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# Copyright 2025 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 AutoencoderKLKVAE
from ...testing_utils import enable_full_determinism, floats_tensor, torch_device
from ..test_modeling_common import ModelTesterMixin
from .testing_utils import AutoencoderTesterMixin
enable_full_determinism()
class AutoencoderKLKVAETests(ModelTesterMixin, AutoencoderTesterMixin, unittest.TestCase):
model_class = AutoencoderKLKVAE
main_input_name = "sample"
base_precision = 1e-2
def get_autoencoder_kl_kvae_config(self):
return {
"in_channels": 3,
"channels": 32,
"num_enc_blocks": 1,
"num_dec_blocks": 1,
"z_channels": 4,
"double_z": True,
"ch_mult": (1, 2),
"sample_size": 32,
}
@property
def dummy_input(self):
batch_size = 2
num_channels = 3
sizes = (32, 32)
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 = self.get_autoencoder_kl_kvae_config()
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_gradient_checkpointing_is_applied(self):
expected_set = {
"KVAEEncoder2D",
"KVAEDecoder2D",
}
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
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