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split tests_modeling_utils (#223)
* split tests_modeling_utils * Fix SD tests .to(device) * fix merge * Fix style Co-authored-by: anton-l <anton@huggingface.co>
This commit is contained in:
Kashif Rasul
GitHub
165
tests/test_modeling_common.py
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165
tests/test_modeling_common.py
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@@ -0,0 +1,165 @@
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# coding=utf-8
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# Copyright 2022 HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import inspect
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import tempfile
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import numpy as np
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import torch
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from diffusers.testing_utils import torch_device
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from diffusers.training_utils import EMAModel
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class ModelTesterMixin:
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def test_from_pretrained_save_pretrained(self):
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init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
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model = self.model_class(**init_dict)
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model.to(torch_device)
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model.eval()
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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new_model = self.model_class.from_pretrained(tmpdirname)
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new_model.to(torch_device)
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with torch.no_grad():
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image = model(**inputs_dict)
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if isinstance(image, dict):
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image = image["sample"]
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new_image = new_model(**inputs_dict)
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if isinstance(new_image, dict):
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new_image = new_image["sample"]
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max_diff = (image - new_image).abs().sum().item()
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self.assertLessEqual(max_diff, 5e-5, "Models give different forward passes")
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def test_determinism(self):
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init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
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model = self.model_class(**init_dict)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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first = model(**inputs_dict)
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if isinstance(first, dict):
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first = first["sample"]
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second = model(**inputs_dict)
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if isinstance(second, dict):
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second = second["sample"]
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out_1 = first.cpu().numpy()
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out_2 = second.cpu().numpy()
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out_1 = out_1[~np.isnan(out_1)]
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out_2 = out_2[~np.isnan(out_2)]
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max_diff = np.amax(np.abs(out_1 - out_2))
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self.assertLessEqual(max_diff, 1e-5)
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def test_output(self):
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init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
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model = self.model_class(**init_dict)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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output = model(**inputs_dict)
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if isinstance(output, dict):
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output = output["sample"]
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self.assertIsNotNone(output)
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expected_shape = inputs_dict["sample"].shape
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self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
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def test_forward_signature(self):
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init_dict, _ = self.prepare_init_args_and_inputs_for_common()
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model = self.model_class(**init_dict)
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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["sample", "timestep"]
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self.assertListEqual(arg_names[:2], expected_arg_names)
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def test_model_from_config(self):
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init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
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model = self.model_class(**init_dict)
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model.to(torch_device)
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model.eval()
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# test if the model can be loaded from the config
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# and has all the expected shape
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_config(tmpdirname)
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new_model = self.model_class.from_config(tmpdirname)
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new_model.to(torch_device)
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new_model.eval()
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# check if all paramters shape are the same
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for param_name in model.state_dict().keys():
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param_1 = model.state_dict()[param_name]
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param_2 = new_model.state_dict()[param_name]
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self.assertEqual(param_1.shape, param_2.shape)
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with torch.no_grad():
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output_1 = model(**inputs_dict)
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if isinstance(output_1, dict):
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output_1 = output_1["sample"]
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output_2 = new_model(**inputs_dict)
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if isinstance(output_2, dict):
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output_2 = output_2["sample"]
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self.assertEqual(output_1.shape, output_2.shape)
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def test_training(self):
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init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
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model = self.model_class(**init_dict)
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model.to(torch_device)
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model.train()
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output = model(**inputs_dict)
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if isinstance(output, dict):
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output = output["sample"]
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noise = torch.randn((inputs_dict["sample"].shape[0],) + self.output_shape).to(torch_device)
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loss = torch.nn.functional.mse_loss(output, noise)
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loss.backward()
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def test_ema_training(self):
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init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
