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# coding=utf-8
# Copyright 2022 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 inspect
import tempfile
import unittest
import numpy as np
import torch
from diffusers import (
AutoencoderKL,
BDDMPipeline,
DDIMPipeline,
DDIMScheduler,
DDPMPipeline,
DDPMScheduler,
GlidePipeline,
GlideSuperResUNetModel,
GlideTextToImageUNetModel,
GradTTSPipeline,
GradTTSScheduler,
LatentDiffusionPipeline,
LatentDiffusionUncondPipeline,
NCSNpp,
PNDMPipeline,
PNDMScheduler,
ScoreSdeVePipeline,
ScoreSdeVeScheduler,
ScoreSdeVpPipeline,
ScoreSdeVpScheduler,
TemporalUNet,
UNetGradTTSModel,
UNetLDMModel,
UNetModel,
VQModel,
)
from diffusers.configuration_utils import ConfigMixin
from diffusers.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.bddm.pipeline_bddm import DiffWave
from diffusers.testing_utils import floats_tensor, slow, torch_device
torch.backends.cuda.matmul.allow_tf32 = False
class ConfigTester(unittest.TestCase):
def test_load_not_from_mixin(self):
with self.assertRaises(ValueError):
ConfigMixin.from_config("dummy_path")
def test_save_load(self):
class SampleObject(ConfigMixin):
config_name = "config.json"
def __init__(
self,
a=2,
b=5,
c=(2, 5),
d="for diffusion",
e=[1, 3],
):
self.register_to_config(a=a, b=b, c=c, d=d, e=e)
obj = SampleObject()
config = obj.config
assert config["a"] == 2
assert config["b"] == 5
assert config["c"] == (2, 5)
assert config["d"] == "for diffusion"
assert config["e"] == [1, 3]
with tempfile.TemporaryDirectory() as tmpdirname:
obj.save_config(tmpdirname)
new_obj = SampleObject.from_config(tmpdirname)
new_config = new_obj.config
# unfreeze configs
config = dict(config)
new_config = dict(new_config)
assert config.pop("c") == (2, 5) # instantiated as tuple
assert new_config.pop("c") == [2, 5] # saved & loaded as list because of json
assert config == new_config
class ModelTesterMixin:
def test_from_pretrained_save_pretrained(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(tmpdirname)
new_model = self.model_class.from_pretrained(tmpdirname)
new_model.to(torch_device)
with torch.no_grad():
image = model(**inputs_dict)
new_image = new_model(**inputs_dict)
max_diff = (image - new_image).abs().sum().item()
self.assertLessEqual(max_diff, 5e-5, "Models give different forward passes")
def test_determinism(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with torch.no_grad():
first = model(**inputs_dict)
second = model(**inputs_dict)
out_1 = first.cpu().numpy()
out_2 = second.cpu().numpy()
out_1 = out_1[~np.isnan(out_1)]
out_2 = out_2[~np.isnan(out_2)]
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
def test_output(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with torch.no_grad():
output = model(**inputs_dict)
self.assertIsNotNone(output)
expected_shape = inputs_dict["x"].shape
self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
def test_forward_signature(self):
init_dict, _ = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
signature = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
arg_names = [*signature.parameters.keys()]
expected_arg_names = ["x", "timesteps"]
self.assertListEqual(arg_names[:2], expected_arg_names)
def test_model_from_config(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
# test if the model can be loaded from the config
# and has all the expected shape
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_config(tmpdirname)
new_model = self.model_class.from_config(tmpdirname)
new_model.to(torch_device)
new_model.eval()
# check if all paramters shape are the same
for param_name in model.state_dict().keys():
param_1 = model.state_dict()[param_name]
param_2 = new_model.state_dict()[param_name]
self.assertEqual(param_1.shape, param_2.shape)
with torch.no_grad():
output_1 = model(**inputs_dict)
output_2 = new_model(**inputs_dict)
self.assertEqual(output_1.shape, output_2.shape)
def test_training(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.train()
output = model(**inputs_dict)
