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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 BriaFiboTransformer2DModel
from ...testing_utils import enable_full_determinism, torch_device
from ..test_modeling_common import ModelTesterMixin
enable_full_determinism()
class BriaFiboTransformerTests(ModelTesterMixin, unittest.TestCase):
model_class = BriaFiboTransformer2DModel
main_input_name = "hidden_states"
# We override the items here because the transformer under consideration is small.
model_split_percents = [0.8, 0.7, 0.7]
# Skip setting testing with default: AttnProcessor
uses_custom_attn_processor = True
@property
def dummy_input(self):
batch_size = 1
num_latent_channels = 48
num_image_channels = 3
height = width = 16
sequence_length = 32
embedding_dim = 64
hidden_states = torch.randn((batch_size, height * width, num_latent_channels)).to(torch_device)
encoder_hidden_states = torch.randn((batch_size, sequence_length, embedding_dim)).to(torch_device)
text_ids = torch.randn((sequence_length, num_image_channels)).to(torch_device)
image_ids = torch.randn((height * width, num_image_channels)).to(torch_device)
timestep = torch.tensor([1.0]).to(torch_device).expand(batch_size)
return {
"hidden_states": hidden_states,
"encoder_hidden_states": encoder_hidden_states,
"img_ids": image_ids,
"txt_ids": text_ids,
"timestep": timestep,
"text_encoder_layers": [encoder_hidden_states[:, :, :32], encoder_hidden_states[:, :, :32]],
}
@property
def input_shape(self):
return (16, 16)
@property
def output_shape(self):
return (256, 48)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"patch_size": 1,
"in_channels": 48,
"num_layers": 1,
"num_single_layers": 1,
"attention_head_dim": 8,
"num_attention_heads": 2,
"joint_attention_dim": 64,
"text_encoder_dim": 32,
"pooled_projection_dim": None,
"axes_dims_rope": [0, 4, 4],
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_gradient_checkpointing_is_applied(self):
expected_set = {"BriaFiboTransformer2DModel"}
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
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