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model = self.model_class(**init_dict)
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model.to(torch_device)
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model.train()
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ema_model = EMAModel(model, device=torch_device)
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output = model(**inputs_dict)
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if isinstance(output, dict):
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output = output["sample"]
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noise = torch.randn((inputs_dict["sample"].shape[0],) + self.output_shape).to(torch_device)
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loss = torch.nn.functional.mse_loss(output, noise)
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loss.backward()
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ema_model.step(model)
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@@ -13,42 +13,10 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import inspect
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import math
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import tempfile
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import unittest
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import numpy as np
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import torch
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import PIL
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from diffusers import UNet2DConditionModel # noqa: F401 TODO(Patrick) - need to write tests with it
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from diffusers import (
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AutoencoderKL,
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DDIMPipeline,
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DDIMScheduler,
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DDPMPipeline,
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DDPMScheduler,
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KarrasVePipeline,
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KarrasVeScheduler,
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LDMPipeline,
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LDMTextToImagePipeline,
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LMSDiscreteScheduler,
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PNDMPipeline,
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PNDMScheduler,
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ScoreSdeVePipeline,
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ScoreSdeVeScheduler,
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StableDiffusionPipeline,
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UNet2DModel,
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VQModel,
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)
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from diffusers.configuration_utils import ConfigMixin, register_to_config
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from diffusers.pipeline_utils import DiffusionPipeline
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from diffusers.testing_utils import floats_tensor, slow, torch_device
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from diffusers.training_utils import EMAModel
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torch.backends.cuda.matmul.allow_tf32 = False
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class SampleObject(ConfigMixin):
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@@ -129,892 +97,3 @@ class ConfigTester(unittest.TestCase):
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assert config.pop("c") == (2, 5) # instantiated as tuple
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assert new_config.pop("c") == [2, 5] # saved & loaded as list because of json
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assert config == new_config
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class ModelTesterMixin:
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def test_from_pretrained_save_pretrained(self):
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init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
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model = self.model_class(**init_dict)
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model.to(torch_device)
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model.eval()
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_pretrained(tmpdirname)
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new_model = self.model_class.from_pretrained(tmpdirname)
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new_model.to(torch_device)
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with torch.no_grad():
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image = model(**inputs_dict)
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if isinstance(image, dict):
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image = image["sample"]
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new_image = new_model(**inputs_dict)
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if isinstance(new_image, dict):
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new_image = new_image["sample"]
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max_diff = (image - new_image).abs().sum().item()
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self.assertLessEqual(max_diff, 5e-5, "Models give different forward passes")
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def test_determinism(self):
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init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
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model = self.model_class(**init_dict)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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first = model(**inputs_dict)
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if isinstance(first, dict):
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first = first["sample"]
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second = model(**inputs_dict)
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if isinstance(second, dict):
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second = second["sample"]
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out_1 = first.cpu().numpy()
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out_2 = second.cpu().numpy()
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out_1 = out_1[~np.isnan(out_1)]
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out_2 = out_2[~np.isnan(out_2)]
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max_diff = np.amax(np.abs(out_1 - out_2))
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self.assertLessEqual(max_diff, 1e-5)
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def test_output(self):
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init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
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model = self.model_class(**init_dict)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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output = model(**inputs_dict)
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if isinstance(output, dict):
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output = output["sample"]
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self.assertIsNotNone(output)
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expected_shape = inputs_dict["sample"].shape
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self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match")
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def test_forward_signature(self):
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init_dict, _ = self.prepare_init_args_and_inputs_for_common()
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model = self.model_class(**init_dict)
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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["sample", "timestep"]
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self.assertListEqual(arg_names[:2], expected_arg_names)
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def test_model_from_config(self):
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init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