noise = torch.randn((inputs_dict["x"].shape[0],) + self.output_shape).to(torch_device)
loss = torch.nn.functional.mse_loss(output, noise)
loss.backward()
class UnetModelTests(ModelTesterMixin, unittest.TestCase):
model_class = UNetModel
@property
def dummy_input(self):
batch_size = 4
num_channels = 3
sizes = (32, 32)
noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor([10]).to(torch_device)
return {"x": noise, "timesteps": time_step}
@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 = {
"ch": 32,
"ch_mult": (1, 2),
"num_res_blocks": 2,
"attn_resolutions": (16,),
"resolution": 32,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_from_pretrained_hub(self):
model, loading_info = UNetModel.from_pretrained("fusing/ddpm_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 = UNetModel.from_pretrained("fusing/ddpm_dummy")
model.eval()
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
noise = torch.randn(1, model.config.in_channels, model.config.resolution, model.config.resolution)
time_step = torch.tensor([10])
with torch.no_grad():
output = model(noise, time_step)
output_slice = output[0, -1, -3:, -3:].flatten()
# fmt: off
expected_output_slice = torch.tensor([0.2891, -0.1899, 0.2595, -0.6214, 0.0968, -0.2622, 0.4688, 0.1311, 0.0053])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
class GlideSuperResUNetTests(ModelTesterMixin, unittest.TestCase):
model_class = GlideSuperResUNetModel
@property
def dummy_input(self):
batch_size = 4
num_channels = 6
sizes = (32, 32)
low_res_size = (4, 4)
noise = torch.randn((batch_size, num_channels // 2) + sizes).to(torch_device)
low_res = torch.randn((batch_size, 3) + low_res_size).to(torch_device)
time_step = torch.tensor([10] * noise.shape[0], device=torch_device)
return {"x": noise, "timesteps": time_step, "low_res": low_res}
@property
def input_shape(self):
return (3, 32, 32)
@property
def output_shape(self):
return (6, 32, 32)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"attention_resolutions": (2,),
"channel_mult": (1, 2),
"in_channels": 6,
"out_channels": 6,
"model_channels": 32,
"num_head_channels": 8,
"num_heads_upsample": 1,
"num_res_blocks": 2,
"resblock_updown": True,
"resolution": 32,
"use_scale_shift_norm": True,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_output(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with torch.no_grad():
output = model(**inputs_dict)
output, _ = torch.split(output, 3, dim=1)
self.assertIsNotNone(output)
expected_shape = inputs_dict["x"].shape
self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
def test_from_pretrained_hub(self):
model, loading_info = GlideSuperResUNetModel.from_pretrained(
"fusing/glide-super-res-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 = GlideSuperResUNetModel.from_pretrained("fusing/glide-super-res-dummy")
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
noise = torch.randn(1, 3, 64, 64)
low_res = torch.randn(1, 3, 4, 4)
time_step = torch.tensor([42] * noise.shape[0])
with torch.no_grad():
output = model(noise, time_step, low_res)
output, _ = torch.split(output, 3, dim=1)
output_slice = output[0, -1, -3:, -3:].flatten()
# fmt: off
expected_output_slice = torch.tensor([-22.8782, -23.2652, -15.3966, -22.8034, -23.3159, -15.5640, -15.3970, -15.4614, - 10.4370])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
class GlideTextToImageUNetModelTests(ModelTesterMixin, unittest.TestCase):
model_class = GlideTextToImageUNetModel
@property
def dummy_input(self):
batch_size = 4
num_channels = 3
sizes = (32, 32)
transformer_dim = 32
seq_len = 16
noise = torch.randn((batch_size, num_channels) + sizes).to(torch_device)
emb = torch.randn((batch_size, seq_len, transformer_dim)).to(torch_device)
time_step = torch.tensor([10] * noise.shape[0], device=torch_device)
return {"x": noise, "timesteps": time_step, "transformer_out": emb}
@property
def input_shape(self):
return (3, 32, 32)
@property
def output_shape(self):
return (6, 32, 32)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"attention_resolutions": (2,),
"channel_mult": (1, 2),
"in_channels": 3,
