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import unittest |
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from diffusers import AutoencoderKLCosmos |
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from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, torch_device |
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from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin |
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enable_full_determinism() |
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class AutoencoderKLCosmosTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase): |
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model_class = AutoencoderKLCosmos |
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main_input_name = "sample" |
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base_precision = 1e-2 |
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def get_autoencoder_kl_cosmos_config(self): |
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return { |
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"in_channels": 3, |
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"out_channels": 3, |
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"latent_channels": 4, |
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"encoder_block_out_channels": (8, 8, 8, 8), |
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"decode_block_out_channels": (8, 8, 8, 8), |
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"attention_resolutions": (8,), |
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"resolution": 64, |
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"num_layers": 2, |
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"patch_size": 4, |
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"patch_type": "haar", |
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"scaling_factor": 1.0, |
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"spatial_compression_ratio": 4, |
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"temporal_compression_ratio": 4, |
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} |
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@property |
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def dummy_input(self): |
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batch_size = 2 |
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num_frames = 9 |
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num_channels = 3 |
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height = 32 |
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width = 32 |
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image = floats_tensor((batch_size, num_channels, num_frames, height, width)).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, 9, 32, 32) |
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@property |
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def output_shape(self): |
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return (3, 9, 32, 32) |
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def prepare_init_args_and_inputs_for_common(self): |
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init_dict = self.get_autoencoder_kl_cosmos_config() |
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inputs_dict = self.dummy_input |
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return init_dict, inputs_dict |
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def test_gradient_checkpointing_is_applied(self): |
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expected_set = { |
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"CosmosEncoder3d", |
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"CosmosDecoder3d", |
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} |
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super().test_gradient_checkpointing_is_applied(expected_set=expected_set) |
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@unittest.skip("Not sure why this test fails. Investigate later.") |
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def test_effective_gradient_checkpointing(self): |
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pass |
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@unittest.skip("Unsupported test.") |
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def test_forward_with_norm_groups(self): |
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pass |
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