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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 gc
import os
import unittest
import torch
from diffusers import ZImageTransformer2DModel
from ...testing_utils import IS_GITHUB_ACTIONS, torch_device
from ..test_modeling_common import ModelTesterMixin, TorchCompileTesterMixin
# Z-Image requires torch.use_deterministic_algorithms(False) due to complex64 RoPE operations
# Cannot use enable_full_determinism() which sets it to True
os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":16:8"
torch.use_deterministic_algorithms(False)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
if hasattr(torch.backends, "cuda"):
torch.backends.cuda.matmul.allow_tf32 = False
@unittest.skipIf(
IS_GITHUB_ACTIONS,
reason="Skipping test-suite inside the CI because the model has `torch.empty()` inside of it during init and we don't have a clear way to override it in the modeling tests.",
)
class ZImageTransformerTests(ModelTesterMixin, unittest.TestCase):
model_class = ZImageTransformer2DModel
main_input_name = "x"
# We override the items here because the transformer under consideration is small.
model_split_percents = [0.9, 0.9, 0.9]
def prepare_dummy_input(self, height=16, width=16):
batch_size = 1
num_channels = 16
embedding_dim = 16
sequence_length = 16
hidden_states = [torch.randn((num_channels, 1, height, width)).to(torch_device) for _ in range(batch_size)]
encoder_hidden_states = [
torch.randn((sequence_length, embedding_dim)).to(torch_device) for _ in range(batch_size)
]
timestep = torch.tensor([0.0]).to(torch_device)
return {"x": hidden_states, "cap_feats": encoder_hidden_states, "t": timestep}
@property
def dummy_input(self):
return self.prepare_dummy_input()
@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 = {
"all_patch_size": (2,),
"all_f_patch_size": (1,),
"in_channels": 16,
"dim": 16,
"n_layers": 1,
"n_refiner_layers": 1,
"n_heads": 1,
"n_kv_heads": 2,
"qk_norm": True,
"cap_feat_dim": 16,
"rope_theta": 256.0,
"t_scale": 1000.0,
"axes_dims": [8, 4, 4],
"axes_lens": [256, 32, 32],
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def setUp(self):
gc.collect()
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.synchronize()
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
def tearDown(self):
super().tearDown()
gc.collect()
if torch.cuda.is_available():
torch.cuda.empty_cache()
torch.cuda.synchronize()
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
def test_gradient_checkpointing_is_applied(self):
expected_set = {"ZImageTransformer2DModel"}
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
@unittest.skip("Test is not supported for handling main inputs that are lists.")
def test_training(self):
super().test_training()
@unittest.skip("Test is not supported for handling main inputs that are lists.")
def test_ema_training(self):
super().test_ema_training()
@unittest.skip("Test is not supported for handling main inputs that are lists.")
def test_effective_gradient_checkpointing(self):
super().test_effective_gradient_checkpointing()
@unittest.skip(
"Test needs to be revisited. But we need to ensure `x_pad_token` and `cap_pad_token` are cast to the same dtype as the destination tensor before they are assigned to the padding indices."
)
def test_layerwise_casting_training(self):
super().test_layerwise_casting_training()
@unittest.skip("Test is not supported for handling main inputs that are lists.")
def test_outputs_equivalence(self):
super().test_outputs_equivalence()
@unittest.skip("Test will pass if we change to deterministic values instead of empty in the DiT.")
def test_group_offloading(self):
super().test_group_offloading()
@unittest.skip("Test will pass if we change to deterministic values instead of empty in the DiT.")
def test_group_offloading_with_disk(self):
super().test_group_offloading_with_disk()
class ZImageTransformerCompileTests(TorchCompileTesterMixin, unittest.TestCase):
model_class = ZImageTransformer2DModel
different_shapes_for_compilation = [(4, 4), (4, 8), (8, 8)]
def prepare_init_args_and_inputs_for_common(self):
return ZImageTransformerTests().prepare_init_args_and_inputs_for_common()
def prepare_dummy_input(self, height, width):
return ZImageTransformerTests().prepare_dummy_input(height=height, width=width)
@unittest.skip(
"The repeated block in this model is ZImageTransformerBlock, which is used for noise_refiner, context_refiner, and layers. As a consequence of this, the inputs recorded for the block would vary during compilation and full compilation with fullgraph=True would trigger recompilation at least thrice."
)
def test_torch_compile_recompilation_and_graph_break(self):
super().test_torch_compile_recompilation_and_graph_break()
@unittest.skip("Fullgraph AoT is broken")
def test_compile_works_with_aot(self):
super().test_compile_works_with_aot()
@unittest.skip("Fullgraph is broken")
def test_compile_on_different_shapes(self):
super().test_compile_on_different_shapes()
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