"out_channels": 6,
"model_channels": 32,
"num_head_channels": 8,
"num_heads_upsample": 1,
"num_res_blocks": 2,
"resblock_updown": True,
"resolution": 32,
"use_scale_shift_norm": True,
"transformer_dim": 32,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_output(self):
init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
model = self.model_class(**init_dict)
model.to(torch_device)
model.eval()
with torch.no_grad():
output = model(**inputs_dict)
output, _ = torch.split(output, 3, dim=1)
self.assertIsNotNone(output)
expected_shape = inputs_dict["x"].shape
self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
def test_from_pretrained_hub(self):
model, loading_info = GlideTextToImageUNetModel.from_pretrained(
"fusing/unet-glide-text2im-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 = GlideTextToImageUNetModel.from_pretrained("fusing/unet-glide-text2im-dummy")
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
noise = torch.randn((1, model.config.in_channels, model.config.resolution, model.config.resolution)).to(
torch_device
)
emb = torch.randn((1, 16, model.config.transformer_dim)).to(torch_device)
time_step = torch.tensor([10] * noise.shape[0], device=torch_device)
model.to(torch_device)
with torch.no_grad():
output = model(noise, time_step, emb)
output, _ = torch.split(output, 3, dim=1)
output_slice = output[0, -1, -3:, -3:].cpu().flatten()
# fmt: off
expected_output_slice = torch.tensor([2.7766, -10.3558, -14.9149, -0.9376, -14.9175, -17.7679, -5.5565, -12.9521, -12.9845])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
model_class = UNetLDMModel
@property
def dummy_input(self):
batch_size = 4
num_channels = 4
sizes = (32, 32)
noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor([10]).to(torch_device)
return {"x": noise, "timesteps": time_step}
@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 = {
"image_size": 32,
"in_channels": 4,
"out_channels": 4,
"model_channels": 32,
"num_res_blocks": 2,
"attention_resolutions": (16,),
"channel_mult": (1, 2),
"num_heads": 2,
"conv_resample": True,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_from_pretrained_hub(self):
model, loading_info = UNetLDMModel.from_pretrained("fusing/unet-ldm-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 = UNetLDMModel.from_pretrained("fusing/unet-ldm-dummy")
model.eval()
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
noise = torch.randn(1, model.config.in_channels, model.config.image_size, model.config.image_size)
time_step = torch.tensor([10] * noise.shape[0])
with torch.no_grad():
output = model(noise, time_step)
output_slice = output[0, -1, -3:, -3:].flatten()
# fmt: off
expected_output_slice = torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
def test_output_pretrained_spatial_transformer(self):
model = UNetLDMModel.from_pretrained("fusing/unet-ldm-dummy-spatial")
model.eval()
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
noise = torch.randn(1, model.config.in_channels, model.config.image_size, model.config.image_size)
context = torch.ones((1, 16, 64), dtype=torch.float32)
time_step = torch.tensor([10] * noise.shape[0])
with torch.no_grad():
output = model(noise, time_step, context=context)
output_slice = output[0, -1, -3:, -3:].flatten()
# fmt: off
expected_output_slice = torch.tensor([61.3445, 56.9005, 29.4339, 59.5497, 60.7375, 34.1719, 48.1951, 42.6569, 25.0890])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3))
class UNetGradTTSModelTests(ModelTesterMixin, unittest.TestCase):
model_class = UNetGradTTSModel
@property
def dummy_input(self):
batch_size = 4
num_features = 32
seq_len = 16
noise = floats_tensor((batch_size, num_features, seq_len)).to(torch_device)
condition = floats_tensor((batch_size, num_features, seq_len)).to(torch_device)
mask = floats_tensor((batch_size, 1, seq_len)).to(torch_device)
time_step = torch.tensor([10] * batch_size).to(torch_device)
return {"x": noise, "timesteps": time_step, "mu": condition, "mask": mask}
@property
def input_shape(self):
return (4, 32, 16)
@property
