
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def get_encoder_hidden_states(self, seed=0, shape=(4, 77, 768), fp16=False):
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generator = torch.Generator(device=torch_device).manual_seed(seed)
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dtype = torch.float16 if fp16 else torch.float32
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return torch.randn(shape, device=torch_device, generator=generator, dtype=dtype)
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@parameterized.expand(
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[
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# fmt: off
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[33, 4, [-0.4424, 0.1510, -0.1937, 0.2118, 0.3746, -0.3957, 0.0160, -0.0435]],
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[47, 0.55, [-0.1508, 0.0379, -0.3075, 0.2540, 0.3633, -0.0821, 0.1719, -0.0207]],
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[21, 0.89, [-0.6479, 0.6364, -0.3464, 0.8697, 0.4443, -0.6289, -0.0091, 0.1778]],
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[9, 1000, [0.8888, -0.5659, 0.5834, -0.7469, 1.1912, -0.3923, 1.1241, -0.4424]],
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# fmt: on
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]
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)
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def test_compvis_sd_v1_4(self, seed, timestep, expected_slice):
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model = self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4")
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latents = self.get_latents(seed)
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encoder_hidden_states = self.get_encoder_hidden_states(seed)
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with torch.no_grad():
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sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
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assert sample.shape == latents.shape
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output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
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expected_output_slice = torch.tensor(expected_slice)
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assert torch.allclose(output_slice, expected_output_slice, atol=1e-4)
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@parameterized.expand(
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[
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# fmt: off
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[83, 4, [-0.2323, -0.1304, 0.0813, -0.3093, -0.0919, -0.1571, -0.1125, -0.5806]],
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[17, 0.55, [-0.0831, -0.2443, 0.0901, -0.0919, 0.3396, 0.0103, -0.3743, 0.0701]],
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[8, 0.89, [-0.4863, 0.0859, 0.0875, -0.1658, 0.9199, -0.0114, 0.4839, 0.4639]],
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[3, 1000, [-0.5649, 0.2402, -0.5518, 0.1248, 1.1328, -0.2443, -0.0325, -1.0078]],
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# fmt: on
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]
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)
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@require_torch_gpu
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def test_compvis_sd_v1_4_fp16(self, seed, timestep, expected_slice):
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model = self.get_unet_model(model_id="CompVis/stable-diffusion-v1-4", fp16=True)
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latents = self.get_latents(seed, fp16=True)
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encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)
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with torch.no_grad():
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sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
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assert sample.shape == latents.shape
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output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
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expected_output_slice = torch.tensor(expected_slice)
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assert torch.allclose(output_slice, expected_output_slice, atol=1e-4)
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@parameterized.expand(
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[
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# fmt: off
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[33, 4, [-0.4430, 0.1570, -0.1867, 0.2376, 0.3205, -0.3681, 0.0525, -0.0722]],
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[47, 0.55, [-0.1415, 0.0129, -0.3136, 0.2257, 0.3430, -0.0536, 0.2114, -0.0436]],
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[21, 0.89, [-0.7091, 0.6664, -0.3643, 0.9032, 0.4499, -0.6541, 0.0139, 0.1750]],
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[9, 1000, [0.8878, -0.5659, 0.5844, -0.7442, 1.1883, -0.3927, 1.1192, -0.4423]],
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# fmt: on
