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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 gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AsymmetricAutoencoderKL
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
backend_empty_cache,
enable_full_determinism,
floats_tensor,
load_hf_numpy,
require_torch_accelerator,
require_torch_gpu,
skip_mps,
slow,
torch_all_close,
torch_device,
)
from ..test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class AutoencoderKLTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
model_class = AsymmetricAutoencoderKL
main_input_name = "sample"
base_precision = 1e-2
def get_asym_autoencoder_kl_config(self, block_out_channels=None, norm_num_groups=None):
block_out_channels = block_out_channels or [2, 4]
norm_num_groups = norm_num_groups or 2
init_dict = {
"in_channels": 3,
"out_channels": 3,
"down_block_types": ["DownEncoderBlock2D"] * len(block_out_channels),
"down_block_out_channels": block_out_channels,
"layers_per_down_block": 1,
"up_block_types": ["UpDecoderBlock2D"] * len(block_out_channels),
"up_block_out_channels": block_out_channels,
"layers_per_up_block": 1,
"act_fn": "silu",
"latent_channels": 4,
"norm_num_groups": norm_num_groups,
"sample_size": 32,
"scaling_factor": 0.18215,
}
return init_dict
@property
def dummy_input(self):
batch_size = 4
num_channels = 3
sizes = (32, 32)
image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
mask = torch.ones((batch_size, 1) + sizes).to(torch_device)
return {"sample": image, "mask": mask}
@property
def input_shape(self):
return (3, 32, 32)
@property
def output_shape(self):
return (3, 32, 32)
def prepare_init_args_and_inputs_for_common(self):
init_dict = self.get_asym_autoencoder_kl_config()
inputs_dict = self.dummy_input
return init_dict, inputs_dict
@unittest.skip("Unsupported test.")
def test_forward_with_norm_groups(self):
pass
@slow
class AsymmetricAutoencoderKLIntegrationTests(unittest.TestCase):
def get_file_format(self, seed, shape):
return f"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy"
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
backend_empty_cache(torch_device)
def get_sd_image(self, seed=0, shape=(4, 3, 512, 512), fp16=False):
dtype = torch.float16 if fp16 else torch.float32
image = torch.from_numpy(load_hf_numpy(self.get_file_format(seed, shape))).to(torch_device).to(dtype)
return image
def get_sd_vae_model(self, model_id="cross-attention/asymmetric-autoencoder-kl-x-1-5", fp16=False):
revision = "main"
torch_dtype = torch.float32
model = AsymmetricAutoencoderKL.from_pretrained(
model_id,
torch_dtype=torch_dtype,
revision=revision,
)
model.to(torch_device).eval()
return model
def get_generator(self, seed=0):
generator_device = "cpu" if not torch_device.startswith("cuda") else "cuda"
if torch_device != "mps":
return torch.Generator(device=generator_device).manual_seed(seed)
return torch.manual_seed(seed)
@parameterized.expand(
[
# fmt: off
[
33,
[-0.0336, 0.3011, 0.1764, 0.0087, -0.3401, 0.3645, -0.1247, 0.1205],
[-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824],
],
[
47,
[0.4400, 0.0543, 0.2873, 0.2946, 0.0553, 0.0839, -0.1585, 0.2529],
[-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089],
],
# fmt: on
]
)
def test_stable_diffusion(self, seed, expected_slice, expected_slice_mps):
model = self.get_sd_vae_model()
image = self.get_sd_image(seed)
generator = self.get_generator(seed)
with torch.no_grad():
sample = model(image, generator=generator, sample_posterior=True).sample
assert sample.shape == image.shape
output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
expected_output_slice = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice)
assert torch_all_close(output_slice, expected_output_slice, atol=5e-3)
@parameterized.expand(
[
# fmt: off
[
33,
[-0.0340, 0.2870, 0.1698, -0.0105, -0.3448, 0.3529, -0.1321, 0.1097],
[-0.0344, 0.2912, 0.1687, -0.0137, -0.3462, 0.3552, -0.1337, 0.1078],
],
[
47,
[0.4397, 0.0550, 0.2873, 0.2946, 0.0567, 0.0855, -0.1580, 0.2531],
[0.4397, 0.0550, 0.2873, 0.2946, 0.0567, 0.0855, -0.1580, 0.2531],
],
# fmt: on
]
)
def test_stable_diffusion_mode(self, seed, expected_slice, expected_slice_mps):
model = self.get_sd_vae_model()
image = self.get_sd_image(seed)
with torch.no_grad():
sample = model(image).sample
assert sample.shape == image.shape
output_slice = sample[-1, -2:, -2:, :2].flatten().float().cpu()
expected_output_slice = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice)
assert torch_all_close(output_slice, expected_output_slice, atol=3e-3)
@parameterized.expand(
[
# fmt: off
[13, [-0.0521, -0.2939, 0.1540, -0.1855, -0.5936, -0.3138, -0.4579, -0.2275]],
[37, [-0.1820, -0.4345, -0.0455, -0.2923, -0.8035, -0.5089, -0.4795, -0.3106]],
# fmt: on
]
)
@require_torch_accelerator
@skip_mps
def test_stable_diffusion_decode(self, seed, expected_slice):
model = self.get_sd_vae_model()
encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64))
with torch.no_grad():
sample = model.decode(encoding).sample
assert list(sample.shape) == [3, 3, 512, 512]
output_slice = sample[-1, -2:, :2, -2:].flatten().cpu()
expected_output_slice = torch.tensor(expected_slice)
assert torch_all_close(output_slice, expected_output_slice, atol=2e-3)
@parameterized.expand([(13,), (16,), (37,)])
@require_torch_gpu
@unittest.skipIf(
not is_xformers_available(),
reason="xformers is not required when using PyTorch 2.0.",
)
def test_stable_diffusion_decode_xformers_vs_2_0(self, seed):
model = self.get_sd_vae_model()
encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64))
with torch.no_grad():
sample = model.decode(encoding).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
sample_2 = model.decode(encoding).sample
assert list(sample.shape) == [3, 3, 512, 512]
assert torch_all_close(sample, sample_2, atol=5e-2)
@parameterized.expand(
[
# fmt: off
[33, [-0.3001, 0.0918, -2.6984, -3.9720, -3.2099, -5.0353, 1.7338, -0.2065, 3.4267]],
[47, [-1.5030, -4.3871, -6.0355, -9.1157, -1.6661, -2.7853, 2.1607, -5.0823, 2.5633]],
# fmt: on
]
)
def test_stable_diffusion_encode_sample(self, seed, expected_slice):
model = self.get_sd_vae_model()
image = self.get_sd_image(seed)
generator = self.get_generator(seed)
with torch.no_grad():
dist = model.encode(image).latent_dist
sample = dist.sample(generator=generator)
assert list(sample.shape) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
output_slice = sample[0, -1, -3:, -3:].flatten().cpu()
expected_output_slice = torch.tensor(expected_slice)
tolerance = 3e-3 if torch_device != "mps" else 1e-2
assert torch_all_close(output_slice, expected_output_slice, atol=tolerance)
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