def output_shape(self):
return (4, 32, 16)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"dim": 64,
"groups": 4,
"dim_mults": (1, 2),
"n_feats": 32,
"pe_scale": 1000,
"n_spks": 1,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_from_pretrained_hub(self):
model, loading_info = UNetGradTTSModel.from_pretrained("fusing/unet-grad-tts-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 = UNetGradTTSModel.from_pretrained("fusing/unet-grad-tts-dummy")
model.eval()
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
num_features = model.config.n_feats
seq_len = 16
noise = torch.randn((1, num_features, seq_len))
condition = torch.randn((1, num_features, seq_len))
mask = torch.randn((1, 1, seq_len))
time_step = torch.tensor([10])
with torch.no_grad():
output = model(noise, time_step, condition, mask)
output_slice = output[0, -3:, -3:].flatten()
# fmt: off
expected_output_slice = torch.tensor([-0.0690, -0.0531, 0.0633, -0.0660, -0.0541, 0.0650, -0.0656, -0.0555, 0.0617])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3))
class TemporalUNetModelTests(ModelTesterMixin, unittest.TestCase):
model_class = TemporalUNet
@property
def dummy_input(self):
batch_size = 4
num_features = 14
seq_len = 16
noise = floats_tensor((batch_size, seq_len, num_features)).to(torch_device)
time_step = torch.tensor([10] * batch_size).to(torch_device)
return {"x": noise, "timesteps": time_step}
@property
def input_shape(self):
return (4, 16, 14)
@property
def output_shape(self):
return (4, 16, 14)
def prepare_init_args_and_inputs_for_common(self):
init_dict = {
"training_horizon": 128,
"dim": 32,
"dim_mults": [1, 4, 8],
"predict_epsilon": False,
"clip_denoised": True,
"transition_dim": 14,
"cond_dim": 3,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_from_pretrained_hub(self):
model, loading_info = TemporalUNet.from_pretrained(
"fusing/ddpm-unet-rl-hopper-hor128", 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 = TemporalUNet.from_pretrained("fusing/ddpm-unet-rl-hopper-hor128")
model.eval()
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
num_features = model.transition_dim
seq_len = 16
noise = torch.randn((1, seq_len, num_features))
time_step = torch.full((num_features,), 0)
with torch.no_grad():
output = model(noise, time_step)
output_slice = output[0, -3:, -3:].flatten()
# fmt: off
expected_output_slice = torch.tensor([-0.2714, 0.1042, -0.0794, -0.2820, 0.0803, -0.0811, -0.2345, 0.0580, -0.0584])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3))
class NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
model_class = NCSNpp
@property
def dummy_input(self):
batch_size = 4
num_channels = 3
sizes = (32, 32)
noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor(batch_size * [10]).to(torch_device)
return {"x": noise, "timesteps": time_step}
@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 = {
"image_size": 32,
"ch_mult": [1, 2, 2, 2],
"nf": 32,
"fir": True,
"progressive": "output_skip",
"progressive_combine": "sum",
"progressive_input": "input_skip",
"scale_by_sigma": True,
"skip_rescale": True,
"embedding_type": "fourier",
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_from_pretrained_hub(self):
model, loading_info = NCSNpp.from_pretrained("fusing/cifar10-ncsnpp-ve", 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_ve_small(self):
model = NCSNpp.from_pretrained("fusing/ncsnpp-cifar10-ve-dummy")
model.eval()
model.to(torch_device)
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
batch_size = 4
num_channels = 3
sizes = (32, 32)
noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor(batch_size * [1e-4]).to(torch_device)
with torch.no_grad():
output = model(noise, time_step)
output_slice = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
expected_output_slice = torch.tensor([0.1315, 0.0741, 0.0393, 0.0455, 0.0556, 0.0180, -0.0832, -0.0644, -0.0856])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
def test_output_pretrained_ve_large(self):
model = NCSNpp.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy")