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model = self.model_class(**init_dict)
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model.to(torch_device)
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model.eval()
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# test if the model can be loaded from the config
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# and has all the expected shape
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with tempfile.TemporaryDirectory() as tmpdirname:
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model.save_config(tmpdirname)
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new_model = self.model_class.from_config(tmpdirname)
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new_model.to(torch_device)
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new_model.eval()
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# check if all paramters shape are the same
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for param_name in model.state_dict().keys():
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param_1 = model.state_dict()[param_name]
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param_2 = new_model.state_dict()[param_name]
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self.assertEqual(param_1.shape, param_2.shape)
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with torch.no_grad():
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output_1 = model(**inputs_dict)
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if isinstance(output_1, dict):
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output_1 = output_1["sample"]
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output_2 = new_model(**inputs_dict)
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if isinstance(output_2, dict):
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output_2 = output_2["sample"]
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self.assertEqual(output_1.shape, output_2.shape)
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def test_training(self):
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init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
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model = self.model_class(**init_dict)
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model.to(torch_device)
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model.train()
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output = model(**inputs_dict)
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if isinstance(output, dict):
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output = output["sample"]
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noise = torch.randn((inputs_dict["sample"].shape[0],) + self.output_shape).to(torch_device)
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loss = torch.nn.functional.mse_loss(output, noise)
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loss.backward()
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def test_ema_training(self):
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init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common()
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model = self.model_class(**init_dict)
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model.to(torch_device)
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model.train()
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ema_model = EMAModel(model, device=torch_device)
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output = model(**inputs_dict)
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if isinstance(output, dict):
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output = output["sample"]
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noise = torch.randn((inputs_dict["sample"].shape[0],) + self.output_shape).to(torch_device)
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loss = torch.nn.functional.mse_loss(output, noise)
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loss.backward()
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ema_model.step(model)
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class UnetModelTests(ModelTesterMixin, unittest.TestCase):
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model_class = UNet2DModel
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@property
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def dummy_input(self):
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batch_size = 4
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num_channels = 3
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sizes = (32, 32)
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noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
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time_step = torch.tensor([10]).to(torch_device)
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return {"sample": noise, "timestep": time_step}
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@property
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def input_shape(self):
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return (3, 32, 32)
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@property
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def output_shape(self):
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return (3, 32, 32)
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def prepare_init_args_and_inputs_for_common(self):
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init_dict = {
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"block_out_channels": (32, 64),
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"down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
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"up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
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"attention_head_dim": None,
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"out_channels": 3,
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"in_channels": 3,
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"layers_per_block": 2,
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"sample_size": 32,
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}
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inputs_dict = self.dummy_input
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return init_dict, inputs_dict
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# TODO(Patrick) - Re-add this test after having correctly added the final VE checkpoints
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# def test_output_pretrained(self):
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# model = UNet2DModel.from_pretrained("fusing/ddpm_dummy_update", subfolder="unet")
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# model.eval()
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#
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# torch.manual_seed(0)
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# if torch.cuda.is_available():
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# torch.cuda.manual_seed_all(0)
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#
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# noise = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
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# time_step = torch.tensor([10])
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#
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# with torch.no_grad():
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# output = model(noise, time_step)["sample"]
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#
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# output_slice = output[0, -1, -3:, -3:].flatten()
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# fmt: off
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# expected_output_slice = torch.tensor([0.2891, -0.1899, 0.2595, -0.6214, 0.0968, -0.2622, 0.4688, 0.1311, 0.0053])
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# fmt: on
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# self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
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class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
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model_class = UNet2DModel
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@property
