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# coding=utf-8
# Copyright 2024 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
from diffusers import AutoencoderKLMochi
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
torch_device,
)
from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class AutoencoderKLMochiTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
model_class = AutoencoderKLMochi
main_input_name = "sample"
base_precision = 1e-2
def get_autoencoder_kl_mochi_config(self):
return {
"in_channels": 15,
"out_channels": 3,
"latent_channels": 4,
"encoder_block_out_channels": (32, 32, 32, 32),
"decoder_block_out_channels": (32, 32, 32, 32),
"layers_per_block": (1, 1, 1, 1, 1),
"act_fn": "silu",
"scaling_factor": 1,
}
@property
def dummy_input(self):
batch_size = 2
num_frames = 7
num_channels = 3
sizes = (16, 16)
image = floats_tensor((batch_size, num_channels, num_frames) + sizes).to(torch_device)
return {"sample": image}
@property
def input_shape(self):
return (3, 7, 16, 16)
@property
def output_shape(self):
return (3, 7, 16, 16)
def prepare_init_args_and_inputs_for_common(self):
init_dict = self.get_autoencoder_kl_mochi_config()
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_gradient_checkpointing_is_applied(self):
expected_set = {
"MochiDecoder3D",
"MochiDownBlock3D",
"MochiEncoder3D",
"MochiMidBlock3D",
"MochiUpBlock3D",
}
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
@unittest.skip("Unsupported test.")
def test_forward_with_norm_groups(self):
"""
tests/models/autoencoders/test_models_autoencoder_mochi.py::AutoencoderKLMochiTests::test_forward_with_norm_groups -
TypeError: AutoencoderKLMochi.__init__() got an unexpected keyword argument 'norm_num_groups'
"""
pass
@unittest.skip("Unsupported test.")
def test_model_parallelism(self):
"""
tests/models/autoencoders/test_models_autoencoder_mochi.py::AutoencoderKLMochiTests::test_outputs_equivalence -
RuntimeError: values expected sparse tensor layout but got Strided
"""
pass
@unittest.skip("Unsupported test.")
def test_outputs_equivalence(self):
"""
tests/models/autoencoders/test_models_autoencoder_mochi.py::AutoencoderKLMochiTests::test_outputs_equivalence -
RuntimeError: values expected sparse tensor layout but got Strided
"""
pass
@unittest.skip("Unsupported test.")
def test_sharded_checkpoints_device_map(self):
"""
tests/models/autoencoders/test_models_autoencoder_mochi.py::AutoencoderKLMochiTests::test_sharded_checkpoints_device_map -
RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cuda:5!
"""
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