model.eval()
model.to(torch_device)
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
batch_size = 4
num_channels = 3
sizes = (32, 32)
noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor(batch_size * [1e-4]).to(torch_device)
with torch.no_grad():
output = model(noise, time_step)
output_slice = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
expected_output_slice = torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
def test_output_pretrained_vp(self):
model = NCSNpp.from_pretrained("fusing/cifar10-ddpmpp-vp")
model.eval()
model.to(torch_device)
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
batch_size = 4
num_channels = 3
sizes = (32, 32)
noise = torch.randn((batch_size, num_channels) + sizes).to(torch_device)
time_step = torch.tensor(batch_size * [9.0]).to(torch_device)
with torch.no_grad():
output = model(noise, time_step)
output_slice = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
expected_output_slice = torch.tensor([0.3303, -0.2275, -2.8872, -0.1309, -1.2861, 3.4567, -1.0083, 2.5325, -1.3866])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
class VQModelTests(ModelTesterMixin, unittest.TestCase):
model_class = VQModel
@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 {"x": 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 = {
"ch": 64,
"out_ch": 3,
"num_res_blocks": 1,
"attn_resolutions": [],
"in_channels": 3,
"resolution": 32,
"z_channels": 3,
"n_embed": 256,
"embed_dim": 3,
"sane_index_shape": False,
"ch_mult": (1,),
"dropout": 0.0,
"double_z": False,
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_forward_signature(self):
pass
def test_training(self):
pass
def test_from_pretrained_hub(self):
model, loading_info = VQModel.from_pretrained("fusing/vqgan-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 = VQModel.from_pretrained("fusing/vqgan-dummy")
model.eval()
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
image = torch.randn(1, model.config.in_channels, model.config.resolution, model.config.resolution)
with torch.no_grad():
output = model(image)
output_slice = output[0, -1, -3:, -3:].flatten()
# fmt: off
expected_output_slice = torch.tensor([-1.1321, 0.1056, 0.3505, -0.6461, -0.2014, 0.0419, -0.5763, -0.8462, -0.4218])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
class AutoEncoderKLTests(ModelTesterMixin, unittest.TestCase):
model_class = AutoencoderKL
@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 {"x": 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 = {
"ch": 64,
"ch_mult": (1,),
"embed_dim": 4,
"in_channels": 3,
"num_res_blocks": 1,
"out_ch": 3,
"resolution": 32,
"z_channels": 4,
"attn_resolutions": [],
}
inputs_dict = self.dummy_input
return init_dict, inputs_dict
def test_forward_signature(self):
pass
def test_training(self):
pass
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.eval()
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(0)
image = torch.randn(1, model.config.in_channels, model.config.resolution, model.config.resolution)
with torch.no_grad():
output = model(image, sample_posterior=True)
output_slice = output[0, -1, -3:, -3:].flatten()
# fmt: off
expected_output_slice = torch.tensor([-0.0814, -0.0229, -0.1320, -0.4123, -0.0366, -0.3473, 0.0438, -0.1662, 0.1750])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
class PipelineTesterMixin(unittest.TestCase):
def test_from_pretrained_save_pretrained(self):
# 1. Load models
model = UNetModel(ch=32, ch_mult=(1, 2), num_res_blocks=2, attn_resolutions=(16,), resolution=32)
schedular = DDPMScheduler(timesteps=10)
ddpm = DDPMPipeline(model, schedular)
with tempfile.TemporaryDirectory() as tmpdirname:
ddpm.save_pretrained(tmpdirname)
new_ddpm = DDPMPipeline.from_pretrained(tmpdirname)
generator = torch.manual_seed(0)
image = ddpm(generator=generator)
generator = generator.manual_seed(0)
new_image = new_ddpm(generator=generator)
assert (image - new_image).abs().sum() < 1e-5, "Models don't give the same forward pass"
@slow
def test_from_pretrained_hub(self):
model_path = "fusing/ddpm-cifar10"