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def dummy_input(self):
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batch_size = 4
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num_channels = 4
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sizes = (32, 32)
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noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
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time_step = torch.tensor([10]).to(torch_device)
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return {"sample": noise, "timestep": time_step}
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@property
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def input_shape(self):
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return (4, 32, 32)
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@property
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def output_shape(self):
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return (4, 32, 32)
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def prepare_init_args_and_inputs_for_common(self):
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init_dict = {
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"sample_size": 32,
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"in_channels": 4,
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"out_channels": 4,
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"layers_per_block": 2,
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"block_out_channels": (32, 64),
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"attention_head_dim": 32,
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"down_block_types": ("DownBlock2D", "DownBlock2D"),
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"up_block_types": ("UpBlock2D", "UpBlock2D"),
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}
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inputs_dict = self.dummy_input
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return init_dict, inputs_dict
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def test_from_pretrained_hub(self):
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model, loading_info = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)
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self.assertIsNotNone(model)
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self.assertEqual(len(loading_info["missing_keys"]), 0)
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model.to(torch_device)
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image = model(**self.dummy_input)["sample"]
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assert image is not None, "Make sure output is not None"
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def test_output_pretrained(self):
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model = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update")
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model.eval()
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torch.manual_seed(0)
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if torch.cuda.is_available():
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torch.cuda.manual_seed_all(0)
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|
||||
noise = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
|
||||
time_step = torch.tensor([10] * noise.shape[0])
|
||||
|
||||
with torch.no_grad():
|
||||
output = model(noise, time_step)["sample"]
|
||||
|
||||
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))
|
||||
|
||||
|
||||
# TODO(Patrick) - Re-add this test after having cleaned up LDM
|
||||
# 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.sample_size, model.config.sample_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 NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
|
||||
model_class = UNet2DModel
|
||||
|
||||
@property
|
||||
def dummy_input(self, sizes=(32, 32)):
|
||||
batch_size = 4
|
||||
num_channels = 3
|
||||
|
||||
noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
|
||||
time_step = torch.tensor(batch_size * [10]).to(torch_device)
|
||||
|
||||
return {"sample": noise, "timestep": 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 = {
|
||||
"block_out_channels": [32, 64, 64, 64],
|
||||
"in_channels": 3,
|
||||
"layers_per_block": 1,
|
||||
"out_channels": 3,
|
||||
"time_embedding_type": "fourier",
|
||||
"norm_eps": 1e-6,
|
||||
"mid_block_scale_factor": math.sqrt(2.0),
|
||||
"norm_num_groups": None,
|
||||
"down_block_types": [
|
||||
"SkipDownBlock2D",
|
||||
"AttnSkipDownBlock2D",
|
||||
"SkipDownBlock2D",
|
||||
"SkipDownBlock2D",
|
||||
],
|
||||
"up_block_types": [
|
||||
"SkipUpBlock2D",
|
||||
"SkipUpBlock2D",
|
||||
"AttnSkipUpBlock2D",
|
||||
"SkipUpBlock2D",
|
||||
],
|
||||
}
|
||||
inputs_dict = self.dummy_input
|
||||
return init_dict, inputs_dict
|
||||
|
||||
def test_from_pretrained_hub(self):
|
||||
model, loading_info = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256", output_loading_info=True)
|
||||
self.assertIsNotNone(model)
|
||||
self.assertEqual(len(loading_info["missing_keys"]), 0)
|
||||
|
||||
model.to(torch_device)
|
||||
inputs = self.dummy_input
|
||||
noise = floats_tensor((4, 3) + (256, 256)).to(torch_device)
|
||||
inputs["sample"] = noise
|
||||
image = model(**inputs)
|
||||
|
||||
assert image is not None, "Make sure output is not None"
|
||||
|
||||
def test_output_pretrained_ve_mid(self):
|
||||
model = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256")
|
||||
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 = (256, 256)
|
||||
|
||||
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)["sample"]
|
||||
|
||||
output_slice = output[0, -3:, -3:, -1].flatten().cpu()
|
||||
# fmt: off
|
||||
expected_output_slice = torch.tensor([-4836.2231, -6487.1387, -3816.7969, -7964.9253, -10966.2842, -20043.6016, 8137.0571, 2340.3499, 544.6114])
|
||||
# fmt: on
|
||||
|
||||
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
|
||||
|
||||
def test_output_pretrained_ve_large(self):
|
||||
model = UNet2DModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update")
|
||||
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)["sample"]
|
||||
|
||||
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))
|
||||
|
||||
|
||||
class VQModelTests(ModelTesterMixin, unittest.TestCase):
|
||||
model_class = VQModel
|
||||
|
||||
@property
|
||||
def dummy_input(self, sizes=(32, 32)):
|
||||
batch_size = 4
|
||||
num_channels = 3
|
||||
|
||||
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": 3,
|
||||
}
|
||||
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.sample_size, model.config.sample_size)
|
||||
with torch.no_grad():
|
||||
output = model(image)
|
||||
|
||||
output_slice = output[0, -1, -3:, -3:].flatten()
|
||||
# fmt: off
|
||||
expected_output_slice = torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143])
|
||||
# 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 {"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
|
||||
|
||||
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.sample_size, model.config.sample_size)
|
||||
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([-4.0078e-01, -3.8304e-04, -1.2681e-01, -1.1462e-01, 2.0095e-01, 1.0893e-01, -8.8248e-02, -3.0361e-01, -9.8646e-03])
|
||||
# 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 = UNet2DModel(
|
||||
block_out_channels=(32, 64),
|
||||
layers_per_block=2,
|
||||
sample_size=32,
|
||||
in_channels=3,
|
||||
out_channels=3,
|
||||
down_block_types=("DownBlock2D", "AttnDownBlock2D"),
|
||||
up_block_types=("AttnUpBlock2D", "UpBlock2D"),
|
||||
)
|
||||
schedular = DDPMScheduler(num_train_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, output_type="numpy")["sample"]
|
||||
generator = generator.manual_seed(0)
|
||||
new_image = new_ddpm(generator=generator, output_type="numpy")["sample"]
|
||||
|
||||
assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
|
||||
|
||||
@slow
|
||||
def test_from_pretrained_hub(self):
|
||||
model_path = "google/ddpm-cifar10-32"
|
||||
|
||||
ddpm = DDPMPipeline.from_pretrained(model_path)
|
||||
ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path)
|
||||
|
||||
ddpm.scheduler.num_timesteps = 10
|
||||
ddpm_from_hub.scheduler.num_timesteps = 10
|
||||
|
||||
generator = torch.manual_seed(0)
|
||||
|
||||
image = ddpm(generator=generator, output_type="numpy")["sample"]
|
||||
generator = generator.manual_seed(0)
|
||||
new_image = ddpm_from_hub(generator=generator, output_type="numpy")["sample"]
|
||||
|
