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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 AutoencoderKLLTX2Video
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 AutoencoderKLLTX2VideoTests(ModelTesterMixin, AutoencoderTesterMixin, unittest.TestCase):
model_class = AutoencoderKLLTX2Video
main_input_name = "sample"
base_precision = 1e-2
def get_autoencoder_kl_ltx_video_config(self):
return {
"in_channels": 3,
"out_channels": 3,
"latent_channels": 8,
"block_out_channels": (8, 8, 8, 8),
"decoder_block_out_channels": (16, 32, 64),
"layers_per_block": (1, 1, 1, 1, 1),
"decoder_layers_per_block": (1, 1, 1, 1),
"spatio_temporal_scaling": (True, True, True, True),
"decoder_spatio_temporal_scaling": (True, True, True),
"decoder_inject_noise": (False, False, False, False),
"downsample_type": ("spatial", "temporal", "spatiotemporal", "spatiotemporal"),
"upsample_residual": (True, True, True),
"upsample_factor": (2, 2, 2),
"timestep_conditioning": False,
"patch_size": 1,
"patch_size_t": 1,
"encoder_causal": True,
"decoder_causal": False,
"encoder_spatial_padding_mode": "zeros",
# Full model uses `reflect` but this does not have deterministic backward implementation, so use `zeros`
"decoder_spatial_padding_mode": "zeros",
}
@property
def dummy_input(self):
batch_size = 2
num_frames = 9
num_channels = 3
sizes = (16, 16)
image = floats_tensor((batch_size, num_channels, num_frames) + sizes).to(torch_device)
input_dict = {"sample": image}
return input_dict
@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_ltx_video_config()
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_gradient_checkpointing_is_applied(self):
expected_set = {
"LTX2VideoEncoder3d",
"LTX2VideoDecoder3d",
"LTX2VideoDownBlock3D",
"LTX2VideoMidBlock3d",
"LTX2VideoUpBlock3d",
}
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
@unittest.skip("Unsupported test.")
def test_outputs_equivalence(self):
pass
@unittest.skip("AutoencoderKLLTXVideo does not support `norm_num_groups` because it does not use GroupNorm.")
def test_forward_with_norm_groups(self):
pass
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