# 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 @property 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} @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": 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))