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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 SD3Transformer2DModel
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
torch_device,
)
from ..test_modeling_common import ModelTesterMixin
enable_full_determinism()
class SD3TransformerTests(ModelTesterMixin, unittest.TestCase):
model_class = SD3Transformer2DModel
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 = embedding_dim = 32
pooled_embedding_dim = embedding_dim * 2
sequence_length = 154
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)
pooled_prompt_embeds = torch.randn((batch_size, pooled_embedding_dim)).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,
"pooled_projections": pooled_prompt_embeds,
"timestep": timestep,
}
@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 = {
"sample_size": 32,
"patch_size": 1,
"in_channels": 4,
"num_layers": 4,
"attention_head_dim": 8,
"num_attention_heads": 4,
"caption_projection_dim": 32,
"joint_attention_dim": 32,
"pooled_projection_dim": 64,
"out_channels": 4,
"pos_embed_max_size": 96,
"dual_attention_layers": (),
"qk_norm": None,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_enable_works(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.enable_xformers_memory_efficient_attention()
assert model.transformer_blocks[0].attn.processor.__class__.__name__ == "XFormersJointAttnProcessor", (
"xformers is not enabled"
)
@unittest.skip("SD3Transformer2DModel 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 = {"SD3Transformer2DModel"}
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
class SD35TransformerTests(ModelTesterMixin, unittest.TestCase):
model_class = SD3Transformer2DModel
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 = embedding_dim = 32
pooled_embedding_dim = embedding_dim * 2
sequence_length = 154
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)
pooled_prompt_embeds = torch.randn((batch_size, pooled_embedding_dim)).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,
"pooled_projections": pooled_prompt_embeds,
"timestep": timestep,
}
@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 = {
"sample_size": 32,
"patch_size": 1,
"in_channels": 4,
"num_layers": 4,
"attention_head_dim": 8,
"num_attention_heads": 4,
"caption_projection_dim": 32,
"joint_attention_dim": 32,
"pooled_projection_dim": 64,
"out_channels": 4,
"pos_embed_max_size": 96,
"dual_attention_layers": (0,),
"qk_norm": "rms_norm",
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available(),
reason="XFormers attention is only available with CUDA and `xformers` installed",
)
def test_xformers_enable_works(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.enable_xformers_memory_efficient_attention()
assert model.transformer_blocks[0].attn.processor.__class__.__name__ == "XFormersJointAttnProcessor", (
"xformers is not enabled"
)
@unittest.skip("SD3Transformer2DModel 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 = {"SD3Transformer2DModel"}
super().test_gradient_checkpointing_is_applied(expected_set=expected_set)
def test_skip_layers(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict).to(torch_device)
# Forward pass without skipping layers
output_full = model(**inputs_dict).sample
# Forward pass with skipping layers 0 (since there's only one layer in this test setup)
inputs_dict_with_skip = inputs_dict.copy()
inputs_dict_with_skip["skip_layers"] = [0]
output_skip = model(**inputs_dict_with_skip).sample
# Check that the outputs are different
self.assertFalse(
torch.allclose(output_full, output_skip, atol=1e-5), "Outputs should differ when layers are skipped"
)
# Check that the outputs have the same shape
self.assertEqual(output_full.shape, output_skip.shape, "Outputs should have the same shape")
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