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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 AutoencoderKLMagvit
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, torch_device
from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class AutoencoderKLMagvitTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
model_class = AutoencoderKLMagvit
main_input_name = "sample"
base_precision = 1e-2
def get_autoencoder_kl_magvit_config(self):
return {
"in_channels": 3,
"latent_channels": 4,
"out_channels": 3,
"block_out_channels": [8, 8, 8, 8],
"down_block_types": [
"SpatialDownBlock3D",
"SpatialTemporalDownBlock3D",
"SpatialTemporalDownBlock3D",
"SpatialTemporalDownBlock3D",
],
"up_block_types": [
"SpatialUpBlock3D",
"SpatialTemporalUpBlock3D",
"SpatialTemporalUpBlock3D",
"SpatialTemporalUpBlock3D",
],
"layers_per_block": 1,
"norm_num_groups": 8,
"spatial_group_norm": True,
}
@property
def dummy_input(self):
batch_size = 2
num_frames = 9
num_channels = 3
height = 16
width = 16
image = floats_tensor((batch_size, num_channels, num_frames, height, width)).to(torch_device)
return {"sample": image}
@property
def input_shape(self):
return (3, 9, 16, 16)
@property
def output_shape(self):
return (3, 9, 16, 16)
def prepare_init_args_and_inputs_for_common(self):
init_dict = self.get_autoencoder_kl_magvit_config()
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_gradient_checkpointing_is_applied(self):
expected_set = {"EasyAnimateEncoder", "EasyAnimateDecoder"}
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
@unittest.skip("Not quite sure why this test fails. Revisit later.")
def test_effective_gradient_checkpointing(self):
pass
@unittest.skip("Unsupported test.")
def test_forward_with_norm_groups(self):
pass
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