||||
assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
|
||||
|
||||
@slow
|
||||
def test_from_pretrained_hub_pass_model(self):
|
||||
model_path = "google/ddpm-cifar10-32"
|
||||
|
||||
# pass unet into DiffusionPipeline
|
||||
unet = UNet2DModel.from_pretrained(model_path)
|
||||
ddpm_from_hub_custom_model = DiffusionPipeline.from_pretrained(model_path, unet=unet)
|
||||
|
||||
ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path)
|
||||
|
||||
ddpm_from_hub_custom_model.scheduler.num_timesteps = 10
|
||||
ddpm_from_hub.scheduler.num_timesteps = 10
|
||||
|
||||
generator = torch.manual_seed(0)
|
||||
|
||||
image = ddpm_from_hub_custom_model(generator=generator, output_type="numpy")["sample"]
|
||||
generator = generator.manual_seed(0)
|
||||
new_image = ddpm_from_hub(generator=generator, output_type="numpy")["sample"]
|
||||
|
||||
assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
|
||||
|
||||
@slow
|
||||
def test_output_format(self):
|
||||
model_path = "google/ddpm-cifar10-32"
|
||||
|
||||
pipe = DDIMPipeline.from_pretrained(model_path)
|
||||
|
||||
generator = torch.manual_seed(0)
|
||||
images = pipe(generator=generator, output_type="numpy")["sample"]
|
||||
assert images.shape == (1, 32, 32, 3)
|
||||
assert isinstance(images, np.ndarray)
|
||||
|
||||
images = pipe(generator=generator, output_type="pil")["sample"]
|
||||
assert isinstance(images, list)
|
||||
assert len(images) == 1
|
||||
assert isinstance(images[0], PIL.Image.Image)
|
||||
|
||||
# use PIL by default
|
||||
images = pipe(generator=generator)["sample"]
|
||||
assert isinstance(images, list)
|
||||
assert isinstance(images[0], PIL.Image.Image)
|
||||
|
||||
@slow
|
||||
def test_ddpm_cifar10(self):
|
||||
model_id = "google/ddpm-cifar10-32"
|
||||
|
||||
unet = UNet2DModel.from_pretrained(model_id)
|
||||
scheduler = DDPMScheduler.from_config(model_id)
|
||||
scheduler = scheduler.set_format("pt")
|
||||
|
||||
ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)
|
||||
|
||||
generator = torch.manual_seed(0)
|
||||
image = ddpm(generator=generator, output_type="numpy")["sample"]
|
||||
|
||||
image_slice = image[0, -3:, -3:, -1]
|
||||
|
||||
assert image.shape == (1, 32, 32, 3)
|
||||
expected_slice = np.array([0.41995, 0.35885, 0.19385, 0.38475, 0.3382, 0.2647, 0.41545, 0.3582, 0.33845])
|
||||
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
||||
|
||||
@slow
|
||||
def test_ddim_lsun(self):
|
||||
model_id = "google/ddpm-ema-bedroom-256"
|
||||
|
||||
unet = UNet2DModel.from_pretrained(model_id)
|
||||
scheduler = DDIMScheduler.from_config(model_id)
|
||||
|
||||
ddpm = DDIMPipeline(unet=unet, scheduler=scheduler)
|
||||
|
||||
generator = torch.manual_seed(0)
|
||||
image = ddpm(generator=generator, output_type="numpy")["sample"]
|
||||
|
||||
image_slice = image[0, -3:, -3:, -1]
|
||||
|
||||
assert image.shape == (1, 256, 256, 3)
|
||||
expected_slice = np.array([0.00605, 0.0201, 0.0344, 0.00235, 0.00185, 0.00025, 0.00215, 0.0, 0.00685])
|
||||
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
||||
|
||||
@slow
|
||||
def test_ddim_cifar10(self):
|
||||
model_id = "google/ddpm-cifar10-32"
|
||||
|
||||
unet = UNet2DModel.from_pretrained(model_id)
|
||||
scheduler = DDIMScheduler(tensor_format="pt")
|
||||
|
||||
ddim = DDIMPipeline(unet=unet, scheduler=scheduler)
|
||||
|
||||
generator = torch.manual_seed(0)
|
||||
image = ddim(generator=generator, eta=0.0, output_type="numpy")["sample"]
|
||||
|
||||
image_slice = image[0, -3:, -3:, -1]
|
||||
|
||||
assert image.shape == (1, 32, 32, 3)
|
||||
expected_slice = np.array([0.17235, 0.16175, 0.16005, 0.16255, 0.1497, 0.1513, 0.15045, 0.1442, 0.1453])
|
||||
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
||||
|
||||
@slow
|
||||
def test_pndm_cifar10(self):
|
||||
model_id = "google/ddpm-cifar10-32"
|
||||
|
||||
unet = UNet2DModel.from_pretrained(model_id)
|
||||
scheduler = PNDMScheduler(tensor_format="pt")
|
||||
|
||||
pndm = PNDMPipeline(unet=unet, scheduler=scheduler)
|
||||
generator = torch.manual_seed(0)
|
||||
image = pndm(generator=generator, output_type="numpy")["sample"]
|
||||
|
||||
image_slice = image[0, -3:, -3:, -1]
|
||||
|
||||
assert image.shape == (1, 32, 32, 3)
|
||||
expected_slice = np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125])
|
||||
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
||||
|
||||
@slow
|
||||
def test_ldm_text2img(self):
|
||||
ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
|
||||
|
||||
prompt = "A painting of a squirrel eating a burger"
|
||||
generator = torch.manual_seed(0)
|
||||
image = ldm([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="numpy")[
|
||||
"sample"
|
||||
]
|
||||
|
||||
image_slice = image[0, -3:, -3:, -1]
|
||||
|
||||
assert image.shape == (1, 256, 256, 3)
|
||||
expected_slice = np.array([0.9256, 0.9340, 0.8933, 0.9361, 0.9113, 0.8727, 0.9122, 0.8745, 0.8099])
|
||||
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
||||
|
||||
@slow
|
||||
def test_ldm_text2img_fast(self):
|
||||
ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
|
||||
|
||||
prompt = "A painting of a squirrel eating a burger"
|
||||
generator = torch.manual_seed(0)
|
||||
image = ldm(prompt, generator=generator, num_inference_steps=1, output_type="numpy")["sample"]
|
||||
|
||||
image_slice = image[0, -3:, -3:, -1]
|
||||
|
||||
assert image.shape == (1, 256, 256, 3)
|
||||
expected_slice = np.array([0.3163, 0.8670, 0.6465, 0.1865, 0.6291, 0.5139, 0.2824, 0.3723, 0.4344])
|
||||
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
||||
|
||||
@slow
|
||||
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
|
||||
def test_stable_diffusion(self):
|
||||
# make sure here that pndm scheduler skips prk
|
||||
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1").to(torch_device)
|
||||
|
||||
prompt = "A painting of a squirrel eating a burger"
|
||||
generator = torch.Generator(device=torch_device).manual_seed(0)
|
||||
with torch.autocast("cuda"):
|
||||
output = sd_pipe(
|
||||
[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="np"
|
||||
)
|
||||
|
||||
image = output["sample"]
|
||||
|
||||
image_slice = image[0, -3:, -3:, -1]
|
||||
|
||||
assert image.shape == (1, 512, 512, 3)
|
||||
expected_slice = np.array([0.8887, 0.915, 0.91, 0.894, 0.909, 0.912, 0.919, 0.925, 0.883])
|
||||
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
||||
|
||||
@slow
|
||||
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
|
||||
def test_stable_diffusion_fast_ddim(self):
|
||||
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1").to(torch_device)
|
||||
|
||||
scheduler = DDIMScheduler(
|
||||
beta_start=0.00085,
|
||||
beta_end=0.012,
|
||||
beta_schedule="scaled_linear",
|
||||
clip_sample=False,
|
||||
set_alpha_to_one=False,
|
||||
)
|
||||
sd_pipe.scheduler = scheduler
|
||||
|
||||
prompt = "A painting of a squirrel eating a burger"
|
||||
generator = torch.Generator(device=torch_device).manual_seed(0)
|
||||
|
||||
with torch.autocast("cuda"):
|
||||
output = sd_pipe([prompt], generator=generator, num_inference_steps=2, output_type="numpy")
|
||||
image = output["sample"]
|
||||
|
||||
image_slice = image[0, -3:, -3:, -1]
|
||||
|
||||
assert image.shape == (1, 512, 512, 3)
|
||||
expected_slice = np.array([0.8354, 0.83, 0.866, 0.838, 0.8315, 0.867, 0.836, 0.8584, 0.869])
|
||||
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
|
||||
|
||||
@slow
|
||||
def test_score_sde_ve_pipeline(self):
|
||||
model_id = "google/ncsnpp-church-256"
|
||||
model = UNet2DModel.from_pretrained(model_id)
|
||||
|
||||
scheduler = ScoreSdeVeScheduler.from_config(model_id)
|
||||
|
||||
sde_ve = ScoreSdeVePipeline(unet=model, scheduler=scheduler)
|
||||
|
||||
torch.manual_seed(0)
|
||||
image = sde_ve(num_inference_steps=300, output_type="numpy")["sample"]
|
||||
|
||||
image_slice = image[0, -3:, -3:, -1]
|
||||
|
||||
assert image.shape == (1, 256, 256, 3)
|
||||
|
||||
expected_slice = np.array([0.64363, 0.5868, 0.3031, 0.2284, 0.7409, 0.3216, 0.25643, 0.6557, 0.2633])
|
||||
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
||||
|
||||
@slow
|
||||
def test_ldm_uncond(self):
|
||||
ldm = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256")
|
||||
|
||||
generator = torch.manual_seed(0)
|
||||
image = ldm(generator=generator, num_inference_steps=5, output_type="numpy")["sample"]
|
||||
|
||||
image_slice = image[0, -3:, -3:, -1]
|
||||
|
||||
assert image.shape == (1, 256, 256, 3)
|
||||
expected_slice = np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447])
|
||||
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
