# coding=utf-8 # Copyright 2022 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import inspect import tempfile import unittest import numpy as np import torch from diffusers import ( AutoencoderKL, BDDMPipeline, DDIMPipeline, DDIMScheduler, DDPMPipeline, DDPMScheduler, GlidePipeline, GlideSuperResUNetModel, GlideTextToImageUNetModel, GradTTSPipeline, GradTTSScheduler, LatentDiffusionPipeline, LatentDiffusionUncondPipeline, NCSNpp, PNDMPipeline, PNDMScheduler, ScoreSdeVePipeline, ScoreSdeVeScheduler, ScoreSdeVpPipeline, ScoreSdeVpScheduler, TemporalUNet, UNetGradTTSModel, UNetLDMModel, UNetModel, UNetUnconditionalModel, VQModel, ) from diffusers.configuration_utils import ConfigMixin from diffusers.pipeline_utils import DiffusionPipeline from diffusers.pipelines.bddm.pipeline_bddm import DiffWave from diffusers.testing_utils import floats_tensor, slow, torch_device from diffusers.training_utils import EMAModel torch.backends.cuda.matmul.allow_tf32 = False class ConfigTester(unittest.TestCase): def test_load_not_from_mixin(self): with self.assertRaises(ValueError): ConfigMixin.from_config("dummy_path") def test_save_load(self): class SampleObject(ConfigMixin): config_name = "config.json" def __init__( self, a=2, b=5, c=(2, 5), d="for diffusion", e=[1, 3], ): self.register_to_config(a=a, b=b, c=c, d=d, e=e) obj = SampleObject() config = obj.config assert config["a"] == 2 assert config["b"] == 5 assert config["c"] == (2, 5) assert config["d"] == "for diffusion" assert config["e"] == [1, 3] with tempfile.TemporaryDirectory() as tmpdirname: obj.save_config(tmpdirname) new_obj = SampleObject.from_config(tmpdirname) new_config = new_obj.config # unfreeze configs config = dict(config) new_config = dict(new_config) assert config.pop("c") == (2, 5) # instantiated as tuple assert new_config.pop("c") == [2, 5] # saved & loaded as list because of json assert config == new_config class ModelTesterMixin: def test_from_pretrained_save_pretrained(self): init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() model = self.model_class(**init_dict) model.to(torch_device) model.eval() with tempfile.TemporaryDirectory() as tmpdirname: model.save_pretrained(tmpdirname) new_model = self.model_class.from_pretrained(tmpdirname) new_model.to(torch_device) with torch.no_grad(): image = model(**inputs_dict) new_image = new_model(**inputs_dict) max_diff = (image - new_image).abs().sum().item() self.assertLessEqual(max_diff, 5e-5, "Models give different forward passes") def test_determinism(self): init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() model = self.model_class(**init_dict) model.to(torch_device) model.eval() with torch.no_grad(): first = model(**inputs_dict) second = model(**inputs_dict) out_1 = first.cpu().numpy() out_2 = second.cpu().numpy() out_1 = out_1[~np.isnan(out_1)] out_2 = out_2[~np.isnan(out_2)] max_diff = np.amax(np.abs(out_1 - out_2)) self.assertLessEqual(max_diff, 1e-5) def test_output(self): init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() model = self.model_class(**init_dict) model.to(torch_device) model.eval() with torch.no_grad(): output = model(**inputs_dict) self.assertIsNotNone(output) expected_shape = inputs_dict["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", "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["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) 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 = 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 {"sample": noise, "timesteps": time_step} @property def input_shape(self): return (3, 32, 32) @property def output_shape(self): return (3, 32, 32) def prepare_init_args_and_inputs_for_common(self): init_dict = { "ch": 32, "ch_mult": (1, 2), "num_res_blocks": 2, "attn_resolutions": (16,), "resolution": 32, } inputs_dict = self.dummy_input return init_dict, inputs_dict def test_from_pretrained_hub(self): model, loading_info = UNetModel.from_pretrained("fusing/ddpm_dummy", output_loading_info=True) self.assertIsNotNone(model) self.assertEqual(len(loading_info["missing_keys"]), 0) model.to(torch_device) image = model(**self.dummy_input) assert image is not None, "Make sure output is not None" def test_output_pretrained(self): model = UNetModel.from_pretrained("fusing/ddpm_dummy") model.eval() torch.manual_seed(0) if torch.cuda.is_available(): torch.cuda.manual_seed_all(0) noise = torch.randn(1, model.config.in_channels, model.config.resolution, model.config.resolution) time_step = torch.tensor([10]) with torch.no_grad(): output = model(noise, time_step) output_slice = output[0, -1, -3:, -3:].flatten() # fmt: off expected_output_slice = torch.tensor([0.2891, -0.1899, 0.2595, -0.6214, 0.0968, -0.2622, 0.4688, 0.1311, 0.0053]) # fmt: on self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2)) class GlideSuperResUNetTests(ModelTesterMixin, unittest.TestCase): model_class = GlideSuperResUNetModel @property def dummy_input(self): batch_size = 4 num_channels = 6 sizes = (32, 32) low_res_size = (4, 4) noise = torch.randn((batch_size, num_channels // 2) + sizes).to(torch_device) low_res = torch.randn((batch_size, 3) + low_res_size).to(torch_device) time_step = torch.tensor([10] * noise.shape[0], device=torch_device) return {"sample": noise, "timesteps": time_step, "low_res": low_res} @property def input_shape(self): return (3, 32, 32) @property def output_shape(self): return (6, 32, 32) def prepare_init_args_and_inputs_for_common(self): init_dict = { "attention_resolutions": (2,), "channel_mult": (1, 2), "in_channels": 6, "out_channels": 6, "model_channels": 32, "num_head_channels": 8, "num_heads_upsample": 1, "num_res_blocks": 2, "resblock_updown": True, "resolution": 32, "use_scale_shift_norm": True, } inputs_dict = self.dummy_input return init_dict, inputs_dict def test_output(self): init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() model = self.model_class(**init_dict) model.to(torch_device) model.eval() with torch.no_grad(): output = model(**inputs_dict) output, _ = torch.split(output, 3, dim=1) self.assertIsNotNone(output) expected_shape = inputs_dict["sample"].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 {"sample": noise, "timesteps": time_step, "transformer_out": emb} @property def input_shape(self): return (3, 32, 32) @property def output_shape(self): return (6, 32, 32) def prepare_init_args_and_inputs_for_common(self): init_dict = { "attention_resolutions": (2,), "channel_mult": (1, 2), "in_channels": 3, "out_channels": 6, "model_channels": 32, "num_head_channels": 8, "num_heads_upsample": 1, "num_res_blocks": 2, "resblock_updown": True, "resolution": 32, "use_scale_shift_norm": True, "transformer_dim": 32, } inputs_dict = self.dummy_input return init_dict, inputs_dict def test_output(self): init_dict, inputs_dict = self.prepare_init_args_and_inputs_for_common() model = self.model_class(**init_dict) model.to(torch_device) model.eval() with torch.no_grad(): output = model(**inputs_dict) output, _ = torch.split(output, 3, dim=1) self.assertIsNotNone(output) expected_shape = inputs_dict["sample"].shape self.assertEqual(output.shape, expected_shape, "Input and output shapes do not match") def test_from_pretrained_hub(self): model, loading_info = GlideTextToImageUNetModel.from_pretrained( "fusing/unet-glide-text2im-dummy", output_loading_info=True ) self.assertIsNotNone(model) self.assertEqual(len(loading_info["missing_keys"]), 0) model.to(torch_device) image = model(**self.dummy_input) assert image is not None, "Make sure output is not None" def test_output_pretrained(self): model = GlideTextToImageUNetModel.from_pretrained("fusing/unet-glide-text2im-dummy") torch.manual_seed(0) if torch.cuda.is_available(): torch.cuda.manual_seed_all(0) noise = torch.randn((1, model.config.in_channels, model.config.resolution, model.config.resolution)).to( torch_device ) emb = torch.randn((1, 16, model.config.transformer_dim)).to(torch_device) time_step = torch.tensor([10] * noise.shape[0], device=torch_device) model.to(torch_device) with torch.no_grad(): output = model(noise, time_step, emb) output, _ = torch.split(output, 3, dim=1) output_slice = output[0, -1, -3:, -3:].cpu().flatten() # fmt: off expected_output_slice = torch.tensor([2.7766, -10.3558, -14.9149, -0.9376, -14.9175, -17.7679, -5.5565, -12.9521, -12.9845]) # fmt: on self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3)) class UNetLDMModelTests(ModelTesterMixin, unittest.TestCase): model_class = UNetUnconditionalModel @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, "timesteps": time_step} @property def input_shape(self): return (4, 32, 32) @property def output_shape(self): return (4, 32, 32) def prepare_init_args_and_inputs_for_common(self): init_dict = { "image_size": 32, "in_channels": 4, "out_channels": 4, "num_res_blocks": 2, "attention_resolutions": (16,), "resnet_input_channels": [32, 32], "resnet_output_channels": [32, 64], "num_head_channels": 32, "conv_resample": True, } inputs_dict = self.dummy_input return init_dict, inputs_dict def test_from_pretrained_hub(self): model, loading_info = UNetUnconditionalModel.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 = UNetUnconditionalModel.from_pretrained("fusing/unet-ldm-dummy") model.eval() torch.manual_seed(0) if torch.cuda.is_available(): torch.cuda.manual_seed_all(0) noise = torch.randn(1, model.config.in_channels, model.config.image_size, model.config.image_size) time_step = torch.tensor([10] * noise.shape[0]) with torch.no_grad(): output = model(noise, time_step) output_slice = output[0, -1, -3:, -3:].flatten() # fmt: off expected_output_slice = torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800]) # fmt: on self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3)) def test_output_pretrained_spatial_transformer(self): model = UNetLDMModel.from_pretrained("fusing/unet-ldm-dummy-spatial") model.eval() torch.manual_seed(0) if torch.cuda.is_available(): torch.cuda.manual_seed_all(0) noise = torch.randn(1, model.config.in_channels, model.config.image_size, model.config.image_size) context = torch.ones((1, 16, 64), dtype=torch.float32) time_step = torch.tensor([10] * noise.shape[0]) with torch.no_grad(): output = model(noise, time_step, context=context) output_slice = output[0, -1, -3:, -3:].flatten() # fmt: off expected_output_slice = torch.tensor([61.3445, 56.9005, 29.4339, 59.5497, 60.7375, 34.1719, 48.1951, 42.6569, 25.0890]) # fmt: on self.assertTrue(torch.allclose(output_slice, expected_output_slice, atol=1e-3)) class UNetGradTTSModelTests(ModelTesterMixin, unittest.TestCase): model_class = UNetGradTTSModel @property def dummy_input(self): batch_size = 4 num_features = 32 seq_len = 16 noise = floats_tensor((batch_size, num_features, seq_len)).to(torch_device) condition = floats_tensor((batch_size, num_features, seq_len)).to(torch_device) mask = floats_tensor((batch_size, 1, seq_len)).to(torch_device) time_step = torch.tensor([10] * batch_size).to(torch_device) return {"sample": noise, "timesteps": time_step, "mu": condition, "mask": mask} @property def input_shape(self): return (4, 32, 16) @property def output_shape(self): return (4, 32, 16) def prepare_init_args_and_inputs_for_common(self): init_dict = { "dim": 64, "groups": 4, "dim_mults": (1, 2), "n_feats": 32, "pe_scale": 1000, "n_spks": 1, } inputs_dict = self.dummy_input return init_dict, inputs_dict def test_from_pretrained_hub(self): model, loading_info = UNetGradTTSModel.from_pretrained("fusing/unet-grad-tts-dummy", output_loading_info=True) self.assertIsNotNone(model) self.assertEqual(len(loading_info["missing_keys"]), 0) model.to(torch_device) image = model(**self.dummy_input) assert image is not None, "Make sure output is not None" def test_output_pretrained(self): model = UNetGradTTSModel.from_pretrained("fusing/unet-grad-tts-dummy") model.eval() torch.manual_seed(0) if torch.cuda.is_available(): torch.cuda.manual_seed_all(0) num_features = model.config.n_feats seq_len = 16 noise = torch.randn((1, num_features, seq_len)) condition = torch.randn((1, num_features, seq_len)) mask = torch.randn((1, 1, seq_len)) time_step = torch.tensor([10]) with torch.no_grad(): output = model(noise, time_step, condition, mask) output_slice = output[0, -3:, -3:].flatten() # fmt: off expected_output_slice = torch.tensor([-0.0690, -0.0531, 0.0633, -0.0660, -0.0541, 0.0650, -0.0656, -0.0555, 0.0617]) # fmt: on self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3)) class TemporalUNetModelTests(ModelTesterMixin, unittest.TestCase): model_class = TemporalUNet @property def dummy_input(self): batch_size = 4 num_features = 14 seq_len = 16 noise = floats_tensor((batch_size, seq_len, num_features)).to(torch_device) time_step = torch.tensor([10] * batch_size).to(torch_device) return {"sample": noise, "timesteps": time_step} @property def input_shape(self): return (4, 16, 14) @property def output_shape(self): return (4, 16, 14) def prepare_init_args_and_inputs_for_common(self): init_dict = { "training_horizon": 128, "dim": 32, "dim_mults": [1, 4, 8], "predict_epsilon": False, "clip_denoised": True, "transition_dim": 14, "cond_dim": 3, } inputs_dict = self.dummy_input return init_dict, inputs_dict def test_from_pretrained_hub(self): model, loading_info = TemporalUNet.from_pretrained( "fusing/ddpm-unet-rl-hopper-hor128", output_loading_info=True ) self.assertIsNotNone(model) self.assertEqual(len(loading_info["missing_keys"]), 0) model.to(torch_device) image = model(**self.dummy_input) assert image is not None, "Make sure output is not None" def test_output_pretrained(self): model = TemporalUNet.from_pretrained("fusing/ddpm-unet-rl-hopper-hor128") model.eval() torch.manual_seed(0) if torch.cuda.is_available(): torch.cuda.manual_seed_all(0) num_features = model.transition_dim seq_len = 16 noise = torch.randn((1, seq_len, num_features)) time_step = torch.full((num_features,), 0) with torch.no_grad(): output = model(noise, time_step) output_slice = output[0, -3:, -3:].flatten() # fmt: off expected_output_slice = torch.tensor([-0.2714, 0.1042, -0.0794, -0.2820, 0.0803, -0.0811, -0.2345, 0.0580, -0.0584]) # fmt: on self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-3)) class NCSNppModelTests(ModelTesterMixin, unittest.TestCase): model_class = NCSNpp @property def dummy_input(self): batch_size = 4 num_channels = 3 sizes = (32, 32) noise = floats_tensor((batch_size, num_channels) + sizes).to(torch_device) time_step = torch.tensor(batch_size * [10]).to(torch_device) return {"sample": noise, "timesteps": time_step} @property def input_shape(self): return (3, 32, 32) @property def output_shape(self): return (3, 32, 32) def prepare_init_args_and_inputs_for_common(self): init_dict = { "image_size": 32, "ch_mult": [1, 2, 2, 2], "nf": 32, "fir": True, "progressive": "output_skip", "progressive_combine": "sum", "progressive_input": "input_skip", "scale_by_sigma": True, "skip_rescale": True, "embedding_type": "fourier", } inputs_dict = self.dummy_input return init_dict, inputs_dict def test_from_pretrained_hub(self): model, loading_info = NCSNpp.from_pretrained("fusing/cifar10-ncsnpp-ve", output_loading_info=True) self.assertIsNotNone(model) self.assertEqual(len(loading_info["missing_keys"]), 0) model.to(torch_device) image = model(**self.dummy_input) assert image is not None, "Make sure output is not None" def test_output_pretrained_ve_small(self): model = NCSNpp.from_pretrained("fusing/ncsnpp-cifar10-ve-dummy") model.eval() model.to(torch_device) torch.manual_seed(0) if torch.cuda.is_available(): torch.cuda.manual_seed_all(0) batch_size = 4 num_channels = 3 sizes = (32, 32) noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device) time_step = torch.tensor(batch_size * [1e-4]).to(torch_device) with torch.no_grad(): output = model(noise, time_step) output_slice = output[0, -3:, -3:, -1].flatten().cpu() # fmt: off expected_output_slice = torch.tensor([0.1315, 0.0741, 0.0393, 0.0455, 0.0556, 0.0180, -0.0832, -0.0644, -0.0856]) # fmt: on self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2)) def test_output_pretrained_ve_large(self): model = NCSNpp.