ddpm = DDPMPipeline.from_pretrained(model_path)
ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path)
ddpm.noise_scheduler.num_timesteps = 10
ddpm_from_hub.noise_scheduler.num_timesteps = 10
generator = torch.manual_seed(0)
image = ddpm(generator=generator)
generator = generator.manual_seed(0)
new_image = ddpm_from_hub(generator=generator)
assert (image - new_image).abs().sum() < 1e-5, "Models don't give the same forward pass"
@slow
def test_ddpm_cifar10(self):
model_id = "fusing/ddpm-cifar10"
unet = UNetModel.from_pretrained(model_id)
noise_scheduler = DDPMScheduler.from_config(model_id)
noise_scheduler = noise_scheduler.set_format("pt")
ddpm = DDPMPipeline(unet=unet, noise_scheduler=noise_scheduler)
generator = torch.manual_seed(0)
image = ddpm(generator=generator)
image_slice = image[0, -1, -3:, -3:].cpu()
assert image.shape == (1, 3, 32, 32)
expected_slice = torch.tensor(
[-0.5712, -0.6215, -0.5953, -0.5438, -0.4775, -0.4539, -0.5172, -0.4872, -0.5105]
)
assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2
@slow
def test_ddim_cifar10(self):
model_id = "fusing/ddpm-cifar10"
unet = UNetModel.from_pretrained(model_id)
noise_scheduler = DDIMScheduler(tensor_format="pt")
ddim = DDIMPipeline(unet=unet, noise_scheduler=noise_scheduler)
generator = torch.manual_seed(0)
image = ddim(generator=generator, eta=0.0)
image_slice = image[0, -1, -3:, -3:].cpu()
assert image.shape == (1, 3, 32, 32)
expected_slice = torch.tensor(
[-0.6553, -0.6765, -0.6799, -0.6749, -0.7006, -0.6974, -0.6991, -0.7116, -0.7094]
)
assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2
@slow
def test_pndm_cifar10(self):
model_id = "fusing/ddpm-cifar10"
unet = UNetModel.from_pretrained(model_id)
noise_scheduler = PNDMScheduler(tensor_format="pt")
pndm = PNDMPipeline(unet=unet, noise_scheduler=noise_scheduler)
generator = torch.manual_seed(0)
image = pndm(generator=generator)
image_slice = image[0, -1, -3:, -3:].cpu()
assert image.shape == (1, 3, 32, 32)
expected_slice = torch.tensor(
[-0.6872, -0.7071, -0.7188, -0.7057, -0.7515, -0.7191, -0.7377, -0.7565, -0.7500]
)
assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2
@slow
@unittest.skip("Skipping for now as it takes too long")
def test_ldm_text2img(self):
model_id = "fusing/latent-diffusion-text2im-large"
ldm = LatentDiffusionPipeline.from_pretrained(model_id)
prompt = "A painting of a squirrel eating a burger"
generator = torch.manual_seed(0)
image = ldm([prompt], generator=generator, num_inference_steps=20)
image_slice = image[0, -1, -3:, -3:].cpu()
assert image.shape == (1, 3, 256, 256)
expected_slice = torch.tensor([0.7295, 0.7358, 0.7256, 0.7435, 0.7095, 0.6884, 0.7325, 0.6921, 0.6458])
assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2
@slow
def test_ldm_text2img_fast(self):
model_id = "fusing/latent-diffusion-text2im-large"
ldm = LatentDiffusionPipeline.from_pretrained(model_id)
prompt = "A painting of a squirrel eating a burger"
generator = torch.manual_seed(0)
image = ldm([prompt], generator=generator, num_inference_steps=1)
image_slice = image[0, -1, -3:, -3:].cpu()
assert image.shape == (1, 3, 256, 256)
expected_slice = torch.tensor([0.3163, 0.8670, 0.6465, 0.1865, 0.6291, 0.5139, 0.2824, 0.3723, 0.4344])
assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2
@slow
def test_glide_text2img(self):
model_id = "fusing/glide-base"
glide = GlidePipeline.from_pretrained(model_id)
prompt = "a pencil sketch of a corgi"
generator = torch.manual_seed(0)
image = glide(prompt, generator=generator, num_inference_steps_upscale=20)
image_slice = image[0, :3, :3, -1].cpu()
assert image.shape == (1, 256, 256, 3)
expected_slice = torch.tensor([0.7119, 0.7073, 0.6460, 0.7780, 0.7423, 0.6926, 0.7378, 0.7189, 0.7784])
assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2
@slow
def test_grad_tts(self):
model_id = "fusing/grad-tts-libri-tts"
grad_tts = GradTTSPipeline.from_pretrained(model_id)
noise_scheduler = GradTTSScheduler()
grad_tts.noise_scheduler = noise_scheduler
text = "Hello world, I missed you so much."
