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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 HiDreamImageTransformer2DModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
torch_device,
)
from ..test_modeling_common import ModelTesterMixin
enable_full_determinism()
class HiDreamTransformerTests(ModelTesterMixin, unittest.TestCase):
model_class = HiDreamImageTransformer2DModel
main_input_name = "hidden_states"
model_split_percents = [0.8, 0.8, 0.9]
@property
def dummy_input(self):
batch_size = 2
num_channels = 4
height = width = 32
embedding_dim_t5, embedding_dim_llama, embedding_dim_pooled = 8, 4, 8
sequence_length = 8
hidden_states = torch.randn((batch_size, num_channels, height, width)).to(torch_device)
encoder_hidden_states_t5 = torch.randn((batch_size, sequence_length, embedding_dim_t5)).to(torch_device)
encoder_hidden_states_llama3 = torch.randn((batch_size, batch_size, sequence_length, embedding_dim_llama)).to(
torch_device
)
pooled_embeds = torch.randn((batch_size, embedding_dim_pooled)).to(torch_device)
timesteps = torch.randint(0, 1000, size=(batch_size,)).to(torch_device)
return {
"hidden_states": hidden_states,
"encoder_hidden_states_t5": encoder_hidden_states_t5,
"encoder_hidden_states_llama3": encoder_hidden_states_llama3,
"pooled_embeds": pooled_embeds,
"timesteps": timesteps,
}
@property
def input_shape(self):
return (4, 32, 32)
@property
def output_shape(self):
return (4, 32, 32)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"patch_size": 2,
"in_channels": 4,
"out_channels": 4,
"num_layers": 1,
"num_single_layers": 1,
"attention_head_dim": 8,
"num_attention_heads": 4,
"caption_channels": [8, 4],
"text_emb_dim": 8,
"num_routed_experts": 2,
"num_activated_experts": 2,
"axes_dims_rope": (4, 2, 2),
"max_resolution": (32, 32),
"llama_layers": (0, 1),
"force_inference_output": True, # TODO: as we don't implement MoE loss in training tests.
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
@unittest.skip("HiDreamImageTransformer2DModel uses a dedicated attention processor. This test doesn't apply")
def test_set_attn_processor_for_determinism(self):
pass
def test_gradient_checkpointing_is_applied(self):
expected_set = {"HiDreamImageTransformer2DModel"}
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
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