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# coding=utf-8
# Copyright 2024 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 DiTTransformer2DModel, Transformer2DModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
slow,
torch_device,
)
from ..test_modeling_common import ModelTesterMixin
enable_full_determinism()
class DiTTransformer2DModelTests(ModelTesterMixin, unittest.TestCase):
model_class = DiTTransformer2DModel
main_input_name = "hidden_states"
@property
def dummy_input(self):
batch_size = 4
in_channels = 4
sample_size = 8
scheduler_num_train_steps = 1000
num_class_labels = 4
hidden_states = floats_tensor((batch_size, in_channels, sample_size, sample_size)).to(torch_device)
timesteps = torch.randint(0, scheduler_num_train_steps, size=(batch_size,)).to(torch_device)
class_label_ids = torch.randint(0, num_class_labels, size=(batch_size,)).to(torch_device)
return {"hidden_states": hidden_states, "timestep": timesteps, "class_labels": class_label_ids}
@property
def input_shape(self):
return (4, 8, 8)
@property
def output_shape(self):
return (8, 8, 8)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"in_channels": 4,
"out_channels": 8,
"activation_fn": "gelu-approximate",
"num_attention_heads": 2,
"attention_head_dim": 4,
"attention_bias": True,
"num_layers": 1,
"norm_type": "ada_norm_zero",
"num_embeds_ada_norm": 8,
"patch_size": 2,
"sample_size": 8,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_output(self):
super().test_output(
expected_output_shape=(self.dummy_input[self.main_input_name].shape[0],) + self.output_shape
)
def test_correct_class_remapping_from_dict_config(self):
init_dict, _ = self.prepare_init_args_and_inputs_for_common()
model = Transformer2DModel.from_config(init_dict)
assert isinstance(model, DiTTransformer2DModel)
def test_correct_class_remapping_from_pretrained_config(self):
config = DiTTransformer2DModel.load_config("facebook/DiT-XL-2-256", subfolder="transformer")
model = Transformer2DModel.from_config(config)
assert isinstance(model, DiTTransformer2DModel)
@slow
def test_correct_class_remapping(self):
model = Transformer2DModel.from_pretrained("facebook/DiT-XL-2-256", subfolder="transformer")
assert isinstance(model, DiTTransformer2DModel)
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