||||
|
||||
@slow
|
||||
def test_ddpm_ddim_equality(self):
|
||||
model_id = "google/ddpm-cifar10-32"
|
||||
|
||||
unet = UNet2DModel.from_pretrained(model_id)
|
||||
ddpm_scheduler = DDPMScheduler(tensor_format="pt")
|
||||
ddim_scheduler = DDIMScheduler(tensor_format="pt")
|
||||
|
||||
ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler)
|
||||
ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler)
|
||||
|
||||
generator = torch.manual_seed(0)
|
||||
ddpm_image = ddpm(generator=generator, output_type="numpy")["sample"]
|
||||
|
||||
generator = torch.manual_seed(0)
|
||||
ddim_image = ddim(generator=generator, num_inference_steps=1000, eta=1.0, output_type="numpy")["sample"]
|
||||
|
||||
# the values aren't exactly equal, but the images look the same visually
|
||||
assert np.abs(ddpm_image - ddim_image).max() < 1e-1
|
||||
|
||||
@unittest.skip("(Anton) The test is failing for large batch sizes, needs investigation")
|
||||
def test_ddpm_ddim_equality_batched(self):
|
||||
model_id = "google/ddpm-cifar10-32"
|
||||
|
||||
unet = UNet2DModel.from_pretrained(model_id)
|
||||
ddpm_scheduler = DDPMScheduler(tensor_format="pt")
|
||||
ddim_scheduler = DDIMScheduler(tensor_format="pt")
|
||||
|
||||
ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler)
|
||||
ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler)
|
||||
|
||||
generator = torch.manual_seed(0)
|
||||
ddpm_images = ddpm(batch_size=4, generator=generator, output_type="numpy")["sample"]
|
||||
|
||||
generator = torch.manual_seed(0)
|
||||
ddim_images = ddim(batch_size=4, generator=generator, num_inference_steps=1000, eta=1.0, output_type="numpy")[
|
||||
"sample"
|
||||
]
|
||||
|
||||
# the values aren't exactly equal, but the images look the same visually
|
||||
assert np.abs(ddpm_images - ddim_images).max() < 1e-1
|
||||
|
||||
@slow
|
||||
def test_karras_ve_pipeline(self):
|
||||
model_id = "google/ncsnpp-celebahq-256"
|
||||
model = UNet2DModel.from_pretrained(model_id)
|
||||
scheduler = KarrasVeScheduler(tensor_format="pt")
|
||||
|
||||
pipe = KarrasVePipeline(unet=model, scheduler=scheduler)
|
||||
|
||||
generator = torch.manual_seed(0)
|
||||
image = pipe(num_inference_steps=20, generator=generator, output_type="numpy")["sample"]
|
||||
|
||||
image_slice = image[0, -3:, -3:, -1]
|
||||
assert image.shape == (1, 256, 256, 3)
|
||||
expected_slice = np.array([0.26815, 0.1581, 0.2658, 0.23248, 0.1550, 0.2539, 0.1131, 0.1024, 0.0837])
|
||||
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
||||
|
||||
@slow
|
||||
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
|
||||
def test_lms_stable_diffusion_pipeline(self):
|
||||
model_id = "CompVis/stable-diffusion-v1-1"
|
||||
pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True).to(torch_device)
|
||||
scheduler = LMSDiscreteScheduler.from_config(model_id, subfolder="scheduler", use_auth_token=True)
|
||||
pipe.scheduler = scheduler
|
||||
|
||||
prompt = "a photograph of an astronaut riding a horse"
|
||||
generator = torch.Generator(device=torch_device).manual_seed(0)
|
||||
image = pipe([prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy")[
|
||||
"sample"
|
||||
]
|
||||
|
||||
image_slice = image[0, -3:, -3:, -1]
|
||||
assert image.shape == (1, 512, 512, 3)
|
||||
expected_slice = np.array([0.9077, 0.9254, 0.9181, 0.9227, 0.9213, 0.9367, 0.9399, 0.9406, 0.9024])
|
||||
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
||||
|
||||
288
tests/test_models_unet.py
Normal file
288
tests/test_models_unet.py
Normal file
@@ -0,0 +1,288 @@
|
||||
# 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 math
|
||||
import unittest
|
||||
|
||||
import torch
|
||||
|
||||
from diffusers import UNet2DModel
|
||||
from diffusers.testing_utils import floats_tensor, torch_device
|
||||
|
||||
from .test_modeling_common import ModelTesterMixin
|
||||
|
||||
|
||||
class UnetModelTests(ModelTesterMixin, unittest.TestCase):
|
||||
model_class = UNet2DModel
|
||||
|
||||
@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 {"sample": noise, "timestep": 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 = {
|
||||
"block_out_channels": (32, 64),
|
||||
"down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
|
||||
"up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
|
||||
"attention_head_dim": None,
|
||||
"out_channels": 3,
|
||||
"in_channels": 3,
|
||||
"layers_per_block": 2,
|
||||
"sample_size": 32,
|
||||
}
|
||||
inputs_dict = self.dummy_input
|
||||
return init_dict, inputs_dict
|
||||
|
||||
|
||||
# TODO(Patrick) - Re-add this test after having correctly added the final VE checkpoints
|
||||
# def test_output_pretrained(self):
|
||||
# model = UNet2DModel.from_pretrained("fusing/ddpm_dummy_update", subfolder="unet")
|
||||
# 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.sample_size, model.config.sample_size)
|
||||
# time_step = torch.tensor([10])
|
||||
#
|
||||
# with torch.no_grad():
|
||||
# output = model(noise, time_step)["sample"]
|
||||
#
|
||||
# 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 UNetLDMModelTests(ModelTesterMixin, unittest.TestCase):
|
||||
model_class = UNet2DModel
|
||||
|
||||
@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 {"sample": noise, "timestep": 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 = {
|
||||
"sample_size": 32,
|
||||
"in_channels": 4,
|
||||
"out_channels": 4,
|
||||
"layers_per_block": 2,
|
||||
"block_out_channels": (32, 64),
|
||||
"attention_head_dim": 32,
|
||||
"down_block_types": ("DownBlock2D", "DownBlock2D"),
|
||||
"up_block_types": ("UpBlock2D", "UpBlock2D"),
|
||||
}
|
||||
inputs_dict = self.dummy_input
|
||||
return init_dict, inputs_dict
|
||||
|
||||
def test_from_pretrained_hub(self):
|
||||
model, loading_info = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update", output_loading_info=True)
|
||||
|
||||
self.assertIsNotNone(model)
|
||||
self.assertEqual(len(loading_info["missing_keys"]), 0)
|
||||
|
||||
model.to(torch_device)
|
||||
image = model(**self.dummy_input)["sample"]
|
||||
|
||||
assert image is not None, "Make sure output is not None"
|
||||
|
||||
def test_output_pretrained(self):
|
||||
model = UNet2DModel.from_pretrained("fusing/unet-ldm-dummy-update")
|
||||
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.sample_size, model.config.sample_size)
|
||||
time_step = torch.tensor([10] * noise.shape[0])
|
||||
|
||||
with torch.no_grad():
|
||||
output = model(noise, time_step)["sample"]
|
||||
|
||||
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))
|
||||
|
||||
|
||||
# TODO(Patrick) - Re-add this test after having cleaned up LDM
|
||||
# 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.sample_size, model.config.sample_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 NCSNppModelTests(ModelTesterMixin, unittest.TestCase):
|
||||
model_class = UNet2DModel
|
||||
|
||||
@property
|
||||
def dummy_input(self, sizes=(32, 32)):
|
||||
batch_size = 4
|
||||
num_channels = 3
|
||||
|
||||
noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device)
|
||||
time_step = torch.tensor(batch_size * [10]).to(torch_device)
|
||||
|
||||
return {"sample": noise, "timestep": 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 = {
|
||||
"block_out_channels": [32, 64, 64, 64],
|
||||
"in_channels": 3,
|
||||
"layers_per_block": 1,
|
||||
"out_channels": 3,
|
||||
"time_embedding_type": "fourier",
|
||||
"norm_eps": 1e-6,
|
||||
"mid_block_scale_factor": math.sqrt(2.0),
|
||||
"norm_num_groups": None,
|
||||
"down_block_types": [
|
||||
"SkipDownBlock2D",
|
||||
"AttnSkipDownBlock2D",
|
||||
"SkipDownBlock2D",
|
||||
"SkipDownBlock2D",
|
||||
],
|
||||
"up_block_types": [
|
||||
"SkipUpBlock2D",
|
||||
"SkipUpBlock2D",
|
||||
"AttnSkipUpBlock2D",
|
||||
"SkipUpBlock2D",
|
||||
],
|
||||
}
|
||||
inputs_dict = self.dummy_input
|
||||
return init_dict, inputs_dict
|
||||
|
||||
def test_from_pretrained_hub(self):
|
||||
model, loading_info = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256", output_loading_info=True)
|
||||
self.assertIsNotNone(model)
|
||||
self.assertEqual(len(loading_info["missing_keys"]), 0)
|
||||
|
||||
model.to(torch_device)
|
||||
inputs = self.dummy_input
|
||||
noise = floats_tensor((4, 3) + (256, 256)).to(torch_device)
|
||||
inputs["sample"] = noise
|
||||
image = model(**inputs)
|
||||
|
||||
assert image is not None, "Make sure output is not None"
|
||||
|
||||
def test_output_pretrained_ve_mid(self):
|
||||
model = UNet2DModel.from_pretrained("google/ncsnpp-celebahq-256")
|
||||
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 = (256, 256)