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]
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)
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def test_compvis_sd_v1_5(self, seed, timestep, expected_slice):
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model = self.get_unet_model(model_id="runwayml/stable-diffusion-v1-5")
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latents = self.get_latents(seed)
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encoder_hidden_states = self.get_encoder_hidden_states(seed)
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with torch.no_grad():
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sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
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assert sample.shape == latents.shape
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output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
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expected_output_slice = torch.tensor(expected_slice)
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assert torch.allclose(output_slice, expected_output_slice, atol=1e-4)
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@parameterized.expand(
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[
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# fmt: off
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[83, 4, [-0.2695, -0.1669, 0.0073, -0.3181, -0.1187, -0.1676, -0.1395, -0.5972]],
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[17, 0.55, [-0.1290, -0.2588, 0.0551, -0.0916, 0.3286, 0.0238, -0.3669, 0.0322]],
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[8, 0.89, [-0.5283, 0.1198, 0.0870, -0.1141, 0.9189, -0.0150, 0.5474, 0.4319]],
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[3, 1000, [-0.5601, 0.2411, -0.5435, 0.1268, 1.1338, -0.2427, -0.0280, -1.0020]],
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# fmt: on
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]
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)
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@require_torch_gpu
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def test_compvis_sd_v1_5_fp16(self, seed, timestep, expected_slice):
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model = self.get_unet_model(model_id="runwayml/stable-diffusion-v1-5", fp16=True)
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latents = self.get_latents(seed, fp16=True)
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encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)
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with torch.no_grad():
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sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
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assert sample.shape == latents.shape
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output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
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expected_output_slice = torch.tensor(expected_slice)
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assert torch.allclose(output_slice, expected_output_slice, atol=1e-4)
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@parameterized.expand(
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[
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# fmt: off
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[33, 4, [-0.7639, 0.0106, -0.1615, -0.3487, -0.0423, -0.7972, 0.0085, -0.4858]],
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[47, 0.55, [-0.6564, 0.0795, -1.9026, -0.6258, 1.8235, 1.2056, 1.2169, 0.9073]],
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[21, 0.89, [0.0327, 0.4399, -0.6358, 0.3417, 0.4120, -0.5621, -0.0397, -1.0430]],
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[9, 1000, [0.1600, 0.7303, -1.0556, -0.3515, -0.7440, -1.2037, -1.8149, -1.8931]],
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# fmt: on
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]
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)
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def test_compvis_sd_inpaint(self, seed, timestep, expected_slice):
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model = self.get_unet_model(model_id="runwayml/stable-diffusion-inpainting")
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latents = self.get_latents(seed, shape=(4, 9, 64, 64))
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encoder_hidden_states = self.get_encoder_hidden_states(seed)
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with torch.no_grad():
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sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
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assert sample.shape == (4, 4, 64, 64)
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output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
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expected_output_slice = torch.tensor(expected_slice)
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assert torch.allclose(output_slice, expected_output_slice, atol=1e-4)
