From 45a09bebf38a201e92002739a63eaa4b1f608920 Mon Sep 17 00:00:00 2001 From: Patrick von Platen Date: Mon, 27 Jun 2022 10:46:39 +0200 Subject: [PATCH] add first files --- tests/test_layers_utils.py | 738 +++++++++++++++++++++++++++++++++++++ 1 file changed, 738 insertions(+) create mode 100755 tests/test_layers_utils.py diff --git a/tests/test_layers_utils.py b/tests/test_layers_utils.py new file mode 100755 index 00000000..db4ed6eb --- /dev/null +++ b/tests/test_layers_utils.py @@ -0,0 +1,738 @@ +# coding=utf-8 +# Copyright 2022 HuggingFace Inc. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import inspect +import tempfile +import unittest + +import numpy as np +import torch + +from diffusers import ( + BDDMPipeline, + DDIMPipeline, + DDIMScheduler, + DDPMPipeline, + DDPMScheduler, + GlidePipeline, + GlideSuperResUNetModel, + GlideTextToImageUNetModel, + GradTTSPipeline, + GradTTSScheduler, + LatentDiffusionPipeline, + PNDMPipeline, + PNDMScheduler, + UNetGradTTSModel, + UNetLDMModel, + UNetModel, +) +from diffusers.configuration_utils import ConfigMixin +from diffusers.pipeline_utils import DiffusionPipeline +from diffusers.pipelines.pipeline_bddm import DiffWave +from diffusers.testing_utils import floats_tensor, slow, torch_device + + +torch.backends.cuda.matmul.allow_tf32 = False + + +class ConfigTester(unittest.TestCase): + def test_load_not_from_mixin(self): + with self.assertRaises(ValueError): + ConfigMixin.from_config("dummy_path") + + def test_save_load(self): + class SampleObject(ConfigMixin): + config_name = "config.json" + + def __init__( + self, + a=2, + b=5, + c=(2, 5), + d="for diffusion", + e=[1, 3], + ): + self.register_to_config(a=a, b=b, c=c, d=d, e=e) + + obj = SampleObject() + config = obj.config + + assert config["a"] == 2 + assert config["b"] == 5 + assert config["c"] == (2, 5) + assert config["d"] == "for diffusion" + assert config["e"] == [1, 3] + + with tempfile.TemporaryDirectory() as tmpdirname: + obj.save_config(tmpdirname) + new_obj = SampleObject.from_config(tmpdirname) + new_config = new_obj.config + + # unfreeze configs + config = dict(config) + new_config = dict(new_config) + + assert config.pop("c") == (2, 5) # instantiated as tuple + assert new_config.pop("c") == [2, 5] # saved & loaded as list because of json + assert config == new_config + + +class ModelTesterMixin: + def test_from_pretrained_save_pretrained(self): + init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() + + model = self.model_class(**init_dict) + model.to(torch_device) + model.eval() + + with tempfile.TemporaryDirectory() as tmpdirname: + model.save_pretrained(tmpdirname) + new_model = self.model_class.from_pretrained(tmpdirname) + new_model.to(torch_device) + + with torch.no_grad(): + image = model(**inputs_dict) + new_image = new_model(**inputs_dict) + + max_diff = (image - new_image).abs().sum().item() + self.assertLessEqual(max_diff, 1e-5, "Models give different forward passes") + + def test_determinism(self): + init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() + model = self.model_class(**init_dict) + model.to(torch_device) + model.eval() + with torch.no_grad(): + first = model(**inputs_dict) + second = model(**inputs_dict) + + out_1 = first.cpu().numpy() + out_2 = second.cpu().numpy() + out_1 = out_1[~np.isnan(out_1)] + out_2 = out_2[~np.isnan(out_2)] + max_diff = np.amax(np.abs(out_1 - out_2)) + self.assertLessEqual(max_diff, 1e-5) + + def test_output(self): + init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() + model = self.model_class(**init_dict) + model.to(torch_device) + model.eval() + + with torch.no_grad(): + output = model(**inputs_dict) + + self.assertIsNotNone(output) + expected_shape = inputs_dict["x"].shape + self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match") + + def test_forward_signature(self): + init_dict, _ = self.prepare_init_args_and_inputs_for_common() + + model = self.model_class(**init_dict) + signature = inspect.signature(model.forward) + # signature.parameters is an OrderedDict => so arg_names order is deterministic + arg_names = [*signature.parameters.keys()] + + expected_arg_names = ["x", "timesteps"] + self.assertListEqual(arg_names[:2], expected_arg_names) + + def test_model_from_config(self): + init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() + + model = self.model_class(**init_dict) + model.to(torch_device) + model.eval() + + # test if the model can be loaded from the config + # and has all the expected shape + with tempfile.TemporaryDirectory() as tmpdirname: + model.save_config(tmpdirname) + new_model = self.model_class.from_config(tmpdirname) + new_model.to(torch_device) + new_model.eval() + + # check if all paramters shape are the same + for param_name in model.state_dict().keys(): + param_1 = model.state_dict()[param_name] + param_2 = new_model.state_dict()[param_name] + self.assertEqual(param_1.shape, param_2.shape) + + with torch.no_grad(): + output_1 = model(**inputs_dict) + output_2 = new_model(**inputs_dict) + + self.assertEqual(output_1.shape, output_2.shape) + + def test_training(self): + init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() + + model = self.model_class(**init_dict) + model.to(torch_device) + model.train() + output = model(**inputs_dict) + noise = torch.randn((inputs_dict["x"].shape[0],) + self.get_output_shape).to(torch_device) + loss = torch.nn.functional.mse_loss(output, noise) + loss.backward() + + +class UnetModelTests(ModelTesterMixin, unittest.TestCase): + model_class = UNetModel + + @property + def dummy_input(self): + batch_size = 4 + num_channels = 3 + sizes = (32, 32) + + noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device) + time_step = torch.tensor([10]).to(torch_device) + + return {"x": noise, "timesteps": time_step} + + @property + def get_input_shape(self): + return (3, 32, 32) + + @property + def get_output_shape(self): + return (3, 32, 32) + + def prepare_init_args_and_inputs_for_common(self): + init_dict = { + "ch": 32, + "ch_mult": (1, 2), + "num_res_blocks": 2, + "attn_resolutions": (16,), + "resolution": 32, + } + inputs_dict = self.dummy_input + return init_dict, inputs_dict + + def test_from_pretrained_hub(self): + model, loading_info = UNetModel.from_pretrained("fusing/ddpm_dummy", output_loading_info=True) + self.assertIsNotNone(model) + self.assertEqual(len(loading_info["missing_keys"]), 0) + + model.to(torch_device) + image = model(**self.dummy_input) + + assert image is not None, "Make sure output is not None" + + def test_output_pretrained(self): + model = UNetModel.from_pretrained("fusing/ddpm_dummy") + model.eval() + + torch.manual_seed(0) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(0) + + noise = torch.randn(1, model.config.in_channels, model.config.resolution, model.config.resolution) + time_step = torch.tensor([10]) + + with torch.no_grad(): + output = model(noise, time_step) + + output_slice = output[0, -1, -3:, -3:].flatten() + # fmt: off + expected_output_slice = torch.tensor([0.2891, -0.1899, 0.2595, -0.6214, 0.0968, -0.2622, 0.4688, 0.1311, 0.0053]) + # fmt: on + self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3)) + + +class GlideSuperResUNetTests(ModelTesterMixin, unittest.TestCase): + model_class = GlideSuperResUNetModel + + @property + def