|
||||
|
||||
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)["sample"]
|
||||
|
||||
output_slice = output[0, -3:, -3:, -1].flatten().cpu()
|
||||
# fmt: off
|
||||
expected_output_slice = torch.tensor([-4836.2231, -6487.1387, -3816.7969, -7964.9253, -10966.2842, -20043.6016, 8137.0571, 2340.3499, 544.6114])
|
||||
# fmt: on
|
||||
|
||||
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
|
||||
|
||||
def test_output_pretrained_ve_large(self):
|
||||
model = UNet2DModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update")
|
||||
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)["sample"]
|
||||
|
||||
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))
|
||||
91
tests/test_models_vae.py
Normal file
91
tests/test_models_vae.py
Normal file
@@ -0,0 +1,91 @@
|
||||
# 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 unittest
|
||||
|
||||
import torch
|
||||
|
||||
from diffusers import AutoencoderKL
|
||||
from diffusers.testing_utils import floats_tensor, torch_device
|
||||
|
||||
from .test_modeling_common import ModelTesterMixin
|
||||
|
||||
|
||||
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 {"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
|
||||
|
||||
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.sample_size, model.config.sample_size)
|
||||
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([-4.0078e-01, -3.8304e-04, -1.2681e-01, -1.1462e-01, 2.0095e-01, 1.0893e-01, -8.8248e-02, -3.0361e-01, -9.8646e-03])
|
||||
# fmt: on
|
||||
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
|
||||
90
tests/test_models_vq.py
Normal file
90
tests/test_models_vq.py
Normal file
@@ -0,0 +1,90 @@
|
||||
# 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 unittest
|
||||
|
||||
import torch
|
||||
|
||||
from diffusers import VQModel
|
||||
from diffusers.testing_utils import floats_tensor, torch_device
|
||||
|
||||
from .test_modeling_common import ModelTesterMixin
|
||||
|
||||
|
||||
class VQModelTests(ModelTesterMixin, unittest.TestCase):
|
||||
model_class = VQModel
|
||||
|
||||
@property
|
||||
def dummy_input(self, sizes=(32, 32)):
|
||||
batch_size = 4
|
||||
num_channels = 3
|
||||
|
||||
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": 3,
|
||||
}
|
||||
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.sample_size, model.config.sample_size)
|
||||
with torch.no_grad():
|
||||
output = model(image)
|
||||
|
||||
output_slice = output[0, -1, -3:, -3:].flatten()
|
||||
# fmt: off
|
||||
expected_output_slice = torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143])
|
||||
# fmt: on
|
||||
self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2))
|
||||
392
tests/test_pipelines.py
Normal file
392
tests/test_pipelines.py
Normal file
@@ -0,0 +1,392 @@
|
||||
# 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 tempfile
|
||||
import unittest
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
import PIL
|
||||
from diffusers import (
|
||||
DDIMPipeline,
|
||||
DDIMScheduler,
|
||||
DDPMPipeline,
|
||||
DDPMScheduler,
|
||||
KarrasVePipeline,
|
||||
KarrasVeScheduler,
|
||||
LDMPipeline,
|
||||
LDMTextToImagePipeline,
|
||||
LMSDiscreteScheduler,
|
||||
PNDMPipeline,
|
||||
PNDMScheduler,
|
||||
ScoreSdeVePipeline,
|
||||
ScoreSdeVeScheduler,
|
||||
StableDiffusionPipeline,
|
||||
UNet2DModel,
|
||||
)
|
||||
from diffusers.pipeline_utils import DiffusionPipeline
|
||||
from diffusers.testing_utils import slow, torch_device
|
||||
|
||||
|
||||
torch.backends.cuda.matmul.allow_tf32 = False
|
||||
|
||||
|
||||
class PipelineTesterMixin(unittest.TestCase):
|
||||
def test_from_pretrained_save_pretrained(self):
|
||||
# 1. Load models
|
||||
model = UNet2DModel(
|
||||
block_out_channels=(32, 64),
|
||||
layers_per_block=2,
|
||||
sample_size=32,
|
||||
in_channels=3,
|
||||
out_channels=3,
|
||||
down_block_types=("DownBlock2D", "AttnDownBlock2D"),
|
||||
up_block_types=("AttnUpBlock2D", "UpBlock2D"),
|
||||
)
|
||||
schedular = DDPMScheduler(num_train_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, output_type="numpy")["sample"]
|
||||
generator = generator.manual_seed(0)
|
||||
new_image = new_ddpm(generator=generator, output_type="numpy")["sample"]
|
||||
|
||||
assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
|
||||
|
||||
@slow
|
||||
def test_from_pretrained_hub(self):
|
||||
model_path = "google/ddpm-cifar10-32"
|
||||
|
||||
ddpm = DDPMPipeline.from_pretrained(model_path)
|
||||
ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path)
|
||||
|
||||
ddpm.scheduler.num_timesteps = 10
|
||||
ddpm_from_hub.scheduler.num_timesteps = 10
|
||||
|
||||
generator = torch.manual_seed(0)
|
||||
|
||||
image = ddpm(generator=generator, output_type="numpy")["sample"]
|
||||
generator = generator.manual_seed(0)
|
||||
new_image = ddpm_from_hub(generator=generator, output_type="numpy")["sample"]
|
||||
|
||||
assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
|
||||
|
||||
@slow
|
||||
def test_from_pretrained_hub_pass_model(self):
|
||||
model_path = "google/ddpm-cifar10-32"
|
||||
|
||||
# pass unet into DiffusionPipeline
|
||||
unet = UNet2DModel.from_pretrained(model_path)
|
||||
ddpm_from_hub_custom_model = DiffusionPipeline.from_pretrained(model_path, unet=unet)
|
||||
|
||||
ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path)
|
||||
|
||||
ddpm_from_hub_custom_model.scheduler.num_timesteps = 10
|
||||
ddpm_from_hub.scheduler.num_timesteps = 10
|
||||
|
||||
generator = torch.manual_seed(0)
|
||||
|
||||
image = ddpm_from_hub_custom_model(generator=generator, output_type="numpy")["sample"]
|
||||
generator = generator.manual_seed(0)
|
||||
new_image = ddpm_from_hub(generator=generator, output_type="numpy")["sample"]
|
||||
|
||||
assert np.abs(image - new_image).sum() < 1e-5, "Models don't give the same forward pass"
|
||||
|
||||
@slow
|
||||
def test_output_format(self):
|
||||
model_path = "google/ddpm-cifar10-32"
|
||||
|
||||
pipe = DDIMPipeline.from_pretrained(model_path)
|
||||
|
||||
generator = torch.manual_seed(0)
|
||||
images = pipe(generator=generator, output_type="numpy")["sample"]
|
||||
assert images.shape == (1, 32, 32, 3)
|
||||
assert isinstance(images, np.ndarray)
|
||||
|
||||
images = pipe(generator=generator, output_type="pil")["sample"]
|
||||
assert isinstance(images, list)
|
||||
assert len(images) == 1
|
||||
assert isinstance(images[0], PIL.Image.Image)
|
||||
|
||||
# use PIL by default
|
||||
images = pipe(generator=generator)["sample"]
|
||||
assert isinstance(images, list)
|
||||
assert isinstance(images[0], PIL.Image.Image)
|
||||
|
||||
@slow
|
||||
def test_ddpm_cifar10(self):
|
||||
model_id = "google/ddpm-cifar10-32"
|
||||
|
||||
unet = UNet2DModel.from_pretrained(model_id)
|
||||
scheduler = DDPMScheduler.from_config(model_id)
|
||||
scheduler = scheduler.set_format("pt")
|
||||
|
||||
ddpm = DDPMPipeline(unet=unet, scheduler=scheduler)
|
||||
|
||||
generator = torch.manual_seed(0)
|
||||
image = ddpm(generator=generator, output_type="numpy")["sample"]
|
||||
|
||||
image_slice = image[0, -3:, -3:, -1]
|
||||
|
||||
assert image.shape == (1, 32, 32, 3)
|
||||
expected_slice = np.array([0.41995, 0.35885, 0.19385, 0.38475, 0.3382, 0.2647, 0.41545, 0.3582, 0.33845])
|
||||
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
||||
|
||||
@slow
|
||||
def test_ddim_lsun(self):
|
||||
model_id = "google/ddpm-ema-bedroom-256"
|
||||
|
||||
unet = UNet2DModel.from_pretrained(model_id)
|
||||
scheduler = DDIMScheduler.from_config(model_id)
|
||||
|
||||
ddpm = DDIMPipeline(unet=unet, scheduler=scheduler)
|
||||
|
||||
generator = torch.manual_seed(0)
|
||||
image = ddpm(generator=generator, output_type="numpy")["sample"]
|
||||
|
||||
image_slice = image[0, -3:, -3:, -1]
|
||||
|
||||
assert image.shape == (1, 256, 256, 3)
|
||||
expected_slice = np.array([0.00605, 0.0201, 0.0344, 0.00235, 0.00185, 0.00025, 0.00215, 0.0, 0.00685])
|
||||
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
||||
|
||||
@slow
|
||||
def test_ddim_cifar10(self):
|
||||
model_id = "google/ddpm-cifar10-32"
|
||||
|
||||
unet = UNet2DModel.from_pretrained(model_id)
|
||||
scheduler = DDIMScheduler(tensor_format="pt")
|
||||
|
||||
ddim = DDIMPipeline(unet=unet, scheduler=scheduler)
|
||||
|
||||
generator = torch.manual_seed(0)
|
||||
image = ddim(generator=generator, eta=0.0, output_type="numpy")["sample"]
|
||||
|
||||
image_slice = image[0, -3:, -3:, -1]
|
||||
|
||||
assert image.shape == (1, 32, 32, 3)
|
||||
expected_slice = np.array([0.17235, 0.16175, 0.16005, 0.16255, 0.1497, 0.1513, 0.15045, 0.1442, 0.1453])
|
||||