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@parameterized.expand(
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[
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# fmt: off
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[83, 4, [-0.1047, -1.7227, 0.1067, 0.0164, -0.5698, -0.4172, -0.1388, 1.1387]],
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[17, 0.55, [0.0975, -0.2856, -0.3508, -0.4600, 0.3376, 0.2930, -0.2747, -0.7026]],
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[8, 0.89, [-0.0952, 0.0183, -0.5825, -0.1981, 0.1131, 0.4668, -0.0395, -0.3486]],
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[3, 1000, [0.4790, 0.4949, -1.0732, -0.7158, 0.7959, -0.9478, 0.1105, -0.9741]],
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# fmt: on
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]
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)
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@require_torch_gpu
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def test_compvis_sd_inpaint_fp16(self, seed, timestep, expected_slice):
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model = self.get_unet_model(model_id="runwayml/stable-diffusion-inpainting", fp16=True)
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latents = self.get_latents(seed, shape=(4, 9, 64, 64), fp16=True)
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encoder_hidden_states = self.get_encoder_hidden_states(seed, fp16=True)
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with torch.no_grad():
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sample = model(latents, timestep=timestep, encoder_hidden_states=encoder_hidden_states).sample
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assert sample.shape == (4, 4, 64, 64)
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output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
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expected_output_slice = torch.tensor(expected_slice)
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assert torch.allclose(output_slice, expected_output_slice, atol=1e-4)
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@ -0,0 +1,303 @@
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# coding=utf-8
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# Copyright 2022 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import gc
|
||||
import unittest
|
||||
|
||||
import torch
|
||||
|
||||
from diffusers import AutoencoderKL
|
||||
from diffusers.modeling_utils import ModelMixin
|
||||
from diffusers.utils import floats_tensor, require_torch_gpu, slow, torch_device
|
||||
from parameterized import parameterized
|
||||
|
||||
from ..test_modeling_common import ModelTesterMixin
|
||||
|
||||
|
||||
torch.backends.cuda.matmul.allow_tf32 = False
|
||||
|
||||
|
||||
class AutoencoderKLTests(ModelTesterMixin, unittest.TestCase):
|
||||
model_class = AutoencoderKL
|
||||
|
||||
@property
|
||||
def dummy_input(self):
|
||||
batch_size = 4
|
||||
num_channels = 3
|
||||
sizes = (32, 32)
|
||||
|
||||
image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
|
||||
|
||||
return {"sample": image}
|
||||
|
||||
@property
|
||||
def input_shape(self):
|
||||
return (3, 32, 32)
|
||||
|
||||
@property
|
||||
def output_shape(self):
|
||||
return (3, 32, 32)
|
||||
|
||||
def prepare_init_args_and_inputs_for_common(self):
|
||||
init_dict = {
|
||||
"block_out_channels": [32, 64],
|
||||
"in_channels": 3,
|
||||
"out_channels": 3,
|
||||
"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
|
||||
"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
|
||||
"latent_channels": 4,
|
||||
}
|
||||
inputs_dict = self.dummy_input
|
||||
return init_dict, inputs_dict
|
||||
|
||||
def test_forward_signature(self):
|
||||
pass
|
||||
|
||||
def test_training(self):
|
||||
pass
|
||||
|
||||
def test_from_pretrained_hub(self):
|
||||
model, loading_info = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy", output_loading_info=True)
|
||||
self.assertIsNotNone(model)
|
||||
self.assertEqual(len(loading_info["missing_keys"]), 0)
|
||||
|
||||
model.to(torch_device)
|
||||
image = model(**self.dummy_input)
|
||||
|
||||
assert image is not None, "Make sure output is not None"
|
||||
|
||||
def test_output_pretrained(self):
|
||||
model = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy")
|
||||
model = model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
# One-time warmup pass (see #372)
|
||||
if torch_device == "mps" and isinstance(model, ModelMixin):
|
||||
image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
|
||||
image = image.to(torch_device)
|
||||
with torch.no_grad():
|
||||
_ = model(image, sample_posterior=True).sample
|
||||
generator = torch.manual_seed(0)
|
||||
else:
|
||||
generator = torch.Generator(device=torch_device).manual_seed(0)
|
||||
|
||||
image = torch.randn(
|
||||
1,
|
||||
model.config.in_channels,
|
||||
model.config.sample_size,
|
||||
model.config.sample_size,
|
||||
generator=torch.manual_seed(0),
|
||||
)
|
||||
image = image.to(torch_device)
|
||||
with torch.no_grad():
|
||||
output = model(image, sample_posterior=True, generator=generator).sample
|
||||
|
||||
output_slice = output[0, -1, -3:, -3:].flatten().cpu()
