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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 AuraFlowTransformer2DModel
from ...testing_utils import enable_full_determinism, torch_device
from ..test_modeling_common import ModelTesterMixin
enable_full_determinism()
class AuraFlowTransformerTests(ModelTesterMixin, unittest.TestCase):
model_class = AuraFlowTransformer2DModel
main_input_name = "hidden_states"
# We override the items here because the transformer under consideration is small.
model_split_percents = [0.7, 0.6, 0.6]
@property
def dummy_input(self):
batch_size = 2
num_channels = 4
height = width = embedding_dim = 32
sequence_length = 256
hidden_states = torch.randn((batch_size, num_channels, height, width)).to(torch_device)
encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
timestep = torch.randint(0, 1000, size=(batch_size,)).to(torch_device)
return {
"hidden_states": hidden_states,
"encoder_hidden_states": encoder_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 = {
"sample_size": 32,
"patch_size": 2,
"in_channels": 4,
"num_mmdit_layers": 1,
"num_single_dit_layers": 1,
"attention_head_dim": 8,
"num_attention_heads": 4,
"caption_projection_dim": 32,
"joint_attention_dim": 32,
"out_channels": 4,
"pos_embed_max_size": 256,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_gradient_checkpointing_is_applied(self):
expected_set = {"AuraFlowTransformer2DModel"}
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
@unittest.skip("AuraFlowTransformer2DModel uses its own dedicated attention processor. This test does not apply")
def test_set_attn_processor_for_determinism(self):
pass
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