# coding=utf-8
# Copyright 2023 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, AutoencoderKL, AutoencoderTiny
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
enable_full_determinism,
floats_tensor,
load_hf_numpy,
require_torch_gpu,
slow,
torch_all_close,
torch_device,
)
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class AutoencoderKLTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
model_class = AutoencoderKL
main_input_name = "sample"
base_precision = 1e-2
@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)
return {"sample": image}
@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 = {
"block_out_channels": [32, 64],
"in_channels": 3,
"out_channels": 3,
"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
"latent_channels": 4,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_forward_signature(self):
pass
def test_training(self):
pass
@unittest.skipIf(torch_device == "mps", "Gradient checkpointing skipped on MPS")
def test_gradient_checkpointing(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)
assert not model.is_gradient_checkpointing and model.training
out = model(**inputs_dict).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model.zero_grad()
labels = torch.randn_like(out)
loss = (out - labels).mean()
loss.backward()
# re-instantiate the model now enabling gradient checkpointing
model_2 = self.model_class(**init_dict)
# clone model
model_2.load_state_dict(model.state_dict())
model_2.to(torch_device)
model_2.enable_gradient_checkpointing()
assert model_2.is_gradient_checkpointing and model_2.training
out_2 = model_2(**inputs_dict).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model_2.zero_grad()
loss_2 = (out_2 - labels).mean()
loss_2.backward()
# compare the output and parameters gradients
self.assertTrue((loss - loss_2).abs() < 1e-5)
named_params = dict(model.named_parameters())
named_params_2 = dict(model_2.named_parameters())
for name, param in named_params.items():
self.assertTrue(torch_all_close(param.grad.data, named_params_2[name].grad.data, atol=5e-5))
def test_from_pretrained_hub(self):
model, loading_info = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy", output_loading_info=True)
self.assertIsNotNone(model)
self.assertEqual(len(loading_info["missing_keys"]), 0)
model.to(torch_device)
image = model(**self.dummy_input)
assert image is not None, "Make sure output is not None"
def test_output_pretrained(self):
model = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy")
model = model.to(torch_device)
model.eval()
if torch_device == "mps":
generator = torch.manual_seed(0)
else:
generator = torch.Generator(device=torch_device).manual_seed(0)
image = torch.randn(
1,
model.config.in_channels,
model.config.sample_size,
model.config.sample_size,
generator=torch.manual_seed(0),
)
image = image.to(torch_device)
with torch.no_grad():
output = model(image, sample_posterior=True, generator=generator).sample
output_slice = output[0, -1, -3:, -3:].flatten().cpu()
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
if torch_device == "mps":
expected_output_slice = torch.tensor(
[
-4.0078e-01,
-3.8323e-04,
-1.2681e-01,
-1.1462e-01,
2.0095e-01,
1.0893e-01,
-8.8247e-02,
-3.0361e-01,
-9.8644e-03,
]
)
elif torch_device == "cpu":
expected_output_slice = torch.tensor(
[-0.1352, 0.0878, 0.0419, -0.0818, -0.1069, 0.0688, -0.1458, -0.4446, -0.0026]
)
else:
expected_output_slice = torch.tensor(
[-0.2421, 0.4642, 0.2507, -0.0438, 0.0682, 0.3160, -0.2018, -0.0727, 0.2485]
)
self.assertTrue(torch_all_close(output_slice, expected_output_slice, rtol=1e-2))
class AsymmetricAutoencoderKLTests(ModelTesterMixin, UNetTesterMixin, unittest.TestCase):
model_class = AsymmetricAutoencoderKL
main_input_name = "sample"
base_precision = 1e-2
@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 = {
"in_channels": 3,
"out_channels": 3,
"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
"down_block_out_channels": [32, 64],
"layers_per_down_block": 1,
"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
"up_block_out_channels": [32, 64],
"layers_per_up_block": 1,
"act_fn": "silu",
"latent_channels": 4,
"norm_num_groups": 32,
"sample_size": 32,
"scaling_factor": 0.18215,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_forward_signature(self):
pass
def test_forward_with_norm_groups(self):
pass
class AutoencoderTinyTests(ModelTesterMixin, unittest.TestCase):
model_class = AutoencoderTiny
main_input_name = "sample"
base_precision = 1e-2
@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)
return {"sample": image}
@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 = {
"in_channels": 3,
"out_channels": 3,
"encoder_block_out_channels": (32, 32),
"decoder_block_out_channels": (32, 32),
"num_encoder_blocks": (1, 2),
"num_decoder_blocks": (2, 1),
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_outputs_equivalence(self):
pass
@slow
class AutoencoderTinyIntegrationTests(unittest.TestCase):
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_file_format(self, seed, shape):
return f"gaussian_noise_s={seed}_shape={'_'.join([str(s) for s in shape])}.npy"
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="hf-internal-testing/taesd-diffusers", fp16=False):
torch_dtype = torch.float16 if fp16 else torch.float32
model = AutoencoderTiny.from_pretrained(model_id, torch_dtype=torch_dtype)
model.to(torch_device).eval()
return model
@parameterized.expand(
[
[(1, 4, 73, 97), (1, 3, 584, 776)],
[(1, 4, 97, 73), (1, 3, 776, 584)],
[(1, 4, 49, 65), (1, 3, 392, 520)],
[(1, 4, 65, 49), (1, 3, 520, 392)],