|
||||
|
||||
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
|
||||
# the expected output slices are not the same for CPU and GPU.
|
||||
if torch_device == "mps":
|
||||
expected_output_slice = torch.tensor(
|
||||
[
|
||||
-4.0078e-01,
|
||||
-3.8323e-04,
|
||||
-1.2681e-01,
|
||||
-1.1462e-01,
|
||||
2.0095e-01,
|
||||
1.0893e-01,
|
||||
-8.8247e-02,
|
||||
-3.0361e-01,
|
||||
-9.8644e-03,
|
||||
]
|
||||
)
|
||||
elif torch_device == "cpu":
|
||||
expected_output_slice = torch.tensor(
|
||||
[-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]
|
||||
)
|
||||
else:
|
||||
expected_output_slice = torch.tensor(
|
||||
[-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]
|
||||
)
|
||||
|
||||
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
|
||||
|
||||
|
||||
@slow
|
||||
class AutoencoderKLIntegrationTests(unittest.TestCase):
|
||||
def tearDown(self):
|
||||
# clean up the VRAM after each test
|
||||
super().tearDown()
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
def get_sd_image(self, seed=0, shape=(4, 3, 512, 512), fp16=False):
|
||||
batch_size, channels, height, width = shape
|
||||
generator = torch.Generator(device=torch_device).manual_seed(seed)
|
||||
dtype = torch.float16 if fp16 else torch.float32
|
||||
image = torch.randn(batch_size, channels, height, width, device=torch_device, generator=generator, dtype=dtype)
|
||||
|
||||
return image
|
||||
|
||||
def get_sd_vae_model(self, model_id="CompVis/stable-diffusion-v1-4", fp16=False):
|
||||
revision = "fp16" if fp16 else None
|
||||
torch_dtype = torch.float16 if fp16 else torch.float32
|
||||
|
||||
model = AutoencoderKL.from_pretrained(
|
||||
model_id, subfolder="vae", torch_dtype=torch_dtype, revision=revision, device_map="auto"
|
||||
)
|
||||
model.to(torch_device).eval()
|
||||
|
||||
return model
|
||||
|
||||
def get_generator(self, seed=0):
|
||||
return torch.Generator(device=torch_device).manual_seed(seed)
|
||||
|
||||
@parameterized.expand(
|
||||
[
|
||||
# fmt: off
|
||||
[33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824]],
|
||||
[47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089]],
|
||||
# fmt: on
|
||||
]
|
||||
)
|
||||
def test_stable_diffusion(self, seed, expected_slice):
|
||||
model = self.get_sd_vae_model()
|
||||
image = self.get_sd_image(seed)
|
||||
generator = self.get_generator(seed)
|
||||
|
||||
with torch.no_grad():
|
||||
sample = model(image, generator=generator, sample_posterior=True).sample
|
||||
|
||||
assert sample.shape == image.shape
|
||||
|
||||
output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
|
||||
expected_output_slice = torch.tensor(expected_slice)
|
||||
|
||||
assert torch.allclose(output_slice, expected_output_slice, atol=1e-4)
|
||||
|
||||
@parameterized.expand(
|
||||
[
|
||||
# fmt: off
|
||||
[33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
|
||||
[47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
|
||||
# fmt: on
|
||||
]
|
||||
)
|
||||
@require_torch_gpu
|
||||
def test_stable_diffusion_fp16(self, seed, expected_slice):
|
||||
model = self.get_sd_vae_model(fp16=True)
|
||||
image = self.get_sd_image(seed, fp16=True)
|
||||
generator = self.get_generator(seed)
|
||||
|
||||
with torch.no_grad():
|
||||
sample = model(image, generator=generator, sample_posterior=True).sample
|
||||
|
||||
assert sample.shape == image.shape
|
||||
|
||||
output_slice = sample[-1, -2:, :2, -2:].flatten().float().cpu()
|
||||
expected_output_slice = torch.tensor(expected_slice)
|
||||
|
||||
assert torch.allclose(output_slice, expected_output_slice, atol=1e-4)
|
||||
|
||||
@parameterized.expand(
|
||||
[
|
||||
# fmt: off
|
||||
[33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814]],
|
