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# coding=utf-8
# 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 OmniGenTransformer2DModel
from diffusers.utils.testing_utils import enable_full_determinism, torch_device
from ..test_modeling_common import ModelTesterMixin
enable_full_determinism()
class OmniGenTransformerTests(ModelTesterMixin, unittest.TestCase):
model_class = OmniGenTransformer2DModel
main_input_name = "hidden_states"
uses_custom_attn_processor = True
model_split_percents = [0.1, 0.1, 0.1]
@property
def dummy_input(self):
batch_size = 2
num_channels = 4
height = 8
width = 8
sequence_length = 24
hidden_states = torch.randn((batch_size, num_channels, height, width)).to(torch_device)
timestep = torch.rand(size=(batch_size,), dtype=hidden_states.dtype).to(torch_device)
input_ids = torch.randint(0, 10, (batch_size, sequence_length)).to(torch_device)
input_img_latents = [torch.randn((1, num_channels, height, width)).to(torch_device)]
input_image_sizes = {0: [[0, 0 + height * width // 2 // 2]]}
attn_seq_length = sequence_length + 1 + height * width // 2 // 2
attention_mask = torch.ones((batch_size, attn_seq_length, attn_seq_length)).to(torch_device)
position_ids = torch.LongTensor([list(range(attn_seq_length))] * batch_size).to(torch_device)
return {
"hidden_states": hidden_states,
"timestep": timestep,
"input_ids": input_ids,
"input_img_latents": input_img_latents,
"input_image_sizes": input_image_sizes,
"attention_mask": attention_mask,
"position_ids": position_ids,
}
@property
def input_shape(self):
return (4, 8, 8)
@property
def output_shape(self):
return (4, 8, 8)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"hidden_size": 16,
"num_attention_heads": 4,
"num_key_value_heads": 4,
"intermediate_size": 32,
"num_layers": 20,
"pad_token_id": 0,
"vocab_size": 1000,
"in_channels": 4,
"time_step_dim": 4,
"rope_scaling": {"long_factor": list(range(1, 3)), "short_factor": list(range(1, 3))},
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_gradient_checkpointing_is_applied(self):
expected_set = {"OmniGenTransformer2DModel"}
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
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