generator = torch.manual_seed(0)
# generate mel spectograms using text
mel_spec = grad_tts(text, generator=generator)
assert mel_spec.shape == (1, 80, 143)
expected_slice = torch.tensor(
[-6.7584, -6.8347, -6.3293, -6.6437, -6.7233, -6.4684, -6.1187, -6.3172, -6.6890]
)
assert (mel_spec[0, :3, :3].cpu().flatten() - expected_slice).abs().max() < 1e-2
@slow
def test_score_sde_ve_pipeline(self):
model = NCSNpp.from_pretrained("fusing/ffhq_ncsnpp")
scheduler = ScoreSdeVeScheduler.from_config("fusing/ffhq_ncsnpp")
sde_ve = ScoreSdeVePipeline(model=model, scheduler=scheduler)
torch.manual_seed(0)
image = sde_ve(num_inference_steps=2)
expected_image_sum = 3382849024.0
expected_image_mean = 1075.3788
assert (image.abs().sum() - expected_image_sum).abs().cpu().item() < 1e-2
assert (image.abs().mean() - expected_image_mean).abs().cpu().item() < 1e-4
@slow
def test_score_sde_vp_pipeline(self):
model = NCSNpp.from_pretrained("fusing/cifar10-ddpmpp-vp")
scheduler = ScoreSdeVpScheduler.from_config("fusing/cifar10-ddpmpp-vp")
sde_vp = ScoreSdeVpPipeline(model=model, scheduler=scheduler)
torch.manual_seed(0)
image = sde_vp(num_inference_steps=10)
expected_image_sum = 4183.2012
expected_image_mean = 1.3617
assert (image.abs().sum() - expected_image_sum).abs().cpu().item() < 1e-2
assert (image.abs().mean() - expected_image_mean).abs().cpu().item() < 1e-4
@slow
def test_ldm_uncond(self):
ldm = LatentDiffusionUncondPipeline.from_pretrained("fusing/latent-diffusion-celeba-256")
generator = torch.manual_seed(0)
image = ldm(generator=generator, num_inference_steps=5)
image_slice = image[0, -1, -3:, -3:].cpu()
assert image.shape == (1, 3, 256, 256)
expected_slice = torch.tensor([0.5025, 0.4121, 0.3851, 0.4806, 0.3996, 0.3745, 0.4839, 0.4559, 0.4293])
assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2
def test_module_from_pipeline(self):
model = DiffWave(num_res_layers=4)
noise_scheduler = DDPMScheduler(timesteps=12)
bddm = BDDMPipeline(model, noise_scheduler)
# check if the library name for the diffwave moduel is set to pipeline module
self.assertTrue(bddm.config["diffwave"][0] == "bddm")
# check if we can save and load the pipeline
with tempfile.TemporaryDirectory() as tmpdirname:
bddm.save_pretrained(tmpdirname)
_ = BDDMPipeline.from_pretrained(tmpdirname)
# check if the same works using the DifusionPipeline class
bddm = DiffusionPipeline.from_pretrained(tmpdirname)
self.assertTrue(bddm.config["diffwave"][0] == "bddm")
tests/test_modeling_utils.py
View file @ db7ec72d
......@@ -880,7 +880,7 @@ class VQModelTests(ModelTesterMixin, unittest.TestCase):
# fmt: off
expected_output_slice = torch.tensor([-1.1321, 0.1056, 0.3505, -0.6461, -0.2014, 0.0419, -0.5763, -0.8462, -0.4218])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3))
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
class AutoEncoderKLTests(ModelTesterMixin, unittest.TestCase):
......@@ -951,7 +951,7 @@ class AutoEncoderKLTests(ModelTesterMixin, unittest.TestCase):
# fmt: off
expected_output_slice = torch.tensor([-0.0814, -0.0229, -0.1320, -0.4123, -0.0366, -0.3473, 0.0438, -0.1662, 0.1750])
# fmt: on
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3))
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
class PipelineTesterMixin(unittest.TestCase):
......
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