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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.models.transformers import TransformerTemporalModel
from ...testing_utils import (
enable_full_determinism,
torch_device,
)
from ..test_modeling_common import ModelTesterMixin
enable_full_determinism()
class TemporalTransformerTests(ModelTesterMixin, unittest.TestCase):
model_class = TransformerTemporalModel
main_input_name = "hidden_states"
@property
def dummy_input(self):
batch_size = 2
num_channels = 4
height = width = 32
hidden_states = torch.randn((batch_size, num_channels, height, width)).to(torch_device)
timestep = torch.randint(0, 1000, size=(batch_size,)).to(torch_device)
return {
"hidden_states": hidden_states,
"timestep": timestep,
}
@property
def input_shape(self):
return (4, 32, 32)
@property
def output_shape(self):
return (4, 32, 32)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"num_attention_heads": 8,
"attention_head_dim": 4,
"in_channels": 4,
"num_layers": 1,
"norm_num_groups": 1,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
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