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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 CogView3PlusTransformer2DModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
torch_device,
)
from ..test_modeling_common import ModelTesterMixin
enable_full_determinism()
class CogView3PlusTransformerTests(ModelTesterMixin, unittest.TestCase):
model_class = CogView3PlusTransformer2DModel
main_input_name = "hidden_states"
uses_custom_attn_processor = True
model_split_percents = [0.7, 0.6, 0.6]
@property
def dummy_input(self):
batch_size = 2
num_channels = 4
height = 8
width = 8
embedding_dim = 8
sequence_length = 8
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)
original_size = torch.tensor([height * 8, width * 8]).unsqueeze(0).repeat(batch_size, 1).to(torch_device)
target_size = torch.tensor([height * 8, width * 8]).unsqueeze(0).repeat(batch_size, 1).to(torch_device)
crop_coords = torch.tensor([0, 0]).unsqueeze(0).repeat(batch_size, 1).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,
"original_size": original_size,
"target_size": target_size,
"crop_coords": crop_coords,
"timestep": timestep,
}
@property
def input_shape(self):
return (1, 4, 8, 8)
@property
def output_shape(self):
return (1, 4, 8, 8)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"patch_size": 2,
"in_channels": 4,
"num_layers": 2,
"attention_head_dim": 4,
"num_attention_heads": 2,
"out_channels": 4,
"text_embed_dim": 8,
"time_embed_dim": 8,
"condition_dim": 2,
"pos_embed_max_size": 8,
"sample_size": 8,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_gradient_checkpointing_is_applied(self):
expected_set = {"CogView3PlusTransformer2DModel"}
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
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