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# coding=utf-8
# Copyright 2024 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 copy
import os
import tempfile
import unittest
import numpy as np
import torch
from diffusers import MotionAdapter, UNet2DConditionModel, UNetMotionModel
from diffusers.utils import logging
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
torch_device,
)
from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin
logger = logging.get_logger(__name__)
enable_full_determinism()
class UNetMotionModelTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
model_class = UNetMotionModel
main_input_name = "sample"
@property
def dummy_input(self):
batch_size = 4
num_channels = 4
num_frames = 4
sizes = (16, 16)
noise = floats_tensor((batch_size, num_channels, num_frames) + sizes).to(torch_device)
time_step = torch.tensor([10]).to(torch_device)
encoder_hidden_states = floats_tensor((batch_size, 4, 16)).to(torch_device)
return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}
@property
def input_shape(self):
return (4, 4, 16, 16)
@property
def output_shape(self):
return (4, 4, 16, 16)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"block_out_channels": (16, 32),
"norm_num_groups": 16,
"down_block_types": ("CrossAttnDownBlockMotion", "DownBlockMotion"),
"up_block_types": ("UpBlockMotion", "CrossAttnUpBlockMotion"),
"cross_attention_dim": 16,
"num_attention_heads": 2,
"out_channels": 4,
"in_channels": 4,
"layers_per_block": 1,
"sample_size": 16,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_from_unet2d(self):
torch.manual_seed(0)
unet2d = UNet2DConditionModel()
torch.manual_seed(1)
model = self.model_class.from_unet2d(unet2d)
model_state_dict = model.state_dict()
for param_name, param_value in unet2d.named_parameters():
self.assertTrue(torch.equal(model_state_dict[param_name], param_value))
def test_freeze_unet2d(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.freeze_unet2d_params()
for param_name, param_value in model.named_parameters():
if "motion_modules" not in param_name:
self.assertFalse(param_value.requires_grad)
else:
self.assertTrue(param_value.requires_grad)
def test_loading_motion_adapter(self):
model = self.model_class()
adapter = MotionAdapter()
model.load_motion_modules(adapter)
for idx, down_block in enumerate(model.down_blocks):
adapter_state_dict = adapter.down_blocks[idx].motion_modules.state_dict()
for param_name, param_value in down_block.motion_modules.named_parameters():
self.assertTrue(torch.equal(adapter_state_dict[param_name], param_value))
for idx, up_block in enumerate(model.up_blocks):
adapter_state_dict = adapter.up_blocks[idx].motion_modules.state_dict()
for param_name, param_value in up_block.motion_modules.named_parameters():
self.assertTrue(torch.equal(adapter_state_dict[param_name], param_value))
mid_block_adapter_state_dict = adapter.mid_block.motion_modules.state_dict()
for param_name, param_value in model.mid_block.motion_modules.named_parameters():
self.assertTrue(torch.equal(mid_block_adapter_state_dict[param_name], param_value))
def test_saving_motion_modules(self):
torch.manual_seed(0)
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_motion_modules(tmpdirname)
self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "diffusion_pytorch_model.safetensors")))
adapter_loaded = MotionAdapter.from_pretrained(tmpdirname)
torch.manual_seed(0)
model_loaded = self.model_class(**init_dict)
model_loaded.load_motion_modules(adapter_loaded)
model_loaded.to(torch_device)
with torch.no_grad():
output = model(**inputs_dict)[0]
output_loaded = model_loaded(**inputs_dict)[0]
max_diff = (output - output_loaded).abs().max().item()
self.assertLessEqual(max_diff, 1e-4, "Models give different forward passes")
@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.mid_block.attentions[0].transformer_blocks[0].attn1.processor.__class__.__name__
== "XFormersAttnProcessor"
), "xformers is not enabled"
def test_gradient_checkpointing_is_applied(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model_class_copy = copy.copy(self.model_class)
modules_with_gc_enabled = {}
# now monkey patch the following function:
# def _set_gradient_checkpointing(self, module, value=False):
# if hasattr(module, "gradient_checkpointing"):
# module.gradient_checkpointing = value
def _set_gradient_checkpointing_new(self, module, value=False):
if hasattr(module, "gradient_checkpointing"):
module.gradient_checkpointing = value
modules_with_gc_enabled[module.__class__.__name__] = True
