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| # coding=utf-8 | |
| # Copyright 2023 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 VQModel | |
| from diffusers.utils import floats_tensor, torch_device | |
| from ..test_modeling_common import ModelTesterMixin | |
| torch.backends.cuda.matmul.allow_tf32 = False | |
| class VQModelTests(ModelTesterMixin, unittest.TestCase): | |
| model_class = VQModel | |
| def dummy_input(self, sizes=(32, 32)): | |
| batch_size = 4 | |
| num_channels = 3 | |
| image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device) | |
| return {"sample": image} | |
| def input_shape(self): | |
| return (3, 32, 32) | |
| 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": 3, | |
| } | |
| 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 = VQModel.from_pretrained("fusing/vqgan-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 = VQModel.from_pretrained("fusing/vqgan-dummy") | |
| model.to(torch_device).eval() | |
| torch.manual_seed(0) | |
| if torch.cuda.is_available(): | |
| torch.cuda.manual_seed_all(0) | |
| 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(): | |
| output = model(image).sample | |
| output_slice = output[0, -1, -3:, -3:].flatten().cpu() | |
| # fmt: off | |
| expected_output_slice = torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]) | |
| # fmt: on | |
| self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3)) | |