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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.transformer_prx import PRXTransformer2DModel
from ...testing_utils import enable_full_determinism, torch_device
from ..test_modeling_common import ModelTesterMixin
enable_full_determinism()
class PRXTransformerTests(ModelTesterMixin, unittest.TestCase):
model_class = PRXTransformer2DModel
main_input_name = "hidden_states"
uses_custom_attn_processor = True
@property
def dummy_input(self):
return self.prepare_dummy_input()
@property
def input_shape(self):
return (16, 16, 16)
@property
def output_shape(self):
return (16, 16, 16)
def prepare_dummy_input(self, height=16, width=16):
batch_size = 1
num_latent_channels = 16
sequence_length = 16
embedding_dim = 1792
hidden_states = torch.randn((batch_size, num_latent_channels, height, width)).to(torch_device)
encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
timestep = torch.tensor([1.0]).to(torch_device).expand(batch_size)
return {
"hidden_states": hidden_states,
"timestep": timestep,
"encoder_hidden_states": encoder_hidden_states,
}
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"in_channels": 16,
"patch_size": 2,
"context_in_dim": 1792,
"hidden_size": 1792,
"mlp_ratio": 3.5,
"num_heads": 28,
"depth": 4, # Smaller depth for testing
"axes_dim": [32, 32],
"theta": 10_000,
}
inputs_dict = self.prepare_dummy_input()
return init_dict, inputs_dict
def test_gradient_checkpointing_is_applied(self):
expected_set = {"PRXTransformer2DModel"}
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
if __name__ == "__main__":
unittest.main()
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