dummy_input(self): + batch_size = 4 + num_channels = 6 + sizes = (32, 32) + low_res_size = (4, 4) + + noise = torch.randn((batch_size, num_channels // 2) + sizes).to(torch_device) + low_res = torch.randn((batch_size, 3) + low_res_size).to(torch_device) + time_step = torch.tensor([10] * noise.shape[0], device=torch_device) + + return {"x": noise, "timesteps": time_step, "low_res": low_res} + + @property + def get_input_shape(self): + return (3, 32, 32) + + @property + def get_output_shape(self): + return (6, 32, 32) + + def prepare_init_args_and_inputs_for_common(self): + init_dict = { + "attention_resolutions": (2,), + "channel_mult": (1, 2), + "in_channels": 6, + "out_channels": 6, + "model_channels": 32, + "num_head_channels": 8, + "num_heads_upsample": 1, + "num_res_blocks": 2, + "resblock_updown": True, + "resolution": 32, + "use_scale_shift_norm": True, + } + inputs_dict = self.dummy_input + return init_dict, inputs_dict + + def test_output(self): + init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() + model = self.model_class(**init_dict) + model.to(torch_device) + model.eval() + + with torch.no_grad(): + output = model(**inputs_dict) + + output, _ = torch.split(output, 3, dim=1) + + self.assertIsNotNone(output) + expected_shape = inputs_dict["x"].shape + self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match") + + def test_from_pretrained_hub(self): + model, loading_info = GlideSuperResUNetModel.from_pretrained( + "fusing/glide-super-res-dummy", output_loading_info=True + ) + self.assertIsNotNone(model) + self.assertEqual(len(loading_info["missing_keys"]), 0) + + model.to(torch_device) + image = model(**self.dummy_input) + + assert image is not None, "Make sure output is not None" + + def test_output_pretrained(self): + model = GlideSuperResUNetModel.from_pretrained("fusing/glide-super-res-dummy") + + torch.manual_seed(0) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(0) + + noise = torch.randn(1, 3, 64, 64) + low_res = torch.randn(1, 3, 4, 4) + time_step = torch.tensor([42] * noise.shape[0]) + + with torch.no_grad(): + output = model(noise, time_step, low_res) + + output, _ = torch.split(output, 3, dim=1) + output_slice = output[0, -1, -3:, -3:].flatten() + # fmt: off + expected_output_slice = torch.tensor([-22.8782, -23.2652, -15.3966, -22.8034, -23.3159, -15.5640, -15.3970, -15.4614, - 10.4370]) + # fmt: on + self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3)) + + +class GlideTextToImageUNetModelTests(ModelTesterMixin, unittest.TestCase): + model_class = GlideTextToImageUNetModel + + @property + def dummy_input(self): + batch_size = 4 + num_channels = 3 + sizes = (32, 32) + transformer_dim = 32 + seq_len = 16 + + noise = torch.randn((batch_size, num_channels) + sizes).to(torch_device) + emb = torch.randn((batch_size, seq_len, transformer_dim)).to(torch_device) + time_step = torch.tensor([10] * noise.shape[0], device=torch_device) + + return {"x": noise, "timesteps": time_step, "transformer_out": emb} + + @property + def get_input_shape(self): + return (3, 32, 32) + + @property + def get_output_shape(self): + return (6, 32, 32) + + def prepare_init_args_and_inputs_for_common(self): + init_dict = { + "attention_resolutions": (2,), + "channel_mult": (1, 2), + "in_channels": 3, + "out_channels": 6, + "model_channels": 32, + "num_head_channels": 8, + "num_heads_upsample": 1, + "num_res_blocks": 2, + "resblock_updown": True, + "resolution": 32, + "use_scale_shift_norm": True, + "transformer_dim": 32, + } + inputs_dict = self.dummy_input + return init_dict, inputs_dict + + def test_output(self): + init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() + model = self.model_class(**init_dict) + model.to(torch_device) + model.eval() + + with torch.no_grad(): + output = model(**inputs_dict) + + output, _ = torch.split(output, 3, dim=1) + + self.assertIsNotNone(output) + expected_shape = inputs_dict["x"].shape + self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match") + + def test_from_pretrained_hub(self): + model, loading_info = GlideTextToImageUNetModel.from_pretrained( + "fusing/unet-glide-text2im-dummy", output_loading_info=True + ) + self.assertIsNotNone(model) + self.assertEqual(len(loading_info["missing_keys"]), 0) + + model.to(torch_device) + image = model(**self.dummy_input) + + assert image is not None, "Make sure output is not None" + + def test_output_pretrained(self): + model = GlideTextToImageUNetModel.from_pretrained("fusing/unet-glide-text2im-dummy") + + torch.manual_seed(0) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(0) + + noise = torch.randn((1, model.config.in_channels, model.config.resolution, model.config.resolution)).to( + torch_device + ) + emb = torch.randn((1, 16, model.config.transformer_dim)).to(torch_device) + time_step = torch.tensor([10] * noise.shape[0], device=torch_device) + + with torch.no_grad(): + output = model(noise, time_step, emb) + + output, _ = torch.split(output, 3, dim=1) + output_slice = output[0, -1, -3:, -3:].flatten() + # fmt: off + expected_output_slice = torch.tensor([2.7766, -10.3558, -14.9149, -0.9376, -14.9175, -17.7679, -5.5565, -12.9521, -12.9845]) + # fmt: on + self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3)) + + +class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase): + model_class = UNetLDMModel + + @property + def dummy_input(self): + batch_size = 4 + num_channels = 4 + sizes = (32, 32) + + noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device) + time_step = torch.tensor([10]).to(torch_device) + + return {"x": noise, "timesteps": time_step} + + @property + def get_input_shape(self): + return (4, 32, 32) + + @property + def get_output_shape(self): + return (4, 32, 32) + + def prepare_init_args_and_inputs_for_common(self): + init_dict = { + "image_size": 32, + "in_channels": 4, + "out_channels": 4, + "model_channels": 32, + "num_res_blocks": 2, + "attention_resolutions": (16,), + "channel_mult": (1, 2), + "num_heads": 2, + "conv_resample": True, + } + inputs_dict = self.dummy_input + return init_dict, inputs_dict + + def test_from_pretrained_hub(self): + model, loading_info = UNetLDMModel.from_pretrained("fusing/unet-ldm-dummy", output_loading_info=True) + self.assertIsNotNone(model) + self.assertEqual(len(loading_info["missing_keys"]), 0) + + model.to(torch_device) + image = model(**self.dummy_input) + + assert image is not None, "Make sure output is not None" + + def test_output_pretrained(self): + model = UNetLDMModel.from_pretrained("fusing/unet-ldm-dummy") + model.eval() + + torch.manual_seed(0) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(0) + + noise = torch.randn(1, model.config.in_channels, model.config.image_size, model.config.image_size) + time_step = torch.tensor([10] * noise.shape[0]) + + with torch.no_grad(): + output = model(noise, time_step) + + output_slice = output[0, -1, -3:, -3:].flatten() + # fmt: off + expected_output_slice = torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800]) + # fmt: on + + self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3)) + + +class UNetGradTTSModelTests(ModelTesterMixin, unittest.TestCase): + model_class = UNetGradTTSModel + + @property + def dummy_input(self): + batch_size = 4 + num_features = 32 + seq_len = 16 + + noise = floats_tensor((batch_size, num_features, seq_len)).to(torch_device) + condition = floats_tensor((batch_size, num_features, seq_len)).to(torch_device) + mask = floats_tensor((batch_size, 1, seq_len)).to(torch_device) + time_step = torch.tensor([10] * batch_size).to(torch_device) + + return {"x": noise, "timesteps": time_step, "mu": condition, "mask": mask} + + @property + def get_input_shape(self): + return (4, 32, 16) + + @property + def get_output_shape(self): + return (4, 32, 16) + + def prepare_init_args_and_inputs_for_common(self): + init_dict = { + "dim": 64, + "groups": 4, + "dim_mults": (1, 2), + "n_feats": 32, + "pe_scale": 1000, + "n_spks": 1, + } + inputs_dict = self.dummy_input + return init_dict, inputs_dict + + def test_from_pretrained_hub(self): + model, loading_info = UNetGradTTSModel.from_pretrained("fusing/unet-grad-tts-dummy", output_loading_info=True) + self.assertIsNotNone(model) + self.assertEqual(len(loading_info["missing_keys"]), 0) + + model.to(torch_device) + image = model(**self.dummy_input) + + assert image is not None, "Make sure output is not None" + + def test_output_pretrained(self): + model = UNetGradTTSModel.from_pretrained("fusing/unet-grad-tts-dummy") + model.eval() + + torch.manual_seed(0) + if torch.cuda.is_available(): + torch.cuda.manual_seed_all(0) + + num_features = model.config.n_feats + seq_len = 16 + noise = torch.randn((1, num_features, seq_len)) + condition = torch.randn((1, num_features, seq_len)) + mask = torch.randn((1, 1, seq_len)) + time_step = torch.tensor([10]) + + with torch.no_grad(): + output = model(noise, time_step, condition, mask) + + output_slice = output[0, -3:, -3:].flatten() + # fmt: off + expected_output_slice = torch.tensor([-0.0690, -0.0531, 0.0633, -0.0660, -0.0541, 0.0650, -0.0656, -0.0555, 0.0617]) + # fmt: on + + self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3)) + + +class PipelineTesterMixin(unittest.TestCase): + def test_from_pretrained_save_pretrained(self): + # 1. Load models + model = UNetModel(ch=32, ch_mult=(1, 2), num_res_blocks=2, attn_resolutions=(16,), resolution=32) + schedular = DDPMScheduler(timesteps=10) + + ddpm = DDPMPipeline(model, schedular) + + with tempfile.TemporaryDirectory() as tmpdirname: + ddpm.save_pretrained(tmpdirname) + new_ddpm = DDPMPipeline.from_pretrained(tmpdirname) + + generator = torch.manual_seed(0) + + image = ddpm(generator=generator) + generator = generator.manual_seed(0) + new_image = new_ddpm(generator=generator) + + assert (image - new_image).abs().sum() < 1e-5, "Models don't give the same forward pass" + + @slow + def test_from_pretrained_hub(self): + model_path = "fusing/ddpm-cifar10" + + ddpm = DDPMPipeline.from_pretrained(model_path) + ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path) + + ddpm.noise_scheduler.num_timesteps = 10 + ddpm_from_hub.noise_scheduler.num_timesteps = 10 + + generator = torch.manual_seed(0) + + image = ddpm(generator=generator) + generator = generator.manual_seed(0) + new_image = ddpm_from_hub(generator=generator) + + assert (image - new_image).abs().sum() < 1e-5, "Models don't give the same forward pass" + + @slow + def test_ddpm_cifar10(self): + generator = torch.manual_seed(0) + model_id = "fusing/ddpm-cifar10" + + unet = UNetModel.from_pretrained(model_id) + noise_scheduler = DDPMScheduler.from_config(model_id) + noise_scheduler = noise_scheduler.set_format("pt") + + ddpm = DDPMPipeline(unet=unet, noise_scheduler=noise_scheduler) + image = ddpm(generator=generator) + + image_slice = image[0, -1, -3:, -3:].cpu() + + assert image.shape == (1, 3, 32, 32) + expected_slice = torch.tensor([0.2250, 0.3375, 0.2360, 0.0930, 