||||
[47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085]],
|
||||
# fmt: on
|
||||
]
|
||||
)
|
||||
def test_stable_diffusion_mode(self, seed, expected_slice):
|
||||
model = self.get_sd_vae_model()
|
||||
image = self.get_sd_image(seed)
|
||||
|
||||
with torch.no_grad():
|
||||
sample = model(image).sample
|
||||
|
||||
assert sample.shape == image.shape
|
||||
|
||||
output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
|
||||
expected_output_slice = torch.tensor(expected_slice)
|
||||
|
||||
assert torch.allclose(output_slice, expected_output_slice, atol=1e-4)
|
||||
|
||||
@parameterized.expand(
|
||||
[
|
||||
# fmt: off
|
||||
[13, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
|
||||
[37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
|
||||
# fmt: on
|
||||
]
|
||||
)
|
||||
@require_torch_gpu
|
||||
def test_stable_diffusion_decode(self, seed, expected_slice):
|
||||
model = self.get_sd_vae_model()
|
||||
encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64))
|
||||
|
||||
with torch.no_grad():
|
||||
sample = model.decode(encoding).sample
|
||||
|
||||
assert list(sample.shape) == [3, 3, 512, 512]
|
||||
|
||||
output_slice = sample[-1, -2:, :2, -2:].flatten().cpu()
|
||||
expected_output_slice = torch.tensor(expected_slice)
|
||||
|
||||
assert torch.allclose(output_slice, expected_output_slice, atol=1e-4)
|
||||
|
||||
@parameterized.expand(
|
||||
[
|
||||
# fmt: off
|
||||
[27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
|
||||
[16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
|
||||
# fmt: on
|
||||
]
|
||||
)
|
||||
def test_stable_diffusion_decode_fp16(self, seed, expected_slice):
|
||||
model = self.get_sd_vae_model(fp16=True)
|
||||
encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64), fp16=True)
|
||||
|
||||
with torch.no_grad():
|
||||
sample = model.decode(encoding).sample
|
||||
|
||||
assert list(sample.shape) == [3, 3, 512, 512]
|
||||
|
||||
output_slice = sample[-1, -2:, :2, -2:].flatten().float().cpu()
|
||||
expected_output_slice = torch.tensor(expected_slice)
|
||||
|
||||
assert torch.allclose(output_slice, expected_output_slice, atol=1e-4)
|
||||
|
||||
@parameterized.expand(
|
||||
[
|
||||
# fmt: off
|
||||
[33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
|
||||
[47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
|
||||
# fmt: on
|
||||
]
|
||||
)
|
||||
def test_stable_diffusion_encode_sample(self, seed, expected_slice):
|
||||
model = self.get_sd_vae_model()
|
||||
image = self.get_sd_image(seed)
|
||||
generator = self.get_generator(seed)
|
||||
|
||||
with torch.no_grad():
|
||||
dist = model.encode(image).latent_dist
|
||||
sample = dist.sample(generator=generator)
|
||||
|
||||
assert list(sample.shape) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
|
||||
|
||||
output_slice = sample[0, -1, -3:, -3:].flatten().cpu()
|
||||
expected_output_slice = torch.tensor(expected_slice)
|
||||
|
||||
assert torch.allclose(output_slice, expected_output_slice, atol=1e-4)
|
||||
|
|
@ -4,7 +4,7 @@ from diffusers import FlaxAutoencoderKL
|
|||
from diffusers.utils import is_flax_available
|
||||
from diffusers.utils.testing_utils import require_flax
|
||||
|
||||
from .test_modeling_common_flax import FlaxModelTesterMixin
|
||||
from ..test_modeling_common_flax import FlaxModelTesterMixin
|
||||
|
||||
|
||||
if is_flax_available():
|
||||
|
|
@ -20,7 +20,7 @@ import torch
|
|||
from diffusers import VQModel
|
||||
from diffusers.utils import floats_tensor, torch_device
|
||||
|
||||
from .test_modeling_common import ModelTesterMixin
|
||||
from ..test_modeling_common import ModelTesterMixin
|
||||
|
||||
|
||||
torch.backends.cuda.matmul.allow_tf32 = False
|
||||
|
|
