diff --git a/tests/test_modeling_common.py b/tests/test_modeling_common.py new file mode 100644 index 0000000000..e192380505 --- /dev/null +++ b/tests/test_modeling_common.py @@ -0,0 +1,165 @@ +# 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 numpy as np +import torch + +from diffusers.testing_utils import torch_device +from diffusers.training_utils import EMAModel + + +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) + if isinstance(image, dict): + image = image["sample"] + + new_image = new_model(**inputs_dict) + + if isinstance(new_image, dict): + new_image = new_image["sample"] + + 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) + if isinstance(first, dict): + first = first["sample"] + + second = model(**inputs_dict) + if isinstance(second, dict): + second = second["sample"] + + 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) + + if isinstance(output, dict): + output = output["sample"] + + self.assertIsNotNone(output) + expected_shape = inputs_dict["sample"].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 = ["sample", "timestep"] + 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) + + if isinstance(output_1, dict): + output_1 = output_1["sample"] + + output_2 = new_model(**inputs_dict) + + if isinstance(output_2, dict): + output_2 = output_2["sample"] + + 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) + + if isinstance(output, dict): + output = output["sample"] + + noise = torch.randn((inputs_dict["sample"].shape[0],) + self.output_shape).to(torch_device) + loss = torch.nn.functional.mse_loss(output, noise) + loss.backward() + + def test_ema_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() + ema_model = EMAModel(model, device=torch_device) + + output = model(**inputs_dict) + + if isinstance(output, dict): + output = output["sample"] + + noise = torch.randn((inputs_dict["sample"].shape[0],) + self.output_shape).to(torch_device) + loss = torch.nn.functional.mse_loss(output, noise) + loss.backward() + ema_model.step(model) diff --git a/tests/test_modeling_utils.py b/tests/test_modeling_utils.py index 9e42fb5f28..ab5b1e86bc 100755 --- a/tests/test_modeling_utils.py +++ b/tests/test_modeling_utils.py @@ -13,42 +13,10 @@ # See the License for the specific language governing permissions and # limitations under the License. -import inspect -import math import tempfile import unittest -import numpy as np -import torch - -import PIL -from diffusers import UNet2DConditionModel # noqa: F401 TODO(Patrick) - need to write tests with it -from diffusers import ( - AutoencoderKL, - DDIMPipeline, - DDIMScheduler, - DDPMPipeline, - DDPMScheduler, - KarrasVePipeline, - KarrasVeScheduler, - LDMPipeline, - LDMTextToImagePipeline, - LMSDiscreteScheduler, - PNDMPipeline, - PNDMScheduler, - ScoreSdeVePipeline, - ScoreSdeVeScheduler, - StableDiffusionPipeline, - UNet2DModel, - VQModel, -) from diffusers.configuration_utils import ConfigMixin, register_to_config -from diffusers.pipeline_utils import DiffusionPipeline -from diffusers.testing_utils import floats_tensor, slow, torch_device -from diffusers.training_utils import EMAModel - - -torch.backends.cuda.matmul.allow_tf32 = False class SampleObject(ConfigMixin): @@ -129,892 +97,3 @@ class ConfigTester(unittest.TestCase): 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) - if isinstance(image, dict): - image = image["sample"] - - new_image = new_model(**inputs_dict) - - if isinstance(new_image, dict): - new_image = new_image["sample"] - - 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) - if isinstance(first, dict): - first = first["sample"] - - second = model(**inputs_dict) - if isinstance(second, dict): - second = second["sample"] - - 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) - - if isinstance(output, dict): - output = output["sample"] - - self.assertIsNotNone(output) - expected_shape = inputs_dict["sample"].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 = ["sample", "timestep"] - 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) - - if isinstance(output_1, dict): - output_1 = output_1["sample"] - - output_2 = new_model(**inputs_dict) - - if isinstance(output_2, dict): - output_2 = output_2["sample"] - - 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) - - if isinstance(output, dict): - output = output["sample"] - - noise = torch.randn((inputs_dict["sample"].shape[0],) + self.output_shape).to(torch_device) - loss = torch.nn.functional.mse_loss(output, noise) - loss.backward() - - def test_ema_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() - ema_model = EMAModel(model, device=torch_device) - - output = model(**inputs_dict) - - if isinstance(output, dict): - output = output["sample"] - - noise = torch.randn((inputs_dict["sample"].shape[0],) + self.output_shape).to(torch_device) - loss = torch.nn.functional.mse_loss(output, noise) - loss.backward() - ema_model.step(model) - - -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)) - - -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 diff --git a/tests/test_models_unet.py b/tests/test_models_unet.py new file mode 100644 index 0000000000..2db065d4f2 --- /dev/null +++ b/tests/test_models_unet.py @@ -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)) diff --git a/tests/test_models_vae.py b/tests/test_models_vae.py new file mode 100644 index 0000000000..e30c63a074 --- /dev/null +++ b/tests/test_models_vae.py @@ -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)) diff --git a/tests/test_models_vq.py b/tests/test_models_vq.py new file mode 100644 index 0000000000..a846c55b3b --- /dev/null +++ b/tests/test_models_vq.py @@ -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)) diff --git a/tests/test_pipelines.py b/tests/test_pipelines.py new file mode 100644 index 0000000000..e6375ce06f --- /dev/null +++ b/tests/test_pipelines.py @@ -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