0.3440, 0.3156, 0.1937, 0.3585, 0.1761]) + assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2 + + @slow + def test_ddim_cifar10(self): + generator = torch.manual_seed(0) + model_id = "fusing/ddpm-cifar10" + + unet = UNetModel.from_pretrained(model_id) + noise_scheduler = DDIMScheduler(tensor_format="pt") + + ddim = DDIMPipeline(unet=unet, noise_scheduler=noise_scheduler) + image = ddim(generator=generator, eta=0.0) + + image_slice = image[0, -1, -3:, -3:].cpu() + + assert image.shape == (1, 3, 32, 32) + expected_slice = torch.tensor( + [-0.7383, -0.7385, -0.7298, -0.7364, -0.7414, -0.7239, -0.6737, -0.6813, -0.7068] + ) + assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2 + + @slow + def test_pndm_cifar10(self): + generator = torch.manual_seed(0) + model_id = "fusing/ddpm-cifar10" + + unet = UNetModel.from_pretrained(model_id) + noise_scheduler = PNDMScheduler(tensor_format="pt") + + pndm = PNDMPipeline(unet=unet, noise_scheduler=noise_scheduler) + image = pndm(generator=generator) + + image_slice = image[0, -1, -3:, -3:].cpu() + + assert image.shape == (1, 3, 32, 32) + expected_slice = torch.tensor( + [-0.7888, -0.7870, -0.7759, -0.7823, -0.8014, -0.7608, -0.6818, -0.7130, -0.7471] + ) + assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2 + + @slow + def test_ldm_text2img(self): + model_id = "fusing/latent-diffusion-text2im-large" + ldm = LatentDiffusionPipeline.from_pretrained(model_id) + + prompt = "A painting of a squirrel eating a burger" + generator = torch.manual_seed(0) + image = ldm([prompt], generator=generator, num_inference_steps=20) + + image_slice = image[0, -1, -3:, -3:].cpu() + + assert image.shape == (1, 3, 256, 256) + expected_slice = torch.tensor([0.7295, 0.7358, 0.7256, 0.7435, 0.7095, 0.6884, 0.7325, 0.6921, 0.6458]) + assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2 + + @slow + def test_glide_text2img(self): + model_id = "fusing/glide-base" + glide = GlidePipeline.from_pretrained(model_id) + + prompt = "a pencil sketch of a corgi" + generator = torch.manual_seed(0) + image = glide(prompt, generator=generator, num_inference_steps_upscale=20) + + image_slice = image[0, :3, :3, -1].cpu() + + assert image.shape == (1, 256, 256, 3) + expected_slice = torch.tensor([0.7119, 0.7073, 0.6460, 0.7780, 0.7423, 0.6926, 0.7378, 0.7189, 0.7784]) + assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2 + + @slow + def test_grad_tts(self): + model_id = "fusing/grad-tts-libri-tts" + grad_tts = GradTTSPipeline.from_pretrained(model_id) + noise_scheduler = GradTTSScheduler() + grad_tts.noise_scheduler = noise_scheduler + + text = "Hello world, I missed you so much." + generator = torch.manual_seed(0) + + # generate mel spectograms using text + mel_spec = grad_tts(text, generator=generator) + + assert mel_spec.shape == (1, 80, 143) + expected_slice = torch.tensor( + [-6.7584, -6.8347, -6.3293, -6.6437, -6.7233, -6.4684, -6.1187, -6.3172, -6.6890] + ) + assert (mel_spec[0, :3, :3].cpu().flatten() - expected_slice).abs().max() < 1e-2 + + def test_module_from_pipeline(self): + model = DiffWave(num_res_layers=4) + noise_scheduler = DDPMScheduler(timesteps=12) + + bddm = BDDMPipeline(model, noise_scheduler) + + # check if the library name for the diffwave moduel is set to pipeline module + self.assertTrue(bddm.config["diffwave"][0] == "pipeline_bddm") + + # check if we can save and load the pipeline + with tempfile.TemporaryDirectory() as tmpdirname: + bddm.save_pretrained(tmpdirname) + _ = BDDMPipeline.from_pretrained(tmpdirname) + # check if the same works using the DifusionPipeline class + _ = DiffusionPipeline.from_pretrained(tmpdirname)