@ -1,132 +0,0 @@
|
|||
# coding=utf-8
|
||||
# Copyright 2022 HuggingFace Inc.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import unittest
|
||||
|
||||
import torch
|
||||
|
||||
from diffusers import AutoencoderKL
|
||||
from diffusers.modeling_utils import ModelMixin
|
||||
from diffusers.utils import floats_tensor, torch_device
|
||||
|
||||
from .test_modeling_common import ModelTesterMixin
|
||||
|
||||
|
||||
torch.backends.cuda.matmul.allow_tf32 = False
|
||||
|
||||
|
||||
class AutoencoderKLTests(ModelTesterMixin, unittest.TestCase):
|
||||
model_class = AutoencoderKL
|
||||
|
||||
@property
|
||||
def dummy_input(self):
|
||||
batch_size = 4
|
||||
num_channels = 3
|
||||
sizes = (32, 32)
|
||||
|
||||
image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
|
||||
|
||||
return {"sample": image}
|
||||
|
||||
@property
|
||||
def input_shape(self):
|
||||
return (3, 32, 32)
|
||||
|
||||
@property
|
||||
def output_shape(self):
|
||||
return (3, 32, 32)
|
||||
|
||||
def prepare_init_args_and_inputs_for_common(self):
|
||||
init_dict = {
|
||||
"block_out_channels": [32, 64],
|
||||
"in_channels": 3,
|
||||
"out_channels": 3,
|
||||
"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
|
||||
"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
|
||||
"latent_channels": 4,
|
||||
}
|
||||
inputs_dict = self.dummy_input
|
||||
return init_dict, inputs_dict
|
||||
|
||||
def test_forward_signature(self):
|
||||
pass
|
||||
|
||||
def test_training(self):
|
||||
pass
|
||||
|
||||
def test_from_pretrained_hub(self):
|
||||
model, loading_info = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy", output_loading_info=True)
|
||||
self.assertIsNotNone(model)
|
||||
self.assertEqual(len(loading_info["missing_keys"]), 0)
|
||||
|
||||
model.to(torch_device)
|
||||
image = model(**self.dummy_input)
|
||||
|
||||
assert image is not None, "Make sure output is not None"
|
||||
|
||||
def test_output_pretrained(self):
|
||||
model = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy")
|
||||
model = model.to(torch_device)
|
||||
model.eval()
|
||||
|
||||
# One-time warmup pass (see #372)
|
||||
if torch_device == "mps" and isinstance(model, ModelMixin):
|
||||
image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
|
||||
image = image.to(torch_device)
|
||||
with torch.no_grad():
|
||||
_ = model(image, sample_posterior=True).sample
|
||||
generator = torch.manual_seed(0)
|
||||
else:
|
||||
generator = torch.Generator(device=torch_device).manual_seed(0)
|
||||
|
||||
image = torch.randn(
|
||||
1,
|
||||
model.config.in_channels,
|
||||
model.config.sample_size,
|
||||
model.config.sample_size,
|
||||
generator=torch.manual_seed(0),
|
||||
)
|
||||
image = image.to(torch_device)
|
||||
with torch.no_grad():
|
||||
output = model(image, sample_posterior=True, generator=generator).sample
|
||||
|
||||
output_slice = output[0, -1, -3:, -3:].flatten().cpu()
|
||||
|
||||
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
|
||||
# the expected output slices are not the same for CPU and GPU.
|
||||
if torch_device == "mps":
|
||||
expected_output_slice = torch.tensor(
|
||||
[
|
||||
-4.0078e-01,
|
||||
-3.8323e-04,
|
||||
-1.2681e-01,
|
||||
-1.1462e-01,
|
||||
2.0095e-01,
|
||||
1.0893e-01,
|
||||
-8.8247e-02,
|
||||
-3.0361e-01,
|
||||
-9.8644e-03,
|
||||
]
|
||||
)
|
||||
elif torch_device == "cpu":
|
||||
expected_output_slice = torch.tensor(
|
||||
[-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]
|
||||
)
|
||||
else:
|
||||
expected_output_slice = torch.tensor(
|
||||
[-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]
|
||||
)
|
||||
|
||||
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
|
||||
Loading…
Reference in New Issue