133 lines
4.3 KiB
Python
133 lines
4.3 KiB
Python
# 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 unittest
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import torch
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from diffusers import AutoencoderKL
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from diffusers.modeling_utils import ModelMixin
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from diffusers.utils import floats_tensor, torch_device
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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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class AutoencoderKLTests(ModelTesterMixin, unittest.TestCase):
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model_class = AutoencoderKL
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@property
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def dummy_input(self):
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batch_size = 4
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num_channels = 3
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sizes = (32, 32)
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image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
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return {"sample": image}
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@property
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def input_shape(self):
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return (3, 32, 32)
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@property
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def output_shape(self):
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return (3, 32, 32)
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def prepare_init_args_and_inputs_for_common(self):
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init_dict = {
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"block_out_channels": [32, 64],
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"in_channels": 3,
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"out_channels": 3,
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"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
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"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
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"latent_channels": 4,
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}
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inputs_dict = self.dummy_input
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return init_dict, inputs_dict
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def test_forward_signature(self):
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pass
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def test_training(self):
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pass
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def test_from_pretrained_hub(self):
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model, loading_info = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy", output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertEqual(len(loading_info["missing_keys"]), 0)
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model.to(torch_device)
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image = model(**self.dummy_input)
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assert image is not None, "Make sure output is not None"
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def test_output_pretrained(self):
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model = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy")
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model = model.to(torch_device)
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model.eval()
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# One-time warmup pass (see #372)
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if torch_device == "mps" and isinstance(model, ModelMixin):
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image = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
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image = image.to(torch_device)
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with torch.no_grad():
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_ = model(image, sample_posterior=True).sample
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generator = torch.manual_seed(0)
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else:
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generator = torch.Generator(device=torch_device).manual_seed(0)
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image = torch.randn(
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1,
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model.config.in_channels,
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model.config.sample_size,
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model.config.sample_size,
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generator=torch.manual_seed(0),
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)
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image = image.to(torch_device)
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with torch.no_grad():
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output = model(image, sample_posterior=True, generator=generator).sample
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output_slice = output[0, -1, -3:, -3:].flatten().cpu()
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# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
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# the expected output slices are not the same for CPU and GPU.
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if torch_device == "mps":
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expected_output_slice = torch.tensor(
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[
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-4.0078e-01,
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-3.8323e-04,
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-1.2681e-01,
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-1.1462e-01,
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2.0095e-01,
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1.0893e-01,
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-8.8247e-02,
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-3.0361e-01,
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-9.8644e-03,
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]
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)
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elif torch_device == "cpu":
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expected_output_slice = torch.tensor(
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[-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]
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)
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else:
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expected_output_slice = torch.tensor(
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[-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]
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)
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self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
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