model_class_copy._set_gradient_checkpointing = _set_gradient_checkpointing_new
model = model_class_copy(**init_dict)
model.enable_gradient_checkpointing()
EXPECTED_SET = {
"CrossAttnUpBlockMotion",
"CrossAttnDownBlockMotion",
"UNetMidBlockCrossAttnMotion",
"UpBlockMotion",
"Transformer2DModel",
"DownBlockMotion",
}
assert set(modules_with_gc_enabled.keys()) == EXPECTED_SET
assert all(modules_with_gc_enabled.values()), "All modules should be enabled"
def test_feed_forward_chunking(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
init_dict["block_out_channels"] = (32, 64)
init_dict["norm_num_groups"] = 32
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with torch.no_grad():
output = model(**inputs_dict)[0]
model.enable_forward_chunking()
with torch.no_grad():
output_2 = model(**inputs_dict)[0]
self.assertEqual(output.shape, output_2.shape, "Shape doesn't match")
assert np.abs(output.cpu() - output_2.cpu()).max() < 1e-2
def test_pickle(self):
# enable deterministic behavior for gradient checkpointing
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
with torch.no_grad():
sample = model(**inputs_dict).sample
sample_copy = copy.copy(sample)
assert (sample - sample_copy).abs().max() < 1e-4
def test_from_save_pretrained(self, expected_max_diff=5e-5):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
torch.manual_seed(0)
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname, safe_serialization=False)
torch.manual_seed(0)
new_model = self.model_class.from_pretrained(tmpdirname)
new_model.to(torch_device)
with torch.no_grad():
image = model(**inputs_dict)
if isinstance(image, dict):
image = image.to_tuple()[0]
new_image = new_model(**inputs_dict)
if isinstance(new_image, dict):
new_image = new_image.to_tuple()[0]
max_diff = (image - new_image).abs().max().item()
self.assertLessEqual(max_diff, expected_max_diff, "Models give different forward passes")
def test_from_save_pretrained_variant(self, expected_max_diff=5e-5):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
torch.manual_seed(0)
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname, variant="fp16", safe_serialization=False)
torch.manual_seed(0)
new_model = self.model_class.from_pretrained(tmpdirname, variant="fp16")
# non-variant cannot be loaded
with self.assertRaises(OSError) as error_context:
self.model_class.from_pretrained(tmpdirname)
# make sure that error message states what keys are missing
assert "Error no file named diffusion_pytorch_model.bin found in directory" in str(error_context.exception)
new_model.to(torch_device)
with torch.no_grad():
image = model(**inputs_dict)
if isinstance(image, dict):
image = image.to_tuple()[0]
new_image = new_model(**inputs_dict)
if isinstance(new_image, dict):
new_image = new_image.to_tuple()[0]
max_diff = (image - new_image).abs().max().item()
self.assertLessEqual(max_diff, expected_max_diff, "Models give different forward passes")
def test_forward_with_norm_groups(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
init_dict["norm_num_groups"] = 16
init_dict["block_out_channels"] = (16, 32)
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with torch.no_grad():
output = model(**inputs_dict)
if isinstance(output, dict):
output = output.to_tuple()[0]
self.assertIsNotNone(output)
expected_shape = inputs_dict["sample"].shape
self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
def test_asymmetric_motion_model(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
init_dict["layers_per_block"] = (2, 3)
init_dict["transformer_layers_per_block"] = ((1, 2), (3, 4, 5))
init_dict["reverse_transformer_layers_per_block"] = ((7, 6, 7, 4), (4, 2, 2))
init_dict["temporal_transformer_layers_per_block"] = ((2, 5), (2, 3, 5))
init_dict["reverse_temporal_transformer_layers_per_block"] = ((5, 4, 3, 4), (3, 2, 2))
init_dict["num_attention_heads"] = (2, 4)
init_dict["motion_num_attention_heads"] = (4, 4)
init_dict["reverse_motion_num_attention_heads"] = (2, 2)
init_dict["use_motion_mid_block"] = True
init_dict["mid_block_layers"] = 2
init_dict["transformer_layers_per_mid_block"] = (1, 5)
init_dict["temporal_transformer_layers_per_mid_block"] = (2, 4)
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with torch.no_grad():
output = model(**inputs_dict)
if isinstance(output, dict):
output = output.to_tuple()[0]
self.assertIsNotNone(output)
expected_shape = inputs_dict["sample"].shape
self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
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