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy") model.eval() model.to(torch_device) torch.manual_seed(0) if torch.cuda.is_available(): torch.cuda.manual_seed_all(0) batch_size = 4 num_channels = 3 sizes = (32, 32) noise = torch.ones((batch_size, num_channels) + sizes).to(torch_device) time_step = torch.tensor(batch_size * [1e-4]).to(torch_device) with torch.no_grad(): output = model(noise, time_step) output_slice = output[0, -3:, -3:, -1].flatten().cpu() # fmt: off expected_output_slice = torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256]) # fmt: on self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2)) def test_output_pretrained_vp(self): model = NCSNpp.from_pretrained("fusing/cifar10-ddpmpp-vp") model.eval() model.to(torch_device) torch.manual_seed(0) if torch.cuda.is_available(): torch.cuda.manual_seed_all(0) batch_size = 4 num_channels = 3 sizes = (32, 32) noise = torch.randn((batch_size, num_channels) + sizes).to(torch_device) time_step = torch.tensor(batch_size * [9.0]).to(torch_device) with torch.no_grad(): output = model(noise, time_step) output_slice = output[0, -3:, -3:, -1].flatten().cpu() # fmt: off expected_output_slice = torch.tensor([0.3303, -0.2275, -2.8872, -0.1309, -1.2861, 3.4567, -1.0083, 2.5325, -1.3866]) # fmt: on self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2)) class VQModelTests(ModelTesterMixin, unittest.TestCase): model_class = VQModel @property def dummy_input(self): batch_size = 4 num_channels = 3 sizes = (32, 32) image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device) return {"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 = { "ch": 64, "out_ch": 3, "num_res_blocks": 1, "attn_resolutions": [], "in_channels": 3, "resolution": 32, "z_channels": 3, "n_embed": 256, "embed_dim": 3, "sane_index_shape": False, "ch_mult": (1,), "dropout": 0.0, "double_z": False, } inputs_dict = self.dummy_input return init_dict, inputs_dict def test_forward_signature(self): pass def test_training(self): pass def test_from_pretrained_hub(self): model, loading_info = VQModel.from_pretrained("fusing/vqgan-dummy", output_loading_info=True) self.assertIsNotNone(model) self.assertEqual(len(loading_info["missing_keys"]), 0) model.to(torch_device) image = model(**self.dummy_input) assert image is not None, "Make sure output is not None" def test_output_pretrained(self): model = VQModel.from_pretrained("fusing/vqgan-dummy") model.eval() torch.manual_seed(0) if torch.cuda.is_available(): torch.cuda.manual_seed_all(0) image = torch.randn(1, model.config.in_channels, model.config.resolution, model.config.resolution) with torch.no_grad(): output = model(image) output_slice = output[0, -1, -3:, -3:].flatten() # fmt: off expected_output_slice = torch.tensor([-1.1321, 0.1056, 0.3505, -0.6461, -0.2014, 0.0419, -0.5763, -0.8462, -0.4218]) # fmt: on self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2)) class AutoEncoderKLTests(ModelTesterMixin, unittest.TestCase): model_class = AutoencoderKL @property def dummy_input(self): batch_size = 4 num_channels = 3 sizes = (32, 32) image = floats_tensor((batch_size, num_channels) + sizes).to(torch_device) return {"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 = { "ch": 64, "ch_mult": (1,), "embed_dim": 4, "in_channels": 3, "num_res_blocks": 1, "out_ch": 3, "resolution": 32, "z_channels": 4, "attn_resolutions": [], } inputs_dict = self.dummy_input return init_dict, inputs_dict def test_forward_signature(self): pass def test_training(self): pass def test_from_pretrained_hub(self): model, loading_info = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy", output_loading_info=True) self.assertIsNotNone(model) self.assertEqual(len(loading_info["missing_keys"]), 0) model.to(torch_device) image = model(**self.dummy_input) assert image is not None, "Make sure output is not None" def test_output_pretrained(self): model = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy") model.eval() torch.manual_seed(0) if torch.cuda.is_available(): torch.cuda.manual_seed_all(0) image = torch.randn(1, model.config.in_channels, model.config.resolution, model.config.resolution) with torch.no_grad(): output = model(image, sample_posterior=True) output_slice = output[0, -1, -3:, -3:].flatten() # fmt: off expected_output_slice = torch.tensor([-0.0814, -0.0229, -0.1320, -0.4123, -0.0366, -0.3473, 0.0438, -0.1662, 0.1750]) # fmt: on self.assertTrue(torch.allclose(output_slice, expected_output_slice, rtol=1e-2)) class PipelineTesterMixin(unittest.TestCase): def test_from_pretrained_save_pretrained(self): # 1. Load models model = UNetModel(ch=32, ch_mult=(1, 2), num_res_blocks=2, attn_resolutions=(16,), resolution=32) schedular = DDPMScheduler(timesteps=10) ddpm = DDPMPipeline(model, schedular) with tempfile.TemporaryDirectory() as tmpdirname: ddpm.save_pretrained(tmpdirname) new_ddpm = DDPMPipeline.from_pretrained(tmpdirname) generator = torch.manual_seed(0) image = ddpm(generator=generator) generator = generator.manual_seed(0) new_image = new_ddpm(generator=generator) assert (image - new_image).abs().sum() < 1e-5, "Models don't give the same forward pass" @slow def test_from_pretrained_hub(self): model_path = "fusing/ddpm-cifar10" ddpm = DDPMPipeline.from_pretrained(model_path) ddpm_from_hub = DiffusionPipeline.from_pretrained(model_path) ddpm.noise_scheduler.num_timesteps = 10 ddpm_from_hub.noise_scheduler.num_timesteps = 10 generator = torch.manual_seed(0) image = ddpm(generator=generator) generator = generator.manual_seed(0) new_image = ddpm_from_hub(generator=generator) assert (image - new_image).abs().sum() < 1e-5, "Models don't give the same forward pass" @slow def test_ddpm_cifar10(self): model_id = "fusing/ddpm-cifar10" unet = UNetModel.from_pretrained(model_id) noise_scheduler = DDPMScheduler.from_config(model_id) noise_scheduler = noise_scheduler.set_format("pt") ddpm = DDPMPipeline(unet=unet, noise_scheduler=noise_scheduler) generator = torch.manual_seed(0) image = ddpm(generator=generator) image_slice = image[0, -1, -3:, -3:].cpu() assert image.shape == (1, 3, 32, 32) expected_slice = torch.tensor( [-0.5712, -0.6215, -0.5953, -0.5438, -0.4775, -0.4539, -0.5172, -0.4872, -0.5105] ) assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2 @slow def test_ddim_cifar10(self): model_id = "fusing/ddpm-cifar10" unet = UNetModel.from_pretrained(model_id) noise_scheduler = DDIMScheduler(tensor_format="pt") ddim = DDIMPipeline(unet=unet, noise_scheduler=noise_scheduler) generator = torch.manual_seed(0) image = ddim(generator=generator, eta=0.0) image_slice = image[0, -1, -3:, -3:].cpu() assert image.shape == (1, 3, 32, 32) expected_slice = torch.tensor( [-0.6553, -0.6765, -0.6799, -0.6749, -0.7006, -0.6974, -0.6991, -0.7116, -0.7094] ) assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2 @slow def test_pndm_cifar10(self): model_id = "fusing/ddpm-cifar10" unet = UNetModel.from_pretrained(model_id) noise_scheduler = PNDMScheduler(tensor_format="pt") pndm = PNDMPipeline(unet=unet, noise_scheduler=noise_scheduler) generator = torch.manual_seed(0) image = pndm(generator=generator) image_slice = image[0, -1, -3:, -3:].cpu() assert image.shape == (1, 3, 32, 32) expected_slice = torch.tensor( [-0.6872, -0.7071, -0.7188, -0.7057, -0.7515, -0.7191, -0.7377, -0.7565, -0.7500] ) assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2 @slow @unittest.skip("Skipping for now as it takes too long") def test_ldm_text2img(self): model_id = "fusing/latent-diffusion-text2im-large" ldm = LatentDiffusionPipeline.from_pretrained(model_id) prompt = "A painting of a squirrel eating a burger" generator = torch.manual_seed(0) image = ldm([prompt], generator=generator, num_inference_steps=20) image_slice = image[0, -1, -3:, -3:].cpu() assert