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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
import torch
from diffusers import AutoencoderKLCogVideoX
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 AutoencoderKLCogVideoXTests(ModelTesterMixin, AutoencoderTesterMixin, unittest.TestCase):
model_class = AutoencoderKLCogVideoX
main_input_name = "sample"
base_precision = 1e-2
def get_autoencoder_kl_cogvideox_config(self):
return {
"in_channels": 3,
"out_channels": 3,
"down_block_types": (
"CogVideoXDownBlock3D",
"CogVideoXDownBlock3D",
"CogVideoXDownBlock3D",
"CogVideoXDownBlock3D",
),
"up_block_types": (
"CogVideoXUpBlock3D",
"CogVideoXUpBlock3D",
"CogVideoXUpBlock3D",
"CogVideoXUpBlock3D",
),
"block_out_channels": (8, 8, 8, 8),
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 2,
"temporal_compression_ratio": 4,
}
@property
def dummy_input(self):
batch_size = 4
num_frames = 8
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, 8, 16, 16)
@property
def output_shape(self):
return (3, 8, 16, 16)
def prepare_init_args_and_inputs_for_common(self):
init_dict = self.get_autoencoder_kl_cogvideox_config()
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_gradient_checkpointing_is_applied(self):
expected_set = {
"CogVideoXDownBlock3D",
"CogVideoXDecoder3D",
"CogVideoXEncoder3D",
"CogVideoXUpBlock3D",
"CogVideoXMidBlock3D",
}
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
def test_forward_with_norm_groups(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
init_dict["norm_num_groups"] = 16
init_dict["block_out_channels"] = (16, 32, 32, 32)
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with torch.no_grad():
output = model(**inputs_dict)
if isinstance(output, dict):
output = output.to_tuple()[0]
self.assertIsNotNone(output)
expected_shape = inputs_dict["sample"].shape
self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
@unittest.skip("Unsupported test.")
def test_outputs_equivalence(self):
pass
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