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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 pytest
import torch
from diffusers import AutoencoderDC
from diffusers.utils.torch_utils import randn_tensor
from ...testing_utils import IS_GITHUB_ACTIONS, enable_full_determinism, torch_device
from ..testing_utils import BaseModelTesterConfig, MemoryTesterMixin, ModelTesterMixin, TrainingTesterMixin
from .testing_utils import NewAutoencoderTesterMixin
enable_full_determinism()
class AutoencoderDCTesterConfig(BaseModelTesterConfig):
@property
def main_input_name(self):
return "sample"
@property
def model_class(self):
return AutoencoderDC
@property
def output_shape(self):
return (3, 32, 32)
@property
def generator(self):
return torch.Generator("cpu").manual_seed(0)
def get_init_dict(self):
return {
"in_channels": 3,
"latent_channels": 4,
"attention_head_dim": 2,
"encoder_block_types": (
"ResBlock",
"EfficientViTBlock",
),
"decoder_block_types": (
"ResBlock",
"EfficientViTBlock",
),
"encoder_block_out_channels": (8, 8),
"decoder_block_out_channels": (8, 8),
"encoder_qkv_multiscales": ((), (5,)),
"decoder_qkv_multiscales": ((), (5,)),
"encoder_layers_per_block": (1, 1),
"decoder_layers_per_block": [1, 1],
"downsample_block_type": "conv",
"upsample_block_type": "interpolate",
"decoder_norm_types": "rms_norm",
"decoder_act_fns": "silu",
"scaling_factor": 0.41407,
}
def get_dummy_inputs(self):
batch_size = 4
num_channels = 3
sizes = (32, 32)
image = randn_tensor((batch_size, num_channels, *sizes), generator=self.generator, device=torch_device)
return {"sample": image}
class TestAutoencoderDC(AutoencoderDCTesterConfig, ModelTesterMixin):
base_precision = 1e-2
@pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16], ids=["fp16", "bf16"])
def test_from_save_pretrained_dtype_inference(self, tmp_path, dtype):
if dtype == torch.bfloat16 and IS_GITHUB_ACTIONS:
pytest.skip("Skipping bf16 test inside GitHub Actions environment")
super().test_from_save_pretrained_dtype_inference(tmp_path, dtype)
class TestAutoencoderDCTraining(AutoencoderDCTesterConfig, TrainingTesterMixin):
"""Training tests for AutoencoderDC."""
class TestAutoencoderDCMemory(AutoencoderDCTesterConfig, MemoryTesterMixin):
"""Memory optimization tests for AutoencoderDC."""
@pytest.mark.skipif(IS_GITHUB_ACTIONS, reason="Skipping test inside GitHub Actions environment")
def test_layerwise_casting_memory(self):
super().test_layerwise_casting_memory()
class TestAutoencoderDCSlicingTiling(AutoencoderDCTesterConfig, NewAutoencoderTesterMixin):
"""Slicing and tiling tests for AutoencoderDC."""
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