image.shape == (1, 3, 256, 256) expected_slice = torch.tensor([0.7295, 0.7358, 0.7256, 0.7435, 0.7095, 0.6884, 0.7325, 0.6921, 0.6458]) assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2 @slow def test_ldm_text2img_fast(self): model_id = "fusing/latent-diffusion-text2im-large" ldm = LatentDiffusionPipeline.from_pretrained(model_id) prompt = "A painting of a squirrel eating a burger" generator = torch.manual_seed(0) image = ldm([prompt], generator=generator, num_inference_steps=1) image_slice = image[0, -1, -3:, -3:].cpu() assert image.shape == (1, 3, 256, 256) expected_slice = torch.tensor([0.3163, 0.8670, 0.6465, 0.1865, 0.6291, 0.5139, 0.2824, 0.3723, 0.4344]) assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2 @slow def test_glide_text2img(self): model_id = "fusing/glide-base" glide = GlidePipeline.from_pretrained(model_id) prompt = "a pencil sketch of a corgi" generator = torch.manual_seed(0) image = glide(prompt, generator=generator, num_inference_steps_upscale=20) image_slice = image[0, :3, :3, -1].cpu() assert image.shape == (1, 256, 256, 3) expected_slice = torch.tensor([0.7119, 0.7073, 0.6460, 0.7780, 0.7423, 0.6926, 0.7378, 0.7189, 0.7784]) assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2 @slow def test_grad_tts(self): model_id = "fusing/grad-tts-libri-tts" grad_tts = GradTTSPipeline.from_pretrained(model_id) noise_scheduler = GradTTSScheduler() grad_tts.noise_scheduler = noise_scheduler text = "Hello world, I missed you so much." generator = torch.manual_seed(0) # generate mel spectograms using text mel_spec = grad_tts(text, generator=generator) assert mel_spec.shape == (1, 80, 143) expected_slice = torch.tensor( [-6.7584, -6.8347, -6.3293, -6.6437, -6.7233, -6.4684, -6.1187, -6.3172, -6.6890] ) assert (mel_spec[0, :3, :3].cpu().flatten() - expected_slice).abs().max() < 1e-2 @slow def test_score_sde_ve_pipeline(self): model = NCSNpp.from_pretrained("fusing/ffhq_ncsnpp") scheduler = ScoreSdeVeScheduler.from_config("fusing/ffhq_ncsnpp") sde_ve = ScoreSdeVePipeline(model=model, scheduler=scheduler) torch.manual_seed(0) image = sde_ve(num_inference_steps=2) expected_image_sum = 3382849024.0 expected_image_mean = 1075.3788 assert (image.abs().sum() - expected_image_sum).abs().cpu().item() < 1e-2 assert (image.abs().mean() - expected_image_mean).abs().cpu().item() < 1e-4 @slow def test_score_sde_vp_pipeline(self): model = NCSNpp.from_pretrained("fusing/cifar10-ddpmpp-vp") scheduler = ScoreSdeVpScheduler.from_config("fusing/cifar10-ddpmpp-vp") sde_vp = ScoreSdeVpPipeline(model=model, scheduler=scheduler) torch.manual_seed(0) image = sde_vp(num_inference_steps=10) expected_image_sum = 4183.2012 expected_image_mean = 1.3617 assert (image.abs().sum() - expected_image_sum).abs().cpu().item() < 1e-2 assert (image.abs().mean() - expected_image_mean).abs().cpu().item() < 1e-4 @slow def test_ldm_uncond(self): ldm = LatentDiffusionUncondPipeline.from_pretrained("fusing/latent-diffusion-celeba-256") generator = torch.manual_seed(0) image = ldm(generator=generator, num_inference_steps=5) image_slice = image[0, -1, -3:, -3:].cpu() assert image.shape == (1, 3, 256, 256) expected_slice = torch.tensor( [-0.1202, -0.1005, -0.0635, -0.0520, -0.1282, -0.0838, -0.0981, -0.1318, -0.1106] ) assert (image_slice.flatten() - expected_slice).abs().max() < 1e-2 def test_module_from_pipeline(self): model = DiffWave(num_res_layers=4) noise_scheduler = DDPMScheduler(timesteps=12) bddm = BDDMPipeline(model, noise_scheduler) # check if the library name for the diffwave moduel is set to pipeline module self.assertTrue(bddm.config["diffwave"][0] == "bddm") # check if we can save and load the pipeline with tempfile.TemporaryDirectory() as tmpdirname: bddm.save_pretrained(tmpdirname) _ = BDDMPipeline.from_pretrained(tmpdirname) # check if the same works using the DifusionPipeline class bddm = DiffusionPipeline.from_pretrained(tmpdirname) self.assertTrue(bddm.config["diffwave"][0] == "bddm")