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
||||
|
||||
@slow
|
||||
def test_pndm_cifar10(self):
|
||||
model_id = "google/ddpm-cifar10-32"
|
||||
|
||||
unet = UNet2DModel.from_pretrained(model_id)
|
||||
scheduler = PNDMScheduler(tensor_format="pt")
|
||||
|
||||
pndm = PNDMPipeline(unet=unet, scheduler=scheduler)
|
||||
generator = torch.manual_seed(0)
|
||||
image = pndm(generator=generator, output_type="numpy")["sample"]
|
||||
|
||||
image_slice = image[0, -3:, -3:, -1]
|
||||
|
||||
assert image.shape == (1, 32, 32, 3)
|
||||
expected_slice = np.array([0.1564, 0.14645, 0.1406, 0.14715, 0.12425, 0.14045, 0.13115, 0.12175, 0.125])
|
||||
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
||||
|
||||
@slow
|
||||
def test_ldm_text2img(self):
|
||||
ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
|
||||
|
||||
prompt = "A painting of a squirrel eating a burger"
|
||||
generator = torch.manual_seed(0)
|
||||
image = ldm([prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="numpy")[
|
||||
"sample"
|
||||
]
|
||||
|
||||
image_slice = image[0, -3:, -3:, -1]
|
||||
|
||||
assert image.shape == (1, 256, 256, 3)
|
||||
expected_slice = np.array([0.9256, 0.9340, 0.8933, 0.9361, 0.9113, 0.8727, 0.9122, 0.8745, 0.8099])
|
||||
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
||||
|
||||
@slow
|
||||
def test_ldm_text2img_fast(self):
|
||||
ldm = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256")
|
||||
|
||||
prompt = "A painting of a squirrel eating a burger"
|
||||
generator = torch.manual_seed(0)
|
||||
image = ldm(prompt, generator=generator, num_inference_steps=1, output_type="numpy")["sample"]
|
||||
|
||||
image_slice = image[0, -3:, -3:, -1]
|
||||
|
||||
assert image.shape == (1, 256, 256, 3)
|
||||
expected_slice = np.array([0.3163, 0.8670, 0.6465, 0.1865, 0.6291, 0.5139, 0.2824, 0.3723, 0.4344])
|
||||
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
||||
|
||||
@slow
|
||||
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
|
||||
def test_stable_diffusion(self):
|
||||
# make sure here that pndm scheduler skips prk
|
||||
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1").to(torch_device)
|
||||
|
||||
prompt = "A painting of a squirrel eating a burger"
|
||||
generator = torch.Generator(device=torch_device).manual_seed(0)
|
||||
with torch.autocast("cuda"):
|
||||
output = sd_pipe(
|
||||
[prompt], generator=generator, guidance_scale=6.0, num_inference_steps=20, output_type="np"
|
||||
)
|
||||
|
||||
image = output["sample"]
|
||||
|
||||
image_slice = image[0, -3:, -3:, -1]
|
||||
|
||||
assert image.shape == (1, 512, 512, 3)
|
||||
expected_slice = np.array([0.8887, 0.915, 0.91, 0.894, 0.909, 0.912, 0.919, 0.925, 0.883])
|
||||
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
||||
|
||||
@slow
|
||||
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
|
||||
def test_stable_diffusion_fast_ddim(self):
|
||||
sd_pipe = StableDiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-1").to(torch_device)
|
||||
|
||||
scheduler = DDIMScheduler(
|
||||
beta_start=0.00085,
|
||||
beta_end=0.012,
|
||||
beta_schedule="scaled_linear",
|
||||
clip_sample=False,
|
||||
set_alpha_to_one=False,
|
||||
)
|
||||
sd_pipe.scheduler = scheduler
|
||||
|
||||
prompt = "A painting of a squirrel eating a burger"
|
||||
generator = torch.Generator(device=torch_device).manual_seed(0)
|
||||
|
||||
with torch.autocast("cuda"):
|
||||
output = sd_pipe([prompt], generator=generator, num_inference_steps=2, output_type="numpy")
|
||||
image = output["sample"]
|
||||
|
||||
image_slice = image[0, -3:, -3:, -1]
|
||||
|
||||
assert image.shape == (1, 512, 512, 3)
|
||||
expected_slice = np.array([0.8354, 0.83, 0.866, 0.838, 0.8315, 0.867, 0.836, 0.8584, 0.869])
|
||||
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3
|
||||
|
||||
@slow
|
||||
def test_score_sde_ve_pipeline(self):
|
||||
model_id = "google/ncsnpp-church-256"
|
||||
model = UNet2DModel.from_pretrained(model_id)
|
||||
|
||||
scheduler = ScoreSdeVeScheduler.from_config(model_id)
|
||||
|
||||
sde_ve = ScoreSdeVePipeline(unet=model, scheduler=scheduler)
|
||||
|
||||
torch.manual_seed(0)
|
||||
image = sde_ve(num_inference_steps=300, output_type="numpy")["sample"]
|
||||
|
||||
image_slice = image[0, -3:, -3:, -1]
|
||||
|
||||
assert image.shape == (1, 256, 256, 3)
|
||||
|
||||
expected_slice = np.array([0.64363, 0.5868, 0.3031, 0.2284, 0.7409, 0.3216, 0.25643, 0.6557, 0.2633])
|
||||
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
||||
|
||||
@slow
|
||||
def test_ldm_uncond(self):
|
||||
ldm = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256")
|
||||
|
||||
generator = torch.manual_seed(0)
|
||||
image = ldm(generator=generator, num_inference_steps=5, output_type="numpy")["sample"]
|
||||
|
||||
image_slice = image[0, -3:, -3:, -1]
|
||||
|
||||
assert image.shape == (1, 256, 256, 3)
|
||||
expected_slice = np.array([0.4399, 0.44975, 0.46825, 0.474, 0.4359, 0.4581, 0.45095, 0.4341, 0.4447])
|
||||
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
||||
|
||||
@slow
|
||||
def test_ddpm_ddim_equality(self):
|
||||
model_id = "google/ddpm-cifar10-32"
|
||||
|
||||
unet = UNet2DModel.from_pretrained(model_id)
|
||||
ddpm_scheduler = DDPMScheduler(tensor_format="pt")
|
||||
ddim_scheduler = DDIMScheduler(tensor_format="pt")
|
||||
|
||||
ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler)
|
||||
ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler)
|
||||
|
||||
generator = torch.manual_seed(0)
|
||||
ddpm_image = ddpm(generator=generator, output_type="numpy")["sample"]
|
||||
|
||||
generator = torch.manual_seed(0)
|
||||
ddim_image = ddim(generator=generator, num_inference_steps=1000, eta=1.0, output_type="numpy")["sample"]
|
||||
|
||||
# the values aren't exactly equal, but the images look the same visually
|
||||
assert np.abs(ddpm_image - ddim_image).max() < 1e-1
|
||||
|
||||
@unittest.skip("(Anton) The test is failing for large batch sizes, needs investigation")
|
||||
def test_ddpm_ddim_equality_batched(self):
|
||||
model_id = "google/ddpm-cifar10-32"
|
||||
|
||||
unet = UNet2DModel.from_pretrained(model_id)
|
||||
ddpm_scheduler = DDPMScheduler(tensor_format="pt")
|
||||
ddim_scheduler = DDIMScheduler(tensor_format="pt")
|
||||
|
||||
ddpm = DDPMPipeline(unet=unet, scheduler=ddpm_scheduler)
|
||||
ddim = DDIMPipeline(unet=unet, scheduler=ddim_scheduler)
|
||||
|
||||
generator = torch.manual_seed(0)
|
||||
ddpm_images = ddpm(batch_size=4, generator=generator, output_type="numpy")["sample"]
|
||||
|
||||
generator = torch.manual_seed(0)
|
||||
ddim_images = ddim(batch_size=4, generator=generator, num_inference_steps=1000, eta=1.0, output_type="numpy")[
|
||||
"sample"
|
||||
]
|
||||
|
||||
# the values aren't exactly equal, but the images look the same visually
|
||||
assert np.abs(ddpm_images - ddim_images).max() < 1e-1
|
||||
|
||||
@slow
|
||||
def test_karras_ve_pipeline(self):
|
||||
model_id = "google/ncsnpp-celebahq-256"
|
||||
model = UNet2DModel.from_pretrained(model_id)
|
||||
scheduler = KarrasVeScheduler(tensor_format="pt")
|
||||
|
||||
pipe = KarrasVePipeline(unet=model, scheduler=scheduler)
|
||||
|
||||
generator = torch.manual_seed(0)
|
||||
image = pipe(num_inference_steps=20, generator=generator, output_type="numpy")["sample"]
|
||||
|
||||
image_slice = image[0, -3:, -3:, -1]
|
||||
assert image.shape == (1, 256, 256, 3)
|
||||
expected_slice = np.array([0.26815, 0.1581, 0.2658, 0.23248, 0.1550, 0.2539, 0.1131, 0.1024, 0.0837])
|
||||
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
||||
|
||||
@slow
|
||||
@unittest.skipIf(torch_device == "cpu", "Stable diffusion is supposed to run on GPU")
|
||||
def test_lms_stable_diffusion_pipeline(self):
|
||||
model_id = "CompVis/stable-diffusion-v1-1"
|
||||
pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token=True).to(torch_device)
|
||||
scheduler = LMSDiscreteScheduler.from_config(model_id, subfolder="scheduler", use_auth_token=True)
|
||||
pipe.scheduler = scheduler
|
||||
|
||||
prompt = "a photograph of an astronaut riding a horse"
|
||||
generator = torch.Generator(device=torch_device).manual_seed(0)
|
||||
image = pipe([prompt], generator=generator, guidance_scale=7.5, num_inference_steps=10, output_type="numpy")[
|
||||
"sample"
|
||||
]
|
||||
|
||||
image_slice = image[0, -3:, -3:, -1]
|
||||
assert image.shape == (1, 512, 512, 3)
|
||||
expected_slice = np.array([0.9077, 0.9254, 0.9181, 0.9227, 0.9213, 0.9367, 0.9399, 0.9406, 0.9024])
|
||||
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
|
||||
Reference in New Issue
Block a user