[(1, 4, 49, 49), (1, 3, 392, 392)],
]
)
def test_tae_tiling(self, in_shape, out_shape):
model = self.get_sd_vae_model()
model.enable_tiling()
with torch.no_grad():
zeros = torch.zeros(in_shape).to(torch_device)
dec = model.decode(zeros).sample
assert dec.shape == out_shape
def test_stable_diffusion(self):
model = self.get_sd_vae_model()
image = self.get_sd_image(seed=33)
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([0.0093, 0.6385, -0.1274, 0.1631, -0.1762, 0.5232, -0.3108, -0.0382])
assert torch_all_close(output_slice, expected_output_slice, atol=3e-3)
@parameterized.expand([(True,), (False,)])
def test_tae_roundtrip(self, enable_tiling):
# load the autoencoder
model = self.get_sd_vae_model()
if enable_tiling:
model.enable_tiling()
# make a black image with a white square in the middle,
# which is large enough to split across multiple tiles
image = -torch.ones(1, 3, 1024, 1024, device=torch_device)
image[..., 256:768, 256:768] = 1.0
# round-trip the image through the autoencoder
with torch.no_grad():
sample = model(image).sample
# the autoencoder reconstruction should match original image, sorta
def downscale(x):
return torch.nn.functional.avg_pool2d(x, model.spatial_scale_factor)
assert torch_all_close(downscale(sample), downscale(image), atol=0.125)
@slow
class AutoencoderKLIntegrationTests(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()
torch.cuda.empty_cache()
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="CompVis/stable-diffusion-v1-4", fp16=False):
revision = "fp16" if fp16 else None
torch_dtype = torch.float16 if fp16 else torch.float32
model = AutoencoderKL.from_pretrained(
model_id,
subfolder="vae",
torch_dtype=torch_dtype,
revision=revision,
)
model.to(torch_device)
return model
def get_generator(self, seed=0):
if torch_device == "mps":
return torch.manual_seed(seed)
return torch.Generator(device=torch_device).manual_seed(seed)
@parameterized.expand(
[
# fmt: off
[33, [-0.1603, 0.9878, -0.0495, -0.0790, -0.2709, 0.8375, -0.2060, -0.0824], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2376, 0.1168, 0.1332, -0.4840, -0.2508, -0.0791, -0.0493, -0.4089], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# 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=3e-3)
@parameterized.expand(
[
# fmt: off
[33, [-0.0513, 0.0289, 1.3799, 0.2166, -0.2573, -0.0871, 0.5103, -0.0999]],
[47, [-0.4128, -0.1320, -0.3704, 0.1965, -0.4116, -0.2332, -0.3340, 0.2247]],
# fmt: on
]
)
@require_torch_gpu
def test_stable_diffusion_fp16(self, seed, expected_slice):
model = self.get_sd_vae_model(fp16=True)
image = self.get_sd_image(seed, fp16=True)
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)
assert torch_all_close(output_slice, expected_output_slice, atol=1e-2)
@parameterized.expand(
[
# fmt: off
[33, [-0.1609, 0.9866, -0.0487, -0.0777, -0.2716, 0.8368, -0.2055, -0.0814], [-0.2395, 0.0098, 0.0102, -0.0709, -0.2840, -0.0274, -0.0718, -0.1824]],
[47, [-0.2377, 0.1147, 0.1333, -0.4841, -0.2506, -0.0805, -0.0491, -0.4085], [0.0350, 0.0847, 0.0467, 0.0344, -0.0842, -0.0547, -0.0633, -0.1131]],
# 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, [-0.2051, -0.1803, -0.2311, -0.2114, -0.3292, -0.3574, -0.2953, -0.3323]],
[37, [-0.2632, -0.2625, -0.2199, -0.2741, -0.4539, -0.4990, -0.3720, -0.4925]],
# fmt: on
]
)
@require_torch_gpu
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=1e-3)
@parameterized.expand(
[
# fmt: off
[27, [-0.0369, 0.0207, -0.0776, -0.0682, -0.1747, -0.1930, -0.1465, -0.2039]],
[16, [-0.1628, -0.2134, -0.2747, -0.2642, -0.3774, -0.4404, -0.3687, -0.4277]],
# fmt: on
]
)
@require_torch_gpu
def test_stable_diffusion_decode_fp16(self, seed, expected_slice):
model = self.get_sd_vae_model(fp16=True)
encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64), fp16=True)
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().float().cpu()
expected_output_slice = torch.tensor(expected_slice)
assert torch_all_close(output_slice, expected_output_slice, atol=5e-3)
@parameterized.expand([(13,), (16,), (27,)])
@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_fp16(self, seed):
model = self.get_sd_vae_model(fp16=True)
encoding = self.get_sd_image(seed, shape=(3, 4, 64, 64), fp16=True)
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=1e-1)
@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=1e-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)
def test_stable_diffusion_model_local(self):
model_id = "stabilityai/sd-vae-ft-mse"
model_1 = AutoencoderKL.from_pretrained(model_id).to(torch_device)
url = "https://huggingface.co/stabilityai/sd-vae-ft-mse-original/blob/main/vae-ft-mse-840000-ema-pruned.safetensors"
model_2 = AutoencoderKL.from_single_file(url).to(torch_device)
image = self.get_sd_image(33)
with torch.no_grad():
sample_1 = model_1(image).sample
sample_2 = model_2(image).sample
assert sample_1.shape == sample_2.shape
output_slice_1 = sample_1[-1, -2:, -2:, :2].flatten().float().cpu()
output_slice_2 = sample_2[-1, -2:, -2:, :2].flatten().float().cpu()
assert torch_all_close(output_slice_1, output_slice_2, atol=3e-3)
@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()
torch.cuda.empty_cache()
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):
if torch_device == "mps":
return torch.manual_seed(seed)
return torch.Generator(device=torch_device).manual_seed(seed)
@parameterized.expand(
[
# fmt: off
[33, [-0.0344, 0.2912, 0.1687, -0.0137, -0.3462, 0.3552, -0.1337, 0.1078], [-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_gpu
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)